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JACIII Vol.27 No.4 pp. 594-602
doi: 10.20965/jaciii.2023.p0594
(2023)

Research Paper:

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

Yuan Xu*, Di Zhang**, Tianlang Xian**, Zhizhang Ma***, Hui Gao**,†, and Yuanyuan Ma**

*Qinghai 906 Engineering Survey and Design Institute LLC
77 Haiyan Road, Xining, Qinghai 810007, China

**China University of Geosciences
388 Lumo Road, Wuhan 430074, China

Corresponding author

***Quality Supervision Station, Water Conservancy Construction Engineering in Hainan Tibetan Autonomous Prefecture
Xinghai East Road, Chengbei District, Chabucia, Gonghe, Hainan Tibetan Autonomous Prefecture, Qinghai 813000, China

Received:
December 31, 2022
Accepted:
March 12, 2023
Published:
July 20, 2023
Keywords:
water well, drilling fluid, OS-ELM, database
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.

ELM feedforward neural network structure

ELM feedforward neural network structure

Cite this article as:
Y. Xu, D. Zhang, T. Xian, Z. Ma, H. Gao, and Y. Ma, “Two-Direction Prediction Method of Drilling Fluid Based on OS-ELM for Water Well Drilling,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.4, pp. 594-602, 2023.
Data files:
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