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}
Pub Date : 2019-09-02DOI: 10.3997/2214-4609.201902195
K. Bobek
{"title":"Automatic Recognition of Lithological Units in Gas-bearing Shale Complex with Neural Networks (the Baltic Basin, Poland)","authors":"K. Bobek","doi":"10.3997/2214-4609.201902195","DOIUrl":"https://doi.org/10.3997/2214-4609.201902195","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":"130026049","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: 10.3997/2214-4609.201902270
P. Renard, C. Jäggli, Y. Dagasan, J. Straubhaar
{"title":"The Posterior Population Expansion Ensemble Method to Invert Categorical Fields","authors":"P. Renard, C. Jäggli, Y. Dagasan, J. Straubhaar","doi":"10.3997/2214-4609.201902270","DOIUrl":"https://doi.org/10.3997/2214-4609.201902270","url":null,"abstract":"","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"22 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":"123872074","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: 10.3997/2214-4609.201902221
J. Püttmann, U. Eickelberg, J. Hohenegger
Summary Statistical analysis are presented for the description of a porosity-permeability system in order to transfer tectonic facies classification to log data and to improve flow unit determination. Two working hypothesis are investigated: a) Porosities at each measured section point represent an accumulation of distinct porosity classes and b) Significant periods can be identified in oscillating porosities. The four major workflow steps of the statistical analysis are described. Decomposition, non-linear regression, and periodograms delivered encouraging results to understand the porosity composition of the multi-fractured dolomite. Five porosity components of high statistical significance are identified and related to tectonic influence factors. Furthermore, results of sinusoidal regression show significant trends, which might be related to deformation history and complexes. Decomposition of oscillating functions resulted in classes of significant periods, where sinusoidal oscillations with specific period lengths are represented. Finally, statistical analysis reveal different porosity distributions depending on the logging tool generation, which can have a considerable impact on the reserve estimation. Statistical analysis of log data -if applicable - are a fast and cost-effective approach to support reservoir characterisation. The study show that the use of statistical analysis of log data can provide significant information to develop or validate static and dynamic reservoir models
{"title":"Tease out More - Advanced Porosity Analysis in Fractured Reservoirs Combining Statistical Method with Outcrop Data","authors":"J. Püttmann, U. Eickelberg, J. Hohenegger","doi":"10.3997/2214-4609.201902221","DOIUrl":"https://doi.org/10.3997/2214-4609.201902221","url":null,"abstract":"Summary Statistical analysis are presented for the description of a porosity-permeability system in order to transfer tectonic facies classification to log data and to improve flow unit determination. Two working hypothesis are investigated: a) Porosities at each measured section point represent an accumulation of distinct porosity classes and b) Significant periods can be identified in oscillating porosities. The four major workflow steps of the statistical analysis are described. Decomposition, non-linear regression, and periodograms delivered encouraging results to understand the porosity composition of the multi-fractured dolomite. Five porosity components of high statistical significance are identified and related to tectonic influence factors. Furthermore, results of sinusoidal regression show significant trends, which might be related to deformation history and complexes. Decomposition of oscillating functions resulted in classes of significant periods, where sinusoidal oscillations with specific period lengths are represented. Finally, statistical analysis reveal different porosity distributions depending on the logging tool generation, which can have a considerable impact on the reserve estimation. Statistical analysis of log data -if applicable - are a fast and cost-effective approach to support reservoir characterisation. The study show that the use of statistical analysis of log data can provide significant information to develop or validate static and dynamic reservoir models","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"124 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":"122370849","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: 10.3997/2214-4609.201902176
E. Kneller, L. Teixeira, B. Hak, N. Cruz, Teresa Oliveira, J. M. Cruz, R. Cunha
Summary The creation of reservoir model properties has become an art of bringing together hard and soft data, gathering ideas of geologists and geophysicists, constraining them with measured values in- and outside wells. Through lifecycle of the oil field the information coverage is growing - new wells are being drilled, new seismic acquisitions are performed, and new geological concepts are developed. The Brazilian pre-salt fields are no exception. However, these fields experience additional challenges, where the carbonates show significant lateral and vertical variability and the salt layer limits illumination and penetration of the seismic signal. In this paper, we investigate performance of three techniques on the Lula field: simulation, which "propagates" properties between wells; deterministic inversion, which transforms seismic amplitudes into elastic properties; and geostatistical inversion, which combines simulation and seismic-driven inversion. We demonstrate that geostatistical inversion brings together the best of both techniques and helps address the challenges of characterization of pre-salt carbonates.
