Solving problems of the oil and gas sector using machine learning algorithms

solved by machine learning algorithms. These tasks include the
study of the interference of wells, the classification of wells
according to their technological and geophysical characteristics, the
assessment of the effectiveness of ongoing and planned geological
and technical measures, the forecast of oil production for individual
wells and the total oil production for a group of wells, the forecast
of the base level of oil production, the forecast of reservoir
pressures and mapping. For each task, the features of building
machine learning models and examples of input data are described.
All of the above tasks are related to regression or classification
problems. Of particular interest is the issue of well placement
optimisation. Such a task cannot be directly solved using a single
neural network. It can be attributed to the problems of optimal
control theory, which are usually solved using dynamic
programming methods. A paper is considered where field
management and well placement are based on a reinforcement
learning algorithm with Markov chains and Bellman's optimality
equation. The disadvantages of the proposed approach are revealed.
To eliminate them, a new approach of reinforcement learning based
on the Alpha Zero algorithm is proposed. This algorithm is best
known in the field of gaming artificial intelligence, beating the
world champions in chess and Go. It combines the properties of
dynamic and stochastic programming. The article discusses in detail
the principle of operation of the algorithm and identifies common
features that make it possible to consider this algorithm as a
possible promising solution for the problem of optimising the
placement of a grid of wells.