Quantitative Frac Pack Analysis Using Dual Tracer Logs and Down Hole Gauges
Bibliographic record
Abstract
Abstract A combination of multiple down hole gauges and dual density / tracer logs were utilized to quantitatively evaluate distribution of fluid and proppant across a long perforated interval separated in two lobes and to quantify the annular pack percentage across the entire completion interval during a deepwater frac pack treatment in GoM. It was important to evaluate the achievement of an effective fracture in both lobes and define the annular pack percentage across the entire completion interval to be able to produce the well to its potential. This technique quantitatively evaluated the entire frac pack process and determined screen out events in separate lobes and annular pack efficiency. The analysis also defined the dynamics of the treatment fluid and proppant slurry movement during the frac pack pumping operation and their final placement. Several expected as well as unexpected conclusions and observations were identified. In summary, the diagnosis indicated that higher percentage of treatment fluid and proppant was received by the upper sandstone lobe. The exact proppant concentration at lower lobe screen out was identified. A baseline pre-pack value was established, which allowed the annular pack percentage to be calculated across the entire interval. It also provided detailed information on the sequence of events during washout at the crossover tool. All of these allowed the operator to confidently maximize deliverability from the subject well, which is currently producing 110 MMcfd. The results from this case history and the technique described should result in a step change in frac pack evaluation. Quantitative evaluation eliminates any doubts about the effectiveness of the annular pack and allows operators to produce their assets at maximum deliverability. Additionally, it assists future completion designs and type selection.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".