Pub Date : 2021-08-04DOI: 10.3997/2214-4609.202156019
É. G. Mironov, M.S. Shikhragimov, G. Sozonenko
Summary Currently, the information search in regulatory documentation for well construction and repairs is carried out primarily manually. This imposes restrictions on the convenience and speed of the supervisor’s work, who is controlling these activities. In this article, the development of the offline search system for the oil company internal regulatory documentation is considered and tested on real queries from «Gazprom Neft» production sites. The proposed search system is installed on the supervisor’s automated workplace (tablet) and demonstrates the best results, when using algorithm based on ElasticSearch. This enables successfully process 72,4% of queries with an average processing time of less than 0,9 second.
{"title":"Development of The Offline Search System for Company Internal Regulatory Documentation for Supervision of Drilling Processes","authors":"É. G. Mironov, M.S. Shikhragimov, G. Sozonenko","doi":"10.3997/2214-4609.202156019","DOIUrl":"https://doi.org/10.3997/2214-4609.202156019","url":null,"abstract":"Summary Currently, the information search in regulatory documentation for well construction and repairs is carried out primarily manually. This imposes restrictions on the convenience and speed of the supervisor’s work, who is controlling these activities. In this article, the development of the offline search system for the oil company internal regulatory documentation is considered and tested on real queries from «Gazprom Neft» production sites. The proposed search system is installed on the supervisor’s automated workplace (tablet) and demonstrates the best results, when using algorithm based on ElasticSearch. This enables successfully process 72,4% of queries with an average processing time of less than 0,9 second.","PeriodicalId":266953,"journal":{"name":"Data Science in Oil and Gas 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129611629","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 : 2021-08-04DOI: 10.3997/2214-4609.202156005
S.V. Zaycev, R.D. Ahmetsafin, S.A. Budennyj, S. Zhuravlev, K.V. Kiselev, R. V. Orlov, A. S. Smelov, G.S. Grigorev, V. Gulin, V. Ananev
Summary Non-seismic methods (NSM) in geophysics are a crucial addition to classical seismic information. It helps to make decisions at early stages of geological exploration in case of limited information value conditions and provide a new knowledge about geological structure. While seismic exploration remains as the most spreading technique in field geophysics, non-seismic methods predominantly play a role of auxiliary methods, more often particular cases advocate self-sufficiency of NSM in application to exploration geophysical problems. The restoration of structural boundaries is especially important to restore structural boundaries in the space between seismic survey profiles. A simple solution in the form of interpolation does not provide the necessary prediction accuracy, and requires the creation of a complex, often nonlinear model, which is possible using machine learning (ML) methods. There is a large number of features at one measurement point – the values of the geophysical fields and their transformations (derivatives, filters in a window of different widths). The analysis of the importunateness of each feature before training the ML algorithm allows you to increase the accuracy of the constructed model.
{"title":"Usage of Machine Learning Algorithms for Structural Boundaries Reconstruction Using The Non-Seismic Methods Data with Feature Selection","authors":"S.V. Zaycev, R.D. Ahmetsafin, S.A. Budennyj, S. Zhuravlev, K.V. Kiselev, R. V. Orlov, A. S. Smelov, G.S. Grigorev, V. Gulin, V. Ananev","doi":"10.3997/2214-4609.202156005","DOIUrl":"https://doi.org/10.3997/2214-4609.202156005","url":null,"abstract":"Summary Non-seismic methods (NSM) in geophysics are a crucial addition to classical seismic information. It helps to make decisions at early stages of geological exploration in case of limited information value conditions and provide a new knowledge about geological structure. While seismic exploration remains as the most spreading technique in field geophysics, non-seismic methods predominantly play a role of auxiliary methods, more often particular cases advocate self-sufficiency of NSM in application to exploration geophysical problems. The restoration of structural boundaries is especially important to restore structural boundaries in the space between seismic survey profiles. A simple solution in the form of interpolation does not provide the necessary prediction accuracy, and requires the creation of a complex, often nonlinear model, which is possible using machine learning (ML) methods. There is a large number of features at one measurement point – the values of the geophysical fields and their transformations (derivatives, filters in a window of different widths). The analysis of the importunateness of each feature before training the ML algorithm allows you to increase the accuracy of the constructed model.","PeriodicalId":266953,"journal":{"name":"Data Science in Oil and Gas 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114727919","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 : 2021-08-04DOI: 10.3997/2214-4609.202156040
V. I. Bulaev
Summary The paper presents a general view of the pipeline for deploying a machine learning model to production. It is shown that today the infrastructural costs of embedding ML into the production circuit can exceed the costs of creating and training a model by almost an order of magnitude.
