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\n\n \n \n 2025\n \n \n (1)\n \n \n
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\n \n \n\n \n \n \n \n \n \n Quantifying Model Misrepresentation in Geophysical Inversion for Critical Mineral Exploration.\n \n \n \n \n\n\n \n Yin, Z.; Miltenberger, A.; Topinka, M.; Wang, L.; Mukerji, T.; and Caers, J.\n\n\n \n\n\n\n IEEE Transactions on Geoscience and Remote Sensing, 63: 1-12. 2025.\n \n\n\n\n
\n\n\n\n \n \n![\"Quantifying](\"//bibbase.org/img/filetypes/pdf.svg\"\n\t "\\"Paper\\"\n\t")
Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n
\n\n\n@article{\n title = {Quantifying Model Misrepresentation in Geophysical Inversion for Critical Mineral Exploration},\n type = {article},\n year = {2025},\n keywords = {Bayes factor,Bayes methods,Data models,Geometry,Gravity,Inverse problems,Minerals,Monte Carlo methods,Shape,Stochastic processes,Uncertainty,gravity,hypothesis test,sequential Monte Carlo (SMC),time-domain electromagnetic},\n pages = {1-12},\n volume = {63},\n id = {2efa32dd-ba7d-38eb-a3b2-370379532b53},\n created = {2025-05-26T23:38:10.030Z},\n file_attached = {true},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2025-11-03T06:22:06.894Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {Geophysical inversion often leans on simplified geological models due to a lack of detailed geological information in greenfield critical mineral exploration. While the actual orebodies are more complex in geometry, the simplified model representation can introduce large uncertainties, thereby adversely affecting decisions for field development. We introduce a stochastic quantification and treatment of the model misrepresentation errors when inverting geophysical data. To test whether misrepresentation errors exist, we provide a generalized hypothesis testing approach. We start with the assumption that our current geometry model accurately represents the subsurface (null hypothesis). We then use geophysical data to test this assumption. If the null hypothesis is rejected, the geometry is known to be false. We employ a Bayes factor to quantify where in space the model error is most significant. The latter will be used as information to reparametrize the assumed geometry, to reduce the misrepresentation errors. Our approach also allows accounting for data error and model error jointly, by using a pushforward formulation of the inverse problem. We provide a sequential Monte Carlo (SMC) sampling algorithm to solve the pushforward formulation, enabling practical computation of the Bayes factor. Both the synthetic and real field studies showed the Bayes factor can locate where the model is misrepresented. The real field application also shows that, through mitigating the misrepresentation errors, we reduced the uncertainty in key decision-making parameters concerning the field development, including the prospecting conductors’ volume and depth. We further provide open-source code to facilitate practical applications.},\n bibtype = {article},\n author = {Yin, Z and Miltenberger, A and Topinka, M and Wang, L and Mukerji, T and Caers, J},\n doi = {10.1109/TGRS.2025.3541538},\n journal = {IEEE Transactions on Geoscience and Remote Sensing}\n}\n\n Geophysical inversion often leans on simplified geological models due to a lack of detailed geological information in greenfield critical mineral exploration. While the actual orebodies are more complex in geometry, the simplified model representation can introduce large uncertainties, thereby adversely affecting decisions for field development. We introduce a stochastic quantification and treatment of the model misrepresentation errors when inverting geophysical data. To test whether misrepresentation errors exist, we provide a generalized hypothesis testing approach. We start with the assumption that our current geometry model accurately represents the subsurface (null hypothesis). We then use geophysical data to test this assumption. If the null hypothesis is rejected, the geometry is known to be false. We employ a Bayes factor to quantify where in space the model error is most significant. The latter will be used as information to reparametrize the assumed geometry, to reduce the misrepresentation errors. Our approach also allows accounting for data error and model error jointly, by using a pushforward formulation of the inverse problem. We provide a sequential Monte Carlo (SMC) sampling algorithm to solve the pushforward formulation, enabling practical computation of the Bayes factor. Both the synthetic and real field studies showed the Bayes factor can locate where the model is misrepresented. The real field application also shows that, through mitigating the misrepresentation errors, we reduced the uncertainty in key decision-making parameters concerning the field development, including the prospecting conductors’ volume and depth. We further provide open-source code to facilitate practical applications.\n
\n\n\n\n
\n\n \n \n 2024\n \n \n (4)\n \n \n
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\n \n \n\n \n \n \n \n \n Quantifying uncertainty in ultra-deepwater carbonate facies modeling.\n \n \n \n\n\n \n Kloeckner, J.; Yin, Z.; Carvalho, P., R.; Marques, D., M.; Costa, J., F., C.; and Caers, J.\n\n\n \n\n\n\n Geoenergy Science and Engineering, 240: 213049. 9 2024.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n\n@article{\n title = {Quantifying uncertainty in ultra-deepwater carbonate facies modeling},\n type = {article},\n year = {2024},\n pages = {213049},\n volume = {240},\n month = {9},\n publisher = {Elsevier},\n day = {1},\n id = {fa32924e-e0bc-378b-a3ee-b0d35d4a60fc},\n created = {2024-11-15T06:53:16.136Z},\n accessed = {2024-11-14},\n file_attached = {false},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2024-11-15T06:54:18.731Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n folder_uuids = {b6f706b8-bef2-4dc2-83ab-cddf23111a24},\n private_publication = {false},\n bibtype = {article},\n author = {Kloeckner, Jonas and Yin, Zhen and Carvalho, Paulo R.M. and Marques, Diego M. and Costa, João Felipe C.L. and Caers, Jef},\n doi = {10.1016/J.GEOEN.2024.213049},\n journal = {Geoenergy Science and Engineering}\n}\n\n \n\n \n \n \n \n \n \n Masked Autoregressive Flow for Geochemical Anomaly Detection with Application to Li–Cs–Ta Pegmatites Exploration of the Superior Craton, Canada.\n \n \n \n \n\n\n \n Scheidt, C.; Mathieu, L.; Yin, Z.; Wang, L.; and Caers, J.\n\n\n \n\n\n\n Natural Resources Research. 2024.\n \n\n\n\n
\n\n\n\n \n \n![\"Masked](\"//bibbase.org/img/filetypes/link.svg\"\n\t "\\"Website\\"\n\t")
Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n@article{\n title = {Masked Autoregressive Flow for Geochemical Anomaly Detection with Application to Li–Cs–Ta Pegmatites Exploration of the Superior Craton, Canada},\n type = {article},\n year = {2024},\n websites = {https://doi.org/10.1007/s11053-024-10409-2},\n id = {d793d8fa-1680-3555-a5ff-2355f81936b5},\n created = {2024-11-15T06:54:39.036Z},\n file_attached = {false},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2024-11-15T06:54:50.836Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {In mineral exploration, geochemical anomaly detection aims at identifying areas where geochemical properties differ from the surrounding areas, indicating possible mineralization. Robust outlier detection can help better identify potential anomalies. However, standard outlier detection techniques tend to work only in low-dimensional and Gaussian space, hence the need of a more robust outlier detection technique that can be used in the space of geochemical elements, which has high complexity and dimensionality. In this paper, a novel machine learning-based outlier detection technique is proposed. The masked autoregressive flow (MAF) was used to model the density of the high-dimensional geochemical space. Once successfully trained, the MAF provides a Gaussian space on which standard outlier detection techniques (here robust Mahalanobis distance) can be applied more successfully. The proposed method was applied to a high-quality lake sediment geochemical data acquired in Quebec, Canada, in an area with known Li–Cs–Ta (LCT) pegmatites. Results are very encouraging, with the detection of many of the known occurrences of LCT pegmatites and the discovery of potential new targets for further exploration. Hence, the method described here can be used to explore for LCT pegmatites.},\n bibtype = {article},\n author = {Scheidt, C and Mathieu, L and Yin, Z and Wang, L and Caers, J},\n doi = {10.1007/s11053-024-10409-2},\n journal = {Natural Resources Research}\n}\n\n In mineral exploration, geochemical anomaly detection aims at identifying areas where geochemical properties differ from the surrounding areas, indicating possible mineralization. Robust outlier detection can help better identify potential anomalies. However, standard outlier detection techniques tend to work only in low-dimensional and Gaussian space, hence the need of a more robust outlier detection technique that can be used in the space of geochemical elements, which has high complexity and dimensionality. In this paper, a novel machine learning-based outlier detection technique is proposed. The masked autoregressive flow (MAF) was used to model the density of the high-dimensional geochemical space. Once successfully trained, the MAF provides a Gaussian space on which standard outlier detection techniques (here robust Mahalanobis distance) can be applied more successfully. The proposed method was applied to a high-quality lake sediment geochemical data acquired in Quebec, Canada, in an area with known Li–Cs–Ta (LCT) pegmatites. Results are very encouraging, with the detection of many of the known occurrences of LCT pegmatites and the discovery of potential new targets for further exploration. Hence, the method described here can be used to explore for LCT pegmatites.\n
\n\n\n\n \n\n \n \n \n \n \n \n Constructing Priors for Geophysical Inversions Constrained by Surface and Borehole Geochemistry.\n \n \n \n \n\n\n \n Wei, X.; Yin, Z.; Scheidt, C.; Darnell, K.; Wang, L.; and Caers, J.\n\n\n \n\n\n\n Surveys in Geophysics, 45(4): 1047-1079. 2024.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n@article{\n title = {Constructing Priors for Geophysical Inversions Constrained by Surface and Borehole Geochemistry},\n type = {article},\n year = {2024},\n pages = {1047-1079},\n volume = {45},\n websites = {https://doi.org/10.1007/s10712-024-09843-x},\n id = {e14b5387-792f-3b80-9f5c-09dac2ead61a},\n created = {2024-11-15T06:55:46.732Z},\n file_attached = {false},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2024-11-15T06:55:57.071Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {Prior model construction is a fundamental component in geophysical inversion, especially Bayesian inversion. The prior model, usually derived from available geological information, can reduce the uncertainty of model characteristics during the inversion. However, the prior geological data for inferring a prior distribution model are often limited in real cases. Our work presents a novel framework to create 3D geophysical prior models using soil geochemistry and borehole rock sample measurements. We focus on the Bayesian inversion, which enables encoding of knowledge and multiple non-geophysical data into the prior. The new framework developed in our research comprises three main parts, namely correlation analysis, prior model reconstruction, and Bayesian inversion. We investigate the correlations between surface and subsurface geochemical features, as well as the correlation between geochemistry and geophysics, using canonical correlation analysis for the surface and borehole geochemistry. Based on the resulting correlations, we construct the prior susceptibility model. The informed prior model is then tested using geophysical forward modeling and outlier detection methods. In this test, we aim to falsify the prior model, which happens when the model cannot predict the field geophysical observation. To obtain the posterior models, the reliable prior models are incorporated into a Bayesian inversion framework. Using a real case of exploration in the Central African Copperbelt, we illustrate the workflow of constructing the high-resolution 3D stratigraphic model conditioned on soil geochemistry, borehole data, and airborne geophysics.},\n bibtype = {article},\n author = {Wei, Xiaolong and Yin, Zhen and Scheidt, Celine and Darnell, Kris and Wang, Lijing and Caers, Jef},\n doi = {10.1007/s10712-024-09843-x},\n journal = {Surveys in Geophysics},\n number = {4}\n}\n\n Prior model construction is a fundamental component in geophysical inversion, especially Bayesian inversion. The prior model, usually derived from available geological information, can reduce the uncertainty of model characteristics during the inversion. However, the prior geological data for inferring a prior distribution model are often limited in real cases. Our work presents a novel framework to create 3D geophysical prior models using soil geochemistry and borehole rock sample measurements. We focus on the Bayesian inversion, which enables encoding of knowledge and multiple non-geophysical data into the prior. The new framework developed in our research comprises three main parts, namely correlation analysis, prior model reconstruction, and Bayesian inversion. We investigate the correlations between surface and subsurface geochemical features, as well as the correlation between geochemistry and geophysics, using canonical correlation analysis for the surface and borehole geochemistry. Based on the resulting correlations, we construct the prior susceptibility model. The informed prior model is then tested using geophysical forward modeling and outlier detection methods. In this test, we aim to falsify the prior model, which happens when the model cannot predict the field geophysical observation. To obtain the posterior models, the reliable prior models are incorporated into a Bayesian inversion framework. Using a real case of exploration in the Central African Copperbelt, we illustrate the workflow of constructing the high-resolution 3D stratigraphic model conditioned on soil geochemistry, borehole data, and airborne geophysics.\n
