Artificial intelligence (AI) is an evolving set of technologies used for solving a wide range of applied issues. The core of AI is machine learning (ML)—a complex of algorithms and methods that address the problems of classification, clustering, and forecasting. The practical application of AI&ML holds promising prospects. Therefore, the researches in this area are intensive. However, the industrial applications of AI and its more intensive use in society are not widespread at the present time. The challenges of widespread AI applications need to be considered from both the AI (internal problems) and the societal (external problems) perspective. This consideration will identify the priority steps for more intensive practical application of AI technologies, their introduction, and involvement in industry and society. The article presents the identification and discussion of the challenges of the employment of AI technologies in the economy and society of resource-based countries. The systematization of AI&ML technologies is implemented based on publications in these areas. This systematization allows for the specification of the organizational, personnel, social and technological limitations. This paper outlines the directions of studies in AI and ML, which will allow us to overcome some of the limitations and achieve expansion of the scope of AI&ML applications.

The long-term development of hydrocarbon reservoirs (HCR) in the geological environment, complex deformation processes occur. Gravity monitoring is carried out to evaluate the possible geodynamic risk and negative consequences from HCR. As a result, the interrelationships of the continuously changing field-geological situation (changes in production volumes, changes in reservoir pressure, processes of fluid injection into productive formations) are investigated. The main tool for solving the gravity inversion when determining areas of increased industrial hazard is the solution of the gravity direct problem. In these studies, proceeding from a given initial approximation of the environment, the problem is realized through successive approximations. To assess such distributions, the authors of the article recommend using the simulated annealing technique within the framework of stochastic optimization. It is aimed at fitting the optimal parameters of the medium provided that a minimal residual of the gravity field values occurs. The approach is implemented using three simple mathematical models of the geological medium such as horizontal prism, homogeneous sphere, and vertical ledge. This technique allows fitting the media values simultaneously by a pair of its parameters. The operation of the algorithm is described and the simulation results are provided. The results showed acceptable accuracy of the algorithm for solving the direct gravity problem by the proposed method. The simulated annealing technique made it possible to increase the reliability of the HCR model while reduce the time for the analysis of the gravity field.

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