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Optimization/Inversion—a Technique to Efficiently and Effectively Calibrate Petroleum System Models


Hermanrud, Christian1, Jay E. Leonard2, Thomas A. Schutter2, Margaret A. Lessenger2, Marianne S. Karplus2, Christopher N. Wold2, Veit J. Matt2 (1) Statoil ASA, Trondheim, Norway (2) Platte River Associates, Inc, Boulder, CO


Petroleum system model calibration has traditionally been constrained to trial-and-error or linear search methods. With complicated models, these approaches prove time consum­ing and may not identify the best results. Optimization/inversion is an advanced technique widely used in science and engineering to efficiently and effectively calibrate a model to measured data. With the large numbers of wells and associated geophysical log data avail­able today, computerized optimization has the potential to become a valuable tool for petro­leum system model calibration.

Statoil and Platte River Associates, Inc. have developed an optimizer that utilizes an adaptive simulated annealing (ASA) inversion algorithm. ASA samples the parameter space efficiently and increases the chance of finding the error surface’s global minima. The opti­mizer allows simultaneous, fast, automated calibration of a large number of models to measured data such as bottom hole temperatures, maturity (%Ro), pressure, porosity, and permeability. The algorithm identifies the optimized values of user-specified model parame­ters such that the model outputs most closely match the measured data. The user can select a set of model parameters including lithologic, thermal, stratigraphic (i.e., eroded section), and diagenesis parameters. The optimizer provides an error response surface as well as the optimized model parameter values. Furthermore, the optimizer sensitivity results enable the user to determine if additional data are required and the relative importance of various parameters to producing accurate models. The optimization/inversion process streamlines calibration, improves modelling results, and enables sophisticated determination of default parameters for basin modelling studies.