Data mining and machine learning for porosity, saturation, and shear velocity prediction: recent experience and results

References

1. Archie, G.E.
   [1942]. The electrical resistivity log as an aid in determining some reservoir characteristics.Transaction American Institute of Mechanical Engineers, 146, 54–62.
   [Google Scholar]
2. Bergstra, J., Komer, B., Eliasmith, C., Yamins, D. and Cox, D.D.
   [2015]. Hyperopt: a python library for model selection and hyperparameter optimization.Computational Science and Discovery, 8(1), p.014008.
   [Google Scholar]
3. Bestagini, P., Vincenzo, L. and Tubaro, S.
   [2017]. A machine learning approach to facies classification using well logs.SEG Technical Program Expanded Abstracts, 2137–2142.
   [Google Scholar]
4. Bhatt, A. and Helle, H.B.
   [1999]. Porosity, Permeability and TOC Prediction from Well Logs Using a Neural Network Approach.61st EAGE Conference & Exhibition, Extended Abstract.
   [Google Scholar]
5. Castagna, J.P., Batzle, M.L. and Kan, T.K.
   [1993]. Rock physics – The link between rock properties and AV O response. In offset-Dependent Reflectivity – Theory and Practice of AVO analysis, investigations in Geophysics, No. 8 ed. J.P. Castagna and M. Backus. Tulsa, OK: Society of Exploration Geophysicists, 135–171.
   [Google Scholar]
6. Chen, T. and Guestrin, C.
   [2016]. XGBoost: A scalable tree boosting system.Proceedings of the 22nd ACM and SIGKDD international conference on knowledge discovery and data mining, August, 785–794.
   [Google Scholar]
7. Dvorkin, J. and Nur, A.
   [1996]. Elasticity of high porosity sandstones: Theory for two North Sea data sets.Geophysics, 61, 1363–1370.
   [Google Scholar]
8. Grana, D., Azevedo, L. and Liu, M.
   [2020]. A comparison of deep machine learning and Monte Carlo methods for facies classification from seismic data.Geophysics, 85, 41–52.
   [Google Scholar]
9. Greenberg, M.L., and Castagna, J.P.
   [1992]. Shear-wave velocity estimation in porous rocks: Theoretical formulation, preliminary verification and applications.Geophysical Prospecting, 40, 195–209.
   [Google Scholar]
10. Hall, B.
    [2016]. Facies classification using machine learning.The Leading Edge, 35, 906–909.
    [Google Scholar]
11. Jiang, L., Castagna, J.P. and Russell, B.
    [2020]. Porosity prediction using machine learning.90th SEG Annual Meeting Conference & Exhibition, Expanded Abstract.
    [Google Scholar]
12. Kramer, M.
    [2005]. R2 statistics for mixed models.15th Annual Conference on Applied Statistics in Agriculture.
    [Google Scholar]
13. Krief, M., Garat, J., Stellingwerff, J., and Ventre, J.
    [1990]. A petrophysical interpretation using the velocities of P and S waves (full-waveform sonic):The Log Analyst, 31, November, 355–369.
    [Google Scholar]
14. Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J. and Chintala, S.
    [2019]. PyTorch: An Imperative Style, High-Performance Deep Learning Library.Advances in Neural Information Processing Systems, 32, 8024–8035.
    [Google Scholar]
15. Poupon, A., and Leveaux, J.
    [1971]. Evaluation of water saturation in shaly formations.Trans. Soc. Prof. Well Log Analysts, 12th Annual symposium, Paper O.
    [Google Scholar]
16. Ramm, M. and Bjørlykke, K.
    [1994]. Porosity/depth trends in reservoir sandstones: assessing the quantitative effects of varying pore-pressure, temperature history and mineralogy, Norwegian Shelf data.Clay Minerals, 29, 475–490.
    [Google Scholar]
17. Raymer, L.L., Hunt, E.R. and Gardner, J.S.
    [1980]. An improved sonic transit time-to-porosity transform.Society of Petrophysicists and Well Log Analysts SPWLA 21st Annual Logging Symposium, July, paper.
    [Google Scholar]
18. Silva, E., Seabra, C., de Campos, R. and Augusto, F.
    [2015]. Impact of Sonic Calibration in the Predictability between a Seismic Trace and its corresponding Reflectivity.14th International Congress of the Brazilian Geophysical Society, August 2015.
    [Google Scholar]
19. Simandoux, P.
    [1963]. Mesures dielectriques en milieu poreux, application a mesure des saturations en eau: Etude du comportement des massifs argileux.Revue de l’Institut Français du Pétrole, 18, supplementary Issue, 193–215.
    [Google Scholar]
20. White, R.
    [1997]. Accuracy of well ties – practical procedures and examples.67th SEG Annual Meeting Conference & Exhibition, Expanded Abstract.
    [Google Scholar]
21. Wyllie, M.R.J., Gregory, A.R. and Gardner, L.W.
    [1956]. Elastic wave velocities in heterogeneous and porous media.Geophysics, 21, 41–70.
    [Google Scholar]