{"title":"The Lifecycle of a Machine Learning System in Production","authors":"V. I. Bulaev","doi":"10.3997/2214-4609.202156040","DOIUrl":"https://doi.org/10.3997/2214-4609.202156040","url":null,"abstract":"Summary The paper presents a general view of the pipeline for deploying a machine learning model to production. It is shown that today the infrastructural costs of embedding ML into the production circuit can exceed the costs of creating and training a model by almost an order of magnitude.","PeriodicalId":266953,"journal":{"name":"Data Science in Oil and Gas 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133544162","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 : 2021-08-04DOI: 10.3997/2214-4609.202156029
I. Frolova, S. Frolov, N. Kayurov, K. S. Serdyuk
Summary The implemented approach in the software made it possible to integrate disparate data of an oil and gas producing enterprise on the basis of a single digital platform. The goal of automated preparation of a business plan for the development of an oil and gas producing enterprise with the level of downhole detail to the level of contractual terms has been achieved. As a result of the program implementation of the process approach, the calculation of net cash flow for each well and infrastructure facilities was implemented, which made it possible to improve the quality of calculations and the level of justification of the indicators laid down for the calculation of the business plan. Integration within the digital platform with automatic production forecasting based on measurement data, data on technological modes and virtual production electricity consumption depending on planned production. This software product is being implemented at oil and gas producing enterprises in Russia. In the future, it is planned to expand the functionality based on the proposed scalable ontological model. For example, the selection of optimal development options based on the specified time limits, finances, technical characteristics and target function. In addition, it is planned to expand the intellectual analysis of actual data and factor analysis of deviations.
{"title":"Features of automated preparation of a business plan for the development of an oil and gas asset based on a digital platform","authors":"I. Frolova, S. Frolov, N. Kayurov, K. S. Serdyuk","doi":"10.3997/2214-4609.202156029","DOIUrl":"https://doi.org/10.3997/2214-4609.202156029","url":null,"abstract":"Summary The implemented approach in the software made it possible to integrate disparate data of an oil and gas producing enterprise on the basis of a single digital platform. The goal of automated preparation of a business plan for the development of an oil and gas producing enterprise with the level of downhole detail to the level of contractual terms has been achieved. As a result of the program implementation of the process approach, the calculation of net cash flow for each well and infrastructure facilities was implemented, which made it possible to improve the quality of calculations and the level of justification of the indicators laid down for the calculation of the business plan. Integration within the digital platform with automatic production forecasting based on measurement data, data on technological modes and virtual production electricity consumption depending on planned production. This software product is being implemented at oil and gas producing enterprises in Russia. In the future, it is planned to expand the functionality based on the proposed scalable ontological model. For example, the selection of optimal development options based on the specified time limits, finances, technical characteristics and target function. In addition, it is planned to expand the intellectual analysis of actual data and factor analysis of deviations.","PeriodicalId":266953,"journal":{"name":"Data Science in Oil and Gas 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129712880","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 : 2021-08-04DOI: 10.3997/2214-4609.202156038
E. Anokhina, G. Erokhin, A. Kamyshnikov, R. Simonov
Summary The prospects for the oil and gas potential of the Pre-Jurassic complex in one field in Western Siberia are associated with the weathering crust. To solve the problem of identifying highly productive zones, a complex interpretation of information on the material composition of rocks and the results of clustering of APS and gamma-ray log data was performed
