Research on Measurement and Prediction of Well Blowout Fluid Flow Rate based on Machine Vision

Authors

  • Haimin Cai
  • He Zhang

DOI:

https://doi.org/10.54691/1pvk2p61

Keywords:

Machine Vision; Flow Rate Measurement; Image Processing; Feature Recognition; Drilling Blowout.

Abstract

A machine vision-based method for measuring and predicting well blowout fluid flow rate is proposed to solve the problems of limited quantitative identification of well blowout fluid flow rate, inability to install traditional measurement equipment in the field, and inability to accurately measure and predict blowout fluid flow rate. This method establishes an image processing model based on ORB-BF-RANSAC for blowout sequence images, feature extraction and feature identification are performed on the blowout images from consecutive frames in order to obtain the coordinates of the feature points within the well blowout images. The pixel displacement of the feature point pairs is then calculated. Subsequently, the true displacement of the feature points is calculated using the pinhole imaging principle. Finally, the true displacement is divided by the number of frames to calculate the blowout fluid flow rate. Compared to the speed of mass flow meter calibration, the method achieves a measurement accuracy of 91%. Based on the above method, a well blowout fluid flow rate prediction method is designed by introducing Spatial Attention Mechanism (SAM) to improve the Graph Convolutional Networks (GCN). The prediction model proposed in this paper is more effective compared with other prediction methods, and the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) are 12.39%, 18.72% and 9.16%.

Downloads

Download data is not yet available.

References

P. Blotto, M. Bonuccelli, G. Morale, E. Dellarole, M. Falcitelli, F. Podenzani, Development of a Integrated Approach to the Risk Analysis of a Blow-out Accident, SPE International Conference on Health, Safety, and Environment in Oil and Gas Exploration and Production, 2004.

S. Rassenfoss, Report Recounts the Missed Signals Leading to a Blowout That Killed Five, (JPT Emerging Technology Senior Editor) View rights & permissions Vol.71(No.8) (2019) 40-42. https://doi.org/10.2118/0819-0040-jpt.

W.F. Jr., Deepwater Blowout, A Case History; Shallow Gas Hazards Hide-in-the-Weeds, 2007.

T.J. Crone, M. Tolstoy, Magnitude of the 2010 Gulf of Mexico Oil Leak, Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA Department of Earth and Environmental Sciences, Columbia University, Palisades, NY 10964, USA Vol.330(No.6004) (2010) 634. https://doi.org/10.1126/science.1195840.

C.B. Paris, M.L. Hénaff, Z.M. Aman, A. Subramaniam, J. Helgers, D.-P. Wang, V.H. Kourafalou, A. Srinivasan, Evolution of the Macondo Well Blowout: Simulating the Effects of the Circulation and Synthetic Dispersants on the Subsea Oil Transport, University of Miami, Rosenstiel School of Marine and Atmospheric Science, 4600 Rickenbacker Causeway, Miami, Florida 33149-1098, United States;University of Miami, Cooperative Institute for Marine and Atmospheric Studi Vol.46(No.24) (2012) 13293-13302. https://doi.org/10.1021/es303197h.

G.E.E. Jerry Westerweel, R.J. Adrian, Particle Image Velocimetry for Complex and Turbulent Flows, Annual Review of Fluid Mechanics Vol.45(No.1) (2013) 409-436.

A. Hitelman, Y. Edan, A. Godo, R. Berenstein, J. Lepar, I. Halachmi, Biometric identification of sheep via a machine-vision system, Precision Livestock Farming (PLF) Lab., Agricultural Engineering Institute, Agricultural Research Organization (A.R.O.) - Volcani Institute, 68 Hamaccabim Road, P.O.B 15159, Rishon Lezion 7505101, Israel Dept. of I Vol.194 (2022) 106713. https://doi.org/ 10.1016/j.compag.2022.106713.

