Pub Date : 2019-09-02DOI: 10.3997/2214-4609.201902248
N. Kutukova
{"title":"Optimization of the Development of the Yurubcheno-Tokhomsky Field Based on the Conceptual Geological Model","authors":"N. Kutukova","doi":"10.3997/2214-4609.201902248","DOIUrl":"https://doi.org/10.3997/2214-4609.201902248","url":null,"abstract":"","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"50 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":"127093999","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: 10.3997/2214-4609.201902178
C. Sanchis, R. Hauge, H. Kjønsberg
Summary Bayesian inversion is used for the prediction of lithology and fluids from AVO seismic data. We assume a multidimensional Gaussian rock physics prior model for the elastic parameters. In this study, we look at the role of the elastic parameters variance in the prior model and how it can impact facies predictions. When the facies classes contained in the prior model have different variance, this difference influences the inversion beyond just adding uncertainty to the seismic reflections. We examine the balance between the influence of this variance and the match with expected seismic data. Our results show that although the variance influence may lead to unexpected results in synthetic scenarios, it also helps to predict the facies configuration when the seismic data follows the prior distribution and forward model.
{"title":"Expecting the Unexpected: The Influence of Elastic Parameter Variance on Bayesian Facies Inversion","authors":"C. Sanchis, R. Hauge, H. Kjønsberg","doi":"10.3997/2214-4609.201902178","DOIUrl":"https://doi.org/10.3997/2214-4609.201902178","url":null,"abstract":"Summary Bayesian inversion is used for the prediction of lithology and fluids from AVO seismic data. We assume a multidimensional Gaussian rock physics prior model for the elastic parameters. In this study, we look at the role of the elastic parameters variance in the prior model and how it can impact facies predictions. When the facies classes contained in the prior model have different variance, this difference influences the inversion beyond just adding uncertainty to the seismic reflections. We examine the balance between the influence of this variance and the match with expected seismic data. Our results show that although the variance influence may lead to unexpected results in synthetic scenarios, it also helps to predict the facies configuration when the seismic data follows the prior distribution and forward model.","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":"128500284","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: 10.3997/2214-4609.201902170
O. Kolbjørnsen, P. Dahle, M. Bjerke, B. Bakke, K. Straith
We consider the problem of depth conversion at the Edvard Grieg field. Measurements of deep directional resistivity suggest that the top surface on Edvard Grieg is much smoother than what is indicated by the interpretation of seismic reflectors. We investigate this problem by tools of standard depth conversion, by integrating measurements from deep directional resistivity into the standard kriging equations. We propose a statistical model which is able to reveal whether we should introduce a smoothing term for the time interpretations to improve the mapping of the top surface.
{"title":"Using Deep Directional Resistivity for Model Selection and Uncertainty Reduction in the Edvard Grieg Depth Conversion","authors":"O. Kolbjørnsen, P. Dahle, M. Bjerke, B. Bakke, K. Straith","doi":"10.3997/2214-4609.201902170","DOIUrl":"https://doi.org/10.3997/2214-4609.201902170","url":null,"abstract":"We consider the problem of depth conversion at the Edvard Grieg field. Measurements of deep directional resistivity suggest that the top surface on Edvard Grieg is much smoother than what is indicated by the interpretation of seismic reflectors. We investigate this problem by tools of standard depth conversion, by integrating measurements from deep directional resistivity into the standard kriging equations. We propose a statistical model which is able to reveal whether we should introduce a smoothing term for the time interpretations to improve the mapping of the top surface.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"205 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":"133827139","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: 10.3997/2214-4609.201902273
L. D. Figueiredo, D. Grana, M. Roisenberg, B. B. Rodrigues
Summary We present a Markov chain Monte Carlo method for the computation of the posterior distribution of discrete and continuous properties in geophysical inverse problems. Mixture distributions, Gaussian or non-parametric, have been proposed to model the multimodal behaviour or subsurface properties. However, due to the spatial correlation of subsurface properties, the number of modes of the mixture distribution increases exponentially with the number of samples in the data vector. In this work, we propose a new Markov chain Monte Carlo method based on two steps. First, we update the configuration of the discrete property (for example, facies or rock types), then we update the configuration of the continuous properties (for example, elastic or petrophysical properties). The first step can be performed according to a jump move, where a new configuration is proposed, or a local move, where the configuration of the previous iteration is preserved. The second step is performed by sampling the new configuration of continuous properties either from the analytical expression of the Gaussian distribution of the continuous properties conditioned by the facies configuration in the Gaussian-linear case, or by numerically sampling from the non-parametric conditional distribution in the non-Gaussian and non-linear case. The methodology is demonstrated through the application to synthetic and real datasets.
