The current state of the Permian-Carboniferous reservoir of the Usinskoye field development is characterized by a constant decline of its average oil rate and a permanent increase of its water cut. In such conditions, a large number of various well work-over techniques are annually carried out to maintain the oil production level of the reservoir, each of these techniques is associated with the risk of not recovering the cost by additional oil income. Therefore, it is necessary to have the efficiency forecasting for each work-over technique. Traditionally, this problem is solved with the help of linear correlations of well oil rate after the work-over technique and different geological and production parameters. There are other methods based on different kinds of machine learning such as neural networks, fuzzy logic, decision trees, and other advanced statistical approaches. However, a general theory of the work-over technique efficiency forecasting has not been created yet. This article does not fully answer the question of the extent to which such forecasting can be successful regardless of the method used, but this question was first formulated, and it was showed why, despite existing objective limitations, the proposed adaptive version of the well work-over technique efficiency forecasting can be useful.
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