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Enregistrement W4236006325 · doi:10.2523/69423-ms

A Decision-Making Expert System for the Oil Transport System

2001· article· en· W4236006325 sur OpenAlexafffundabout
Abdulatif Abdulah, Rafiqul Islam

Notice bibliographique

RevueProceedings of SPE Latin American and Caribbean Petroleum Engineering Conference · 2001
Typearticle
Langueen
DomaineEngineering
ThématiqueReservoir Engineering and Simulation Methods
Établissements canadiensDalhousie University
Organismes subventionnairesNatural Sciences and Engineering Research Council of Canada
Mots-clésCitationLatin AmericansPetroleumComputer sciencePetroleum industryOperations researchLibrary scienceEngineeringPolitical scienceGeology

Résumé

récupéré en direct d'OpenAlex

A Decision-Making Expert System for the Oil Transport System Abdulatif Abdulah; Abdulatif Abdulah Dalhousie University Search for other works by this author on: This Site Google Scholar Rafiqul Islam Rafiqul Islam Dalhousie University Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, Buenos Aires, Argentina, March 2001. Paper Number: SPE-69423-MS https://doi.org/10.2118/69423-MS Published: March 25 2001 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Abdulah, Abdulatif, and Rafiqul Islam. "A Decision-Making Expert System for the Oil Transport System." Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, Buenos Aires, Argentina, March 2001. doi: https://doi.org/10.2118/69423-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Latin America and Caribbean Petroleum Engineering Conference Search Advanced Search AbstractThe problem of pipeline corrosion within the oil and gas industry costs the world economy billions of dollars every year in maintenance, repairs and too often in damage control. These costs are passed on, reflected in increased prices to the world's petroleum product consumers. With the advent of widely available computing and communications technology, it is logical that we should seek relief from corrosion and maintenance problems in the form of a high-tech solution. To this end the authors have developed an expert system, the Petroleum Corrosion and Coating Expert System (PCCES) equipped with an extensive knowledgebase of physical and chemical phenomena and the metallurgical characteristics of the pipes themselves. Essentially a complex decision tree, the expert considers factors in a real-world situation and attempts to produce appropriate conclusions based on inference from the knowledgebase. For greater ease of use, the expert system relies on a Java applet design, eliminating the need for proprietary client-side software.IntroductionThe deterioration of the components of gas and oil pipelines costs billions of dollars every year. It was estimated that in the United States alone the problem of corrosion cost 33 billion dollars in 1989 GNP. The application of control strategy for protection and coating of these pipelines would help the efforts to conserve natural resources1.Human experts in the field may give good advice on how control and protection strategies may be implemented, but human experts are not always available to the on-site maintenance personnel. Geographical and cost considerations make it impossible to provide live expert input at every turn. When, as is usually the case, the questions and tasks involved are of a repetitive nature (i.e. one case of pipeline maintenance bears much resemblance to another), there is a strong motivation for the design of an Expert system; a simple application of Artificial Intelligence able to analyze a situation based on real-world information and utilizes coded concrete information from human experts (knowledge) to infer appropriate actions and advise on-site personnel.The primary development goals for this expert system are as follows:Optimization of pipeline maintenance operations, thereby optimizing investments in Oil and Gas pipeline hardware and maintenance.Provision of a system suitable both for training and for direct application to everyday pipeline maintenance problems.Providing regular maintenance personnel instantly with the information needed to solve problems, which would normally require input from a human expert.Increasing the speed and reliability of solutions implemented in the field.Cost reduction through optimization.Assistance in the automation of uncomfortable and monotonous operations.Enabling wider access to technical knowledge within organizations.Design of the Expert SystemThe knowledge acquisition, representation and knowledgebase development for a pipeline corrosion and coating expert system begins with the acquisition of expert information, accomplished both by interview and review of published information. For the initial development we have seeded the system with knowledge from Dr. R. Islam, a noted authority in the field of petroleum engineering.As development continues, information from other experts and a wide ranging literature search will be integrated into the expert system's knowledgebase to provide it with an ever-widening area of expertise. Keywords: use case, interface class, abdulatif abdulah, expert system, midstream oil & gas, sequence diagram, information, software, database, spe 69423 Subjects: Information Management and Systems, Artificial intelligence This content is only available via PDF. 2001. Society of Petroleum Engineers You can access this article if you purchase or spend a download.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,377
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,012
Tête enseignante GPT0,238
Écart entre enseignants0,226 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2001
Routes d'admission3
Résumé présentoui

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