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An objective, spreadsheet-based approach to parameter estimation for basin-scale numerical modeling

An objective, spreadsheet-based approach to parameter estimation for basin-scale numerical modeling

Annual Meeting Expanded Abstracts - American Association of Petroleum Geologists 13(Pages 60

Numerical modeling of the stratigraphic and thermal evolution of sedimentary basins has become common practice for evaluating hydrocarbon potential. The PC-based software is relatively easy to run and most programs provide default values for matrix and fluid properties. Although users are encouraged to tailor parameters to subject basins, this arduous task encourages the use of default parameters for "typical" lithologies. Another common time-saving practice involves "guestimating" proportions of end-member lithologies within each stratigraphic unit. These two shortcuts can significantly impact model results. To reduce the uncertainty inherent in basin-scale modeling, we devised an objective and internally consistent method for estimating matrix properties and applying them to basin stratigraphy. Two macro-based, spreadsheet programs were developed to facilitate this approach. One spreadsheet uses mud or lithology logs to determine the proportions of rock types present within each stratigraphic unit. The other spreadsheet, WALDO (Well Analysis from Log and Down-hole data, available from authors), uses these data to relate matrix parameters to specific lithologies. For example, lithology data are used to calibrate "Vshale" (shale fraction) values determined from gamma-ray logs. The spreadsheet also helps define porosity-depth curves for each lithology from sonic logs and core measurements. Permeability and thermal conductivity data also are correlated to specific lithologies in order to define porosity-permeability and porosity-thermal conductivity functions. In tests using datasets from the Alaska North Slope, this objective, lithology-based approach significantly improved the overall fit of model results to key empirical constraints (temperature, pressure, vitrinite reflectance), while reducing the amount of model calibration required.

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