Sealed Wellbore Pressure Monitoring in the Montney: Early Learnings
Bibliographic record
Abstract
Abstract Characterizing fracture geometry in unconventional reservoirs is essential to optimizing field development. Surveillance data is critical to understand how fractures propagate both vertically and laterally in any given formation. This paper is focused on low-cost, practical solutions to this problem, primarily Sealed Wellbore Pressure Monitoring (SWPM). SWPM is a novel technology recently developed by Devon Energy, which employs a sealed monitoring well to detect the arrival of hydraulic fractures from an adjacent treatment well via a pressure pulse. SWPM has recently been employed in unconventional plays in the U.S. This paper reports the results from its first application in Canada, in the Montney formation in British Columbia. SWPM data was collected from monitoring wells across four pads in the Montney, located in north-east B.C. The Montney consists of multiple stacked development targets, which emphasizes the importance of fracture characterization for optimal well placement and fracture design. Data collected from SWPM was compared with other diagnostics such as production interference testing, and fracture modeling. By integrating the information from these diagnostics, it is possible to better calibrate hydraulic fracture models and make better field development decisions earlier, with more confidence. This paper summarizes the key learnings, challenges, and limitations from the SWPM pilot. In terms of hydraulic fracture geometry, lateral fracture propagation was consistently very fast (long fracture lengths) in the Upper target; whereas in the Middle target, lateral fracture growth was shorter and fracture height growth was greater. This behavior was generally consistent with expectations based on the minimum horizontal stress profile and fracture modeling in the area. The SWPM data correlated reasonably well with production interference tests. A new metric (SWPM Intensity) was found to have the best relationship with the interference test data. This relationship is crucial as it links hydraulic fracture geometry to propped, flowing geometry. In conjunction with other diagnostics, early learnings from SWPM data have already provided significant value in informing field development decisions in the Montney. The novel SWPM Intensity metric provides an early indication of expected production interference between wells, and therefore an indication of how to balance completion intensity with well spacing. Moreover, by better understanding hydraulic fracture geometry and its relationship to propped geometry, completion designs and well spacing can be better customized by layer.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".