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.
Determining formation pressure in the well extraction zones is a key task in monitoring the development of hydrocarbon fields. Direct measurements of formation pressure require prolonged well shutdowns, resulting in underproduction and the possibility of technical problems with the subsequent start-up of wells. The impossibility of simultaneous shutdown of all wells of the pool makes it difficult to assess the real energy state of the deposit. This article presents research aimed at developing an indirect method for determining the formation pressure without shutting down the wells for investigation, which enables to determine its value at any time. As a mathematical basis, two artificial intelligence methods are used – multidimensional regression analysis and a neural network. The technique based on the construction of multiple regression equations shows sufficient performance, but high sensitivity to the input data. This technique enables to study the process of formation pressure establishment during different periods of deposit development. Its application is expedient in case of regular actual determinations of indicators used as input data. The technique based on the artificial neural network enables to reliably determine formation pressure even with a minimal set of input data and is implemented as a specially designed software product. The relevant task of continuing the research is to evaluate promising prognostic features of artificial intelligence methods for assessing the energy state of deposits in hydrocarbon extraction zones.
The article studies the formation features of the bottomhole zones in productive formations during operation of production wells in the north of the Perm Territory. Their distinctive feature is the high gas saturation of formation oil. The most widely used parameter in Russian and world practice – the skin factor was used as a criterion characterizing the state of the bottomhole zone. Analysis of scientific publications has shown that one of the main problems of applying the skin factor to assess the state of bottomhole zones is the ambiguity of interpretations of its physical meaning and the impossibility of identifying the prevailing factors that form its value. The paper proposes an approach to identifying such factors in the conditions of the fields under consideration, based on multivariate correlation-regression analysis. Choice of this tool is due to the complexity of the processes occurring in the “formation – bottomhole zone – well” system. When describing complex multifactorial processes, the chosen method demonstrates a high degree of reliability. For a large number of wells in the region, significant material was collected and summarized, including the results of determining the skin factor (1102 values) during hydrodynamic investigations, as well as data on the values of various geological and technological indicators, which can probably be statistically related to the value of the skin factor. A series of multidimensional mathematical models has been built; the skin factor was used as a predicted parameter, and data on the values of geological and technological indicators were used as independent indicators. Analysis of the constructed models is a key stage of this study. Set of parameters included in the multidimensional models, sequence of their inclusion and contribution to the total value of the achieved determination coefficient as the main indicator for the performance of the constructed models were studied. It has been established that the main factor influencing the state of the bottomhole zone is oil degassing. Significant differences in the formation features of the skin factor in the terrigenous and carbonate sediments at the fields under consideration have been determined.