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

doi:10.46544/ams.v26i2.11

Abstract

The article describes the tasks of the oil and gas sector that can be 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.

Highlights

Machine learning algorithms began to spread to all areas of the industry, and the oil&gas sector was not an exception (Meng et al, 2020; Roscher et al, 2020; Goodfellow et al, 2016)
In MLsystems, it is rather difficult to interpret the decisions made by them, in contrast to mathematical models and expert systems, they are simpler to implement and practically have no limitations as the complexity of modelling increases (Piscopo et al, 2019). This statement may seem dubious: how can the implementation of machine learning make development easier and not complicate an already complex system? The reason is that machine learning allows you to get the desired result without any knowledge of the mathematical model of the analysed process - algorithms can be unified for any field of application (Toms et al, 2020; Blišťanová et al, 2014; Blišťanová et al, 2015)
The prediction of total oil production or oil flow rate using neural networks is implemented in the NNet software, which was developed to solve a whole range of tasks in oil-field geology (Yuzevych et al, 2019)

Summary

Introduction

Machine learning algorithms began to spread to all areas of the industry, and the oil&gas sector was not an exception (Meng et al, 2020; Roscher et al, 2020; Goodfellow et al, 2016). The horizontal structure is used for the tasks of assessing the interference of wells and predicting the total oil production for fragments of the field. This structure has a number of disadvantages: a directly proportional increase in the number of dataset fields with an increase in the number of studied wells and sensitivity to information gaps for individual wells. The vertical structure is suitable for various classification tasks and tasks of predicting oil production for individual wells (Arteeva and Zemenkov, 2020; Vankov et al, 2020). The disadvantages of this structure include the sensitivity to errors in the data on injection and production wells. We will consider the tasks of the oil and gas sector in more detail

Material and Methods

Conclusions