Pub Date : 2019-09-02DOI: 10.3997/2214-4609.201902267
M. Gineste, J. Eidsvik
Summary Probabilistic inversion of subsurface elastic properties using seismic reflection data is considered. The methodology makes use of data partitioning as a divide-and-conquer strategy, while the conditioning to data makes use of an iterative ensemble Kalman smoother. Augmenting the ensemble Kalman framework with an variational approach is found suitable when conditioning on larger sets of seismic waveform data. The methodology is exemplified using a synthetic case for the inversion of acoustic- and shear velocity and density.
{"title":"Seismic Waveform Inversion of Elastic Properties Using an Iterative Ensemble Kalman Smoother","authors":"M. Gineste, J. Eidsvik","doi":"10.3997/2214-4609.201902267","DOIUrl":"https://doi.org/10.3997/2214-4609.201902267","url":null,"abstract":"Summary Probabilistic inversion of subsurface elastic properties using seismic reflection data is considered. The methodology makes use of data partitioning as a divide-and-conquer strategy, while the conditioning to data makes use of an iterative ensemble Kalman smoother. Augmenting the ensemble Kalman framework with an variational approach is found suitable when conditioning on larger sets of seismic waveform data. The methodology is exemplified using a synthetic case for the inversion of acoustic- and shear velocity and density.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"1 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":"123963003","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: 10.3997/2214-4609.201902200
V. Zaccardi, A. Abadpour, N. Haller, P. Berthet, D. Rappin, J. Grange-Praderas
{"title":"Integrated Geo-modelling and Ensemble History Matching of Highly Faulted Turbiditic Reservoir Model","authors":"V. Zaccardi, A. Abadpour, N. Haller, P. Berthet, D. Rappin, J. Grange-Praderas","doi":"10.3997/2214-4609.201902200","DOIUrl":"https://doi.org/10.3997/2214-4609.201902200","url":null,"abstract":"","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"10 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":"127262618","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: 10.3997/2214-4609.201902243
G. Massonnat
{"title":"Random Walk for Simulation of Geobodies: A New Process-like Methodology for Reservoir Modelling","authors":"G. Massonnat","doi":"10.3997/2214-4609.201902243","DOIUrl":"https://doi.org/10.3997/2214-4609.201902243","url":null,"abstract":"","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"1 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":"129570919","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: 10.3997/2214-4609.201902214
D. Garner
Summary A key impact on reservoir studies is a rigorous strategy around facies for modeling. The industry practices across small to large companies are highly variable regarding generating facies logs. Geomodeling workflows and geostatistical algorithms treat the facies log variable as hard conditioning information. Facies logs in practice have errors and carry petrophysical inconsistencies, real quality issues, which are not head-on addressed by the time they are used in a geomodeling workflow. Establishing electrofacies modeling best practices in the petroleum industry can help improve the preparation of facies logs for modeling and improve the fidelity of many geomodeling processes. This material presents basic theory, practical considerations, and example results from up to four different fields, depending on poster size. Further discussion is intended to further illustrate benefits of the use of electrofacies and help mature the understanding of the workflows which are not widely used.
