Pub Date : 2019-09-02DOI: 10.3997/2214-4609.201902254
M. Cyz, L. Azevedo
Summary The main goal of reservoir characterization is the description of the subsurface rock properties (i.e. porosity, volume of minerals and fluid saturations). This is commonly done in a sequential, two-step, approach: elastic properties are inferred from seismic inversion, which are then used to compute rock properties by applying calibrated rock physics models. However, this sequential procedure may lead to biased predictions as the uncertainties may not be propagated through the entire process. To overcome these limitations, here we propose the inference of shale rock properties directly from seismic data using a geostatistical direct shale rock physics AVA inversion. The purpose of the proposed geostatistical direct shale rock physics AVA inversion is to extract the properties included in the composition of a shale volume, such as brittleness, TOC and porosity from the seismic reflection data. The proposed method is applied to a real dataset from a Lower Paleozoic shale reservoir in Northern Poland.
{"title":"Geostatistical Seismic Shale Rock Physics AVA Inversion","authors":"M. Cyz, L. Azevedo","doi":"10.3997/2214-4609.201902254","DOIUrl":"https://doi.org/10.3997/2214-4609.201902254","url":null,"abstract":"Summary The main goal of reservoir characterization is the description of the subsurface rock properties (i.e. porosity, volume of minerals and fluid saturations). This is commonly done in a sequential, two-step, approach: elastic properties are inferred from seismic inversion, which are then used to compute rock properties by applying calibrated rock physics models. However, this sequential procedure may lead to biased predictions as the uncertainties may not be propagated through the entire process. To overcome these limitations, here we propose the inference of shale rock properties directly from seismic data using a geostatistical direct shale rock physics AVA inversion. The purpose of the proposed geostatistical direct shale rock physics AVA inversion is to extract the properties included in the composition of a shale volume, such as brittleness, TOC and porosity from the seismic reflection data. The proposed method is applied to a real dataset from a Lower Paleozoic shale reservoir in Northern Poland.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"18 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134289952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-02DOI: 10.3997/2214-4609.201902171
D. Renard, N. Desassis, X. Freulon
Summary New methodology for infering proportions and lithotype rule used for PluriGaussian Model
多元高斯模型中比例推断和岩型规则的新方法
{"title":"Inference of PluriGaussian Model Parameters in SPDE Framework","authors":"D. Renard, N. Desassis, X. Freulon","doi":"10.3997/2214-4609.201902171","DOIUrl":"https://doi.org/10.3997/2214-4609.201902171","url":null,"abstract":"Summary New methodology for infering proportions and lithotype rule used for PluriGaussian Model","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121939552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-02DOI: 10.3997/2214-4609.201902204
E. Barrela, L. Azevedo, A. Soares, L. Guerreiro
Summary This paper shows the application to a real field case of an iterative geostatistical history matching technique, integrating geological and engineering consistency. Current trends reflect a growing interest on developing workflows that simultaneously integrate petrophysical modeling with dynamic calibration of reservoir models to historical production data. Contrary to manual history matching techniques, where model perturbation often disregards geological or physical realism leading to poor production forecast, this example introduces geological consistency through geostatistical simulation and physical realism by using streamline regionalization, while holding the predictive capability of resulting petrophysical models. This is achieved by iteratively updating the reservoir static properties using stochastic sequential simulation and co-simulation, constrained to production data, while using streamline information for electing preponderant flow production regions of the model, focusing property perturbation. In order to capture the complex subsurface heterogeneities of the reservoir, petrophysical property realizations are obtained using the direct sequential simulation and co-simulation with multi-local distribution functions. The location and proportion of reservoir facies is also automatically updated throughout the iterative procedure, using Bayesian Classification. The technique was successfully applied to a real case study, located in North-East onshore Brazil, resulting in multiple history matched models that better reproduce historic data.
