Uncertainty Analysis of a CO2 Sequestration Project Using Surrogate Reservoir Modeling Technique
- Shohreh Amini (West Virginia U.) | Shahab D. Mohaghegh (West Virginia U.) | Razi Gaskari (Intelligent Solution Inc) | Grant Bromhal (National Energy Technology Laboratory/US DOE)
- Document ID
- Society of Petroleum Engineers
- SPE Western Regional Meeting, 21-23 March, Bakersfield, California, USA
- Publication Date
- Document Type
- Conference Paper
- 2012. Society of Petroleum Engineers
- 7.6.6 Artificial Intelligence, 6.5.1 Air Emissions, 5.4 Enhanced Recovery, 5.5 Reservoir Simulation, 6.1.5 Human Resources, Competence and Training, 7.6.4 Data Mining, 2.2.2 Perforating, 4.2 Pipelines, Flowlines and Risers, 4.1.4 Gas Processing, 6.5.7 Climate Change, 7.4.4 Energy Policy and Regulation, 1.6 Drilling Operations, 7.2.1 Risk, Uncertainty and Risk Assessment, 5.1.5 Geologic Modeling, 5.10.1 CO2 Capture and Sequestration, 4.6 Natural Gas, 4.3.4 Scale, 6.5.3 Waste Management, 5.4.2 Gas Injection Methods, 5.5.8 History Matching
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While CO2 Capture and Sequestration (CCS) is considered a part of the solution to overcoming the ever increasing level of CO2 in the atmosphere, one must be sure that significant new hazards are not created by the CO2 injection process. The risks involved in different stages of a CO2 sequestration project are related to geological and operational uncertainties. This paper presents the application of a grid-based Surrogate Reservoir Model (SRM) to a real case CO2 sequestration project in which CO2 were injected into a depleted gas reservoir. An SRM is a customized model that accurately mimics reservoir simulation behavior by using Artificial Intelligence & Data Mining techniques. Initial steps for developing the SRM included constructing a reservoir simulation model with a commercial software, history matching the model with available field data and then running the model under different operational scenarios or/and different geological realizations. The process was followed by extracting some static and dynamic data from a handful of simulation runs to construct a spatio-temporal database that is representative of the process being modeled. Finally, the SRM was trained, calibrated, and validated.
The most widely used Quantitative Risk Analysis (QRA) techniques, such as Monte Carlo simulation, require thousands of simulation runs to effectively perform the uncertainty analysis and subsequently risk assessment of a project. Performing a comprehensive risk analysis that requires several thousands of simulation runs becomes impractical when the time required for a single simulation run (especially in a geologically complex reservoir) exceeds only a few minutes. Making use of surrogate reservoir models (SRMs) can make this process practical since SRM runs can be performed in minutes.
Using this Surrogate Reservoir Model enables us to predict the pressure and CO2 distribution throughout the reservoir with a reasonable accuracy in seconds. Consequently, application of SRM in analyzing the uncertainty associated with reservoir characteristics and operational constraints of the CO2 sequestration project is presented.
Despite all the efforts in shifting the energy sources to the renewable and atmosphere friendly source of energy, fossil fuels are still the most essential source of energy for industries and transportation. Considering the demand growth it is believed that fossil fuel consumption will continue to increase through the next century. As a result, concerns about the greenhouse gas emission and its impact on global warming and climate change are increasing. This has encouraged focus on two different approaches of reducing CO2 in the atmosphere. The first one is the preventive methods which aim at minimizing CO2 emission in to the atmosphere through improved efficiency, renewable energy supplies, carbon-free fuel consumption and nuclear fission, and the second approach is to apply the remedial methods through which the CO2 concentration in the atmosphere is reduced [1,2].
|File Size||705 KB||Number of Pages||10|