\n\n\n\n \n\n \n \n \n \n \n \n Falsification of magmatic intrusion models using outcrops, drillholes, and geophysics.\n \n \n \n \n\n\n \n Wei, X.; Yin, Z.; and Caers, J.\n\n\n \n\n\n\n of SEG Global Meeting Abstracts. International Workshop on Gravity, Electrical & Magnetic Methods and Their Applications, Shenzhen, China, May 19?22, 2024, pages 364-367. Society of Exploration Geophysicists and Chinese Geophysical Society, 8 2024.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n@inbook{\n type = {inbook},\n year = {2024},\n pages = {364-367},\n websites = {https://doi.org/10.1190/GEM2024-091.1},\n month = {8},\n publisher = {Society of Exploration Geophysicists and Chinese Geophysical Society},\n day = {23},\n series = {SEG Global Meeting Abstracts},\n id = {15172638-03d6-32ef-bfe6-a071ef0fa77a},\n created = {2024-11-15T06:55:46.890Z},\n file_attached = {false},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2024-11-15T06:55:54.807Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n source_type = {CHAP},\n notes = {doi:10.1190/GEM2024-091.1},\n private_publication = {false},\n abstract = {Magmatic intrusions can host a variety of minerals and often serve as key targets in mineral exploration endeavors. During exploration, the manual construction of an ?optimal? intrusion model based on the available data is a standard practice, which then forms the foundation for subsequent interpretations and decision-making processes. However, this optimal-model-to-interpretation paradigm that relies heavily on expertise presents a significant challenge in assessing the uncertainty of the inferred model. To address the problem, we have developed an innovative and automatic framework with a core component: falsification. The first step involves the stochastic construction of prior magmatic intrusion models using outcrop and drillhole data. We can also derive a prior distribution of the physical property (e.g., density contrast) for the intrusive body from rock sample measurements or textbooks. The second step attempts to falsify the prior geometric representations and the prior physical property, obtained from the first step, using geophysical data. The unfalsified prior models can serve as realistic and valid priori for diverse Bayesian inversion methods, resulting in more reliable quantification of the posterior uncertainty. Our study highlights the necessity and significance of falsification in the prior model construction.},\n bibtype = {inbook},\n author = {Wei, Xiaolong and Yin, Zhen and Caers, Jef},\n doi = {doi:10.1190/GEM2024-091.1},\n chapter = {Falsification of magmatic intrusion models using outcrops, drillholes, and geophysics},\n title = {International Workshop on Gravity, Electrical & Magnetic Methods and Their Applications, Shenzhen, China, May 19?22, 2024}\n}\n\n Magmatic intrusions can host a variety of minerals and often serve as key targets in mineral exploration endeavors. During exploration, the manual construction of an ?optimal? intrusion model based on the available data is a standard practice, which then forms the foundation for subsequent interpretations and decision-making processes. However, this optimal-model-to-interpretation paradigm that relies heavily on expertise presents a significant challenge in assessing the uncertainty of the inferred model. To address the problem, we have developed an innovative and automatic framework with a core component: falsification. The first step involves the stochastic construction of prior magmatic intrusion models using outcrop and drillhole data. We can also derive a prior distribution of the physical property (e.g., density contrast) for the intrusive body from rock sample measurements or textbooks. The second step attempts to falsify the prior geometric representations and the prior physical property, obtained from the first step, using geophysical data. The unfalsified prior models can serve as realistic and valid priori for diverse Bayesian inversion methods, resulting in more reliable quantification of the posterior uncertainty. Our study highlights the necessity and significance of falsification in the prior model construction.\n
\n\n\n\n
\n\n \n \n 2023\n \n \n (6)\n \n \n
\n \n \n \n
\n \n \n\n \n \n \n \n \n \n Data Science for the Geosciences.\n \n \n \n \n\n\n \n Wang, L.; Yin, D., Z.; and Caers, J.\n\n\n \n\n\n\n Cambridge University Press, 2023.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n\n@book{\n title = {Data Science for the Geosciences},\n type = {book},\n year = {2023},\n websites = {https://www.cambridge.org/core/books/data-science-for-the-geosciences/64E10197819920B0B5F36472B3B872C4},\n publisher = {Cambridge University Press},\n city = {Cambridge},\n id = {453c538a-182a-3360-ad57-ac457cb5a2f4},\n created = {2023-03-21T19:37:11.464Z},\n file_attached = {false},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2024-11-15T06:51:24.691Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {BOOK},\n folder_uuids = {36a4a608-b213-4dff-bcf0-199620822767,47776c85-b5e1-4ae8-901b-94a445992e19},\n private_publication = {false},\n bibtype = {book},\n author = {Wang, Lijing and Yin, David Zhen and Caers, Jef},\n doi = {DOI:}\n}\n\n \n\n \n \n \n \n \n Simulation of hydrogen generation via in-situ combustion gasification of heavy oil.\n \n \n \n\n\n \n Song, P.; Li, Y.; Yin, Z.; Ifticene, M., A.; and Yuan, Q.\n\n\n \n\n\n\n International Journal of Hydrogen Energy, 49. 2023.\n \n\n\n\n
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\n\n\n@article{\n title = {Simulation of hydrogen generation via in-situ combustion gasification of heavy oil},\n type = {article},\n year = {2023},\n volume = {49},\n id = {81ff0539-3a4b-37a4-bc24-2ca41eab18ce},\n created = {2024-11-15T06:46:15.419Z},\n file_attached = {false},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2024-11-15T07:15:56.113Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n folder_uuids = {b6f706b8-bef2-4dc2-83ab-cddf23111a24},\n private_publication = {false},\n abstract = {With the increasing demand for clean hydrogen (H2) energy, new emerging technologies for in-situ H2 production from hydrocarbon reservoirs have attracted the attention of researchers and industry. This presents an opportunity for the petroleum industry to contribute to the energy transition. One technology in the field pilot stage is in-situ combustion gasification (ISCG). However, the mechanism behind this process is not fully understood and additional experimental and modeling work is required. To address this issue, we developed a laboratory-scale simulation model for ISCG. Statistical methods were combined to investigate the sensitivity and interactions of different parameters that control the process. The results showed that 34 mol.% H2 can be generated at 800 °C. Higher temperatures yielded higher H2 concentrations with coke gasification and water-gas shift reactions dominating hydrogen generation. This study provides valuable knowledge about the process and lays a foundation for future lab-scale ISCG experiments.},\n bibtype = {article},\n author = {Song, Ping and Li, Yunan and Yin, Zhen and Ifticene, Mohamed Amine and Yuan, Qingwang},\n doi = {10.1016/j.ijhydene.2023.09.248},\n journal = {International Journal of Hydrogen Energy}\n}\n\n With the increasing demand for clean hydrogen (H2) energy, new emerging technologies for in-situ H2 production from hydrocarbon reservoirs have attracted the attention of researchers and industry. This presents an opportunity for the petroleum industry to contribute to the energy transition. One technology in the field pilot stage is in-situ combustion gasification (ISCG). However, the mechanism behind this process is not fully understood and additional experimental and modeling work is required. To address this issue, we developed a laboratory-scale simulation model for ISCG. Statistical methods were combined to investigate the sensitivity and interactions of different parameters that control the process. The results showed that 34 mol.% H2 can be generated at 800 °C. Higher temperatures yielded higher H2 concentrations with coke gasification and water-gas shift reactions dominating hydrogen generation. This study provides valuable knowledge about the process and lays a foundation for future lab-scale ISCG experiments.\n
\n\n\n\n \n\n \n \n \n \n \n Unraveling the uncertainty of geological interfaces through data-knowledge-driven trend surface analysis.\n \n \n \n\n\n \n Wang, L.; Peeters, L.; MacKie, E., J.; Yin, Z.; and Caers, J.\n\n\n \n\n\n\n Computers and Geosciences, 178. 2023.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n\n\n \n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@article{\n title = {Unraveling the uncertainty of geological interfaces through data-knowledge-driven trend surface analysis},\n type = {article},\n year = {2023},\n volume = {178},\n id = {a6fcd975-e7d6-3ecb-9064-c66a5fab1242},\n created = {2024-11-15T06:47:25.943Z},\n file_attached = {false},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2024-11-15T06:49:59.982Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n folder_uuids = {b6f706b8-bef2-4dc2-83ab-cddf23111a24},\n private_publication = {false},\n abstract = {Modeling complex geological interfaces is a common task in geosciences. Many data sources are available for geological interface modeling, including borehole data and geophysical surveys. Geological knowledge, such as the delineation from geologists, is difficult to quantify but likely adds value to geological interface modeling. To integrate all information, we present a data-knowledge-driven trend surface analysis method to construct stochastic geological interfaces. We design a Metropolis–Hastings sampling framework to sample stochastic trend interfaces and quantify the uncertainty of geological interfaces given all information sources. This method is suitable for both explicit and implicit representations of geological interfaces. We demonstrate our method in three different test cases: modeling stochastic interfaces of Greenland subglacial topography, magmatic intrusion, and buried river valleys in Australia.},\n bibtype = {article},\n author = {Wang, Lijing and Peeters, Luk and MacKie, Emma J. and Yin, Zhen and Caers, Jef},\n doi = {10.1016/j.cageo.2023.105419},\n journal = {Computers and Geosciences}\n}\n\n Modeling complex geological interfaces is a common task in geosciences. Many data sources are available for geological interface modeling, including borehole data and geophysical surveys. Geological knowledge, such as the delineation from geologists, is difficult to quantify but likely adds value to geological interface modeling. To integrate all information, we present a data-knowledge-driven trend surface analysis method to construct stochastic geological interfaces. We design a Metropolis–Hastings sampling framework to sample stochastic trend interfaces and quantify the uncertainty of geological interfaces given all information sources. This method is suitable for both explicit and implicit representations of geological interfaces. We demonstrate our method in three different test cases: modeling stochastic interfaces of Greenland subglacial topography, magmatic intrusion, and buried river valleys in Australia.\n
