Pub Date : 2019-09-02DOI: 10.3997/2214-4609.201902212
E. Kovalevskiy, M. Volkova
Summary The cause of the low efficiency of geostatistical seismic inversion based on sequential Gaussian simulation (SGS) is explained as follows. In spite of the non-stationary type of initial borehole impedance sections, SGS generates stationary realizations of the same vertical sections. This results in the stationary cubes of predicted impedance values in which all deterministic features are erased. This article proposes using impedance section realizations obtained from a fuzzy model rather than from SGS. The first accurately represent the local statistics of borehole impedance sections, which allows the resulting impedance cubes to clearly show the deterministic features of a geological object. The method is illustrated with an example of the inversion for a real seismic cube.
{"title":"Stochastic Seismic Inversion Based on a Fuzzy Model","authors":"E. Kovalevskiy, M. Volkova","doi":"10.3997/2214-4609.201902212","DOIUrl":"https://doi.org/10.3997/2214-4609.201902212","url":null,"abstract":"Summary The cause of the low efficiency of geostatistical seismic inversion based on sequential Gaussian simulation (SGS) is explained as follows. In spite of the non-stationary type of initial borehole impedance sections, SGS generates stationary realizations of the same vertical sections. This results in the stationary cubes of predicted impedance values in which all deterministic features are erased. This article proposes using impedance section realizations obtained from a fuzzy model rather than from SGS. The first accurately represent the local statistics of borehole impedance sections, which allows the resulting impedance cubes to clearly show the deterministic features of a geological object. The method is illustrated with an example of the inversion for a real seismic cube.","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":"114189545","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: 10.3997/2214-4609.201902203
T. Buckle, R. Hutton, V. Demyanov, D. Arnold, A. Antropov, E. Kharyba, M. Pilipenko, L. Stulov
Summary We present a data driven workflow to improve local history match quality by identifying model regions from correlation between production response and geological modelling parameters for use in an assisted history matching framework. This paper outlines the implementation and results from a large mature field case study. Regions are identified by calculating the partial correlation between individual well production misfits and uncertain geological modelling parameters across 500 models. Wells are then categorised into three groups based on their correlations: positive, negative and insignificant. A probabilistic neural network (PNN) is trained on the location of each well and its group. A map of regions can then be calculated using the PNN. The parameters used to define the region map are then varied separately in each region in an assisted history matching loop. In the full field case study, an 8.8% improvement in oil rate misfit within the positively correlated well group was achieved by regional modification of the net-to-gross multiplier, with no detrimental effect on the other groups match quality. This case study demonstrates the effective identification and utilisation of geologically and dynamically inferred regions which improve the local history match
{"title":"Improving Local History Match Using Machine Learning Generated Regions from Production Response and Geological Parameter Correlations","authors":"T. Buckle, R. Hutton, V. Demyanov, D. Arnold, A. Antropov, E. Kharyba, M. Pilipenko, L. Stulov","doi":"10.3997/2214-4609.201902203","DOIUrl":"https://doi.org/10.3997/2214-4609.201902203","url":null,"abstract":"Summary We present a data driven workflow to improve local history match quality by identifying model regions from correlation between production response and geological modelling parameters for use in an assisted history matching framework. This paper outlines the implementation and results from a large mature field case study. Regions are identified by calculating the partial correlation between individual well production misfits and uncertain geological modelling parameters across 500 models. Wells are then categorised into three groups based on their correlations: positive, negative and insignificant. A probabilistic neural network (PNN) is trained on the location of each well and its group. A map of regions can then be calculated using the PNN. The parameters used to define the region map are then varied separately in each region in an assisted history matching loop. In the full field case study, an 8.8% improvement in oil rate misfit within the positively correlated well group was achieved by regional modification of the net-to-gross multiplier, with no detrimental effect on the other groups match quality. This case study demonstrates the effective identification and utilisation of geologically and dynamically inferred regions which improve the local history match","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"60 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":"125096441","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: 10.3997/2214-4609.201902205
P. Raanes, G. Evensen, A. Stordal
{"title":"Revising the Method of Ensemble Randomized Maximum Likelihood","authors":"P. Raanes, G. Evensen, A. Stordal","doi":"10.3997/2214-4609.201902205","DOIUrl":"https://doi.org/10.3997/2214-4609.201902205","url":null,"abstract":"","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"09 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":"127313170","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: 10.3997/2214-4609.201902272
T. Fjeldstad, D. Grana, H. Omre
Summary We consider joint prediction of lithology/fluid classes, petrophysical properties and elastic attributes in a Bayesian spatial framework based on a set of geophysical observations. A probabilistic model accounting for both vertical and lateral spatial dependency is proposed based on a Markov random field prior model for the lithology/fluid classes. We discuss in specific the rock physics model for the elastic attributes, which is well-known to be multimodal and skewed due to the presence of different lithology/fluid classes and saturation effects of the subsurface. The posterior model is assessed by an efficient Markov chain Monte Carlo algorithm. The proposed workflow is demonstrated on a Norwegian Sea gas discovery, with realistic spatial continuity in the predictions.
