Multiple Realisations for Robust Prediction of Reservoir Performance.
MEPO is a step change in the reservoir engineering workflow. It substantially enhances the value you will get from your existing reservoir simulation models, by enabling a "multiple realisation" approach – allowing you to not merely model your reservoir, but to optimise it for maximum performance. MEPO can be used to improve your reservoir decisions in a number of areas – from determining the best locations for infill wells, to selecting the optimal development scenario, understanding uncertainty, or semi-automating laborious tasks such as history matching. Best of all, it works in harmony with your existing reservoir simulator, and is simple to understand and use.
MEPO uses modern optimisation and experimental design techniques to better explore the solution space of your problem, and facilitates the use of the multiple model realisation approach when facing key decision gates.
MEPO workflows now extend back to your geomodel thanks to the MEPO Link plugin for Petrel, allowing you to include static model parameters such as fault positions as history matching parameters, or to give you greater options in optimising well location. The MEPO Link plugin is available exclusively from the Schlumberger Ocean Store here
MEPO will enable you to make better decisions by improving work processes and controlling uncertainties in your simulation projects
- Understand uncertainties and mitigate risk
- Optimize development plans, production strategies, well targets…
- Enhance your reservoir understanding cost efficiently
Improve communication and collaboration across asset teams
- Enhance understanding of field dynamics by more efficiently sharing, analyzing and visualizing results
- Conduct integrated modeling with geo tools and production networks
Read more about how the different ways optimisation technology can enhance your production in the article "Optimizing Optimization", or get an overview of the benefits of MEPO in the article "Reservoir optimisation software: the next generation".
For more info about MEPO and our supporting services see:
User Group Meetings
MEPO articles and publications
"BG has chosen MEPO as the best software to launch uncertainty-based reservoir modeling workflows across the group. We wanted a single "end-to-end" tool to implement these new workflows, and have been very pleased with the impact it has had on our business."
Steve Griffiths, Head of Reservoir Engineering, BG Group plc
How it works
The objective of history matching is to minimise the difference between the simulated and observed production data. Assisted history matching is automating this process by applying a so-called optimisation algorithm. In MEPO, two implementations of Evolutionary Algorithms are used to efficiently find the alternative solutions to a history matching problem: Evolution Strategies and Genetic Algorithms.
Reservoir simulation models become more complex and need to be capable of delivering results for decision gates in shorter time periods. At the same time, reservoir simulation deals with substantial modelling uncertainties. Recent workflows in uncertainty quantification therefore aim at generating alternative simulation models rather than producing one unique reservoir model. Hence, optimisation techniques should:
- Have a broad application area with little introduction and customisation effort
- Deliver good solutions within the framework of uncertainty
- Be robust with satisfactory performance
- Be simple to understand
- Follow a transparent workflow
- Deliver reproducible results.
Evolutionary Algorithms satisfy all these requirements, and therefore provide a methodology for challenging deployment of algorithms for an increasing number of problems that become more and more complex and require solutions in less and less time. Evolutionary Algorithms belong to the class of direct search methods. They use only the objective function value to determine new search steps, and do not require any gradient information from the optimisation problem. Therefore, they can be used in cases for which gradient information is not available and where traditional algorithms fail because of significant non-linearities or discontinuities in the search space. Evolutionary Algorithms have proven to be robust and easy to adapt to different engineering problems.
The nature of Evolutionary Algorithms is to use parallel structures in generating parent-to-child sequences. This principal feature can easily be transferred to parallel structures of an optimisation program, allowing parallel computing to be used. The scalability of this methodology will have an important effect on the applicability of numerical optimisations in case of very time consuming simulations.
Evolutionary Algorithms are generally accepted as robust and generalised problem solvers. They are applicable to a wide range of problems, including cases of discontinuities and non-linearities in the search space. Evolutionary Algorithms deliver approximately equally good performance over a wide range of problem statements, and are therefore well suited for history matching projects with a diversity of specific problem statements.
For more information about optimisation methods and the algorithms used in MEPO, see MEPO Publications.
- ECLIPSE© (Schlumberger)
- IMEX™, STARS™, GEM™ (CMG)
- VIP™, Nexus© (Landmark)
- MoReS© (Shell)
- CHEARS© (Chevron)
- PSIM© (ConocoPhilips)
- 3DSL® (StreamSim Technologies)
- IPOS©, OLGA© (SPT Group)
Center for Reservoir Optimisation
SPT believes in providing a complete solution, not just technology. We have a highly skilled team of experts in our main offices available to provide consulting services using MEPO, whether it is to help you get started or to take on complete projects. In addition, in Oslo SPT Group’s Center for Reservoir Optimisation (CRO) combines unique expertise on optimisation workflows, state of the art MEPO technology, a powerful computational facility, and a team of experienced reservoir engineers to help you reach decisions faster and with greater confidence.
Joint Industry Projects
SPT believes in engaging with our customers because this helps us develop products that efficiently address real problems. To this end we have initiated several joint industry projects (JIPs) aimed at building on the MEPO framework to address specific industry challenges for which no adequate solution currently exists.
The MEPO FORWARD I JIP focused on the area of production optimisation and field management and was run in partnership with BG, ConocoPhillips, ENI and Statoil. This JIP is now complete and the technology developed had been implemented in MEPO 4.0 and later releases.
The MEPO FORWARD II JIP started in late 2011 and looks at the development of enhanced optimisation techniques and workflows with application to integrated modelling. Current partners are BG, DONG, ENI and Total but is still open to further participants.