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To support remaining economic recoverable reserves and undeveloped reserves compliant with SEC rules, different types of recovery analogous reservoir sequences need to be established. Based on the SEC reserves estimation standards and the actual development characteristics of the oilfield, a classified and optimal evaluation method of analogous reservoirs, which can select a reasonable analogous reservoir fast, has been proposed. This method mainly includes 7 steps to select analogous reservoirs. With practical application in an oilfield, this method has obtained good effects. Selection results can cover all the typical reservoir blocks, and it provides a reliable basis for analogizing the target reservoir in the future. The method herein could provide a new method to select analogous reservoirs based on SEC, and it can be widely used in other oil fields.
To support remaining economic recoverable reserves and undeveloped reserves compliant with SEC rules, different types of recovery analogous reservoir sequences need to be established. Based on the SEC reserves estimation standards and the actual development characteristics of the oilfield, a classified and optimal evaluation method of analogous reservoirs, which can select a reasonable analogous reservoir fast, has been proposed. This method mainly includes 7 steps to select analogous reservoirs. With practical application in an oilfield, this method has obtained good effects. Selection results can cover all the typical reservoir blocks, and it provides a reliable basis for analogizing the target reservoir in the future. The method herein could provide a new method to select analogous reservoirs based on SEC, and it can be widely used in other oil fields.
Нефтегазовая отрасль является важнейшей отраслью экономики России, которая вносит значительный вклад в экономический рост и развитие страны. В данной отрасли производственные процессы обучения являются одним из ключевых элементов, которые влияют на эффективность работы предприятий и на достижение высоких результатов. В настоящее время развитие машинного обучения во всем мире наблюдает стремительный рост, и его применение находит все больше отраслей экономики. Одной из таких отраслей является нефтегазовая промышленность, которая активно использует современные технологии, включая машинное обучение, для оптимизации производственных процессов и повышения эффективности добычи нефти и газа. Россия, являющаяся одним изкрупнейших производителей нефти и газа в мире, также активно развивает применение машинного обучения в нефтегазовой отрасли. Однако, в связи с быстрым развитием технологий и изменением экономической ситуации, компании должны готовиться к изменениям и постоянно совершенствовать свои методы и подходы для достижения успеха. В данной статье мы рассмотрим применение машинного обучения в нефтегазовой отрасли России, а также определим, как выпускники вузов, специализирующихся на машинном обучении, могут работать в этой отрасли. Мы также рассмотрим перспективы развития нефтегазовой отрасли в России в будущем, с учетом быстрого развития технологий и изменения экономической и политической ситуации в мире. The oil and gas industry is the most important sector of the Russian economy, which makes a significant contribution to the economic growth and development of the country. In this industry, production training processes are one of the key elements that affect the efficiency of enterprises and the achievement of high results. Currently, the development of machine learning around the world is seeing rapid growth, and more and more sectors of the economy are using it. One of these industries is the oil and gas industry, which actively uses modern technologies, including machine learning, to optimize production processes and improve the efficiency of oil and gas production. Russia, which is one of the largest oil and gas producers in the world, is also actively developing the use of machine learning in the oil and gas industry. However, due to the rapid development of technology and the changing economic situation, companies must prepare for changes and constantly improve their methods and approaches to achieve success. In this article, we will look at the application of machine learning in the Russian oil and gas industry, and also determine how graduates of universities specializing in machine learning can work in this industry. We will also consider the prospects for the development of the oil and gas industry in Russia in the future, taking into account the rapid development of technology and the changing economic and political situation in the world.
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