Tariq Zeeshan, Yan Bicheng, Sun Shuyu, Gudala Manojkumar, Aljawad Murtada Saleh, Murtaza Mobeen, Mahmoud Mohamed
Ali I. Al-Naimi Petroleum Engineering Research Center, Physical Science and Engineering (PSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal23955-6900, Saudi Arabia.
Energy Resources and Petroleum Engineering Program, Physical Science and Engineering (PSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal23955-6900, Saudi Arabia.
ACS Omega. 2022 Nov 4;7(45):41314-41330. doi: 10.1021/acsomega.2c05066. eCollection 2022 Nov 15.
Unconventional oil and gas reservoirs are usually classified by extremely low porosity and permeability values. The most economical way to produce hydrocarbons from such reservoirs is by creating artificially induced channels. To effectively design hydraulic fracturing jobs, accurate values of rock breakdown pressure are needed. Conducting hydraulic fracturing experiments in the laboratory is a very expensive and time-consuming process. Therefore, in this study, different machine learning (ML) models were efficiently utilized to predict the breakdown pressure of tight rocks. In the first part of the study, to measure the breakdown pressures, a comprehensive hydraulic fracturing experimental study was conducted on various rock specimens. A total of 130 experiments were conducted on different rock types such as shales, sandstone, tight carbonates, and synthetic samples. Rock mechanical properties such as Young's modulus (), Poisson's ratio (ν), unconfined compressive strength, and indirect tensile strength (σ) were measured before conducting hydraulic fracturing tests. ML models were used to correlate the breakdown pressure of the rock as a function of fracturing experimental conditions and rock properties. In the ML model, we considered experimental conditions, including the injection rate, overburden pressures, and fracturing fluid viscosity, and rock properties including Young's modulus (), Poisson's ratio (ν), UCS, and indirect tensile strength (σ), porosity, permeability, and bulk density. ML models include artificial neural networks (ANNs), random forests, decision trees, and the K-nearest neighbor. During training of ML models, the model hyperparameters were optimized by the grid-search optimization approach. With the optimal setting of the ML models, the breakdown pressure of the unconventional formation was predicted with an accuracy of 95%. The accuracy of all ML techniques was quite similar; however, an explicit empirical correlation from the ANN technique is proposed. The empirical correlation is the function of all input features and can be used as a standalone package in any software. The proposed methodology to predict the breakdown pressure of unconventional rocks can minimize the laboratory experimental cost of measuring fracture parameters and can be used as a quick assessment tool to evaluate the development prospect of unconventional tight rocks.
非常规油气藏通常以极低的孔隙度和渗透率值来分类。从这类油藏中开采碳氢化合物最经济的方法是创建人工诱导通道。为了有效地设计水力压裂作业，需要准确的岩石破裂压力值。在实验室进行水力压裂实验是一个非常昂贵且耗时的过程。因此，在本研究中，有效地利用了不同的机器学习（ML）模型来预测致密岩石的破裂压力。在研究的第一部分，为了测量破裂压力，对各种岩石样本进行了全面的水力压裂实验研究。对页岩、砂岩、致密碳酸盐岩和合成样本等不同岩石类型总共进行了130次实验。在进行水力压裂测试之前，测量了岩石的力学性能，如杨氏模量（）、泊松比（ν）、无侧限抗压强度和间接抗拉强度（σ）。ML模型用于将岩石的破裂压力与压裂实验条件和岩石特性相关联。在ML模型中，我们考虑了实验条件，包括注入速率、上覆压力和压裂液粘度，以及岩石特性，包括杨氏模量（）、泊松比（ν）、无侧限抗压强度、间接抗拉强度（σ）、孔隙度、渗透率和体积密度。ML模型包括人工神经网络（ANNs）、随机森林、决策树和K近邻算法。在ML模型的训练过程中，通过网格搜索优化方法对模型超参数进行了优化。在ML模型的最优设置下，对非常规地层的破裂压力进行了预测，准确率达到了95%。所有ML技术的准确率相当相似；然而，提出了一种来自ANN技术的显式经验关联式。该经验关联式是所有输入特征的函数，可以在任何软件中作为独立的程序包使用。所提出的预测非常规岩石破裂压力的方法可以将测量裂缝参数的实验室实验成本降至最低，并且可以用作评估非常规致密岩石开发前景的快速评估工具。
