Matches in SemOpenAlex for { <https://semopenalex.org/work/W3206626471> ?p ?o ?g. }
Showing items 1 to 76 of
76
with 100 items per page.
- W3206626471 abstract "Abstract Constructing and maintaining integrity for different types of wells requires accurate assessment of posed risk level, especially when one barrier element or group of barriers fails. Risk assessment and well integrity (WI) categorization is conducted typically using traditional spreadsheets and in-house software that contain their own inherent errors. This is mainly because they are subjected to the understanding and the interpretation of the assigned team to WI data. Because of these limitations, industrial practices involve the collection and analysis of failure data to estimate risk level through certain established probability/likelihood matrices. However, those matrices have become less efficient due to the possible bias in failure data and consequent misleading assessment. The main objective of this work is to utilize machine learning (ML) algorithms to develop a powerful model and predict WI risk category of gas-lifted wells. ML algorithms implemented in this study are; logistic regression, decision trees, random forest, support vector machines, k-nearest neighbors, and gradient boosting algorithms. In addition, those algorithms are used to develop physical equation to predict risk category. Three thousand WI and gas-lift datasets were collected, preprocessed, and fed into the ML model. The newly developed model can predict well risk level and provide a unique methodology to convert associated failure risk of each element in the well envelope into tangible value. This shows the total potential risk and hence the status of well-barrier integrity overall. The implementation of ML can enhance brownfield asset operations, reduce intervention costs, better control WI through the field, improve business performance, and optimize production." @default.
- W3206626471 created "2021-10-25" @default.
- W3206626471 creator A5003321391 @default.
- W3206626471 creator A5019298007 @default.
- W3206626471 creator A5080076922 @default.
- W3206626471 date "2021-10-18" @default.
- W3206626471 modified "2023-09-23" @default.
- W3206626471 title "Machine Learning Application for Gas Lift Performance and Well Integrity" @default.
- W3206626471 cites W2053236197 @default.
- W3206626471 cites W2057799714 @default.
- W3206626471 cites W2059121759 @default.
- W3206626471 cites W2504255386 @default.
- W3206626471 cites W2509594888 @default.
- W3206626471 cites W2512510272 @default.
- W3206626471 cites W2531257189 @default.
- W3206626471 cites W2585540576 @default.
- W3206626471 cites W2623061109 @default.
- W3206626471 cites W2897611820 @default.
- W3206626471 cites W2897739082 @default.
- W3206626471 cites W3011343706 @default.
- W3206626471 cites W3016559868 @default.
- W3206626471 doi "https://doi.org/10.2118/205134-ms" @default.
- W3206626471 hasPublicationYear "2021" @default.
- W3206626471 type Work @default.
- W3206626471 sameAs 3206626471 @default.
- W3206626471 citedByCount "3" @default.
- W3206626471 countsByYear W32066264712022 @default.
- W3206626471 crossrefType "proceedings-article" @default.
- W3206626471 hasAuthorship W3206626471A5003321391 @default.
- W3206626471 hasAuthorship W3206626471A5019298007 @default.
- W3206626471 hasAuthorship W3206626471A5080076922 @default.
- W3206626471 hasConcept C112930515 @default.
- W3206626471 hasConcept C119857082 @default.
- W3206626471 hasConcept C12174686 @default.
- W3206626471 hasConcept C12267149 @default.
- W3206626471 hasConcept C124101348 @default.
- W3206626471 hasConcept C127413603 @default.
- W3206626471 hasConcept C139002025 @default.
- W3206626471 hasConcept C154945302 @default.
- W3206626471 hasConcept C169258074 @default.
- W3206626471 hasConcept C200601418 @default.
- W3206626471 hasConcept C38652104 @default.
- W3206626471 hasConcept C41008148 @default.
- W3206626471 hasConcept C71924100 @default.
- W3206626471 hasConcept C84525736 @default.
- W3206626471 hasConceptScore W3206626471C112930515 @default.
- W3206626471 hasConceptScore W3206626471C119857082 @default.
- W3206626471 hasConceptScore W3206626471C12174686 @default.
- W3206626471 hasConceptScore W3206626471C12267149 @default.
- W3206626471 hasConceptScore W3206626471C124101348 @default.
- W3206626471 hasConceptScore W3206626471C127413603 @default.
- W3206626471 hasConceptScore W3206626471C139002025 @default.
- W3206626471 hasConceptScore W3206626471C154945302 @default.
- W3206626471 hasConceptScore W3206626471C169258074 @default.
- W3206626471 hasConceptScore W3206626471C200601418 @default.
- W3206626471 hasConceptScore W3206626471C38652104 @default.
- W3206626471 hasConceptScore W3206626471C41008148 @default.
- W3206626471 hasConceptScore W3206626471C71924100 @default.
- W3206626471 hasConceptScore W3206626471C84525736 @default.
- W3206626471 hasLocation W32066264711 @default.
- W3206626471 hasOpenAccess W3206626471 @default.
- W3206626471 hasPrimaryLocation W32066264711 @default.
- W3206626471 hasRelatedWork W3127425528 @default.
- W3206626471 hasRelatedWork W3143658565 @default.
- W3206626471 hasRelatedWork W3195168932 @default.
- W3206626471 hasRelatedWork W3204641204 @default.
- W3206626471 hasRelatedWork W3210877509 @default.
- W3206626471 hasRelatedWork W4205958290 @default.
- W3206626471 hasRelatedWork W4283016678 @default.
- W3206626471 hasRelatedWork W4283762323 @default.
- W3206626471 hasRelatedWork W4312707991 @default.
- W3206626471 hasRelatedWork W4321636153 @default.
- W3206626471 isParatext "false" @default.
- W3206626471 isRetracted "false" @default.
- W3206626471 magId "3206626471" @default.
- W3206626471 workType "article" @default.