Pub Date : 2019-09-02DOI: 10.3997/2214-4609.201902247
K. A. Khudhur, O. Fabusuyi, L. Azevedo, A. Soares
{"title":"Integration of Production Optimization Strategy in Reservoir Petrophysical Models","authors":"K. A. Khudhur, O. Fabusuyi, L. Azevedo, A. Soares","doi":"10.3997/2214-4609.201902247","DOIUrl":"https://doi.org/10.3997/2214-4609.201902247","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":"130383222","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: 10.3997/2214-4609.201902174
N. Desassis, D. Renard, M. Pereira, X. Freulon
Summary In this work, the Stochastic Partial Differential Equation approach is used to model the underlying Gaussian random fields in the PluriGaussian models. This approach allows to perform conditional simulations with computational complexity nearly independent of the size of the data sets. Furthermore, by using non-homogeneous operators, this framework allows to handle varying anisotropies and model complex geological structures. The model is presented and the proposed simulation algorithm is described. The methodology is illustrated through two synthetic data sets.
{"title":"PluriGaussian Simulations with the Stochastic Partial Differential Equation (SPDE) Approach","authors":"N. Desassis, D. Renard, M. Pereira, X. Freulon","doi":"10.3997/2214-4609.201902174","DOIUrl":"https://doi.org/10.3997/2214-4609.201902174","url":null,"abstract":"Summary In this work, the Stochastic Partial Differential Equation approach is used to model the underlying Gaussian random fields in the PluriGaussian models. This approach allows to perform conditional simulations with computational complexity nearly independent of the size of the data sets. Furthermore, by using non-homogeneous operators, this framework allows to handle varying anisotropies and model complex geological structures. The model is presented and the proposed simulation algorithm is described. The methodology is illustrated through two synthetic data sets.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"113 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":"121342936","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: 10.3997/2214-4609.201902183
E. Kharyba, S. Frolov, M. Blagojević, J. Kukavic, R. Yagfarov, A. Belanozhka
Summary The purpose of this project was to calculate oil reserves in order to make a decision on further workover activities. The main uncertainties faced were: permeability and porosity due to the lack of core from the target interval, PVT parameters due to the absence of downhole oil sample, 2D seismic profiles instead of 3D. In addition, there were risks connected to the proximity of OWC and faults possibly intersecting the wellbore.
{"title":"Uncertainty Evaluation to Improve Geological Understanding for More Reliable Hydrocarbon Reserves Assessment: Case Study","authors":"E. Kharyba, S. Frolov, M. Blagojević, J. Kukavic, R. Yagfarov, A. Belanozhka","doi":"10.3997/2214-4609.201902183","DOIUrl":"https://doi.org/10.3997/2214-4609.201902183","url":null,"abstract":"Summary The purpose of this project was to calculate oil reserves in order to make a decision on further workover activities. The main uncertainties faced were: permeability and porosity due to the lack of core from the target interval, PVT parameters due to the absence of downhole oil sample, 2D seismic profiles instead of 3D. In addition, there were risks connected to the proximity of OWC and faults possibly intersecting the wellbore.","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":"129912854","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: 10.3997/2214-4609.201902228
T. Chugunova, J. Straubhaar, P. Renard
{"title":"Multiple Point Statistics with Pyramids Application on the Multi-scale Multi-structure Training Images","authors":"T. Chugunova, J. Straubhaar, P. Renard","doi":"10.3997/2214-4609.201902228","DOIUrl":"https://doi.org/10.3997/2214-4609.201902228","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":"130684602","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: 10.3997/2214-4609.201902180
K. Fossum, R. Lorentzen
Summary In this work we present two unique workflows for assisted history matching of seismic and production data, and demonstrate the methods on a real field case. Both workflows use an iterative ensemble smoother for the data assimilation, but differ in data representation and localization method. Further, publicly available seismic data are inverted for acoustic impedance using two different approaches. In addition, correlated data noise is estimated for the 4D attributes using different techniques. History matching results are presented for selected production and seismic data, and estimated parameters are shown for one layer in the model. Both workflows demonstrate that ensemble based iterative smoothers can successfully assimilate large amounts of correlated data. Despite methodological differences in the workflows, both methods are able to make significant improvements to the data match. The work demonstrates promising advances towards assisted assimilation of big data-sets for real field cases.
