Digital transformation of gas production

Dmitrievskiy, A N; Eremin, N.A.; Stolyarov, V.E.

doi:10.1088/1757-899x/700/1/012052

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…For example, the WSN has become a widely-used technology in oil and gas fields among the upstream, midstream, and downstream sectors for monitoring the important production processes and controlling the vital operations [3][4][5]. In [6], it explicitly discussed the advantages of digital technologies of gas production from a different viewpoint, and these promising advantages are included in the case of some special gas wells; with these advanced digital gas exploitation tools, the terms of cost-effective exploitation are most likely to be extended from decreasing production stage, and finally it would contribute to increased labor productivity.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Energy Management Information System with Machine Learning Algorithms in Oil and Gas Industry

Guo

et al. 2023

Wireless Communications and Mobile Computing

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Oil and gas will continue to play an increasingly important role in global economy development and prosperity for the upcoming years. However, a fact which sometimes is ignored is that the oil and gas industry is an intensive energy-consumption industry and a major contributor to greenhouse gas emissions as well. Recently, with the arrival of the “double carbon” era, the energy-intensive production of oil and gas has caused people to pay more attention. Currently, big data technologies as well as the Internet of things (IoT) are developing at a rapid pace, and the metering infrastructure technology also shows huge advancement; oil and gas companies have built many information systems and developed many functions to acquire the energy consumption and oil production data for the aim of reducing the oil production cost via the emerging information technologies, and it turned out that these investments do improve the energy efficiency and lower oil production cost. Unfortunately, due to the different development standards, some systems store the same data with different values; so, therefore, there is a “data barrier” among these systems and somehow discount the share and analysis of these data resources. To this end, in our work, based on these rich data resources from the other information systems, we discussed an intelligent energy management information system with four machine learning algorithms to enhance the analysis of data resources over energy consumption and oil production. First, we presented the innovation of energy consumption data fusion with the method “Data Lake”; then, four different machine learning algorithms: Support Vector Machine (SVM), Linear Regression (LR), Extreme Learning Machine (ELM), and Artificial Neural Networks (ANN) are installed in the proposed system for predicting the oil and gas production and total energy consumption. In order to aggregate these data into dashboard views that help managers make decisions about operations, we also demonstrated the process of energy consumption data visualization with emerging open-source software tool ECharts. Finally, a real life application of the proposed system was summarized in case of total energy consumption prediction and the system output shows good prediction accuracy which proves the feasibility and benefits of the intelligent energy management information system.

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Section: Introductionmentioning

confidence: 99%

An Intelligent Energy Management Information System with Machine Learning Algorithms in Oil and Gas Industry

Guo

et al. 2023

Wireless Communications and Mobile Computing

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“…The main digital technologies used in oil and gas enterprises are Big Data, neurotechnology and artificial intelligence, distributed registry systems, the industrial Internet of Things, robotics and sensor components, as well as virtual and augmented reality technologies [12,13].…”

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confidence: 99%

Digital Technologies in Arctic Oil and Gas Resources Extraction: Global Trends and Russian Experience

et al. 2022

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The paper is devoted to the analysis of the current and the forecast of the prospective state of introducing digital technologies into the oil and gas mining industry of the Russian Arctic. The authors of the paper analyzed the global trends that define the process of digital technologies’ introduction into the oil and gas mining industry. They also reviewed the Russian companies’ experience in this sphere. The main trends and prospects for the development of oil and gas resources extraction in the Russian Arctic in the digitalization sphere were identified. Together with this, the researchers considered prospects for digital technologies’ introduction into the oil and gas industry, observing their competition with RES. As a result, the authors have come to the following conclusions: (1) in Russian companies, digitalization is being more actively introduced into the processes of general enterprise management. (2) The main purpose of Russian oil and gas sector digitalization is to increase the efficiency of business process management, while the key constraining factors of digitalization are the lack of qualified personnel, lack of material and technical base and cyber-security threats aggravation. (3) The prospects of introducing a new package of sanctions can become both an incentive for a qualitative leap in Russian software development/implementation and an obstacle to the development of Arctic projects due to their rise in price. (4) The COVID-19 pandemic factor is one of the incentives for the widespread introduction of production and various business processes automation in the oil and gas industry, as well as the development of digital communications. (5) The leader in the digital technology development industry among Russian oil and gas companies is “Gazprom Neft” PJSC, followed by “NK Rosneft” PJSC. (6) “Gazprom” PJSC continues to lag behind in the sphere of digitalization; however, qualitative changes here should be expected in 2022. (7) The “sensitivity parameters” influencing the industry digitalization process in the Arctic region are the high dependence on foreign technological solutions and software, characteristics of the entire Russian oil and gas industry, and the problem of ensuring cybersecurity in Arctic oil and gas projects and power outages. (8) For the Arctic regions, the use of RES as the main source of electricity is the most optimal and promising solution; however, hydrocarbon energy will still dominate the market in the foreseeable future.

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Application of artificial intelligence methods for identifying and predicting complications in the construction of oil and gas wells: problems and solutions

et al. 2020

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This paper poses and solves the problem of using artificial intelligence methods for processing large volumes of geodata from geological and technological measurement stations in order to identify and predict complications during well drilling. Digital modernization of the life cycle of wells using artificial intelligence methods, in particular, helps to improve the efficiency of drilling oil and gas wells. In the course of creating and training artificial neural networks, regularities were modeled with a given accuracy, hidden relationships between geological and geophysical, technical and technological parameters were revealed. The clustering of multidimensional data volumes from various types of sensors used to measure parameters during well drilling has been carried out. Artificial intelligence classification models have been developed to predict the operational results of the well construction. The analysis of these issues is carried out, and the main directions for their solution are determined.

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Digital transformation of gas production

Cited by 4 publications

References 7 publications

An Intelligent Energy Management Information System with Machine Learning Algorithms in Oil and Gas Industry

An Intelligent Energy Management Information System with Machine Learning Algorithms in Oil and Gas Industry

Digital Technologies in Arctic Oil and Gas Resources Extraction: Global Trends and Russian Experience

Application of artificial intelligence methods for identifying and predicting complications in the construction of oil and gas wells: problems and solutions

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