Abstract Permeability estimation has a major role in mapping quality of the reservoir, reservoir engineering calculation, reserve estimation, numerical reservoir simulation and planning for the drilling operations. In carbonate formations, it is of great challenge to predict permeability by reason of natural heterogeneity, nonuniformity of rock, complexity and nonlinearity of parameters. Various approaches have been developed for measuring/predicting this parameter, which are associated with high expenditures, time consuming processes and low accuracy. In this study, comprehensive efforts have been made to the development of radial basis function neural network (RBF-ANN), multilayer perceptron neural network (MLP-ANN), least square support vector machine (LSSVM), adaptive neuro-fuzzy inference system (ANFIS), genetic programming (GP), and committee machine intelligent system (CMIS). For this purpose, a widespread databank of 701 core permeability datapoints as a function of well log data was adopted from the open literature for heterogonous formations. Moreover, several optimization techniques like genetic algorithm (GA), particle swarm optimization (PSO), and levenberg marquardt (LM) were employed to enhance the prediction capability of the proposed tools in this study. For assessing the models efficiency, several tools like crossplot and error distribution diagram were applied in association with statistical calculation. As a result, the CMIS model is identified as the most accurate model with the highest determination coefficient (R2 near to unity) and the lowest root mean square error (RMSE near to zero). As a result of GP mathematical strategy, a new user-friendly empirically-derived correlation was developed for rapid and accurate estimation of reservoir permeability. The outcome of outlier detection shows the validity of dataset used for modeling, and the effective porosity is perceived to be the most affecting parameter on the permeability estimation in terms of sensitivity analysis. The main novelty of this modeling study was the proposal of CMIS and GP-based empirically-derived models for the first time in literature. To this end, the outcome of this study can be of great value for reservoir engineers dealing with simulation and characterization of the heterogonous carbonate reservoirs.

中文翻译：

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非均质碳酸盐岩油藏渗透率的严格预测：智能建模和相关性开发

摘要 渗透率估算在储层绘图质量、储层工程计算、储量估算、储层数值模拟和钻井作业规划等方面具有重要作用。在碳酸盐岩地层中，由于天然非均质性、岩石的不均匀性、参数的复杂性和非线性，预测渗透率具有很大的挑战。已经开发了各种方法来测量/预测该参数，这些方法与高支出、耗时的过程和低准确度相关联。本研究在径向基函数神经网络（RBF-ANN）、多层感知器神经网络（MLP-ANN）、最小二乘支持向量机（LSSVM）、自适应神经模糊推理系统（ ANFIS）、遗传编程（GP）、和委员会机器智能系统（CMIS）。为此，从非均质地层的公开文献中采用了作为测井数据的函数的 701 个岩心渗透率数据点的广泛数据库。此外，采用遗传算法 (GA)、粒子群优化 (PSO) 和 levenberg marquardt (LM) 等几种优化技术来增强本研究中所提出工具的预测能力。为了评估模型效率，使用了交会图和误差分布图等多种工具与统计计算相关联。因此，CMIS 模型被确定为具有最高决定系数（R2 接近于 1）和最低均方根误差（RMSE 接近于零）的最准确模型。作为 GP 数学策略的结果，开发了一种新的用户友好的经验派生相关性，用于快速准确地估计储层渗透率。异常值检测的结果表明用于建模的数据集的有效性，并且在敏感性分析方面，有效孔隙度被认为是对渗透率估计影响最大的参数。该建模研究的主要新颖之处在于首次在文献中提出了基于 CMIS 和 GP 的经验派生模型。为此，本研究的结果对于处理非均质碳酸盐岩储层模拟和表征的储层工程师具有重要价值。在敏感性分析方面，有效孔隙度被认为是对渗透率估计影响最大的参数。该建模研究的主要新颖之处在于首次在文献中提出了基于 CMIS 和 GP 的经验派生模型。为此，本研究的结果对于处理非均质碳酸盐岩储层模拟和表征的储层工程师具有重要价值。在敏感性分析方面，有效孔隙度被认为是对渗透率估计影响最大的参数。该建模研究的主要新颖之处在于首次在文献中提出了基于 CMIS 和 GP 的经验派生模型。为此，本研究的结果对于处理非均质碳酸盐岩储层模拟和表征的储层工程师具有重要价值。