Development of Marginal/Mature Oil Fields: A Case Study of the Sinclair Field
Bibliographic record
Abstract
Abstract Development of marginal/mature fields has become popular because of a significant decline in new field discoveries and high oil prices. In particular, small size fields of this kind are more challenging because of limited options for development. This paper presents a study on the Sinclair field located in Alberta, Canada. The field has 19 wells, six of which are horizontal, and have been in production for more than 20 years. Despite the quality of oil (40°API, 1.5 cp) and rock properties (20% average porosity, water-wet sandstone), the current production is less than 100 bbl/D for the whole field. The field is now undergoing waterflooding. The main challenges are the thin pay zone (~4 m), severe water production and a puzzling recovery factor of approximately 10%. The current study consists of three phases: numerical reservoir modelling and history match to understand the reasons for low oil production and to analyze the hydrodynamic characteristics of the field, characterization of reservoir and interwell connectivity using static and production data and proposing an enhanced oil recovery technique supported by field scale numerical simulation. After modelling and history matching stages, potential reserves locations are estimated for possible dilute surfactant injection. Based on interwell connectivity, different injection schemes that use some producers as injectors are tested. The obtained results are subject to further evaluation and analysis to derive the economic viability of the 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".