Pub Date : 2019-09-03DOI: 10.3997/2214-4609.201902223
M. Lemay, F. Ors, J. Grimaud, J. Rivoirard, I. Cojan, X. Freulon
Channelized turbidite systems are associated with extensive hydrocarbon reservoirs. Yet building realistic turbidite reservoir models is still a challenge. We take advantage of some similarities between fluvial and turbidite environments to transpose the Flumy model , initially dedicated to fluvial reservoirs (Lopez al., 2008), to channelized turbidite systems by simulating the main processes at play in the submarine realm : channel lateral migration, avulsion, aggradation, overflowing, flow stripping, and sediment transport (Lemay 2018). A flow compatible with the input channel geometry parameters is first built. This flow controls the channel evolution through time and thus the stratigraphic architecture of deposits, as well as their grain size. In this study, we present the application of Flumy to the case study of the Benin major valley .
{"title":"Forward Model Applied to Channelized Turbidite Systems: A Case Study of the Benin Major Valley Fill","authors":"M. Lemay, F. Ors, J. Grimaud, J. Rivoirard, I. Cojan, X. Freulon","doi":"10.3997/2214-4609.201902223","DOIUrl":"https://doi.org/10.3997/2214-4609.201902223","url":null,"abstract":"Channelized turbidite systems are associated with extensive hydrocarbon reservoirs. Yet building realistic turbidite reservoir models is still a challenge. We take advantage of some similarities between fluvial and turbidite environments to transpose the Flumy model , initially dedicated to fluvial reservoirs (Lopez al., 2008), to channelized turbidite systems by simulating the main processes at play in the submarine realm : channel lateral migration, avulsion, aggradation, overflowing, flow stripping, and sediment transport (Lemay 2018). A flow compatible with the input channel geometry parameters is first built. This flow controls the channel evolution through time and thus the stratigraphic architecture of deposits, as well as their grain size. In this study, we present the application of Flumy to the case study of the Benin major valley .","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127880570","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: 10.3997/2214-4609.201902263
A. Al-Ali, K. Stephen, Asghar Shams
Summary Recently seismic inversion method and geostatistical tools has been widely used in reservoir modelling workflows due to its excellent ability to capture the complex geobodies. In this study, the objective of this work is to characterize the spatial distribution of the Mishrif carbonate in the West Qurna Oil Field using seismic inversion results, well log data, rock physics model. Identification of the spatial distribution of channel fairway and lithology are keys for constructing Mishrif reservoir model, which have a great impact on the development of the most prolific reservoir in the field Mishrif reservoir.
{"title":"New Insights Into the Spatial Distribution of Complex Carbonate Channels Using Geostatistical Approach: A Case Study","authors":"A. Al-Ali, K. Stephen, Asghar Shams","doi":"10.3997/2214-4609.201902263","DOIUrl":"https://doi.org/10.3997/2214-4609.201902263","url":null,"abstract":"Summary Recently seismic inversion method and geostatistical tools has been widely used in reservoir modelling workflows due to its excellent ability to capture the complex geobodies. In this study, the objective of this work is to characterize the spatial distribution of the Mishrif carbonate in the West Qurna Oil Field using seismic inversion results, well log data, rock physics model. Identification of the spatial distribution of channel fairway and lithology are keys for constructing Mishrif reservoir model, which have a great impact on the development of the most prolific reservoir in the field Mishrif reservoir.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"19 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":"117281648","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: 10.3997/2214-4609.201902192
K. Lee, J. Lim, D. Yoon, S. Kim, T. Lee, J. Choe
{"title":"Application of Long Short-term Memory Algorithm for Prediction of Shale Gas Production in Alberta","authors":"K. Lee, J. Lim, D. Yoon, S. Kim, T. Lee, J. Choe","doi":"10.3997/2214-4609.201902192","DOIUrl":"https://doi.org/10.3997/2214-4609.201902192","url":null,"abstract":"","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":"128349350","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: 10.3997/2214-4609.201902211
N. Ismagilov, Viacheslav Borovitskiy, M. Lifshits, M. Platonova
Summary The work introduces a new method for simulation of facies distribution for two categories based on Fourier analysis of Boolean functions. According this method, two categories of facies distributed along vertical wells are encoded as Boolean functions taking two values. The subsequent simulation process is divided into three consecutive steps. First, Boolean functions of well data are decomposed into a binary version of Fourier series. Then, decomposition coefficients are simulated over 2-dimensional area as stationary random fields. Finally, synthetic data in the interwell space is reconstructed as Fourier sum from simulated coefficients. The new method was implemented in an experimental software and tested on a case of real oil field.
