One Stage Forward or Two Stages Back: What Are We Treating? Identification of Internal Casing Erosion during Hydraulic Fracturing—A Montney Case Study Using Ultrasonic and Fiber-Optic Diagnostics
Bibliographic record
Abstract
Summary Plug-and-perforation (plug-and-perf) multistage hydraulic fracturing completions in unconventional reservoirs rely on complete hydraulic isolation from the previous stage to ensure effective treatment of the active stage. Failure to isolate stages can be a result of partially set plugs, plugs set in wellbore debris or deformed casing, unqualified pressure/temperature rating of plugs, and so on. This paper presents a case study with field examples in which unexpected casing erosion occurred at the setting depths of the dissolvable fracturing (frac) plugs during hydraulic fracturing and subsequently resulted in loss of interstage isolation. A 12-well, four-layer, cube pilot was designed with permanent fiber-optic cable to collect distributed acoustic sensing (DAS), distributed temperature sensing (DTS), and distributed strain sensing (DSS) data as well as downhole pressure gauges for development insight and future completion optimization. The cable was mapped, and oriented perforation techniques placed entry holes opposite the fiber along the wellbore, and no loss of communication was observed during perforating operations. However, fiber-optic signal was lost during hydraulic fracturing operations on one or more stages in all four instrumented horizontal wells. Real-time DAS/DTS analysis indicated the fiber breaks were consistently occurring below the lowermost perforation cluster in the stage, at or very near the frac plug setting depth. Step-down tests were also performed and showed significantly enlarged effective treating area. Based on this observation, post-frac downhole imaging tools were deployed to investigate potential casing and perforation erosion. Downhole imaging data clearly showed the casing was severely eroded at several locations. Additional interrogation of the damage with respect to plug design components indicated that damage always occurred near the plug sealing element. By integrating the analysis of DAS/DTS, step-down tests, and ultrasonic imaging, it was determined that the frac plug bypass was creating a loss of casing integrity at the plug set location. Casing integrity loss resulted in multiple fiber-optic cable breaks and lowered the ability to evenly distribute slurry into treatment clusters. Fiber-optic data analysis showed that 50% of the larger outer diameter (OD) dissolvable frac plugs had bypass compared to 100% bypass for the smaller OD high-expansion, dissolvable plugs. To establish key patterns and identify critical variables that influence stimulation effectiveness, it is important to obtain several different diagnostic data sets and perform an integrated evaluation using all available information. This study also reinforces the need for operators and manufacturers to work together to design and qualify frac plugs against realistic downhole conditions, particularly in areas with potential casing deformation issues. Industry innovation is required to enable fracturing operations to continue through deformed casing. This includes advancing equipment, tools, and techniques for plug-and-perf and other multistage completion methods.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".