{"title":"Joint Bayesian Spatial Inversion of Lithology/fluid Classes, Petrophysical Properties and Elastic Attributes","authors":"T. Fjeldstad, D. Grana, H. Omre","doi":"10.3997/2214-4609.201902272","DOIUrl":"https://doi.org/10.3997/2214-4609.201902272","url":null,"abstract":"Summary We consider joint prediction of lithology/fluid classes, petrophysical properties and elastic attributes in a Bayesian spatial framework based on a set of geophysical observations. A probabilistic model accounting for both vertical and lateral spatial dependency is proposed based on a Markov random field prior model for the lithology/fluid classes. We discuss in specific the rock physics model for the elastic attributes, which is well-known to be multimodal and skewed due to the presence of different lithology/fluid classes and saturation effects of the subsurface. The posterior model is assessed by an efficient Markov chain Monte Carlo algorithm. The proposed workflow is demonstrated on a Norwegian Sea gas discovery, with realistic spatial continuity in the predictions.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"16 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":"126160238","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: 10.3997/2214-4609.201902219
D. Kolyukhin, M. Protasov
Summary The presented paper addresses the modeling and seismic imaging of fractured reservoirs. A three-dimensional statistical model of a discrete fracture network is developed. A flexible and efficient method to generate the random realizations of the statistical model for an arbitrary computational grid is suggested. The problem of scaling the developed fracture model using the analysis of seismic images for different grid steps is studied. Particular attention is paid to the models with a multifractal distribution of fractures.
{"title":"Using Seismic Images for Scaling of Statistical Model of Discrete Fracture Networks","authors":"D. Kolyukhin, M. Protasov","doi":"10.3997/2214-4609.201902219","DOIUrl":"https://doi.org/10.3997/2214-4609.201902219","url":null,"abstract":"Summary The presented paper addresses the modeling and seismic imaging of fractured reservoirs. A three-dimensional statistical model of a discrete fracture network is developed. A flexible and efficient method to generate the random realizations of the statistical model for an arbitrary computational grid is suggested. The problem of scaling the developed fracture model using the analysis of seismic images for different grid steps is studied. Particular attention is paid to the models with a multifractal distribution of fractures.","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":"123555449","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: 10.3997/2214-4609.201902252
S. Ghassemzadeh, M. G. Perdomo, M. Haghigh
Summary Reservoir simulation plays a vital role as oil and gas companies rely on them in the development of new fields. Therefore, a reliable and fast reservoir simulation is a crucial instrument to explore more scenarios and optimize the production. In each simulation, the reservoir is divided into millions of cells, and rock and fluid attributes are assigned to these cells. Then, based on these attributes, flow equations are solved with time-consuming numerical methods. Given the recent progress in machine learning, the possibility of using deep learning in reservoir simulation has been investigated in this paper. In the new approach, fluid flow equations are solved using a deep learning-based simulator instead of time-consuming mathematical approaches. In this paper, we studied 1D Oil Reservoir and 2D Gas Reservoir. Data sets generated using the numerical models were used to create the developed simulators. We used two metrics to evaluate our models: Mean Absolute Percentage Error (MAPE) and correlation coefficient (R2). Given the low value of these matrics (MAPE 0.84 for 1D and MAPE < 0.84%, R2 ≈ 1 for 2D), the results confirmed that the deep learning approach is reasonably accurate and trustworthy when compared with mathematically derived models.