{"title":"Boolean Spectral Analysis in Categorical Reservoir Modelling","authors":"N. Ismagilov, Viacheslav Borovitskiy, M. Lifshits, M. Platonova","doi":"10.3997/2214-4609.201902211","DOIUrl":"https://doi.org/10.3997/2214-4609.201902211","url":null,"abstract":"Summary The work introduces a new method for simulation of facies distribution for two categories based on Fourier analysis of Boolean functions. According this method, two categories of facies distributed along vertical wells are encoded as Boolean functions taking two values. The subsequent simulation process is divided into three consecutive steps. First, Boolean functions of well data are decomposed into a binary version of Fourier series. Then, decomposition coefficients are simulated over 2-dimensional area as stationary random fields. Finally, synthetic data in the interwell space is reconstructed as Fourier sum from simulated coefficients. The new method was implemented in an experimental software and tested on a case of real oil field.","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":"123930261","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: 10.3997/2214-4609.201902245
E. Akhmetvaliev, A. Belanozhka, M. Pilipenko, K. Kyzyma
{"title":"The Data Integration Approach for Prospecting Missed Intervals. An Example Based on Gazprom Neft Assets","authors":"E. Akhmetvaliev, A. Belanozhka, M. Pilipenko, K. Kyzyma","doi":"10.3997/2214-4609.201902245","DOIUrl":"https://doi.org/10.3997/2214-4609.201902245","url":null,"abstract":"","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"336 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":"116468223","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: 10.3997/2214-4609.201902196
E. Nesvold, T. Mukerji
Summary Several studies on deep generative models for use in geomodeling show encouraging results with binary training data. An important question is what type of training data to use, since realistic 3D geology with natural variability is difficult to create. The advent of multiple types of remote sensing data of subaerial and subaqueous sedimentary patterns provides new possibilities in this context. Here, we train a Wasserstein GAN using 20,000 multispectral satellite images of subsections of 40 modern river deltas. The generated output has three facies and a background facies, and all quantitative evaluation methods of the unconditional output show a close overlap between the model and training data distributions. Standard MCMC sampling conditional on soft and hard data works well as long as the likelihood model is balanced against the prior model. Transfer learning, i.e. fine-training a small subset of the network parameters on smaller dataset of interest, such as highly non-stationary images of river deltas with similar characteristics, also shows promising results.
{"title":"Geomodeling Using Generative Adversarial Networks and a Database of Satellite Imagery of Modern River Deltas","authors":"E. Nesvold, T. Mukerji","doi":"10.3997/2214-4609.201902196","DOIUrl":"https://doi.org/10.3997/2214-4609.201902196","url":null,"abstract":"Summary Several studies on deep generative models for use in geomodeling show encouraging results with binary training data. An important question is what type of training data to use, since realistic 3D geology with natural variability is difficult to create. The advent of multiple types of remote sensing data of subaerial and subaqueous sedimentary patterns provides new possibilities in this context. Here, we train a Wasserstein GAN using 20,000 multispectral satellite images of subsections of 40 modern river deltas. The generated output has three facies and a background facies, and all quantitative evaluation methods of the unconditional output show a close overlap between the model and training data distributions. Standard MCMC sampling conditional on soft and hard data works well as long as the likelihood model is balanced against the prior model. Transfer learning, i.e. fine-training a small subset of the network parameters on smaller dataset of interest, such as highly non-stationary images of river deltas with similar characteristics, also shows promising results.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"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":"130954522","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: 10.3997/2214-4609.201902216
S. Nejadi, J. Curkan, P. Durkin, S. Hubbard, I. Gates
Summary The McMurray Formation is composed of large-scale fluvial meander-belt deposits that are highly heterogeneous. Repeated cut and fill events within the formation have led to a complex amalgam of stacked stratigraphic architectural elements. Lithological properties vary both laterally and vertically over short distances in the McMurray Formation. The youngest deposits of the reservoir studied at the Surmont site are well imaged using 3D seismic data; calibration with well-data enables construction of a particularly detailed reservoir model. The underlying deposits are characterized using wire line logs, core data, and stratigraphic dip analysis. For modeling purposes, internal stratigraphic architecture of both reservoir levels is mapped and distinct fluvial meander-belt architectural elements, including point bars, counter point bars, side bars and abandoned channel fills, are characterized as distinct zones. Each zone is characterized by distinct morphology, facies associations, petrophysical properties, and thus, reservoir potential. Deterministic geobody interpretations are implemented to guide geostatistical simulations; spatial distribution of facies are constrained to the mapped architectural elements. Constraining parameter estimations to deterministically interpret meander-belt architectural elements improves the predictive capability of the reservoir model. This modeling workflow preserves geological realism in models, allows spatial uncertainty to be captured adequately, and improves the ability to optimize development.
