Bibliographic record
Abstract
Introduction Distributed temperature (DT) monitoring systems are a relatively new technology being applied to thermal enhanced oil recovery applications. In Canada, most SAGD (Steam Assisted Gravity Drainage) projects are utilizing these systems in early commercial phases to understand the benefits of distributed temperature data, as it relates to drilling and completion designs and management of overall steam conformance in multiple well pairs. As a relatively new technology applied in the extreme operating environment of SAGD, DT monitoring has been a technical challenge for service companies and operators alike. These challenges however, are being overcome through ever evolving completion design modifications, improved fibre optic deployment methods, material improvements and development of more sophisticated surface optical computing technologies. A great deal of effort is being expended to flatten the learning curve towards the benefits of all concerned. The following Project Overview highlights one application of DT data towards a particular thermal project in Canada. It profiles a situation where the client benefited from the application of DT monitoring in a 325 ° C operating environment. DT Operational Methodology Most distributed temperature monitoring systems (DT) are based on multimode optical time domain refractometry. The light from a laser propagates along the fibre and energizes the glass, lattice structure, and molecules. Among the many different backscattering waves is the Raman signal. The Raman signal is a signal used for temperature evaluation. Raman scattering produces frequency- shifted wavelengths which are known as Stokes and anti- Stokes lines. The intensity of the Stokes lines is temperature independent. Anti-Stokes line intensity varies as a function of the temperature of the fibre. The ratio of these two intensities provides a direct measure of absolute temperature at the depth where the signal originated. Field Background Bitumen and water were recently discovered in an aquifer zone above a steam stimulated bitumen reservoir. It was a possibility that the conduit for this fluid transfer was behind pipe in two wells. Previous conventional monitoring had not confirmed casing failures or possible vertical fluid migration from the aquifer. The operator chose to test one of the production wells for the possibility of fluid influx to the upper zone, with the intention of identifying the most likely source. One of the key concerns during the completion of the well was good cement placement. It was a common practice to run a cement bond as a part of the completion process to confirm hydraulic isolation. Detecting flow behind casing was traditionally very difficult. Evaluations conducted in other areas of the field have shown that flow through potential paths, stop after a few thermal cycles which compounds valid indentification of problem areas. DT Technology offered the operator a probable method to identify fluid mobility behind pipe. Monitoring Program and Instrumentation Configuration Pump and rods were pulled from 73 mm tubing to allow the Fibrenet System to be installed. The surface pack-off landing sub assembly was designed to support the full weight of the monitoring system since the control lines were free hanging inside the enclosed tubing with pump-out plug.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.024 |
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".