{"title":"Application of Deep Learning in Reservoir Simulation","authors":"S. Ghassemzadeh, M. G. Perdomo, M. Haghigh","doi":"10.3997/2214-4609.201902252","DOIUrl":"https://doi.org/10.3997/2214-4609.201902252","url":null,"abstract":"Summary Reservoir simulation plays a vital role as oil and gas companies rely on them in the development of new fields. Therefore, a reliable and fast reservoir simulation is a crucial instrument to explore more scenarios and optimize the production. In each simulation, the reservoir is divided into millions of cells, and rock and fluid attributes are assigned to these cells. Then, based on these attributes, flow equations are solved with time-consuming numerical methods. Given the recent progress in machine learning, the possibility of using deep learning in reservoir simulation has been investigated in this paper. In the new approach, fluid flow equations are solved using a deep learning-based simulator instead of time-consuming mathematical approaches. In this paper, we studied 1D Oil Reservoir and 2D Gas Reservoir. Data sets generated using the numerical models were used to create the developed simulators. We used two metrics to evaluate our models: Mean Absolute Percentage Error (MAPE) and correlation coefficient (R2). Given the low value of these matrics (MAPE 0.84 for 1D and MAPE < 0.84%, R2 ≈ 1 for 2D), the results confirmed that the deep learning approach is reasonably accurate and trustworthy when compared with mathematically derived models.","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":"126446879","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: 10.3997/2214-4609.201902188
A. Romi, O. Burachok, M. Nistor, C. Spyrou, Y. Seilov, O. Djuraev, S. Matkivskyi, D. Grytsai, O. Goryacheva, R. Soyma
Summary Most of the fields in the Basin of the current study are represented by multi-stacked thin reservoirs with total thickness up to 2 thousand meters containing oil, gas-condensate and dry gas with high lateral and vertical heterogeneity. The asset in this study is a mature gas field with more than 50 years of production history, that consists from 15 gas-bearing sands of variable gas composition, that are in commingled production through the slotted liner completions while some of the sands are not yet under development and therefore, shouldn't be considered in history matching and excluded from material balance P10 reserves calculation but rather in P50 and P90 resources. This paper shows how the application of stochastic approach for facies modeling followed by petrophysical porosity, in the presence of non-resolutive 3D seismic could help to guide the property distribution and evaluate geological uncertainties. The next very important step in the applied workflow was flow-based ranking and selection of representative case based on connected (drained) volumes that helps to achieve history match for selected base case in the presence of additional high uncertainty in contact levels and quality of production data.
{"title":"Advantage of Stochastic Facies Distribution Modeling for History Matching of Multi-stacked Highly-heterogeneous Field of Dnieper-Donetsk Basin","authors":"A. Romi, O. Burachok, M. Nistor, C. Spyrou, Y. Seilov, O. Djuraev, S. Matkivskyi, D. Grytsai, O. Goryacheva, R. Soyma","doi":"10.3997/2214-4609.201902188","DOIUrl":"https://doi.org/10.3997/2214-4609.201902188","url":null,"abstract":"Summary Most of the fields in the Basin of the current study are represented by multi-stacked thin reservoirs with total thickness up to 2 thousand meters containing oil, gas-condensate and dry gas with high lateral and vertical heterogeneity. The asset in this study is a mature gas field with more than 50 years of production history, that consists from 15 gas-bearing sands of variable gas composition, that are in commingled production through the slotted liner completions while some of the sands are not yet under development and therefore, shouldn't be considered in history matching and excluded from material balance P10 reserves calculation but rather in P50 and P90 resources. This paper shows how the application of stochastic approach for facies modeling followed by petrophysical porosity, in the presence of non-resolutive 3D seismic could help to guide the property distribution and evaluate geological uncertainties. The next very important step in the applied workflow was flow-based ranking and selection of representative case based on connected (drained) volumes that helps to achieve history match for selected base case in the presence of additional high uncertainty in contact levels and quality of production data.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"10 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":"132262903","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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