A. Dhiman, N. Shah, P. Adhikari, S. Kumbhar, I.S. Dhanjal, N. Mehendale, Firefighting robot with deep learning and machine vision, K. J. Somaiya College of Engineering, Mumbai, India K. J. Somaiya College of Engineering, Mumbai, India K. J. Somaiya College of Engineering, Mumbai, India K. J. Somaiya College of Engineering, Mumbai, India K. J. Somai Vol.34(No.4) (2022) 2831-2839. https://doi.org/10.1007/s00521-021-06537-y.

D. Ribli, A. Horváth, Z. Unger, P. Pollner, I. Csabai, Detecting and classifying lesions in mammograms with Deep Learning, Department of Physics of Complex Systems, Eötvös Loránd University, Budapest, Hungary;3rd Department of Internal Medicine, Semmelweis University, Budapest, Hungary;Department of Radiology, Semmelweis University, Budapest, Hungary;MTA Vol.8(No.1) (2018) 1-7. https://doi.org/10.1038/s41598-018-22437-z.

J. Kikani, A Method for Blowout-Rate Prediction for Sour-Gas Wells, Oldenburg Fields, Germany, (Shell E&P Technology Company);(BEB Erdgas & Erdol Gmbh);(BEB Erdgas & Erdol Gmbh) Vol.11(No.3) (1996) 158-162. https://doi.org/10.2118/35582-pa.

P. Oudeman, Analysis of Surface and Wellbore Hydraulics Provides Key to Efficient Blowout Control, (Shell Intl. E&P) Vol.13(No.3) (1998) 163-173. https://doi.org/10.2118/51179-pa.

A.R. Hasan, C.S. Kabir, D. Lin, Modeling Wellbore Dynamics During Oil Well Blowout, International Oil and Gas Conference and Exhibition in China, 2000.

L. Tang, S.M. Masutani, Laminar to Turbulent Flow Liquid-liquid Jet Instability And Breakup, The Thirteenth International Offshore and Polar Engineering Conference, 2003.

Ø. Arild, K.K. Fjelde, A. Merlo, B. Daireaux, T. Løberg, BlowFlow - a New Tool within Blowout Risk Management, IADC/SPE Asia Pacific Drilling Technology Conference and Exhibition, 2008.

E. Hajidavalloo, P. Omidian, Modeling and Simulation of Flow Field Around a Blowout Well, SPE Journal Vol.17(No.1) (2012) 212-218. https://doi.org/10.2118/149777-pa.

A. Turner, P. Loustau, Fiber Optic Sensor Based Monitoring System for Blowout Preventer, Offshore Technology Conference, 2015.

M.D. Dunn, S. Fitzgerald, J.B. Garner, Predicting Hydrocarbon Burn Efficiency of Ignited Blowout for Oil Spill Source Control, IADC/SPE Drilling Conference and Exhibition, 2018.

M. Evestedt, A. Medvedev, GAS JET IMPINGING ON LIQUID SURFACE: CAVITY SHAPE MODELLING AND VIDEO-BASED ESTIMATION, Department of Information Technology, Uppsala University, P. O. Box 337, SE-951 05, SWEDEN Vol.38(No.1) (2005) 1065-1070. https://doi.org/10.3182/ 200507 03-6-cz-1902.00178.

J.D. Osorio-Cano, A. Osorio, eacute, s. F, R. Medina, A method for extracting surface flow velocities and discharge volumes from video images in laboratory, Research group OCEANICOS, Department of Geosciences and Environment, Universidad Nacional de Colombia sede Medellín, Carrera 80 #65-223, Medellín, Colombia; Instituto de Hidráulica Ambiental (IH), Universidad de Canta Vol.33 (2013) 188-196. https://doi.org/10.1016/j.flowmeasinst.2013.07.009.

M.W.d.S. Oliveira, N.R. da Silva, A. Manzanera, O.M. Bruno, Feature extraction on local jet space for texture classification, [ 1 ] Univ Sao Paulo, Inst Math & Comp Sci, USP, BR-13566590 Sao Carlos, SP, Brazil Universidade de Sao Paulo [ 2 ] Univ Sao Paulo, Sao Carlos Inst Phys, Sci Comp Grp, BR-13566590 Sao Carlo Vol.439(No.0) (2015) 160-170. https://doi.org/10.1016/j.physa.2015.06.046.

