A Systematic Approach to Modeling Condensate Liquid Dropout in Britannia Reservoir
Bibliographic record
Abstract
Abstract Britannia field is a gas-condensate reservoir in the central North Sea, and has been producing since 1998. Condensate accumulation near the wellbore continuously impairs well productivity during the first year of production, as wellbore pressure drops below dew point, before stabilising where further well performance deterioration becomes negligible. The magnitude of the initial productivity loss averages 50-60%. Using several Britannia field well examples, this paper presents a comprehensive and systematic production data analysis approach to modeling well productivity deterioration due to condensate accumulation. Production performance of Britannia wells is monitored by routine bottom-hole and wellhead back-pressure curves constructed using separator well test data. Permeability levels within the Britannia reservoir zones are high enough that the wells reach pseudo steady state (pss) flow within about a month. The well performance signature (shape of back-pressure curves) is characterised by performance points moving quickly to the left from the pss line, as condensate accumulates around the wellbore and reduces well deliverability. The workflow described in this paper uses the slope of the back-pressure curve of the late-time performance in order to provide estimates of kh, mechanical skin and non-Darcy flow coefficient. Effective permeability to gas (krgk) in the transient or stabilised deliverability equation is adjusted to match separator test points for estimating condensate blockage as a function of producing time. Reservoir simulation sector models are used to convert gas-relative permeability versus time relationships to gas-relative permeability versus saturation relationships. Pressure transient analysis, production data analysis type curves and the flowing material balance method are collectively utilised to fine-tune the initial estimates of kh and skin used in the deliverability equation. Back-pressure curves are routinely used to identify wells for workover to improve well productivity, and candidates for capillary string installation or to evaluate benefits of additional field-wide compression.
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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) | 0.001 | 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) | 0.000 | 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".