Fluid flow and heat transfer during staged multi-cluster fracturing treatments along horizontal wells — Application for hydraulic fracture characterization using distributed temperature sensing
Bibliographic record
Abstract
We present a technique for quantitatively characterizing fracture parameters during fracturing operation using temperature information recorded by distributed temperature sensing (DTS). A coupled thermo-hydraulic forward model is first developed to describe the fluid flow and heat transfer in the wellbore, fracture, and reservoir. The developed model is solved using the finite-difference approach for both injection and shut-in periods of staged multi-cluster fracturing treatments along horizontal wells. Then, the DTS temperature behavior is studied by conducting a sensitivity analysis of essential parameters. The results show that temperature signals capture changes in the fracture, reservoir, wellbore, and operation parameters, demonstrating DTS temperature data's feasibility in diagnosing fracture properties. The results also indicate that the temperature response at fracture locations shows a V-shape characteristic for both injection and shut-in periods, aiding in identifying the locations of the created fractures. The proposed model integrated with the Genetic Algorithm is applied to interpret DTS data from a shale gas reservoir, providing parameters like injection volume, fracture locations, fracture half-length, and leak-off coefficient at one particular time. These results enhance new insights on utilizing temperature data for fracturing optimization and further improve energy extraction performance from the stimulated reservoirs.
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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 | 0.000 |
| 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 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".