Artificial neural network (ANN) approach to predict unconfined compressive strength (UCS) of oil and gas well cement reinforced with nanoparticles

Yildirim Kocoglu, Phillip D. McElroy, Heber Bibang, Seyedhossein Emadibaladehi, Athar Hussain, Marshall Watson

Research output: Contribution to journalArticlepeer-review

Abstract

The prediction of unconfined compressive strength (UCS) of oil well cement class “H” based on the artificial neural network (ANN) modeling approach is presented in this study. 195 cement samples were embedded with varying dosages of strength enhancing pre-dispersed nanoparticles consisting of nanosilica (nano-SiO2), nano-alumina (nano-Al2O3), and nanotitanium dioxide (nano-TiO2) at various simulated wellbore temperatures. The efficacy of the pre-dispersed nanoparticle solutions was analyzed by transmission electron microscope (TEM)images. Nano-SiO2 and nano-Al2O3 displayed excellent dispersibility throughout the solution. However, nano-TiO2 readily agglomerates which, at high concentrations, is detrimental to the UCS of cement. 70% of the data set was used to train the ANN model, 15% was used for validation, and 15% was used to test the model. The model consisted of one input layer with five nodes, one hidden layer with 12 nodes, and one output layer with one node.12 nodes in
Original languageEnglish
JournalJournal of Natural Gas Science and Engineering
StatePublished - Apr 2021

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