- Long known in-situ complexity of hydraulic induced fractures needs to be accounted for if one expects to achieve optimization fracturing designs during field development, continuously in need of economic efficience.
- Current fracturing models are inadequate in predicting potential complex geometries, very important when assessing shadowing, re-fracture, parent-child interference, well and stage spacing, as well as overall design parameters, important for conformance studies, thus improving recovery factors.
- Classical geomechanical approaches are computationally inefficient, unable to handle the number of natural fracture found at the field scale (sometimes high densities), and complicated to use in classical coupling to reservoir models
- Reservoir models also call for complex physics, more or less integrated in dual- porosity representations, inadequate for unconventional reservoirs, pushing approximations which are hard to calibrate.
- DFN models, more accurate for the description of fractures in unconventional reservoirs, are rarely integrated to reservoir models, not offering both complex geometry description and geomechanical deformation under stress at the scale of an SRV created by the stimulation.
- The next reservoir simulator generation, under the double pressure to integrate more and more of the physical effects at play and the use an ever increasing number of cells – especially for discrete fracture models – needs to offer non-invasive, non- disturbing solutions, which adapt to already existing modeling platforms.
This is an evaluation module enabling:
A- The choice of the calibration/ evaluation of the well/wells considered as representative, thus trying to distinguish within the record patterns and characteristic features.
B- The description of all natural reservoir properties participating to the production, based on data analysis at different scales, characterizing at the same time the reservoir needing stimulation and the far field.
C- The analysis of post-stimulation data records, which helps determining how induced fractures participate to the stimulation process.
D- The analysis of dynamic reservoir characteristics specific to unconventional reservoirs needed by the modeling process predicting production
E- The quick evaluation of the proppant design using a an equivalent fluid model.
F- “Cases Library” which are examples of typical stimulation jobs and “Reservoir Data Base” containing various data pertinent either to the HDSfrac or HDSres modules
This 3D Fracture Generator Module integrates natural characterized planar heterogeneities and potential propagating paths, enabling time and space evolutions, justifying the new term « Adaptative Discrete Fracture Network ». Its main features are:
- Multiscale fractures (meter to km) definition and capacity of mixing different support resolution within the same framework (from seismic to core)
- Deterministic, statistical or geostatisticaly distributed fractures (any driver or combinaison of drivers can be used)
- Compatibility with other DFN software (import/export) including the ability to add any missed (or neglected) fracture set
- Connectivity evaluation and backbone extraction
- Logging and geological contraining
- « Propagating Path » distribution analyzed and generated, also acounting for natural fractures.
- Object proximity fracture density parametrization (support free).
- T-Fracture and abutting characteristics corresponding to geological hypothesis
This innovative meshing tool realistically describes complex topologies (Matrix and Fracture) by reducing the number of unknowns while ensuring K-orthogonal links between nodes, thus favoring simpler numerical schemes. Furthermore in case of strenuous conditions (ex: local high pressure gradients), Automated Meshing Refinement methods manage efficiently any compromise between accuracy and computational cost.
This tool allows multiphase reservoir studies containing matrix and up to 500.000 fractures (complexity) within operational time constrains.
This is the stimulation module, which simulates the hydraulic fracturing process within the chosen well, performed for all stages. It serves as a calibration tool through the match of the BHP and microseismic record, leading to a plausible characterization of the effective SRV. It can use data obtained from the HDSeval or data obtained by different studies or methods. Therefore, the tool can be used independently. A methodology leading to this match is indicated.
Its main simulation features are:
- Full 3D geomechanical ADFN (active discrete fracture network) stimulation from which the “true”concept of height and length of the hydraulic fracture can be obtained. Shadowing and both Mode I and Mode II fractures can be simulated. MINC adapted technique accounts for the transient effects, carried out throughout up to reservoir simulator.
- Ability to simulate one or several wells within the zone of interest, including re-fracturing.
- Multiple zone completions and capacity to simulate various inter-well stimulation schemes (ex. Zipper fracs).
- Multiple injection schedules schemes.
- Capacity to simulate stage by stage stimulation.
- Limited entry perforation designs including erosion.
- The ability to generate models using our “Cases Library” which can be used as analogs, including field properties such as Pc, Kr, K-PHI distributions and geomechanical properties classified by facies type in different shale basins.
- Non-invasive capacities with regard to the reservoir modeling tool – adaptable to ECLIPSE, CMG software or any simulator providing the grid location specifications are known.
The deliverable of the tool are:
- A geomechanically stimulated final ADFN, able to be stimulated, corresponding to the effective SRV activated.
- The aperture distribution within the SRV
- The “back-bone” of the activated DFN, corresponding to 80% of the back-flow
- A pressure and leak-off mapping at different times
- The distribution of “proppant” within the full DFN and the “back-bone”.
- A specialized grid corresponding to the ADFN, used for the geomechanical calculations which can be directly used for reservoir simulation (future development) or indirectly linked to any commercially available – transmissivity factors for both fracture and matrix.
This module transfers the grid obtained in HDSfrac (results of the hydraulic fracturing calculation) to any reservoir model (fracture and matrix connections) without any changes in the host reservoir model. This capacity insures the respect of the appropriate overall fracture geometry within the reservoir modeling framework, therefore respecting the production drainage pattern, thus allowing to process any enhanced recovery method (gas injection, heat extraction, EOR, etc…) or re-fracture operation, limited only by the capacities of the model used. The grid passed on to the reservoir model accounts for transient effects, since it includes an adaptation of the MINC method (applied to the ADFN). Once transferred, any subsequent EOR process, production re-scheduling or production scheme re-design can be performed directly within the reservoir simulator, inasmuch as geomechanical effects are not invoked. For re-fracturing, the use of HDSfrac is needed. This module is independent of the other ones, allowing to import any DFN coming from other sources, but transforming it to a non-invasive version. In a future version a specialized reservoir model, including physical processes specific to unconventional reservoirs will be developed. In that version, the ADFN will represent the reservoir grid, on which both geomechanical and flow calculations will be performed, avoiding time consuming on data handling. Therefore, this module offers a double advantage: either the fracturing grid can be exported to a simulator in a non-invasive way or represents the grid for a new specialized simulator which can be used directly to perform both geomechanical and flow simulations.
The main goal of the tools developed is the use of the information obtained by the simulation effort for the purpose of reservoir management in presence of many wells. As such they are similar to the AI (Artificial Intelligence) and Machine Learning techniques recently re-discovered and used to replace or complement simulation efforts. More details can be found in our Blog. One of the main requirements of such techniques is the need for prior existence of a great amount of data used for training and development. Furthermore, the so-called learning is done classically on 70-80% of the field data, which tends to say that statistical learning is assimilated rather than a physical learning. The question is then how to use simulations as early as possible, using only a few wells to infer conclusions on other wells or zones, reducing the simulation time to the maximum – since it is more time consuming. Our answer is two fold. On one hand, we are developing a relational mapping of what is learned by simulation through a double reservoir scale: the stage-well under study and the well-pad relationship. In the early stages, this mapping is meant to guide the user in the choice of characterization, history match parameters, or design choices. These can help the user to “learn” and establish along with its experience, a validated opinion. At later stages, when many wells are analyzed, depending on their analogy (when the user detects some pattern), data generated by this mapping could be used by a machine learning algorithm.