\n\n\n\n \n\n \n \n \n \n \n GStatSim V1.0: a Python package for geostatistical interpolation and conditional simulation.\n \n \n \n\n\n \n Mackie, E., J.; Field, M.; Wang, L.; Yin, Z.; Schoedl, N.; Hibbs, M.; and Zhang, A.\n\n\n \n\n\n\n Geoscientific Model Development, 16(13). 2023.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n\n\n \n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@article{\n title = {GStatSim V1.0: a Python package for geostatistical interpolation and conditional simulation},\n type = {article},\n year = {2023},\n volume = {16},\n id = {ef17a123-e52f-3078-86e6-53f4b14dbd4e},\n created = {2024-11-15T06:47:27.431Z},\n file_attached = {false},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2024-11-15T06:49:58.174Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n folder_uuids = {b6f706b8-bef2-4dc2-83ab-cddf23111a24},\n private_publication = {false},\n abstract = {The interpolation of geospatial phenomena is a common problem in Earth science applications that can be addressed with geostatistics, where spatial correlations are used to constrain interpolations. In certain applications, it can be particularly useful to a perform geostatistical simulation, which is used to generate multiple non-unique realizations that reproduce the variability in measurements and are constrained by observations. Despite the broad utility of this approach, there are few open-access geostatistical simulation software applications. To address this accessibility issue, we present GStatSim, a Python package for performing geostatistical interpolation and simulation. GStatSim is distinct from previous geostatistical tools in that it emphasizes accessibility for non-experts, geostatistical simulation, and applicability to remote sensing data sets. It includes tools for performing non-stationary simulations and interpolations with secondary constraints. This package is accompanied by a Jupyter Book with user tutorials and background information on different interpolation methods. These resources are intended to significantly lower the technological barrier to using geostatistics and encourage the use of geostatistics in a wider range of applications. We demonstrate the different functionalities of this tool for the interpolation of subglacial topography measurements in Greenland.},\n bibtype = {article},\n author = {Mackie, Emma J. and Field, Michael and Wang, Lijing and Yin, Zhen and Schoedl, Nathan and Hibbs, Matthew and Zhang, Allan},\n doi = {10.5194/gmd-16-3765-2023},\n journal = {Geoscientific Model Development},\n number = {13}\n}\n\n The interpolation of geospatial phenomena is a common problem in Earth science applications that can be addressed with geostatistics, where spatial correlations are used to constrain interpolations. In certain applications, it can be particularly useful to a perform geostatistical simulation, which is used to generate multiple non-unique realizations that reproduce the variability in measurements and are constrained by observations. Despite the broad utility of this approach, there are few open-access geostatistical simulation software applications. To address this accessibility issue, we present GStatSim, a Python package for performing geostatistical interpolation and simulation. GStatSim is distinct from previous geostatistical tools in that it emphasizes accessibility for non-experts, geostatistical simulation, and applicability to remote sensing data sets. It includes tools for performing non-stationary simulations and interpolations with secondary constraints. This package is accompanied by a Jupyter Book with user tutorials and background information on different interpolation methods. These resources are intended to significantly lower the technological barrier to using geostatistics and encourage the use of geostatistics in a wider range of applications. We demonstrate the different functionalities of this tool for the interpolation of subglacial topography measurements in Greenland.\n
\n\n\n\n \n\n \n \n \n \n \n Review of Mathematical and Statistical Concepts.\n \n \n \n\n\n \n Wang, L.; Yin, D., Z.; and Caers, J.\n\n\n \n\n\n\n Data Science for the Geosciences. 2023.\n \n\n\n\n
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\n\n\n\n@inbook{\n type = {inbook},\n year = {2023},\n id = {10a12059-f2ea-3d05-a845-b6a83bc27090},\n created = {2024-11-15T06:48:27.479Z},\n file_attached = {false},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2024-11-15T06:49:53.298Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n folder_uuids = {b6f706b8-bef2-4dc2-83ab-cddf23111a24},\n private_publication = {false},\n bibtype = {inbook},\n author = {Wang, Lijing and Yin, David Zhen and Caers, Jef},\n doi = {10.1017/9781009201391.006},\n chapter = {Review of Mathematical and Statistical Concepts},\n title = {Data Science for the Geosciences}\n}\n\n \n\n \n \n \n \n \n Extreme Value Statistics.\n \n \n \n\n\n \n Wang, L.; Yin, D., Z.; and Caers, J.\n\n\n \n\n\n\n Data Science for the Geosciences. 2023.\n \n\n\n\n
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\n\n\n\n@inbook{\n type = {inbook},\n year = {2023},\n id = {dbf7f38b-9ff1-3a94-ad94-4d96911e362f},\n created = {2024-11-15T06:48:29.584Z},\n file_attached = {false},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2024-11-15T06:49:26.837Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n folder_uuids = {b6f706b8-bef2-4dc2-83ab-cddf23111a24},\n private_publication = {false},\n bibtype = {inbook},\n author = {Wang, Lijing and Yin, David Zhen and Caers, Jef},\n doi = {10.1017/9781009201391.002},\n chapter = {Extreme Value Statistics},\n title = {Data Science for the Geosciences}\n}\n\n
\n\n \n \n 2022\n \n \n (3)\n \n \n
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\n \n \n\n \n \n \n \n \n \n Quantifying Uncertainty in Downscaling of Seismic Data to High-Resolution 3-D Lithological Models.\n \n \n \n \n\n\n \n Yin, Z.; Amaru, M.; Wang, Y.; Li, L.; and Caers, J.\n\n\n \n\n\n\n IEEE Transactions on Geoscience and Remote Sensing, 60: 1-12. 2022.\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@article{\n title = {Quantifying Uncertainty in Downscaling of Seismic Data to High-Resolution 3-D Lithological Models},\n type = {article},\n year = {2022},\n pages = {1-12},\n volume = {60},\n id = {2830a07b-bf47-3e22-8caf-57549bf3a20e},\n created = {2022-08-12T22:26:23.026Z},\n file_attached = {true},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2025-11-03T06:22:13.187Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n folder_uuids = {b6f706b8-bef2-4dc2-83ab-cddf23111a24},\n private_publication = {false},\n abstract = {Building high-resolution lithological models using seismic data can facilitate decision-makings for earth resources development, but they are subject to considerable uncertainties due to the limited seismic resolution. Traditionally, high-resolution lithological models are built deterministically or stochastic simulation is performed using seismic as “soft data” in geostatistical contexts. Many approaches have been developed to do the above, but in this work, we explicitly account for the uncertain relationship between seismic data and lithological models. We introduce a data-driven Bayesian approach to first quantify soft data uncertainty by generating multiple lithology proportions from seismic. The spatial lithology proportion uncertainty arises from the uncertain relationship between low-resolution seismic and lithology proportions. We quantify this proportion uncertainty by learning the statistical relationships with seismic using high-resolution borehole data. Once conditioning the statistical relationships to observed seismic, we can generate multiple realizations of spatial proportions. With the generated proportions as volume averages, a sequential indicator simulation (SISim) is then performed to build high-resolution lithofacies models. This approach is applied to a real channelized turbidite system with four lithofacies. We demonstrate that the generated high-resolution lithological models can preserve the global uncertainties captured by proportion trends while locally matching borehole observations. When compared with the conventional approaches that use deterministic soft data, our statistical learning approach avoids the problem of underestimating reservoir model uncertainty. More importantly, the lithofacies models from the proposed method are less likely to be falsified by observed data. Additionally, an open-source Python library for the uncertainty quantification is provided.},\n bibtype = {article},\n author = {Yin, Z and Amaru, M and Wang, Y and Li, L and Caers, J},\n doi = {10.1109/TGRS.2022.3153934},\n journal = {IEEE Transactions on Geoscience and Remote Sensing}\n}\n\n Building high-resolution lithological models using seismic data can facilitate decision-makings for earth resources development, but they are subject to considerable uncertainties due to the limited seismic resolution. Traditionally, high-resolution lithological models are built deterministically or stochastic simulation is performed using seismic as “soft data” in geostatistical contexts. Many approaches have been developed to do the above, but in this work, we explicitly account for the uncertain relationship between seismic data and lithological models. We introduce a data-driven Bayesian approach to first quantify soft data uncertainty by generating multiple lithology proportions from seismic. The spatial lithology proportion uncertainty arises from the uncertain relationship between low-resolution seismic and lithology proportions. We quantify this proportion uncertainty by learning the statistical relationships with seismic using high-resolution borehole data. Once conditioning the statistical relationships to observed seismic, we can generate multiple realizations of spatial proportions. With the generated proportions as volume averages, a sequential indicator simulation (SISim) is then performed to build high-resolution lithofacies models. This approach is applied to a real channelized turbidite system with four lithofacies. We demonstrate that the generated high-resolution lithological models can preserve the global uncertainties captured by proportion trends while locally matching borehole observations. When compared with the conventional approaches that use deterministic soft data, our statistical learning approach avoids the problem of underestimating reservoir model uncertainty. More importantly, the lithofacies models from the proposed method are less likely to be falsified by observed data. Additionally, an open-source Python library for the uncertainty quantification is provided.\n
\n\n\n\n \n\n \n \n \n \n \n \n Efficacy of Information in Mineral Exploration Drilling.\n \n \n \n \n\n\n \n Caers, J.; Scheidt, C.; Yin, Z.; Wang, L.; Mukerji, T.; and House, K.\n\n\n \n\n\n\n Natural Resources Research, 31(3): 1157-1173. 2022.\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n \n Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@article{\n title = {Efficacy of Information in Mineral Exploration Drilling},\n type = {article},\n year = {2022},\n pages = {1157-1173},\n volume = {31},\n websites = {https://doi.org/10.1007/s11053-022-10030-1},\n id = {fde1ea5e-af05-3dfc-815f-d733794cfdb1},\n created = {2022-09-13T16:53:18.154Z},\n file_attached = {true},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2025-03-02T04:25:41.666Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n folder_uuids = {b6f706b8-bef2-4dc2-83ab-cddf23111a24},\n private_publication = {false},\n abstract = {In mineral exploration, targets need to be confirmed by means of drilling. Such drilling is based on anomalies often detected in geochemical and/or geophysical exploration. Because drilling is costly and because mineral exploration has low success rate, the need to know quickly, with minimal drilling, whether a target is mineable or not, is an important problem. Value of information (VOI) is a decision-theoretic concept that quantifies by how much information reduces risk about a project dollar-value; hence, optimizing its value provides a means for optimizing the location of drilling. However, VOI depends on actual dollar values of success vs. failure, which may be uncertain themselves, or not yet relevant. In this paper, we introduce a new concept, termed efficacy of information (EOI), which is like VOI but without monetary rewards or costs. It quantifies by how much, on average, future information reduces uncertainty on some (discrete) property of interest. We present an efficient method for calculating EOI based on approximately solving a Bayesian inverse problem. We determine the location of a single, or set of boreholes sequentially, by optimizing their EO1. We also apply this method on an actual mineral deposit.},\n bibtype = {article},\n author = {Caers, J and Scheidt, C and Yin, Z and Wang, L and Mukerji, T and House, K},\n doi = {10.1007/s11053-022-10030-1},\n journal = {Natural Resources Research},\n number = {3}\n}\n\n In mineral exploration, targets need to be confirmed by means of drilling. Such drilling is based on anomalies often detected in geochemical and/or geophysical exploration. Because drilling is costly and because mineral exploration has low success rate, the need to know quickly, with minimal drilling, whether a target is mineable or not, is an important problem. Value of information (VOI) is a decision-theoretic concept that quantifies by how much information reduces risk about a project dollar-value; hence, optimizing its value provides a means for optimizing the location of drilling. However, VOI depends on actual dollar values of success vs. failure, which may be uncertain themselves, or not yet relevant. In this paper, we introduce a new concept, termed efficacy of information (EOI), which is like VOI but without monetary rewards or costs. It quantifies by how much, on average, future information reduces uncertainty on some (discrete) property of interest. We present an efficient method for calculating EOI based on approximately solving a Bayesian inverse problem. We determine the location of a single, or set of boreholes sequentially, by optimizing their EO1. We also apply this method on an actual mineral deposit.\n
