Drilling Speed Enhancement Method for Extended Reach Wells Based on Machine Learning and Bayesian Optimization
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摘要:
海上大位移井的井眼轨迹复杂和水平位移较大，导致井下摩阻增加，严重影响钻井效率。根据钻井数据和录井数据等，提出了一种基于机器学习钻速预测与钻井参数优化的大位移井钻井提速方法。首先，对现场原始数据进行滤波、归一化等预处理，进行了相关性分析，得出钻压、转盘转速等钻井参数及井斜角、水平位移等井眼轨迹参数与钻速有显著的相关性；然后，构建了基于BP神经网络、随机森林和支持向量机的钻速预测模型，预测精度评价结果表明，BP神经网络模型表现最优，可以较为准确地预测海上大位移钻井的机械钻速；最后，采用贝叶斯优化算法，以提高钻速为目标进行了钻压、转盘转速和排量等参数优化。优化结果表明，钻井参数优化后，机械钻速平均提升了18.86%。研究结果揭示了钻井参数和井眼轨迹参数对大位移井钻速的影响，为大位移井钻井提速提供了理论依据。
Abstract:The wellbore trajectories of offshore extended reach wells are complex and characterized by large horizontal displacements, leading to increased downhole friction and subsequently affecting drilling efficiency. This paper introduces a novel method for rate of penetration prediction and drilling parameter optimization in extended reach wells using machine learning, based on drilling and logging data. Initially, raw field data were preprocessed and subjected to correlation analysis, revealing significant correlations between drilling parameters such as bit pressure and rotary speed, as well as wellbore trajectory parameters like hole deviation angle and horizontal displacement, with rate of penetration. Based on these findings, rate of penetration prediction models were developed using BP neural networks, random forests, and support vector machines. The prediction accuracy of these models was evaluated using four performance indicators, with the results showing that the BP neural network model outperformed the others, providing relatively accurate rate of penetration predictions for offshore extended reach wells. Furthermore, the Bayesian optimization algorithm was employed to adjust controllable parameters such as bit pressure, rotary speed, and pump rate, resulting in an average increase in rate of penetration by 18.86%. This study elucidates the impact of drilling parameters and wellbore trajectory parameters on rate of penetration; in extended reach wells and provides theoretical evidence for enhancing drilling efficiency.
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图 1 贝叶斯优化算法的采集函数迭代过程
Figure 1. Iteration Process of Acquisition Function in Bayesian Optimization Algorithm
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图 5 钻速优化前后对比
Figure 5. Comparison of Rate of Penetration Before and After Optimization
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图 6 不同深度下的钻速提升效果
Figure 6. Enhancement Effect of Rate of Penetration at Different Depths
表 1 预测模型评价指标
Table 1 Evaluation Metrics for Prediction Models
算法 决定系数 平均绝对误差 均方误差 均方根误差 BP神经网络 0.883 4.81 57.64 7.59 随机森林 0.838 5.59 69.95 8.36 支持向量机 0.680 8.79 215.25 14.67 
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