{"title":"Challenges and Solutions of Geostatistical Inversion for Reservoir Characterization of the Supergiant Lula Field","authors":"E. Kneller, L. Teixeira, B. Hak, N. Cruz, Teresa Oliveira, J. M. Cruz, R. Cunha","doi":"10.3997/2214-4609.201902176","DOIUrl":"https://doi.org/10.3997/2214-4609.201902176","url":null,"abstract":"Summary The creation of reservoir model properties has become an art of bringing together hard and soft data, gathering ideas of geologists and geophysicists, constraining them with measured values in- and outside wells. Through lifecycle of the oil field the information coverage is growing - new wells are being drilled, new seismic acquisitions are performed, and new geological concepts are developed. The Brazilian pre-salt fields are no exception. However, these fields experience additional challenges, where the carbonates show significant lateral and vertical variability and the salt layer limits illumination and penetration of the seismic signal. In this paper, we investigate performance of three techniques on the Lula field: simulation, which \"propagates\" properties between wells; deterministic inversion, which transforms seismic amplitudes into elastic properties; and geostatistical inversion, which combines simulation and seismic-driven inversion. We demonstrate that geostatistical inversion brings together the best of both techniques and helps address the challenges of characterization of pre-salt carbonates.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"22 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":"122857586","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: 10.3997/2214-4609.201902230
E. Nesvold, T. Mukerji
{"title":"Characterizing Connectivity in Heterogeneous Porous Media Using Graph Laplacians","authors":"E. Nesvold, T. Mukerji","doi":"10.3997/2214-4609.201902230","DOIUrl":"https://doi.org/10.3997/2214-4609.201902230","url":null,"abstract":"","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"6 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":"121746983","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: 10.3997/2214-4609.201902218
Abdulmohsen AlAli, K. Marfurt, N. Nakata
{"title":"The Effect of Fracture Clustering on Confined Fractured Zones: Numerical Modeling and Analyses","authors":"Abdulmohsen AlAli, K. Marfurt, N. Nakata","doi":"10.3997/2214-4609.201902218","DOIUrl":"https://doi.org/10.3997/2214-4609.201902218","url":null,"abstract":"","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"126 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":"121937553","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: 10.3997/2214-4609.201902266
R. Moyen, R. Porjesz, P. Roy, R. Sablit, R. Alamer, F. Abdulaziz
{"title":"Adaptive Ensemble-based Petrophysical Inversion for Seismically Constrained Static Model Building","authors":"R. Moyen, R. Porjesz, P. Roy, R. Sablit, R. Alamer, F. Abdulaziz","doi":"10.3997/2214-4609.201902266","DOIUrl":"https://doi.org/10.3997/2214-4609.201902266","url":null,"abstract":"","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"48 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":"121590730","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: 10.3997/2214-4609.201902271
S. B. D. Silva, Paloma Carla Fonte Boa Carvalho, C. D. Costa, J. Araújo, G. Corso
Summary Full-waveform inversion (FWI) is a powerful technique to obtain high-resolution velocity models, which is based on the wave equation. We investigate the frequency-domain FWI of wide-aperture data. We have used a Bayesian inversion framework with l-BGFS algorithm. For the prior information, we have used a spatial covariance operator based on information collected in two wells at the ends of the velocity model. The data uncertainties were estimated according to the distance source-receiver (offset) and the angular frequency to emphasizes the waves with a greater angular range (diving waves). Finally, we report a numerical example using the Marmousi model with a maximum offset of 16,960 meters to demonstrate the effectiveness of the proposed inversion methodology. The proposed strategy has been successful to obtain gas and oil cap structures in high-resolution.
{"title":"A Bayesian Approach for Full-waveform Inversion Using Wide-aperture Seismic Data","authors":"S. B. D. Silva, Paloma Carla Fonte Boa Carvalho, C. D. Costa, J. Araújo, G. Corso","doi":"10.3997/2214-4609.201902271","DOIUrl":"https://doi.org/10.3997/2214-4609.201902271","url":null,"abstract":"Summary Full-waveform inversion (FWI) is a powerful technique to obtain high-resolution velocity models, which is based on the wave equation. We investigate the frequency-domain FWI of wide-aperture data. We have used a Bayesian inversion framework with l-BGFS algorithm. For the prior information, we have used a spatial covariance operator based on information collected in two wells at the ends of the velocity model. The data uncertainties were estimated according to the distance source-receiver (offset) and the angular frequency to emphasizes the waves with a greater angular range (diving waves). Finally, we report a numerical example using the Marmousi model with a maximum offset of 16,960 meters to demonstrate the effectiveness of the proposed inversion methodology. The proposed strategy has been successful to obtain gas and oil cap structures in high-resolution.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"38 2 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":"114820848","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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