Intelligent cement design: Utilizing machine learning algorithms to assure effective long-term well integrity

Arash Shadravan, Mohammadali Tarrahi, Mahmood Amani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Designing drilling fluids, spacers and cement slurries are all often done by trial and error in the laboratory. There are several test data that would not be used in this method and it is hard to digest a plethora of information for users and take intelligent and cost-effective decision to design a fluid with the desired properties. Therefore, trial and error method is considered to be time consuming, very costly and misleading. Today, there is a need for an intelligent system which uses all the available fluid design data stored in a database by which we can benefit from its insights for smart fluid designs. This predictive tool suggest a composition for drilling fluids, spacer fluid or cement slurry by implementing a machine learning algorithm on imported experimental data. This paper investigates and implements a data-driven predictive tool which uses Gaussian Process Regression (GPR). GPR as a novel machine learning method considerably reduces the costs of testing, optimizes the material use, integrates available experimental data and eliminates the user bias. This practical nonlinear regression method fosters an efficient and fast prediction analysis which do not require including complex physics of the underlying intricate chemical fluid behavior while integrating all available data from different databases. GPR has exceptional advantages over traditional regression methods since it does not require a known form for regression function. Also its capability of determining estimation error and confidence interval is unique. This machine learning based tool offers comprehensive insights for intelligent fluid design and considerably reduces the experiment cost. This study showcases an example through which of GPR predicts rheological properties and helped engineers to maintain the fluid rheological hierarchy for better cement jobs and well integrity. Zonal isolation has the utmost importance for carbon sequestration projects.

Original languageEnglish
Title of host publicationCarbon Management Technology Conference 2015
Subtitle of host publicationSustainable and Economical CCUS Options, CMTC 2015
PublisherAIChE
Pages1102-1113
Number of pages12
ISBN (Electronic)9781510818866
Publication statusPublished - 1 Jan 2015
EventCarbon Management Technology Conference 2015: Sustainable and Economical CCUS Options, CMTC 2015 - Sugar Land, United States
Duration: 17 Nov 201519 Nov 2015

Publication series

NameCarbon Management Technology Conference 2015: Sustainable and Economical CCUS Options, CMTC 2015
Volume2

Other

OtherCarbon Management Technology Conference 2015: Sustainable and Economical CCUS Options, CMTC 2015
CountryUnited States
CitySugar Land
Period17/11/1519/11/15

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Media Technology
  • Environmental Science(all)

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  • Cite this

    Shadravan, A., Tarrahi, M., & Amani, M. (2015). Intelligent cement design: Utilizing machine learning algorithms to assure effective long-term well integrity. In Carbon Management Technology Conference 2015: Sustainable and Economical CCUS Options, CMTC 2015 (pp. 1102-1113). (Carbon Management Technology Conference 2015: Sustainable and Economical CCUS Options, CMTC 2015; Vol. 2). AIChE.