Desouky Mahmoud, Tariq Zeeshan, Aljawad Murtada Saleh, Alhoori Hamed, Mahmoud Mohamed, Abdulraheem Abdulazeez
College of Petroleum Engineering & Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.
Department of Computer Science, Northern Illinois University, Dekalb, Illinois 60115, United States.
ACS Omega. 2021 Jul 16;6(29):18782-18792. doi: 10.1021/acsomega.1c01919. eCollection 2021 Jul 27.
In hydraulic fracturing operations, small rounded particles called proppants are mixed and injected with fracture fluids into the targeted formation. The proppant particles hold the fracture open against formation closure stresses, providing a conduit for the reservoir fluid flow. The fracture's capacity to transport fluids is called fracture conductivity and is the product of proppant permeability and fracture width. Prediction of the propped fracture conductivity is essential for fracture design optimization. Several theoretical and few empirical models have been developed in the literature to estimate fracture conductivity, but these models either suffer from complexity, making them impractical, or accuracy due to data limitations. In this research, and for the first time, a machine learning approach was used to generate simple and accurate propped fracture conductivity correlations in unconventional gas shale formations. Around 350 consistent data points were collected from experiments on several important shale formations, namely, Marcellus, Barnett, Fayetteville, and Eagle Ford. Several machine learning models were utilized in this research, such as artificial neural network (ANN), fuzzy logic, and functional network. The ANN model provided the highest accuracy in fracture conductivity estimation with of 0.89 and 0.93 for training and testing data sets, respectively. We observed that a higher accuracy could be achieved by creating a correlation specific for each shale formation individually. Easily obtained input parameters were used to predict the fracture conductivity, namely, fracture orientation, closure stress, proppant mesh size, proppant load, static Young's modulus, static Poisson's ratio, and brittleness index. Exploratory data analysis showed that the features above are important where the closure stress is the most significant.
在水力压裂作业中，被称为支撑剂的小圆形颗粒与压裂液混合并注入目标地层。支撑剂颗粒能使裂缝在地层闭合应力作用下保持张开，为储层流体流动提供通道。裂缝的流体传输能力称为裂缝导流能力，它是支撑剂渗透率与裂缝宽度的乘积。支撑裂缝导流能力的预测对于裂缝设计优化至关重要。文献中已经开发了几种理论模型和少量经验模型来估算裂缝导流能力，但这些模型要么因过于复杂而不实用，要么由于数据限制而缺乏准确性。在本研究中，首次采用机器学习方法在非常规气页岩地层中生成简单且准确的支撑裂缝导流能力关联式。从对几个重要页岩地层（即马塞勒斯、巴尼特、费耶特维尔和伊格尔福德）的实验中收集了约350个一致的数据点。本研究中使用了几种机器学习模型，如人工神经网络（ANN）、模糊逻辑和函数网络。ANN模型在裂缝导流能力估算中提供了最高的准确性，训练数据集和测试数据集的 分别为0.89和0.93。我们观察到，通过为每个页岩地层单独创建关联式可以实现更高的准确性。使用易于获取的输入参数来预测裂缝导流能力，即裂缝方位、闭合应力、支撑剂目数、支撑剂用量、静态杨氏模量、静态泊松比和脆性指数。探索性数据分析表明，上述特征很重要，其中闭合应力最为显著。
