Fuel, Vol.236, 110-123, 2019
Rigorous prognostication of permeability of heterogeneous carbonate oil reservoirs: Smart modeling and correlation development
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 (R-2 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.