Feature Selection for Petroleum Reservoir Evaluation Metrics Using an Improved Differential Evolution Algorithm

In the field of petroleum exploration, accurate evaluation of reservoir quality is crucial for development decision-making. However, reservoir assessment often involves numerous complex metrics, some of which may contain redundancy or noise that affects model accuracy and efficiency. Therefore, employing effective feature selection methods to identify the most representative subset from multiple evaluation metrics becomes a key step in enhancing evaluation model performance. The Differential Evolution (DE) algorithm serves as an efficient global optimization method suitable for solving high-dimensional feature selection problems. Its core concept involves mutation, crossover, and selection operations within a population to gradually approach optimal solutions. In code implementation, DE typically initializes a population of candidate solutions (feature subsets) represented as binary or real-valued vectors, where each dimension corresponds to a feature's selection status. However, traditional DE may suffer from slow convergence or local optima trapping when applied to petroleum reservoir metric selection, necessitating targeted improvements. The enhanced differential evolution algorithm optimizes feature selection through the following approaches with corresponding algorithmic implementations: - Adaptive Parameter Adjustment: Dynamically modifies mutation factors and crossover probabilities during iteration to balance global exploration and local exploitation capabilities, preventing premature convergence. In practice, this can be implemented using fitness-based adjustment rules where parameters evolve alongside population fitness diversity. - Hybrid Strategy Integration: Combines local search algorithms (such as simulated annealing or neighborhood search) to strengthen local optimization capacity and improve feature subset performance in classification or regression tasks. Code-wise, this involves embedding local search operations after DE's main cycle to refine selected feature subsets. - Multi-Objective Optimization: Simultaneously considers classification accuracy and feature subset size in evaluation metrics, determining optimal feature combinations through Pareto optimal solutions. Implementation requires designing fitness functions that incorporate both objectives, often using techniques like NSGA-II for multi-objective handling. In petroleum reservoir evaluation applications, the improved algorithm effectively selects key features from multi-source metrics including well-logging data and core analysis data, significantly reducing data dimensionality while enhancing reservoir prediction model generalization. Experimental results demonstrate that compared to traditional methods, feature selection based on the improved differential evolution algorithm achieves higher computational efficiency and identifies more geologically significant metric combinations, providing more reliable data support for petroleum exploration. Further research could explore integrating the algorithm with other machine learning models or optimizing objective functions based on characteristics of different reservoir types to improve feature selection specificity and practicality. Potential implementations include wrapping the feature selection process as a preprocessing module in scikit-learn pipelines or developing specialized MATLAB toolboxes for reservoir analytics.