  Currently in the petroleum industry, operators often flare the produced gas
instead of commodifying it. The flaring magnitudes are large in some states,
which constitute problems with energy waste and CO2 emissions. In North Dakota,
operators are required to estimate and report the volume flared. The questions
are, how good is the quality of this reporting, and what insights can be drawn
from it? Apart from the company-reported statistics, which are available from
the North Dakota Industrial Commission (NDIC), flared volumes can be estimated
via satellite remote sensing, serving as an unbiased benchmark. Since
interpretation of the Landsat 8 imagery is hindered by artifacts due to glow,
the estimated volumes based on the Visible Infrared Imaging Radiometer Suite
(VIIRS) are used. Reverse geocoding is performed for comparing and contrasting
the NDIC and VIIRS data at different levels, such as county and oilfield. With
all the data gathered and preprocessed, Bayesian learning implemented by MCMC
methods is performed to address three problems: county level model development,
flaring time series analytics, and distribution estimation. First, there is
heterogeneity among the different counties, in the associations between the
NDIC and VIIRS volumes. In light of such, models are developed for each county
by exploiting hierarchical models. Second, the flaring time series, albeit
noisy, contains information regarding trends and patterns, which provide some
insights into operator approaches. Gaussian processes are found to be effective
in many different pattern recognition scenarios. Third, distributional insights
are obtained through unsupervised learning. The negative binomial and GMMs are
found to effectively describe the oilfield flare count and flared volume
distributions, respectively. Finally, a nearest-neighbor-based approach for
operator level monitoring and analytics is introduced.
