Novel hybrid machine learning optimizer algorithms to prediction of fracture density by petrophysical data

One of the challenges in reservoir management is determining the fracture density (FVDC) in reservoir rock. Given the high cost of coring operations and image logs, the ability to predict FVDC from various petrophysical input variables using a supervised learning basis calibrated to the standard well is extremely useful. In this study, a novel machine learning approach is developed to predict FVDC from 12-input variable well-log based on feature selection. To predict the FVDC, combination of two networks of multiple extreme learning machines (MELM) and multi-layer perceptron (MLP) hybrid algorithm with a combination of genetic algorithm (GA) and particle swarm optimizer (PSO) has been used. We use a novel MELM-PSO/GA combination that has never been used before, and the best comparison result between MELM-PSO-related models with performance test data is RMSE = 0.0047 1/m; R2 = 0.9931. According to the performance accuracy analysis, the models are MLP-PSO < MLP-GA < MELM-GA < MELM-PSO. This method can be used in other fields, but it must be recalibrated with at least one well. Furthermore, the developed method provides insights for the use of machine learning to reduce errors and avoid data overfitting in order to create the best possible prediction performance for FVDC prediction.

Automated summary

A novel machine learning approach was developed to predict fracture density (FVDC) in reservoir rock based on 12-input variable well-logs using feature selection. The combination of multiple extreme learning machines (MELM) and multi-layer perceptron (MLP) with a hybrid algorithm of genetic algorithm (GA) and particle swarm optimizer (PSO) was used, resulting in RMSE = 0.0047 1/m; R2 = 0.9931. The proposed method can be applied in other fields but requires recalibration with at least one well, and provides insights for using machine learning to improve prediction performance while avoiding data overfitting.