Statistical Approaches to Quantifying Uncertainty of Monitoring and Performance at Geologic CO2 Storage Sites

<p>Geologic carbon dioxide (CO2) storage is one approach for mitigating concentrations of CO2 in the atmosphere that are caused by stationary anthropogenic inputs. Injecting CO2 into the subsurface for long-term storage is an “engineered-natural system”. This engineered-natural system is complex, with potential interactions during CO2 injection between CO2 and other reservoir fluids and various components of the geologic system. The National Risk Assessment Partnership (NRAP) is an initiative within DOE’s Office of Fossil Energy that is improving the fundamental understanding of the complex science behind engineered-natural systems and is developing the risk assessment tools that are needed for safe, permanent geologic CO2 storage. The NRAP technical approach entails an iterative modeling process that integrates component models into a system model which may then be used to provide quantitative assessments of potential risks and to design monitoring protocols that will effectively monitor risks at a geologic CO2 storage project. A theme throughout all phases of the NRAP approach is quantifying uncertainty and variability. The focus of this dissertation is to contribute statistical methods and/or approaches for quantifying uncertainty and variability with respect to both monitoring and performance at geologic CO2 storage sites. These methods are intended for future use by NRAP or other geologic CO2 storage practitioners and may be incorporated into broader modeling approaches. However, the results and contributions from this work extend beyond geologic CO2 storage and apply to other subsurface engineered-natural systems.</p

CO2 Enhanced Oil Recovery Life Cycle Analysis Model (Rev. 2)

In “How green is my oil?” by Azzolina et al., the authors presented an integrated life-cycle model for CO2-EOR where the CO2 is sourced from a coal-fired power plant. The model was developed entirely in Microsoft Excel® to improve transparency and provide a useful tool for other practitioners. This model is an updated version of the model from the article. The cells have been unlocked so they can be modified. Azzolina, N.A., Peck, W.D., Hamling, J.A., Gorecki, C.D., Ayash, S.C., Doll, T.E., Nakles, D.V., and Melzer, L.S., 2016, How green is my oil? a detailed look at greenhouse gas accounting for CO2-enhanced oil recovery (CO2-EOR) sites: International Journal of Greenhouse Gas Control, v. 51, p. 369–379. DOI: /10.1016/j.ijggc.2016.06.008. Acknowledgment: This material is based upon work supported by the U.S. Department of Energy National Energy Technology Laboratory under Award Number DE-FC26-05NT42592.https://commons.und.edu/eerc-publications/1000/thumbnail.jp

Quantifying the Benefit of Wellbore Leakage Potential Estimates for Prioritizing Long-Term MVA Well Sampling at a CO₂ Storage Site

This work uses probabilistic methods to simulate a hypothetical geologic CO₂ storage site in a depleted oil and gas field, where the large number of legacy wells would make it cost-prohibitive to sample all wells for all measurements as part of the postinjection site care. Deep well leakage potential scores were assigned to the wells using a random subsample of 100 wells from a detailed study of 826 legacy wells that penetrate the basal Cambrian formation on the U.S. side of the U.S./Canadian border. Analytical solutions and Monte Carlo simulations were used to quantify the statistical power of selecting a leaking well. Power curves were developed as a function of (1) the number of leaking wells within the Area of Review; (2) the sampling design (random or judgmental, choosing first the wells with the highest deep leakage potential scores); (3) the number of wells included in the monitoring sampling plan; and (4) the relationship between a well’s leakage potential score and its relative probability of leakage. Cases where the deep well leakage potential scores are fully or partially informative of the relative leakage probability are compared to a noninformative base case in which leakage is equiprobable across all wells in the Area of Review. The results show that accurate prior knowledge about the probability of well leakage adds measurable value to the ability to detect a leaking well during the monitoring program, and that the loss in detection ability due to imperfect knowledge of the leakage probability can be quantified. This work underscores the importance of a data-driven, risk-based monitoring program that incorporates uncertainty quantification into long-term monitoring sampling plans at geologic CO₂ storage sites