Machine Learning in Wax Deposition

• Main Authors: Ul Haq, I., Lal, B.
• Format: Book
• Published: Springer Nature 2023
• Online Access: http://scholars.utp.edu.my/id/eprint/38040/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174780244&doi=10.1007%2f978-3-031-24231-1_8&partnerID=40&md5=3d1db27e798dfd4de6e247b35cdb92ba

One of the biggest sectors providing energy demands is the oil and gas industry. Ensuring a continuous fuel supply necessitates flow assurance. Flow assur­ance is challenging and crucial for subsea pipelines since the seawater temperature, as well as the encircling temperature, is typically much colder than the exterior atmospheric temperature. Wax deposition takes place when the inner pipe temper­ature drops below the crude oil cloud temperature. Such challenges emerge when the paraffins in crude oil crystallise and accumulate on the cold upstream wall. The primary issue affecting flow assurance or decreasing the efficiency of oil and gas transportation is wax deposition. Consequently, this chapter provides an insight into methodologies for reducing and monitoring wax accumulation. In addition, the numerical models for wax deposition are highlighted. In the final section, the role of artificial intelligence (machine learning) in the efficacious prediction of wax or the enhancement of various numerical models is affirmed. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.