{"title":"Markov Chain Monte Carlo Methods for High-dimensional Mixture Distributions","authors":"L. D. Figueiredo, D. Grana, M. Roisenberg, B. B. Rodrigues","doi":"10.3997/2214-4609.201902273","DOIUrl":"https://doi.org/10.3997/2214-4609.201902273","url":null,"abstract":"Summary We present a Markov chain Monte Carlo method for the computation of the posterior distribution of discrete and continuous properties in geophysical inverse problems. Mixture distributions, Gaussian or non-parametric, have been proposed to model the multimodal behaviour or subsurface properties. However, due to the spatial correlation of subsurface properties, the number of modes of the mixture distribution increases exponentially with the number of samples in the data vector. In this work, we propose a new Markov chain Monte Carlo method based on two steps. First, we update the configuration of the discrete property (for example, facies or rock types), then we update the configuration of the continuous properties (for example, elastic or petrophysical properties). The first step can be performed according to a jump move, where a new configuration is proposed, or a local move, where the configuration of the previous iteration is preserved. The second step is performed by sampling the new configuration of continuous properties either from the analytical expression of the Gaussian distribution of the continuous properties conditioned by the facies configuration in the Gaussian-linear case, or by numerically sampling from the non-parametric conditional distribution in the non-Gaussian and non-linear case. The methodology is demonstrated through the application to synthetic and real datasets.","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":"131300496","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: 10.3997/2214-4609.201902238
D. Walsh, T. Manzocchi
Summary Conventional geostatistical modelling methods are unable to reproduce the low connectivity typical of deep marine turbidite reservoirs at high net:gross ratios, because the connectivity of these geomodels is inevitably controlled by their net:gross ratio. Previous studies have developed modelling methods that can honour independently both the low connectivity and high net:gross ratios of these systems at different hierarchical scales, however they are unable to honour available well data. We present a new workflow for building reservoir geomodels conditioned to well data, with realistic levels of sand connectivity and hierarchical stacking.
{"title":"A Workflow for Generating Hierarchical Reservoir Geomodels Conditioned to Well Data with Realistic Sand Connectivity","authors":"D. Walsh, T. Manzocchi","doi":"10.3997/2214-4609.201902238","DOIUrl":"https://doi.org/10.3997/2214-4609.201902238","url":null,"abstract":"Summary Conventional geostatistical modelling methods are unable to reproduce the low connectivity typical of deep marine turbidite reservoirs at high net:gross ratios, because the connectivity of these geomodels is inevitably controlled by their net:gross ratio. Previous studies have developed modelling methods that can honour independently both the low connectivity and high net:gross ratios of these systems at different hierarchical scales, however they are unable to honour available well data. We present a new workflow for building reservoir geomodels conditioned to well data, with realistic levels of sand connectivity and hierarchical stacking.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"55 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":"115404281","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: 10.3997/2214-4609.201902225
J. Peisker, A. Miller, M. Ebner
Summary Stochastic reservoir modeling is an integral part of quantifying subsurface uncertainties. Classical geostatistical methods like Gaussian random function and multi-point geostatistics (MPS) are robust and cheap in computing time. However, these methods are based on mathematical/statistical concepts and therefore lack geological plausibility. Physical modeling with stratigraphic forward modeling (SFM), on the other hand, is capable of generating detailed 3D simulations of the geological realm. Conditioning SFM to e.g. well log data is expensive and not always successful. A hybrid approach of SFM with MPS can support the conditioning. This approach generates concept driven models that match the well data while also keeping geological continuity. Experiments were done on the mature 7th Tortonian oil reservoir in, Austria. Classical geostatistical approaches failed to generate enough dynamically diverse prior models to envelop the production data. First one geological process (SFM) model was generated and conditioned to well data. The result was then used as a training image (TI) for MPS. These results better match the wells while still preserving the geological information from SFM. All simulation models have been initialized and dynamically simulated. In comparison with the common geostatistical approach, they are dynamically more diverse while being more constrained by geological concepts.
