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

Springer Science and Business Media LLC - Tập 11 - Trang 4375-4397 - 2021
Meysam Rajabi1, Shadfar Davoodi2, Saeed Beheshtian3, Ahmed E. Radwan4, Hamzeh Ghorbani5, Nima Mohamadian6, Mehdi Ahmadi Alvar7
1Department of Mining Engineering, Birjand University of Technology, Birjand, Iran
2School of Earth Sciences and Engineering, Tomsk Polytechnic University, Tomsk, Russia
3Department of Petroleum Engineering, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran
4Faculty of Geography and Geology, Institute of Geological Sciences, Jagiellonian University, Kraków, Poland
5Young Researchers and Elite Club, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
6Young Researchers and Elite Club, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran
7Department of Computer Engineering, Faculty of Engineering, Shahid Chamran University, Ahvaz, Iran

Tóm tắt

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.

Tài liệu tham khảo

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