Permeability Prediction of Carbonate Reservoir Based on Nuclear Magnetic Resonance (NMR) Logging and Machine Learning

Jianpeng Zhao (), Qi Wang, Wei Rong, Jingbo Zeng, Yawen Ren and Hui Chen
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Jianpeng Zhao: School of Earth Sciences & Engineering, Xi’an Shiyou University, Xi’an 710065, China
Qi Wang: School of Earth Sciences & Engineering, Xi’an Shiyou University, Xi’an 710065, China
Wei Rong: Geological Research Institute, China Petroleum Logging Co., Ltd., Xi’an 710075, China
Jingbo Zeng: Geological Research Institute, China Petroleum Logging Co., Ltd., Xi’an 710075, China
Yawen Ren: Geological Research Institute, China Petroleum Logging Co., Ltd., Xi’an 710075, China
Hui Chen: Geological Research Institute, China Petroleum Logging Co., Ltd., Xi’an 710075, China

Energies, 2024, vol. 17, issue 6, 1-15

Abstract: Reservoir permeability is an important parameter for reservoir characterization and the estimation of current and future production from hydrocarbon reservoirs. Logging data is an important means of evaluating the continuous permeability curve of the whole well section. Nuclear magnetic resonance logging measurement results are less affected by lithology and have obvious advantages in interpreting permeability. The Coates model, SDR model, and other complex mathematical equations used in NMR logging may achieve a precise approximation of the permeability values. However, the empirical parameters in those models often need to be determined according to the nuclear magnetic resonance experiment, which is time-consuming and expensive. Machine learning, as an efficient data mining method, has been increasingly applied to logging interpretation. XGBoost algorithm is applied to the permeability interpretation of carbonate reservoirs. Based on the actual logging interpretation data, with the proportion of different pore components and the logarithmic mean value of T2 in the NMR logging interpretation results as the input variables, a regression prediction model is established through XGBoost algorithm to predict the permeability curve, and the optimization of various parameters in XGBoost algorithm is discussed. The determination coefficient is utilized to check the overall fitting between measured permeability versus predicted ones. It is found that XGBoost algorithm achieved overall better performance than the traditional models.

Keywords: machine learning; permeability prediction; carbonate reservoir; NMR logging; XGBoost method (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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