The majority of the technically recoverable natural gas is present in unconventional reservoirs such as tight sands, shale, and coal beds. On account of the implicit high costs associated with these unconventional and deep gas assets, it is of paramount importance to garner as much knowledge from potentially disparate and limited data sources. The objectives must be minimization of drilling and completion times, constraint of risks, quantification of uncertainty and ultimately maximization of the lifetime of a well’s productivity.
This paper illustrates an advanced analytical methodology that aggregates and integrates data from a multitude of sources, performs a robust quality control, correlates and discovers patterns to enable engineers to determine more efficient and accurate exploitation strategies. It proposes an approach based on an advanced analytical framework that introduces an exploratory data analysis step for reservoir characterization, followed by determination of key production indicators and multivariate methodologies to classify wells. With improved understanding of inherent correlation between geology, reservoir, rock mechanics, frackpack process and proppant fluid and well performance, it is feasible to cluster the wells in accordance to the overriding production indicators, thus dividing the field into clearly defined segments. The statistical output enables mapping via a transparent classification of best possible wells to known geology and reservoir conditions to identify and locate poorly-drained zones and effectively neccessitate less drilling to achieve production targets.