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Data Science in Oil and Gas 2021
- Conference date: August 4-6, 2021
- Location: Online
- Published: 04 August 2021
1 - 20 of 31 results
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Usage of Machine Learning Algorithms for Structural Boundaries Reconstruction Using The Non-Seismic Methods Data with Feature Selection
SummaryNon-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.
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Lithology Classification by Depositional Environment and Well Log Data Using XGBoost Algorithm
Authors T. Micić Ponjiger, S. Šešum, M.V. Naugolnov and O. PilipenkoSummaryThe 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“.
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Technology for Predicting the Lithology Cube Using Kolmogorov Neural Networks
Authors I.I. priezzhev, D.A. Danko and E.E. TaikulakovSummaryFor the prediction of the lithofacies cube, it is proposed to use new age full-functional Kolmogorov neural networks. These three-layer neural networks, which can be positioned as a new generation of neural networks, have a high degree of freedom comparable to deep multi-layer neural networks. For a more accurate lithofacies cube, it is suggested to perform the forecast in two stages. At the first stage, a separate forecast of each lithofacie is made in the form of a probability cube. At the second stage, the connection of such cubes into one lithofacies cube is based on the principle of maximum probability.
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Using Unmanned Aircraft for Seismic Acquisition Supervising and Terrain Scouting Case Study in Republic of Serbia
Authors M.V. Naugolnov, I.Yu. Bogatyrev, M. Bozic, K.A. Kultysheva, V. Stevanovic and A. NestorovicSummarySupervision of seismic acquisition is an essential stage in ensuring both the quality of geophysical information and operational safety during field work. The tasks of supervision support include monitoring of seismic equipment, safety rules implementation, such as, for example, wearing personal protective equipment, as well as compliance with environmental protection requirements, i.e. prevention of oil spills, leaving household waste after completion of work, etc. The modern development of unmanned aerial vehicle (UAV) technologies, coupled with machine learning and computer vision methods makes it possible to create a digital seismic supervisor.
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The Use of Hybrid Digital Models and Tracer-Based Dynamic Production Profiling in Intelligent Field Development
Authors E.A. Malyavko, D.S. Tatarinov, V.N. Ogienko and S.I. UrvantsevSummaryToday, 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.
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Development of Software for Reserves` Audit
Authors V.O. Dulov, V.M. Khomik, O.N. Gustaia and S.M. ValitovSummaryThe 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.
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Intelligent Technology for Drilling and Well Construction in Russian Oil and Gas Fields
Authors D.S. Filippova, E.A. Safarova, V.E. Stolyarov and N.A. EreminSummaryThe novelty of the implemented solutions lies in the improvement of drilling technologies based on the application of modeling algorithms and finding the optimal network configuration to perform a reliable forecast based on the artificial neural network model. Without comprehensive automation, it is impossible to reduce the role of personnel, which implies the robotization of part of the drilling process and technologies of descent operations. The presented concept of a geographically distributed system of intelligent monitoring and management is easily adaptable to various technological processes when working in emergency situations due to information support of construction processes. The introduction of technologies provides a reduction in operating costs, an increase in gas and oil production of about 10% and a reduction in well downtime of at least 50 % from the classic technologies of drilling, construction and operation in remote fields.
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Application of Machine Learning Algorithms for Prediction of Reservoir Properties in Bazhenov Formation from Simultaneous Inversion
Authors A.S Ugryumov, A.V. Kolomytsev, B.S. Plotnikov and A.A KasyanenkoSummaryThe work explores how different machine learning algorithms can be used to predict Bazhenov formation reservoir properties such as rock type, heavy hydrocarbons and kerogen volume fraction, total organic carbon content, total, effective and dynamic porosity and water saturation from the results of simultaneous inversion of seismic data. The workflow for data processing and handling is proposed and application of various machine-learning models is investigated. Finally, practical issues of data interoperability between different pieces of software are discussed and tips on implementation of the obtained trends in reservoir modeling are given.
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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.V. Stoupakova, R.S. Sautkin, E.A. Ablya and M.A. BolshakovaSummaryIn 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.
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Automated Full-Bore Core Description Application for Production Purposes. From an Idea to IT-Product
SummaryThe 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.
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Well Clustering for The Subsequent Identification of Candidate Wells for Hydraulic Fracturing
By Ch.R. AitovSummaryThis 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
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Machine learning for classification of seismic data
Authors S.I. Litvinov, P.S. Bekeshko and O.O. AdamovichSummaryThis 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.
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Development of The Offline Search System for Company Internal Regulatory Documentation for Supervision of Drilling Processes
Authors E.G. Mironov, M.S. Shikhragimov and G.V. SozonenkoSummaryCurrently, 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.
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Application of The Approximation Neural Network Method for Interpretation of Geoelectric Field Data
Authors I.E. Obornev, M.I. Shimelevich, E.A. Obornev and E.A. RodionovSummaryThe 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.
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Approximation Approach to Solving The Inverse Problem of Geoelectrics Using Neural Networks
Authors M.I. Shimelevich, I.E. Obornev, E.A. Obornev and E.A. RodionovSummaryThe 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.
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Using of Machine Learning Algorithms for Development Analysis of a Brown oil Field Located in The Basement Rocks
Authors M.V. Naugolnov, A.V. Antropov and J. ArsićSummaryThe 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.
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Development of an Application for automatic Quality Control of Seismic Data
SummaryThe 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.
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Modeling of Two-Phase Fluid Flow in a Well Using Machine Learning Algorithms
Authors K.A. Pechko, I.S. Senkin and E.V. BelonogovSummaryBottom 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.
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Velocity Model Determining on Refracted Wave Data for Accounting of Variations in Upper Part of Seismic-Geological Section
SummaryAn 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.
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Application of Modern Mathematical Methods for Detailed Study of Target Objects of Medium
SummaryThe 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.
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