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Record W1989510902 · doi:10.2118/2001-032-ea

Leveraging the Reach: Monitoring Remote Assets With Geostationary Satellite Systems

2001· article· en· W1989510902 on OpenAlexaboutno aff
B. Svinterud

Bibliographic record

VenueCanadian International Petroleum Conference · 2001
Typearticle
Languageen
FieldComputer Science
TopicDistributed and Parallel Computing Systems
Canadian institutionsnot available
Fundersnot available
KeywordsGeostationary orbitComputer scienceSatelliteSatellite broadcastingRemote sensingGeostationary Operational Environmental SatelliteGeologyAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Using geostationary satellite systems to monitor compressor lubrication systems is a practical and costeffective option for energy and petroleum companies today. The prices of satellite terminals have dropped by a factor of five in the past five years, and these compact communications devices are now available for US$500. Low-priced hardware and affordable service make satellite systems attractive for compressor monitoring. This extended abstract explains how energy and petroleum companies can benefit from using satellite services to monitor divider block lubrication systems. It describes how these services can maximize compressor life cycles, cut operating costs, increase labor efficiency, and maximize production volumes. Introduction The oil and gas industry depends on critical production and monitoring data from the field. The more thorough, accurate, and timely the data, the more effectively companies can run their businesses. To achieve these results, oil and gas companies are now turning to satellite data monitoring systems such as GlobalWave from Vistar Datacom to handle their data communication needs. Three important factors influence this trend: steadily declining prices, field-tested reliability, and ease of use. The advent of spot beam technology allows satellite power to focus more narrowly on coverage areas. These concentrated beams have enabled Vistar to design a power-efficient satellite transceiver that measures a mere six by four inches. Consequently, companies can deploy GlobalWave technology in a few minutes at a fraction of the cost of much larger traditional communications systems. GlobalWave service equips oil and gas companies to monitor important equipment characteristics such as compressor operation status, static pressure, differential pressure, and temperature. Given that lubrication problems account for 80 per cent of compressor failures, keeping a close eye on compressor lubrication status is vital. Insufficient lubrication can cause cylinders to seize in a couple of minutes. Too much lubrication can also impair equipment and needlessly increase operating costs. Either way, companies incur unnecessary expenses to repair or replace stricken compressors. Working in tandem with divider block technology, the GlobalWave system can immediately notify production operators if lubrication levels exceed a preset high or low threshold value. This prompt notification allows companies to act quickly to protect their assets, reduce costs, and maintain production volumes. THE MOVE TO DIVIDER BLOCK LUBRICATION SYSTEMS Compressor reliability hinges as much on the system that distributes cylinder lubrication as it does on proper lubrication and lubrication rates. Recognizing the importance of cylinder and packing lubrication systems, companies throughout the U.S. and Canada are spending millions of dollars to retrofit their compressors with state-of-the-art lubrication technology. The older self-priming vacuum style pump-to-point systems are yielding to divider block systems whose positive displacement series flow valves properly proportion and distribute lubrication. These divider block systems monitor divider block piston movement and measure the flow of lubricating fluid. Divider block pumps avoid many of the problems associated with the self-priming vacuum style pump-to-point pumps by using a gravity or suction system. But more important, divider block systems can be automated to shut down and issue alarms should lubrication problems occur.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.638
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.242
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2001
Admission routes1
Has abstractyes

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