Porosity prediction of carbonate reservoir based on neural network

Liyan Yang; Xiangdong Peng

doi:10.1117/12.2640460

15 July 2022 Porosity prediction of carbonate reservoir based on neural network

Liyan Yang, Xiangdong Peng

Proceedings Volume 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022); 1225803 (2022) https://doi.org/10.1117/12.2640460
Event: International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 2022, Qingdao, China

Abstract

Porosity, as one of the important reservoir physical parameters, plays an important role in reservoir evaluation. Considering the actual work needs, finding a low-cost and efficient method to obtain high-precision porosity has become an important topic of reservoir evaluation. Due to the complex nonlinear mapping relationship and timing characteristics between logging parameters and porosity, a model of deep learning is proposed to predict the porosity of carbonate reservoir according to the existing logging data. Firstly, on the basis of core analysis and geological and logging data, data preprocessing is carried out for carbonate reservoir logging data, including core depth homing, logging data standardization and normalization. The second step is to establish the prediction model of reservoir parameters by using proper learning samples. The third step is to evaluate the predicted effect of porosity model and modify the model by using superposition diagram method and error statistics method. The calculation demerit of the final model is compared with the traditional results.The comparison results in the last step show that the prediction results of reservoir parameters by neural network are more accurate than those by traditional methods.

Citation Download Citation

Liyan Yang and Xiangdong Peng "Porosity prediction of carbonate reservoir based on neural network", Proc. SPIE 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 1225803 (15 July 2022); https://doi.org/10.1117/12.2640460

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