A novel machine learning model for autonomous analysis and diagnosis of well integrity failures in artificial-lift production systems

Salem, Adel M.; Yakoot, Mostafa S.; Mahmoud, Omar

doi:10.46690/ager.2022.02.05

Cited by 6 publications

(1 citation statement)

References 22 publications

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“…With the development of the computational power, the image treatment driven by the artificial intelligence algorithm is adopted frequently in the geological and mining engineering [16][17][18][19][20][21][22][23][24][25][26][27]. For example, under different light source positions, Saricam and Ozturk [16] located the positions of the fractures in terms of the shadows, segmented the drilling core pieces, and calculated the RQD value.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Algorithm for Rock Core RQD Based on Object Detection and Image Segmentation to Suppress Noise and Vibration

Xiong,

Wang,

Zhang

et al. 2024

Advances in Civil Engineering

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In the construction of the civil engineering infrastructure, the noise and vibration are affected by the geological conditions, adopting specific construction techniques based on the geological conditions is of great significance in suppressing the noise and vibration caused by the construction. To classify and evaluate the rock mass quality, the rock quality designation (RQD) is adopted widely in the geological and mining engineering. Traditionally, to obtain RQD, lengths of drilling core pieces are measured and RQD is calculated manually, which is labor-expensive and time-consuming. With the development of the computational power, the image treatment driven by the computer vision creates a potential approach to obtain RQD automatically. In the present work, the image treatment process with the aid of the object detection and the image segmentation is adopted to obtain RQD automatically, based on the similarity of features such as color and texture, the segment anything model is adopted to detect the rock cores in the image, then, the YOLOv8 algorithm is adopted to train the model, and the gap features of the rock chip segments are extracted for segmentation of different rock core segments. To test the performance of the proposed approach, 10 boreholes from Shapingba Railway Comprehensive Reconstruction Project are adopted to conduct the case study. Compared to the traditional manual approach, RQD obtained by the proposed approach is relatively accurate and obviously efficient, namely, the average error is less than 5% and the time consumed is less than 70%.

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Section: Introductionmentioning

confidence: 99%

Intelligent Algorithm for Rock Core RQD Based on Object Detection and Image Segmentation to Suppress Noise and Vibration

Xiong,

Wang,

Zhang

et al. 2024

Advances in Civil Engineering

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Architecture of Pension Service Platform on Account of Machine Learning and Big Data Analysis

Chen

2023

Lecture Notes in Electrical Engineering

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Dimensionless Data-Driven Model for Cuttings Concentration Prediction in Eccentric Annuli: Statistical and Parametric Approach

Saad,

Gawish,

Mahmoud

2024

Arab J Sci Eng

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A novel machine learning model for autonomous analysis and diagnosis of well integrity failures in artificial-lift production systems

Abstract: O. A novel machine learning model for autonomous analysis and diagnosis of well integrity failures in artificial-lift production systems. Advances in

Cited by 6 publications

References 22 publications

Intelligent Algorithm for Rock Core RQD Based on Object Detection and Image Segmentation to Suppress Noise and Vibration

Intelligent Algorithm for Rock Core RQD Based on Object Detection and Image Segmentation to Suppress Noise and Vibration

Architecture of Pension Service Platform on Account of Machine Learning and Big Data Analysis

Dimensionless Data-Driven Model for Cuttings Concentration Prediction in Eccentric Annuli: Statistical and Parametric Approach

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