\n\n\n\n \n\n \n \n \n \n \n \n Sequential Value of Information for Subsurface Exploration Drilling.\n \n \n \n \n\n\n \n Hall, T.; Scheidt, C.; Wang, L.; Yin, Z.; Mukerji, T.; and Caers, J.\n\n\n \n\n\n\n Natural Resources Research, 31(5): 2413-2434. 2022.\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n \n Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@article{\n title = {Sequential Value of Information for Subsurface Exploration Drilling},\n type = {article},\n year = {2022},\n pages = {2413-2434},\n volume = {31},\n websites = {https://doi.org/10.1007/s11053-022-10078-z},\n id = {00576bf4-143d-348f-8c13-042a2330eca3},\n created = {2023-01-02T16:32:24.821Z},\n file_attached = {true},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2024-11-15T06:43:19.657Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n folder_uuids = {b6f706b8-bef2-4dc2-83ab-cddf23111a24},\n private_publication = {false},\n abstract = {Quantitative methods are needed for systematic decision-making during exploration for subsurface resources, but few methods exist that fully incorporate the interaction of geological, operational, and financial conditions. The sequential nature of planning where to drill for subsurface exploration is not commonly addressed by conventional techniques in a quantitative fashion, despite its foundational relevance to hypothesis testing. Value of information (VOI) can incorporate various aspects of subsurface exploration decision-making as well as sequence. Here, we use VOI to determine the optimal sequence and placement of exploration boreholes when varying conditions such as target resource volume and drilling cost. Using VOI, we show that the optimal placement and selection of exploration boreholes change when planning to drill one borehole at a time compared to planning to drill two boreholes sequentially. A formulation and tutorial explanation of VOI for sequential decision situations are shown using a synthetic case. We demonstrate a test case using data from a real metal deposit.},\n bibtype = {article},\n author = {Hall, T and Scheidt, C and Wang, L and Yin, Z and Mukerji, T and Caers, J},\n doi = {10.1007/s11053-022-10078-z},\n journal = {Natural Resources Research},\n number = {5}\n}\n\n Quantitative methods are needed for systematic decision-making during exploration for subsurface resources, but few methods exist that fully incorporate the interaction of geological, operational, and financial conditions. The sequential nature of planning where to drill for subsurface exploration is not commonly addressed by conventional techniques in a quantitative fashion, despite its foundational relevance to hypothesis testing. Value of information (VOI) can incorporate various aspects of subsurface exploration decision-making as well as sequence. Here, we use VOI to determine the optimal sequence and placement of exploration boreholes when varying conditions such as target resource volume and drilling cost. Using VOI, we show that the optimal placement and selection of exploration boreholes change when planning to drill one borehole at a time compared to planning to drill two boreholes sequentially. A formulation and tutorial explanation of VOI for sequential decision situations are shown using a synthetic case. We demonstrate a test case using data from a real metal deposit.\n
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\n\n \n \n 2021\n \n \n (1)\n \n \n
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\n \n \n\n \n \n \n \n \n \n Stochastic modeling of subglacial topography exposes uncertainty in water routing at Jakobshavn Glacier.\n \n \n \n \n\n\n \n MacKie, E., J.; Schroeder, D., M.; Zuo, C.; Yin, Z.; and Caers, J.\n\n\n \n\n\n\n Journal of Glaciology, 67(261): 75-83. 2021.\n \n\n\n\n
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\n\n\n@article{\n title = {Stochastic modeling of subglacial topography exposes uncertainty in water routing at Jakobshavn Glacier},\n type = {article},\n year = {2021},\n keywords = {Glacial geomorphology,glacier hydrology,radio-echo sounding,subglacial processes},\n pages = {75-83},\n volume = {67},\n websites = {https://www.cambridge.org/core/article/stochastic-modeling-of-subglacial-topography-exposes-uncertainty-in-water-routing-at-jakobshavn-glacier/602E8B877835D78AB8F3E9A386D51A14},\n publisher = {Cambridge University Press},\n edition = {2020/10/12},\n id = {bee73b9e-2e7b-368f-a8e5-44d077dee7b7},\n created = {2021-07-06T04:28:02.816Z},\n file_attached = {true},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2024-11-15T06:43:20.312Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n folder_uuids = {b6f706b8-bef2-4dc2-83ab-cddf23111a24},\n private_publication = {false},\n abstract = {Subglacial topography is an important feature in numerous ice-sheet analyses and can drive the routing of water at the bed. Bed topography is primarily measured with ice-penetrating radar. Significant gaps, however, remain in data coverage that require interpolation. Topographic interpolations are typically made with kriging, as well as with mass conservation, where ice flow dynamics are used to constrain bed geometry. However, these techniques generate bed topography that is unrealistically smooth at small scales, which biases subglacial water flowpath models and makes it difficult to rigorously quantify uncertainty in subglacial drainage patterns. To address this challenge, we adapt a geostatistical simulation method with probabilistic modeling to stochastically simulate bed topography such that the interpolated topography retains the spatial statistics of the ice-penetrating radar data. We use this method to simulate subglacial topography using mass conservation topography as a secondary constraint. We apply a water routing model to each of these realizations. Our results show that many of the flowpaths significantly change with each topographic realization, demonstrating that geostatistical simulation can be useful for assessing confidence in subglacial flowpaths.},\n bibtype = {article},\n author = {MacKie, Emma J and Schroeder, Dustin M and Zuo, Chen and Yin, Zhen and Caers, Jef},\n doi = {DOI: 10.1017/jog.2020.84},\n journal = {Journal of Glaciology},\n number = {261}\n}\n\n Subglacial topography is an important feature in numerous ice-sheet analyses and can drive the routing of water at the bed. Bed topography is primarily measured with ice-penetrating radar. Significant gaps, however, remain in data coverage that require interpolation. Topographic interpolations are typically made with kriging, as well as with mass conservation, where ice flow dynamics are used to constrain bed geometry. However, these techniques generate bed topography that is unrealistically smooth at small scales, which biases subglacial water flowpath models and makes it difficult to rigorously quantify uncertainty in subglacial drainage patterns. To address this challenge, we adapt a geostatistical simulation method with probabilistic modeling to stochastically simulate bed topography such that the interpolated topography retains the spatial statistics of the ice-penetrating radar data. We use this method to simulate subglacial topography using mass conservation topography as a secondary constraint. We apply a water routing model to each of these realizations. Our results show that many of the flowpaths significantly change with each topographic realization, demonstrating that geostatistical simulation can be useful for assessing confidence in subglacial flowpaths.\n
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\n \n \n\n \n \n \n \n \n \n Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0).\n \n \n \n \n\n\n \n Yin, Z.; Strebelle, S.; and Caers, J.\n\n\n \n\n\n\n Geoscientific Model Development, 13(2): 651-672. 2 2020.\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n \n Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@article{\n title = {Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0)},\n type = {article},\n year = {2020},\n pages = {651-672},\n volume = {13},\n websites = {https://www.geosci-model-dev.net/13/651/2020/},\n month = {2},\n day = {19},\n id = {ce77ca1d-2966-3958-a6b4-04b5861315b4},\n created = {2020-02-19T18:16:17.588Z},\n accessed = {2020-02-19},\n file_attached = {true},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2024-11-15T06:43:20.074Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n folder_uuids = {b6f706b8-bef2-4dc2-83ab-cddf23111a24},\n private_publication = {false},\n abstract = {<p><![CDATA[Abstract. Geological uncertainty quantification is critical to subsurface modeling and prediction, such as groundwater, oil or gas, and geothermal resources, and needs to be continuously updated with new data. We provide an automated method for uncertainty quantification and the updating of geological models using borehole data for subsurface developments within a Bayesian framework. Our methodologies are developed with the Bayesian evidential learning protocol for uncertainty quantification. Under such a framework, newly acquired borehole data directly and jointly update geological models (structure, lithology, petrophysics, and fluids), globally and spatially, without time-consuming model rebuilding. To address the above matters, an ensemble of prior geological models is first constructed by Monte Carlo simulation from prior distribution. Once the prior model is tested by means of a falsification process, a sequential direct forecasting is designed to perform the joint uncertainty quantification. The direct forecasting is a statistical learning method that learns from a series of bijective operations to establish “Bayes–linear-Gauss” statistical relationships between model and data variables. Such statistical relationships, once conditioned to actual borehole measurements, allow for fast-computation posterior geological models. The proposed framework is completely automated in an open-source project. We demonstrate its application by applying it to a generic gas reservoir dataset. The posterior results show significant uncertainty reduction in both spatial geological model and gas volume prediction and cannot be falsified by new borehole observations. Furthermore, our automated framework completes the entire uncertainty quantification process efficiently for such large models.]]></p>},\n bibtype = {article},\n author = {Yin, Zhen and Strebelle, Sebastien and Caers, Jef},\n doi = {10.5194/gmd-13-651-2020},\n journal = {Geoscientific Model Development},\n number = {2}\n}\n\n \n
\n\n\n\n \n\n \n \n \n \n \n \n A Tree-Based Direct Sampling Method for Stochastic Surface and Subsurface Hydrological Modeling.\n \n \n \n \n\n\n \n Zuo, C.; Yin, Z.; Pan, Z.; MacKie, E., J.; and Caers, J.\n\n\n \n\n\n\n Water Resources Research, 56(2): e2019WR026130. 2 2020.\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n \n Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@article{\n title = {A Tree-Based Direct Sampling Method for Stochastic Surface and Subsurface Hydrological Modeling},\n type = {article},\n year = {2020},\n pages = {e2019WR026130},\n volume = {56},\n websites = {https://doi.org/10.1029/2019WR026130},\n month = {2},\n publisher = {John Wiley & Sons, Ltd},\n day = {1},\n id = {f2080bb4-278d-3c57-baca-bb56f3bf9809},\n created = {2020-03-02T19:52:41.493Z},\n file_attached = {true},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2024-11-15T06:43:20.139Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n notes = {doi: 10.1029/2019WR026130},\n folder_uuids = {b6f706b8-bef2-4dc2-83ab-cddf23111a24},\n private_publication = {false},\n abstract = {Abstract Direct sampling (DS) is a versatile multiple-point statistics method for generating spatial-temporal geostatistical models. DS is known for being able to address a variety of training images and hence spatiotemporal stochastic modeling problems. One limitation of DS is the central processing unit (CPU) time, mostly attributed to the use of a random search for patterns in the training image. To improve CPU performance, we propose a tree-based direct sampling (TDS) method. In our method, training patterns are grouped according to their similarities combined with a clustering tree for fast lookup. Rather than patterns, we store locations in our database. During the simulation, TDS applies a tree-driven search approach. Two objectives, similarity and diversity, are used to rapidly retrieve patterns and prevent trapping into local optima. We also introduce a way to speed up simulation by means of pasting patterns with adaptive size. The performance of our TDS is investigated using a 2-D benchmark training image. Moreover, we apply the proposed method to two real cases including gap filling the bedrock topography in Antarctica from radar to better understand subglacial hydrology and creating 3-D groundwater models in the Danish aquifer system. Based on several quantitative evaluations, we find the proposed TDS is comparable to DS in terms of simulation quality, while significantly saves CPU time.},\n bibtype = {article},\n author = {Zuo, Chen and Yin, Zhen and Pan, Zhibin and MacKie, Emma J and Caers, Jef},\n doi = {10.1029/2019WR026130},\n journal = {Water Resources Research},\n number = {2}\n}\n\n Abstract Direct sampling (DS) is a versatile multiple-point statistics method for generating spatial-temporal geostatistical models. DS is known for being able to address a variety of training images and hence spatiotemporal stochastic modeling problems. One limitation of DS is the central processing unit (CPU) time, mostly attributed to the use of a random search for patterns in the training image. To improve CPU performance, we propose a tree-based direct sampling (TDS) method. In our method, training patterns are grouped according to their similarities combined with a clustering tree for fast lookup. Rather than patterns, we store locations in our database. During the simulation, TDS applies a tree-driven search approach. Two objectives, similarity and diversity, are used to rapidly retrieve patterns and prevent trapping into local optima. We also introduce a way to speed up simulation by means of pasting patterns with adaptive size. The performance of our TDS is investigated using a 2-D benchmark training image. Moreover, we apply the proposed method to two real cases including gap filling the bedrock topography in Antarctica from radar to better understand subglacial hydrology and creating 3-D groundwater models in the Danish aquifer system. Based on several quantitative evaluations, we find the proposed TDS is comparable to DS in terms of simulation quality, while significantly saves CPU time.\n
