Evaluating the well performance obtained by different completion designs has been typically restricted to the observed production outcome and its associated completion cost. However, given the multiple factors impacting well productivity in shale and tight reservoirs (S&T), it has been historically challenging to determine, with high certainty, the economic advantage of one design over another. The presented case of study approaches this subject in a probabilistic manner by increasing the sample size being considered and simultaneously leveraging a modern Rate Transient Analysis (RTA) methodology that better describes the developed fracture network by accounting for exhibited "atypical" flow regimes and previously observed hydraulic fracture geometries noted as fracture swarms. The characteristic parameters and Expected Ultimate Recovery (EUR) obtained for each of the 168 wells this method was applied to, were grouped by bench, neighborhood, and completion design, conforming the distribution of values used for the statistical inferences created. Finally, combining the information obtained from the characteristic parameter values and the EUR, with the different completion cost, a statistically robust recommendation is made regarding the economic convenience of the different completion designs.


The Vaca Muerta Formation, located in Neuquen basin of Argentina, has arguably become during the last decade, the most active unconventional hub outside North America. Although Permian and Eagle Ford basins have both significantly higher level of activity, well productivity in Vaca Muerta has proven to be comparable to US plays (Wood Mackenzie, 2020). As any other unconventional development, S&T projects targeting the Vaca Muerta Formation are continuously aiming to improve their overall performance by increasing focus in optimizing high economic impact variables; and, given the duo of well spacing and stimulation design determine the overall hydrocarbon recovery and constitute a significant portion of the development cost, they could arguably be considered as the most important decisions impacting the economics of these S&T developments (Malhotra et al., 2021).

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