Abstract
This paper uses a case study to describe an approach for optimizing the economic development of one of the most prolific areas in the Marcellus shale, located in Bradford County, PA. A project was built to optimize well spacing, landing, completion design, and conduct uncertainty evaluations for upcoming pad developments by utilizing a fully integrated hydraulic fracturing, reservoir, and geomechanics numerical simulator. One of the key questions addressed in this study was whether single or multiple benches are optimal for maximizing economic value.
Our structured modeling process is: (a) define the area of influence, (b) identify and model the physics of a pad representative of the area of influence, (c) establish history match objectives and input parameter uncertainty ranges, (d) calibrate the model to selected objectives, (e) employ a multigenerational algorithm for economic development optimization, and (f) evaluate potential outcomes using a Monte Carlo uncertainty analysis. This multidisciplinary effort integrates static/dynamic data, completion diagnostics, and Marcellus formation production. The fully-coupled 3D model allows for physics-based estimates of fracture propagation, production, and economics for each scenario. Robust optimization ensures our development aligns with current industry and gas market dynamics.
The current case study successfully matched fracture and production observations for a five well pad, including a parent-child situation, which had almost ten years of production data. Proper pad selection, calibration, and uncertainty analysis, grounded in a physics-based model, provided a robust understanding of reservoir architecture and performance. In our specific case, the pads developed in the area consisted only of a single bench, and despite partial information in the calibration data, our evaluation yielded multiple attractive economic development options. Findings from this model were applied to other pads in the area in an iterative process of refining models. We treat simulation models as evergreen and continue to re-evaluate and update as new field data are available.
The case study presented showcases an effective methodology for optimizing unconventional reservoir development. Our integrated modeling approach, incorporating geology and dynamic data, enriches our reservoir characterization and is a powerful tool for economic reservoir optimization. The multi-bench evaluation and criteria for pad selection is an added utility of our learnings. The novelty lies in the multi-disciplinary phase project design, ensuring a comprehensive and iterative optimization process. This methodology may be applied broadly to enhance stimulation strategies and optimize pad development in unconventional reservoirs.
