Data Science in Oil and Gas 2021最新文献_第3页

The Use of Hybrid Digital Models and Tracer-Based Dynamic Production Profiling in Intelligent Field Development 混合数字模型和基于示踪剂的动态生产剖面在智能油田开发中的应用

Data Science in Oil and Gas 2021 Pub Date : 2021-08-04 DOI: 10.3997/2214-4609.202156010

E. Malyavko, D. Tatarinov, V. Ogienko, S. Urvantsev

{"title":"The Use of Hybrid Digital Models and Tracer-Based Dynamic Production Profiling in Intelligent Field Development","authors":"E. Malyavko, D. Tatarinov, V. Ogienko, S. Urvantsev","doi":"10.3997/2214-4609.202156010","DOIUrl":"https://doi.org/10.3997/2214-4609.202156010","url":null,"abstract":"Summary Today, the global oil and gas industry needs a solution to process huge data arrays due to a growing number of wells studied and emerging technologies that yield a broader range of information about the geological and technical factors of field development. Therefore, digital analytical tools are required enabling quick analysis of the data on production, well interventions, reservoir pressure, well interference, voidage replacement, and field studies. This paper describes the approaches to data processing and analysis employed during marker-based well logging at several large fields in the Russian Federation. The mentioned technology involves the use of quantum dot marker-reporters as high-precision flow indicators to obtain data on the flow profile and composition in horizontal wells for many years without well interventions. Data analysis was performed using hybrid digital models based on geological and reservoir modeling and a simplified physical reservoir model, involving machine-learning algorithms underlain by neural networks. This platform provides for structured storage of geological and engineering data and enables using dynamic production logging data in stochastic and traditional geological and reservoir modeling. A case study is described to demonstrate how the waterflooding system operation was optimized by applying complex analysis algorithms, generating a notable economic effect.","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":"125688443","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

Using Unmanned Aircraft for Seismic Acquisition Supervising and Terrain Scouting Case Study in Republic of Serbia 利用无人机进行地震采集监测和地形侦察在塞尔维亚共和国的案例研究

Data Science in Oil and Gas 2021 Pub Date : 2021-08-04 DOI: 10.3997/2214-4609.202156009

M. Naugolnov, I. Bogatyrev, M. Božić, K. Kultysheva, V. Stevanović, A. Nestorović

引用次数: 0

Interpretation of Distributed Fluid Temperature Logging in a Producer with Gradient Optimization and Uncertainty Analysis 用梯度优化和不确定性分析解释油田分布流体温度测井

Data Science in Oil and Gas 2021 Pub Date : 2021-08-04 DOI: 10.3997/2214-4609.202156035

A.E. Karakulev, L.A. Kotlyar, I. Sofronov

{"title":"Interpretation of Distributed Fluid Temperature Logging in a Producer with Gradient Optimization and Uncertainty Analysis","authors":"A.E. Karakulev, L.A. Kotlyar, I. Sofronov","doi":"10.3997/2214-4609.202156035","DOIUrl":"https://doi.org/10.3997/2214-4609.202156035","url":null,"abstract":"Summary The paper provides an approach for interpreting downhole distributed temperature sensing (DTS) and the results of its application in cases of synthetic and real production data. The outcome of such interpretation is a profile of fluid flows from reservoir layers. The given problem, however, is ambiguous, that is why the suggested approach consists of three steps: formulation of the inverse problem based on minimization of the constructed functional with the developed fast gradient optimization method, massive parallel inversions to collect a set of different interpretations and Bayesian inference of the most probable flow profiles incorporating uncertainty. All three issues are discussed in detail. Modifications of gradient optimizer making it fast and robust are described along with regularization allowing us to approach global functional minimum for synthetic data (illustration is provided) and decrease the ambiguity for real data. Explanation and example of how statistical analysis turns a set of interpretations into the most probable flow profiles and corresponding uncertainty with EM-clustering using Dirichlet distribution are included. All in all, the developed approach for effective evaluation of flow profiles and their statistical analysis can become a useful tool in oil and gas industry automating a big part of DTS interpretation process.","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":"134495923","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

