Two-Direction Prediction Method of Drilling Fluid Based on OS-ELM for Water Well Drilling

In this study, a drilling fluid prediction method based on an online sequential extreme learning machine (OS-ELM) is proposed, which is prepared for water well drilling on the muddy clay formation of Tarim Basin, Qinghai Province. First, we investigated the mechanism linking mix ratio to fluid perfo...

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Vydané v:Journal of advanced computational intelligence and intelligent informatics Ročník 27; číslo 4; s. 594 - 602
Hlavní autori: Xu, Yuan, Zhang, Di, Xian, Tianlang, Ma, Zhizhang, Gao, Hui, Ma, Yuanyuan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Tokyo Fuji Technology Press Co. Ltd 01.07.2023
Predmet:
Additives
Algorithms
Artificial neural networks
Construction accidents & safety
Drilling
Drilling fluids
Drilling machines (tools)
Drilling muds
Efficiency
Engineering
Hydrology
Machine learning
Parameter modification
Prediction models
Rheology
Slurries
Viscosity
Water wells
Well drilling
ISSN:1343-0130, 1883-8014
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Abstract In this study, a drilling fluid prediction method based on an online sequential extreme learning machine (OS-ELM) is proposed, which is prepared for water well drilling on the muddy clay formation of Tarim Basin, Qinghai Province. First, we investigated the mechanism linking mix ratio to fluid performance, allowing us to employ an OS-ELM algorithm derived from the extreme learning machine. Particularly, the proposed prediction method is bidirectional to identify an appropriate slurry formulation. The forward prediction model is established to predict the fluid performance, where the mud additive contents are inputs, and the drilling fluid properties parameters are outputs. Correspondingly, the backward prediction model is established to modify the slurry formula, where differences in the drilling fluid properties are inputs and percentages of slurry additives amount are output. The simulation results show that the two-direction OS-ELM prediction model can better predict the drilling fluid properties in water well drilling.
AbstractList In this study, a drilling fluid prediction method based on an online sequential extreme learning machine (OS-ELM) is proposed, which is prepared for water well drilling on the muddy clay formation of Tarim Basin, Qinghai Province. First, we investigated the mechanism linking mix ratio to fluid performance, allowing us to employ an OS-ELM algorithm derived from the extreme learning machine. Particularly, the proposed prediction method is bidirectional to identify an appropriate slurry formulation. The forward prediction model is established to predict the fluid performance, where the mud additive contents are inputs, and the drilling fluid properties parameters are outputs. Correspondingly, the backward prediction model is established to modify the slurry formula, where differences in the drilling fluid properties are inputs and percentages of slurry additives amount are output. The simulation results show that the two-direction OS-ELM prediction model can better predict the drilling fluid properties in water well drilling.
Author Xu, Yuan
Ma, Yuanyuan
Ma, Zhizhang
Xian, Tianlang
Gao, Hui
Zhang, Di
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Cites_doi 10.1007/s11432-014-5269-3
10.1002/rnc.4639
10.1097/01.BACK.0000605144.87361.a1
10.1016/j.measurement.2021.109673
10.1016/j.neucom.2007.02.009
10.1016/j.optlastec.2018.10.057
10.1016/j.ast.2018.09.044
10.1016/S1876-3804(22)60055-7
10.1002/app.47292
10.1016/j.automatica.2020.108973
10.1016/j.jfranklin.2020.01.013
10.3233/IDA-184377
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SubjectTerms Additives
Algorithms
Artificial neural networks
Construction accidents & safety
Drilling
Drilling fluids
Drilling machines (tools)
Drilling muds
Efficiency
Engineering
Hydrology
Machine learning
Parameter modification
Prediction models
Rheology
Slurries
Viscosity
Water wells
Well drilling
Title Two-Direction Prediction Method of Drilling Fluid Based on OS-ELM for Water Well Drilling
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