Approaches of Combining Machine Learning with NMR-Based Pore Structure Characterization for Reservoir Evaluation

Wenjun Zhao, Tangyan Liu (), Jian Yang, Zhuo Zhang, Cheng Feng and Jizhou Tang
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Wenjun Zhao: State Key Laboratory of Marine Geology, Tongji University, Shanghai 200092, China
Tangyan Liu: Faculty of Petroleum, China University of Petroleum-Beijing at Karamay, Karamay 834000, China
Jian Yang: Engineering Technology Research Institute of Southwest Oil & Gas Field Company, PetroChina, Chengdu 610017, China
Zhuo Zhang: State Key Laboratory of Marine Geology, Tongji University, Shanghai 200092, China
Cheng Feng: Faculty of Petroleum, China University of Petroleum-Beijing at Karamay, Karamay 834000, China
Jizhou Tang: State Key Laboratory of Marine Geology, Tongji University, Shanghai 200092, China

Sustainability, 2024, vol. 16, issue 7, 1-23

Abstract: Tight gas, a category of unconventional natural gas, relies 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 adding rules. Therefore, it is possible to make reservoir evaluations 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 7000 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 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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