A Case History on the Use of Down-Hole Sensors in a Field Producing from Long Horizontal/Multilateral Wells
Bibliographic record
Abstract
Abstract All of the production wells in this field were drilled and completed as horizontal and multi-lateral wells. The well designs range from a single lateral in a single sand body to where the upper and lower horizontal laterals intersect several sand lenses. The oil, an extra-heavy (9 API), high viscosity oil, requires a completion using artificial lift due to the low reservoir pressure, which will not support a column of water. The use of down-hole pressure and temperature sensors with Surface Read-Out (SRO) was an integral part of the original well completions on production wells to monitor the individual well and pump performance. Vertical monitoring wells, drilled and completed through the multiple sand lenses present, expanded the use of down-hole sensors as data was sought on the area extent and pressure drawdown in the various sands being produced. Efforts to determine the contribution to flow and the pressure losses encountered in horizontal wells led to the use of multiple sensors installed at depths along the long horizontal lateral. A change to the drilling of complex multi-lateral wells resulted in the use of tandem sensors to determine the relative contribution to flow from the lower and the upper lateral(s). All of these approaches, combined with the inability to use conventional Production Logging Tool techniques, led to the application of new technology combining fibre optics with multiple sensors to obtain a real time alternative to a PLT. As many as 15 surface read-out sensors were successfully installed in 7000 feet long horizontal well sections with measured depths up to 10,000 feet.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".