Injection of water-based hydraulic fracturing fluid (HFF) into tight shale gas/oil formations can increase formation permeability and enhance production rates, but this process frequently causes mineral scale formation that can occlude pore space and hinder flow. To identify the most important factors that control the formation of mineral scales, we applied a novel global sensitivity analysis method—distance-based generalized sensitivity analysis (DGSA)—to a reactive transport model (RTM) that was previously built and calibrated to simulate precipitation of barite [BaSO₄] and iron (hydr)oxide [Fe(OH)₃] in shale matrices and on fracture surfaces. Reactive transport simulations were run with model parameters randomly sampled based on assigned uncertainties. Modeling results for barite and Fe(OH)₃ formation were clustered using machine-learning algorithms. A list of ranked critical input parameters was obtained after statistical quantification of cumulative distribution functions of input parameters. We found that barite formation is most sensitive to the rate of sulfate ion generation, which is determined by the pyrite dissolution rate coefficient and oxidant availability. In addition, barite formation is sensitive to the initial amounts of barite in HFF and shale, followed by barite thermodynamics/kinetics. For Fe(OH)₃ formation, the ranked factors are Fe(OH)₃ precipitation rate coefficients, initial HFF pH, initial Fe(OH)₃ amount in HFF, and oxidant availability. Our results provide insights into managing mineral scale formation during hydraulic fracturing to enhance production. Meanwhile, this study serves as an example of global sensitivity analysis of RTMs using the efficient, straightforward, and open-source DGSA method.

中文翻译：

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水力压裂过程中矿垢形成反应性输运模型的整体敏感性分析

将水基水力压裂液（HFF）注入致密页岩气/油地层中可以增加地层渗透性并提高生产率，但是此过程经常会导致矿物垢形成，从而堵塞孔隙空间并阻碍流动。为了确定控制矿物垢形成的最重要因素，我们将一种新颖的全局灵敏度分析方法-基于距离的广义灵敏度分析（DGSA）-应用于先前建立并校准以模拟降水的反应输运模型（RTM）晶石[BaSO ₄ ]和（氢氧化）铁[Fe（OH）_3的合成在页岩基质和裂缝表面中。基于分配的不确定性，对随机模型参数进行反应性运输模拟。使用机器学习算法对重晶石和Fe（OH）₃形成的建模结果进行了聚类。在对输入参数的累积分布函数进行统计量化之后，获得了排序后的关键输入参数的列表。我们发现，重晶石的形成对硫酸根离子的生成速率最为敏感，而硫酸根离子的生成速率取决于黄铁矿的溶解速率系数和氧化剂的可用性。另外，重晶石的形成对HFF和页岩中重晶石的初始量敏感，随后对重晶石的热力学/动力学敏感。对于Fe（OH）_3的形成，排序因素为Fe（OH）₃沉淀率系​​数，初始HFF pH，初始Fe（OH）₃在HFF中的含量以及氧化剂的利用率。我们的结果提供了在水力压裂过程中管理矿物垢形成的见解，以提高产量。同时，本研究为使用高效，直接且开源的DGSA方法对RTM进行全局敏感性分析的示例。