Wavelet Transform Processing in Detecting Failures in Offshore Well Production

Authors

  • Priscila Esposte Coutinho Universidade Federal Fluminense
  • Larissa Haringer Martins da Silveira
  • Marcio Cataldi
  • Fabiana Rodrigues Leta
  • Antônio Orestes de Salvo Castro
  • Cláudio Benevenuto de Campos Lima
  • Gilson Brito Alves Lima

DOI:

https://doi.org/10.21712/lajer.2022.v9.n1.p1-11

Keywords:

Offshore wells, Oil production, Wavelet transform, Predictor variables, Time series

Abstract

Brazil has a significant offshore oil production, which dates back to the late 1960s and is currently focused on exploring pre-salt reservoirs. The drilling technology Petrobras uses is considered a world standard: in 2020, it allowed offshore production to reach 97% of the country’s total oil production. During the process, however, unwanted events, and even operational failures may occur, which are capable of significant damage. Thus, failure detection is extremely important to prevent production losses or delays, to reduce costs and to avoid accidents. This study uses a real, public database on offshore production, and proposes using wavelet transforms to detect production failures. With the technique, we pinpointed which time intervals between measurements showed relevant variability, and then clustered the data, according to mobile averages, to shrink the record number. Using wavelet transforms, we analyzed which variables could be used as predictors of production failures and identified the temperature read by the Temperature and Pressure Transducer sensor (T-TPT) and the pressure at the Production Choke sensor (P-PCK) as possible predictor variables. We also observed the creation of a filtered series, averaged from the original data series, which maintained its variability, showing the viability of record regrouping in shorter series.

Downloads

Download data is not yet available.

References

Aguiar-Conraria, L and Soares, MJ (2010) ‘Oil and the macroeconomy: using Wavelets to analyze old issues’, Empirical Economics, v. 4, n. 3, pp. 645 – 655. Springer Science and Business Media LLC. <https://doi.org/10.1007/s00181-010-0371-x>.

Agência Nacional de Petróleo, Gás Natural e Biocombustíveis – ANP (2021), ‘Boletim Anual de Recursos e Reservas’. < https://www.gov.br/anp/pt-br/centrais-de-conteudo/dados-estatisticos/arquivos-reservas-nacionais-de-petroleo-e-gas-natural/boletim_reservas_2020.pdf> (accessed 11 August 2021).

Asgarian, B, Aghaeidoost, V and Shokrgozar, HR (2016) ‘Damage detection of jacket type offshore platforms using rate of signal energy using Wavelet packet transform’, Marine Structures, v. 45, pp. 1–21. Elsevier BV. <http://dx.doi.org/10.1016/j.marstruc.2015.10.003>.

Blain, GC and Kayano, MT (2011) ‘118 years of monthly Standardized Precipitation Index data: meteorological series of Campinas, state of São Paulo’, Revista Brasileira de Meteorologia, 26 (1), pp. 137–148. <https://doi.org/10.1590/S0102-77862011000100012>.

Bolzan, MJA (2006) ‘Wavelet transform: a necessity’, Revista Brasileira de Ensino de Física, v. 28, n. 4, pp. 563–567. <https://doi.org/10.1590/S1806-11172006000400019>.

D’Almeida, AL (2015) Indústria do Petróleo no Brasil e no Mundo: Formação, Desenvolvimento e Ambiência Atual. São Paulo: Edgard Blucher.

Goswami, JC and Chan, AK (2011) Fundamentals of Wavelets: Theory, Algorithms, and Applications, 2nd edn. New Jersey: John Wiley & Sons, Inc. 219 p. ISBN 978-0-470-48413-5

Hammond, DK, Vandergheynst, P and Gribonval, R (2011) ‘Wavelets on graphs via spectral graph theory’, Applied and Computational Harmonic Analysis, v. 30, n. 2, pp. 129–150. Elsevier BV. <http://dx.doi.org/10.1016/j.acha.2010.04.005>.

Korovin, IS and Khisamutdinov, MV (2014) ‘Hybrid Method of Dynamograms Wavelet Analysis for Oil-Production Equipment State Identification’, Advanced Materials Research, v. 909, pp. 252–259. Trans Tech Publications, Ltd. <http://dx.doi.org/10.4028/www.scientific.net/amr.909.252>.

Layouni, M, Hamdi, MS and Tahar, S (2017) ‘Detection and sizing of metal-loss defects in oil and gas pipelines using pattern-adapted Wavelets and machine learning’, Applied Soft Computing, v.52, pp. 247–261. Elsevier BV. <http://dx.doi.org/10.1016/j.asoc.2016.10.040>.

