Researchers at University of Louisiana at Lafayette (ULL), and their partners from Missouri University of Science and Technology and Schlumberger Carbon Services, will use novel statistical-neural genetic algorithm methods to study the leakage risks for wells exposed to CO2. At a typical CO2 injection site, the CO2 plume could reach abandoned and active wells. There is an increased risk of CO2 leakage through these wells, particularly if the well construction quality is not well known. In many cases, construction information for older wells is limited or does not exist. The methodology and output of this study may be used to evaluate the leakage risks from existing wells at current and future CO2 storage sites, as well as older wells where all of the well construction data is not available by comparing similar well attributes and risks between similar types of wells.
This study will first develop a comprehensive database of the wells in the Texas Gulf Coast area using electronic records that exist in the Railroad Commission of Texas, in addition to hard copy and microfilm data of older wells that exist in other government agency and private files. The database will be fed into a novel hybrid model of a neural-genetic algorithm, being developed as part of this project, to perform risk analysis across the database, and identify wells that should be subjected to remedial action. Statistical analyses will be performed by the model on general well attributes such as classification, well type, well construction details and materials, location, geology, geomechanical properties, wireline log data, mechanical integrity test data, plugging and abandonment reports, CO2 exposure duration, and reported well integrity problems of the wells. Multivariate statistical techniques, such as factor, regression and cluster, shall be used to analyze the database. In addition, the model output will be verified by a combination of wireline logs, core sampling, and testing that includes X-ray and scanning electron microscopy, cement logging, pH, and fluids testing.
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 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 project will analyze industry and regulatory historical well data, develop a software program that estimates risk, utilize the program and historical well data to predict the risk of long term wellbore leakage based on similar well attributes, and perform field studies to confirm that the identified wells are leaking.
The project makes a vital contribution to the scientific, technical, and institutional knowledge base, needed to establish frameworks for the development of commercial-scale carbon capture and storage (CCS). An analysis will be conducted of available industry and regulatory agency-based data to assess risks of well failure by various factors, such as age of construction, region, construction materials, incident reports, logging and mechanical integrity testing. Specifically, this project will help meet Carbon Storage Program goals of ensuring 99 percent storage permanence, and the development of Best Practices Manuals.
The project team will analyze industry and regulatory well data to develop a well construction database as an input to a software program that estimates risk of long term wellbore leakage based on similar well attributes. Specific project objectives will include:
The creation of likely leakage scenarios for specific well attributes.
The compilation of a wellbore database from existing wells from the Gulf Coast area in Texas and statistical analysis of the data.
The development of a neural-genetic algorithm model which predicts leakage risk for wells based on the Gulf Coast area in Texas (Figure 1).
The verification of model results by conducting field sampling including sidewall core samples, pressure testing data, and well logs of existing wells and the comparison of those results with the model.
The main objective of this project is the development of a novel hybrid model, utilizing a neural-genetic algorithm which can be used to predict long term leakage risks for wells exposed to CO2. The model is fed with data from wells in the Texas Gulf Coast area that have been subject to statistical analysis for key parameters listed above.
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