A Novel Hydraulic Fracturing Model Fully Coupled With Geomechanics and Reservoir Simulator
Bibliographic record
Abstract
Abstract Unconventional fracturing techniques, such as high rate waterfracs, waterflooding or steam stimulation, produced water and cuttings re-injection, CO2 sequestration, N2, CBM stimulation etc., are difficult to model because of strong interactions among the fracturing process, geomechanical changes in the porous media, and reservoir fluid flow. The resulting strong poroelastic/thermoelastic effects, permeability/porosity changes, and possible rock failure (shear fracturing) make current conventional fracturing models inadequate in such circumstances. Therefore, it is necessary to develop new models which include all of these mechanisms and which are capable of integrated data analysis. This paper presents a new fracturing model with all of these mechanisms included. The model fully couples fracture mechanics with reservoir and geomechanics simulation. This methodology allows us to model fracture initialization and propagation, post-frac multiphase cleanup in the reservoir and fracture, and pre- and post-frac well performance in a changing stress and pressure environment, all within the same system. The model couples a three-dimensional (3-D) finite element geomechanics model with a conventional 3-D finite difference reservoir flow simulator. The geomechanics module implicitly models fracture propagation via displacements on the fracture face. The flow and geomechanics/fracturing are coupled in an iterative manner that is equivalent to full coupling of geomechanical modeling. The 3-D (planar) fracture geometry and pressure are the common dynamic boundary conditions for the flow and stress modules. The new iterative process yields smooth fracture propagation, and the model has been tested on classical fracturing problems. A field example where we model a waterfrac stimulation performed in Bossier tight gas sands is presented to demonstrate the models' capabilities as well as the validity and advantages of the approach. The results show that the model is capable of matching a complex history of injections and calibrating the stress-dependency of formation permeability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".