logo

EbookBell.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link:  https://ebookbell.com/faq 


We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.


For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookBell Team

Machine Learning Applications In Subsurface Energy Resource Management State Of The Art And Future Prognosis Srikanta Mishra

  • SKU: BELL-48669816
Machine Learning Applications In Subsurface Energy Resource Management State Of The Art And Future Prognosis Srikanta Mishra
$ 31.00 $ 45.00 (-31%)

5.0

18 reviews

Machine Learning Applications In Subsurface Energy Resource Management State Of The Art And Future Prognosis Srikanta Mishra instant download after payment.

Publisher: CRC Press
File Extension: PDF
File size: 20.85 MB
Pages: 378
Author: Srikanta Mishra
ISBN: 9781032074559, 1032074558
Language: English
Year: 2024

Product desciption

Machine Learning Applications In Subsurface Energy Resource Management State Of The Art And Future Prognosis Srikanta Mishra by Srikanta Mishra 9781032074559, 1032074558 instant download after payment.

The utilization of machine learning (ML) techniques to understand hidden patterns and build data-driven predictive models from complex multivariate datasets is rapidly increasing in many applied science and engineering disciplines,
including geo-energy. Motivated by these developments, Machine Learning Applications in Subsurface Energy Resource Management presents a current snapshot of the state of the art and future outlook for ML applications to manage subsurface energy resources (e.g., oil and gas, geologic carbon sequestration, and geothermal energy).

Related Products