Development of a genetic algorithm (GA) code in python language for fracture porosity analysis

Abstract

Machine Learning (ML) techniques are more and more applied in hydrocarbon exploration and production (E&P) in general, and in petrophysics in particular. In this research, a Genetic Algorithm (GA) code was developed in Python language to analyze the fracture porosity of a Fractured Granite Basement (FGB) reservoir, which is difficult to calculate due to the reservoir heterogeneity caused by fracture networks. The study well was in the Cuu long basin, Vietnam. The steps of GA code development include defining the GA and evaluation functions, calculating fracture porosity, training and generating new population as well as printing and plotting the results of the models. For main GA functions, the Multiple Linear Regression (MLR) and Root Mean Square Error (RMSE) formulas were used. The best model was evaluated based on the least total prediction error, cost and execution time. The fracture porosity was first calculated by a conventional method and further used to train the GA models, among which the GA model consisting of 1080-training data with 100 population showed the best performance.