Part 1: Empirical Workflow for Predicting Infill Performance in the Marcellus

Chin, Adam (NCS Multistage) | Staruiala, Adam (NCS Multistage) | Behmanesh, Hamid (NCS Multistage) | Anderson, David (NCS Multistage) | Alonzo, Christopher (NCS Multistage) | Jones, David (Chesapeake Energy) | Barraza, Saul Rivera (Chesapeake Energy) | Lasecki, Leo (Chesapeake Energy) | McBride, Kyle (Chesapeake Energy)



As most of the major unconventional plays in North America are well into the development phase, optimizing infill development is a central focus for most operators. There are numerous case studies where operators have invested in several different technologies (micro-seismic, tracers, fiber optics, interference tests, etc.) to try and better understand and optimize child well performance (Jaripatke et al. 2018, Kumar et al. 2018, Manchanda et al. 2018). These case studies have revealed many phenomena which can impact infill performance including depletion, asymmetric fracture growth, and geo-mechanical effects. These factors frequently lead to less productive infill wells. It is not uncommon for child wells to under-perform their type well, which is often generated based on parent performance, and potentially scaled up to account for modern completion design. This is a major issue for operators when it comes to booking reserves and allocating capital to the most economic projects. In order to help address this issue, this paper proposes an analytical workflow designed to account for the adverse impacts that depletion and geo-mechanical effects may have on a child well, generating probabilistic forecasts that accurately predict a range of outcomes that the child well is capable of.

The physics that govern the interactions between parent and child wells are very complex. Attempting to model these physics by integrating a geo-model, hydraulic fracture modeling, geo-mechanics and reservoir simulation is a massive time investment and requires a high degree of technical expertise in several domains. Furthermore, each of these exercises comes with a set of assumptions, the impacts of which can be compounded upon integration. The focus of this paper is to present a practical workflow that can be used to predict child well performance. This workflow has been applied to a set of wells in the Marcellus. The first iteration of the workflow accounted for the effects of pressure depletion on initial reservoir pressure determination, however it over-predicted the infill type curve. This led to a second iteration of the workflow which incorporated the impact of geo-mechanical effects on the cluster efficiency of the child well. This refined workflow was validated using several parent-child datasets from the study area, as well as additional data from surrounding areas. This workflow incorporates empirical field data to tune certain inputs, making it adaptable to different formations.