Adding Depth: The Superiority of Volumetric Perforation Erosion Analysis for Evaluating Stimulation Performance and Optimizing Completion Designs
Bibliographic record
Abstract
Objectives/Scope Permanent fiber optics and high-resolution acoustic imaging are two diagnostic tools commonly used to assess completion design performance in horizontal wells. Fiber optics offer real-time data at the cluster level but can be costly and prone to mechanical failure. In contrast, acoustic imaging is much more affordable but only captures temporal snapshots of data. This paper thoroughly examines how advanced perforation (perf) erosion analysis with three-dimensional (3D) measurements can produce analytical results comparable to fiber. Methods, Procedures, Process Perf tunnel volumes are calculated using measurements obtained from high-resolution acoustic imaging technology, which has been laboratory-validated against a metrology-grade laser scanner. These measurements are used to generate a novel diagnostic plot that explains perf erosion behavior in conjunction with theoretical growth cases. This plot is also used to empirically determine uneroded perf tunnel volumes for different regions around the borehole, which are subtracted from eroded volumes to calculate growth at the perf, cluster, and stage levels. The growth volumes are then used to calculate treatment uniformity with respect to inferred proppant placement for different stage configurations. Results, Observations, Conclusions Treatment uniformity values determined from volumetric perf erosion analysis are strikingly similar to those determined from permanent fiber optics using Distributed Acoustic Sensing (DAS) data collected in real-time during stimulation. In the case of a horizontal Niobrara well analyzed with both diagnostic technologies, the conclusions independently determined from both datasets yielded the same answer in terms of which stage configuration should be utilized for future field development plans. Hydraulic fracture profiles (HFPs) were also generated for stages that had all perf measurements available for analysis. When these HFPs were directly compared to those generated from permanent fiber optics for the same stages, they tended to show a tremendous amount of similarity with respect to inferred cluster-level proppant placement and treatment bias. This alignment between fundamentally different diagnostics not only demonstrates the strength and veracity of volumetric perf erosion analysis, but it also helps validate the new method of empirically determining uneroded perf tunnel volumes using 3D measurements. Novel/Additive Information Although acoustic imaging technology has been used extensively for several years, erosional analyses have typically been performed using exit hole measurements exclusively. Only recently has 3D volumetric perf erosion analysis been evaluated. It is clear from the learnings that exit hole measurements alone do not account for all the subtle nuances associated with proppant placement and erosional phenomena. These additional measurements allow perfs to be represented in 3D space, which has helped bridge the gap between different diagnostics that historically lacked alignment and left operators wondering in which dataset to place their confidence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".