Fast Induction-Log Inversion Using Quasi-Newton Updates

Abstract

Interpretation of induction-logging data is difficult because of its diffusive nature and its large volume of investigation. To overcome these difficulties, various techniques have been applied in the past. These techniques include deconvolution filters, leastsquares inversion based on the maximization of entropy and on neural-network techniques. Among them, the maximum-entropy method is the most robust. Unfortunately, it is computationally intensive and can only be carried out in computing centers. To reduce this intensity and accelerate the inversion process, a new method has been developed for induction-log inversion using quasi-Newton updates of the Jacobian matrix. Using the new method, 200 ft of log data can be inverted in 80 seconds on an 800-MHz notebook computer.