Quantitative production analysis and EUR prediction from unconventional reservoirs using a data-driven drainage volume formulation Academic Article uri icon


  • 2019, Springer Nature Switzerland AG. A novel data-driven approach was previously introduced for production analysis of unconventional reservoirs without the traditional rate transient analysis/pressure transient analysis assumptions. The approach relied on a w() function, which is drainage volume geometry function to characterize the flow geometry from the transient drainage volume. It has been used to rank refracturing candidates and to determine optimal fracture spacing. In this paper, we generalize the previous study to improve the amount of quantitative reservoir information obtained during the production analysis. Our approach is based upon a transient generalization of the Matthews-Brons-Hazebroek definition of the pseudo-steady state drainage volume. It is obtained from an asymptotic solution of the diffusivity equation in heterogeneous and/or fractured media. Given field pressure and flow rate data, we can calculate the transient well drainage volume with time. The time evolution of the drainage volume can be inverted to estimate w() function, which contains information of underlying flow geometries, and which is then used for quantitative analysis. The power and utility of the proposed methodology is first validated with synthetic examples and then demonstrated using a well from the Montney shale. In the examples studied, we are able to identify linear flow, the onset of fracture interference, complex nonlinear flow, and the development of the stimulated reservoir volume (SRV), leading to the quantitative calculation of matrix permeability, fracture surface area, and volume of the SRV. The proposed approach is a data-driven model-free analysis of production data without the presumption of specific flow regimes. It provides a simple and intuitive understanding of the transient drainage volume and instantaneous recovery efficiency, irrespective of the complexity of the reservoir depletion geometry. We show an improved approach for the w() inversion which yields better physical resolution and which can identify more detailed characteristics of the underlying flow geometry than previous studies, e.g., complex near-fracture flow, linear flow, and fracture interference. The results of the analysis have been used for the characterization of hydraulic fracture and reservoir properties, including the prediction of fracture surface area, matrix permeability, and SRV, and extended to the calculation of estimated ultimate recovery.

published proceedings

  • Computational Geosciences

author list (cited authors)

  • Wang, Z., Malone, A., & King, M. J.

citation count

  • 10

complete list of authors

  • Wang, Zhenzhen||Malone, Andrew||King, Michael J

publication date

  • April 2020