Seismic Well Tie using Geophysical Logs obtained from K-Nearest Neighbor Regression Algorithm

Matheus Radamés Silva Barbosa, Vinicius Carneiro, Alexsandro Guerra Cerqueira

Abstract


This paper aims to verify if the seismic slowness log estimated through the supervised machine learning K-nearest neighbor (KNN) algorithm can be a feasible alternative to replace the sonic well log as input for the seismic well tie in a dataset from the Recôncavo Basin. The training and optimization of the regression were performed in a dataset composed of 17 well logs with petrophysical information of gamma-ray, deep and shallow resistivities, and the geological formation, e.g, Pojuca, Marfim, Maracangalha, Candeias, São Sebastião, Água Grande, and Sergi Formations. The metric to evaluate the regressions was the mean absolute error of the measured property and the prediction. The holdout cross-validation technique was applied to avoid overfitting, and a well log was separated as a blind test to verify the prediction in an unknown dataset. Furthermore, synthetic seismic traces were generated from the slowness log and the prediction using the KNN. The comparison between them shows outstanding results in the visual analysis of the peaks and amplitudes of the main seismic events. In addition, the comparison between the seismic traces close to the synthetic seismic traces reveals a better correlation to the calculated traces using the slowness predicted by the KNN algorithm.

Keywords


K-nearest neighbor; seismic well tie; Recôncavo Basin; sonic log

Full Text:

PDF

References


AKINNIKAWE, O., S. LYNE, J. ROBERTS, and DEVON ENERGY CORP E&P, 2018, Synthetic Well Log Generation Using Machine Learning Techniques: SPE Unconventional Resources Technology Conference, doi: 10.15530/urtec-2018-2877021.

BARBOSA, M.R.S., A.G. CERQUEIRA, and V.C. SANTANA, 2021, Non-linear regression model as a replacement for seismic slowness log data in the construction of synthetic traces: 17th International Congress of the Brazilian Geophysical Society, Rio de Janeiro, RJ, Brazil, Sociedade Brasileira de Geofísica, SBGf.

BULHÕES, F.C, C.D.M.R. FORMENTO, J.C.S. de O. LYRIO, G.A.S. de AMORIM, G.D. FERREIRA, E.S. PEREIRA, and R.F. CASTRO, 2015, Geostatistical 3D Density Modeling: Integrating Seismic Velocity and Well logs: 14th International Congress of the Brazilian Geophysical Society, Rio de Janeiro, RJ, Brazil, Sociedade Brasileira de Geofísica, SBGf, doi: 10.1200/sbgf2015-054.

CRANGANU, C., and M. BREABAN, 2013, Using support vector regression to estimate sonic log distributions: A case study from the Anadarko Basin, Oklahoma: Journal of Petroleum Science and Engineering, Elsevier, 103, 1–13, doi: 10.1016/j.petrol.2013.02.011.

Da Silva, A.A.N., B.F. Bahia, T.C.S, Sant’ana, V.C. Santana, and M. Holz, 2014, Modelagem de perfis geofísicos sintéticos em poços da bacia do Recôncavo: 14th International Congress of the Brazilian Geophysical Society, and EXPOGEF. Rio de Janeiro, RJ, Brazil. Sociedade Brasileira de Geofísica, SBGf, doi: 10.22564/6simbgf2014.035.

ERTEL, W., 2018, Introduction to artificial intelligence: 2nd ed., Springer, Switzerland, 356 pp, ISBN: 978-3319584867.

FACELI, K., A.C. LORENA, J. GAMA, and A.C.P.L.F. de CARVALHO, 2019, Inteligência artificial: uma abordagem de aprendizado de máquina: Rio de Janeiro, Brazil, LTC, 378 pp, ISBN: 978-8512618805.

GARDNER, G.H.F., L.W. GARDNER, and A.R. GREGORY, 1974, Formation velocity and density – the diagnostic basics for stratigraphic traps: Geophysics, 39, 770–780, doi: 10.1190/1.1440465.

