1 research outputs found
Quantifying the Benefit of Wellbore Leakage Potential Estimates for Prioritizing Long-Term MVA Well Sampling at a CO<sub>2</sub> Storage Site
This
work uses probabilistic methods to simulate a hypothetical
geologic CO<sub>2</sub> 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 CO<sub>2</sub> storage sites