Machine Learning and Flow Assurance in Oil and Gas Production

Language: English

Published by Springer Nature Switzerland Mrz 2024, 2024

3031242335 / 9783031242335

  • Softcover
  • New
See all details

Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.

5-star seller

AbeBooks seller since January 11, 2012

View this seller's items
Softcover

Condition: New

US$ 200.86

US$ 26.20 shipping 
Ships from Germany to U.S.A.

Quantity: 2 available

Add to basket
Free 30-day returns

Item description from seller

This item is printed on demand - it takes 3-4 days longer - Neuware -This 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 gasindustry 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 assurancemodels 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 muchattention 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 learningin 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 flowassurance 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. 188 pp. Englisch.…

Seller Inventory # 9783031242335

Title
Machine Learning and Flow Assurance in Oil and Gas Production
Author
Bhajan Lal
Publisher
Springer Nature Switzerland Mrz 2024
Publication year
2024
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3031242335
ISBN 13
9783031242335
Item weight
295 grams
Dimensions
235x155x11 mm

BuchWeltWeit Ludwig Meier e.K.

Bergisch Gladbach, Germany

5-star seller

AbeBooks seller since January 11, 2012

Shipping rates from Germany to U.S.A.

Item5 to 15 business days5 to 15 business days
First itemUS$ 26.20US$ 26.20
Delivery times are set by sellers and vary by carrier and location. Orders passing through Customs may face delays and buyers are responsible for any associated duties or fees. Sellers may contact you regarding additional charges to cover any increased costs to ship your items.

Payment methods

  • Visa
  • Mastercard
  • American Express
  • Apple Pay
  • Google Pay
  • Bank Wire Transfer
  • Check
  • Paypal

Seller's business information

BuchWeltWeit Ludwig Meier e.K.

Germany