Project No: FE0001163
Performer: West Virginia University
Traci Rodosta Carbon Storage Program Technology Manager National Energy Technology Laboratory 3610 Collins Ferry Road P.O. Box 880 Morgantown, WV 26507 304-285-1345 email@example.com Michael McMillian Project Manager National Energy Technology Laboratory 3610 Collins Ferry Road P.O. Box 88 Morgantown, WV 26507-0880 304-285-4669 firstname.lastname@example.org Shahab D. Mohaghegh Principal Investigator West Virginia University Research Corporation c/o Sponsored Programs 886 Chestnut Ridge Road P.O. Box 6845 Morgantown, WV 26505-6845 304-293-7682 ext. 3405 email@example.com
DOE Share: $1,344,618.00
Performer Share: $336,508.00
Total Award Value: $1,681,126.00
Performer website: West Virginia University - http://www.wvu.edu
Through its core research and development program administered by the National Energy Technology Laboratory (NETL), the U.S. Department of Energy (DOE) emphasizes monitoring, verification, and accounting (MVA), as well as computer simulation and risk assessment, of possible carbon dioxide (CO2) leakage at CO2 geologic storage sites. MVA efforts focus on the development and deployment of technologies that can provide an accurate accounting of stored CO2, with a high level of confidence that the CO2 will remain stored underground permanently. Effective application of these MVA technologies will ensure the safety of geologic storage projects with respect to both human health and the environment, and can provide the basis for establishing carbon credit trading markets for geologically storing CO2. The new technology is based on the concept of "smart fields," which is rapidly gaining support and popularity in the oil and gas industry. Smart fields integrate digital information technology with the latest monitoring techniques to provide continuous knowledge and control of reservoir operations and processes. Under this concept, hundreds of millions of dollars have been invested to successfully develop highly sensitive PDGs that are capable of operating in harsh environments for long periods. The PDGs collect and transmit high-frequency data streams in real time to remote control centers to be analyzed and used for reservoir management. The project team will use the pattern recognition power of state-of-the-art Artificial Intelligence and Data Mining (AI&DM) technology to develop a methodology, residing in a computer program, to recognize patterns from simulated realistic pressure data acquired from PDGs located within the reservoir model (Figure 1). This software will be capable of, but not limited to, locating point sources within a reservoir from which CO2 is leaking based on changes in pressure data. The methodology will autonomously cleanse and summarize raw data collected from in-situ pressure gauges, to prepare the data for processing and analysis. Upon completion, the methodology will be validated by its ability to accurately identify the location of simulated leakage points in a model of an existing heterogeneous reservoir. Once the approximate location of potential CO2 leakage is identified, the information will be communicated via e-mails, text messages, or other means to those performing "at" or "near" surface monitoring location for more precise detection and analysis. The main objective of this project is to develop the next generation of intelligent software that is able to take maximum advantage of the data collected by PDGs to continuously and autonomously monitor and verify CO2 storage in geologic formations as part of the effort to assure CO2 storage permanence in the subsurface. Further, the project team will investigate the feasibility of using this technology to monitor the growth and advancement of the CO2 plume during the injection process.
Program Background and Project Benefits
It will be necessary to improve existing monitoring technologies, develop novel systems, and protocols to satisfy regulations to track the fate of subsurface CO2 and quantify any emissions from reservoirs. The Carbon Storage Program is sponsoring the development of technologies and protocols by 2020 that are broadly applicable in different geologic storage classes and have sufficient accuracy to account for greater than 99 percent of all injected CO2. If necessary, the tools will support project developers to help quantify emissions from carbon capture, utilization, and storage (CCUS) projects in the unlikely event that CO2 migrates out of the injection zone. Finally, coupled with our increased understanding of these systems and reservoir models, MVA tools will help in the development of one of DOE’s goals to quantify storage capacity within ± 30 percent accuracy. Successful performance of this project will improve the understanding of factors affecting CO2 storage permanence and capacity, and increase confidence that CO2 placed in geologic formations is accurately tracked and that any leakage is detected and quickly treated. Results of the project are expected to be broadly applicable to a variety of geologic storage technologies and projects seeking to model potential CO2 leakage from geologic formations.
To date, researchers have selected two sites (one in Mattoon, Illinois and a second in Citronelle, Alabama) to be used as models in the development of the software.
The following provides summaries of the accomplishments to date for each site:
Mattoon, IL Site
Surrogate Reservoir Models (SRM) were constructed and datasets were generated. The SRMs were built with the purpose of pressure and water saturation prediction with a reasonable accuracy as well as to predict the CO2 mole fraction in the reservoir.
The dataset was divided into two sets due to the pressure and rate turbulence at the beginning. The first data set was generated in the time interval of the first to the seventh month while the time scale for the second dataset was the first to the seventh year. The first to seventh month dataset results showed a maximum error of ~6.5 percent for the water saturation and pressure. The first to seventh year dataset results showed a maximum error of ~3.4 percent for the water saturation and ~3.8 percent error for the pressure case.
Citronelle, AL Site
Heterogeneous porosity and permeability maps were generated for the model using values interpreted from resistivity and induction logs. Two different porosity values were interpreted based on true resistivity and induction logs for 51 simulation layers in 48 wells. Nine different models were built based on multiple geological realizations obtained from the two different porosity distribution and four porosity-permeability correlations.
Simulations were performed in order to generate data streams from the flow model. This was done by modeling an array of slim holes in the reservoir where Permanent Downhole Pressure Gauges (PDPG) might be installed. Then simulated carbon dioxide leakage data recoded during the simulation runs were collected. A method was developed for distorting the data from the pressure readings to emulate background noise inherent to field measurements using techniques such as including random pressure spikes and adding random white noise. Data cleansing routines were incorporated as a data pre-processing step for the high resolution data. These routines had the ability to remove outliers and reduce white noise using mathematical techniques such as moving average.
A study on leakage modeling and simulating in the reservoir was performed. A Leakage Detection System (LDS) was developed using pressure data received in high frequency streams from simulated Permanent Down-hole Gauges (PDG). A set of simulation runs were completed that provided simulated pressure behavior in the observations wells with respect to leakage rates and locations.