Predicting the Migration of CO2 Plume Using Injection Data and a Distance-Metric Approach to Reservoir-Model Selection
- Sayantan Bhowmik (University of Texas at Austin) | Sanjay Srinivasan (U. of Texas at Austin) | Steven Lawrence Bryant (U. of Texas at Austin)
- Document ID
- Society of Petroleum Engineers
- SPE International Conference on CO2 Capture, Storage, and Utilization, 10-12 November, New Orleans, Louisiana, USA
- Publication Date
- Document Type
- Conference Paper
- 2010. Society of Petroleum Engineers
- 1.2.3 Rock properties, 5.5 Reservoir Simulation, 4.6 Natural Gas, 5.1.5 Geologic Modeling, 5.1.9 Four-Dimensional and Four-Component Seismic, 5.6.5 Tracers, 5.1.1 Exploration, Development, Structural Geology, 5.4 Enhanced Recovery, 4.3.4 Scale, 4.1.2 Separation and Treating, 5.5.8 History Matching, 5.4.2 Gas Injection Methods
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During the operation of a geological carbon storage project, verifying that the CO2 plume remains within the permitted zone will be of particular interest both to regulators and to operators. We demonstrate a model selection algorithm that refines an initial suite of subsurface models representing the prior uncertainty to create a posterior set of subsurface models that reflect injection performance consistent with that observed. Such posterior models can be used to represent uncertainty in the future migration of the CO2 plume. Because only injection data is required, the method provides a very inexpensive method to map the migration of the plume and the associated uncertainty in migration paths. We illustrate the method using a field data set.
Geological sequestration of CO2 in deep saline aquifers is one techonology to remove CO2 in sufficient quantities from the atmosphere to reduce the carbon footprint of fossil fuel consumption. Field scale projects for geological storage (sequestration) have been undertaken in Algeria, the North Sea and the US to study the feasibility of this method on a large scale. Accurately monitoring the movement of CO2 plume is of great interest to the storage community. A variety of techniques have been employed, including time-lapse seismic and satellite measurements of surface deflection. Such methods are effective but are expensive, in the case of seismic, or not universally applicable, in the case of satellite measurements when the ground vegetation cover is too thick or seasonally variable. More importantly, these monitoring techniques typically provide snapshots of the current state of the plume, and are not suited for predicting the future migration of the plume unless used in conjunction with an elaborate reservoir model updating scheme. The ability to predict future movement of the plume is of great interest to operators and regulators, especially if undertaking remedial measures to prevent the movement of the plume outside the area of storage arises.
In this work we describe an approach for reservoir model selection based on easily available injection well data. The selection process yields a final set of most probable aquifer models, rather than a single "best fit?? model. The set of most probable models can be used for a probabilistic estimate of current plume location and for a forecast of subsequent migration of the CO2 plume.
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