Pub Date : 2019-09-02DOI: 10.3997/2214-4609.201902186
A. Sidubaev, A. Melnikova, K. Grigoryev, S. Tarasov
Summary The formation of the exploration program is a complex process that requires an integrated approach. A successful exploration program is based on a multi-level analysis of all possible geological uncertainties, probabilistic assessment of reserves and resources, analysis of tornado charts, maps of variation coefficients, and calculation of the value of information. This work considers the consistent formation and execution of exploration works on the example of Alexander Zhagrin field, which allowed to start production in two years after discovery of oil field in autonomous conditions. The research region is located in the Khanty-Mansiisk autonomous districtof the Tyumen region. The main potentially productive formation is the river-dominated delta sediments of the Cretaceous complex represented by the stratum AS-9.
{"title":"An Integrated Approach to Uncertainty Management on the Example of Alexander Zhagrin Field","authors":"A. Sidubaev, A. Melnikova, K. Grigoryev, S. Tarasov","doi":"10.3997/2214-4609.201902186","DOIUrl":"https://doi.org/10.3997/2214-4609.201902186","url":null,"abstract":"Summary The formation of the exploration program is a complex process that requires an integrated approach. A successful exploration program is based on a multi-level analysis of all possible geological uncertainties, probabilistic assessment of reserves and resources, analysis of tornado charts, maps of variation coefficients, and calculation of the value of information. This work considers the consistent formation and execution of exploration works on the example of Alexander Zhagrin field, which allowed to start production in two years after discovery of oil field in autonomous conditions. The research region is located in the Khanty-Mansiisk autonomous districtof the Tyumen region. The main potentially productive formation is the river-dominated delta sediments of the Cretaceous complex represented by the stratum AS-9.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"13 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":"123107232","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: 10.3997/2214-4609.201902202
A. Thenon, A. Abadpour, T. Chugunova
{"title":"Updating MPS Facies Realizations Using the Ensemble Smoother with Multiple Data Assimilation","authors":"A. Thenon, A. Abadpour, T. Chugunova","doi":"10.3997/2214-4609.201902202","DOIUrl":"https://doi.org/10.3997/2214-4609.201902202","url":null,"abstract":"","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":"128013505","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: 10.3997/2214-4609.201902226
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: 10.3997/2214-4609.201902230
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: 10.3997/2214-4609.201902221
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: 10.3997/2214-4609.201902189
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: 10.3997/2214-4609.201902260
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}
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