LEADER 05423nam 22007215 450 001 9910682598903321 005 20251009080449.0 010 $a3-031-24231-9 024 7 $a10.1007/978-3-031-24231-1 035 $a(MiAaPQ)EBC7214035 035 $a(Au-PeEL)EBL7214035 035 $a(DE-He213)978-3-031-24231-1 035 $a(PPN)269099913 035 $a(CKB)26262627200041 035 $a(EXLCZ)9926262627200041 100 $a20230311d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning and Flow Assurance in Oil and Gas Production /$fedited by Bhajan Lal, Cornelius Borecho Bavoh, Jai Krishna Sahith Sayani 205 $a1st ed. 2023. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2023. 215 $a1 online resource (179 pages) 311 08$aPrint version: Lal, Bhajan Machine Learning and Flow Assurance in Oil and Gas Production Cham : Springer International Publishing AG,c2023 9783031242304 320 $aIncludes bibliographical references. 327 $aChapter 1: Machine Learning and Flow Assurance Issues -- Chapter 2: Machine Learning in Oil and Gas Industry -- Chapter 3: Multiphase Flow Systems and Potential of Machine Learning Approaches in Cutting Transport and Liquid Loading Scenarios -- Chapter 4: Machine Learning in Corrosion -- Chapter 5: Machine Learning in Asphaltenes Mitigation -- Chapter 6: Machine learning for Scale deposition in oil and gas industry -- Chapter 7: Machine Learning in CO2 sequestration -- Chapter 8: Machine Learning in Wax Deposition -- Chapter 9: Machine Learning Application in Gas Hydrates -- Chapter 10: Machine Learning Application Guidelines in Flow Assurance. 330 $aThis book is useful to flow assurance engineers, students, and industries who wish to be flow assurance authorities in the twenty-first-century oil and gas industry. The use of digital or artificial intelligence methods in flow assurance has increased recently to achieve fast results without any thorough training effectively. Generally, flow assurance covers all risks associated with maintaining the flow of oil and gas during any stage in the petroleum industry. Flow assurance in the oil and gas industry covers the anticipation, limitation, and/or prevention of hydrates, wax, asphaltenes, scale, and corrosion during operation. Flow assurance challenges mostly lead to stoppage of production or plugs, damage to pipelines or production facilities, economic losses, and in severe cases blowouts and loss of human lives. A combination of several chemical and non-chemical techniques is mostly used to prevent flow assurance issues in the industry. However, the use of models to anticipate, limit, and/or prevent flow assurance problems is recommended as the best and most suitable practice. The existing proposed flow assurance models on hydrates, wax, asphaltenes, scale, and corrosion management are challenged with accuracy and precision. They are not also limited by several parametric assumptions. Recently, machine learning methods have gained much attention as best practices for predicting flow assurance issues. Examples of these machine learning models include conventional approaches such as artificial neural network, support vector machine (SVM), least square support vector machine (LSSVM), random forest (RF), and hybrid models. The use of machine learning in flow assurance is growing, and thus, relevant knowledge and guidelines on their application methods and effectiveness are needed for academic, industrial, and research purposes. In this book, the authors focus on the use and abilities of various machine learning methods in flow assurance. Initially, basic definitions and use of machine learning in flow assurance are discussed in a broader scope within the oil and gas industry. The rest of the chapters discuss the use of machine learning in various flow assurance areas such as hydrates, wax, asphaltenes, scale, and corrosion. Also, the use of machine learning in practical field applications is discussed to understand the practical use of machine learning in flow assurance. 606 $aCogeneration of electric power and heat 606 $aFossil fuels 606 $aPetrology 606 $aProduction engineering 606 $aIndustrial engineering 606 $aFluid mechanics 606 $aFossil Fuel 606 $aPetrology 606 $aProcess Engineering 606 $aIndustrial and Production Engineering 606 $aEngineering Fluid Dynamics 615 0$aCogeneration of electric power and heat. 615 0$aFossil fuels. 615 0$aPetrology. 615 0$aProduction engineering. 615 0$aIndustrial engineering. 615 0$aFluid mechanics. 615 14$aFossil Fuel. 615 24$aPetrology. 615 24$aProcess Engineering. 615 24$aIndustrial and Production Engineering. 615 24$aEngineering Fluid Dynamics. 676 $a060 676 $a665.5440285631 702 $aLal$b Bhajan 702 $aBavoh$b Cornelius Borecho 702 $aSahith Sayani$b Jai Krishna 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910682598903321 996 $aMachine learning and flow assurance in oil and gas production$93374612 997 $aUNINA