Perf Guns, They’re Not Just for Holes Anymore! Perf Cluster Tracer Injection
Bibliographic record
Abstract
Abstract Oilfield water and oil tracers have historically been pumped into the fracturing fluid during well stimulation. This paper will introduce an alternative method of injecting oilfield tracers that utilizes perforating guns with energetic propellant to force the tracer into a perforation cluster prior to fracturing operations. Direct tracer injection from perforating guns offers several advantages to operators that are interested in oilfield tracer diagnostics, they include: Monitoring a wells oil or water returns down to an individual cluster level of resolution. Energetic propellent assisted perforation cluster breakdown. Direct tracer injection into clusters for wells that are: Perforated but not hydraulically fractured. Bullhead refracturing treatments with long open intervals of newly fired perforations. Not sufficiently isolated between stages from poor cementing or leaking plugs. Not isolated or experimentally isolated between stages. Oilfield tracers in solid form were first injected into perforation clusters with energetic propellants on two Marcellus Shale wells. The primary purpose of this experiment was to determine if Perforation Gun Tracing (PGT) could be used to provide flow-assurance diagnostic information to the well operator. Additionally, standard liquid water tracers were also injected into the flowstream during the corresponding fracture treatment stages and used as a control for the PGT. Both tracer injection methods indicated that the toe side of the wellbore was contributing to the fluid returns profile and also showed similar trends in tracer response over time. This experiment showed that PGT could provide valuable diagnostic information to well operators. Several additional field trials that exploit the unique benefits of PGT were completed after the success of the initial experiment and are included in the case studies section of the paper. In each case, PGT was able to provide the intended diagnostic which included flow-assurance, flow-profiling, fracture driven interactions and/or refracturing effectiveness. In refracturing, PGT has tremendous benefits because the energetic propellent can help the new perforations breakdown and compete with the existing broken down and eroded perforations. Additionally, unique tracers are injected at several known cluster depths throughout the lateral. Any returns of the unique tracers in the flowback water will correspond to fracturing fluid that has contacted the depth of that specific traced cluster. This provides an operator valuable diagnostic information to determine how deep their refracture treatment was able to reach into the lateral. PGT also delivers information on the returns of an individual cluster, without post-frac well intervention or permanent hardware installation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".