A New Numerical Methodology for Shale Reservoir Performance Evaluation
Authors: Mongalvy V. , Chaput E. , Agarwal S. , Lu L. 
Published: September 2011

Reservoir engineering for shale gas and shale oil plays is a new discipline requiring tailored workflows and tools to model physical specificities and to quantify uncertainties. Though decline cure analysis (DCA) is commonly used for evaluating Estimated Ultimate Recovery (EUR) when production data are available, it does not allow extrapolating results to varying geological or completion conditions, optimizing play development, and does not address quantification of associated uncertainties of particular significance at early development stages when only little production data is available.

The paper presents a dedicated well performance modeling workflow (named SHARP for "Shale Reservoir Performance") developed to specifically address these issues. SHARP combines a specific "3□ 3k 3S" sector model with an uncertainty platform. Whereas conventional approaches attempt to investigate uncertainty around a base-case solution, SHARP considers all key variables, either natural or related to hydraulic fracturing, as unknowns. Embedded experimental design plans are used to identify parameters governing well performance, and the solution space is then screened exhaustively to identify an array of possible History Match solutions. Input constraints, integrating all available information from dynamic to micro-seismic and petrophysical data, are used as prior knowledge to narrow down the solution space. Once semi-automated history-match is achieved, the model is used in predictive mode to address well and development optimization within the residual uncertainty space.

SHARP is used for well performance history match, EUR estimation, upsides estimation, development optimization (spacing, fracturing optimization) and for phenomenological understanding. It allows quantification of uncertainty associated with these tasks. These applications are illustrated with a real case study on Barnett shale data from the public domain.

The example shows: (a) the relative influence of systems such as propped fractures, induced un-propped fracture network, matrix petrophysics, stimulated and unstimulated rock volumes on well performance, thereby allowing to optimize operational development and completion strategies in a context of uncertainty, and (b) drainage areas, recovery factors per system and contributions of the different systems to production through time.

SHARP has been used in various in-house studies, covering projects of various maturity levels. At exploration stage, it can help assessing the potential and risks of a business case with enhanced confidence. At development stage, it provides insights on optimization opportunities through identification and better understanding of governing dynamics. Finally, it helps structure and share the knowledge between the different entities involved and accelerate the learning curve.

After the introduction, this paper presents the SHARP concept and gives a brief summary of the current understandings of transport physics in gas shale. We then discuss and attempt to quantify uncertainty and approximations required with the usage of commercial reservoir simulators. Thereafter, the "3 □ 3k 3S" concept model is presented, and initialized to suit the Barnett case study. The methodology leading to the exhaustive set of history matching solutions is described, and finally results are analyzed.