{"title":"Fluid Flow Consistent Geostatistical History Matching of an Onshore Reservoir","authors":"E. Barrela, L. Azevedo, A. Soares, L. Guerreiro","doi":"10.3997/2214-4609.201902204","DOIUrl":"https://doi.org/10.3997/2214-4609.201902204","url":null,"abstract":"Summary This paper shows the application to a real field case of an iterative geostatistical history matching technique, integrating geological and engineering consistency. Current trends reflect a growing interest on developing workflows that simultaneously integrate petrophysical modeling with dynamic calibration of reservoir models to historical production data. Contrary to manual history matching techniques, where model perturbation often disregards geological or physical realism leading to poor production forecast, this example introduces geological consistency through geostatistical simulation and physical realism by using streamline regionalization, while holding the predictive capability of resulting petrophysical models. This is achieved by iteratively updating the reservoir static properties using stochastic sequential simulation and co-simulation, constrained to production data, while using streamline information for electing preponderant flow production regions of the model, focusing property perturbation. In order to capture the complex subsurface heterogeneities of the reservoir, petrophysical property realizations are obtained using the direct sequential simulation and co-simulation with multi-local distribution functions. The location and proportion of reservoir facies is also automatically updated throughout the iterative procedure, using Bayesian Classification. The technique was successfully applied to a real case study, located in North-East onshore Brazil, resulting in multiple history matched models that better reproduce historic data.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130611720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-02DOI: 10.3997/2214-4609.201902257
A. Volkova, V. Merkulov
{"title":"Iterative Approach of Gravity and Magnetic Inversion through Geostatistics","authors":"A. Volkova, V. Merkulov","doi":"10.3997/2214-4609.201902257","DOIUrl":"https://doi.org/10.3997/2214-4609.201902257","url":null,"abstract":"","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125362992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-02DOI: 10.3997/2214-4609.201902242
D. Otoo, D. Hodgetts
Summary A major challenge in reservoir modeling is the accurate representation of lithofacies in a defined framework to honor geologic knowledge and available subsurface data. Considering the impact of lithofacies distribution on reservoir petrophysics, a two-stage methodology was applied to enhance lithofacies characterization in the Hugin formation, Volve field. The approach applies the Truncated Gaussian Simulation method that relies on sediment patterns and variograms, derived from geological process simulations. The methodology involves: (1) application of the geological process modeling (Petrel-GPMTM) software to reproduce stratigraphic models of the shallow-marine to marginal-marine Hugin formation (2) define lithofacies distribution in GPM outputs by using the property calculator tool in PetrelTM. Resultant lithofacies trends and variograms are applied to constrain facies modeling. Data includes: seismic data and 24 complete suites of well logs. The Hugin formation consists of a complex mix of wave and riverine sediment deposits within a period of transgression of the Viking Graben. Twenty depositional models were reproduced using different geological process scenarios. GPM-based facies models show an improvement in lithofacies representation, evident in the geologically realistic distribution of lithofacies in inter-well volumes, leading to the conclusion that a robust stratigraphic model provides an important stratigraphic framework for modeling facies heterogeneities.
{"title":"Using Geological Process Modeling to Enhance Lithofacies Distribution in a 3-D Model: An Example","authors":"D. Otoo, D. Hodgetts","doi":"10.3997/2214-4609.201902242","DOIUrl":"https://doi.org/10.3997/2214-4609.201902242","url":null,"abstract":"Summary A major challenge in reservoir modeling is the accurate representation of lithofacies in a defined framework to honor geologic knowledge and available subsurface data. Considering the impact of lithofacies distribution on reservoir petrophysics, a two-stage methodology was applied to enhance lithofacies characterization in the Hugin formation, Volve field. The approach applies the Truncated Gaussian Simulation method that relies on sediment patterns and variograms, derived from geological process simulations. The methodology involves: (1) application of the geological process modeling (Petrel-GPMTM) software to reproduce stratigraphic models of the shallow-marine to marginal-marine Hugin formation (2) define lithofacies distribution in GPM outputs by using the property calculator tool in PetrelTM. Resultant lithofacies trends and variograms are applied to constrain facies modeling. Data includes: seismic data and 24 complete suites of well logs. The Hugin formation consists of a complex mix of wave and riverine sediment deposits within a period of transgression of the Viking Graben. Twenty depositional models were reproduced using different geological process scenarios. GPM-based facies models show an improvement in lithofacies representation, evident in the geologically realistic distribution of lithofacies in inter-well volumes, leading to the conclusion that a robust stratigraphic model provides an important stratigraphic framework for modeling facies heterogeneities.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116678705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-02DOI: 10.3997/2214-4609.201902276
H. Debeye
Summary Geostatistical simultaneous facies inversion based on the Bayesian inference method is presented. Recent debate on the topic has been focused on the one-step versus the two-step approach. Here we side-step this topic by investigating and discussing the trace-by-trace versus the spatial full 3D inversion method. Experiments are done to compare several variations of trace-by-trace with no lateral conditioning, trace-by-trace with lateral conditioning and full 3D methods with lateral conditioning. Conditioning is based on either exponential or Gaussian variograms. With several QCs it is shown that quality of results improves going from trace-by-trace to full 3D inversion. Likewise quality of results improves going from conditioning based on exponential variograms to conditioning based on Gaussian variograms. The full 3D method with lateral conditioning based on Gaussian variograms beats the other schemes with respect to the look and feel and statistics of the facies realizations.
