Pub Date : 2019-09-02DOI: 10.3997/2214-4609.201902197
Xiaodong Luo, R. Lorentzen, T. Bhakta
{"title":"Ensemble-based Kernel Learning to Handle Rock-physics-model Imperfection in Seismic History Matching: A Real Field Case Study","authors":"Xiaodong Luo, R. Lorentzen, T. Bhakta","doi":"10.3997/2214-4609.201902197","DOIUrl":"https://doi.org/10.3997/2214-4609.201902197","url":null,"abstract":"","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":"115131004","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: 10.3997/2214-4609.201902264
M. Pereira, C. Magneron, N. Desassis
Summary An innovative geostatistical filtering approach is presented in this paper. It is based on Stochastic Partial Differential Equations (SPDE) and the idea is to solve kriging equations with the finite element method which requires the subdivision of a whole domain into simpler parts. This approach enables to deal with local variographic parameters while using a unique neighborhood even on large datasets. It opens the door to the operational processing of the most complex noise issues on seismic data. Post-stack and pre-stack. The methodology is described in details and two case studies are presented.
{"title":"Geostatistical Filtering of Noisy Seismic Data Using Stochastic Partial Differential Equations (SPDE)","authors":"M. Pereira, C. Magneron, N. Desassis","doi":"10.3997/2214-4609.201902264","DOIUrl":"https://doi.org/10.3997/2214-4609.201902264","url":null,"abstract":"Summary An innovative geostatistical filtering approach is presented in this paper. It is based on Stochastic Partial Differential Equations (SPDE) and the idea is to solve kriging equations with the finite element method which requires the subdivision of a whole domain into simpler parts. This approach enables to deal with local variographic parameters while using a unique neighborhood even on large datasets. It opens the door to the operational processing of the most complex noise issues on seismic data. Post-stack and pre-stack. The methodology is described in details and two case studies are presented.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"24 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":"126556466","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: 10.3997/2214-4609.201902179
H. Al-Ibadi, K. Stephen, E. Mackay
Summary This paper examines the impact of heterogeneity on Low Salinity Water flooding (LSWF) for a realistic field scale models. We examine various scenarios of permeability variations to cover a wide range of heterogeneity possibilities. Since heterogeneity is known to induce fingering and crossflow effects at the fine scale during conventional water flooding. We analyse these effects where the LSWF process is related to a change in wettability, to determine what should be captured, in terms of solute dispersion, in typical coarse scale simulation models.
{"title":"Geological Heterogeneous Effect on Fluid Flow and Solute Transport during Low Salinity Water Flooding","authors":"H. Al-Ibadi, K. Stephen, E. Mackay","doi":"10.3997/2214-4609.201902179","DOIUrl":"https://doi.org/10.3997/2214-4609.201902179","url":null,"abstract":"Summary This paper examines the impact of heterogeneity on Low Salinity Water flooding (LSWF) for a realistic field scale models. We examine various scenarios of permeability variations to cover a wide range of heterogeneity possibilities. Since heterogeneity is known to induce fingering and crossflow effects at the fine scale during conventional water flooding. We analyse these effects where the LSWF process is related to a change in wettability, to determine what should be captured, in terms of solute dispersion, in typical coarse scale simulation models.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"26 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":"123943610","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: 10.3997/2214-4609.201902260
S. Bhukta, Eman Al-Shehri, Sunil Kumar Singh, P. K. Nath, A. Al-Ajmi, B. Khan, A. Najem
{"title":"Seismic Tools to Mitigate the Challenges of Thin Tight Carbonate Reservoir: A Case Study","authors":"S. Bhukta, Eman Al-Shehri, Sunil Kumar Singh, P. K. Nath, A. Al-Ajmi, B. Khan, A. Najem","doi":"10.3997/2214-4609.201902260","DOIUrl":"https://doi.org/10.3997/2214-4609.201902260","url":null,"abstract":"","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"24 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":"126043159","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: 10.3997/2214-4609.201902189
W. Al-Mudhafar
Summary Several cases have been conducted to address the permeability modeling and estimation, but all were not accurate because of the heteroscedasticity between data. Therefore, integrating the microfacies sequences into permeability modeling became a crucial to obtain accurate prediction and then improve the overall reservoir characterization. The discrete microfacies distribution leads to distinct regression lines given each microfacies type. Therefore, the Random Forest (RF) algorithm was considered in this paper for microfacies classification and Smooth Generalized Additive Modeling (sGAM) was considered for permeability modeling as a function of well logging data and the predicted discrete microfacies distribution. The well logging records that were incorporated into the microfacies classification and permeability modeling: SP, ILD and density porosity logs. These two approaches were adopted in a well in a sandstone reservoir, located in East Texas basin. The effectiveness of using RF and sGAM approaches was investigated by their performance to handle wide ranges of data given the five microfacies types. More specifically, the Random Forest Modeling was super accurate to predict the microfacies distribution at the missing intervals for the same well and other wells. Moreover, the sGAM resulted to obtain accurate modeling and prediction of permeability in high and low permeable intervals.
