Possibility of using machine learning algorithms to assess the effectiveness of acid hydraulic fracturing

• Published in: IOP conference series. Earth and environmental science 2022-05, Vol.1021 (1), p.12044
• Main Authors: Kochnev, A A, Torsunova, D V, Boyarov, V Y, Krivoshchekov, S N, Silaev, E V
• Format: Article
• Language: English
• Subjects: Acids; Algorithms; Economic forecasting; Geology; Hydraulic fracturing; Learning algorithms; Machine learning; Multiple regression models; Oil; Parameters; Petroleum production; Physical properties; Statistical analysis; Statistical methods; Technology
• ISSN: 1755-1307; 1755-1315
• DOI: 10.1088/1755-1315/1021/1/012044

At the fields of the Perm Territory, there is a significant application of hydraulic fracturing technology to stimulate the production of carbonate reservoirs. The analysis of the efficiency of the acid hydraulic fracturing technology by the example of the fields of the Perm Territory is presented in the article. The effectiveness of the technology is largely determined by the geological and physical conditions of the fields. Using the methods of mathematical statistics, the main geological and physical parameters which most significantly affect the effectiveness of measures for acid hydraulic fracturing have been identified. A multiple regression model was built to find out the complex influence of the parameters on the increase in oil production after the measures. Various machine learning algorithms have been used to predict the increase in oil and fluid input rates after acid fracturing. Different algorithms showed different results. For the problems of forecasting oil production after the application of acid fracturing, the most successful one was the support vector method.