Abstract

The paper presents a comparison of the methods for assessing the baseline oil production level based on the use of nonparametric statistics, integral and differential models, and neural network algorithms.

Highlights

  • As is known, geological and engineering operations (GEO) are directed methods of influencing the development of a field for the purpose of increasing the oil production

  • Depending on the action mechanism and the action object, the diversity of GEO can be divided into several groups: methods of oil production intensification, physicochemical methods, hydrodynamic methods, and gas methods

  • GEOs are considered to be the most effective with the smallest discrepancy between the actual and basic indicators that would be characteristic for the baseline level of the object development, i.e. the method used before the performance of GEO

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Summary

Introduction

Geological and engineering operations (GEO) are directed methods of influencing the development of a field for the purpose of increasing the oil production. Construction and further support of a special hydrodynamic model of the field can be used to calculate the baseline oil production level and assess the effectiveness of geological and engineering operations. This approach, in addition to substantial economic costs, requires special training of specialists in the area of physical process modeling. In other words, it is practically not suitable in production conditions when solving operational problems of optimization of the field development system

Methods for forecasting the baseline oil production
Findings
Artificial neural networks
Full Text
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