{"title":"Assisted History Matching of 4D Seismic Data - A Comparative Study","authors":"K. Fossum, R. Lorentzen","doi":"10.3997/2214-4609.201902180","DOIUrl":"https://doi.org/10.3997/2214-4609.201902180","url":null,"abstract":"Summary In this work we present two unique workflows for assisted history matching of seismic and production data, and demonstrate the methods on a real field case. Both workflows use an iterative ensemble smoother for the data assimilation, but differ in data representation and localization method. Further, publicly available seismic data are inverted for acoustic impedance using two different approaches. In addition, correlated data noise is estimated for the 4D attributes using different techniques. History matching results are presented for selected production and seismic data, and estimated parameters are shown for one layer in the model. Both workflows demonstrate that ensemble based iterative smoothers can successfully assimilate large amounts of correlated data. Despite methodological differences in the workflows, both methods are able to make significant improvements to the data match. The work demonstrates promising advances towards assisted assimilation of big data-sets for real field cases.","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":"134046324","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: 10.3997/2214-4609.201902182
J. Paglia, J. Eidsvik
Summary We study a drilling situation based on real data, where the high-level problem concerns mud weight prediction and a decision about casing in a section of a well plan. Sensitivity analysis is done to select the most relevant input parameters for the mud weight window. In doing so, we study how the uncertainties in the inputs affects uncertainties in the mud weight window. Our approach for this is based on distance-based generalized sensitivity analysis, and we discover that the pore pressure and unconfined compressive strength are the most important input parameters. Building on this insight, a statistical model is fitted for the mud weight window and the two main input parameters, keeping in mind their geostatistical trends and dependencies. Finally we use the fitted model to the decision situation concerning casing, in a trade-off between drilling risks and costs. We conduct value of information analysis to determine the optimal data gathering scheme at a given depth, for making better decisions about casing or not. In spite of being case specific, we aim to develop a workflow that could be applied in other drilling contexts.
{"title":"A Statistical Workflow for Mud Weight Prediction and Improved Drilling Decisions","authors":"J. Paglia, J. Eidsvik","doi":"10.3997/2214-4609.201902182","DOIUrl":"https://doi.org/10.3997/2214-4609.201902182","url":null,"abstract":"Summary We study a drilling situation based on real data, where the high-level problem concerns mud weight prediction and a decision about casing in a section of a well plan. Sensitivity analysis is done to select the most relevant input parameters for the mud weight window. In doing so, we study how the uncertainties in the inputs affects uncertainties in the mud weight window. Our approach for this is based on distance-based generalized sensitivity analysis, and we discover that the pore pressure and unconfined compressive strength are the most important input parameters. Building on this insight, a statistical model is fitted for the mud weight window and the two main input parameters, keeping in mind their geostatistical trends and dependencies. Finally we use the fitted model to the decision situation concerning casing, in a trade-off between drilling risks and costs. We conduct value of information analysis to determine the optimal data gathering scheme at a given depth, for making better decisions about casing or not. In spite of being case specific, we aim to develop a workflow that could be applied in other drilling contexts.","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":"131904425","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: 10.3997/2214-4609.201902213
A. Al-Shamali, N. Verma, R. Quttainah, A. Tiwary, G. Alawi, M. A. Raisi
{"title":"Integrated Res Characterization Tool to Construct High Resolution Geological Model in MR FM of DF Field","authors":"A. Al-Shamali, N. Verma, R. Quttainah, A. Tiwary, G. Alawi, M. A. Raisi","doi":"10.3997/2214-4609.201902213","DOIUrl":"https://doi.org/10.3997/2214-4609.201902213","url":null,"abstract":"","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"174 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":"125795934","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: 10.3997/2214-4609.201902210
Z. Yin, J. Caers
{"title":"Monte Carlo-based Framework for Quantification and Updating of Geological Model Uncertainty with Borehole Data","authors":"Z. Yin, J. Caers","doi":"10.3997/2214-4609.201902210","DOIUrl":"https://doi.org/10.3997/2214-4609.201902210","url":null,"abstract":"","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"51 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":"122380860","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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