Long-Term Forecasting and Optimization of Non-Stationary Well Operation Modes Through Neural Networks Simulation

Abstract

The article discusses the results of long-term forecasting of non-stationary technological modes of production wells using neural network modeling methods. The main difficulty in predicting unsteady modes is to reproduce the response of producing wells to a sharp change in the mode of one of the wells. Such jumps, as a rule, lead to a rapid increase in the forecast error. Training and forecasting of modes was carried out on the data of numerical hydrodynamic modeling. Two fields with significantly different properties, the number of wells and their modes of operation were selected as objects of modeling. Non-stationarity was set by changing the regime on one or several production wells at different points in time. The LSTM recurrent neural network carried out forecasting of production technological parameters. This made it possible to take into account the time-lagging influence of the wells on each other. It is shown that the LSTM neural network allows predicting unsteady technological modes of well operation with an accuracy of up to 5% for a period of 10 years. The solution of the problem of optimization of oil production is considered on the example of one of the models. It is shown that the optimal solution found by the neural network differs from the solution found by hydrodynamic modeling by 5%. At the same time, a significant gain in calculation time was achieved.