Abstract Drilling optimization can have direct and indirect cost savings implications on drilling a well. As a result, over the years, attention has been given to developing analytical and data-driven models that effectively predict and optimize the rate of penetration (ROP). This paper focuses on another aspect of improving drilling performance - predicting and optimizing torque-on-bit (TOB). TOB is a critical component in estimating the energy expanded during drilling called mechanical specific energy (MSE). TOB was modeled as a function of rotary speed (RPM), weight-on-bit (WOB), flow rate, pump pressure, and unconfined compressive strength (UCS). Five regression-based machine learning algorithms - linear regression, ridge regression, support vector machines, random forest, and boosted trees were used to build TOB models. The performance of the five algorithms was compared using the root mean square error (RMSE) and results showed that boosted trees was the best performing across all the formations. Three direct search optimization algorithms - Nelder Mead, differential evolution, particle swarm optimization (PSO) were used to optimize TOB and results showed that PSO consistently produced minimized TOB values in all the 12 formations. Finally, hypothesis testing was used to statistically test if there was a significant difference between the measured and optimized TOB values. The p-value obtained for each formation was less than the significance level of 5%, indicating the minimized TOB values were significant.

中文翻译：

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使用机器学习算法的钻压 (TOB) 预测和优化

摘要 钻井优化对钻井具有直接和间接的成本节约影响。因此，多年来，人们一直关注开发能够有效预测和优化渗透率 (ROP) 的分析和数据驱动模型。本文侧重于提高钻井性能的另一个方面——预测和优化钻压 (TOB)。TOB 是估算钻井过程中膨胀能量的关键组成部分，称为机械比能 (MSE)。TOB 被建模为转速 (RPM)、钻压 (WOB)、流速、泵压力和无侧限抗压强度 (UCS) 的函数。五种基于回归的机器学习算法——线性回归、岭回归、支持向量机、随机森林和提升树被用于构建 TOB 模型。使用均方根误差 (RMSE) 比较了五种算法的性能，结果表明提升树在所有构造中的性能最好。三种直接搜索优化算法 - Nelder Mead、差分进化、粒子群优化 (PSO) 用于优化 TOB，结果表明 PSO 在所有 12 个地层中始终产生最小的 TOB 值。最后，假设检验用于统计检验测量的和优化的 TOB 值之间是否存在显着差异。为每个地层获得的 p 值小于 5% 的显着性水平，表明最小化的 TOB 值是显着的。三种直接搜索优化算法 - Nelder Mead、差分进化、粒子群优化 (PSO) 用于优化 TOB，结果表明 PSO 在所有 12 个地层中始终产生最小的 TOB 值。最后，假设检验用于统计检验测量的和优化的 TOB 值之间是否存在显着差异。为每个地层获得的 p 值小于 5% 的显着性水平，表明最小化的 TOB 值是显着的。三种直接搜索优化算法 - Nelder Mead、差分进化、粒子群优化 (PSO) 用于优化 TOB，结果表明 PSO 在所有 12 个地层中始终产生最小的 TOB 值。最后，假设检验用于统计检验测量的和优化的 TOB 值之间是否存在显着差异。为每个地层获得的 p 值小于 5% 的显着性水平，表明最小化的 TOB 值是显着的。假设检验用于统计检验测量的和优化的 TOB 值之间是否存在显着差异。为每个地层获得的 p 值小于 5% 的显着性水平，表明最小化的 TOB 值是显着的。假设检验用于统计检验测量的和优化的 TOB 值之间是否存在显着差异。为每个地层获得的 p 值小于 5% 的显着性水平，表明最小化的 TOB 值是显着的。