Field application study on automatic history matching using particle swarm optimization

Sanghyun Lee, Karl Dunbar Stephen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)


The traditional trial and error approach of history matching to obtain an accurate model requires engineers to control each uncertain parameter and can be quite time consuming and inefficient. However, automatic history matching (AHM), assisted by computers, is an efficient process to control a large number of parameters simultaneously by an algorithm that integrates a static model with dynamic data to minimize a misfit for improving reliability. It helps to reduce simulation run time as well. Particle Swarm Optimization (PSO) is a population based stochastic algorithm that can explore parameter space combined with the least squares single objective function. The process of AHM can adopt parameterization and realization methods to reduce inverse problems. In this study, realizations of various reservoir properties such as porosity, net to gross, relative permeability, horizontal and vertical permeability, and aquifer size were chosen for controlling throughout the AHM. History matching was conducted to validate the efficiency of each method. The guidelines for optimized AHM with a stochastic algorithm are also disccussed. The realization and parameterization methods improved matching results in a full-field application with resulting in a reduced misfit and in less. A stochastic algorithm generates multiple models to deduce control parameters to reduce a misfit. In this study we identified that PSO converged effectively with updated control parameters. The optimized AHM improved the accuracy of a full-field model although some misfit remained in the match to bottomhole pressure. We found that updating with too many parameters makes the problem difficult to solve while using too few leads to false convergence. In addition, while the simulation run time is critical, a full-field simulation model with reduced computational overhead is benefitial. In this study, we observed that the PSO was an efficient algorithm to update control parameters to reduce a misfit. Using the parameterization and realization as an assisted method helped find better results. Overall this study can be used as a guideline to optimize the process of history matching.

Original languageEnglish
Title of host publicationSPE Reservoir Characterisation and Simulation Conference and Exhibition 2019
PublisherSociety of Petroleum Engineers
ISBN (Electronic)9781613996690
Publication statusPublished - 17 Sept 2019
EventSPE Reservoir Characterisation and Simulation Conference and Exhibition 2019 - Jumeirah at Etihad Towers, Abu Dhabi, United Arab Emirates
Duration: 17 Sept 201919 Sept 2019


ConferenceSPE Reservoir Characterisation and Simulation Conference and Exhibition 2019
Abbreviated titleRCSC 2019
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Internet address


  • Automatic history matching
  • Parameterization
  • Particle swarm optimization
  • Realization
  • Stochastic algorithm

ASJC Scopus subject areas

  • Fuel Technology
  • Geotechnical Engineering and Engineering Geology
  • Geochemistry and Petrology


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