Mist Drilling in Low-Pressured, Highly Fractured Carbonates Improves Well Test Results and Fracture Identification from Image Logs
Bibliographic record
Abstract
Abstract Underbalanced drilling (UBD) is an effective technique used to prevent severe fluid losses typically encountered when drilling through vuggy and fractured carbonates. This paper discusses the successful application of UBD to improve drilling performance and data acquisition quality in an ultra heavy oil carbonate resource located in Alberta, Canada. Logistical challenges associated with mobilizing UBD equipment to a remote location during extreme winter conditions are also presented. A series of appraisal drilling campaigns were conducted over a four year period in the ultra heavy oil-bearing Grosmont carbonates to acquire subsurface data in support of field development planning. Prior to 2010, the primary drilling fluid used was water-based polymer mud and, when heavy losses were encountered, polymer-based lost-circulation material. The formation damage caused by drilling with these materials hampered hydrology testing and masked image log responses resulting in an inaccurate view of the reservoir architecture and an inappropriate development concept. During the winter 2010 drilling campaign, a change was made from overbalanced to underbalanced drilling using mist in the bitumen-saturated carbonates. The main objective of mist drilling was to reduce near-wellbore damage and improve interpretability of hydrology test data. The application of mist drilling was proven to be operationally feasible despite the challenges posed by severe winter weather conditions during drilling, which are exacerbated by the poor infrastructure surrounding the remote Grosmont well locations. Excellent quality image logs and much improved hydrology test data were obtained in the mist-drilled wells compared to appraisal wells drilled overbalanced in past campaigns. Skin factors were reduced by a factor of 100 and fractures became more discernible on image logs. Consequently, understanding of permeability architecture in this heavily fractured carbonate reservoir was greatly enhanced. The successful experience with mist drilling in these fractured carbonates paves the way for better fracture identification and reservoir characterization for the resource. The data acquired from the UBD wells have impacted key decisions related to technology testing and future development planning for the Grosmont lease.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".