Увеличение вовлеченных в разработку запасов нефти с помощью картирования ВНК в процессе бурения методом сверхглубокого электромагнитного каротажа
Bibliographic record
Abstract
Summary The real-time interpretation of Extra-Deep Azimuthal Resistivity LWD measurements resulted in the extended reservoir characterization of the lithological heterogeneity of the Fort McMurray Formation, including clean sand facies, inclined heterolithic stratification (IHS) facies, and mud-filled channel facies. This lithology was compounded by fluid heterogeneity within the reservoir, including irregular Oil-Water Contacts (OWC), partial reservoir charging, lean zones and top gas zones. The increase in actual exploited oil reserves (quantitative), was estimated at more than 50 percent compared to the projected reserves exploited by the planned wellbore trajectory. This new formation evaluation approach was proven while drilling four horizontal producers in the unconsolidated oil reserves with high reservoir heterogeneity, which stressed the need for operators to fully understand their subsurface in order to maximize oil recovery. This new logging-while-drilling approach offers an opportunity to better understand the oil reservoir, which ultimately leads to increased production performance of Oil Sands projects. Utilization of this technology in future projects will fundamentally change the efficiency of drilling and completion practices within the oil industry.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".