Improved Permeability Prediction in Heterogeneous Carbonate Reservoirs: A new Approach Using Rock Typing and Wireline Formation Testing
- Iulian N. Hulea (Shell International BV) | Daniela Frese (Shell International BV) | Shyam Ramaswami (Shell International BV)
- Document ID
- Society of Petrophysicists and Well-Log Analysts
- SPWLA 56th Annual Logging Symposium, 18-22 July, Long Beach, California, USA
- Publication Date
- Document Type
- Conference Paper
- 2015. held jointly by the Society of Petrophysicists and Well Log Analysts (SPWLA) and the submitting authors
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- 270 since 2007
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Carbonate rocks are a synonym for heterogeneity. Attempts to characterize carbonate reservoirs follow various rock typing routes in order to predict saturation and permeability. The rock typing methods focus mainly on Special Core Analysis output where core derived permeability and capillary pressures play a central role. Given the late stage in a typical project where these results become available, a full integration with larger volume sampling methods (such as wireline formation testing, and well testing) is usually not brought to a closure. For a heterogeneous formation we attempt to bring this information in agreement and highlight differences between the results of different sampling methods. More specifically, we focus on the agreement between various permeability scales to accurately model our reservoir.
Also, we will show that by using fluid mobility data from wireline formation testing pressure measurements one can improve the accuracy of rock –typing predicted permeability by comparing mobility against the predicted permeability obtained via various averaging methods. In addition, a new method of QCing and ensuring consistency between static and dynamic models will be presented. In populating a reservoir model, the permeability and saturation are typically forward modeled in parallel streams without a consistency check between these parameters. Where core permeability is not available we will show that a permeability curve can be derived based on input data already available in the model: via capillary pressures. The advantage of this method is that it can be calibrated directly to well performance.
|File Size||1 MB||Number of Pages||10|