A Novel Method of Deep Learning for Shear Velocity Prediction in a Tight Sandstone Reservoir

Ren Jiang, Zhifeng Ji, Wuling Mo, Suhua Wang, Mingjun Zhang, Wei Yin, Zhen Wang, Yaping Lin, Xueke Wang and Umar Ashraf
Additional contact information
Ren Jiang: Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China
Zhifeng Ji: Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China
Wuling Mo: Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China
Suhua Wang: Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China
Mingjun Zhang: Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China
Wei Yin: Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China
Zhen Wang: Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China
Yaping Lin: Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China
Xueke Wang: Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China
Umar Ashraf: Institute for Ecological Research and Pollution Control of Plateau Lakes, School of Ecology and Environmental Science, Yunnan University, Kunming 650500, China

Energies, 2022, vol. 15, issue 19, 1-20

Abstract: Shear velocity is an important parameter in pre-stack seismic reservoir description. However, in the real study, the high cost of array acoustic logging leads to lacking a shear velocity curve. Thus, it is crucial to use conventional well-logging data to predict shear velocity. The shear velocity prediction methods mainly include empirical formulas and theoretical rock physics models. When using the empirical formula method, calibration should be performed to fit the local data, and its accuracy is low. When using rock physics modeling, many parameters about the pure mineral must be optimized simultaneously. We present a deep learning method to predict shear velocity from several conventional logging curves in tight sandstone of the Sichuan Basin. The XGBoost algorithm has been used to automatically select the feature curves as the model’s input after quality control and cleaning of the input data. Then, we construct a deep-feed neuro network model (DFNN) and decompose the whole model training process into detailed steps. During the training process, parallel training and testing methods were used to control the reliability of the trained model. It was found that the prediction accuracy is higher than the empirical formula and the rock physics modeling method by well validation.

Keywords: shear velocity prediction; tight sandstone; deep learning; rock physics modeling; deep feed neuro network; Sichuan Basin (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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