{"title":"Use of Geostatistical Algorithms for Complex Interpretation of Well Data and Prediction of Reservoir Distribution Zones","authors":"E. Anokhina, G. Erokhin, A. Kamyshnikov, R. Simonov","doi":"10.3997/2214-4609.202156038","DOIUrl":"https://doi.org/10.3997/2214-4609.202156038","url":null,"abstract":"Summary The prospects for the oil and gas potential of the Pre-Jurassic complex in one field in Western Siberia are associated with the weathering crust. To solve the problem of identifying highly productive zones, a complex interpretation of information on the material composition of rocks and the results of clustering of APS and gamma-ray log data was performed","PeriodicalId":266953,"journal":{"name":"Data Science in Oil and Gas 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128775178","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 : 2021-08-04DOI: 10.3997/2214-4609.202156015
K. O. Osipov, M. E. Elsheikh, A. Stoupakova, R. Sautkin, E. Ablya, M. Bolshakova
Summary In this work, the principal components analysis was applied along with a correlation heatmap to determine the relationship between liquid hydrocarbons samples and source rock samples in the Nile Delta Basin. The principal component analysis made it possible to display oil samples with source rock samples in a single space, and the correlation heatmap helped to determine geological factors that stand for axes of this plot. Based on the results of the study, it was possible to determine the characteristics of source rocks for hydrocarbons which are consist of kerogen type III and have an initial hydrogen Index of less than 200 mg HC/g TOC, that produced liquid hydrocarbons at the early and main stages of oil window. The Miocene source rocks are the closest to the studied oils in terms of depositional environment.
{"title":"Application of the principal component analysis for hydrocarbons - source rocks correlation in the Nile Delta Basin, Egypt","authors":"K. O. Osipov, M. E. Elsheikh, A. Stoupakova, R. Sautkin, E. Ablya, M. Bolshakova","doi":"10.3997/2214-4609.202156015","DOIUrl":"https://doi.org/10.3997/2214-4609.202156015","url":null,"abstract":"Summary In this work, the principal components analysis was applied along with a correlation heatmap to determine the relationship between liquid hydrocarbons samples and source rock samples in the Nile Delta Basin. The principal component analysis made it possible to display oil samples with source rock samples in a single space, and the correlation heatmap helped to determine geological factors that stand for axes of this plot. Based on the results of the study, it was possible to determine the characteristics of source rocks for hydrocarbons which are consist of kerogen type III and have an initial hydrogen Index of less than 200 mg HC/g TOC, that produced liquid hydrocarbons at the early and main stages of oil window. The Miocene source rocks are the closest to the studied oils in terms of depositional environment.","PeriodicalId":266953,"journal":{"name":"Data Science in Oil and Gas 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123202058","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 : 2021-08-04DOI: 10.3997/2214-4609.202156012
V.O. Dulov, V.M. Khomik, O.N. Gustaia, S. Valitov
Summary The software that performs engineering and economic calculations for reserves' audit has been developed. The calculation process is carried out according to the methodology adopted by the company and meets the official guidelines of SPE (PRMS) and SEC. Modern tools were used for development of the software, including the use of one of the most popular programming languages, well-known libraries and machine learning tools.
{"title":"Development of Software for Reserves` Audit","authors":"V.O. Dulov, V.M. Khomik, O.N. Gustaia, S. Valitov","doi":"10.3997/2214-4609.202156012","DOIUrl":"https://doi.org/10.3997/2214-4609.202156012","url":null,"abstract":"Summary The software that performs engineering and economic calculations for reserves' audit has been developed. The calculation process is carried out according to the methodology adopted by the company and meets the official guidelines of SPE (PRMS) and SEC. Modern tools were used for development of the software, including the use of one of the most popular programming languages, well-known libraries and machine learning tools.","PeriodicalId":266953,"journal":{"name":"Data Science in Oil and Gas 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115372553","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 : 2021-08-04DOI: 10.3997/2214-4609.202156022
M. Naugolnov, A. Antropov, J. Arsić
Summary The purpose of the work is a new approach to the development analysis of brown oil field, that is located in basement rocks. Analysis is done for the tasks of the future implementation of the pressure maintenance system with the usage of advanced analytics tools and machine learning algorithms. The solution is based on the integration of well performance data and field studies, as well as on the study of the mutual influence of wells as a factor characterizing the fracture throughput, wells clasterisation and production forecast.
