Improved Communication and Autonomy with Downhole Memory Logging Tools
Bibliographic record
Abstract
Abstract Openhole logging tools have been used without wireline in memory logging for 20 years, in an important and growing market. A new system in field trials in Canada and Russia in 2019 further expands the operating envelope overlap between wireline and logging-while-drilling by making step changes in communications, autonomy, performance, and reliability. The new approach advances the logging of horizontal and challenging wells, and permits operations in managed pressure drilling and foam drilled wells. The vast majority of openhole memory work is achieved with a hydro-mechanical system that indicates successful deployment but lacks two-way communication between the engineer at surface and the tools downhole. Pressure-pulse communications have been used for 10 years with a wide range of measurements including memory logging with wireline formation testers. The experience gained from operating these systems informed the development of a new system that uses drillpipe rotation to communicate to the tools, pressure pulses to reply for the uplink, and a more powerful downhole processor. These enhancements in autonomy and communication improve the outcome of logging jobs. The system incorporates a new rotation downlink method which employs data from an angular rate sensor to identify a series of commands sent by rotating the drillstring. Control software in the downhole tools executes the commands, and replies are transmitted uphole by pressure pulses. The toolstring is released from a safe ‘garage’ position inside the drillpipe and deployed into openhole, with the top of the toolstring retained by a no-go. The engineer is supplied with far more diagnostic information than previously, including the axial position of the tools, with context sensitive encoding to provide maximum troubleshooting information to the surface over a limited bandwidth channel. The pressure-pulse downlink remains in place as a secondary method. Other material improvements include high data sampling rate, debris tolerance and downhole recovery strategies. All of these advances improve the autonomy of the downhole memory equipment as well as the real-time communication and control from the surface. Autonomous memory logging toolstrings, with powerful downhole software and rotation downlink communications, are important components in improving the performance and reliability of these successful and innovative formation evaluation systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".