\n\n\n\n \n\n \n \n \n \n \n \n A Monte Carlo-based framework for risk-return analysis in mineral prospectivity mapping.\n \n \n \n \n\n\n \n Wang, Z.; Yin, Z.; Caers, J.; and Zuo, R.\n\n\n \n\n\n\n Geoscience Frontiers. 2020.\n \n\n\n\n
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\n\n\n@article{\n title = {A Monte Carlo-based framework for risk-return analysis in mineral prospectivity mapping},\n type = {article},\n year = {2020},\n keywords = {Geostatistics,Mineral exploration,Risk vs return,Uncertainty quantification},\n websites = {http://www.sciencedirect.com/science/article/pii/S1674987120300529},\n id = {3391f265-2de5-3f21-bf34-626224e69a4c},\n created = {2020-06-09T00:53:20.659Z},\n file_attached = {true},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2024-11-15T06:43:20.082Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n folder_uuids = {b6f706b8-bef2-4dc2-83ab-cddf23111a24},\n private_publication = {false},\n abstract = {Quantification of a mineral prospectivity mapping (MPM) heavily relies on geological, geophysical and geochemical analysis, which combines various evidence layers into a single map. However, MPM is subject to considerable uncertainty due to lack of understanding of the metallogenesis and limited spatial data samples. In this paper, we provide a framework that addresses how uncertainty in the evidence layers can be quantified and how such uncertainty is propagated to the prediction of mineral potential. More specifically, we use Monte Carlo simulation to jointly quantify uncertainties on all uncertain evidence variables, categorized into geological, geochemical and geophysical. On stochastically simulated sets of the multiple input layers, logistic regression is employed to produce different quantifications of the mineral potential in terms of probability. Uncertainties we address lie in the downscaling of magnetic data to a scale that makes such data comparable with known mineral deposits. Additionally, we deal with the limited spatial sampling of geochemistry that leads to spatial uncertainty. Next, we deal with the conceptual geological uncertainty related to how the spatial extent of the influence of evidential geological features such as faults, granite intrusions and sedimentary formations. Finally, we provide a novel way to interpret the established uncertainty in a risk-return analysis to decide areas with high potential but at the same time low uncertainty on that potential. Our methods are illustrated and compared with traditional deterministic MPM on a real case study of prospecting skarn Fe deposition in southwestern Fujian, China.},\n bibtype = {article},\n author = {Wang, Ziye and Yin, Zhen and Caers, Jef and Zuo, Renguang},\n doi = {https://doi.org/10.1016/j.gsf.2020.02.010},\n journal = {Geoscience Frontiers}\n}\n\n Quantification of a mineral prospectivity mapping (MPM) heavily relies on geological, geophysical and geochemical analysis, which combines various evidence layers into a single map. However, MPM is subject to considerable uncertainty due to lack of understanding of the metallogenesis and limited spatial data samples. In this paper, we provide a framework that addresses how uncertainty in the evidence layers can be quantified and how such uncertainty is propagated to the prediction of mineral potential. More specifically, we use Monte Carlo simulation to jointly quantify uncertainties on all uncertain evidence variables, categorized into geological, geochemical and geophysical. On stochastically simulated sets of the multiple input layers, logistic regression is employed to produce different quantifications of the mineral potential in terms of probability. Uncertainties we address lie in the downscaling of magnetic data to a scale that makes such data comparable with known mineral deposits. Additionally, we deal with the limited spatial sampling of geochemistry that leads to spatial uncertainty. Next, we deal with the conceptual geological uncertainty related to how the spatial extent of the influence of evidential geological features such as faults, granite intrusions and sedimentary formations. Finally, we provide a novel way to interpret the established uncertainty in a risk-return analysis to decide areas with high potential but at the same time low uncertainty on that potential. Our methods are illustrated and compared with traditional deterministic MPM on a real case study of prospecting skarn Fe deposition in southwestern Fujian, China.\n
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\n\n \n \n 2019\n \n \n (3)\n \n \n
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\n \n \n\n \n \n \n \n \n \n Fast assimilation of frequently acquired 4D seismic data for reservoir history matching.\n \n \n \n \n\n\n \n Yin, Z.; Feng, T.; and MacBeth, C.\n\n\n \n\n\n\n Computers & Geosciences, 128: 30-40. 7 2019.\n \n\n\n\n
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\n\n\n@article{\n title = {Fast assimilation of frequently acquired 4D seismic data for reservoir history matching},\n type = {article},\n year = {2019},\n pages = {30-40},\n volume = {128},\n websites = {https://www.sciencedirect.com/science/article/pii/S0098300418308951},\n month = {7},\n publisher = {Pergamon},\n day = {1},\n id = {c36a2f67-74bb-3f80-a1a1-e8c3d99af63f},\n created = {2019-04-11T18:13:08.859Z},\n accessed = {2019-04-11},\n file_attached = {true},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2024-11-15T06:43:19.656Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b6f706b8-bef2-4dc2-83ab-cddf23111a24},\n private_publication = {false},\n abstract = {The study in this paper proposed a new framework for history matching of frequently acquired 4D seismic data. To achieve this goal, the large volumes of seismic data from the many repeated 4D monitors are firstly condensed into a single attribute by directly correlating them to the reservoir production and injection performances. This ‘well2seis’ cross-correlation is achieved by defining a linear relationship between pressure and saturation-related 4D seismic responses and the cumulative changes of reservoir fluid volumes derived from wells. It is shown that such a cross-disciplinary attribute not only reduces the amount of 4D seismic data for history matching, but also enhances the seismic data reliability since the 4D seismic observations are conditioned by low-uncertainty production data from reservoir engineering domain. In the second step, Morris sensitivity analysis is adapted to fast diagnose the uncertainty reservoir model parameters that are sensitive to the well2seis attributes. To quantitatively assimilate the well2seis observations to calibrate these uncertainty parameters, we then proposed a well2seis objective function that quantifies the mismatch between the observed and model simulated well2seis attributes. Ensemble Smoother with Multiple Data Assimilation (ES-MDA) is performed at the end to iteratively assimilate the well2seis observations by minimizing the well2seis misfit. Application of the proposed workflow to a North Sea field case shows that, when history matching to the observed well2seis attribute that honours the information from seismic and reservoir engineering domains, it can significantly reduce the uncertainty of key reservoir parameters, hence improving the matching quality to both 4D seismic and production observations and enhancing the prediction reliability of the reservoir models. Compared to traditional history matching approaches that attempt to match individual seismic time-lapse attributes and production observations, this approach is observed to significantly boost the history matching efficiency by reducing the number of time-consuming iterations.},\n bibtype = {article},\n author = {Yin, Zhen and Feng, Tao and MacBeth, Colin},\n doi = {10.1016/J.CAGEO.2019.04.001},\n journal = {Computers & Geosciences}\n}\n\n The study in this paper proposed a new framework for history matching of frequently acquired 4D seismic data. To achieve this goal, the large volumes of seismic data from the many repeated 4D monitors are firstly condensed into a single attribute by directly correlating them to the reservoir production and injection performances. This ‘well2seis’ cross-correlation is achieved by defining a linear relationship between pressure and saturation-related 4D seismic responses and the cumulative changes of reservoir fluid volumes derived from wells. It is shown that such a cross-disciplinary attribute not only reduces the amount of 4D seismic data for history matching, but also enhances the seismic data reliability since the 4D seismic observations are conditioned by low-uncertainty production data from reservoir engineering domain. In the second step, Morris sensitivity analysis is adapted to fast diagnose the uncertainty reservoir model parameters that are sensitive to the well2seis attributes. To quantitatively assimilate the well2seis observations to calibrate these uncertainty parameters, we then proposed a well2seis objective function that quantifies the mismatch between the observed and model simulated well2seis attributes. Ensemble Smoother with Multiple Data Assimilation (ES-MDA) is performed at the end to iteratively assimilate the well2seis observations by minimizing the well2seis misfit. Application of the proposed workflow to a North Sea field case shows that, when history matching to the observed well2seis attribute that honours the information from seismic and reservoir engineering domains, it can significantly reduce the uncertainty of key reservoir parameters, hence improving the matching quality to both 4D seismic and production observations and enhancing the prediction reliability of the reservoir models. Compared to traditional history matching approaches that attempt to match individual seismic time-lapse attributes and production observations, this approach is observed to significantly boost the history matching efficiency by reducing the number of time-consuming iterations.\n
\n\n\n\n \n\n \n \n \n \n \n Fast assimilation of frequently acquired 4D seismic data for reservoir history matching.\n \n \n \n\n\n \n Yin, Z.; Feng, T.; and MacBeth, C.\n\n\n \n\n\n\n Computers and Geosciences, 128. 2019.\n \n\n\n\n
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\n\n\n@article{\n title = {Fast assimilation of frequently acquired 4D seismic data for reservoir history matching},\n type = {article},\n year = {2019},\n keywords = {Data assimilation,ES-MDA,Morris sensitivity analysis,Seismic history matching,Uncertainty reduction},\n volume = {128},\n id = {587afbb6-46f4-3cb1-8046-fe0f74730ea0},\n created = {2019-04-21T23:59:00.000Z},\n file_attached = {false},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2020-11-29T16:01:26.801Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {© 2019 The study in this paper proposed a new framework for history matching of frequently acquired 4D seismic data. To achieve this goal, the large volumes of seismic data from the many repeated 4D monitors are firstly condensed into a single attribute by directly correlating them to the reservoir production and injection performances. This ‘well2seis’ cross-correlation is achieved by defining a linear relationship between pressure and saturation-related 4D seismic responses and the cumulative changes of reservoir fluid volumes derived from wells. It is shown that such a cross-disciplinary attribute not only reduces the amount of 4D seismic data for history matching, but also enhances the seismic data reliability since the 4D seismic observations are conditioned by low-uncertainty production data from reservoir engineering domain. In the second step, Morris sensitivity analysis is adapted to fast diagnose the uncertainty reservoir model parameters that are sensitive to the well2seis attributes. To quantitatively assimilate the well2seis observations to calibrate these uncertainty parameters, we then proposed a well2seis objective function that quantifies the mismatch between the observed and model simulated well2seis attributes. Ensemble Smoother with Multiple Data Assimilation (ES-MDA) is performed at the end to iteratively assimilate the well2seis observations by minimizing the well2seis misfit. Application of the proposed workflow to a North Sea field case shows that, when history matching to the observed well2seis attribute that honours the information from seismic and reservoir engineering domains, it can significantly reduce the uncertainty of key reservoir parameters, hence improving the matching quality to both 4D seismic and production observations and enhancing the prediction reliability of the reservoir models. Compared to traditional history matching approaches that attempt to match individual seismic time-lapse attributes and production observations, this approach is observed to significantly boost the history matching efficiency by reducing the number of time-consuming iterations.},\n bibtype = {article},\n author = {Yin, Z. and Feng, T. and MacBeth, C.