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Data Science in Oil and Gas 2021
- Conference date: August 4-6, 2021
- Location: Online
- Published: 04 August 2021
21 - 31 of 31 results
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Features of automated preparation of a business plan for the development of an oil and gas asset based on a digital platform
Authors I.I. Frolova, S.A. Frolov, N.K. Kayurov and K.S. SerdyukSummaryThe 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.
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Minimum Viable Product Continuous Creation Strategy of Cloud Solution Using The Example of a New Hybrid Three-Phase Flow Metering Method
Authors A.A. Zhirkina, Y. Pico, L.E. Dovgilovich and A.M. KuvichkoSummaryEvery high-tech digital product at some phase faces a problem of transfer from an internal-use prototype to a functional digital product, that can be implemented and used by the wide audience. This problem becomes even more difficult when it comes to cloud solutions. One of the possible ways to avoid this painful transition is to use the continuous MVP creation and support strategy, with minimum of resources applied. Real life example of new downhole three-phase flowmetering algorithm implemented at one of the North Caspian oilfields, has shown us that by starting developer-client interactions at the very first stages of the algorithm development we achieve several benefits, such as securing the customer interest in the technology being developed, getting extensive feedback on both algorithmic and user-experience parts of the setup, adapting the user interface and demonstrating new cloud approach to the delivery of digital solutions. Moreover, extensive field-test allows the developer to validate the algorithm performance and make necessary corrections in the core technology itself. Web-framework, that has been used for a front-end development also made it possible to create user-friendly interfaces without engaging with a dedicated front-end engineer and create business logic by hands of the technology developer themselves.
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An Investigation of Relation of The Attenuation Parameter of Reflected Seismic Signals with Values of Pore Pressure in The Medium
Authors N.A. Goreyavchev, S.S. Sanin, K.A. Kornienko and G.M. MitrofanovSummaryThe paper presents the results of testing the hypothesis that there is a relationship between the value of the attenuation parameter of seismic signals observed at the surface and the pressure measured in the wells. Hypothesis testing was performed on the basis of field seismic data that underwent standard processing. The attenuation parameter values determined from them were used to obtain correlations between attenuation and pressure. Based on these relationships, predicted pressure values were calculated. Comparison of the predicted and measured pressures showed their high accuracy. This confirmed the validity of the hypothesis under consideration.
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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 and D.E. SyresinSummaryThe 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.
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Ground Roll Noise Attenuation in The Low-Frequency Space Using a Heuristic Approach
Authors D.G. Semin, M.V. Shavkunov and L.A. KovalenkoSummarySeismic data processing is a long, complex and iterative process implemented at Gazprom Neft. One of the stages of this process is the ground roll noise attenuation. Implementation requires the presence in the company of specialists-geophysicists with strong mathematical background, who currently solve this problem by manually selecting combinations of various predetermined filters. The company has been dealing with this task for a long time and has accumulated a representative sample of data where manual noise attenuation has been implemented. This information can be used as an automatic hypothesis test for new filtering methods. Based on this approach, we propose a method for obtaining ground roll noise masks and a method for its attenuation in the Fourier frequency domain (FK transform) based on heuristic rules.
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Autonomous Reservoir Management with Deep Reinforcement Learning
Authors Y.E. Pico and A.A. LemikhovSummaryThe introduction of intelligent completion systems opens the opportunity to approach reservoir optimization as optimal control problem. Moreover, improving in Deep Reinforcement Learning make viable solving the optimal control problem to achieve autonomous control. We show how using intelligent completions and reservoir modeling, the task of autonomous choke control is solved. The present article is one of the first attempts to analyze and compare efficiency of novel DRL algorithms applied to autonomous reservoir control problem.
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Interpretation of Distributed Fluid Temperature Logging in a Producer with Gradient Optimization and Uncertainty Analysis
Authors A.E. Karakulev, L.A. Kotlyar and I.L. SofronovSummaryThe 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.
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Application of Deep Autoencoders for Novelty and Anomaly Detection in Well Testing Data Analysis
Authors A.R. Valeev, D.E. Syresin and I.V. VrabieSummaryThe 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.
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Use of Geostatistical Algorithms for Complex Interpretation of Well Data and Prediction of Reservoir Distribution Zones
Authors E. Anokhina, G. Erokhin, A. Kamyshnikov and R. SimonovSummaryThe 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
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Capabilities of Convolutional Neural Networks Based Algorithms for Solving Resistivity Logging Tasks
Authors K.N. Danilovskiy, A.M. Petrov, A.R. Leonenko and K.V. SukhorukovaSummaryRussian 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.
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The Lifecycle of a Machine Learning System in Production
By V.I. BulaevSummaryThe 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.
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