Reservoir Management Aspects of the Rejuvenation of the Forties Field, UKCS
Bibliographic record
Abstract
Abstract When Apache Corporation bought the Forties field in 2003, the field had produced about 2.5 billion barrels of oil from an original oil in place of nearly 5 billion barrels. Historically, cumulative production from some wells had been more than 80 million barrels. By contrast, the remaining drilling targets in 2003 were much smaller, as well as being distributed across the entire field (18 km by 10 km) and reservoir section. In addition, the facilities were nearly 30 years old and designed for fluid rates very different from those current in 2003. Since 2003, Apache has rejuvenated the field through a combination of facilities improvements, workovers, and drilling new wells. Management of the Forties reservoir during this rejuvenation has had to take into account the challenges of accessing small, widely distributed targets and the constraints of facilities projects being run in parallel. Initially, 4D seismic was used as the main tool to identify drilling locations. This was an appropriate technique to identify the larger targets but became less certain as thinner oil columns and deeper parts of the section were targeted. During this initial phase of drilling a fine-scale reservoir model was constructed and is now used with streamline simulation as a tool to assess targets and optimise the waterflood. The installation of multiphase well testing equipment and a widespread use of electric submersible pumps with permanent downhole gauges have provided new insights into reservoir performance. Sand control techniques have been introduced in order to manage the potential for sand production. Understanding and working with the interdependency of reservoir management, drilling, and facilities has been essential throughout the process. The combination of 4D seismic and simulation techniques which has guided the rejuvenation of Forties are presented, along with the interactions with facility-related projects.
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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".