Method for estimating oil well production in complex fault block reservoirs based on adaptive genetic algorithm

In complex fault block reservoirs, there are numerous faults with complex distribution. The presence of faults can disrupt the continuity of oil reservoirs, making it difficult to accurately determine the connectivity of oil layers and leading to increased errors in estimating oil well production. Therefore, a new method for estimating oil well production in complex fault block reservoirs based on adaptive genetic algorithm is proposed. Construct an objective function system for predicting oil well production, quantitatively analyze the relationship between production and multiple complex factors such as geological structure, reservoir characteristics, and production methods, and apply correction factors to optimize the theoretical model. Adopting an adaptive genetic algorithm framework to solve the objective function, dynamically adjusting the crossover and mutation probabilities, preventing premature convergence problems, and improving the search efficiency of the global optimal solution. After iterative operations such as encoding, individual selection, cross recombination, and gene mutation, the estimated oil well production is obtained. The test results show that the adaptive genetic algorithm has high and stable long-term prediction accuracy and low time cost in estimating oil well production in complex fault block reservoirs.