Under-Balanced Drilling Experience in a Shallow Clastic Oil Field, Offshore Sabah, South China Sea
Bibliographic record
Abstract
Abstract Following encouraging results of Under-Balanced Drilling ("UBD") applications in the North Sea and onshore Canada, Shell Malaysia Exploration and Production ("SM-EP") embarked upon the application of this technology in a shallow clastic oil field in offshore Sabah. Relatively little information is available in the public domain regarding the effectiveness of UBD in clastic oil reservoirs. This paper provides factual evidence relevant to the assessment of UBD-induced added value. The paper also provides a comparison between the cost and benefit of UBD with that of Under-Balanced Perforating ("UBP") in a two-well trial. Every pressure regime, reservoir fluid type, geological circumstance, etc, requires a specific operational configuration custom made for the situation at hand. This is a major source of UBD-induced incremental cost. Different reservoirs require different UBD configurations so analogies that assist the business case of a UBD project are currently limited and are difficult to find. This was the first offshore application of crude oil and hydrocarbon gas injection as drilling fluid in jointed pipe operations. A description of the incentives and risks of our specific geological circumstances is provided. Detail will be provided regarding our UBD design, main operational-risk mitigation measures, and the strategy used to appraise the added value of UBD. The complexities, incentives, and pitfalls of our UBD strategy will be chronicled. The initial results of this first-ever UBD trial within SM_EP will be published, along with the lessons that we have learned.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".