{"title":"Combining Stratigraphic Forward Modelling with Multiple-point Statistics - A Case Study from Seismic to Tracer Response","authors":"J. Peisker, A. Miller, M. Ebner","doi":"10.3997/2214-4609.201902225","DOIUrl":"https://doi.org/10.3997/2214-4609.201902225","url":null,"abstract":"Summary Stochastic reservoir modeling is an integral part of quantifying subsurface uncertainties. Classical geostatistical methods like Gaussian random function and multi-point geostatistics (MPS) are robust and cheap in computing time. However, these methods are based on mathematical/statistical concepts and therefore lack geological plausibility. Physical modeling with stratigraphic forward modeling (SFM), on the other hand, is capable of generating detailed 3D simulations of the geological realm. Conditioning SFM to e.g. well log data is expensive and not always successful. A hybrid approach of SFM with MPS can support the conditioning. This approach generates concept driven models that match the well data while also keeping geological continuity. Experiments were done on the mature 7th Tortonian oil reservoir in, Austria. Classical geostatistical approaches failed to generate enough dynamically diverse prior models to envelop the production data. First one geological process (SFM) model was generated and conditioned to well data. The result was then used as a training image (TI) for MPS. These results better match the wells while still preserving the geological information from SFM. All simulation models have been initialized and dynamically simulated. In comparison with the common geostatistical approach, they are dynamically more diverse while being more constrained by geological concepts.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"5 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":"126086349","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: 10.3997/2214-4609.201902250
S. Anyosa, S. Bunting, J. Eidsvik, A. Romdhane
{"title":"A Simulation Analysis of CO2 Capture and Underground Storage Monitoring in Smeaheia","authors":"S. Anyosa, S. Bunting, J. Eidsvik, A. Romdhane","doi":"10.3997/2214-4609.201902250","DOIUrl":"https://doi.org/10.3997/2214-4609.201902250","url":null,"abstract":"","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"7 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":"126457300","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: 10.3997/2214-4609.201902187
A. Pradhan, T. Mukerji
Summary Geophysical basin modeling helps constrain the non-uniqueness of seismic velocity inversion methods by employing basin modeling to incorporate geo-history constraints into inversion. Traditionally, basin modeling is performed in a deterministic manner and thus does not facilitate uncertainty quantification. We present a Bayesian approach for propagation of basin modeling uncertainties into velocity models. Our methodology constitutes defining prior probability distributions on uncertain basin modeling parameters and likelihood models on basin modeling calibration data. Posterior realizations of basin models are generated by sampling the prior, performing Monte-Carlo basin simulations and evaluating the corresponding likelihood values. These posterior models are finally linked to velocity models by rock physics modeling. We demonstrate the applicability of our proposed workflow using a 2D real case study from Gulf of Mexico.
{"title":"A Bayesian Approach to Uncertainty Quantification in Geophysical Basin Modeling","authors":"A. Pradhan, T. Mukerji","doi":"10.3997/2214-4609.201902187","DOIUrl":"https://doi.org/10.3997/2214-4609.201902187","url":null,"abstract":"Summary Geophysical basin modeling helps constrain the non-uniqueness of seismic velocity inversion methods by employing basin modeling to incorporate geo-history constraints into inversion. Traditionally, basin modeling is performed in a deterministic manner and thus does not facilitate uncertainty quantification. We present a Bayesian approach for propagation of basin modeling uncertainties into velocity models. Our methodology constitutes defining prior probability distributions on uncertain basin modeling parameters and likelihood models on basin modeling calibration data. Posterior realizations of basin models are generated by sampling the prior, performing Monte-Carlo basin simulations and evaluating the corresponding likelihood values. These posterior models are finally linked to velocity models by rock physics modeling. We demonstrate the applicability of our proposed workflow using a 2D real case study from Gulf of Mexico.","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":"125846792","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: 10.3997/2214-4609.201902261
A. Livers-Douglas, Matthew Burton-Kelly, B. Oster, Wesley D. Peck
Summary The Energy & Environmental Research Center is investigating the feasibility of safely and permanently storing at least 50 million tonnes of CO2 in North Dakota, United States. A regional geologic model of the injection target was created: the eolian sandstones of the Permian Broom Creek Formation. This study demonstrates how seismic data, covering a subset of the overall model region, were integrated using both multiple-point statistics (MPS) and variogram analysis. Seismic geobody interpretation enabled MPS training image development to define a lithofacies distribution, which was then used to constrain petrophysical property distributions. Alternatively, a seismic porosity inversion volume was used to calculate variograms, which were then applied in property distributions throughout the greater region. The mean and standard deviation of the porosity distributions were nearly identical in both, but porosity in the MPS case was bimodal (attributed to the facies model) versus a unimodal distribution in the variogram analysis case. These results do not indicate one approach is altogether better than the other, but geologic characteristics and control point density may make one approach more suitable. Relative agreement between the methods indicates the biggest overall benefit to a project occurs simply in having seismic data to inform model construction.
