Development and Implementation of the AVAILS+ Collaborative Forecasting Tool for Production Assurance in the Kuwait Oil Company, North Kuwait (KOC NK)
Bibliographic record
Abstract
Summary AVAILS+ is a short-term forecasting tool designed to lead the production-assurance efforts of the North Kuwait Asset [Sabriyah, Raudhatain, Ratqa, Abdali, and Bahrah fields of the Kuwait Oil Company (KOC)]. The term "AVAILS" is a shorthand term for "Available Production," whereas the "+" connotes the extended capabilities of both the technology (suite of dashboard metrics) and the methodology (team building and collaboration). The tool has been developed jointly between KOC North Kuwait (KOC NK) and Quantum Reservoir Impact (QRI). AVAILS+ design principles are firmly rooted within Reservoir Competency Asymmetric Assessment (RCAA) (Saleri and Toronyi 2011), QRI's empirically driven investigative process for qualifying and quantifying reservoir fundamentals. As a technology, the tool can best be described as an "enterprise mashup," a collection of exploration-and-production data stores integrated into a reservoir-analytics engine with dashboards for tracking primary drivers of the production forecast. This high degree of data integration, coupled with its visual nature (dashboards), enables better cross-organization transparency and collaboration with respect to execution of the recovery plan for production assurance. There is nothing novel about short-term forecasts, metrics, dashboards, or fit-for-purpose databases—all which are components of this enterprise mashup. What is unique is the way in which AVAILS + elegantly unifies these components into a strategic decision-making engine for KOC NK. There have been genuine new insights within this business-intelligence approach to managing the reservoirs of KOC NK, all leading the workforce to an improved understanding of reservoir fundamentals and, consequently, better, more-informed, and more-timely decisions.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".