Abstract
We demonstrate that although barometric pressures are complicated signals comprised of numerous frequencies, it is a subset of these frequencies that drive the overwhelming majority of gas transport in fractured rock. Using an inverse numerical analysis, we demonstrate that a single barometric component with seasonally modulated amplitude approximates gas transport due to a measured barometric signal. If past barometric tendencies are expected to continue at a location, the identification of this frequency can facilitate accurate long term predictions of barometrically induced gas transport negating the need to consider stochastic realizations of future barometric variations. Additionally, we perform an analytical analysis that indicates that there is a set of barometric frequencies, consistent with the inverse numerical analysis, with high production efficiency. Based on the corroborating inverse numerical and analytical analyses, we conclude that there is a set of dominant gas transport frequencies in barometric records.
Highlights
Predictions of gas transport can be improved for many applications by better understanding the effect of barometric variations on gas transport in fractured rock
Many previous researchers have assumed that barometric variations due to roughly weekly weather patterns are important frequencies to consider for gas transport in fractured rock
From this we identify a set of highly efficient frequencies for producing gas that are clustered around the frequency identified in the inverse numerical analysis
Summary
We present an inverse analysis using a numerical model of gas transport within a fractured domain demonstrating that a dominant transport frequency can be identified that reproduces gas transport simulated using the measured barometric pressures. We perform an analytical analysis of gas production efficiency using the decomposed barometric pressure record. From this we identify a set of highly efficient frequencies for producing gas that are clustered around the frequency identified in the inverse numerical analysis. We provide conclusions based on the corroborating evidence from the inverse numerical and analytical analyses
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