利用机器学习模型预测天然裂缝性油藏的流体流动行为

Mustafa Mudhafar Shawkat, A. R. Risal, Noor J. Mahdi, Ziauddin Safari, Maryam H. Naser, A. A. Zand
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引用次数: 0

摘要

由于构造运动导致裂缝的渗透率和导流能力增加,天然裂缝性储层是最具挑战性的储层之一。渗透率和导电性的不稳定性对流体流动路径的影响导致了流体从基质向裂缝的转移过程中的问题以及生产过程中的流体损失。此外,这些复杂性使得工程师难以估计生产过程中的流体流动。裂缝性质的研究对于模拟流体流动路径(如裂缝孔隙度、渗透率和形状因子)具有重要意义,这些因素对流体流动的稳定性至关重要。为此,本研究引入了决策树(DT)、随机森林(RF)、k -最近回归(KNR)、岭回归(RR)和LASSO回归模型等新模型。研究了天然裂缝性储层的裂缝性质,如裂缝孔隙度(FP)和形状因子(SF)。本研究中使用的数据集收集自之前的研究(即德克萨斯州油气田),以建立基于智能的流体流动特性预测模型。预测过程基于孔隙间流动系数、储气性比、井筒半径、基质渗透率和裂缝渗透率作为输入数据。该研究揭示了采用机器学习(ML)模型的积极发现,并且在使用统计准确性指标方面具有优越性。总体而言,该研究强调了自然裂缝油藏分析的计算机辅助模型的实施,提供了更多关于广泛执行技术的细节,例如注入或制造人工裂缝,以尽量减少碳氢化合物的损失或泄漏。
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Fluid Flow Behavior Prediction in Naturally Fractured Reservoirs Using Machine Learning Models
The naturally fractured reservoirs are one of the most challenging due to the tectonic movements that are caused to increase the permeability and conductivity of the fractures. The instability of the permeability and conductivity effects on the fluid flow path causes problems during the transfer of the fluids from the matrix to the fractures and fluid losses during production. In addition, these complications made it difficult for engineers to estimate fluid flow during production. The fracture properties’ study is important to model the fluid flow paths such as the fracture porosity, permeability, and the shape factor, which are considered essential in the stability of fluid flow. To examine this, this research introduced new models including decision tree (DT), random forest (RF), K-nearest regression (KNR), ridge regression (RR), and LASSO regression model,. The research studied the fracture properties in naturally fractured reservoirs like the fracture porosity (FP) and the shape factor (SF). The datasets used in this study were collected from previous studies “i.e., Texas oil and gas fields” to build an intelligence-based predictive model for fluid flow characteristics. The prediction process was conducted based on interporosity flow coefficient, storativity ratio, wellbore radius, matrix permeability, and fracture permeability as input data. This study revealed a positive finding for the adopted machine learning (ML) models and was superior in using statistical accuracy metrics. Overall, the research emphasized the implementation of computer-aided models for naturally fractured reservoir analysis, giving more details on the extensive execution techniques, such as injection or the creation of artificial cracks, to minimize hydrocarbon losses or leakage.
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