应力场约束的多信息裂缝建模技术在页岩油开发中的应用

Application of Stress Field-constrained Multi-information Fracture Modeling Technology in the Development of Shale Oil

摘要

玛北风城组页岩油已成为准噶尔盆地增储上产的重点接替领域，风城组储层基质物性较差，裂缝成为其高产的主控因素，针对不同的裂缝发育程度的储层需要采取差异化的配套压裂工艺。常规井震联合裂缝建模方法精度较低，难以定量表征多尺度分方位裂缝。为此，从以下方面开展研究：1）依据井上分方位裂缝信息结合地震进行分方位裂缝建模，提高研究针对性；2）开展构造演化分析及多信息约束的应力场反演，在应力场的驱动下模拟不同时期的裂缝发育情况；3）通过神经网络深度学习的方法，将测井、地震、应力场三者裂缝信息有效融为一体，实现对于不同尺度、不同期次裂缝的精确表征。通过以上方法的应用，天然裂缝预测符合率提升至80%以上，支撑了M井区6口井的压裂方案设计。已投产井日产能提升22%以上，助力油田实现降本增效。

Abstract

The shale oil in the Fengcheng Formation of the Mabei Area has emerged as a key successor field for reserve and production growth in the Junggar Basin. The matrix physical properties of the Fengcheng Formation reservoirs are relatively poor, with fractures serving as the primary controlling factor for high production. Additionally, differentiated supporting fracturing technologies are required for reservoirs with varying degrees of fracture development. Traditional well-seismic joint fracture modeling methods exhibit low accuracy and struggle to quantitatively characterize multi-scale, azimuthally distributed fractures. This paper innovates methods in the following aspects: 1) azimuthal fracture modeling based on well data and seismic information is conducted to enhance the pertinence of the research; 2) tectonic evolution analysis and multi-constraint stress field inversion are performed to simulate the distribution of fracture development in different geological stages driven by paleotectonic stress field; 3) through the method of deep learning, the fracture information from well logging, seismic data, and stress fields is effectively integrated into a unified framework, enabling precise characterization of fractures of different scales and periods. The application of these methods raises the accuracy of natural fracture prediction to more than 80%, providing technical support for the fracturing design of 6 wells in the M Area. The daily production capacity of individual wells has increased by more than 22%, contributing to cost reduction and efficiency enhancement in the oilfield.

关键词

页岩油 / 裂缝建模 / 地震属性 / 古应力场 / 神经网络融合 / 缝网模拟

Key words

shale oil / fracture modeling / seismic attribute / paleostress field / neural network fusion / fracture simulation

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基金资助

中国石油天然气股份有限公司重大科技专项(2023ZZ15YJ02)