@article{DUAN2023, 
author = {Taizhong DUAN and Wenbiao ZHANG and Zhiliang HE and Yanfeng LIU and Qiqi MA and Meng LI and Peiqing LIAN and Yuan HUANG},
title = {Deep learning-based geological modeling of ultra-deep fault-karst reservoirs in Shunbei oilfield, Tarim Basin},
year = {2023},
journal = {Oil & Gas Geology},
volume = {44},
number = {1},
pages = {203-212},
keywords = {fault-karst reservoir, training dataset, deep learning, geomodelling, Shunbei oilfield, Tarim Basin},
url = {https://www.sciopen.com/article/10.11743/ogg20230117},
doi = {10.11743/ogg20230117},
abstract = {Fault-karst reservoir is of a special type distributed in the Ordovician strata in the Tarim Basin, China. It’s characterized by deep burial, complex genesis and strong heterogeneity. Due to sparse well data and low seismic quality and other adverse conditions, its accurate characterization and fine modeling are faced with great challenges. In the study, an integration of drilling, core, outcrop and 3D seismic data is applied to build a deep learning-based training dataset for the fault-karst reservoir with the guidance of architecture mode of fault-controlled fractured-vuggy reservoir. Based on the comprehensive analysis of deep learning network, we propose a deep learning-based modeling method suitable for fault-karst reservoirs. The results show that the “in-situ, equal-scale” training dataset established based on multi-source data is the basis for deep learning-based modeling of fault-karst reservoirs. The selected pix 2 pix (P2P) neural network could realize the 3D model prediction of fault-karst reservoirs by seismic data. A 3D faultkarst reservoir model is then established for the south segment of the No. 5 fault zone in Shunbei area following the built of training network. The model is conformed to the geological mode and distribution pattern of the reservoir type on all fronts, and also highly consistent with the reservoir prediction based on drilling data. One of the key research directions therefore lies in improving the accuracy and conditional degree of deep learning-based geological modeling of fault-karst reservoirs.}
}