Interpreting Uncemented Multistage Hydraulic-Fracturing Completion Effectiveness By Use of Fiber-Optic DTS Injection Data
Bibliographic record
Abstract
Summary Tight gas, low-permeability reservoirs offer a tremendous challenge with respect to effectively completing and draining a target reservoir. Openhole-packer completions in horizontal wells offer a cost-efficient means of accessing the entire lateral section, assuming the target pay can be effectively stimulated. The challenge with openhole completions compared with more-conventional cased, cemented, and limited-entry perforated completions is understanding and controlling hydraulic-fracture geometry—specifically, the number and location of fracture-initiation points and the fracturing-fluid flow into the near-wellbore (NWB) region of the reservoir. Fiber-optic-based distributed temperature sensing (DTS) offers a method for identifying, quantifying, and evaluating the NWB fracture geometry, the fracturing-fluid distribution in these broad openhole sections, and overall stimulation effectiveness. DTS can also reveal success or issues with respect to effective zonal isolation when using mechanical isolation during the hydraulic-fracturing process. In this particular case study, a lateral well in a basin-centered gas area was completed with swell-packer interval isolation by use of fracture sleeves for reservoir access. By coupling fracture-treatment responses and openhole log characteristics with the NWB DTS data during pumping and warm back, an integrated assessment of the completion stimulation effectiveness and efficiency was performed. The end result of this assessment provided an improved understanding of the current completion performance and allowed optimization of openhole completion projects for future wells in this same area.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".