改进遗传算法及其在钻井液设计中的运用

在深井、超深井以及地层复杂等条件下，为了避免或减少钻井事故的发生，达到优质快速钻井的目的，选择合适的钻井液体系至关重要。基于案例推理（CBR，Case-Based Reasoning）的钻井液设计中，钻井液体系由岩性、井型和井深等属性推理得出，但属性权重的分配会对推理结果产生显著的影响；遗传算法在优化属性权重时，存在收敛速度慢、收敛精度低的缺点。针对上述问题，提出一种解决CBR中属性权重分配问题的改进遗传算法。首先，对遗传算子进行改进：选择算子方面，利用指数尺度变换法优化个体选择；交叉算子方面，对算术交叉中的比例因子进行自适应调整；变异算子方面，改进个体变异方向，保持种群多样性。其次，从个体适应度和交叉个体的差异程度两方面实现交叉概率自适应调整。最后，通过对UCI数据集的对比实验，证明了改进后的遗传算法能改善全局收敛性能，提高CBR的准确率。将该算法运用到基于CBR的钻井液设计中，实验结果表明，所提方法能够优化属性权重的分配，进而提高钻井液设计的质量。

Improved Genetic Algorithm and its Application in the Design of Drilling Fluid

The selection of a proper drilling fluid system is the key to enabling fast and high-quality drilling operations while avoiding or reducing the occurrence of drilling accidents when working in deep wells, ultra-deep wells, and complex formations. When designing the drilling fluid using case-based reasoning (CBR), the drilling fluid system can be derived from multiple attributes such as lithology, well type, and well depth. However, the derivation results can be substantially affected by each attribute's weight assignment. The genetic algorithm suffers from slow convergence and low convergence precision when used for optimization of the attribute weights. Considering this issue, this study proposes an improved genetic algorithm to address the issue of attribute weight assignment in CBR. Initially, the genetic operator is improved using the following techniques. An exponential scale transformation method is used to optimize the selection of the individual operator. A self-adaptive adjustment is performed on the scale factors in the arithmetic crossover. With reference to the mutation operator, the mutation direction of each individual is modified to maintain the diversity of the population. Next, the self-adaptive adjustment of the crossover probability is realized from two aspects, namely the individual fitness and the level of variation between crossover individuals. Finally, by performing comparative experiments on the UCI dataset, we proved that the improved genetic algorithm can enhance the global convergence performance and increase the accuracy of CBR. Experimental results demonstrate that applying the improved genetic algorithm to the CBR-based drilling fluid design can effectively optimize the weight assignments of each attribute and therefore improve the quality of drilling fluid.