Petroleum Science >2022,??lssue 4:??- DOI: https://doi.org/10.1016/j.petsci.2022.02.008.
Predicting gas-bearing distribution using DNN based on multi-component seismic data: Quality evaluation using structural and fracture factor Open?Access
文章信息
作者：Kai Zhang, Nian-Tian Lin, Jiu-Qiang Yang, Zhi-Wei Jin, Gui-Hua Li, Ren-Wei Ding
作者單位：
投稿時(shí)間：
引用方式：Kai Zhang, Nian-Tian Lin, Jiu-Qiang Yang, Zhi-Wei Jin, Gui-Hua Li, Ren-Wei Ding, Predicting gas-bearing distribution using DNN based on multi-component seismic data: Quality evaluation using structural and fracture factors, Petroleum Science, Volume 19, Issue 4, 2022, Pages 1566-1581, https://doi.org/10.1016/j.petsci.2022.02.008.
文章摘要
Abstract: The tight-fractured gas reservoir of the Upper Triassic Xujiahe Formation in the Western Sichuan Depression has low porosity and permeability. This study presents a DNN-based method for identifying gas-bearing strata in tight sandstone. First, multi-component composite seismic attributes are obtained. The strong nonlinear relationships between multi-component composite attributes and gas-bearing reservoirs can be constrained through a DNN. Therefore, we identify and predict the gas-bearing strata using a DNN. Then, sample data are fed into the DNN for training and testing. After optimized network parameters are determined by the performance curves and empirical formulas, the best deep learning gas-bearing prediction model is determined. The composite seismic attributes can then be fed into the model to extrapolate the hydrocarbon-bearing characteristics from known drilling areas to the entire region for predicting the gas reservoir distribution. Finally, we assess the proposed method in terms of the structure and fracture characteristics and predict favorable exploration areas for identifying gas reservoirs.
關(guān)鍵詞
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Keywords: Multi-component seismic exploration; Tight sandstone gas reservoir prediction; Deep neural network (DNN); Reservoir quality evaluation; Fracture prediction; Structural characteristics