{"title":"Incorporating Discrete Microfacies Sequences to Improve Permeability Estimation in Sandstone Reservoirs","authors":"W. Al-Mudhafar","doi":"10.3997/2214-4609.201902189","DOIUrl":"https://doi.org/10.3997/2214-4609.201902189","url":null,"abstract":"Summary Several cases have been conducted to address the permeability modeling and estimation, but all were not accurate because of the heteroscedasticity between data. Therefore, integrating the microfacies sequences into permeability modeling became a crucial to obtain accurate prediction and then improve the overall reservoir characterization. The discrete microfacies distribution leads to distinct regression lines given each microfacies type. Therefore, the Random Forest (RF) algorithm was considered in this paper for microfacies classification and Smooth Generalized Additive Modeling (sGAM) was considered for permeability modeling as a function of well logging data and the predicted discrete microfacies distribution. The well logging records that were incorporated into the microfacies classification and permeability modeling: SP, ILD and density porosity logs. These two approaches were adopted in a well in a sandstone reservoir, located in East Texas basin. The effectiveness of using RF and sGAM approaches was investigated by their performance to handle wide ranges of data given the five microfacies types. More specifically, the Random Forest Modeling was super accurate to predict the microfacies distribution at the missing intervals for the same well and other wells. Moreover, the sGAM resulted to obtain accurate modeling and prediction of permeability in high and low permeable intervals.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"250 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":"125774085","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: 10.3997/2214-4609.201902173
L. Adamson, A. Kidd, T. Frenz, M. Smith
Summary This paper shows the importance of uncertainty quantification for the mature small pool offshore field development. Mature small pool offshore field development is associated with high risks and high costs. Often projects in such fields are economically marginal therefore, the uncertainty quantification is very important to understand the full range of outcomes before making the ultimate decision on a given project. In this paper, we suggest an integrated workflow to account for a vast number of both static and dynamic uncertainties. To include the uncertainties into the project, we create an Uncertainty Matrix to group a huge number of uncertainties into a much manageable number of variables. In this work, we also address the challenges of capturing geological realism through facies modelling and propagating it whilst performing Uncertainty Studies. We demonstrate the application of the suggested workflow on a mature North Sea Brent Field with a limited data set. The subsequent results directly influence an infill well drilling decision on this field, which currently has two production wells and one injection well to date.
{"title":"To Drill Or Not To Drill? Mature North Sea Field Case Study","authors":"L. Adamson, A. Kidd, T. Frenz, M. Smith","doi":"10.3997/2214-4609.201902173","DOIUrl":"https://doi.org/10.3997/2214-4609.201902173","url":null,"abstract":"Summary This paper shows the importance of uncertainty quantification for the mature small pool offshore field development. Mature small pool offshore field development is associated with high risks and high costs. Often projects in such fields are economically marginal therefore, the uncertainty quantification is very important to understand the full range of outcomes before making the ultimate decision on a given project. In this paper, we suggest an integrated workflow to account for a vast number of both static and dynamic uncertainties. To include the uncertainties into the project, we create an Uncertainty Matrix to group a huge number of uncertainties into a much manageable number of variables. In this work, we also address the challenges of capturing geological realism through facies modelling and propagating it whilst performing Uncertainty Studies. We demonstrate the application of the suggested workflow on a mature North Sea Brent Field with a limited data set. The subsequent results directly influence an infill well drilling decision on this field, which currently has two production wells and one injection well to date.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"38 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":"116650897","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: 10.3997/2214-4609.201902226
V. Dall’Alba, P. Renard, J. Straubhaar, B. Issautier, C. Duvail, Y. Caballero
{"title":"3D Multiple-points Statistics Simulations of the Roussillon Continental Pliocene Reservoir Using DeeSse","authors":"V. Dall’Alba, P. Renard, J. Straubhaar, B. Issautier, C. Duvail, Y. Caballero","doi":"10.3997/2214-4609.201902226","DOIUrl":"https://doi.org/10.3997/2214-4609.201902226","url":null,"abstract":"","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"29 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":"124896073","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: 10.3997/2214-4609.201902181
R. Gazizov, A. Bezrukov, B. Feoktistov
Summary Here a mathematical approach which can be used for comparison of three methods for modeling Gaussian random fields is developed. Namely, the known methods of Sequential Gaussian Simulation and Spectral Modeling as well as new method based of Fourier transform and spectral modeling random fields of Fourier coefficients are considered. We show that these methods give equivalent result when specific Gaussian fields are modeled. Also we discuss advantages and limitations of these methods, their applicability in practice problems, computational complexity and ways for their effective realizations.
