Optimal Horizontal Well Placement: Formation Boundary Mapping While Drilling
Bibliographic record
Abstract
Summary In Western Canada several oil and gas producers are seeing the benefit of developing their field by drilling horizontal production wells. The success of horizontal wells depends on the ability to stay in the target formation while drilling the lateral section. In the past the data that could be collected while drilling has been limited. Cuttings analysis and logging while drilling (LWD) allowed operators to identify when the well bore leaves the zone of interest. However, because traditional LWD measurement have had a shallow depth of investigation geosteering horizontal wells has been reactive. Typically the well has to drill out of the zone of interest before a problem is identified. New technology indroduced by Schlumberger in 2003 takes a proactive rather then a reactive approach to geosteering horizontal wells. Deep reading and directional sensitive resistivity measurements allow formation resistivity contacts to be mapped in real time. These measurements have a depth of investigation of up to 5 meters. With the ability to see formation contacts from this distance well bores can be placed relative to the formation contacts, reducing the risk of exiting the reservoir and allowing for more productive horizontal wells. This technology was first brought to Western Canada in 2006 and had a significant impact on how horizontal wells were placed in different environments in 2007. Theory and/or Method In order to be successful while drilling horizontal wells a method is need to keep the lateral section in the reservoir. This is not easy in complex geology where sub-seismic features will make it difficult to know where exactely to place the well. The ability to map formation contacts in real time, without exiting the reservoir reduce this sub-seismic uncertainity. Using Directional-Deep Resistivity Logging While Drilling (DDR-LWD) allow for this real time boundary mapping and enables operators to drill better horizontal wells.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".