Acquiring Microresistivity Borehole Images in Deviated and Horizontal Wells Using Shuttle-Deployed Memory Tools
Bibliographic record
Abstract
Abstract Borehole images have broad applications in geological, petrophysical, and geomechanical studies. The advent of the small-diameter memory resistivity micro imaging tool improves operational efficiency in a broad range of well types. In spite of the tool's small size and weight, its design provides coverage and image quality that matches or exceeds that of previous generation imaging tools. It is deployed with or without a wireline and is not constrained by wireline data transmission rates because data are recorded to internal memory. Deploying the tool inside the drillpipe on the well shuttle facilitates access into highly deviated wells and past bad hole conditions without compromising borehole coverage. Finding fractures in deep and tight rocks has become a high priority among explorationists around the world. Recent discoveries have shown that fractures can play an important role in the productivity of low-permeability plays, such as coalbed methane or shale gas. The only logging technology with the resolution to detect and identify these small features within the reservoirs is borehole imaging, where 2 mm details can be visualized. Deviated wells in the Western Canadian Sedimentary Basin were logged using memory borehole imaging tools. The tools were housed inside a special drill collar while running in the hole, allowing rotation and circulation and were deployed using a messenger system and pressure pulses. The tools recorded microresistivity data to memory as the drillpipe was then tripped to surface. In all wells data was recovered, processed, and interpreted using software specially developed for this new memory-based technology. The resulting images were the equal of electrical borehole images obtained using conventional wireline deployment. Using the memory imaging tool housed in the special drill collars protects the tools while tripping into the well. Since there is no wireline and no wet latches the shuttle system is more robust than conventional tool-pusher systems. This reduces risk and logging operation time while simultaneously delivering high-resolution borehole images that allow these fractured reservoirs to be properly evaluated.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".