Intelligent Field Management: Real Time Monitoring and Proactive Optimization of Greater Ekofisk Area
Bibliographic record
Abstract
Abstract An online real-time production optimization and monitoring system for the Greater Ekofisk Area of Norway was installed in 2006. The system has evolved significantly since installation; changes driven by multi-disciplinary teams in coordination with the ConocoPhillips Production Optimization Centre (POC). The POC, Subsurface Production Delivery and Reservoir Optimization teams are tasked with assessing real time data from nominally 160 active producers and injectors in order to minimize losses and thereby maximize field production. This online system integrates data from the complete production system: reservoir to export meters. The system realises the importance of visualisation with respect to monitoring field performance, streamlined decisions, and reduced man hours mining data and analysis. The system allows real time monitoring of all wells and associated instrumentation parameters along with field three phase production allocated to the well level. The system alerts an engineer's attention when a well's performance is outside predefined tolerances thereby enabling continuous optimization of the combined field network. This paper demonstrates how the online system addresses the following challenges: Real-time monitoring of separator loadings, production/injection well performance Daily production/injection volume losses allocation Quick screening and allocation issues Generation of updated well models with the latest well test data for further analysis (nodal analysis, lift performance analysis) Generation of updated network models for what-if studies The tool has an open architecture that allows information to be shared with other software packages. It is also capable of controlling and using results from other software that have open access. The tool is used daily by the POC to review the overall performance of the Greater Ekofisk Area.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".