Time-Lapse Pulsed Neutron Well Logging in Oil Sands for Monitoring Steam Chamber Development
Bibliographic record
Abstract
Abstract Steam-assisted gravity drainage (SAGD) technology, although a relatively new oil recovery method, has already proved its value in economic development of heavy-oil sands in Western Canada. The SAGD process requires a lifetime monitoring of steam chamber growth to optimize reservoir development, improve oil recovery, and minimize environmental impact. Operators have widely used pulsed neutron well logs to monitor their life cycles of oil sand reservoirs. Time-lapse pulsed neutron logs acquired in observation wells enable operators to effectively track the growth of the steam chamber and identify the changes of formation fluid saturations. We present high-temperature pulsed neutron logging technology and an algorithm to quantify steam, heavy oil and water saturations in SAGD wells. One of the major challenges in well logging operation is to withstand the thermal shock from the steam chamber. Reservoir temperature often varies abruptly, by as much as 250 degrees C in a very short interval, so the logging tool must be stable in drastic temperature variations. Well logging conditions such as a steam-filled wellbore, extra completion hardware and bad cement quality are challenging factors as well. Furthermore, formation fluid saturation analysis in Canadian oil sands is typically complex because the formation water salinity is relatively fresh but varies, clay properties are not homogeneous, and SAGD operations create conditions in which three-phase fluids coexist in the formation. These environmental conditions make it difficult to rely only on commonly used thermal neutron capture cross-section measurements (formation sigma). In this paper, case study examples present the above-mentioned challenges and solutions to identify the multi-component formation fluids. The multi-detector pulsed neutron well logging instrument has been modified with a custom-designed heat flask to handle the extreme temperature variations in the SAGD environment. This heat-flask equipped instrument ensures a stable data acquisition in the presence of rapid and extreme temperature variation and enables a prolonged and time-efficient operation through effective heat management. For saturation analysis, we demonstrate an advanced algorithm to quantify three fluid components using a combination of gamma ray ratio and carbon/oxygen (C/O) measurements.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".