},\n doi = {10.1016/j.cageo.2019.04.001},\n journal = {Computers and Geosciences}\n}\n\n © 2019 The study in this paper proposed a new framework for history matching of frequently acquired 4D seismic data. To achieve this goal, the large volumes of seismic data from the many repeated 4D monitors are firstly condensed into a single attribute by directly correlating them to the reservoir production and injection performances. This ‘well2seis’ cross-correlation is achieved by defining a linear relationship between pressure and saturation-related 4D seismic responses and the cumulative changes of reservoir fluid volumes derived from wells. It is shown that such a cross-disciplinary attribute not only reduces the amount of 4D seismic data for history matching, but also enhances the seismic data reliability since the 4D seismic observations are conditioned by low-uncertainty production data from reservoir engineering domain. In the second step, Morris sensitivity analysis is adapted to fast diagnose the uncertainty reservoir model parameters that are sensitive to the well2seis attributes. To quantitatively assimilate the well2seis observations to calibrate these uncertainty parameters, we then proposed a well2seis objective function that quantifies the mismatch between the observed and model simulated well2seis attributes. Ensemble Smoother with Multiple Data Assimilation (ES-MDA) is performed at the end to iteratively assimilate the well2seis observations by minimizing the well2seis misfit. Application of the proposed workflow to a North Sea field case shows that, when history matching to the observed well2seis attribute that honours the information from seismic and reservoir engineering domains, it can significantly reduce the uncertainty of key reservoir parameters, hence improving the matching quality to both 4D seismic and production observations and enhancing the prediction reliability of the reservoir models. Compared to traditional history matching approaches that attempt to match individual seismic time-lapse attributes and production observations, this approach is observed to significantly boost the history matching efficiency by reducing the number of time-consuming iterations.\n
\n\n\n\n \n\n \n \n \n \n \n Monte Carlo-based framework for quantification and updating of geological model uncertainty with borehole data.\n \n \n \n\n\n \n Yin, Z.; and Caers, J.\n\n\n \n\n\n\n In 4th EAGE Conference on Petroleum Geostatistics, 2019. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Monte Carlo-based framework for quantification and updating of geological model uncertainty with borehole data},\n type = {inproceedings},\n year = {2019},\n id = {2c08f3b5-7128-337a-b811-82ae38dfc4ed},\n created = {2019-10-17T23:59:00.000Z},\n file_attached = {false},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2021-01-15T11:34:44.041Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {© EAGE 2019. Uncertainty quantification is of importance for reservoir appraisals. In this work, we provide an automated method for uncertainty quantification of geological model using well borehole data for the reservoir appraisal. In our method, when new wells are drilled, multiple components of the geological model are updated jointly and automatically by means of a sequential decomposition following geological rules. During updating, we extend the direct forecasting method to perform such joint model uncertainty reduction. Our approach also enables updating geological model uncertainty without conventional model rebuilding, which significantly reduces the time-consumption. The application to a gas reservoir shows that, this proposed framework can efficiently update the geological model and reduce the prediction uncertainty of the gas storage volume jointly with all model variables.},\n bibtype = {inproceedings},\n author = {Yin, Z. and Caers, J.},\n doi = {10.3997/2214-4609.201902210},\n booktitle = {4th EAGE Conference on Petroleum Geostatistics}\n}\n\n © EAGE 2019. Uncertainty quantification is of importance for reservoir appraisals. In this work, we provide an automated method for uncertainty quantification of geological model using well borehole data for the reservoir appraisal. In our method, when new wells are drilled, multiple components of the geological model are updated jointly and automatically by means of a sequential decomposition following geological rules. During updating, we extend the direct forecasting method to perform such joint model uncertainty reduction. Our approach also enables updating geological model uncertainty without conventional model rebuilding, which significantly reduces the time-consumption. The application to a gas reservoir shows that, this proposed framework can efficiently update the geological model and reduce the prediction uncertainty of the gas storage volume jointly with all model variables.\n
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\n\n \n \n 2018\n \n \n (1)\n \n \n
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\n \n \n\n \n \n \n \n \n \n A workflow for building surface-based reservoir models using NURBS curves, coons patches, unstructured tetrahedral meshes and open-source libraries.\n \n \n \n \n\n\n \n Zhang, Z.; Yin, Z.; and Yan, X.\n\n\n \n\n\n\n Computers & Geosciences, 121: 12-22. 12 2018.\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n \n Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@article{\n title = {A workflow for building surface-based reservoir models using NURBS curves, coons patches, unstructured tetrahedral meshes and open-source libraries},\n type = {article},\n year = {2018},\n pages = {12-22},\n volume = {121},\n websites = {https://www.sciencedirect.com/science/article/pii/S0098300417311196},\n month = {12},\n publisher = {Pergamon},\n day = {1},\n id = {05e88040-3443-3fa3-8eed-6f7465b8250d},\n created = {2019-04-13T00:14:39.527Z},\n accessed = {2019-04-12},\n file_attached = {true},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2024-11-15T06:43:20.139Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n folder_uuids = {b6f706b8-bef2-4dc2-83ab-cddf23111a24},\n private_publication = {false},\n abstract = {Surface-based models have been built to represent complex reservoir geometries. This paper presents a workflow for building surface-based reservoir models using NURBS curves, Coons patches and unstructured tetrahedral volume meshes. Surfaces are created as Coons patches based on NURBS curves. The surface mesh of the entire model is hybrid consisting of quadrilaterals and triangles. Geological regions are represented as volumes bounded by surfaces. Unstructured tetrahedral meshes are built to adapt to the bounding surfaces. Well configurations of location and geometry are particularly flexible, facilitated by mesh adaptation. All libraries for curve, surface and mesh generation are open-source. They are free-of-charge for non-commercial uses. The workflow provides a flexible alternative to commercial software packages for building surface-based models and unstructured meshes. The workflow is validated by simulating two-phase immiscible displacement and comparing to the analytical solution.},\n bibtype = {article},\n author = {Zhang, Zhao and Yin, Zhen and Yan, Xia},\n doi = {10.1016/J.CAGEO.2018.09.001},\n journal = {Computers & Geosciences}\n}\n\n Surface-based models have been built to represent complex reservoir geometries. This paper presents a workflow for building surface-based reservoir models using NURBS curves, Coons patches and unstructured tetrahedral volume meshes. Surfaces are created as Coons patches based on NURBS curves. The surface mesh of the entire model is hybrid consisting of quadrilaterals and triangles. Geological regions are represented as volumes bounded by surfaces. Unstructured tetrahedral meshes are built to adapt to the bounding surfaces. Well configurations of location and geometry are particularly flexible, facilitated by mesh adaptation. All libraries for curve, surface and mesh generation are open-source. They are free-of-charge for non-commercial uses. The workflow provides a flexible alternative to commercial software packages for building surface-based models and unstructured meshes. The workflow is validated by simulating two-phase immiscible displacement and comparing to the analytical solution.\n
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\n\n \n \n 2017\n \n \n (1)\n \n \n
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\n \n \n\n \n \n \n \n \n \n Improving 4D seismic interpretation and seismic history matching using the well2seis technique.\n \n \n \n \n\n\n \n Yin, Z.; Ayzenberg, M.; and Macbeth, C.\n\n\n \n\n\n\n In 1st EAGE Workshop on Practical Reservoir Monitoring, PRM 2017, 2017. \n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n@inproceedings{\n title = {Improving 4D seismic interpretation and seismic history matching using the well2seis technique},\n type = {inproceedings},\n year = {2017},\n id = {97fa757d-8195-321e-b53e-5737e3ec716f},\n created = {2018-05-28T17:39:10.343Z},\n file_attached = {true},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2024-02-27T17:38:18.795Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n folder_uuids = {b6f706b8-bef2-4dc2-83ab-cddf23111a24},\n private_publication = {false},\n abstract = {A well2seis technique is proposed to incorporate available reservoir data from both 4D seismic and well production domains through cross-correlation, to prevent biased reservoir interpretation. The subsequent well2seis attribute shows the capacity to enhance dynamic reservoir interpretations by quantitatively integrating 4D seismic responses with their corresponding well behaviours. It reduces the interpretation workload by avoiding the need to work on many individual 4D differences separately. A joint workflow is then proposed to close the loop between the observed data and the predictions from the simulation model using the well2seis results. This workflow sequentially makes use of direct updating (for updating static models) and assisted history matching (for updating dynamic models) procedures. In the assisted history matching stage, the well2seis correlation attribute successfully acts as an alternative history matching attribute to the conventional production or 4D seismic data. The application of the proposed approach to a North Sea field shows that both the seismic and production history matching results are simultaneously improved to a satisfactory degree when minimising the misfit between the observed and simulated well2seis attributes.},\n bibtype = {inproceedings},\n author = {Yin, Z. and Ayzenberg, M. and Macbeth, C.},\n doi = {10.3997/2214-4609.201700035},\n booktitle = {1st EAGE Workshop on Practical Reservoir Monitoring, PRM 2017}\n}\n\n A well2seis technique is proposed to incorporate available reservoir data from both 4D seismic and well production domains through cross-correlation, to prevent biased reservoir interpretation. The subsequent well2seis attribute shows the capacity to enhance dynamic reservoir interpretations by quantitatively integrating 4D seismic responses with their corresponding well behaviours. It reduces the interpretation workload by avoiding the need to work on many individual 4D differences separately. A joint workflow is then proposed to close the loop between the observed data and the predictions from the simulation model using the well2seis results. This workflow sequentially makes use of direct updating (for updating static models) and assisted history matching (for updating dynamic models) procedures. In the assisted history matching stage, the well2seis correlation attribute successfully acts as an alternative history matching attribute to the conventional production or 4D seismic data. The application of the proposed approach to a North Sea field shows that both the seismic and production history matching results are simultaneously improved to a satisfactory degree when minimising the misfit between the observed and simulated well2seis attributes.\n
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\n\n \n \n 2016\n \n \n (4)\n \n \n
