Prediction of SRV and Optimization of Fracturing in Tight Gas and Shale Using a Fully Elasto-Plastic Coupled Geomechanical Model
Bibliographic record
Abstract
Abstract Hydraulic fracturing is essential for economical development of tight gas and shale gas reservoirs. Current techniques are unable to predict the SRV dependence on frac job and rock mechanics parameters, which precludes any meaningful optimization. In our previous work on the SRV propagation prediction by means of combined tensile/shear fracturing model, the applications showed relatively narrow, focused SRV that resembled behavior dominated by a single fracture. In this work, we significantly improved the model by implementing rigorous full Newton elasto-plastic solution of fracturing problems by pseudo-continuum technique. The results reveal interesting features of complex fracturing occurring in tight formations, which are in broad agreement with the shapes of SRV’s obtained from microseismic imaging. Flexibility of the code to select either tensile or shear fracturing mechanism or combination of both allows various scenarios to be examined. Different cases of 2-D and 3-D simulations will be presented which demonstrate some important features of the process. First, it is found that a wide SRV can result in cases where initial reservoir conditions are close to shear fracturing point such as in formations with microfractures, partially cemented natural fractures and abnormally high initial pore pressure. The SRV width is found to depend on the horizontal stress contrast as expected. Second, wide SRV growth is associated with constant or increasing pumping pressure for further failed zone growth due to the loss of elastic coupling by off-planar failure propagation. Further, under high injection pressure, an efficient fracture elasto-plastic constitutive model developed can drive both maximum and minimum effective stresses to zero or tensile and therefore creation of tensile fracture can be predicted simultaneously with shear fracturing. This will then provide means of modeling proppant transport. The new model is a significant step towards development of an integrated predictive tool for the optimization of shale gas development.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".