Underwater Gas Leak Quantification by Convolutional Neural Network Using Images

被引:0
|
作者
Caldas, Gustavo Luis Rodrigues [1 ,2 ]
Moreira, Roger Matsumoto [3 ]
de Souza Jr, Mauricio B. [1 ,2 ]
机构
[1] Univ Fed Rio Janeiro, Sch Chem, Chem & Biochem Proc Engn Program, BR-21941909 Rio De Janeiro, RJ, Brazil
[2] Univ Fed Rio Janeiro, Chem Engn Program, Inst Alberto Luiz Coimbra Pos Grad & Pesquisa Engn, BR-21941914 Rio De Janeiro, RJ, Brazil
[3] Univ Fed Fluminense, Sch Engn, BR-24210240 Niteroi, RJ, Brazil
关键词
subsea leaks; U-Net; bubble diameter; computer vision; deep learning; BUBBLE FORMATION; SIZE;
D O I
10.3390/pr13010118
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Exploration and production activities in deep-water oil and gas reservoirs can directly impact the surrounding ecosystems. Thus, a tool capable of measuring oil and gas leaks based on surveillance images, especially in pre-mature stages, is of great importance for ensuring safety and environmental protection. In the present work, a Convolutional Neural Network (U-Net) is applied to leak images using transfer learning and hyperparameter optimization, aiming to predict bubble diameter and flow rate. The data were extracted from a reduced model leak experiment, with a total of 77,676 frames processed, indicating a Big Data context. The results agreed with the data obtained in the laboratory: for the flow rate prediction, coefficients of determination by transfer learning and hyperparameter optimization were, respectively, 0.938 and 0.941. Therefore, this novel methodology has potential applications in the oil and gas industry, in which leaks captured by a camera are measured, supporting decision-making in the early stages and building a framework of a mitigation strategy in industrial environments.
引用
收藏
页数:38
相关论文
共 50 条
  • [1] Underwater object Images Classification Based on Convolutional Neural Network
    Zhu, Keqing
    Tian, Jie
    Huang, Haining
    2018 IEEE 3RD INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING (ICSIP), 2018, : 301 - 305
  • [2] Underwater Fleck Detection Using Convolutional Neural Network
    Pushpa Mala S.
    Prajwal Raju P.
    Poojashree B.
    Hebbar R.
    Bedre V.
    Manasa K.R.
    Journal of The Institution of Engineers (India): Series B, 2024, 105 (02) : 365 - 373
  • [3] A joint framework for underwater sequence images stitching based on deep neural network convolutional neural network
    Sheng, Mingwei
    Tang, Songqi
    Cui, Zhuang
    Wu, Wanqi
    Wan, Lei
    INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS, 2020, 17 (02)
  • [4] Gas leak detection in galvanised steel pipe with internal flow noise using convolutional neural network
    Song, Yanjue
    Li, Suzhen
    Process Safety and Environmental Protection, 2021, 146 : 736 - 744
  • [5] Gas leak detection in galvanised steel pipe with internal flow noise using convolutional neural network
    Song Yanjue
    Li Suzhen
    PROCESS SAFETY AND ENVIRONMENTAL PROTECTION, 2021, 146 : 736 - 744
  • [6] Classification of Histopathological Images Using Convolutional Neural Network
    Hatipoglu, Nuh
    Bilgin, Gokhan
    2014 4TH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS (IPTA), 2014, : 295 - 300
  • [7] Object Recognition in Images using Convolutional Neural Network
    Duth, Sudharshan P.
    Raj, Swathi
    PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON INVENTIVE SYSTEMS AND CONTROL (ICISC 2018), 2018, : 718 - 722
  • [8] Classification of Tank Images Using Convolutional Neural Network
    Liu, Ying
    Yu, Yongbin
    Wang, Lin
    Nyima, Tashi
    Zhaxi, Nima
    Huang, Hang
    Deng, Quanxin
    PROCEEDINGS OF 2020 IEEE 4TH INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC 2020), 2020, : 210 - 214
  • [9] Accuracy improvement of quantification information using super-resolution with convolutional neural network for microscopy images
    Kang, Mi-Sun
    Cha, Eunju
    Kang, Eunhee
    Ye, Jong Chul
    Her, Nam-Gu
    Oh, Jeong-Woo
    Nam, Do-Hyun
    Kim, Myoung-Hee
    Yang, Sejung
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2020, 58 (58)
  • [10] Adaptive Receiver Using Convolutional Neural Network for Underwater Wireless Communication
    Li, Min
    Wang, Ronghai
    Zhao, Limei
    Luo, Jinsheng
    Wu, Peiyang
    PROCEEDINGS OF 2023 THE 12TH INTERNATIONAL CONFERENCE ON NETWORKS, COMMUNICATION AND COMPUTING, ICNCC 2023, 2023, : 194 - 199