This paper summarizes results from a collaborative industry study involving seven operators and ten pad-scale datasets across four different shale plays (Delaware, Midland, Bakken, and Montney). This paper specifically focuses on a subset of the datasets from the Midland and Delaware Basins. The project had three main objectives: (a) compare/contrast observations between datasets and basins, (b) develop general insights into parent/child interactions and techniques for mitigating impact, and (c) provide customized economic optimization recommendations for each individual operator and dataset. Each dataset was a high quality ‘science pad,’ involving a full suite of high-quality diagnostics. For each dataset, the observations were synthesized into lists of ‘key observations’ that describe key properties, such as: (a) fracture length and height, (b) productive/drained length and height, (c) GOR, water cut, and RTA trends, (d) production allocations by layer, (e) parent well response to the frac hits, (f) relative performance of the wells, and (g) perforation efficiency and erosion. Next, using an integrated hydraulic fracturing and reservoir simulator, numerical models were constructed to match the key observations of each dataset. The history matching process involved varying formation properties such as permeability, relative permeability, and fracture toughness. After all the models were calibrated, a set of roughly 120 sensitivity analysis simulations was performed on each model. The results were interrogated to identify trends – similarities and differences between the datasets – and to explain ‘what happened and why.’ Finally, the calibrated models were utilized by an automated economic optimization algorithm to provide recommendations for future operations. The simulations do not reveal a ‘silver bullet’ for avoiding parent/child effects. However, they do identify specific changes that could improve performance. 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.
