Ifrene, G.; Irofti, D.; Ni, R.; Egenhoff, S.; Pothana, P. New Insights into Fracture Porosity Estimations Using Machine Learning and Advanced Logging Tools. Fuels2023, 4, 333-353.
Ifrene, G.; Irofti, D.; Ni, R.; Egenhoff, S.; Pothana, P. New Insights into Fracture Porosity Estimations Using Machine Learning and Advanced Logging Tools. Fuels 2023, 4, 333-353.
Ifrene, G.; Irofti, D.; Ni, R.; Egenhoff, S.; Pothana, P. New Insights into Fracture Porosity Estimations Using Machine Learning and Advanced Logging Tools. Fuels2023, 4, 333-353.
Ifrene, G.; Irofti, D.; Ni, R.; Egenhoff, S.; Pothana, P. New Insights into Fracture Porosity Estimations Using Machine Learning and Advanced Logging Tools. Fuels 2023, 4, 333-353.
Abstract
The purpose of this work is to compare two fracture prediction models with real-world data. The pure Artificial Neural Network (ANN) model emphasizes regression analysis, while the hybrid model (SVM-ANN) focuses on the combination of regression and classification analysis or Support Vector Machine. The results were subsequently tested against logging data by combining the Machine Learning approach with advanced logging tools. In this context, we used electrical image logs and the dipole acoustic tool which together allowed the distinction of 404 open fractures and 231 closed fractures and, consequently the estimation of fracture porosity. The results are then fed into two machine-learning algorithms. Pure Artificial Neural Networks and hybrid models are used to establish comprehensive results, which are subsequently tested to check the accuracy of the models. The outputs obtained from the two methods demonstrate that the hybridized model has a lower Root Mean Square Error (RMSE) than pure ANN. The results of our approach strongly suggest that incorporating hybridized machine learning algorithms in fracture porosity estimations can contribute to the development of more trustworthy static reservoir models in simulation programs. Finally, the combination of Machine Learning (ML) and well-log analysis do permit reliable estimation of fracture porosity in the Ahnet field in Algeria, where, in many places, advanced logging data is absent and costly.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
