石油钻采工艺2025，Vol.47Issue(3)：265-276,12.DOI:10.13639/j.odpt.202503036
多目标优化算法在机械比能与机械钻速耦合优化中的应用
Application of multi-objective optimization algorithm in coupling optimization of mechanical specific energy and rate of penetration
摘要
Abstract
Enhancing drilling efficiency is crucial for reducing costs and accelerating energy extraction.This study proposes a multi-objective coupling optimization model based on such objective functions as mechanical specific energy(MSE)and rate of penetration(ROP),aiming to improve the drilling efficiency through deep learning prediction and intelligent algorithm optimization.Initially,various deep learning architectures were evaluated,thereby,CNN-BiGRU-Attention model was selected to predict MSE and ROP,which combines Convolutional Neural Network(CNN),Bidirectional Gated Recurrent Unit(BigRU),and Attention mechanism.Subsequently,a multi-objective coupling optimization model was established,and three optimization algorithms including NSGA-II(Non-dominated Sorting Genetic Algorithm II),SPEA2(Strength Pareto Evolutionary Algorithm 2),and RVEA(Reference Vector-guided Evolutionary Algorithm)were employed to solve the model.The optimization performance of the three algorithms was comparatively analyzed under different conditions of minimum ROP limits,corresponding to 50%,70%,90%,and initial ROP value at the current depth.The results indicated that the RVEA algorithm performed the best in the multi-objective coupling optimization of MSE and ROP.To align more closely with practical applications,torque constraint was introduced,and a corresponding deep learning model was established to investigate the impact of adjustments in rotational speed and drilling pressure on torque.The experimental results demonstrated that the RVEA algorithm could still effectively optimize MSE and ROP even after incorporating torque.This study does not only identify optimal strategies for reducing MSE and increasing ROP under various ROP constraints,but also provides practical theoretical guidance and optimization solutions for drilling engineering parameters.关键词
机械比能/机械钻速/多目标优化/参考向量引导进化算法/钻井优化/深度学习/扭矩约束Key words
mechanical specific energy/rate of penetration/multi-objective optimization/reference vector-guided evolution algorithm/drilling optimization/deep learning/torque constraint分类
能源科技引用本文复制引用
刘伟吉,张家辉,祝效华..多目标优化算法在机械比能与机械钻速耦合优化中的应用[J].石油钻采工艺,2025,47(3):265-276,12.基金项目
国家杰出青年科学基金"钻井提速理论与方法"(编号:52225401). （编号:52225401）
