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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 Previous HitmodelNext Hit 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 Previous HitmodelNext Hit 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 Previous HitmodelNext Hit 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 Previous HitmodelNext Hit parame­ters such that the Previous HitmodelNext Hit outputs most closely match the measured data. The user can select a set of Previous HitmodelNext Hit parameters including lithologic, thermal, stratigraphic (i.e., eroded section), and diagenesis parameters. The optimizer provides an error response surface as well as the optimized Previous HitmodelTop 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.