ROS Theses Repository

View Item 
  •   ROS Home
  • Energy, Geoscience, Infrastructure and Society
  • Doctoral Theses (Energy, Geoscience, Infrastructure and Society)
  • View Item
  •   ROS Home
  • Energy, Geoscience, Infrastructure and Society
  • Doctoral Theses (Energy, Geoscience, Infrastructure and Society)
  • View Item
  •   ROS Home
  • Energy, Geoscience, Infrastructure and Society
  • Doctoral Theses (Energy, Geoscience, Infrastructure and Society)
  • View Item
  • Admin
JavaScript is disabled for your browser. Some features of this site may not work without it.

Uncertainty evaluation of reservoir simulation models using particle swarms and hierarchical clustering

View/Open
KathradaM_0609_pe.pdf (5.047Mb)
Date
2009-06
Author
Kathrada, Muhammad
Metadata
Show full item record
Abstract
History matching production data in finite difference reservoir simulation models has been and always will be a challenge for the industry. The principal hurdles that need to be overcome are finding a match in the first place and more importantly a set of matches that can capture the uncertainty range of the simulation model and to do this in as short a time as possible since the bottleneck in this process is the length of time taken to run the model. This study looks at the implementation of Particle Swarm Optimisation (PSO) in history matching finite difference simulation models. Particle Swarms are a class of evolutionary algorithms that have shown much promise over the last decade. This method draws parallels from the social interaction of swarms of bees, flocks of birds and shoals of fish. Essentially a swarm of agents are allowed to search the solution hyperspace keeping in memory each individual’s historical best position and iteratively improving the optimisation by the emergent interaction of the swarm. An intrinsic feature of PSO is its local search capability. A sequential niching variation of the PSO has been developed viz. Flexi-PSO that enhances the exploration and exploitation of the hyperspace and is capable of finding multiple minima. This new variation has been applied to history matching synthetic reservoir simulation models to find multiple distinct history 3 matches to try to capture the uncertainty range. Hierarchical clustering is then used to post-process the history match runs to reduce the size of the ensemble carried forward for prediction. The success of the uncertainty modelling exercise is then assessed by checking whether the production profile forecasts generated by the ensemble covers the truth case.
URI
http://hdl.handle.net/10399/2268
Collections
  • Doctoral Theses (Energy, Geoscience, Infrastructure and Society)

Browse

All of ROSCommunities & CollectionsBy Issue DateAuthorsTitlesThis CollectionBy Issue DateAuthorsTitles

ROS Administrator

LoginRegister
©Heriot-Watt University, Edinburgh, Scotland, UK EH14 4AS.

Maintained by the Library
Tel: +44 (0)131 451 3577
Library Email: libhelp@hw.ac.uk
ROS Email: open.access@hw.ac.uk

Scottish registered charity number: SC000278

  • About
  • Copyright
  • Accessibility
  • Policies
  • Privacy & Cookies
  • Feedback
AboutCopyright
AccessibilityPolicies
Privacy & Cookies
Feedback
 
©Heriot-Watt University, Edinburgh, Scotland, UK EH14 4AS.

Maintained by the Library
Tel: +44 (0)131 451 3577
Library Email: libhelp@hw.ac.uk
ROS Email: open.access@hw.ac.uk

Scottish registered charity number: SC000278

  • About
  • Copyright
  • Accessibility
  • Policies
  • Privacy & Cookies
  • Feedback
AboutCopyright
AccessibilityPolicies
Privacy & Cookies
Feedback