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\n \n \n\n \n \n \n \n \n \n Evaluation of inter-well connectivity using well fluctuations and 4D seismic data.\n \n \n \n \n\n\n \n Yin, Z.; MacBeth, C.; Chassagne, R.; and Vazquez, O.\n\n\n \n\n\n\n Journal of Petroleum Science and Engineering, 145: 533-547. 9 2016.\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n \n Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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Paper\n \n \n \n Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n@article{\n title = {Evaluation of inter-well connectivity using well fluctuations and 4D seismic data},\n type = {article},\n year = {2016},\n pages = {533-547},\n volume = {145},\n websites = {https://www.sciencedirect.com/science/article/pii/S092041051630239X},\n month = {9},\n publisher = {Elsevier},\n day = {1},\n id = {ad358101-b8ac-3373-862e-d9394018eec3},\n created = {2018-05-28T17:39:10.089Z},\n accessed = {2018-03-08},\n file_attached = {true},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2024-11-15T06:43:19.882Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n folder_uuids = {b6f706b8-bef2-4dc2-83ab-cddf23111a24},\n private_publication = {false},\n abstract = {An integrated methodology is proposed to quantitatively evaluate interwell connectivity by uniting available data from the production and seismic domains, while simultaneously honouring reservoir geology. The Capacitance Model approach for interwell evaluation is selected initially to obtain prior understanding using well production and injection fluctuations. Then, to make proper use of 4D seismic data, we extend the newly developed “well2seis” technique to further predict the well-to-reservoir connectivity by correlating multiple seismic monitor surveys to the well behaviour data. Based on the prior information provided by the Capacitance Model, appropriate wells are selected to provide robust 4D seismic correlation. The final result is generated as a 3D attribute volume, which directly reveals spatial patterns of reservoir connectivity. The proposed methodology is firstly tested on a synthetic case, where it is shown that the well2seis correlation attribute can correctly identify key reservoir flow barriers and conduits. When applied to observed data from the Norne field, the pressure diffusion and fluid flow pathways from injectors to producers are detected, which are consistent with bottom-hole pressure measurements and observed sea water production breakthrough. We also discover a key fault barrier which was not considered in the reservoir model previously and successfully improves the history matching quality. The understanding of the reservoir connectivity is significantly improved compared to using conventional methods or the 4D seismic method independently.},\n bibtype = {article},\n author = {Yin, Zhen and MacBeth, Colin and Chassagne, Romain and Vazquez, Oscar},\n doi = {10.1016/J.PETROL.2016.06.021},\n journal = {Journal of Petroleum Science and Engineering}\n}\n\n An integrated methodology is proposed to quantitatively evaluate interwell connectivity by uniting available data from the production and seismic domains, while simultaneously honouring reservoir geology. The Capacitance Model approach for interwell evaluation is selected initially to obtain prior understanding using well production and injection fluctuations. Then, to make proper use of 4D seismic data, we extend the newly developed “well2seis” technique to further predict the well-to-reservoir connectivity by correlating multiple seismic monitor surveys to the well behaviour data. Based on the prior information provided by the Capacitance Model, appropriate wells are selected to provide robust 4D seismic correlation. The final result is generated as a 3D attribute volume, which directly reveals spatial patterns of reservoir connectivity. The proposed methodology is firstly tested on a synthetic case, where it is shown that the well2seis correlation attribute can correctly identify key reservoir flow barriers and conduits. When applied to observed data from the Norne field, the pressure diffusion and fluid flow pathways from injectors to producers are detected, which are consistent with bottom-hole pressure measurements and observed sea water production breakthrough. We also discover a key fault barrier which was not considered in the reservoir model previously and successfully improves the history matching quality. The understanding of the reservoir connectivity is significantly improved compared to using conventional methods or the 4D seismic method independently.\n
\n\n\n\n \n\n \n \n \n \n \n \n Accessing a North Sea reservoir connectivity from 4D seismic and production data.\n \n \n \n \n\n\n \n Ayzenberg, M.; and Yin, Z.\n\n\n \n\n\n\n In 78th EAGE Conference and Exhibition 2016: Efficient Use of Technology - Unlocking Potential, 2016. \n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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Paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n@inproceedings{\n title = {Accessing a North Sea reservoir connectivity from 4D seismic and production data},\n type = {inproceedings},\n year = {2016},\n id = {55c4cf8b-5416-3bf5-b419-b99cbf8fbb55},\n created = {2018-05-28T17:39:10.369Z},\n file_attached = {true},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2024-02-27T18:22:29.147Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n folder_uuids = {b6f706b8-bef2-4dc2-83ab-cddf23111a24},\n private_publication = {false},\n abstract = {It is a common practice nowadays to interpret 4D seismic in view of the production and injection history. Evaluation of the well influence on 4D seismic is often done manually by simultaneously looking at 4D attributes and production/injection profiles. A well2seis technique was developed to automatically correlate 4D signal to the well activity (e.g. Yin et al., 2015). As opposed to individual 4D vintages, the well2seis correlation attribute provides a framework for estimating the drainage radius of wells and understanding the spatial connectivity of the reservoir for maturing reservoir models. We integrate the well2seis technique in a consistent workflow that assists 4D interpretation for the purpose of model maturation, well planning and generally reservoir management. A workflow is applied to a compartmentalized North Sea reservoir where the inter-field communication pattern needs to be determined. As in-fill drilling of an undrained segment is planned in the near future, the sealing properties of major faults need to be understood in order to optimize the field drainage strategy. The study demonstrates that the workflow can assist in maximizing the value of 4D seismic and production/injection data in the decision making process.},\n bibtype = {inproceedings},\n author = {Ayzenberg, M. and Yin, Z.},\n booktitle = {78th EAGE Conference and Exhibition 2016: Efficient Use of Technology - Unlocking Potential}\n}\n\n It is a common practice nowadays to interpret 4D seismic in view of the production and injection history. Evaluation of the well influence on 4D seismic is often done manually by simultaneously looking at 4D attributes and production/injection profiles. A well2seis technique was developed to automatically correlate 4D signal to the well activity (e.g. Yin et al., 2015). As opposed to individual 4D vintages, the well2seis correlation attribute provides a framework for estimating the drainage radius of wells and understanding the spatial connectivity of the reservoir for maturing reservoir models. We integrate the well2seis technique in a consistent workflow that assists 4D interpretation for the purpose of model maturation, well planning and generally reservoir management. A workflow is applied to a compartmentalized North Sea reservoir where the inter-field communication pattern needs to be determined. As in-fill drilling of an undrained segment is planned in the near future, the sealing properties of major faults need to be understood in order to optimize the field drainage strategy. The study demonstrates that the workflow can assist in maximizing the value of 4D seismic and production/injection data in the decision making process.\n
\n\n\n\n \n\n \n \n \n \n \n Evaluation of reservoir connectivity using 4d seismic and production data.\n \n \n \n\n\n \n Yin, Z.; Ayzenberg, M.; and MacBeth, C.\n\n\n \n\n\n\n In 3rd EAGE Integrated Reservoir Modelling Conference, 2016. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n\n\n \n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Evaluation of reservoir connectivity using 4d seismic and production data},\n type = {inproceedings},\n year = {2016},\n id = {d8fd5f33-08c2-3b2e-a5de-4c5f73a098d4},\n created = {2020-10-24T23:59:00.000Z},\n file_attached = {false},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2020-10-27T09:52:49.506Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {© 2016 EAGE. A well2seis technique is proposed to automatically correlate 4D seismic signal to well production activities. It provides a framework for estimating the drainage radius of wells, and understanding the spatial reservoir connectivity for reservoir model maturation. This technique is incorporated into a consistent workflow that assists 4D interpretation for the purpose of model maturation, well planning and reservoir management. We applied this workflow to a compartmentalized North Sea field where the inter-reservoir communication pattern needs to be determined. The study on the field demonstrates that the well2seis workflow can assist in maximizing the value of 4D seismic and production/injection data in the decision making process.},\n bibtype = {inproceedings},\n author = {Yin, Z. and Ayzenberg, M. and MacBeth, C.},\n doi = {10.3997/2214-4609.201602421},\n booktitle = {3rd EAGE Integrated Reservoir Modelling Conference}\n}\n\n © 2016 EAGE. A well2seis technique is proposed to automatically correlate 4D seismic signal to well production activities. It provides a framework for estimating the drainage radius of wells, and understanding the spatial reservoir connectivity for reservoir model maturation. This technique is incorporated into a consistent workflow that assists 4D interpretation for the purpose of model maturation, well planning and reservoir management. We applied this workflow to a compartmentalized North Sea field where the inter-reservoir communication pattern needs to be determined. The study on the field demonstrates that the well2seis workflow can assist in maximizing the value of 4D seismic and production/injection data in the decision making process.\n
\n\n\n\n \n\n \n \n \n \n \n \n Enhancement of dynamic reservoir interpretation using the well2seis technique.\n \n \n \n \n\n\n \n Yin, Z.\n\n\n \n\n\n\n Ph.D. Thesis, 2016.\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n\n@phdthesis{\n title = {Enhancement of dynamic reservoir interpretation using the well2seis technique},\n type = {phdthesis},\n year = {2016},\n institution = {Heriot-Watt University},\n id = {873e2b17-966e-3619-a125-198f0258c67d},\n created = {2021-11-01T19:53:18.832Z},\n file_attached = {true},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2024-11-15T06:43:19.870Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {yin2016enhancement},\n source_type = {phdthesis},\n folder_uuids = {b6f706b8-bef2-4dc2-83ab-cddf23111a24},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Yin, Zhen}\n}\n\n
\n\n \n \n 2015\n \n \n (2)\n \n \n
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\n \n \n\n \n \n \n \n \n \n Joint interpretation of interwell connectivity by integrating 4D seismic with injection and production fluctuations.\n \n \n \n \n\n\n \n Yin, Z.; Macbeth, C.; and Chassagne, R.\n\n\n \n\n\n\n Europec 2015. 2015.\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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Paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n@article{\n title = {Joint interpretation of interwell connectivity by integrating 4D seismic with injection and production fluctuations},\n type = {article},\n year = {2015},\n id = {fbdf7a38-07c3-385d-ac4f-97a07028e297},\n created = {2018-05-30T04:31:05.795Z},\n file_attached = {true},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2024-11-15T06:43:19.660Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b6f706b8-bef2-4dc2-83ab-cddf23111a24},\n private_publication = {false},\n abstract = {Copyright 2015, Society of Petroleum Engineers.A technique is proposed to quantitatively measure interwell connectivity by correlating multiple 4D seismic monitors to historical well production data. We make use of multiple 4D seismic surveys shot over the same reservoir to generate an array of 4D seismic differences. Then a causative relationship is defined between the 4D seismic signals and changes of reservoir fluid volumes caused by injection and production behavior. This allows us to correlate seismic data directly to well data to generate a "well2seis" volume. It is found that the distribution of the "well2seis" correlation attributes reveals key reservoir connectivity features, such as the seal of faults, inter-reservoir shale and fluid flow pathways between wells, and can therefore enhance our interpretation on interwell connectivity. Combining with conventional interwell methods that are based on injection and production rate variations, this multiple 4D seismic method is found to support the conventional interwell approaches and can provide more reliable and detailed interpretation. Our methodology is tested on a synthetic model extracted from full-field data for a Norwegian Sea reservoir, the fluid flow of which is controlled by fault compartmentalization and inter-reservoir shale. The full structural details and reservoir properties are preserved but three scenarios with different degrees of reservoir connectivity are created. It is found that proposed technique successfully detects the flow paths of the injected fluids in all reservoir scenarios. A volumetric attribute is created that accurately identifies the distinctive types of key flow barriers and conduits for each scenario that are known to be major factors influencing the reservoir dynamics. This proves that the well2seis attribute agrees with geological interpretations better than conventional well connectivity factors based on engineering data only. Additionally, the combination of the two types of methods provides a more robust tool for characterization of the reservoir connectivity by providing both quantitative degree and physical pattern of interwell communication.},\n bibtype = {article},\n author = {Yin, Z. and Macbeth, C. and Chassagne, R.