{"title":"Using of Machine Learning Algorithms for Development Analysis of a Brown oil Field Located in The Basement Rocks","authors":"M. Naugolnov, A. Antropov, J. Arsić","doi":"10.3997/2214-4609.202156022","DOIUrl":"https://doi.org/10.3997/2214-4609.202156022","url":null,"abstract":"Summary The purpose of the work is a new approach to the development analysis of brown oil field, that is located in basement rocks. Analysis is done for the tasks of the future implementation of the pressure maintenance system with the usage of advanced analytics tools and machine learning algorithms. The solution is based on the integration of well performance data and field studies, as well as on the study of the mutual influence of wells as a factor characterizing the fracture throughput, wells clasterisation and production forecast.","PeriodicalId":266953,"journal":{"name":"Data Science in Oil and Gas 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116065349","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 : 2021-08-04DOI: 10.3997/2214-4609.202156037
A. Valeev, D. Syresin, I.V. Vrabie
Summary The novelty detection problem is essential for the study of non-stationary processes, in which the received signals have a wide variability in time. Among such problems we can single out the problem of research of non-stationary multiphase flows in wells. Numerical analysis methods are often used to investigate such flows, but does not always allow to reproduce the complexity and features of real systems, especially at its anomalous behavior. To solve this problem in the problems of well tests, we have developed an approach to detect novelty of some data in relation to other. The proposed model is able to detect variations in time series by analysis of magnitude and dynamic characteristics of the flow parameters. The method is robust to outliers in signals, simply interpreted and has a low computational complexity. The model was evaluated on synthetic data obtained with a multiphase non-stationary flow simulator.
{"title":"Application of Deep Autoencoders for Novelty and Anomaly Detection in Well Testing Data Analysis","authors":"A. Valeev, D. Syresin, I.V. Vrabie","doi":"10.3997/2214-4609.202156037","DOIUrl":"https://doi.org/10.3997/2214-4609.202156037","url":null,"abstract":"Summary The novelty detection problem is essential for the study of non-stationary processes, in which the received signals have a wide variability in time. Among such problems we can single out the problem of research of non-stationary multiphase flows in wells. Numerical analysis methods are often used to investigate such flows, but does not always allow to reproduce the complexity and features of real systems, especially at its anomalous behavior. To solve this problem in the problems of well tests, we have developed an approach to detect novelty of some data in relation to other. The proposed model is able to detect variations in time series by analysis of magnitude and dynamic characteristics of the flow parameters. The method is robust to outliers in signals, simply interpreted and has a low computational complexity. The model was evaluated on synthetic data obtained with a multiphase non-stationary flow simulator.","PeriodicalId":266953,"journal":{"name":"Data Science in Oil and Gas 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129982202","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 : 2021-08-04DOI: 10.3997/2214-4609.202156027
N. Goreyavchev, A. Matveev, G. Dugarov, A. Duchkov, T. Nefedkina, I. Bogatyrev, G. Mitrofanov
Summary The issues of detailed study of target objects of the medium are considered, which are of interest for the processes of exploration and development of oil and gas reservoirs. Consideration is carried out under an example of data preparation for determining the parameters of target fractured objects. It is shown that the use of a set of methods, consisting of ray tracing, 5D interpolation and factor decompositions, made it possible to obtain qualitative data for solving the corresponding inverse problem.
{"title":"Application of Modern Mathematical Methods for Detailed Study of Target Objects of Medium","authors":"N. Goreyavchev, A. Matveev, G. Dugarov, A. Duchkov, T. Nefedkina, I. Bogatyrev, G. Mitrofanov","doi":"10.3997/2214-4609.202156027","DOIUrl":"https://doi.org/10.3997/2214-4609.202156027","url":null,"abstract":"Summary The issues of detailed study of target objects of the medium are considered, which are of interest for the processes of exploration and development of oil and gas reservoirs. Consideration is carried out under an example of data preparation for determining the parameters of target fractured objects. It is shown that the use of a set of methods, consisting of ray tracing, 5D interpolation and factor decompositions, made it possible to obtain qualitative data for solving the corresponding inverse problem.","PeriodicalId":266953,"journal":{"name":"Data Science in Oil and Gas 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128867442","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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