URTeC: 2754 Maximizing Project Value in Vaca Muerta Shale Formation – Part 1: Optimizing High Density Completions – Case of Study Alejandro Lerza* 1 , Sergio Cuervo 1 , Sahil Malhotra 1 , 1. Chevron Corporation – YPF Joint Venture. Copyright 2020, Unconventional Resources Technology Conference (URTeC) DOI 10.15530/urtec-2020-2754 This paper was prepared for presentation at the Unconventional Resources Technology Conference held in Austin, Texas, USA, 20-22 July 2020. The URTeC Technical Program Committee accepted this presentation on the basis of information contained in an abstract submitted by the author(s). The contents of this paper have not been reviewed by URTeC and URTeC does not warrant the accuracy, reliability, or timeliness of any information herein. All information is the responsibility of, and, is subject to corrections by the author(s). Any person or entity that relies on any information obtained from this paper does so at their own risk. The information herein does not necessarily reflect any position of URTeC. Any reproduction, distribution, or storage of any part of this paper by anyone other than the author without the written consent of URTeC is prohibited. Abstract When reviewing the evolution of completion designs during the last 5 years in the different unconventional plays located in United States and Canada, a common trend is clearly shown: the industry has continuously increased the number of clusters and the amount of sand and water pumped per frac stage, while simultaneously reducing the distance between clusters. This “new” type of completion design is commonly called High Density Completions (HDC), and its objective is to improve project’s Net Present Value (NPV) per acre, by maximizing the amount of fracture area generated per well, while maintaining the fractures as close to the well as possible (small fracture length), which allows an increase in recovery per well without generating undesired well interference events. However, deciding what is the optimal completion design from the large number of possible alternatives, is not a trivial task, and is precisely one of the reasons why operators tend to either conduct simulation studies to assess the value of a handful of pre-selected completion alternatives, or closely follow the production results obtained by the different completion designs used by their neighbors, and depending on the analysis results, they might end up testing or even directly implementing such completion alternative as their new standard. Although the previously mentioned methods for improving completion designs has proven to be very effective and supportive of the typical industry fast pace operations, they are both highly constrained to number of the alternatives analyzed (in case of the simulation) or by the ones previously tested in the area, thus, it is not a real optimization but a completion design improvement or enhancement instead, since full universe of possible alternatives is not considered before selecting the one generating the highest value. Furthermore, the well spacing aspect of this complex problem, and its interaction with completion designs, is typically not considered, which results in the incorrect assumption regarding the range of application of the new “optimized” design. This paper introduces a state of the art methodology for optimizing completions designs while considering the interactions between all the variables simultaneously in unconventional plays, which was tested in the Vaca Muerta shale formation, located in Argentinean Neuquen Basin, when the first basin HDC design was developed, selected and tested. The introduced methodology consists on leveraging simulation technology, Design of Experiments (DoE) technique, Monte Carlo simulation (MC) and economic analysis, to calculate the expected production response for hundred thousand different completion designs, assessing the expected impact of each completion variable, and selecting the true optimal completion alternative from the all physically possible suit of solutions based on the desired value driver metric. Selected optimal