Designing a High Resolution Chemical Surveillance Network in a Deepwater Field Off NW Borneo, East Malaysia
Bibliographic record
Abstract
Due to the relative infancy of chemical tracer surveillance, field applications to date have focused on either pilot wells within a sector of the field or at a well or interval level, providing limited qualitative resolution of production performance. The Siakap North - Petai field is intended to be the first field developed employing full chemical tracer coverage across each producing well. This has been made possible through the co-ordinated efforts between service providers and the operator. A total of 40 oil marking and 40 water marking tracers, will be installed in 8 production wells, with resolution varying from 3 Screen joints per (Oil + Water) tracer up to 1 screen joint per (Oil + Water) tracer, depending upon the lithology and therefore level of feedback deemed necessary to help profile production performance within the well. Siakap North - Petai field will be a deepwater subsea tieback to the existing Kikeh FPSO approximately 15km away. Phase 1 development will comprise 8 production and 5 injection wells. As a result, conventional intervention and surveillance techniques will be cost prohibitive. Each production well will commingle oil from two reservoir intervals with differing lithologies (a mixture of laminated thin-beds and amalgamated thick bedded sections). A means of determining flow contribution along the completed interval will help characterise the complex architecture of the field, aid in reservoir management and identify future infill or workover opportunities. This paper will focus on the system design, quality control and application of the chemical tracer network from conception through to field installation. A follow-up paper focusing on sampling and production based observations will be published at a later date, once sufficient production data has been gathered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".