Initial Surveillance in the Cannonball Field: Design, Implementation, and Interpretation
Bibliographic record
Abstract
Abstract The Cannonball West gas and condensate field is located in the Columbus basin, 35–40 miles off the southeast coast of Trinidad in water depths of 240 feet. It is comprised of a single reservoir unit, the TP65 sand, situated at 12,334 ft. true vertical depth subsea. This field was discovered in 2002 by the exploration well, Ironhorse-1 ST1, which penetrated the reservoir down dip of the Sparrow fault block, and just south of the adjacent Red Mango accumulation. The sand was initially over pressured at 6,400 psi; with a temperature of 220 °F. The volumetric estimate of gas in place was 1 trillion cubic feet of gas. This reservoir was developed with three vertical wells, completed as open-hole gravel-packs with 7 5/8" production strings. The well design was based on parameters estimated from the discovery well and a target per-well production rate of 280 million standard cubic feet of gas. Each well was successfully instrumented with permanent downhole pressure and temperature sensors as well as wellhead, casing, pre-and post-choke pressure and temperature sensors, well-by-well ‘wet’ gas rate meters and well flowline acoustic sand detectors. All of this data is available real-time and is continuous. Field startup followed in early 2006. Hydrocarbons are produced via a dedicated normally unmanned platform. This platform is Trinidad's first locally fabricated platform, and is a 9-slot installation with a production capacity of 1 billion cubic feet per day. Fluids flow through a 26″ subsea pipeline to the nearby Cassia B central hub for processing prior to sale to local industry and LNG facilities. The operation is managed by bp Trinidad and Tobago, plc. (bpTT). To quantify completion efficiency, well-by-well drainage areas and connected volumes, and to garner data to help set safe nominally sand-free operating limits, a baseline surveillance program was designed prior to initial production. The purpose of this paper is to detail the design, implementation and interpretation of this baseline testing program. In particular, we show how the design reconciled the need for extended, high quality pressure buildup data with the need to maintain a reliable gas supply, one of bpTT's core reputation values. We also delineate how the test design incorporated multiple datasets allowing redundant, independent calculation of key parameters. Finally, we show how individual well completion condition, drainage area and connected volume were quantified and how the combined results fit within and tie to the seismic interpretation. Implications for inter-well connectivity along with future operations and drilling are discussed in the closing section of the paper.
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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".