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ChevronTexaco, Houston, USA
Corresponding author: dyuq@chevrontexaco.com
| The first 20% of the full text of this article appears below. |
Mature and complex reservoirs are usually covered by many kinds of static and dynamic datai.e. petrophysical, PVT, well testing, geologic, completion, and reservoir production information. This paper illustrates an effective methodology to develop a consistent simulation model, via integration of the available static and dynamic data sets, that allows better understanding of the subsurface reservoir flow characteristics.
A complex gas condensate reservoir, referred to as reservoir X and first put into production 14 years ago, serves as the example in this paper. There is abundance of available data from different sources. However, the data can not be directly applied to build a reservoir prediction model because of the various measurement scalese.g., core = about 0.1 ft long, log = tens of feet away from wellbore, and reservoir simulation = hundreds of feet for the grid size. In some cases, the same parameter is derived from different sourcesi.e., permeability from core samples, logging data, and pressure transient test. Due to the extreme intrinsic complexity of reservoir X, actual subsurface flow characteristics were not well understood before this study. By using the proposed methodology, a reliable, coherent and improved reservoir simulation model was built.
In the past decade, the multidisciplinary approach (usually including geology, geophysics, petrophysics, reservoir engineering, and production engineering) has gained popularity in industry's attempt to achieve more effective reservoir management. This paper focuses on a multidisciplinary approach that integrated PVT data, well tests, geologic, petrophysical, and dynamic reservoir production information to build a reservoir simulation model, which can be used to predict reservoir performance.
| Reservoir X background |
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