Prediction of shear wave velocity in shale reservoir based on TCN

Xiangyu Shi; Zitong Zhang; Kai Zhang; Chunlei Zhang

doi:10.1117/12.2628504

22 April 2022 Prediction of shear wave velocity in shale reservoir based on TCN

Xiangyu Shi, Zitong Zhang, Kai Zhang, Chunlei Zhang

Proceedings Volume 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021); 1217419 (2022) https://doi.org/10.1117/12.2628504
Event: International Conference on Internet of Things and Machine Learning (IoTML 2021), 2021, Shanghai, China

Abstract

Shear wave (S-wave) velocity prediction is important for the evaluation of shale oil and gas reservoir. However, there are some problems with traditional models: the parameters of the petrophysical model are relatively fixed, and the machine learning models do not consider the sequence information of the log data. Therefore, the S-wave velocity prediction model based on Temporal Convolutional Network (TCN) for shale reservoir is proposed. The model can flexibly extract the sequence features by adopting causal convolution and dilation factors and mine the inner relationship between the well logs and the reservoir S-wave velocity to achieve a better prediction performance. Two wells of MY1 and FN4 in shale reservoir in the Permian Fengcheng Formation in Mahu Sag of Junggar Basin, Xinjiang Oilfield are taken as an example. The TCN model achieves optimal results on both MY1 and FN4 with mean relative error (MRE) of 0.84% and 1.39%, respectively, when compared with the results of traditional petrophysical models, machine learning models and conventional deep learning models. This indicates that the TCN model has strong effectiveness and generalization in Swave velocity prediction, which provides a new idea for S-wave velocity prediction in shale reservoir.

Citation Download Citation

Xiangyu Shi, Zitong Zhang, Kai Zhang, and Chunlei Zhang "Prediction of shear wave velocity in shale reservoir based on TCN", Proc. SPIE 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021), 1217419 (22 April 2022); https://doi.org/10.1117/12.2628504

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