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Data Science in Oil and Gas 2021最新文献

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Automated Full-Bore Core Description Application for Production Purposes. From an Idea to IT-Product 用于生产目的的自动化全孔岩心描述应用程序。从一个想法到it产品
Data Science in Oil and Gas 2021 Pub Date : 2021-08-04 DOI: 10.3997/2214-4609.202156016
E. E. Baraboshkin, A. Demidov, E. A. Panchenko, N. Gatina, A. Hahina, D.A. Mamaev, V. Alekseev, R. R. Nyazhemetdinov, A. Tkachev, D. Orlov, D. Koroteev
{"title":"Automated Full-Bore Core Description Application for Production Purposes. From an Idea to IT-Product","authors":"E. E. Baraboshkin, A. Demidov, E. A. Panchenko, N. Gatina, A. Hahina, D.A. Mamaev, V. Alekseev, R. R. Nyazhemetdinov, A. Tkachev, D. Orlov, D. Koroteev","doi":"10.3997/2214-4609.202156016","DOIUrl":"https://doi.org/10.3997/2214-4609.202156016","url":null,"abstract":"Summary The automatization is a modern trend in various field of geology. In this work we present a system which were constructed based on convolutional neural network (CNN) for automated core description. The system was successfully applied to production data. The application of the system speeds up the core description process in 7x. A sedimetnologist spent 40 minutes to describe 60 meters of core in a scale of 1:10cm instead of 5 hours. The results are stored in digital format which removes all paperwork. The system helps to describe most of required lithologic types (rock type and its structure). In case of missed rare lithotype – user can add it to the system. A pipeline to prepare and train the CNN model described.","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":"121849805","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}
引用次数: 0
Application of The Approximation Neural Network Method for Interpretation of Geoelectric Field Data 近似神经网络方法在地电场资料解释中的应用
Data Science in Oil and Gas 2021 Pub Date : 2021-08-04 DOI: 10.3997/2214-4609.202156020
I. Obornev, M. Shimelevich, E. Obornev, E. Rodionov
{"title":"Application of The Approximation Neural Network Method for Interpretation of Geoelectric Field Data","authors":"I. Obornev, M. Shimelevich, E. Obornev, E. Rodionov","doi":"10.3997/2214-4609.202156020","DOIUrl":"https://doi.org/10.3997/2214-4609.202156020","url":null,"abstract":"Summary The paper presents an example of the application of approximation neural network structures to the problem of reconstructing the resistivity distributions of 2D and 3D piecewise linear media from geoelectric data. This problem is reduced to solving a nonlinear operator equation of the first kind. An algorithm was proposed [ Shimelevich et al, 2018 , Obornev et al, 2020 ] for finding an approximate solution of this equation with a total number of parameters of the order of ∼ n 10 ^ 3, based on the use of neural (Kolmogorov) networks of the multilayer perceptron type. This approach, which allows real-time data inversion, is illustrated both on model examples and on profile and areal field survey data.","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":"124719765","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}
引用次数: 0
Lithology Classification by Depositional Environment and Well Log Data Using XGBoost Algorithm 基于沉积环境和测井资料的XGBoost算法岩性分类
Data Science in Oil and Gas 2021 Pub Date : 2021-08-04 DOI: 10.3997/2214-4609.202156006
T. M. Ponjiger, S. Šešum, M. V. Naugolnov, O. Pilipenko
{"title":"Lithology Classification by Depositional Environment and Well Log Data Using XGBoost Algorithm","authors":"T. M. Ponjiger, S. Šešum, M. V. Naugolnov, O. Pilipenko","doi":"10.3997/2214-4609.202156006","DOIUrl":"https://doi.org/10.3997/2214-4609.202156006","url":null,"abstract":"Summary The aim of this paper is to obtain an automatic lithology prediction model by using machine learning (ML) algorithms, with selected well log curves, core description data and sedimentary environment information. This model is applicable for several depositional systems for fields in Pannonian Basin and it’s locally integrated in standard software platform for petrophysicist in Company „Naftna Industrija Srbije“.","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":"132416778","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}
引用次数: 1
Well Clustering for The Subsequent Identification of Candidate Wells for Hydraulic Fracturing 用于后续水力压裂候选井识别的井聚类
Data Science in Oil and Gas 2021 Pub Date : 2021-08-04 DOI: 10.3997/2214-4609.202156017
C. Aitov