{"title":"Geostatistical Analysis of Seismic Data for Regional Modeling of the Broom Creek Formation, North Dakota, USA","authors":"A. Livers-Douglas, Matthew Burton-Kelly, B. Oster, Wesley D. Peck","doi":"10.3997/2214-4609.201902261","DOIUrl":"https://doi.org/10.3997/2214-4609.201902261","url":null,"abstract":"Summary The Energy & Environmental Research Center is investigating the feasibility of safely and permanently storing at least 50 million tonnes of CO2 in North Dakota, United States. A regional geologic model of the injection target was created: the eolian sandstones of the Permian Broom Creek Formation. This study demonstrates how seismic data, covering a subset of the overall model region, were integrated using both multiple-point statistics (MPS) and variogram analysis. Seismic geobody interpretation enabled MPS training image development to define a lithofacies distribution, which was then used to constrain petrophysical property distributions. Alternatively, a seismic porosity inversion volume was used to calculate variograms, which were then applied in property distributions throughout the greater region. The mean and standard deviation of the porosity distributions were nearly identical in both, but porosity in the MPS case was bimodal (attributed to the facies model) versus a unimodal distribution in the variogram analysis case. These results do not indicate one approach is altogether better than the other, but geologic characteristics and control point density may make one approach more suitable. Relative agreement between the methods indicates the biggest overall benefit to a project occurs simply in having seismic data to inform model construction.","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":"126076485","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: 10.3997/2214-4609.201902191
I. Tishchenko, I. Mallinson
Summary Direct Hydrocarbon Indicators (DHI) are commonly used for exploration prospects. Amplitudes as an independent source of information could be used as conditional probability within Bayes Theorem to assess risk of geological success. Following research is aiming to construct predictive model for estimating probability of hydrocarbons observing DHI, P(dhi|hc). In order to build such model, we used Rose & Associates DHI Interpretation and Risk Analysis Consortium database, which contains extensive descriptions of 336 drilled prospects, with known results, across various categories: Geology, Data Quality, Amplitude Characteristics and Pitfalls. Multiple Logistic Regression was used for predicting probability P(dhi|hc). Three methods were considered within the study: two data-driven models - stepwise regression and lasso shrinkage method plus the third one, a combination of data-and expertise- driven approach - stepwise regression plus manual addition of predictors to the model. All three models with key predictors are described and give similar accuracy of prediction − 77%. Performed data analysis and calculated models reveal several insights into R&A DHI Consortium database and amplitude prospects characterisation. The best method to create such models is probably a combination of data and expertise driven approaches, while selection of most appropriate model is a question of company's strategy.
{"title":"Amplitude Supported Prospects, Analysis and Predictive Models for Reducing Risk of Geological Success","authors":"I. Tishchenko, I. Mallinson","doi":"10.3997/2214-4609.201902191","DOIUrl":"https://doi.org/10.3997/2214-4609.201902191","url":null,"abstract":"Summary Direct Hydrocarbon Indicators (DHI) are commonly used for exploration prospects. Amplitudes as an independent source of information could be used as conditional probability within Bayes Theorem to assess risk of geological success. Following research is aiming to construct predictive model for estimating probability of hydrocarbons observing DHI, P(dhi|hc). In order to build such model, we used Rose & Associates DHI Interpretation and Risk Analysis Consortium database, which contains extensive descriptions of 336 drilled prospects, with known results, across various categories: Geology, Data Quality, Amplitude Characteristics and Pitfalls. Multiple Logistic Regression was used for predicting probability P(dhi|hc). Three methods were considered within the study: two data-driven models - stepwise regression and lasso shrinkage method plus the third one, a combination of data-and expertise- driven approach - stepwise regression plus manual addition of predictors to the model. All three models with key predictors are described and give similar accuracy of prediction − 77%. Performed data analysis and calculated models reveal several insights into R&A DHI Consortium database and amplitude prospects characterisation. The best method to create such models is probably a combination of data and expertise driven approaches, while selection of most appropriate model is a question of company's strategy.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"12 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":"130719232","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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