Prediction of Remaining Oil in Oil Reservoirs and Optimization of Development Strategies Based on Machine Learning Algorithms

Currently, understanding the distribution of remaining underground oil has become an important topic in the field of oil and gas exploration. With the development of machine learning algorithms, people hope to quickly analyze a large amount of data and extract useful information from it. The remaining oil (RO) sample library in this article includes data organization, data screening, data cleaning, feature recombination, and data normalization. Based on the principles of reservoir engineering and combined with the oil saturation of grid type reservoir units, the influence of geological, fluid, and oil recovery factors on the distribution of RO was studied, and the initial characteristic parameters were determined. In terms of development strategy optimization, genetic algorithm, particle swarm optimization algorithm and other optimization algorithms were used to find the optimal exploitation plan. The resource utilization rate of genetic algorithm was 0.85, with a cost-benefit value of 1.2; the resource utilization rate of particle swarm optimization was 0.88, with a cost-benefit value of 1.1; the resource utilization rate of simulated annealing was 0.86, with a cost-benefit value of 1.15. The prediction and development strategy of RO in reservoirs based on machine learning algorithms can help to quickly predict the distribution of RO in reservoirs.