{"title":"Well Clustering for The Subsequent Identification of Candidate Wells for Hydraulic Fracturing","authors":"C. Aitov","doi":"10.3997/2214-4609.202156017","DOIUrl":"https://doi.org/10.3997/2214-4609.202156017","url":null,"abstract":"Summary This paper presents a methodology for selecting candidate wells for hydraulic fracturing. The technique is based on well clustering. Allocation of wells into clusters is carried out according to the most coinciding technological indicators of wells operation. Further selection of wells, one cluster or another, for hydraulic fracturing is performed using well-known optimization algorithms","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":"133965453","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}
引用次数: 0
The Approach to Evaluate The Confidence of Flow Rate Prediction Accuracy in The Tasks of Virtual Flow Metering 虚拟流量计量任务中流量预测精度置信度的评估方法
Data Science in Oil and Gas 2021 Pub Date : 2021-08-04 DOI: 10.3997/2214-4609.202156032
E.V. Kupryashin, I.V. Vrabie, D. Syresin
{"title":"The Approach to Evaluate The Confidence of Flow Rate Prediction Accuracy in The Tasks of Virtual Flow Metering","authors":"E.V. Kupryashin, I.V. Vrabie, D. Syresin","doi":"10.3997/2214-4609.202156032","DOIUrl":"https://doi.org/10.3997/2214-4609.202156032","url":null,"abstract":"Summary The paper is devoted to computation of the prediction interval and evaluation of regression accuracy, applied for flowrate computation with virtual flowmeters. Our approach is based on ensembles of neural networks known as Mixture Density Networks and minimizing of the negative-log likelihood function. We investigated the advantages of the applied method to calculate the oil rates and prediction interval using synthetic dataset consisting of 180 wells. The approach has demonstrated to be robust and sensitive the presence of signals variability and noise impact, and to the error caused by the model's uncertainty caused by statistical difference between training and testing datasets.","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":"125610168","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}
引用次数: 0
Modeling of Two-Phase Fluid Flow in a Well Using Machine Learning Algorithms 基于机器学习算法的井中两相流体流动建模
Data Science in Oil and Gas 2021 Pub Date : 2021-08-04 DOI: 10.3997/2214-4609.202156025
K. Pechko, I. Senkin, E. Belonogov
{"title":"Modeling of Two-Phase Fluid Flow in a Well Using Machine Learning Algorithms","authors":"K. Pechko, I. Senkin, E. Belonogov","doi":"10.3997/2214-4609.202156025","DOIUrl":"https://doi.org/10.3997/2214-4609.202156025","url":null,"abstract":"Summary Bottom hole pressure prediction is crucial issue in integrated field modeling. This article proposes a new approach to well modeling implementing machine learning algorithms. In this paper bottomhole pressure is analysed as dependent variable on four parameters such as level of wellhead pressure, flow rate, gas factor and water cut. The model is developed using the \"Random forest\" approach with gradient boosting. The model was tested on synthetic and real data from different wells and fields. The prediction accuracy satisfies company requirements and is more than 90 times faster than traditional empirical correlations.","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":"114782911","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}
引用次数: 0
Capabilities of Convolutional Neural Networks Based Algorithms for Solving Resistivity Logging Tasks 基于卷积神经网络算法求解电阻率测井任务的能力
Data Science in Oil and Gas 2021 Pub Date : 2021-08-04 DOI: 10.3997/2214-4609.202156039
K. Danilovskiy, A. Petrov, A. Leonenko, K. Sukhorukova
{"title":"Capabilities of Convolutional Neural Networks Based Algorithms for Solving Resistivity Logging Tasks","authors":"K. Danilovskiy, A. Petrov, A. Leonenko, K. Sukhorukova","doi":"10.3997/2214-4609.202156039","DOIUrl":"https://doi.org/10.3997/2214-4609.202156039","url":null,"abstract":"Summary Russian unfocused lateral logs (BKZ) are infamously known for their complexity. However, the BKZ was widely used in the Soviet Union, therefore, a large amount of data was measured at various oilfields. Reinterpretation of these logs using modern processing techniques is an urgent task. In this study, we propose a new approach to Russian resistivity logs modeling and processing, based on fully convolutional networks (FCN). FCN architecture allows taking into account signal-forming media domain for every measurement point. Training datasets are created individually for the task from real and numerically simulated data. The results of the proposed approach applying are demonstrated on the algorithm for transforming BKZ signals into focused lateral log. Application of the algorithm to real data makes it possible to check data conditionality, perform accurate depth matching, and also facilitates cross-well correlation with an incomplete set of logs.","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":"124043283","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}
