Fast forward modeling simulation of resistivity well logs using neural networks

Lin Zhang, Mary M Poulton, Zhiyi Zhang, Alberto Mezzatesta, Sirnivasa Chakravarthy

Research output: Contribution to conferencePaper

4 Scopus citations

Abstract

A neural network based approach has been developed to simulate dual laterolog and micro-laterolog responses in a 1D vertical well. Since the well site interpretation needs faster forward modeling capability, our goal is to train a neural network to a desired accuracy and substitute it for rigorous forward modeling to increase the speed of the inversion process. A modular neural network (MNN) has been trained with synthetic data. The training and testing models cover a resistivity range of 0.01 to 5000 ohm m and a thickness range of 0.5 to 50 feet. The MNN can simulate the tool response in our test case with better than 94% accuracy in most cases and the speed is almost 300 times faster than the speed in the traditional forward modeling simulation. The application of this research will be used for fast estimation of formation resistivity for 1D earth models.

Original languageEnglish (US)
StatePublished - Jan 1 1999
Event1999 Society of Exploration Geophysicists Annual Meeting, SEG 1999 - Houston, United States
Duration: Oct 31 1999Nov 5 1999

Other

Other1999 Society of Exploration Geophysicists Annual Meeting, SEG 1999
CountryUnited States
CityHouston
Period10/31/9911/5/99

ASJC Scopus subject areas

  • Geophysics

Fingerprint Dive into the research topics of 'Fast forward modeling simulation of resistivity well logs using neural networks'. Together they form a unique fingerprint.

  • Cite this

    Zhang, L., Poulton, M. M., Zhang, Z., Mezzatesta, A., & Chakravarthy, S. (1999). Fast forward modeling simulation of resistivity well logs using neural networks. Paper presented at 1999 Society of Exploration Geophysicists Annual Meeting, SEG 1999, Houston, United States.