A Practical Simulation Method Capturing Complex Hydraulic-Fracturing Physics
Authors: Carpenter Chris 
Published: September 2016

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 172831, “Forecasting Production in Shale and Tight Reservoirs: A Practical Simulation Method Capturing the Complex Hydraulic-Fracturing Physics,” by Axelle Baroni, Matthieu Delorme, and Nina Khvoenkova, IFP Energies nouvelles, prepared for the 2015 SPE Middle East Oil and Gas Show and Conference, Manama, Bahrain, 8–11 March. The paper has not been peer reviewed.
To understand production from shale reservoirs, the role of hydraulically induced fractures, natural fractures, and their interaction in a formation must be captured. Implementation of such approaches is data-intensive and is accurate only when the field is fully characterized. A model has been developed in which fractures are discretized explicitly and flow is coupled by use of simplified mechanics. It can be considered as an intermediate method between a fully developed discrete-fracture-network (DFN) model and simplified hydraulic-fracturing models.
Introduction
The current view is that many unconventional reservoirs comprise well-developed natural-fracture networks with multiple orientations within which complex hydraulic-fracture patterns develop, as suggested by microseismic data. The unconventional-reservoir modeling process follows a multidisciplinary integrated approach that includes specific aspects of geophysics, geology, laboratory work, and reservoir engineering. To understand the shale-deformation processes, the flow should be coupled with mechanics, yet the complexity of the involved mechanisms governing stimulation modeling precludes very fast simulations.
Characterizing all parameters is a challenge because of the uncertainty of natural- fracture distribution, potential local stress changes, and the lack of reservoir description in three dimensions. The best practice able to capture the most-important phenomena, integrating while being accurately predictive, is not yet defined.
The presented work flow (detailed in Fig. 1 and in the text of the complete paper) focuses on the entire stimulation process, describing a simplified yet physically sound model in a 3D discrete and deformable fracture network (DDFN).
Flow Methodologies
The first step of the work flow is the natural-fracture- network characterization. By use of image and classical logs coupled with other seismic and geological analysis, the natural-fracture model can be constructed. The overall fracture network (natural and hydraulically induced fractures) is defined by one or several fracture families, each being defined deterministically (faults and damaged zone) or by statistical laws (hydraulically induced and natural fractures) for orientation, density, and size. Overall connectivity and fracture hydraulic properties are more uncertain than geometrical ones, usually calibrated from dynamic data. By use of the fracture families’ statistical properties, the discrete network can be generated stochastically (depending on the random seed). Each perforation “shot” is represented by a highly conductive near-well damaged zone of a few meters.