[First Hit]

Datapages, Inc.Print this page

Critical Review, Calibration, and Ranking of Popular Previous HitVelocityNext Hit-Based Pressure Prediction Models

Mario A. Gutierrez, Neil Braunsdorf, and Brent Couzens
Shell International Exploration and Production Inc, Houston, TX

The goal of this presentation is to conduct a critical review of the major physical model-oriented and empirical Previous HitvelocityNext Hit-based models used in pre-drill pore pressure prediction, and introduce a new approach for pore pressure model calibration and analysis. Pore pressure prediction involves a broad range of methodologies to estimate fluid pressures from porosity and Previous HitdepthNext Hit based trends, seismic velocities, and multivariate regressions. Model building is typically characterized by an iterative sequence including model identification, calibration, selection, and diagnostic checking. To predict effective stress and pore pressure, practitioners apply a diverse set of functional forms that relate velocities and fluid pressures, Previous HitusingNext Hit in addition to seismic Previous HitvelocityNext Hit, predictive variables like porosity, Previous HitdepthNext Hit, temperature, etc. Previous HitVelocityNext Hit-based methods, in particular power-law Previous HitfunctionsNext Hit, are very popular in pre-drill pore pressure prediction, because they are simple and generally provide acceptable estimates. One effective way to prioritize the model selection process is by measuring the accuracy of model predictions. Previous HitUsingNext Hit a new approach, multiple functional forms are calibrated by non-linear minimization of the difference between model-predicted and actual measured pore pressures. With this approach, the quality of the pressure estimation is quantified and ranked Previous HitusingTop prediction error statistics, including RMS error, standard deviation, absolute mean, maximum and minimum error, and the number of non-physical predictions. Residual plots provide additional model diagnostics, highlighting systematic errors and the effective predictive range as a function of potentially important independent variables. Examples highlighting the calibration and ranking approach functionalities will be presented.

AAPG Search and Discovery Article #90039©2005 AAPG Calgary, Alberta, June 16-19, 2005