{"title":"Raising the Bar: Electrofacies as a Framework for Improving the Practice of Geomodeling","authors":"D. Garner","doi":"10.3997/2214-4609.201902214","DOIUrl":"https://doi.org/10.3997/2214-4609.201902214","url":null,"abstract":"Summary A key impact on reservoir studies is a rigorous strategy around facies for modeling. The industry practices across small to large companies are highly variable regarding generating facies logs. Geomodeling workflows and geostatistical algorithms treat the facies log variable as hard conditioning information. Facies logs in practice have errors and carry petrophysical inconsistencies, real quality issues, which are not head-on addressed by the time they are used in a geomodeling workflow. Establishing electrofacies modeling best practices in the petroleum industry can help improve the preparation of facies logs for modeling and improve the fidelity of many geomodeling processes. This material presents basic theory, practical considerations, and example results from up to four different fields, depending on poster size. Further discussion is intended to further illustrate benefits of the use of electrofacies and help mature the understanding of the workflows which are not widely used.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"3 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":"123991223","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: 10.3997/2214-4609.201902208
K. Struminskiy, A. Klenitskiy, A. A. Reshytko, D. Egorov, A. Shchepetnov, A. Sabirov, D. Vetrov, A. Semenikhin, O. Osmonalieva, B. Belozerov
{"title":"Well Log Data Standardization, Imputation and Anomaly Detection Using Hidden Markov Models","authors":"K. Struminskiy, A. Klenitskiy, A. A. Reshytko, D. Egorov, A. Shchepetnov, A. Sabirov, D. Vetrov, A. Semenikhin, O. Osmonalieva, B. Belozerov","doi":"10.3997/2214-4609.201902208","DOIUrl":"https://doi.org/10.3997/2214-4609.201902208","url":null,"abstract":"","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"45 10 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":"125687595","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: 10.3997/2214-4609.201902235
W. Zhu, B. Yalcin, S. Khirevich, T. Patzek
{"title":"Correlation Analysis of Fracture Intensity Descriptors with Different Dimensionality in a Geomechanics-constrained 3D Fracture Network","authors":"W. Zhu, B. Yalcin, S. Khirevich, T. Patzek","doi":"10.3997/2214-4609.201902235","DOIUrl":"https://doi.org/10.3997/2214-4609.201902235","url":null,"abstract":"","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"23 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":"121627800","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: 10.3997/2214-4609.201902215
M. Franzel, S. Jones, Ian H. Jermyn, M. Allen, K. McCaffrey
Summary The three-dimensional geometry of fluvial channel sand bodies has received considerably less attention than their internal sedimentology, despite the importance of sandstone body geometry for subsurface reservoir modelling. The aspect ratio (width/thickness, W:T) of fluvial channels is widely used to characterize their geometry. However, this does not provide a full characterization of fluvial sand body shape, since one W:T ratio can correspond to many different channel geometries. The resultant over- or underestimation of the cross-sectional area of a sand body can have significant implications for reservoir models and hydrocarbon volume predictions. There is thus a clear need for the generation of versatile, quantitative, and statistically robust models for sand body shape. The main aim of this research is to develop a new statistically-based approach that will provide quantitative data, derived from outcrop analogues, to fully constrain stochastic fluvial reservoir models. Here, we describe the construction of a new shape database and conduct a preliminary qualitative analysis in order to understand measurement and other uncertainties, and to explore the catalogue of shape configurations. A future quantitative analysis will develop a predictive model to enable forecasting of reservoir channel sand body geometries and shapes that can be built into existing reservoir models.
{"title":"Statistical Characterisation of Fluvial Sand Bodies: Implications for Complex Reservoir Models","authors":"M. Franzel, S. Jones, Ian H. Jermyn, M. Allen, K. McCaffrey","doi":"10.3997/2214-4609.201902215","DOIUrl":"https://doi.org/10.3997/2214-4609.201902215","url":null,"abstract":"Summary The three-dimensional geometry of fluvial channel sand bodies has received considerably less attention than their internal sedimentology, despite the importance of sandstone body geometry for subsurface reservoir modelling. The aspect ratio (width/thickness, W:T) of fluvial channels is widely used to characterize their geometry. However, this does not provide a full characterization of fluvial sand body shape, since one W:T ratio can correspond to many different channel geometries. The resultant over- or underestimation of the cross-sectional area of a sand body can have significant implications for reservoir models and hydrocarbon volume predictions. There is thus a clear need for the generation of versatile, quantitative, and statistically robust models for sand body shape. The main aim of this research is to develop a new statistically-based approach that will provide quantitative data, derived from outcrop analogues, to fully constrain stochastic fluvial reservoir models. Here, we describe the construction of a new shape database and conduct a preliminary qualitative analysis in order to understand measurement and other uncertainties, and to explore the catalogue of shape configurations. A future quantitative analysis will develop a predictive model to enable forecasting of reservoir channel sand body geometries and shapes that can be built into existing reservoir models.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"35 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":"125298222","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: 10.3997/2214-4609.201902184
Pedro Correia, J. Chautru, Y. Meric, F. Geffroy, H. Binet, P. Ruffo, L. Bazzana
Summary The structurally lowest point in a hydrocarbon trap that can retain hydrocarbons is called a Spill Point and characterizing these locations over a depth horizon is a common approach in trap analysis. However, a horizon is an uncertain object typically produced through a time to depth conversion procedure which might involve several different variables like time, velocity, and fault position. Each of those variables brings its own uncertainty. By using geostatistical simulations, we produce different realizations of the depth horizons and further process them individually to determine the probability of presence of reservoirs and spill points associated to highly probable reservoirs. This paper presents a methodology to achieve such results including our analysis algorithm for trap and spill point characterization. By using a case-study we demonstrate that only proper characterization of all relevant realizations in the uncertainty space show us the possible scenarios, and their impact on traps volume.
