塔里木盆地顺北油田超深断溶体深度学习地质建模方法

Deep learning-based geological modeling of ultra-deep fault-karst reservoirs in Shunbei oilfield， Tarim Basin

段太忠 张文彪 何治亮 刘彦锋 马琦琦 李蒙 廉培庆 黄渊

Taizhong DUAN Wenbiao ZHANG Zhiliang HE Yanfeng LIU Qiqi MA Meng LI Peiqing LIAN Yuan HUANG

断控缝洞型储层是分布在中国塔里木盆地奥陶系的一种特殊类型储层，具有埋藏深、成因复杂、非均质性强等特点，受限于井资料稀疏和地震品质低等因素，断控缝洞型储层的准确表征与精细建模面临重要挑战。综合钻测井、岩心、野外露头及三维地震信息，在断控缝洞型储层构型模式指导下，构建了断溶体深度学习训练样本；在深度学习网络综合分析基础上，提出了适用于深层断溶体的深度学习建模方法。研究结果表明：深层少井资料条件下，基于多源数据综合建立的“原位等尺度”训练样本是断溶体深度学习建模的基础；优选的地质体目标图像转换网络可以较好地实现从地震数据到断溶体储层的直接预测。在训练网络搭建基础上，建立了塔里木盆地顺北油田5号断裂带南段的断溶体储层三维模型，该模型多维度符合断控岩溶地质模式及分布规律，与基于钻井资料的储层预测符合率较高。提升断溶体深度学习地质建模的精度和条件化程度是未来的努力攻关方向之一。

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 fault-karst 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.

10.11743/ogg20230117