Refining Geology With the Bit: An Innovative Approach to Well Design and Geosteering
Bibliographic record
Abstract
Abstract Maximizing bitumen recovery from thermal horizontal wells requires optimal placement of the wellbore close to the bottom of the reservoir. Canadian Natural Resources Ltd. is collaborating with Halliburton to utilize logging while drilling (LWD) techniques to achieve optimal well placement in relatively thin heterogeneous reservoirs of the Clearwater Formation in its commercial cyclic steam stimulation operations in east central Alberta, Canada. Halliburton's StrataSteer3D ® service is used both prior to, and during, drilling in real time to plan, model, and steer the horizontal well. Resistivity, and gamma ray are monitored in real-time to detect the base of the reservoir during horizontal drilling and thereby geo-steer the well one meter above the reservoir base. Real-time data are used to recalculate well plans as new surveys and petrophysical data are acquired. Data collected are integrated using DecisionSpace ® for 3D geological modeling and well planning. Geological surfaces are revised while drilling and are applied to successive wells. Log responses are also characterized and correlated to the known area geology. This method of using the bit to steer by, and refine, the geology results in a reduction in overall drilling time by eliminating the need to intentionally ‘tag’ the bottom of the reservoir. Wellbore completion is also improved by ensuring maximum steam contact with the reservoir. The precision of vertical wellbore placement is increased thereby giving greater confidence in subsequent reservoir performance characterization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".