Bibliographic record
Abstract
Gas Field Planning Tool (GFPT) was developed in 1990 by the Shell Group of Companies to fill the need for a tool for gas field planning and development using deterministic subsurface and surface models. Main initiators were Shell Canada, NAM (the Netherlands), Shell Expro (UK) and BSP (Shell Brunei),as these companies are major gas producers. Shell Companies now have several years experience with using the GFPT. Application ranges from simple single field models to corporate-level models with a large number of gas reservoirs and wells. Shell companies now using GFPT models are Shell Expro (UK), BSP (Brunei), SSB (Malaysia), Shell Canada, SPDC (Nigeria), SDA (Australia),Woodside (Australia), PDO (Oman), NAM (the Netherlands), New Business Development (e.g.Lunar Project) and in future also Shell Egypt. NAM currently has a GFPT model for the Anjum field in Friesland and for the Ten Arlo field in the north of Holland. GFPT is currently being migrated to an HFPT (Hydrocarbon Field Planning Tool), which can also be used for planning ofcondensate, oil and water developments and for control of hydrocarbon compositions in the network using PVT de-lumping at the well head (e.g.for LNG plants) and optimisation techniques (linear,non-linear or based on bean-back lists).
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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".