Li, D, Bissyande, TF, Klein, J and Traon, YL (2016) ‘Time Series Classification with Discrete Wavelet Transformed Data’, International Journal of Software Engineering and Knowledge Engineering, v. 26, n.9, pp. 1361–1377. World Scientific Pub Co Pte Lt. <http://dx.doi.org/10.1142/s0218194016400088>.

Martí, L, Sanchez-Pi, N, Molina, J and Garcia, ACB (2015) ‘Anomaly Detection Based on Sensor Data in Petroleum Industry Applications’, Sensors, v. 15, n. 2, pp. 2774–2797. MDPI AG. <http://dx.doi.org/10.3390/s150202774>.

Marques, FSB, Rangel, LAD, Lima, GBA, Gavião, LO, Pinto, HLCP, Colombo, D and Lima, CBC (2019) ‘Modelagem de parâmetros operacionais para suporte à avaliação do processo de perfuração de poços offshore’, Sistemas & Gestão, v. 14, n. 1, pp. 77–85. <http://dx.doi.org/10.20985/1980-5160.2019.v14n1.1481>.

Naccache, T (2011) ‘Oil price cycles and Wavelets’, Energy Economics, v. 33, n. 2, pp. 338–352. Elsevier BV. <http://dx.doi.org/10.1016/j.eneco.2010.12.001>.

Ortiz Neto, JB and Costa, AJD (2007) ‘A Petrobrás e a exploração de petróleo offshore no Brasil: um approach evolucionário’, Revista Brasileira de Economia, v. 61, n. 1, pp. 95–109. <https://doi.org/10.1590/S0034-71402007000100006>.

Ray, PK, Mohanty, SR and Kishor, N (2011) ‘Disturbance detection in grid-connected distributed generation system using Wavelet and S-transform’, Electric Power Systems Research, v. 81, n. 3, pp. 805–819. Elsevier BV. <http://dx.doi.org/10.1016/j.epsr.2010.11.011>.

Reboredo, JC and Rivera-Castro, MA (2014) ‘Wavelet-based evidence of the impact of oil prices on stock returns’, International Review of Economics & Finance, v. 29, pp. 145–176. Elsevier BV. <http://dx.doi.org/10.1016/j.iref.2013.05.014>.

Takei, J, Zainal, MZ, Ramli, R, Matzain, B, Myrland, F and Shariff, A (2010) ‘Flow Instability in Deepwater Flowlines and Risers – A Case Study of Subsea Oil Production from Chinguetti Field, Mauritania’, SPE Asia Pacific Oil and, Gas Conference and Exhibition, Brisbane, Queensland, Australia. <http://dx.doi.org/10.2118/133188-ms>.

Torrence, C and Compo, GP (1998) ‘A Practical Guide to Wavelet Analysis’, Bulletin of The American Meteorological Society, v. 79, n. 1, pp. 61–78. American Meteorological Society. <https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2>.

Vargas, REV et al. (2019) ‘A realistic and public dataset with rare undesirable real events in oil wells’, Journal of Petroleum Science and Engineering, v. 181, n. 3, pp. 106223. Elsevier BV. <http://dx.doi.org/10.1016/j.petrol.2019.106223>.

Vitorino, MI, Silva Dias, PL and Ferreira, NJ (2006) ‘Observational study of the seasonality of the submonthly and intraseasonal signal over the tropics’, Meteorology and Atmospheric Physics, v. 93, pp. 17–35. Springer Science and Business Media LLC. <http://dx.doi.org/10.1007/s00703-005-0162-7>.

Zadkarami, M, Shahbazian, M and Salahshoor, K (2016) ‘Pipeline leakage detection and isolation: an integrated approach of statistical and Wavelet feature extraction with multi-layer perceptron neural network (MLPNN)’, Journal of Loss Prevention in the Process Industries, v. 43, pp. 479–487. Elsevier BV. <http://dx.doi.org/10.1016/j.jlp.2016.06.018>.

Downloads

Published

31-08-2022

How to Cite

Esposte Coutinho, P., Haringer Martins da Silveira, L., Cataldi, M., Rodrigues Leta, F., Orestes de Salvo Castro, A., Benevenuto de Campos Lima, C., & Brito Alves Lima, G. (2022). Wavelet Transform Processing in Detecting Failures in Offshore Well Production. Latin American Journal of Energy Research, 9(1), 1–11. https://doi.org/10.21712/lajer.2022.v9.n1.p1-11

Issue

Section

Petróleo e Gás Natural