A New Tool for Long-Term Monitoring and Management of Kuwait Oil Company KOC Reservoirs through Reservoir Management Performance Index RMPI Concept and Best Practices
Bibliographic record
Abstract
Abstract Maintaining and sustaining reservoir performance and health is a priority that requires ongoing assurance activities that will maximize recovery up to industry best practices. An automated and integrated reservoir performance tool has been developed to provide high-level assurances at both the reservoir and asset level. The Reservoir Management Performance Index (RMPI) is a set of indices that can identify key performance issues involving several aspects of the reservoir'sdevelopment and operational plans. This tool identifies mitigating measures that require action, assures production sustainability, promotes a reservoir-focused organization, and standardizes the reservoir performance evaluation in an organization. RMPIprovides a high-level overview and a platform for all management and operation levels, where observing the same set of results can initiate collective decisions that improve reservoir management. Such a system was developed for a company to monitor and measure the performance against expected standards and forecasts for the large number of reservoirs in its portfolio. This tool measures multiple aspects of reservoir management grouped into four major categories: Energy Management, Forcasting Relaibility, Reserves Management and Operations. The tool is tailored to account for various aspects such as: stage of maturity of the reservoir, primary or secondary depletion stage, etc. Each category consists of multiple individual metrics that combine actual field data with targets/forecasts and use an algorithm to calculate a score. These scores are weighted and aggregated for an overall score in each category and an overall score for the asset/reservoir itself. Several aspects accounted for in the metrics include (but not limited to): pressure management, voidage replacement, water and gas management, production and injection performance, reserves promotion and replacement, current RF, EURF, drilling and workover efficiency, Well-Up time, etc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".