Progress and prospects of brittleness research in unconventional reservoirs
LIU Zhen1,2, ZHANG Junhua1,2, YU Zhengjun3, REN Ruijun3, SUN Youzhuang1,2
1. National Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), Qingdao, Shandong 266580, China; 2. School of Geosciences, China University of Petroleum (East China), Qingdao, Shandong 266580, China; 3. Geophysical Research Institute, Shengli Oilfield Company, SINOPEC, Dongying, Shandong 257022, China
Abstract：This paper briefly reviews the current domestic and international research status and influencing factors related to brittleness. On this basis， we summarize seven main expressions for brittleness indices and expound upon their respective physical interpretations. Brittleness prediction methods for reservoirs are categorized into seismic brittleness inversion based on conventional approaches and brittleness prediction based on deep learning techniques. Meanwhile， the advantages and disadvantages of the two methods are evaluated. Conventional methods are characterized by wide applications， relatively mature algorithms， stability and reliability， and requirements for substantial prior knowledge with limited effects in complex geological conditions. Conversely， deep learning methods feature adaptability to intricate geological conditions， no need for prior information， and straightforward principles and procedures. However， the training process demands significant computational resources and time. Additionally， we introduce new technologies such as direct inversion of Bayesian AVAZ brittleness， machine learning brittleness prediction based on well-logging derivatives and volatility attributes， and hybrid neural network brittleness prediction by employing CNN-LSTM models. Finally， this study provides an outlook on the future development of brittleness prediction techniques.
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