Study of Permian Basin Provides Insight Into Parent-/Child-Well Interactions

Abstract

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 211899, “Results From a Collaborative Parent/Child Industry Study: Permian Basin,” by Mark McClure, SPE, ResFrac; Magdalene Albrecht, SM Energy; and Carl Bernet, Ovintiv, et al. The paper has not been peer reviewed. _ The complete paper summarizes results from a collaborative industry study involving seven operators and 10 pad-scale data sets across four different shale plays. The paper specifically focuses on a subset of the data sets from the Midland and Delaware Basins. The project had three main objectives: compare and contrast observations between data sets and basins, develop general insights into parent- and child-well interactions, and provide customized economic optimization recommendations for each individual operator and data set. The simulations reveal that economic performance can be optimized with customized selection of well spacing, job size, and landing depth based on each company’s objectives and price deck. Methods Modeling Approach. The simulations were performed with a fully integrated hydraulic fracturing, wellbore, geomechanics, and reservoir simulator. The entire life cycle of the wells is captured in a single continuous simulation. The fractures are meshed as true cracks, with apertures on the order of microns to millimeters. Constitutive relations are used to capture transitions from being mechanically open to mechanically closed and vice versa. The simulations are fully 3D, and the equations are solved with a fully coupled scheme. For efficiency, the simulations include only one or a few stages along the wells. Fracture geometry is assumed to be planar, with no more than one dominant hydraulic fracture strand propagating per cluster. The modeling approach is based on the industry’s emerging recognition that hydraulic fractures in shale are planar at large scale, while being rough and complex at small scale. In the simulator, constitutive relations are used to account for the effect of small-scale complexity on processes such as proppant transport, fluid leakoff, and fracture propagation. Modeling Work Flow. For each data set, the modeling process consisted of the following steps: 1. Gather and organize data. 2. Create an initial model based on this data. 3. Create a list of key observations from the data set that constitute the objectives for the history-matching process. 4. Present the initial model and the list of key observations to stakeholders to obtain feedback and check communication. 5. Vary simulation input parameters to match the key observations while frequently communicating with stakeholders. 6. After finalizing the history match, perform a set of sensitivity analysis simulations, varying parameters such as well spacing and parent-well age. 7. Set up an economics model and perform a quantitative optimization of net present value (NPV)/section or discounted return on investment [DROI; defined as time-discounted revenue minus time-discounted operating expense divided by time-discounted capital expenditure (CAPEX)]. The steps of this work flow are detailed in the complete paper.