Underbalanced Drilling in Canada: Tracking the Long-Term Performance of Underbalanced Drilling Projects in Canada
Bibliographic record
Abstract
The development of underbalanced drilling (UBD) for production enhancement has advanced significantly since the advent of this technology in the early 1990s. The basis for the initial judgment as to the success of a UBD campaign was usually limited by the information that was available at the time of project completion: project execution success and initial production rates. However, the full scope of the effect of UBD on the overall economic success of a project remains unknown for many cases. While several underbalanced field developments have sufficient production history, drilling records, and cost data available for analysis, to date the body of published literature lacks thorough, long-term case histories.This paper addresses this scarcity by analyzing several UBD projects in the Western Canadian Sedimentary Basin. The discussion includes a comparison of UBD and completed wells with the offsetting conventional producers in the same reservoir. Comparative analysis using industry-standard decline analysis and economic techniques yield technical and economic insight. To provide a balanced picture of the economic benefits that UBD can bring, both successful and unsuccessful projects are examined. The unsuccessful cases are analyzed to determine the reasons for underperformance, whether they fall into the categories of poor candidate selection or sub-optimal execution.Understanding the magnitude and the driving factors behind the success and failure of UBD projects is critical to the growth and acceptance of the technology. This paper attempts to assist in that understanding and provide a benchmark for thorough comparisons of UBD case histories for the future.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".