Development and Implementation of the AVAILS+ Collaborative Forecasting Tool for Production Assurance in the Kuwait Oil Company, North Kuwait (KOC NK)
Bibliographic record
Abstract
Abstract 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). The tool has been developed jointly between KOC North Kuwait (KOC NK) and Quantum Reservoir Impact (QRI®). AVAILS+ design principles are firmly rooted within RCAA® (Reservoir Competency Asymmetric Assessment)1, 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 E&P 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) enable 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 of 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 the North Kuwait organization. There have been genuine new insights within this business intelligence approach to managing the reservoirs of NK, all leading the workforce to an improved understanding of reservoir fundamentals and, consequently, better, more informed and timely decisions.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".