Autonomous Reservoir Management with Deep Reinforcement Learning 基于深度强化学习的自主水库管理

Data Science in Oil and Gas 2021 Pub Date : 2021-08-04 DOI: 10.3997/2214-4609.202156034

Y.E. Pico, A.A. Lemikhov

引用次数: 0

Intelligent Technology for Drilling and Well Construction in Russian Oil and Gas Fields 俄罗斯油气田钻井与建井智能化技术

Data Science in Oil and Gas 2021 Pub Date : 2021-08-04 DOI: 10.3997/2214-4609.202156013

D. Filippova, E. Safarova, V. Stolyarov, N. Eremin

引用次数: 0

Approximation Approach to Solving The Inverse Problem of Geoelectrics Using Neural Networks 用神经网络求解地电反问题的近似方法

Data Science in Oil and Gas 2021 Pub Date : 2021-08-04 DOI: 10.3997/2214-4609.202156021

M. Shimelevich, I. Obornev, E. Obornev, E. Rodionov

{"title":"Approximation Approach to Solving The Inverse Problem of Geoelectrics Using Neural Networks","authors":"M. Shimelevich, I. Obornev, E. Obornev, E. Rodionov","doi":"10.3997/2214-4609.202156021","DOIUrl":"https://doi.org/10.3997/2214-4609.202156021","url":null,"abstract":"Summary The paper presents an approximation neural network algorithm for solving conditionally correct coefficient inverse problems of geoelectrics in the class of media with piecewise constant electrical conductivity given on a parametrization grid. It is shown that the degree of ambiguity (error) of solutions monotonically increases with an increase in the dimension of the parametrization grid. A method is proposed for constructing an optimal parametrization grid, which has the maximum dimension provided that the a priori estimates of the ambiguity of the solutions do not exceed a given value. It is shown that the inverse problem in the considered class of media is reduced to the classical approximation-interpolation problem using neural network polynomials, the solution of which is the essence of the approximation neural network (ANN) method. The intrinsic error of the ANS method is determined, a posteriori estimates of the ambiguity (error) of the obtained approximate solutions are calculated with the achieved synthesis discrepancy. The method makes it possible to formalize and uniformly obtain solutions to the inverse problem of geoelectrics with the total number of the required parameters of the medium ∼ n 10 ^ 3.","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":"125506470","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

Ground Roll Noise Attenuation in The Low-Frequency Space Using a Heuristic Approach 基于启发式方法的低频空间地滚噪声衰减

Data Science in Oil and Gas 2021 Pub Date : 2021-08-04 DOI: 10.3997/2214-4609.202156033

D. Semin, M. Shavkunov, L.A. Kovalenko

引用次数: 0

An Investigation of Relation of The Attenuation Parameter of Reflected Seismic Signals with Values of Pore Pressure in The Medium 反射地震信号衰减参数与介质孔隙压力关系的研究

Data Science in Oil and Gas 2021 Pub Date : 2021-08-04 DOI: 10.3997/2214-4609.202156031

N. Goreyavchev, S. Sanin, K. A. Kornienko, G. Mitrofanov

引用次数: 0

Application of Machine Learning Algorithms for Prediction of Reservoir Properties in Bazhenov Formation from Simultaneous Inversion 机器学习算法在巴治诺夫组储层物性预测中的应用

Data Science in Oil and Gas 2021 Pub Date : 2021-08-04 DOI: 10.3997/2214-4609.202156014

A.S Ugryumov, A. Kolomytsev, B. Plotnikov, A. Kasyanenko

引用次数: 0

Technology for Predicting the Lithology Cube Using Kolmogorov Neural Networks 基于Kolmogorov神经网络的岩性立方预测技术

Data Science in Oil and Gas 2021 Pub Date : 2021-08-04 DOI: 10.3997/2214-4609.202156007

I. Priezzhev, D. Danko, E. Taikulakov

引用次数: 1