Research on Prediction of the Effects of Oil-Increasing Measures Driven by Data

Abstract

A large number of major oil fields in China have entered the late stages of development, and the decreasing production is increasingly unable to meet the continuously growing demand for energy. Therefore, it is crucial for oilfield production to accurately and rapidly predict the effects of production-increasing measures based on existing data. This paper comprehensively considers three types of data: geological static parameters, production dynamic parameters, and process parameters of measures. Advanced machine learning algorithms such as random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost) are separately used, together with data augmentation techniques and Bayesian optimization algorithms to construct the different enhancing production through measures prediction model. The best prediction model is optimized by comparing the scores of each model. The results of a comprehensive comparison of various models based on the mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R2) indicate that the model based on the extreme gradient boosting algorithm performs the best. The application of data augmentation and optimization algorithms significantly improves the model performance. The accuracy of predicting the oil production enhancement effect for a given measure can reach over 90%. Compared with traditional methods for predicting the effects of measures, this paper addresses the issues of long computational time in numerical simulations and difficulty in exploring the mechanism of oil production enhancement measures in depth, and achieves a rapid and accurate prediction of the multidimensional effect of measures for increasing oil production. This paper employs machine learning algorithms to fully explore the relationship between three types of data and oil production enhancement effects, accurately predicting the effect of measures for increasing oil production. It provides a technical foundation for selecting reasonable measures to increase oil production in oilfields and has certain guiding significance for actual production.

Copyright 2023, IFEDC Organizing Committee.

This paper was prepared for presentation at the 2023 International Field Exploration and Development Conference in Wuhan, China, 20–22 September 2023.

This paper was selected for presentation by the IFEDC Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the IFEDC Technical Team and are subject to correction by the author(s). The material does not necessarily reflect any position of the IFEDC Technical Committee its members. Papers presented at the Conference are subject to publication review by Professional Team of IFEDC Technical Committee. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of IFEDC Organizing Committee is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of IFEDC. Contact email: paper@ifedc.org.