Open AccessApplication of Machine Learning Algorithms for Solving Problems in the Oil and Gas Sector
Author(s) -
Konstantin Maiorov,
Kalashnikov Istu
Publication year - 2021
Publication title -
intellektualʹnye sistemy v proizvodstve
Language(s) - English
Resource type - Journals
eISSN - 2410-9304
pISSN - 1813-7911
DOI - 10.22213/2410-9304-2021-3-55-64
Subject(s) - artificial neural network , machine learning , artificial intelligence , computer science , algorithm , decision tree , reinforcement learning , field (mathematics) , markov decision process , normalization (sociology) , markov process , mathematics , statistics , pure mathematics , sociology , anthropology
The paper examines the life cycle of field development, analyzes the processes of the field development design stage for the application of machine learning methods. For each process, relevant problems are highlighted, existing solutions based on machine learning methods, ideas and problems are proposed that could be effectively solved by machine learning methods. For the main part of the processes, examples of solutions are briefly described; the advantages and disadvantages of the approaches are identified. The most common solution method is feed-forward neural networks. Subject to preliminary normalization of the input data, this is the most versatile algorithm for regression and classification problems. However, in the problem of selecting wells for hydraulic fracturing, a whole ensemble of machine learning models was used, where, in addition to a neural network, there was a random forest, gradient boosting and linear regression. For the problem of optimizing the placement of a grid of oil wells, the disadvantages of existing solutions based on a neural network and a simple reinforcement learning approach based on Markov decision-making process are identified. A deep reinforcement learning algorithm called Alpha Zero is proposed, which has previously shown significant results in the role of artificial intelligence for games. This algorithm is a decision tree search that directs the neural network: only those branches that have received the best estimates from the neural network are considered more thoroughly. The paper highlights the similarities between the tasks for which Alpha Zero was previously used, and the task of optimizing the placement of a grid of oil producing wells. Conclusions are made about the possibility of using and modifying the algorithm of the optimization problem being solved. Аn approach is proposed to take into account symmetric states in a Monte Carlo tree to reduce the number of required simulations.