Improved Safety of Rig Automation with Remote Monitoring and Diagnostics
Bibliographic record
Abstract
Improved Safety of Rig Automation with Remote Monitoring and Diagnostics Bryce Levett Bryce Levett Varco International Search for other works by this author on: This Site Google Scholar Paper presented at the SPE International Conference on Health, Safety, and Environment in Oil and Gas Exploration and Production, Calgary, Alberta, Canada, March 2004. Paper Number: SPE-86600-MS https://doi.org/10.2118/86600-MS Published: March 29 2004 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Levett, Bryce. "Improved Safety of Rig Automation with Remote Monitoring and Diagnostics." Paper presented at the SPE International Conference on Health, Safety, and Environment in Oil and Gas Exploration and Production, Calgary, Alberta, Canada, March 2004. doi: https://doi.org/10.2118/86600-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE International Conference and Exhibition on Health, Safety, Environment, and Sustainability Search Advanced Search AbstractRig automation has focused on improving safety with equipment designed to reduce manpower/human interaction and assume repetitive tasks and operations. As more parts of a drilling rig become automated, there has been a change in how rig personnel interact with equipment. A system of remote monitoring, diagnostics and technical support has been developed to make this interaction more seamless and expand the focus of rig automation. Case studies are presented in this paper of actual field conditions where the remote monitoring system eliminated hazardous operations, discovered operational 'bad habits', and identified areas requiring additional training. Use of the remote diagnostic system prevented the need for physical contact between human and machine in order to solve problems. Documented field incidents have shown benefits of increased safety in many areas when using remote monitoring and management compared to operations without.IntroductionRig automation (as pertains to discussion in this paper) is defined as equipment designed to automate repetitive tasks, reduce human intervention and error. These repetitive tasks are associated with pipe handling operations on the drill floor. Operations can include racking pipe in the derrick, bringing drill pipe to well center, make-up or breakout of connections and pick-up or lay-down of pipe from the derrick to a conveyer.One of the key drivers for the development of this rig automation has been improved safety. The reduction of human presence and physical interaction by humans has been a major goal behind the design of automated equipment. The equipment has evolved into complex robotic designs. Sophisticated sensor technology is employed to determine position relative to other equipment. The optimal path/movement necessary to complete an operation is determined with collision avoidance as a prime factor. Joysticks and touch screens are utilized as control interfaces, requiring only one person to operate the equipment.The sophistication of the control technology has simplified the interaction of the operator and the equipment, but has also added a degree of complexity to troubleshooting when something does not function quite right. Specialized personnel from the equipment manufacturing companies are sometimes dispatched to the rig to assist in troubleshooting. Data about a failed operation is often sparse and personnel have to rely on verbal data, which can be in error. Troubleshooting techniques may require the use of manual overrides to automated control sequences. Equipment can be fully functioned in this mode, however the collision avoidance systems are disabled or bypassed thereby increasing the risk of operation. Even during normal (non-troubleshooting) operations, manual override mode is available and if used can increase the risk of operation.The system discussed in this paper was developed and designed to remotely monitor drilling equipment. Using remote diagnostics, the system helps eliminate the need to dispatch specialized personnel (both onboard rig personnel and onshore specialists) and reduces the use of manual override during both operation and troubleshooting. Keywords: rig automation, artificial intelligence, monitoring, safety, operation, drilling equipment, manual override mode, controller, collision, upstream oil & gas Subjects: Drilling Equipment, Information Management and Systems This content is only available via PDF. 2004. Society of Petroleum Engineers You can access this article if you purchase or spend a download.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".