Managing Drilling Risk in a Mature North Sea Field
Bibliographic record
Abstract
Abstract As fields mature, drilling can become more difficult. The likelihood of losses increases as reservoir pressures decline while higher mud weights are needed to prevent collapse of overburden shales as targets are pushed further from the platform. Drilling parameters for the Forties field have become fairly well established after years of experience yet 65% of the wells drilled between 2002 and 2007 experienced incidents attributed to instability. As field production declined, economic viability demanded a step change in performance. Through a better understanding of the field geomechanics and past drilling events, the Apache drilling team has implemented fit for purpose drilling procedures that have significantly improved drilling efficiency. This paper describes how geomechanics analysis has been used to assist well planning. Incorporating the experience of recent wells, the Forties mechanical earth model has been refined and provides key inputs needed to optimise well plans and adapt drilling practices to changing conditions. The adverse effects of anisotropy is a key reason for wellbore instability in the overburden. A joint Apache-Schlumberger team has been working to integrate geomechanics and geophysics knowledge of the field to quantify the effects of anisotropy related to bedding planes and weak shales. It can be difficult to predict drilling and completion risks during well planning, often due to the lack of distinction between events caused by formation instability and those which are drilling induced (drilling practices, hole cleaning). Capturing and classifying historic drilling events is a fundamental key process in understanding the mechanism and causes of well bore failures. Drilling performance in this mature field has been improved through the collaborative effort of Apache staff and key service providers. The refinement of the wellbore stability model has enabled sound practices and procedures to be developed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".