Damage Interpretation of Properly and Improperly Drilled Underbalanced Horizontals in the Fractured Jean Marie Reservoir Using Novel Modeling and Methodology
Bibliographic record
Abstract
The Jean Marie reservoir in the northeastern part of British Columbia, Canada, is one of the most active horizontally underbalanced drilled reservoirs in the world. The Jean Marie is a depleted, low permeability, fractured carbonate with low initial water saturation making it susceptible to formation damage using conventional drilling fluids.1 Given these reservoir characteristics, horizontal underbalanced drilling is the current method of choice for operators active in the area.Although underbalanced technology has made tremendous improvements in maintaining and ensuring underbalanced conditions at all times during operations, overbalanced incidents do occur while drilling the horizontal section. These incidents can be much more damaging than drilling with a well designed overbalanced fluid. In some cases, an overbalanced event could permanently impair production from a well by as much as 75%.With the aid of real time downhole pressure gauges, these overbalanced incidents can be identified but very often go undetected, leading to a false sense of achieving the goal of minimal damage. The unidentified damage can lead to misinterpretation of production results and disappointment with underbalanced drilling.To exacerbate the problem of identifying damage, traditional pressure transient analysis of wells in the Jean Marie may yield incorrect valuation of the overbalanced incidents by underestimating the skin on the face of the horizontal well. This could be due to the difficulty of performing meaningful transient analysis on a horizontal well in a dual porosity reservoir and a general lack of knowledge of the impact of drilling related damage on fractured or highly heterogeneous reservoirs.This paper will show how, by combining transient productivity index data collected while drilling with the use of history matching techniques, overbalanced incidents can be easily identified and the impact on well productivity can be quantified.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".