Abstract
The objective of this work was to apply artificial intelligence techniques, such as computational vision, image processing, and machine learning, to develop a software for performing the analysis and monitoring of cuttings in vibrating screens to detect flow variations and possible instabilities during the drilling process. An experimental unit was built to emulate the flow of cuttings in the sieves, where three different vibration intensities were applied to the apparatus to allow for three distinct flow speeds. Flowing experiments using quartz, marble, granite, and actual gravel fragments were recorded in videos and used by the computational vision software developed on the Matlab© platform. Videos were processed by the proposed algorithm using different image processing and machine learning techniques. The 2D Discrete Fourier Transform associated with the convolution theorem was the segmentation technique used to detect the rocks in the frames. The estimation of the solids’ velocity was performed with the detectors of correspondence points in sequential frames of the video using the MATLAB’s functions “MSER”, “Harris”, and “SURF”. The MATLAB’s function "regionprops" was also used to estimate the area of the sieve filled with rocks as well as some geometric parameters of them, such as size, circularity, roundness, and eccentricity. The results showed that the proposed computational vision software could perform reliable estimates of the reported features for a range of rock types. Besides, in most cases, it was possible to estimate the area of the sieve filled with rocks and their velocities across the sieve with average errors of less than 10%. Results also suggested that it could be possible to implement a real-time monitoring system for vibrating sieves in drilling operations using the software developed in this work.
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References
Alexandre EB, Lopes MD, Rateke T, Chiarella VF, Sobieranski AC, Comunello E, Wangenheim AV (2013) Air model control using computer vision. Computer on the Beach Full Articles. 128–137
Backes JA (2016) Introduction to Computer Vision using Matlab. Rio de Janeiro: Alta Books
Barbosa VP, Menezes AL, Gedraite R, Ataíde CH (2020) Vibration screening: a detailed study using image analysis techniques to characterize the bed behavior in solid–liquid separation. Min Eng 154:106383. https://doi.org/10.1016/j.mineng.2020.106383
Cipelli CAP, Assis WO, Matta EN, Gomes MM, Gedraite R (2014) Development of an image capture, processing and identification system using LabView. In: 14th National Congress of Scientific Initiation CONIC SEMESP, Mauá Institute of Technology University Center-SP
Costa CAR (1998) Introduction to Digital Image Processing - An approach focused on remote sensing and Spring System features. Technical Report, Embrapa - CNPTIA, p. 45, ISSN 1414–472
Esquef IA, Albuquerque MP, Albuquerque MP (2003) Digital Image Processing. Brazilian Center for Physical Research - CBPF. Available at: http://www.cbpf.br/cat/pdsi/pdf/cap3webfinal.pdf. Access on: 16/01/2019.
Facco P, Santomaso AC, Barolo M (2017) Artificial vision system for particle size characterization from bulk materials. Chem Eng Sci 164:246–257
Fagundes TB (2018) Characterization of oil well drilling gravels by instrumental analytical techniques. Dissertation (In Portuguese). University of São Paulo. São Paulo, p. 223, 2018
Fares K, Amine K, Salah E (2020) A robust blind color image watermarking based on Fourier transform domain. Optik 208:164562. https://doi.org/10.1016/j.ijleo.2020.164562
Fonseca Neto J (1999) Application of the Fourier Transform in digital image processing. Aracaju-SE
Gan C, Cao W-H, Liu K-Z, Wu M (2022) A novel dynamic model for the online prediction of rate of penetration and its industrial application to a drilling process. J Proc Cont. 109:83–92
Haleem N, Bustreo M, Del Bue A (2021) A computer vision based online quality control system for textile yarns. Comp Ind. 133:103550. https://doi.org/10.1016/j.compind.2021.103550
Hamzeloo E, Massinaei M, Mehrshad N (2013) Estimation of particle size distribuition on an industrial conveyor belt using image analysis and neural network. Powder Technol 261:185–190
Hassan H, Ren Z, Zao H, Huang S, Li D, Xiang S, Kang Y, Chen S, Huang B (2022) Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks. Comp Biol Med. 141:105123. https://doi.org/10.1016/j.compbiomed.2021.105123
Ignat A, Păvăloi I (2021) Keypoint selection algorithm for palmprint recognition with SURF. Proc Comp Sci. 192:270–280. https://doi.org/10.1016/j.procs.2021.08.028
Jesus SR, Rocha WCJ, Bittencourt JCN (2020) Performance analysis of detectors and feature descriptors using the Raspberry Pi computer platform. Annals of the XIX Regional School of Computing - Bahia, Alagoas and Sergipe, pp 372–381
