A Novel Nonlinear Prestack Inversion Method Based on Nutcracker Optimization Algorithm with High Convergence Speed

Predicting reservoir parameters with high accuracy is still a crucial work of oil reservoir exploration and development. Due to the limitation of computational efficiency, deterministic methods are primarily used in practical production applications for predicting reservoir parameters. When the nonlinear forward equations are exceptionally complex and the initial model constructed deviates significantly from the true reservoir parameters, deterministic methods may have difficulty obtaining reasonable predictions of reservoir parameters. Compared to deterministic methods, intelligent optimization methods based on nature-inspired metaheuristic algorithms have unique advantages because they do not require derivative information, can achieve global optimization, and have less reliance on initial model. Therefore, they perform better in solving complex nonlinear optimization problems. In this paper, a new intelligent optimization algorithm called Nutcracker Optimization Algorithm (NOA) with a high convergence speed is introduced. By utilizing this optimization algorithm to solve the nonlinear inversion problem constructed by the highly nonlinear exact Zoeppritz equations, we analyze the potential of nonlinear reservoir parameters prediction methods based on intelligent optimization algorithms in practical production applications. The synthetic data test shows that, compared to the classical quantum particle swarm optimization (QPSO) algorithm and the highly-cited whale optimization algorithm (WOA), the prestack nonlinear inversion method based on NOA proposed in this paper ensures high convergence accuracy and exhibits high computational efficiency. It significantly reduces computation time and holds great potential for practical production applications. The field data test shows that the proposed method can rapidly and accurately estimates reservoir parameters, validating the feasibility and effectiveness of the proposed method. This has important theoretical value and practical significance for advancing the application of intelligent optimization algorithms in field reservoir exploration and development.