Matches in SemOpenAlex for { <https://semopenalex.org/work/W3199888957> ?p ?o ?g. }
Showing items 1 to 79 of
79
with 100 items per page.
- W3199888957 abstract "Abstract Well integrity has become a crucial field with increased focus and being published intensively in industry researches. It is important to maintain the integrity of the individual well to ensure that wells operate as expected for their designated life (or higher) with all risks kept as low as reasonably practicable, or as specified. Machine learning (ML) and artificial intelligence (AI) models are used intensively in oil and gas industry nowadays. ML concept is based on powerful algorithms and robust database. Developing an efficient classification model for well integrity (WI) anomalies is now feasible because of having enormous number of well failures and well barrier integrity tests, and analyses in the database. Circa 9000 dataset points were collected from WI tests performed for 800 wells in Gulf of Suez, Egypt for almost 10 years. Moreover, those data have been quality-controlled and quality-assured by experienced engineers. The data contain different forms of WI failures. The contributing parameter set includes a total of 23 barrier elements. Data were structured and fed into 11 different ML algorithms to build an automated systematic tool for calculating imposed risk category of any well. Comparison analysis for the deployed models was performed to infer the best predictive model that can be relied on. 11 models include both supervised and ensemble learning algorithms such as random forest, support vector machine (SVM), decision tree and scalable boosting techniques. Out of 11 models, the results showed that extreme gradient boosting (XGB), categorical boosting (CatBoost), and decision tree are the most reliable algorithms. Moreover, novel evaluation metrics for confusion matrix of each model have been introduced to overcome the problem of existing metrics which don't consider domain knowledge during model evaluation. The innovated model will help to utilize company resources efficiently and dedicate personnel efforts to wells with the high-risk. As a result, progressive improvements on business, safety, environment, and performance of the business. This paper would be a milestone in the design and creation of the Well Integrity Database Management Program through the combination of integrity and ML." @default.
- W3199888957 created "2021-09-27" @default.
- W3199888957 creator A5003321391 @default.
- W3199888957 creator A5019298007 @default.
- W3199888957 creator A5080076922 @default.
- W3199888957 date "2021-09-15" @default.
- W3199888957 modified "2023-10-16" @default.
- W3199888957 title "Multi-Class Taxonomy of Well Integrity Anomalies Applying Inductive Learning Algorithms: Analytical Approach for Artificial-Lift Wells" @default.
- W3199888957 cites W2053236197 @default.
- W3199888957 cites W2059121759 @default.
- W3199888957 cites W2170505850 @default.
- W3199888957 cites W2193967279 @default.
- W3199888957 cites W2256125974 @default.
- W3199888957 cites W2504255386 @default.
- W3199888957 cites W2766994678 @default.
- W3199888957 cites W2784278107 @default.
- W3199888957 cites W2790522369 @default.
- W3199888957 cites W2797283614 @default.
- W3199888957 cites W2944047995 @default.
- W3199888957 cites W3011343706 @default.
- W3199888957 cites W3154193034 @default.
- W3199888957 cites W4237611937 @default.
- W3199888957 doi "https://doi.org/10.2118/206129-ms" @default.
- W3199888957 hasPublicationYear "2021" @default.
- W3199888957 type Work @default.
- W3199888957 sameAs 3199888957 @default.
- W3199888957 citedByCount "3" @default.
- W3199888957 countsByYear W31998889572022 @default.
- W3199888957 crossrefType "proceedings-article" @default.
- W3199888957 hasAuthorship W3199888957A5003321391 @default.
- W3199888957 hasAuthorship W3199888957A5019298007 @default.
- W3199888957 hasAuthorship W3199888957A5080076922 @default.
- W3199888957 hasConcept C11413529 @default.
- W3199888957 hasConcept C119857082 @default.
- W3199888957 hasConcept C119898033 @default.
- W3199888957 hasConcept C12267149 @default.
- W3199888957 hasConcept C124101348 @default.
- W3199888957 hasConcept C139002025 @default.
- W3199888957 hasConcept C154945302 @default.
- W3199888957 hasConcept C169258074 @default.
- W3199888957 hasConcept C41008148 @default.
- W3199888957 hasConcept C45942800 @default.
- W3199888957 hasConcept C46686674 @default.
- W3199888957 hasConcept C48044578 @default.
- W3199888957 hasConcept C5274069 @default.
- W3199888957 hasConcept C77088390 @default.
- W3199888957 hasConcept C84525736 @default.
- W3199888957 hasConceptScore W3199888957C11413529 @default.
- W3199888957 hasConceptScore W3199888957C119857082 @default.
- W3199888957 hasConceptScore W3199888957C119898033 @default.
- W3199888957 hasConceptScore W3199888957C12267149 @default.
- W3199888957 hasConceptScore W3199888957C124101348 @default.
- W3199888957 hasConceptScore W3199888957C139002025 @default.
- W3199888957 hasConceptScore W3199888957C154945302 @default.
- W3199888957 hasConceptScore W3199888957C169258074 @default.
- W3199888957 hasConceptScore W3199888957C41008148 @default.
- W3199888957 hasConceptScore W3199888957C45942800 @default.
- W3199888957 hasConceptScore W3199888957C46686674 @default.
- W3199888957 hasConceptScore W3199888957C48044578 @default.
- W3199888957 hasConceptScore W3199888957C5274069 @default.
- W3199888957 hasConceptScore W3199888957C77088390 @default.
- W3199888957 hasConceptScore W3199888957C84525736 @default.
- W3199888957 hasLocation W31998889571 @default.
- W3199888957 hasOpenAccess W3199888957 @default.
- W3199888957 hasPrimaryLocation W31998889571 @default.
- W3199888957 hasRelatedWork W1978163942 @default.
- W3199888957 hasRelatedWork W2320316938 @default.
- W3199888957 hasRelatedWork W3100297620 @default.
- W3199888957 hasRelatedWork W3173611487 @default.
- W3199888957 hasRelatedWork W4283016678 @default.
- W3199888957 hasRelatedWork W4285298015 @default.
- W3199888957 hasRelatedWork W4293069612 @default.
- W3199888957 hasRelatedWork W4296901315 @default.
- W3199888957 hasRelatedWork W4298012357 @default.
- W3199888957 hasRelatedWork W46572615 @default.
- W3199888957 isParatext "false" @default.
- W3199888957 isRetracted "false" @default.
- W3199888957 magId "3199888957" @default.
- W3199888957 workType "article" @default.