Physical property classification and evaluation method based on the pore structure for conglomerate reservoirs
LIU Shiqiong1, LIU Xiangjun1, SUN Yangsha2, LIU Hongqi1, KONG Yuhua3, LI Xiansheng4
1. State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu, Sichuan 610500, China; 2. Emergency Response Bureau, Zhongshan District, Liupanshui, Liupanshui, Guizhou 553000, China; 3. Research Institute of Exploration and Development of Xinjiang Oilfield Company, Karamay, Xinjiang 834000, China; 4. Geological Exploration & Development Research Institute, CNPC Chuanqing Drilling Engineering Company Limited (CCDC), Chengdu, Sichuan 610051, China
Abstract:Since the discovery of the giant conglomerate oil reservoir in Mahu,Xinjiang,the exploration and development of conglomerate oil and gas reservoirs have attracted increasing attention. Nevertheless,such reservoirs are highly heterogeneous,resulting in difficult reservoir identification and evaluation. Taking the upper Wuerhe Formation in the HM work area as an example,this study classifies conglomerate reservoirs into five categories. After the Sphere-Cylinder model is applied to optimize the inversion of NMR (nuclear magnetic resonance) echo,an optimized T2 spectrum is obtained and then characterized by 12 spectral shape parameters. A spectral shape prediction Vector and a Joint prediction Vector are formed,respectively,by the 12 spectral shape parameters and the spectral eigenvalue. Then,they are trained by SVM (Support Vector Machine) to build a prediction model for reservoir physical property classification parameters. According to the comparison of the results of the prediction model with the reservoir physical property parameters and oil and gas production,the reservoir physical property classification parameters correlate well with the reservoir physical properties and are in good agreement with the test production of the reservoir. The comparison results show that the shape parameters of the T2 spectrum obtained by optimized inversion with the Sphere-Cylinder model and the spectral eigenvalue can well characterize the physical properties of conglomerate oil and gas reservoirs. The results of this paper can help improve the quality and reliability of the classification and evaluation of conglomerate oil and gas reservoirs and provide logging technical support for the efficient exploitation of conglomerate oil and gas reservoirs.
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