},\n journal = {Europec 2015}\n}\n\n Copyright 2015, Society of Petroleum Engineers.A technique is proposed to quantitatively measure interwell connectivity by correlating multiple 4D seismic monitors to historical well production data. We make use of multiple 4D seismic surveys shot over the same reservoir to generate an array of 4D seismic differences. Then a causative relationship is defined between the 4D seismic signals and changes of reservoir fluid volumes caused by injection and production behavior. This allows us to correlate seismic data directly to well data to generate a \"well2seis\" volume. It is found that the distribution of the \"well2seis\" correlation attributes reveals key reservoir connectivity features, such as the seal of faults, inter-reservoir shale and fluid flow pathways between wells, and can therefore enhance our interpretation on interwell connectivity. Combining with conventional interwell methods that are based on injection and production rate variations, this multiple 4D seismic method is found to support the conventional interwell approaches and can provide more reliable and detailed interpretation. Our methodology is tested on a synthetic model extracted from full-field data for a Norwegian Sea reservoir, the fluid flow of which is controlled by fault compartmentalization and inter-reservoir shale. The full structural details and reservoir properties are preserved but three scenarios with different degrees of reservoir connectivity are created. It is found that proposed technique successfully detects the flow paths of the injected fluids in all reservoir scenarios. A volumetric attribute is created that accurately identifies the distinctive types of key flow barriers and conduits for each scenario that are known to be major factors influencing the reservoir dynamics. This proves that the well2seis attribute agrees with geological interpretations better than conventional well connectivity factors based on engineering data only. Additionally, the combination of the two types of methods provides a more robust tool for characterization of the reservoir connectivity by providing both quantitative degree and physical pattern of interwell communication.\n
\n\n\n\n \n\n \n \n \n \n \n \n Enhancement of dynamic reservoir interpretation by correlating multiple 4D seismic monitors to well behavior.\n \n \n \n \n\n\n \n Yin, Z.; Ayzenberg, M.; MacBeth, C.; Feng, T.; and Chassagne, R.\n\n\n \n\n\n\n Interpretation, 3(2): SP35-SP52. 2015.\n \n\n\n\n
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\n\n\n@article{\n title = {Enhancement of dynamic reservoir interpretation by correlating multiple 4D seismic monitors to well behavior},\n type = {article},\n year = {2015},\n pages = {SP35-SP52},\n volume = {3},\n websites = {http://library.seg.org/doi/10.1190/INT-2014-0194.1},\n id = {17e27880-78a5-301a-b369-db4ac1d40dda},\n created = {2019-01-03T00:39:44.550Z},\n file_attached = {true},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2024-11-15T06:43:20.306Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b6f706b8-bef2-4dc2-83ab-cddf23111a24},\n private_publication = {false},\n abstract = {© 2015 Society of Exploration Geophysicists and American Association of Petroleum Geologists. All rights reserved.We have found that dynamic reservoir interpretation can be enhanced by directly correlating the seismic amplitudes from many repeated 4D seismic monitors to the field production and injection history from wells. This "well2seis" crosscorrelation was achieved by defining a linear relationship between the 4D seismic signals and changes in the cumulative fluid volumes at the wells. We also found that the distribution of the well2seis correlation attribute can reveal key reservoir connectivity features, such as the seal of faults, fluid pathways, and communication between neighboring compartments. It can therefore enhance dynamic reservoir description. Based on this enhanced interpretation, we have developed a workflow to close the loop between 4D seismic and reservoir engineering data. First, the reservoir model was directly updated using quantitative information extracted from multiple surveys, by positioning and placing known barriers or conduits to flow. After this process, a seismic-assisted history matching was applied using the well2seis attribute to honor data from the seismic and engineering domains, while remaining consistent with the fault interpretation. Compared to traditional history matching, that attempts to match individual seismic time-lapse amplitudes and production data, our approach used an attribute that condensed available data to effectively enhance the signal. In addition, the approach was observed to improve the history-matching efficiency as well as model predictability. The proposed methodology was applied to a North Sea-field, the production of which was controlled by fault compartmentalization. It successfully detected the communication pathways and sealing property of key faults that are known to be major factors in influencing reservoir development. After history matching, the desired loops were closed by efficiently updating the reservoir simulation model, and this was indicated by a 90% reduction in the misfit errors and 89% lowering of the corresponding uncertainty bounds.},\n bibtype = {article},\n author = {Yin, Zhen and Ayzenberg, Milana and MacBeth, Colin and Feng, Tao and Chassagne, Romain},\n doi = {10.1190/INT-2014-0194.1},\n journal = {Interpretation},\n number = {2}\n}\n\n © 2015 Society of Exploration Geophysicists and American Association of Petroleum Geologists. All rights reserved.We have found that dynamic reservoir interpretation can be enhanced by directly correlating the seismic amplitudes from many repeated 4D seismic monitors to the field production and injection history from wells. This \"well2seis\" crosscorrelation was achieved by defining a linear relationship between the 4D seismic signals and changes in the cumulative fluid volumes at the wells. We also found that the distribution of the well2seis correlation attribute can reveal key reservoir connectivity features, such as the seal of faults, fluid pathways, and communication between neighboring compartments. It can therefore enhance dynamic reservoir description. Based on this enhanced interpretation, we have developed a workflow to close the loop between 4D seismic and reservoir engineering data. First, the reservoir model was directly updated using quantitative information extracted from multiple surveys, by positioning and placing known barriers or conduits to flow. After this process, a seismic-assisted history matching was applied using the well2seis attribute to honor data from the seismic and engineering domains, while remaining consistent with the fault interpretation. Compared to traditional history matching, that attempts to match individual seismic time-lapse amplitudes and production data, our approach used an attribute that condensed available data to effectively enhance the signal. In addition, the approach was observed to improve the history-matching efficiency as well as model predictability. The proposed methodology was applied to a North Sea-field, the production of which was controlled by fault compartmentalization. It successfully detected the communication pathways and sealing property of key faults that are known to be major factors in influencing reservoir development. After history matching, the desired loops were closed by efficiently updating the reservoir simulation model, and this was indicated by a 90% reduction in the misfit errors and 89% lowering of the corresponding uncertainty bounds.\n
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\n \n \n\n \n \n \n \n \n A method to update fault transmissibility multipliers in the flow simulation model directly from 4D seismic.\n \n \n \n\n\n \n Benguigui, A.; Yin, Z.; and Macbeth, C.\n\n\n \n\n\n\n Journal of Geophysics and Engineering, 11(2). 2014.\n \n\n\n\n
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\n\n\n@article{\n title = {A method to update fault transmissibility multipliers in the flow simulation model directly from 4D seismic},\n type = {article},\n year = {2014},\n keywords = {[4D seismic interpretation, fault transmissibility},\n volume = {11},\n id = {ee925f40-7e5b-362f-95f9-75adbf63d5eb},\n created = {2018-05-28T17:39:10.090Z},\n file_attached = {false},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2019-04-13T00:15:00.321Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n folder_uuids = {b6f706b8-bef2-4dc2-83ab-cddf23111a24},\n private_publication = {false},\n abstract = {We propose a new approach to update fault seal estimates in fluid flow simulation models by direct use of 4D seismic amplitudes calibrated by a well geological constraint. The method is suited to compartmentalized reservoirs and based on metrics created from differences in the 4D seismic signature on either side of major faults. The effectiveness of the approach is demonstrated by application to data from the fault controlled Heidrun field in the Norwegian Sea. The results of this application appear favourable and show that our method can detect variations of fault permeability along the major controlling faults in the field. Updates of the field simulation model with the 4D seismic-derived transmissibilities are observed to decrease the mismatch between the predicted and historical field production data in the majority of wells in our sector of interest. © 2014 Sinopec Geophysical Research Institute.},\n bibtype = {article},\n author = {Benguigui, A. and Yin, Z. and Macbeth, C.},\n doi = {10.1088/1742-2132/11/2/025006},\n journal = {Journal of Geophysics and Engineering},\n number = {2}\n}\n\n We propose a new approach to update fault seal estimates in fluid flow simulation models by direct use of 4D seismic amplitudes calibrated by a well geological constraint. The method is suited to compartmentalized reservoirs and based on metrics created from differences in the 4D seismic signature on either side of major faults. The effectiveness of the approach is demonstrated by application to data from the fault controlled Heidrun field in the Norwegian Sea. The results of this application appear favourable and show that our method can detect variations of fault permeability along the major controlling faults in the field. Updates of the field simulation model with the 4D seismic-derived transmissibilities are observed to decrease the mismatch between the predicted and historical field production data in the majority of wells in our sector of interest. © 2014 Sinopec Geophysical Research Institute.\n
\n\n\n\n \n\n \n \n \n \n \n \n Simulation model updating with multiple 4D seismic in a fault-compartmentalized Norwegian sea field.\n \n \n \n \n\n\n \n Yin, Z.; and MacBeth, C.\n\n\n \n\n\n\n In 76th European Association of Geoscientists and Engineers Conference and Exhibition 2014: Experience the Energy - Incorporating SPE EUROPEC 2014, 2014. \n \n\n\n\n
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Paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n@inproceedings{\n title = {Simulation model updating with multiple 4D seismic in a fault-compartmentalized Norwegian sea field},\n type = {inproceedings},\n year = {2014},\n id = {cc4743ce-ec43-3ae5-9e60-bef1df8d1d13},\n created = {2018-05-28T17:39:10.514Z},\n file_attached = {true},\n profile_id = {602a7a04-1e7f-3520-a0c6-a2162c849e33},\n last_modified = {2024-02-27T18:25:21.446Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b6f706b8-bef2-4dc2-83ab-cddf23111a24},\n private_publication = {false},\n abstract = {A new approach is proposed to effectively update fault transmissibility multipliers in a compartmentalized reservoir using many repeated 4D seismic monitors and historic well production data. Our philosophy avoids a time-consuming seismic history matching loop by directly updating the reservoir model using semi-quantitative information extracted from the 4D seismic to drive down the overall misfit. Two methodologies, applied in sequence, are proposed for our workflow using 4D signatures dominated by saturation changes. The first method detects the location of fault barriers and also confirms openly conducting faults. The second estimates transmissibility values for those open faults identified from the first method. Application of our proposed workflow proves that it can help to close the loop between predicted and observed data from both 4D seismic and well history.},\n bibtype = {inproceedings},\n author = {Yin, Z. and MacBeth, C.},\n booktitle = {76th European Association of Geoscientists and Engineers Conference and Exhibition 2014: Experience the Energy - Incorporating SPE EUROPEC 2014}\n}\n\n A new approach is proposed to effectively update fault transmissibility multipliers in a compartmentalized reservoir using many repeated 4D seismic monitors and historic well production data. Our philosophy avoids a time-consuming seismic history matching loop by directly updating the reservoir model using semi-quantitative information extracted from the 4D seismic to drive down the overall misfit. Two methodologies, applied in sequence, are proposed for our workflow using 4D signatures dominated by saturation changes. The first method detects the location of fault barriers and also confirms openly conducting faults. The second estimates transmissibility values for those open faults identified from the first method. Application of our proposed workflow proves that it can help to close the loop between predicted and observed data from both 4D seismic and well history.\n
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