PDF(Pubmed)
Abstract：
Drilling through shale formations can be expensive and time-consuming due to the instability of the wellbore. Further, there is a need to develop inhibitors that are environmentally friendly. Our study discovered a cost-effective solution to this problem using Gum Arabic (ArG). We evaluated the inhibition potential of an ArG clay swelling inhibitor and fluid loss controller in water-based mud (WBM) by conducting a linear swelling test, capillary suction timer test, and zeta potential, fluid loss, and rheology tests. Our results displayed a significant reduction in linear swelling of bentonite clay (Na-Ben) by up to 36.1% at a concentration of 1.0 wt. % ArG. The capillary suction timer (CST) showed that capillary suction time also increased with the increase in the concentration of ArG, which indicates the fluid-loss-controlling potential of ArG. Adding ArG to the drilling mud prominently decreased fluid loss by up to 50%. Further, ArG reduced the shear stresses of the base mud, showing its inhibition and friction-reducing effect. These findings suggest that ArG is a strong candidate for an alternate green swelling inhibitor and fluid loss controller in WBM. Introducing this new green additive could significantly reduce non-productive time and costs associated with wellbore instability while drilling. Further, a dynamic linear swelling model, based on machine learning (ML), was created to forecast the linear swelling capacity of clay samples treated with ArG. The ML model proposed demonstrates exceptional accuracy (R2 score = 0.998 on testing) in predicting the swelling properties of ArG in drilling mud.
摘要：
由于井筒的不稳定性,钻通页岩地层可能是昂贵且耗时的。Further,需要开发环境友好的抑制剂。我们的研究发现了使用阿拉伯树胶（ArG）解决此问题的具有成本效益的解决方案。我们通过进行线性溶胀测试，评估了ArG粘土溶胀抑制剂和水基泥浆（WBM）中流体损失控制器的抑制潜力，毛细管抽吸计时器测试，和zeta电位,流体损失,和流变学测试。我们的结果表明，在1.0wt的浓度下，膨润土粘土（Na-Ben）的线性溶胀显着降低了36.1％。%ArG。毛细管抽吸计时器(CST)显示毛细管抽吸时间也随着ArG浓度的增加而增加,这表明ArG的流体损失控制潜力。向钻井泥浆中添加ArG可显著降低流体损失达50%。Further,ArG降低了基础泥浆的剪切应力，显示其抑制和减摩作用。这些发现表明，ArG是WBM中替代绿色溶胀抑制剂和失水控制剂的有力候选者。引入这种新的绿色添加剂可以显着减少非生产时间和与钻井时井筒不稳定性相关的成本。Further,动态线性膨胀模型，基于机器学习(ML)，是为了预测用ArG处理的粘土样品的线性溶胀能力而创建的。提出的ML模型在预测ArG在钻井泥浆中的溶胀特性方面具有出色的准确性（测试时R2评分=0.998）。