{"title":"Stochastic Realizations of Gaussian Random Fields: Analysis and Comparison of Modeling Methods","authors":"R. Gazizov, A. Bezrukov, B. Feoktistov","doi":"10.3997/2214-4609.201902181","DOIUrl":"https://doi.org/10.3997/2214-4609.201902181","url":null,"abstract":"Summary Here a mathematical approach which can be used for comparison of three methods for modeling Gaussian random fields is developed. Namely, the known methods of Sequential Gaussian Simulation and Spectral Modeling as well as new method based of Fourier transform and spectral modeling random fields of Fourier coefficients are considered. We show that these methods give equivalent result when specific Gaussian fields are modeled. Also we discuss advantages and limitations of these methods, their applicability in practice problems, computational complexity and ways for their effective realizations.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"233 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":"130818864","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: 10.3997/2214-4609.201902175
A. A. Curtis, E. Eslinger, S. Nookala
Summary An explanation is given of both where and why there are several major steps in the reservoir characterisation and modelling process in which geostatistics are of little avail and for which other technologies must be used before geostatistics can then be invoked. A workflow is presented which overcomes one of the more intractable problems in reservoir characterisation: that of moving petrophysical properties, including saturation-dependent properties, from a fine scale to a coarser scale in the absence of suitable grids. Without a rigorous solution to this problem, the subsequent use of geostatistical algorithms to distribute what may be poor quality properties data is questionable. The solution, termed the CUSP workflow, uses a unique parametrisation based on Characteristic Length Variables (CLVs) which honour the principles of hydraulic similitude. A Bayesian-based Probabilistic Multivariate Clustering Analysis is used to carry out the Classification and Propagation of petrophysical properties based on the CLVs. The CUSP workflow is scale independent and has been implemented in readily available software. An example of the application of the workflow to move petrophysical properties from the core-plug scale to the wireline log scale is presented and an example for moving from the log scale to the geocell scale is provided.
{"title":"Geostatistics: Necessary, but Far from Sufficient","authors":"A. A. Curtis, E. Eslinger, S. Nookala","doi":"10.3997/2214-4609.201902175","DOIUrl":"https://doi.org/10.3997/2214-4609.201902175","url":null,"abstract":"Summary An explanation is given of both where and why there are several major steps in the reservoir characterisation and modelling process in which geostatistics are of little avail and for which other technologies must be used before geostatistics can then be invoked. A workflow is presented which overcomes one of the more intractable problems in reservoir characterisation: that of moving petrophysical properties, including saturation-dependent properties, from a fine scale to a coarser scale in the absence of suitable grids. Without a rigorous solution to this problem, the subsequent use of geostatistical algorithms to distribute what may be poor quality properties data is questionable. The solution, termed the CUSP workflow, uses a unique parametrisation based on Characteristic Length Variables (CLVs) which honour the principles of hydraulic similitude. A Bayesian-based Probabilistic Multivariate Clustering Analysis is used to carry out the Classification and Propagation of petrophysical properties based on the CLVs. The CUSP workflow is scale independent and has been implemented in readily available software. An example of the application of the workflow to move petrophysical properties from the core-plug scale to the wireline log scale is presented and an example for moving from the log scale to the geocell scale is provided.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"78 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":"130635169","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：