AN INITIAL GUESS FOR THE LEVENBERG-MARQUARDT ALGORITHM FOR CONDITIONING A STOCHASTIC CHANNEL TO PRESSURE DATA

Abstract

A standard procedure for conditioning a stochastic channel to well-test pressure data requires the minimization of an objective function. The Levenberg-Marquardt algorithm is a natural choice for minimization, but may suffer from slow convergence or converge to a local minimum which gives an unacceptable match of observed pressure data if a poor initial guess is used. In this work, we present a procedure to generate a good initial guess when the Levenberg-Marquardt algorithm is used to condition a stochastic channel to pressure data and well observations of channel facies, channel thickness, and channel top depth. This technique yields improved computational efficiency when the Levenberg-Marquardt method is used as the optimization procedure for generating realizations of the model by the randomized maximum likelihood method.