An intelligent prediction method of fractures in tight carbonate reservoirs

• Published in: Petroleum exploration and development 2022-12, Vol.49 (6), p.1364-1376
• Main Authors: DONG, Shaoqun, ZENG, Lianbo, DU, Xiangyi, BAO, Mingyang, LYU, Wenya, JI, Chunqiu, HAO, Jingru
• Format: Article
• Language: English
• Subjects: artificial intelligence; fracture identification by well logs; fracture network model; interwell fracture density model; interwell fracture trend prediction; tight carbonate reservoir; Zagros Basin
• ISSN: 1876-3804; 1876-3804
• DOI: 10.1016/S1876-3804(23)60355-6

An intelligent prediction method for fractures in tight carbonate reservoir has been established by upgrading single-well fracture identification and interwell fracture trend prediction with artificial intelligence, modifying construction of interwell fracture density model, and modeling fracture network and making fracture property equivalence. This method deeply mines fracture information in multi-source isomerous data of different scales to reduce uncertainties of fracture prediction. Based on conventional fracture indicating parameter method, a prediction method of single-well fractures has been worked out by using 3 kinds of artificial intelligence methods to improve fracture identification accuracy from 3 aspects, small sample classification, multi-scale nonlinear feature extraction, and decreasing variance of the prediction model. Fracture prediction by artificial intelligence using seismic attributes provides many details of inter-well fractures. It is combined with fault-related fracture information predicted by numerical simulation of reservoir geomechanics to improve inter-well fracture trend prediction. An interwell fracture density model for fracture network modeling is built by coupling single-well fracture identification and interwell fracture trend through co-sequential simulation. By taking the tight carbonate reservoir of Oligocene-Miocene AS Formation of A Oilfield in Zagros Basin of the Middle East as an example, the proposed prediction method was applied and verified. The single-well fracture identification improves over 15% compared with the conventional fracture indication parameter method in accuracy rate, and the inter-well fracture prediction improves over 25% compared with the composite seismic attribute prediction. The established fracture network model is well consistent with the fluid production index.