Characterization of low-frequency distributed acoustic sensing signals in hydraulic fracturing stimulation – A coupled flow-geomechanical simulation approach
Bibliographic record
Abstract
Low-frequency distributed acoustic sensing (LF-DAS) is one of the promising diagnostic techniques for detecting and characterizing hydraulic fractures. LF-DAS signals can capture fracture hits and the strain field around the hydraulic fracture. However, the interpretation of field LF-DAS data can be challenging due to the complexity of the underground conditions. This study develops a fracture propagation model to simulate the hydraulic fracturing process. The modelling results are analyzed to examine patterns and trends observed in interpreting field LF-DAS data. The fracture propagation model, coupled with the flow and geomechanical computations, is implemented in the MATLAB Reservoir Simulation Toolbox (MRST). The flow and geomechanical calculations are discretized by the finite volume and the virtual element methods, respectively. The hydraulic fracture is set to propagate along a prescribed path with a specific propagation or activation criterion. The accuracy of our model is validated against the KGD analytical solutions for the leak-off-viscosity, storage-viscosity and leak-off-toughness dominated regimes. The simulated stress and strain features are consistent with those interpreted from field LF-DAS signals. Several case studies and sensitivity analyses demonstrate the approach's utility and examine fracture interference, closure, and stress shadowing effects. The modelling work facilitates interpreting field measurement data by investigating characteristics of fracture hits from adjacent wells. The modelling method provides insights into fracture interference and its implications on optimal designs during hydraulic fracturing stimulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".