高压高压压裂液流变的数据工程和监督ML预测模型-数字实验室方法

AbdulMuqtadir Khan, Shashin Sharan, Kalyanaraman Venugopal, Lalitha Venkataramanan, Asim Najmi
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引用次数: 1

摘要

高温流变性测试对于所有压裂作业设计特定井的破胶剂和添加剂计划至关重要。流变性取决于源水质、测试温度、剪切剖面以及测试中使用的添加剂-稳定剂-破碎剂组合。每个处理过程都需要大量的工作人员来微调最佳的流体配方,需要相应的实验室资源和时间。数据分析智能系统设计可以在分析数学关联之外实现,以减少时间和资源需求。使用高压/高温(HP/HT)流变仪Chandler 5550和ISO 13503-1指南共进行了820次流变试验。温度范围从200°F到336°F,流体系统由硼酸盐和金属交联剂组成。通过整合水源参数、实验室设置细节、添加剂浓度和流变性(一致性和行为指数)结果,准备了一个包含40个输入输出特征的结构化数据库,将每个流变性曲线数字化。然后将ML算法和技术应用于数据库,以预测给定测试参数的流变性。算法输入为水源水质(即单价/二价离子、矿物质、盐度、硬度等)和测试温度。预测的输出被设定为特定粘度和流体稳定性要求的详细流体配方。在测试过程中,为了消除非物理异常值(如爬坡),数据清理和摄取工作进行得非常彻底。随后进行了详细的参数相关性研究,揭示了不同参数,特别是二价离子(Ca+2和Mg+2)和总溶解固形物对流变学的影响。对于不同的试验,训练集与保留集的比值固定为90:10。此外,使用5倍交叉验证来选择最终模型的超参数。为了根据添加剂浓度(这是一个连续的量)预测流体配方/目标流变性,尝试了基于回归的模型。训练了岭回归和集合方法,如随机森林和助推型模型。基于提升的模型对保留数据集的拟合优度(R2)平均为88%。对于现场实施,模型结果用于为实验室技术人员创建数字实验室请求,而不是由压裂设计工程师手动处理此任务。基于物理的数据驱动ML模型将平均HP/HT运行次数从20次减少到5次,从而节省了400%的实验室资源。这种基于ml的工作流是独一无二的,在文献中是不存在的。它可以实现所有大型压裂项目的资源优化,减少生成实验室请求的人工输入,然后进行试错优化,从而节省数千小时的时间,并降低所有实验室设备的维护成本。该技术可以很容易地扩展到设计固井液、钻井泥浆和腐蚀性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data Engineering and Supervised ML Enabled Predictive Model for HPHT Fracturing Fluid Rheology - Digital Laboratory Approach
High-temperature rheology testing is critical for all fracturing applications to design the well-specific breaker and additive schedule. The rheology depends on the source water quality, testing temperatures, shear profiles, and additives-stabilizers-breaker combinations used for the test. The process for each treatment requires extensive staff to fine-tune the optimal fluid formulations requiring proportional laboratory resources and time. Data analytics intelligent system design can be implemented beyond analytical mathematical correlations to reduce the time and resource requirements. A total of 820 rheology tests were conducted using the high-pressure/high-temperature (HP/HT) rheometer Chandler 5550 and ISO 13503-1 guideline. Temperatures ranged from 200 to 336°F and fluid systems consisted of borate and metallic crosslinkers. A structured database with 40 input-output features was prepared to digitize each rheology curve by incorporating the source water parameters, laboratory setup details, additive concentrations, and rheology (consistency and behavior indices) results. ML algorithms and techniques were then applied to the database to predict the rheology for given testing parameters. The algorithm inputs were prepared as the source water quality (i.e., monovalent/divalent ions, minerals, salinity, hardness etc.) and the test temperature. The outputs predicted were set to be the detailed fluid formulation for specified viscosity and fluid stability requirements. Data cleaning and ingestion were done thoroughly to remove nonphysical outliers such as bob-climbing during testing. A detailed parametric correlation study followed and revealed the impact of different parameters, especially divalent ions such as Ca+2 and Mg+2, and total dissolved solids on the rheology. The training set to holdout set ratio was fixed at 90:10 for different trials. Further, 5-fold cross validation was used to choose the hyperparameters for the final model. To predict fluid formulation/target rheology in terms of additive concentrations, which is a continuous quantity, regression-based models were attempted. Ridge regression and ensemble methods such as random forest and boosting type models were trained. Boosting-based models gave an average 88% goodness of fit (R2) for the holdout datasets. For field implementation, the model results were used to create a digital laboratory request for the laboratory technician instead of having the fracturing design engineer manually handle this task. The physics-based data-driven ML model reduced an average HP/HT runs/well from 20 to 5 yielding a 400% laboratory resource savings. This ML-based workflow is unique and does not exist in the literature. It can enable resource optimization for all large-scale fracturing projects and reduce manual laborious input for generating laboratory requests followed by trial-and-error optimizations with a potential of saving thousands of hours and reduce all the laboratory equipment maintenance costs. The technique can easily be extended to designing cementing fluids, drilling muds, and corrosion properties.
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