A Decision-Making Expert System for the Oil Transport System
Bibliographic record
Abstract
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.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".