Reservoir parameter characterization method based on joint probability inversion with structural constraints
ZHANG Jian1,2,3, LI Jingye1,2, WANG Jianhua4, CHEN Xiaohong1,2, LI Yuanqiang1,2, ZHOU Chunlei5
1. National Engineering Laboratory for Offshore Oil Exploration, China University of Petroleum (Beijing), Beijing 102249, China;
2. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102249, China;
3. Faculty of Geoscience and Environmental Engineering, Southwest Jiaotong University, Chengdu, Sichuan 611756, China;
4. CNOOC Research Institute Co. Ltd., Beijing 100028, China;
5. Northwest Branch, Research Institute of Petroleum Exploration & Development, Petrochina, Lanzhou, Gansu 730020, China
Abstract：Current methods for the reservoir parameter prediction and the uncertainty evaluation all use multi-step inversion, which makes it difficult to consider the uncertainty in each step. To this end, a reservoir parameter characterization method based on the joint probability inversion with structural constraints is proposed. The mixed Gaussian joint prior distribution associated with reservoir elastic parameters and physical parameters is first obtained based on well logs, followed by the single Gaussian one according to the sensitivity analysis of petrophysical parameters. Then, the geological structural information and well information are integrated into the inversion process through the least-squares well log interpolation with structural constraints. Finally, the analytical expressions of elastic parameters, physical parameters, and facies are defined by the Bayesian posterior distribution. Compared with traditional methods, the proposed method reduces cumulative errors and improves the accuracy of the prediction of reservoir parameters and uncertainty. The introduction of structural information and well information improves the lateral continuity and resolution of the inversion results. The conditional and blind well tests are carried out according to the actual data in Area M to verify the method. Also, we compare and analyze the difference between the inversion results of the new method and the multi-step one without constraints. The results show that under the assumption of linearized and Gaussian distribution, the new method achieves better inversion results with more accurate posterior probabilities. It objectively characterizes the uncertainties and provides a favorable basis for reservoir characterization and evaluation.
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