Resaturated pay: A new infill target type identified through the application and continuous improvement of 4D seismic at the Forties Field
Bibliographic record
Abstract
Forties is a giant North Sea oil field with an estimated 4.2 billion to 5 billion barrels of oil initially in place. The field has been producing since 1975 and by year-end 2015 had produced more than 2.7 billion barrels of cumulative oil production. The peak production rate exceeded more than 500,000 barrels of oil per day (BOPD) in 1979, and since then more than 3.8 billion barrels of water have been injected into the field. The majority of the field is now water swept due to the combined effect of water injection and extensive depletion supported by a large underlying aquifer. The ability to identify the few remaining unswept portions of the reservoir has elevated 4D seismic into a key technology to derisk all new wells drilled at Forties. The types of 4D targets at Forties are ever-evolving as continuous improvements in seismic acquisition and processing technologies deliver higher quality time-lapse seismic data. With every subtle improvement in 4D repeatability, additional and sometimes unexpected information about the reservoir has come to light. Historically the primary and most robust 4D effect was an increase in impedance (i.e., a “hardening” effect), which could be mapped confidently to identify areas where water was replacing oil. As 4D repeatability improved, it became evident that a decrease in impedance (i.e., a “softening” effect) was observed in many areas in the reservoir. Further analysis revealed that this type of 4D response was highlighting areas where oil had resaturated previously swept reservoir, forming a new type of infill target in the Forties portfolio called resaturation targets. Since drilling the first 4D resaturation target in 2011, 22 of these targets have been drilled with an 86% success rate, delivering 15 million barrels of new oil production from the Forties Field.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 0.002 |
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".