{"title":"Integrated Reservoir Characterization and Multiscale Heterogeneity Modeling of Stacked Meander-belt Deposits, Lower Cretaceous McMurray Formation, Alberta","authors":"S. Nejadi, J. Curkan, P. Durkin, S. Hubbard, I. Gates","doi":"10.3997/2214-4609.201902216","DOIUrl":"https://doi.org/10.3997/2214-4609.201902216","url":null,"abstract":"Summary The McMurray Formation is composed of large-scale fluvial meander-belt deposits that are highly heterogeneous. Repeated cut and fill events within the formation have led to a complex amalgam of stacked stratigraphic architectural elements. Lithological properties vary both laterally and vertically over short distances in the McMurray Formation. The youngest deposits of the reservoir studied at the Surmont site are well imaged using 3D seismic data; calibration with well-data enables construction of a particularly detailed reservoir model. The underlying deposits are characterized using wire line logs, core data, and stratigraphic dip analysis. For modeling purposes, internal stratigraphic architecture of both reservoir levels is mapped and distinct fluvial meander-belt architectural elements, including point bars, counter point bars, side bars and abandoned channel fills, are characterized as distinct zones. Each zone is characterized by distinct morphology, facies associations, petrophysical properties, and thus, reservoir potential. Deterministic geobody interpretations are implemented to guide geostatistical simulations; spatial distribution of facies are constrained to the mapped architectural elements. Constraining parameter estimations to deterministically interpret meander-belt architectural elements improves the predictive capability of the reservoir model. This modeling workflow preserves geological realism in models, allows spatial uncertainty to be captured adequately, and improves the ability to optimize development.","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":"116816546","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: 10.3997/2214-4609.201902201
E. Tolstukhin, E. Barrela, A. Khrulenko, J. Halotel, V. Demyanov
Summary This paper presents a methodology used in a subsurface uncertainty study for redevelopment of an oil field in the North Sea. A fractured chalk reservoir was depleted for more than 30 years with limited water injection. The uncertainty study aims to find an ensemble of geologically consistent scenarios that would honor production history. The scenarios then serve as input for the redevelopment concept selection, well placement and economic evaluation. The challenge in this study was that the field has long production history that must be respected. In addition, the uncertainty that may not be resolved by HM must be preserved in the scenarios in order to estimate all the risks and capture all the potential associated with the remaining oil pockets and future well targets. For the brown field, it is difficult to analyze all the information and utilize its full potential. In this work we use data analytics can improve efficiency of ensemble history matching by analyzing links between the static and dynamic model ensemble update: screening of the initial ensemble, model localization based on spatial analysis dynamic observations to the parameter update and identification of conflicts between groups of production observations that prevent balanced model update.
{"title":"Ensemble History Matching Enhanced with Data Analytics - A Brown Field Study","authors":"E. Tolstukhin, E. Barrela, A. Khrulenko, J. Halotel, V. Demyanov","doi":"10.3997/2214-4609.201902201","DOIUrl":"https://doi.org/10.3997/2214-4609.201902201","url":null,"abstract":"Summary This paper presents a methodology used in a subsurface uncertainty study for redevelopment of an oil field in the North Sea. A fractured chalk reservoir was depleted for more than 30 years with limited water injection. The uncertainty study aims to find an ensemble of geologically consistent scenarios that would honor production history. The scenarios then serve as input for the redevelopment concept selection, well placement and economic evaluation. The challenge in this study was that the field has long production history that must be respected. In addition, the uncertainty that may not be resolved by HM must be preserved in the scenarios in order to estimate all the risks and capture all the potential associated with the remaining oil pockets and future well targets. For the brown field, it is difficult to analyze all the information and utilize its full potential. In this work we use data analytics can improve efficiency of ensemble history matching by analyzing links between the static and dynamic model ensemble update: screening of the initial ensemble, model localization based on spatial analysis dynamic observations to the parameter update and identification of conflicts between groups of production observations that prevent balanced model update.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"29 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":"126024930","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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