Montney Unconventional Gas Play: Managing Choke Wear Using Flow Coefficient Diagnostics
Bibliographic record
Abstract
Abstract The objective of this project was to improve wellsite safety, optimize choke life, and reduce downtime in highly erosive Dawson Montney (British Columbia, Canada) gas wells through use of real-time flow coefficient (Cv) diagnostics. This was achieved by developing a methodology to trend well behaviour, characterize wear patterns and create Wear Reports based on user defined set-points. A 2-phase flow coefficient equation was used to reduce error in higher liquid-gas-ratio wells and to improve universality. Percent Cv deviation (%Dev) is introduced and calculated in relation to the theoretical choke curves and acts as a key wear indicator. A custom Steady-State Slope-Fitting algorithm and workflow was developed to numerically characterize choke behavior. Using well specific slope durations based on flow behavior as well as elimination of statistical outliers, reliable slopes can be calculated. Logical statements comparing the slopes of key parameters to user adjustable setpoints identify wear patterns and create a shortlisted "Wear Report". These wells can be quickly validated using a custom Spotfire based visualization template. A subset of the Dawson Montney Field (107 wells) was originally piloted to validate methodology. Two generalized wear signatures (for auto and manual choke control) were theorized and vetted using a combination of wear trends and failure analyses which showed high internal wear. By comparing slopes of flow rate, choke position, and %Dev to set-point values, wear signatures can be identified. Workflow was easily adapted to be effective with the Dawson Montney standard choke type as well as a new choke pilot. Many wells have exhibited wear signatures and have been correctly identified by the software, thus increasing safety between scheduled onsite choke condition checks. Early indicators have been communicated to field personnel and well specific plans have been made to replace chokes. In many cases, early detection has allowed pre-planning of onsite work to minimize well downtime (from 24–48 hours downtime to 4 hours or less) and maximize cash flow generation ($10–20k accelerated, using typical rates and netbacks). This program has now expanded to over 400 Montney wells and will be monitored by a continuously manned Operation Control Center. Use of a slope fitting algorithm to numerically describe flow/wear behavior, creation of prelim wear set points/limits, and the wear signature identification logic is a novel approach to detecting and mitigating issues related to choke wear, which ultimately improves safety and reduces costs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".