Experiences With Automated History Matching

Abstract

Experiences With Automated History Matching C. S. Kabir; C. S. Kabir ChevronTexaco Overseas Petroleum Search for other works by this author on: This Site Google Scholar M. C. H. Chien; M. C. H. Chien Focus Simulation Inc. Search for other works by this author on: This Site Google Scholar J. L. Landa J. L. Landa ChevronTexaco EPTC Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Reservoir Simulation Symposium, Houston, Texas, February 2003. Paper Number: SPE-79670-MS https://doi.org/10.2118/79670-MS Published: February 03 2003 Connected Content Related to: Experiences With Automated History Matching Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Kabir, C. S., Chien, M. C. H., and J. L. Landa. "Experiences With Automated History Matching." Paper presented at the SPE Reservoir Simulation Symposium, Houston, Texas, February 2003. doi: https://doi.org/10.2118/79670-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Reservoir Simulation Conference Search Advanced Search Abstract Matching historical performance of a reservoir is a time-consuming exercise with uncertain outcome. The current practice of manual matching is subjective and goodness of the match owes largely to the experiences of the team members involved in a study and to the quality and quantity of various input and observed data.This paper describes approaches taken to speed up the history matching effort during the course of replicating multiyear performance of two West African reservoirs. The study entails validation of production data, and matching of single-well and full-field performances with two automated-history-matching (AHM) approaches based on the Gauss-Newton algorithm. These finite-difference results obtained with AHM are contrasted with those obtained from the traditional manual approach.Results show that both AHM approaches shorten length of a study, and preserve objectivity of a history match. The use of grid coarsening results in impressive speed gain that can quickly provide clues whether adjustments of certain parameters are justified for some wells. That multiple parameter adjustments can be made within specified bounds with a few iterations in a 24-hour run is a profound improvement in our speed of learning of key reservoir flow behavior. This speed, in turn, guides the study in a path that leads to rapid conclusion in a fraction of time taken in a traditional study. Keywords: grid, machine learning, spe 79670, internal grid, artificial intelligence, coefficient, drillstem/well testing, sensitivity coefficient, drillstem testing, method 2 Subjects: Reservoir Simulation, Formation Evaluation & Management, History matching, Drillstem/well testing Copyright 2003, Society of Petroleum Engineers You can access this article if you purchase or spend a download.