MAPPING OF RESERVOIR PROPERTIES AND FACIES THROUGH INTEGRATION OF STATIC AND DYNAMIC DATA

Reynolds, Albert C. ; Oliver, Dean S. ; Dong, Yannong ; Liu, Ning ; Gao, Guohua ; Zhang, Fengjun ; Li, Ruijian

University of Tulsa (United States)

关键词: Oil Wells;    02 Petroleum;    Geologic Models;    Site Characterization;    Petroleum Deposits;   

DOI  :  10.2172/837887
RP-ID  :  NONE
RP-ID  :  FC26-00BC15309
RP-ID  :  837887

美国|英语

来源: UNT Digital Library

【 摘 要 】

Knowledge of the distribution of permeability and porosity in a reservoir is necessary for the prediction of future oil production, estimation of the location of bypassed oil, and optimization of reservoir management. The volume of data that can potentially provide information on reservoir architecture and fluid distributions has increased enormously in the past decade. The techniques developed in this research will make it easier to use all the available data in an integrated fashion. While it is relatively easy to generate plausible reservoir models that honor static data such as core, log, and seismic data, it is far more difficult to generate plausible reservoir models that honor dynamic data such as transient pressures, saturations, and flow rates. As a result, the uncertainty in reservoir properties is higher than it could be and reservoir management can not be optimized. In this project, we have developed computationally efficient automatic history matching techniques for generating geologically plausible reservoir models which honor both static and dynamic data. Specifically, we have developed methods for adjusting porosity and permeability fields to match both production and time-lapse seismic data and have also developed a procedure to adjust the locations of boundaries between facies to match production data. In all cases, the history matched rock property fields are consistent with a prior model based on static data and geologic information. Our work also indicates that it is possible to adjust relative permeability curves when history matching production data.