Technology Update: Data Aggregation and Distribution With Human Factors Incorporated
Bibliographic record
Abstract
Technology Update To optimize the value of surface and downhole drilling data, an operator, a software company, and a service company have combined forces to develop a system to seamlessly integrate all forms of data, file types, and communication protocols. To ensure that people on the rig and in the office are able and willing to act on the data, the system includes human-factors engineering. The upstream oil and gas industry first began to gather and stream data from rig sensors in the 1970s and introduced real-time data centers in the early 1980s. Since those early efforts, the industry has made only incremental progress in using available drilling data. These changes typically are limited to improving visual displays, upgrading relational databases for easier reporting, and providing web services for integration. Today, the widespread use of drilling data remains limited by technology deficiencies in rig sensor measurement quality, sensor accuracy and reliability, rig-site aggregation methods, data sampling rates, and the inability to easily share data between proprietary vendor systems. In addition, drilling electronic data recorder (EDR) technology has not kept pace with the technology of other industries or that of other oil and gas sectors. Typical service company aggregation systems use outmoded software and hardware. Consequently, these systems are costly to alter, require long cycle times to add features, cannot process the high-frequency data that are collected by modern sensors, and cannot easily accommodate different back-end engines provided by third parties. In addition, because they have closed architecture, changing user interfaces is notoriously difficult. To address these shortcomings, an operator has built a system that easily accommodates back-end engines, enables rapid prototyping and deployment of new ideas, and separates the back-end from user interfaces. Unlike traditional systems that use only rig data, this new system also uses well information from other data sources. The design basis was to provide users with higher frequency (up to 100 Hz) and higher-quality data than traditional data-aggregation and -distribution systems. This allows drillers to access real-time models and algorithms for analyses, such as stick/slip mitigation, stuck pipe avoidance, optimal tripping practices, hole cleaning, geosteering, and other drilling methods.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".