Data quality analysis of the station of geological and technological researches in recognizing losses and kicks to improve the prediction accuracy of neural network algorithms

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

Chernikov

Eremin

Stolyarov

et al. 2020

Georesursy

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On increasing the productive time of drilling oil and gas wells using machine learning methods

et al. 2020

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The article is devoted to the development of a hybrid method for predicting and preventing the development of troubles in the process of drilling wells based on machine learning methods and modern neural network models. Troubles during the drilling process, such as filtrate leakoff; gas, oil and water shows and sticking, lead to an increase in unproductive time, i.e. time that is not technically necessary for well construction and is caused by various violations of the production process. Several different approaches have been considered, including based on the regression model for predicting the indicator function, which reflects an approach to a developing trouble, as well as anomaly extraction models built both on basic machine learning algorithms and using the neural network model of deep learning. Showing visualized examples of the work of the developed methods on simulation and real data. Intelligent analysis of Big Geodata from geological and technological measurement stations is based on well-proven machine learning algorithms. Based on these data, a neural network model was proposed to prevent troubles and emergencies during the construction of wells. The use of this method will minimize unproductive drilling time.

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Use of fuzzy sets (artificial intelligence) in the oil and gas industry

Афанасьева

2024

Modern Economy Success

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актуальность статьи обусловлена тем, что эффективное использование имеющихся данных обычно представляет наибольшую трудность при моделировании рабочих состояний энергосистемы. Анализ состояния системы на основе устаревшей или неверной информации может привести к принятию решений, существенно отличающихся от оптимальных. Целью статьи является выявление особенностей использования нечетких множеств (искусственного интеллекта) в нефтегазовой отрасли и разработка предложений для практического применения и совершенствования данных процессов. Методология исследования основана на применении системного подхода и общенаучных методов, среди которых: систематизация и обобщение, логический и сравнительный анализ. В статье обосновано, что удобным математическим инструментом описания неопределенности и неточности входных данных и связей между ними является теория нечетких множеств. В статье представлены возможности использования нечеткого моделирования в нефтегазовой отрасли при выборе альтернатив при транспортировке нефтегазовых продуктов. Результаты исследований по применению этой теории во многих областях науки и техники позволяют предположить, что благодаря ее возможностям моделирования неопределенности и неточности входных данных и описания связей между ними она расширит сферу своей деятельности. the relevance of the article is due to the fact that the effective use of available data usually poses the greatest difficulty when modeling the operating states of the power system. Analyzing the state of a system based on outdated or incorrect information can lead to decisions that are significantly different from the optimal ones. The purpose of the article is to identify the features of using fuzzy sets (artificial intelligence) in the oil and gas industry and develop proposals for practical application and improvement of these processes. The research methodology is based on the use of a systematic approach and general scientific methods, including: systematization and generalization, logical and comparative analysis. The article substantiates that a convenient mathematical tool for describing the uncertainty and inaccuracy of input data and the connections between them is the theory of fuzzy sets. The article presents the possibilities of using fuzzy modeling in the oil and gas industry when choosing alternatives when transporting oil and gas products. Research on the application of this theory in many fields of science and technology suggests that its ability to model the uncertainty and imprecision of input data and describe the relationships between them will expand its scope.

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Data quality analysis of the station of geological and technological researches in recognizing losses and kicks to improve the prediction accuracy of neural network algorithms

Cited by 3 publications

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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

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

On increasing the productive time of drilling oil and gas wells using machine learning methods

Use of fuzzy sets (artificial intelligence) in the oil and gas industry

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