A comprehensive formation evaluation method is presented which integrates log, core and well test data on a reservoir using regression analysis. The method employs a capillary pressure curve model correlating four reservoir variables: porosity, water saturation, permeability and capillary pressure. Porosity and saturation are estimated by conventional log analysis, permeability is obtained from empirical correlations with logs (usually porosity) and capillary pressure is directly related to height above the water level.

Depth profiles of the four variables are adjusted by regression analysis constrained by the capillary pressure curve model. Results of the regression include:

  1. An estimate of the water level,

  2. Improved profiles of the four variables which are consistent with both log analysis and capillary pressure theory,

  3. Adjusted log analysis parameters,

  4. Complete synthetic capillary pressure curves for each depth level,

  5. Relative permeability curves (drainage) generated from the capillary pressure curves, and

  6. Estimated effective permeability to hydrocarbons and to water opposite the wellbore.

These results are then integrated with well test data by comparing effective permeabilities from 6. above with the test results. If there is a mismatch, it may be necessary to rerun the regression in order to honor all of the datasets.

The method is illustrated with log, core and test data from an offshore Gulf of Mexico well.

Keywords:
pressure curve model, evaluation, capillary pressure curve model, resistivity, permeability, artificial intelligence, log analysis, upstream oil & gas, water saturation, well logging
Subjects:
Formation Evaluation & Management, Open hole/cased hole log analysis
Copyright 1994, Society of Petroleum Engineers
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