Real-Time Analysis of Formation-Face Pressures in Acid-Fracturing Treatments
Bibliographic record
Abstract
Summary Knowledge of fracture-entry pressures or formation-face pressures (FFPs) during acid-fracturing treatments in real-time mode can help in evaluating the effectiveness of the treatment and improve the decision-making process during execution. In this paper, methods and tools used to generate FFPs in real-time mode with the help of bottomhole-pressure (BHP) data are discussed in detail. The horizontal wells selected for the study were drilled and completed in the North Sea with permanent BHP gauges that enabled constant monitoring of downhole pressures. The tool in discussion uses the combination of treatment data such as surface pressure, fluid density, injection rates, fluid type, wellbore details, and wellbore deviation, along with bottomhole-gauge pressures, to calculate fracture-inlet pressures just outside the casing at active perforation(s) depth. The tool performs the calculations in “live” mode during treatment execution and simultaneously generates a dynamic array of data that assists in “on-the-fly” evaluation and the decision-making process. Several acid-fracture treatments were analyzed using the tool and led to important conclusions related to fracture-propagation modes, acid-exposure times, and the effectiveness of given acid types. The results had a direct influence on the modification of treatment designs and pump schedules to optimize treatment outcomes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".