{"title":"Automatic Scenarios Extraction from Depth Uncertainty Evaluation","authors":"Pedro Correia, J. Chautru, Y. Meric, F. Geffroy, H. Binet, P. Ruffo, L. Bazzana","doi":"10.3997/2214-4609.201902184","DOIUrl":"https://doi.org/10.3997/2214-4609.201902184","url":null,"abstract":"Summary The structurally lowest point in a hydrocarbon trap that can retain hydrocarbons is called a Spill Point and characterizing these locations over a depth horizon is a common approach in trap analysis. However, a horizon is an uncertain object typically produced through a time to depth conversion procedure which might involve several different variables like time, velocity, and fault position. Each of those variables brings its own uncertainty. By using geostatistical simulations, we produce different realizations of the depth horizons and further process them individually to determine the probability of presence of reservoirs and spill points associated to highly probable reservoirs. This paper presents a methodology to achieve such results including our analysis algorithm for trap and spill point characterization. By using a case-study we demonstrate that only proper characterization of all relevant realizations in the uncertainty space show us the possible scenarios, and their impact on traps volume.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"165 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":"122986915","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: 10.3997/2214-4609.201902185
P. Alikhani, A. Guadagnini, F. Inzoli
Summary Data on hydrocarbon reservoir attributes (e.g., permeability, porosity) are only available at a set of sparse locations, thus resulting (at best) in an incomplete knowledge of spatial heterogeneity of the system. This lack of information propagates to uncertainty in our evaluations of reservoir performance and of the resulting oil recovery. We consider a two-phase flow setting taking place in a randomly heterogeneous (correlated) permeability field to assess the feedback between viscous and gravity forces in a numerical Monte Carlo context and finally characterize oil recovery estimates under uncertainty for a water flooding scenario. Our work leads to the following major conclusions: Uncertainty in the spatial distribution of permeability propagates to final oil recovery in a way that depends on the feedback between gravity and viscous forces driving the system. Uncertainty of final oil recovered (as rendered in terms of variance) is smallest for vertical flows, consistent with the observation that the gravity effect is largest in such scenarios and is dominant in controlling the flow dynamics. Uncertainty of final oil recovered tends to be higher when there is competition between the effects of gravity and viscous forces, the latter being influenced by the strength of the spatial variability of permeability.
{"title":"Feedback Between Gravity and Viscous Forces in Two-phase Buckley-Leverett Flow in Randomly Heterogeneous Permeability Fields","authors":"P. Alikhani, A. Guadagnini, F. Inzoli","doi":"10.3997/2214-4609.201902185","DOIUrl":"https://doi.org/10.3997/2214-4609.201902185","url":null,"abstract":"Summary Data on hydrocarbon reservoir attributes (e.g., permeability, porosity) are only available at a set of sparse locations, thus resulting (at best) in an incomplete knowledge of spatial heterogeneity of the system. This lack of information propagates to uncertainty in our evaluations of reservoir performance and of the resulting oil recovery. We consider a two-phase flow setting taking place in a randomly heterogeneous (correlated) permeability field to assess the feedback between viscous and gravity forces in a numerical Monte Carlo context and finally characterize oil recovery estimates under uncertainty for a water flooding scenario. Our work leads to the following major conclusions: Uncertainty in the spatial distribution of permeability propagates to final oil recovery in a way that depends on the feedback between gravity and viscous forces driving the system. Uncertainty of final oil recovered (as rendered in terms of variance) is smallest for vertical flows, consistent with the observation that the gravity effect is largest in such scenarios and is dominant in controlling the flow dynamics. Uncertainty of final oil recovered tends to be higher when there is competition between the effects of gravity and viscous forces, the latter being influenced by the strength of the spatial variability of permeability.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"609 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":"116176996","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: 10.3997/2214-4609.201902193
P. Birkle, M. Zouch, M. Alzaqebah, M. Alwohaibi
{"title":"Machine Learning-based Approach for Automated Identification of Produced Water Types from Conventional and Unconventional Reservoirs","authors":"P. Birkle, M. Zouch, M. Alzaqebah, M. Alwohaibi","doi":"10.3997/2214-4609.201902193","DOIUrl":"https://doi.org/10.3997/2214-4609.201902193","url":null,"abstract":"","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"150 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":"125772615","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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