引用次数: 0
Velocity Model Determining on Refracted Wave Data for Accounting of Variations in Upper Part of Seismic-Geological Section 利用折射波资料确定计算地震地质剖面上部变化的速度模型
Data Science in Oil and Gas 2021 Pub Date : 2021-08-04 DOI: 10.3997/2214-4609.202156026
G. Chernyshov, N. Goreyavchev, A. Matveev, D. A. Litvichenko, A. Duchkov, G. Mitrofanov
{"title":"Velocity Model Determining on Refracted Wave Data for Accounting of Variations in Upper Part of Seismic-Geological Section","authors":"G. Chernyshov, N. Goreyavchev, A. Matveev, D. A. Litvichenko, A. Duchkov, G. Mitrofanov","doi":"10.3997/2214-4609.202156026","DOIUrl":"https://doi.org/10.3997/2214-4609.202156026","url":null,"abstract":"Summary An example of building a velocity model of the upper part of a seismic-geological section is given. Under the model constructing the times of the first arrivals of refracted waves are used. The created model is applied in the problem of static correction, but it can also be utilized for data migration. To improve the efficiency of the proposed technological solution, the methods of machine learning, ray seismic tomography and factors decomposition are used.","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":"121926016","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}
引用次数: 0
Machine learning for classification of seismic data 地震数据分类的机器学习
Data Science in Oil and Gas 2021 Pub Date : 2021-08-04 DOI: 10.3997/2214-4609.202156018
S.I. Litvinov, P. Bekeshko, O. Adamovich
{"title":"Machine learning for classification of seismic data","authors":"S.I. Litvinov, P. Bekeshko, O. Adamovich","doi":"10.3997/2214-4609.202156018","DOIUrl":"https://doi.org/10.3997/2214-4609.202156018","url":null,"abstract":"Summary This paper discusses the possibility of using neural networks to classify seismic data in order to increase the efficiency of data processing, reduce the time for a geophysicist to perform routine tasks and have a positive impact on the economic efficiency of the project. The result of using deep learning for the classification of seismograms in the presence of non-stationary man-made noise in space is presented. The approach made it possible to achieve high classification accuracy. As a result of the work, an important conclusion was made about the possibility of using this approach to search for man-made noise in seismic records.","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":"124860142","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}
引用次数: 0
Development of an Application for automatic Quality Control of Seismic Data 地震资料自动质量控制应用的开发
Data Science in Oil and Gas 2021 Pub Date : 2021-08-04 DOI: 10.3997/2214-4609.202156023
G. Chernyshov, A. Duchkov, A. Matveev, N. Goreyavchev, S. Grubas, D. A. Litvichenko, D. Semin, G. Mitrofanov
{"title":"Development of an Application for automatic Quality Control of Seismic Data","authors":"G. Chernyshov, A. Duchkov, A. Matveev, N. Goreyavchev, S. Grubas, D. A. Litvichenko, D. Semin, G. Mitrofanov","doi":"10.3997/2214-4609.202156023","DOIUrl":"https://doi.org/10.3997/2214-4609.202156023","url":null,"abstract":"Summary The purpose of seismic survey is to build a depth-velocity geological model based on the joint interpretation of seismic and well data. Seismic surveys provide uniform coverage of the studied area, and well data provide more complete and accurate information about the studied geological medium at a discrete set of points (well locations). Well data in conjunction with the analysis of seismic stacks and various attributes are used within one software on the seismic interpretation stage. At the same time, the stages of seismic processing and interpretation are historically separated by different software packages. This reduces the efficiency of teamwork within the same project. Therefore, a relevant objective is the development of convenient software tools for joint work. Ideally, work should be carried out in a single software environment in order to ensure effectiveness of teamwork during a project. Thus, the purpose of the study is to create a series of software tools whic are designed to facilitate the interaction between the stages of processing and interpretation.","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":"131031637","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}
引用次数: 0
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