G. Alcan, M. Ghorbani, A. Kosar, M. Unel, A new visual tracking method for the analysis and characterization of jet flow, Mechatronics Engineering Program, Faculty of Engineering and Natural Sciences, Sabanci University Orhanli, Istanbul, Tuzla, Turkey Vol.51 (2016) 55-67. https://doi.org/ 10.1016/j.flowmeasinst.2016.08.005.

M. Mirzaei, M. Belan, An Application of Image Analysis to Hypersonic Flows, Department of Aerospace Engineering, Politecnico di Milano, Italy Vol.45(No.45) (2013) 01064(1-5). https://doi. org/10.1051/epjconf/20134501064.

P. Chhabra, N.K. Garg, M. Kumar, Content-based image retrieval system using ORB and SIFT features, Department of Computer Science and Engineering, GZS Campus College of Engineering and Technology, Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, India Department of Computational Sciences, Maharaja R Vol.32(No.7) (2020) 2725-2733. https://doi. org/10.1007/s00521-018-3677-9.

X. Wang, J. Zou, D. Shi, An Improved ORB Image Feature Matching Algorithm Based on SURF, 2018 3RD INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION ENGINEERING (ICRAE) (2018) 218-222.

Z. Zhang, L. Wang, W. Zheng, L. Yin, R. Hu, B. Yang, Endoscope image mosaic based on pyramid ORB, School of Innovation and Entrepreneurship, Xi’an Fanyi University, Xi’an 710105, P.R.China;School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, P.R.China;Department of Vol.71(Part B) (2022) 103261. https://doi.org/10.1016/j.bspc. 2021. 103261.

W. Wu, W. Liu, An optimized method based on ransac for fundamental matrix estimation, 2018.

J.S.B.L.X.-H.W.J.-M. Li, Study of the Parallax Correction Algorithm Based on the Multiple Regulatory Factors, School of Electrical and Information Engineering,Jiangsu University,Zhenjiang,212013, China2012.

M.J. Jia, X. Cao, J.R. Gunn, P. Bruza, S. Jiang, B.W. Pogue, Tomographic Cherenkov-excited luminescence scanned imaging with multiple pinhole beams recovered via back-projection reconstruction, Optics letters Vol.44(No.7) (2019) 1552-1555. https://doi.org/10.1364/ol.44.0015 52.

Z. Jiang, Y. Kong, W. Qian, S. Wang, C. Liu, Resolution and signal-to-noise ratio enhancement for synthetic coded aperture imaging via varying pinhole array, ; Jiangnan Univ, Computat Opt Lab, Dept Optoelect Informat Sci & Technol, Sch Sci, Wuxi 214122, Jiangsu, Peoples R China Jiangnan University ; Chinese Acad Sci, Shanghai Inst Opt & Fine Mech, Shanghai 201800, Peoples R China Vol.58(No.22) (2019) 6157-6164. https://doi.org/10.1364/ao.58.006157.

Z. Niu, G. Zhong, H. Yu, A review on the attention mechanism of deep learning, Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China;School of Creative Technologies, University of Portsmouth, Portsmouth PO1 2DJ, UK Vol.452 (2021) 48-62. https://doi.org/10.1016/j.neucom.2021.03.091.

M. Zhang, H. Su, J. Wen, Classification of flower image based on attention mechanism and multi-loss attention network, Department of Information Engineering, Information Institute, GUI Zhou University of Finance and Economics, Guiyang, 550025, China Vol.179(No.C) (2021) 307-317. https://doi.org/10.1016/j.comcom.2021.09.001.

Downloads

Published

2024-01-23

Issue

Section

Articles

How to Cite

Cai, H., & Zhang, H. (2024). Research on Measurement and Prediction of Well Blowout Fluid Flow Rate based on Machine Vision. Frontiers in Sustainable Development, 4(1), 33-47. https://doi.org/10.54691/1pvk2p61

Similar Articles

1-10 of 76

You may also start an advanced similarity search for this article.