Reducing uncertainty in geostatistical description with well testing pressure data
- Univ. of Tulsa, OK (United States)
- Chevron Petroleum Technology Company, La Habra, CA (United States)
Geostatistics has proven to be an effective tool for generating realizations of reservoir properties conditioned to static data, e.g., core and log data and geologic knowledge. Due to the lack of closely spaced data in the lateral directions, there will be significant variability in reservoir descriptions generated by geostatistical simulation, i.e., significant uncertainty in the reservoir descriptions. In past work, we have presented procedures based on inverse problem theory for generating reservoir descriptions (rock property fields) conditioned to pressure data and geostatistical information represented as prior means for log-permeability and porosity and variograms. Although we have shown that the incorporation of pressure data reduces the uncertainty below the level contained in the geostatistical model based only on static information (the prior model), our previous results assumed did not explicitly account for uncertainties in the prior means and the parameters defining the variogram model. In this work, we investigate how pressure data can help detect errors in the prior means. If errors in the prior means are large and are not taken into account, realizations conditioned to pressure data represent incorrect samples of the a posteriori probability density function for the rock property fields, whereas, if the uncertainty in the prior mean is incorporated properly into the model, one obtains realistic realizations of the rock property fields.
- Research Organization:
- BDM Corp., Bartlesville, OK (United States); American Association Petroleum Geologists, Tulsa, OK (United States)
- OSTI ID:
- 508525
- Report Number(s):
- CONF-970317--; ON: DE97004613
- Country of Publication:
- United States
- Language:
- English
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