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742-2

Technical Committee Reservoir Engineering

Adjoint Method Used in History Matching and Optimization Workflows - Phase 2: Revealing Hidden Reservoir Features not captured in Reservoir Simulation Models

The DGMK 742 project is a joint industry project that develops advanced technologies for increasing confidence in the history matching capability of reservoir simulation models. The DGMK 742 project has run for the last 8 years in two phases: Phase I (DGMK 742/1) ran from August 2011 to July 2014, and Phase II (DGMK 742/2) was conducted from May 2016 to April 2019. The focus of the DGMK 742 project is the development of advanced techniques for calibrating reservoir simulation models to available observed data using the adjoint method.
The aim of the DGMK 742/2 project was to develop a workflow based on the adjoint method that can be used to improve reservoir characterization in reservoir simulation model. The developed workflow leveraged the power of the adjoint method (using SenEx) in performing analytical sensitivity-based parameter modifications at grid-level to reveal hidden reservoir features like channels, faults, vertical communication etc. Other detection algorithms were developed over the course of the DGMK 742/2 research project to improve detection of hidden reservoir features without the need of human interaction. The MEPO-tSenEx hotlink developed in DGMK 742/1 was also incorporated into the developed workflow.
Within the framework of the DGMK 742/2 research project, the developed workflow and detection algorithms have been applied both, to synthetic as well as to some real field reservoir simulation models under various practical reservoir settings. Results obtained show that the developed workflow for improving reservoir characterization significantly outperforms other methods existing in literature. Numerous tests performed over the course of the DGMK 742/2 research project highlights benefits and limitations of using the developed workflow.

Authors
L. Ganzer, D. Awofodu
Copyright
2020
Language
English
eBook ISBN
978-3-947716-15-9
Book Series ISSN
0937-9762
Number of Pages
95
Number of Pictures
68
Number of Tables
16