Reproduction of reservoir pressure by machine learning methods and study of its influence on the cracks formation process in hydraulic fracturing

• Published in: Zapiski Gornogo instituta (1999) Vol. 258; pp. 924 - 932
• Main Authors: Filippov, Еvgenii, Zakharov, Lev, Martyushev, Dmitrii, Ponomareva, Inna
• Format: Journal Article
• Language: English
• Published: Saint-Petersburg Mining University 30.12.2022
• Subjects: artificial intelligence; change in correlation coefficient; hydraulic fracturing; liquid flow rates; neural network; random forest; reservoir pressure
• ISSN: 2411-3336; 2541-9404
• DOI: 10.31897/PMI.2022.103

Hydraulic fracturing is an effective way to stimulate oil production, which is currently widely used in various conditions, including complex carbonate reservoirs. In the conditions of the considered field, hydraulic fracturing leads to a significant differentiation of technological efficiency indicators, which makes it expedient to study in detail the crack formation patterns. For all affected wells, the assessment of the resulting fractures spatial orientation was performed using the developed indirect technique, the reliability of which was confirmed by geophysical methods. In the course of the analysis, it was found that in all cases the fracture is oriented in the direction of the development system element area, which is characterized by the maximum reservoir pressure. At the same time, reservoir pressure values for all wells were determined at one point in time (at the beginning of hydraulic fracturing) using machine learning methods. The reliability of the used machine learning methods is confirmed by high convergence with the actual (historical) reservoir pressures obtained during hydrodynamic studies of wells. The obtained conclusion about the influence of the formation pressure on the patterns of fracturing should be taken into account when planning hydraulic fracturing in the considered conditions.