Reservoir Management Challenges of the Terra Nova Offshore Field: Lessons Learned After Five Years of Production
Bibliographic record
Abstract
Abstract The paper describes reservoir management challenges during the first 5 years of the production from the Terra Nova oil field, located offshore Newfoundland and Labrador, Canada. The purpose of the paper is to share lessons learned in the appraisal strategy, data monitoring, subsurface modeling and production management of this subsea/ floating, production, storage, offloading (FPSO) development. The material covers appraisal history, reservoir description, production history, reservoir monitoring, reservoir management, lessons learned and future challenges. The paper will discuss the need for a balanced approach regarding development and delineation of the resources. Since there is no gas export from Terra Nova or any of the Grand Banks fields, surplus gas has to be re-injected. The Terra Nova depletion plan requires that the production from the water injection region and the gas flood regions be balanced for optimum pressure management. The relative richness of the injection gas from the Terra Nova production process has the potential to create miscible gas-oil conditions. We will discuss how slim tube experiments and compositional tracking of the fluids have demonstrated benefits to the oil recovery by operating the gasflood at a reasonable pressure. The Terra Nova field lies not only in a harsh environment but also in an immature, low infrastructure area. This has led to additional challenges regarding reservoir management and future development. Production behavior and history matching have shown that seismically defined fault blocks are transmissible rather than sealing. The presence of leaky faults is beneficial for oil recovery, but presents challenges for precise reservoir simulation modeling and history matching. Terra Nova has extensive amount of data from bottom hole pressure gauges, wireline formation pressures (e.g. RFT/RCI), water cut trends GOR trends, gas tracers, etc. The paper discusses how these data have been integrated in the reservoir management process. Various history matching approaches will be discussed; both manual history matching and assisted techniques (experimental design). This is to the best of our knowledge the first SPE paper from the Grand Banks oil province that describes integrated reservoir management challenges particular to this region.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".