Jiang G, Sun J, He Y, Cui K, Dong T, Yang L, Yang X, Wang X (2021) Novel water-based drilling and completion fluid technology to improve wellbore quality during drilling and protect unconventional reservoirs. Engineering. https://doi.org/10.1016/j.eng.2021.11.014
Kazmi W, Andersen HJA (2015) comparison of interest points and region detectors on structured, range and texture images. J vis Commun Image Represent 32:156–169. https://doi.org/10.1016/j.jvcir.2015.08.004
Khojasteh P, Ahmadyfard A, Tokhmechi B, Mirmahdavi SA (2015) Automatic detection of formations using images of oil well drilling cuttings. J Petrol Sci Eng 125:67–74
Khorram F, Mermarian H, Tokhmechi B, Soltanian-Zadeh H (2011) Limestone chemical componentes estimation using image processing and pattern recognition techniques. J Min Environ. 2:126–135
Kistner M, Jemwa GT, Aldrich C (2013) Monitoring of mineral processing systems by using textural image analysis. Min Eng 52:169–177
Ko YD, Shang H (2011) A neural network-based soft sensor for particle size distribution using image analysis. Powder Technol 212:359–366
Le T-T, Miclet D, Heritier P, Piron E, Chateauneuf A, Berducat M (2018) Morphology characterization of irregular particles using image analysis. application to solid inorganic fertilizers. Comp Elect Agri 147:146–157. https://doi.org/10.1016/j.compag.2018.02.022
Lee MH, Park IK (2017) Performance evaluation of local descriptors for maximally stable extremal regions. J Visual Commun Image Repres 47:62–72. https://doi.org/10.1016/j.jvcir.2017.05.008
Liao C-W, Yu J-H, Tarng Y-S (2010) On-line full scan inspection of particle size and shape using digital image processing. Particuology. 8:286–292
Marana AN, Chiachia G, Guilherme IR, Papa JP, Miura K, Ferreira MVD, Torres F (2009) An intelligent system for petroleum well drilling cutting analysis artificial intelligence system. IEEE Int Conf Adapt Intell Syst. 37:42
Martins FPR (1999) Calibration of metrological patterns using computer vision. In: University of São Paulo. Ph.D. Thesis (In Portuguese). São Paulo, 1999.
Matta EN, Cipell CAP, Gomes MM, Guerreiro FS, Gedraite R, Kunigk L, Assis WO (2014) Contribution to the study of the behavior of a vibrating screen typically used in a drilling fluid treatment unit. XX Brazil Cong Chem Eng Florianópolis SC 2014
Najarian K, Splinter R (2006) Biomedical Signal and Image Processing. CRC Press, Taylor & Francis Group. Curso PISB 2017.2, p.92
Oliveira Filho KS (1999) Fundamentals of Imaging Radiodiagnosis. UFRGS Institute of Physics
Pang S, Du A, Orgun MA, Chen H (2020) Weakly supervised learning for image keypoint matching using graph convolutional networks. Knowl-Based Syst 197:105871. https://doi.org/10.1016/j.knosys.2020.105871
Serapian ABS, Mendes JRP, Miura K (2011) Computer vision system for oil well drilling gravel detection. Proceedings of the 6th Brazilian Congress on Oil and Gas Research and Development (PDPetro), Florianópolis - SC, 2011
Silva FA, Paiva MSV, Artero AO, Piteri MA (2013) Evaluation of keypoint detectors and descriptors. IX Workshop de Visão Computacional, Rio de Janeiro - RJ. ISSN 2526–5997. Anais
Souza GB, Marana AN (2004) Recognition of People in Video Images using light biometric features. VI Workshop on Computer Vision, Paulista State University (UNESP). p. 84–89, 2004
Srividhya S, Prakash S (2017) Performance Evaluation of various features detection algorithms in VSLAM. Indian J Res 6(2):386–388
Thai P, Alam S, Lilith N, Nguyen BT (2022) A computer vision framework using convolutional neural networks for airport-airside surveillance. Transportat Res Part C Emerg Technol 137:103590. https://doi.org/10.1016/j.trc.2022.103590
Yaghoobi H, Mansouri H, Farsangi MAE, Nezamabadi-Pour H (2019) Determining the fragmented rock size distribuition using textural feature extraction of images. Powder Technol 342:630–641
Zacarkim VL, Todt E, Bombardelli FG (2018) Evaluation of IGFTT Keypoints Detector in Indoor Visual SLAM. In: 2018 Latin American Robotic Symposium, 2018 Brazilian Symposium on Robotics (SBR) and 2018 Workshop on Robotics in Education (WRE), 2018, Joao Pessoa. 2018 Latin American Robotic Symposium, 2018 Brazilian Symposium on Robotics (SBR) and 2018 Workshop on Robotics in Education (WRE), p.88
Acknowledgements
The authors would like to thank Universidade Federal Rural do Rio de Janeiro, CAPES (Coordenação de Pessoal de Nível Superior) and CENPES/PETROBRAS Grant No. 4600580875 (A prototypal for drilling fluid properties control) for providing scholarships and supporting for this research.
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Grossi, C.D., Hummel, Y.N., Moura, E.A. et al. Development of computational vision methodologies for monitoring cuttings in the drilling fluid treatment system. Braz. J. Chem. Eng. (2023). https://doi.org/10.1007/s43153-023-00389-w
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DOI: https://doi.org/10.1007/s43153-023-00389-w