The University of Texas at Austin (UT Austin) is developing a prototype modular computational approach for monitoring the location of the CO2 plume as it moves through the subsurface during the injection process—the period when the CO2 is pumped through an injection well into the targeted rock formation. The approach utilizes project injection rate and pressure data as a basis for the modeling input. This enables modeling and monitoring capabilities at negligible incremental cost because injection rate and pressure data will be recorded for operational reasons in every carbon storage project. A goal of the modular computational approach is to take advantage of the inherent flexibility it provides, allowing for other types of data, such as surface deflection or seismic imaging, to be easily included with the rate/pressure data to reduce the uncertainty of the inferred plume location.
The injection data are used to model spatial distributions of subsurface features for a range of hypothetical storage formations (formation rock types and conditions) to delineate the impact of large-scale heterogeneities (baffles, sealing faults, and zones of high permeability) on injection characteristics (rates and pressures). A random walker algorithm is being developed as a fast transfer function that simulates the physics of CO2 injection and migration with sufficient fidelity for the purposes of model discrimination, reducing overall run time. A method to quantitatively measure similarity between model responses is also being developed. These components are then integrated into a software module that takes injection data and a suite of plausible geologic models as inputs and produces a probabilistic assessment of the plume location (Figure 1). The deviation from the expected plume location and the degree of confidence in the deviation will then be quantified.
The resulting software will be tested on synthetic data sets and validated with field data obtained from external CO2 injection projects such as the In Salah Injection Project in Algeria and the various injection projects being performed by the seven NETL-funded RCSPs.
The overall goal of the Department of Energy’s (DOE) Carbon Storage Program is to develop and advance technologies that will significantly improve the effectiveness of geologic carbon storage, reduce the cost of implementation, and prepare for widespread commercial deployment between 2020 and 2030. Research conducted to develop these technologies will ensure safe and permanent storage of carbon dioxide (CO2) to reduce greenhouse gas (GHG) emissions without adversely affecting energy use or hindering economic growth.
Geologic carbon storage involves the injection of CO2 into underground formations that have the ability to securely contain the CO2 permanently. Technologies being developed for geologic carbon storage are focused on five storage types: oil and gas reservoirs, saline formations, unmineable coal seams, basalts, and organic-rich shales. Technologies being developed will work towards meeting carbon storage programmatic goals of (1) estimating CO2 storage capacity +/- 30 percent in geologic formations; (2) ensuring 99 percent storage permanence; (3) improving efficiency of storage operations; and (4) developing Best Practices Manuals. These technologies will lead to future CO2 management for coal-based electric power generating facilities and other industrial CO2 emitters by enabling the storage and utilization of CO2 in all storage types.
The DOE Carbon Storage Program encompasses five Technology Areas: (1) Geologic Storage and Simulation and Risk Assessment (GSRA), (2) Monitoring, Verification, Accounting (MVA) and Assessment, (3) CO2 Use and Re-Use, (4) Regional Carbon Sequestration Partnerships (RCSP), and (5) Focus Area for Sequestration Science. The first three Technology Areas comprise the Core Research and Development (R&D) that includes studies ranging from applied laboratory to pilot-scale research focused on developing new technologies and systems for GHG mitigation through carbon storage. This project is part of the Core R&D GSRA Technology Area and works to develop technologies and simulation tools to ensure secure geologic storage of CO2. It is critical that these technologies are available to aid in characterizing geologic formations before CO2-injection takes place in order to predict the CO2 storage resource and develop CO2 injection techniques that achieve optimal use of the pore space in the reservoir and avoid fracturing the confining zone (caprock). The program’s R&D strategy includes adapting and applying existing technologies that can be utilized in the next five years, while concurrently developing innovative and advanced technologies that will be deployed in the decade beyond. This study is developing a novel computational approach to monitor the location of the CO2 as it is being injected into a formation.
This effort increases the fundamental understanding of processes associated with CO2 injection in geologic formations by demonstrating a quantitative link between inexpensive, routinely measured injection data and large-scale features of CO2 plume migration. The tool can be easily extended to include other sources of monitoring data such as seismic imaging and surface deflection. The modular implementation of the software will be readily integrated with existing technologies for monitoring and performance prediction. The project will produce a low-cost, easily implemented early warning system for unanticipated deviations in plume migration. This will enable operators and regulators to deploy expensive, higher resolution plume monitoring methods in a more cost-effective and targeted way. Additionally, the method provides a quantitative estimate of the uncertainty in plume location, allowing for more informed decisions about whether to acquire additional data or alter injection strategies to help meet the carbon storage programmatic goals of demonstrating storage permanence and improving efficiency of storage operations.
The overall goal of this project is to develop a prototype of a new computational approach for monitoring the location of CO2 plumes during injection that is based on pressure and injection rate data. Key research objectives include:
Implementing a novel Bayesian probabilistic approach for geological model selection using injection data and other information (Figure 2).
Developing modular software that can be readily integrated with existing injection flow simulators.
Demonstrating and verifying that approach on field datasets.
Quantifying the uncertainty in the plume location.
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