Zhao, W.; Liu, T.; Yang, J.; Zhang, Z.; Feng, C.; Tang, J. Approaches of Combining Machine Learning with NMR-Based Pore Structure Characterization for Reservoir Evaluation. Sustainability2024, 16, 2774.
Zhao, W.; Liu, T.; Yang, J.; Zhang, Z.; Feng, C.; Tang, J. Approaches of Combining Machine Learning with NMR-Based Pore Structure Characterization for Reservoir Evaluation. Sustainability 2024, 16, 2774.
Zhao, W.; Liu, T.; Yang, J.; Zhang, Z.; Feng, C.; Tang, J. Approaches of Combining Machine Learning with NMR-Based Pore Structure Characterization for Reservoir Evaluation. Sustainability2024, 16, 2774.
Zhao, W.; Liu, T.; Yang, J.; Zhang, Z.; Feng, C.; Tang, J. Approaches of Combining Machine Learning with NMR-Based Pore Structure Characterization for Reservoir Evaluation. Sustainability 2024, 16, 2774.
Abstract
Tight gas, a category of Deep Green Energy Resources, rely on advanced intelligent monitoring methods for their extraction. Conventional logging for reservoir evaluation relies on logging data and the manual setting of evaluation criteria to classify reservoirs. However, the complexity and heterogeneity of tight reservoirs pose challenges in accurately identifying target layers by using traditional well-logging techniques. Machine learning may hold the key to solving this problem, as it enables computers to learn without being explicitly programmed and manually added rules. Therefore, it is possible to make reservoir evaluation via using machine learning methods. In this paper, the reservoir quality index (RQI) and porous geometric parameters obtained from the optimized inversion of the spherical-tubular model are adopted to evaluate the reservoir. Then, three different machine learning approaches, the random forest (RF) algorithm, support vector machine (SVM) algorithm, and extreme gradient boosting (XGB) algorithm, are utilized for reservoir classification. The selected dataset covers more than 7,000 samples from five wells. The data from four wells are arranged as the training dataset, and the data of the remaining one well is designed as the testing dataset to calculate the prediction accuracies of different machine learning algorithms. Among them, accuracies of RF, SVM, and XGB are all higher than 90%, and XGB owns the highest result by reaching 97%. Machine learning based approaches can greatly assist reservoir prediction by implementing the well-logging data. The research highlights the application of reservoir classification with a higher prediction accuracy by combining machine learning algorithms with NMR logging based pore structure characterization, which can provide a guideline for sweet spot identification within the tight formation. This not only optimizes resource extraction but also aligns with the global shift towards clean and renewable energy sources, promoting sustainability and reducing the carbon footprint associated with conventional energy production. In summary, the fusion of machine learning and NMR logging based reservoir evaluation plays a crucial role in advancing both energy efficiency and the transition to cleaner energy sources.
Keywords
Machine learning; Intelligent evaluation and prediction; Spherical-Tubular model; Petrophysical parameters
Subject
Engineering, Energy and Fuel Technology
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.