MILANI, E.J., and I. DAVISON, 1988, Basement control and transfer tectonics in the Recôncavo-Tucano-Jatobá rift, Northeast Brazil: Tectonophysics, 154, 41–70, doi: 10.1016/0040-1951(88)90227-2.

ORTIZ-BEJAR, J., M. GRAFF, E.S. TELLEZ, J. ORTIZ-BEJAR, and J.C. JACOBO, 2018, K-Nearest Neighbor Regressors Optimized by using Random Search: 2018 IEEE International Autumn Meeting on Power, Electronic and Computing (ROPEC), doi: 10.109/ROPEC.2018.8661399.

PEDREGOSA, F., G. VAROQUAUX, A. GRAMFORT, V. MICHEL, B. THIRION, O. GRISEL, M. BLONDEL, P. PRETTENHOFER, R. WEISS, V. DUBOURG, J. VANDERPLAS, A. PASSOS, D. COURNAPEAU, M. BRUCHER, M. PERROT, and E. DUCHESNAY, 2011, Scikit-learn: Machine learning in Python: Journal of Machine Learning Research, 12, 2825–2830.

REFAEILZADEH, P., L. TANG, and H. LIU, 2009, Cross validation, in LIU, L., and M.T. ÖZSU, Eds., Encyclopedia of database systems: Springer, Boston, MA, p. 532–538, doi: 10.1007/978-0-387-39940-9_565.

ROLON, L., S.D. MOHAGHEGH, S. AMERI, R. GASKARI, and B. McDANIEL, 2005, Developing Synthetic Well Logs for the Upper Devonian Units in Pennsylvania: SPE Eastern Regional Meeting, OnePetro, Morgantown, West Virginia, SPE-98013-MS, doi: 10.2118/98013-MS.

ROLON, L., S.D. MOHAGHEGH, S. AMERI, R. GASKARI, and B. McDANIEL, 2009, Using artificial neural networks to generate synthetic well logs: Journal of Natural Gas Science and Engineering, 1, p. 118–133, doi: 10.1016/j.jngse.2009.08.003.

SILVA, O.B., J.M. CAIXETA, P.S. MILHOMEM and M.D. KOSIN, 2007, Roteiros geológicos – guia de campo da Bacia do Recôncavo, NE do Brasil: Boletim de Geociências da Petrobras, 15, 423–431.

SIMM, R., and M. BACON, 2014, Seismic Amplitude: An interpreter’s Handbook: Cambridge University Press, 279 pp, ISBN: 978-1107011502, doi: 10.1017/CBO9780511984501.

SULEYMANOV, V., H. GAMAL, G. GLATZ, S. ELKATATNY, and A. ABDULRAHEEM, 2021, Real-Time Prediction for Sonic Slowness Logs from Surface Drilling Data Using Machine Learning Techniques: SPE Annual Caspian Technical Conference. OnePetro, doi: 10.2118/207000-MS.

WHITE R.E., and R. SIMM, 2003, Tutorial: good practice in well ties: First Break, 21, doi: 10.3997/1365-2397.21.10.25640.




DOI: http://dx.doi.org/10.22564/brjg.v40i1.2157

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.





 

>> Brazilian Journal of Geophysics - BrJG (online version): ISSN 2764-8044
a partir do v.37n.4 (2019) até o presente

Revista Brasileira de Geofísica - RBGf (online version): ISSN 1809-4511
v.15n.1 (1997) até v.37n.3 (2019)

Revista Brasileira de Geofísica - RBGf (printed version): ISSN 0102-261X
v.1n.1 (1982) até v.33n.1 (2015)

 

Brazilian Journal of Geophysics - BrJG
Sociedade Brasileira de Geofísica - SBGf
Av. Rio Branco 156 sala 2509
Rio de Janeiro, RJ, Brazil
Phone/Fax: +55 21 2533-0064
E-mail: editor@sbgf.org.br

Since 2022, the BrJG publishes all content under Creative Commons CC BY license. All copyrights are reserved to authors.

Creative Commons