Hydraulic fracturing is today a standard when developing unconventional reservoir plays. This is studied through different models, based on a great deal of characterization data gathering and analysis. Unfortunately, numerical limitations impose drastic simplifications (number of fractures, some data being ignored…) leading to simple fracture geometries, lacking observed complexity. This limits any design optimization expectation. Our objective is to show that calibration data used for simpler models, along microseismic measurements, can lead to more realistic hydraulic fracturing geometries. Results can be linked to a reservoir platform, forecasting production. The presented computationally efficient method, within which sensitivity is performed, highlights key parameters governing the stimulation process. This study shows that the tool used is tailored for practical scenario design and evaluation.
The method used to generate realistic fracture geometries implies information at all scales (seismic, log, cores…) as well as numerical tools able to handle geomechanics and fluid flow, over a great number of fractures (as required by the characterization). Thus all data is input into one 3D Representative Deformable Discrete Fracture Network (DDFN), simulating the hydraulic stimulation. Characterization is based on geostatistical concepts applied to both natural and hydraulically induced fractures, driven by geological and geomechanical data. The process is simulated using a one phase hydrodynamic model within the DDFN (specific discretization) under far stress conditions. Fractures behavior is governed by geomechanical laws, reversible and non-reversible, with an approximate proppant model. Various scenarios are tested according to either geomechanical uncertain parameters, or characterization ones. Observed in-situ Bottom Hole Pressure (BHP) and microseismic characteristics (shape, frequency…) are then history-matched.
For each simulated scenario, quality of the history match is shown and discussed, stressing the representativity of the data involved.
The method has shown to be computationally efficient and robust enough to support hundred thousands of fractures while at the same time being able to simulate simpler cases. Also, within the studied framework, ties with already existing reservoir platform are shown. Advantages of such an approach are highlighted including current limitations of classical reservoir models.
This work undergone at different scales demonstrate the new possibilities of computational robust algorithms, within an approach considering both geological settings and geomechanical properties. The model offers the possibility to integrate several scales to an adaptive discretization scheme.