Combined Electric Line Milling and Clean-out to Eliminate Fluid Costs for Multi-Stage Frac Preparation in Southeast Saskatchewan
Bibliographic record
Abstract
Abstract Objectives/Scope Clients utilizing Coiled Tubing (CT) for straddle frac operations in multi-stage horizontal wells often encounter cement stringers preventing the frac bottomhole assembly (BHA) from reaching plug back total depth (PBTD) and the packer from sealing to the casing wall. This paper presents the learnings from a >90 well campaign of preparing for fracking operations using an electric line (e-line) milling and clean-out tool. The wells were mostly cemented, 4.5″ liners with frac sleeves. This technique reduced frac preparation costs in the cemented wells by approximately 30%. Methods, Procedures, Process The common practice in Southeast Saskatchewan (SE Sask) is to perform a “well prep” operation prior to the frac equipment's arrival to the well site. A CT unit equipped with a rotating scraper/mill—and associated fluids—is used for the clean-out, adding to the logistical coordination and well costs. Fluid has several costs associated with it: the cost of the fluid/water itself, heating for winter operations, trucking and disposal. However, “well prep” is considered “cheap insurance” by most operators working in SE Sask compared to the potential costs of a waiting frac crew. Results, Observations, Conclusions An operator in SE Sask has had success with an alternative clean-out solution to replace the use of fluid for well preps by introducing an e-line method consisting of an electric milling & clean-out tool with a casing collar locator (CCL). The mill is conveyed by e-line tractor and is equipped with a scraper mill to confirm the PBTD and ensure that there is no cement debris or sheath present that could negatively affect the frac operation. Various bailers can be added to collect the cement debris in the same run and ensure it is removed from the wellbore. In combination with the clean-out service, a CCL is deployed and logged to surface to pinpoint the exact sleeve location to be referenced during the frac operation. This new, efficient clean-out solution has proved slightly more time-consuming (~3-5 hours) but yielded significant cost savings of approximately 30% per well of prep costs. These savings come from using e-line equipment, eliminating fluid costs and offering inherently safer operations with a low carbon footprint. Novel/Additive Information Moreover, the paper will discuss the future applicability of this ‘additional application’ for pre-logging runs as a means to reduce total completion costs in cemented wells. This is achieved by using the e-line milling tool as a pre-run for casing inspections or cement evaluation logging.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".