{"title":"Spatial Continuity and Simultaneous Seismic Inversion of Facies and Reservoir Properties Ready for Flow Simulation","authors":"H. Debeye","doi":"10.3997/2214-4609.201902276","DOIUrl":"https://doi.org/10.3997/2214-4609.201902276","url":null,"abstract":"Summary Geostatistical simultaneous facies inversion based on the Bayesian inference method is presented. Recent debate on the topic has been focused on the one-step versus the two-step approach. Here we side-step this topic by investigating and discussing the trace-by-trace versus the spatial full 3D inversion method. Experiments are done to compare several variations of trace-by-trace with no lateral conditioning, trace-by-trace with lateral conditioning and full 3D methods with lateral conditioning. Conditioning is based on either exponential or Gaussian variograms. With several QCs it is shown that quality of results improves going from trace-by-trace to full 3D inversion. Likewise quality of results improves going from conditioning based on exponential variograms to conditioning based on Gaussian variograms. The full 3D method with lateral conditioning based on Gaussian variograms beats the other schemes with respect to the look and feel and statistics of the facies realizations.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126534586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-02DOI: 10.3997/2214-4609.201902239
P. Juda, P. Renard, J. Straubhaar
{"title":"K-fold Cross-validation of Multiple-point Statistical Simulations","authors":"P. Juda, P. Renard, J. Straubhaar","doi":"10.3997/2214-4609.201902239","DOIUrl":"https://doi.org/10.3997/2214-4609.201902239","url":null,"abstract":"","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126543627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-02DOI: 10.3997/2214-4609.201902259
V. Lisitsa, M. Novikov, Y. Bazaikin, D. Kolyukhin
{"title":"Seismic Attenuation in Two-Scale Porous Fractured Media — A Numerical Study","authors":"V. Lisitsa, M. Novikov, Y. Bazaikin, D. Kolyukhin","doi":"10.3997/2214-4609.201902259","DOIUrl":"https://doi.org/10.3997/2214-4609.201902259","url":null,"abstract":"","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127337536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-03DOI: 10.3997/2214-4609.201902199
L. Mosser, O. Dubrule, M. Blunt
Summary Numerous geophysical tasks require the solution of ill-posed inverse problems where we seek to find a distribution of earth models that match observed data such as reflected acoustic waveforms or produced hydrocarbon volumes. We present a framework to create stochastic samples of posterior property distributions for ill-posed inverse problems using a gradient-based approach. The spatial distribution of petrophysical properties is created by a deep generative model and controlled by a set of latent variables. A generative adversarial network (GAN) is used to represent a prior distribution of geological models based on a training set of object-based models. We minimize the mismatch between observed ground-truth data and numerical forward-models of the generator output by first computing gradients of the objective function with respect to grid-block properties and using neural network backpropagation to obtain gradients with respect to the latent variables. Synthetic test cases of acoustic waveform inversion and reservoir history matching are presented. In seismic inversion, we use a Metropolis adjusted Langevin algorithm (MALA) to obtain posterior samples. For both synthetic cases, we show that deep generative models such as GANs can be combined in an end-to-end framework to obtain stochastic solutions to geophysical inverse problems.
{"title":"Deep Stochastic Inversion","authors":"L. Mosser, O. Dubrule, M. Blunt","doi":"10.3997/2214-4609.201902199","DOIUrl":"https://doi.org/10.3997/2214-4609.201902199","url":null,"abstract":"Summary Numerous geophysical tasks require the solution of ill-posed inverse problems where we seek to find a distribution of earth models that match observed data such as reflected acoustic waveforms or produced hydrocarbon volumes. We present a framework to create stochastic samples of posterior property distributions for ill-posed inverse problems using a gradient-based approach. The spatial distribution of petrophysical properties is created by a deep generative model and controlled by a set of latent variables. A generative adversarial network (GAN) is used to represent a prior distribution of geological models based on a training set of object-based models. We minimize the mismatch between observed ground-truth data and numerical forward-models of the generator output by first computing gradients of the objective function with respect to grid-block properties and using neural network backpropagation to obtain gradients with respect to the latent variables. Synthetic test cases of acoustic waveform inversion and reservoir history matching are presented. In seismic inversion, we use a Metropolis adjusted Langevin algorithm (MALA) to obtain posterior samples. For both synthetic cases, we show that deep generative models such as GANs can be combined in an end-to-end framework to obtain stochastic solutions to geophysical inverse problems.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125652974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.3997/2214-4609.201902258
Y. Wang, D. Liu, Y. Zhong, L. Li, X. Wang
{"title":"Study on the Fine Prediction of Ediacaran Fractured-vuggy Karst Reservoir","authors":"Y. Wang, D. Liu, Y. Zhong, L. Li, X. Wang","doi":"10.3997/2214-4609.201902258","DOIUrl":"https://doi.org/10.3997/2214-4609.201902258","url":null,"abstract":"","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126445569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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