Data-Driven In-Situ Sonic-Log Synthesis in Shale Reservoirs for Geomechanical Characterization
- Jiabo He (University of Melbourne) | Hao Li (University of Oklahoma) | Siddharth Misra (University of Oklahoma)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- November 2019
- Document Type
- Journal Paper
- 1,225 - 1,239
- 2019.Society of Petroleum Engineers
- machine learning, sonic logs, data driven
- 25 in the last 30 days
- 199 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 12.00|
|SPE Non-Member Price:||USD 35.00|
Compressional-travel-time (DTC) and shear-travel-time (DTS) logs acquired using sonic-logging tools are crucial for subsurface geomechanical characterization. In this study, 13 “easy-to-acquire” conventional logs were processed using six shallow-regression-type supervised-learning models—namely, ordinary least squares (OLS), partial least squares (PLS), ElasticNet (EN), least absolute shrinkage and selection operator (LASSO), multivariate adaptive regression splines (MARS), and artificial-neural network (ANN)—to successfully synthesize DTC and DTS logs. Among the six models, ANN outperforms other models with R2 of 0.87 and 0.85 for the syntheses of DTC and DTS logs, respectively. The six shallow-learning models are trained and tested with 8,481 data points acquired from a 4,240-ft-depth interval of a shale reservoir in Well 1, and the trained models are deployed in Well 2 for purposes of blind testing against 2,920 data points from 1,460-ft-depth interval. After that, five clustering algorithms are applied on subsamples of 13 “easy-to-acquire” logs to identify clusters and compare them with the log-synthesis performance of the shallow-learning models. A dimensionality-reduction algorithm, t-distributed stochastic neighbor embedding (t-SNE), is used to visualize the petrophysical/statistical characteristics of the clustering algorithm. Hierarchical-clustering, density-based spatial clustering of application with noise (DBSCAN), and self-organizing-map (SOM) algorithms are sensitive to outliers and did not effectively differentiate the input data into consistent clusters. A Gaussian-mixture model can differentiate the various formations, but the clusters do not have a strong correlation with the performance of the log-synthesis models. Clusters identified using the K-means method have a strong correlation with the performance of the shallow-learning models. By combining the shallow-learning models for log synthesis with the K-means clustering, we propose a reliable workflow that can synthesize the DTC and DTS logs, as well as generate a reliability indicator for the synthesis logs to help the user better understand the performance of the shallow-learning models during deployment in new wells.
|File Size||1 MB||Number of Pages||15|
Abdi, H. 2010. Partial Least Squares Regression and Projection on Latent Structure Regression (PLS Regression). Wiley Interdiscip Rev Comput Stat 2 (1): 97–106. https://doi.org/10.1002/wics.51.
Akinnikawe, O., Lyne, S., and Roberts, J. 2018. Synthetic Well Log Generation Using Machine Learning Techniques. Presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, Houston, 23–25 July. URTEC-2877021-MS. https://doi.org/10.15530/URTEC-2018-2877021.
Al-Bulushi, N., Araujo, M., Kraaijveld, M. et al. 2007. Predicting Water Saturation Using Artificial Neural Networks (ANNs). Presented at the SPWLA Middle East Regional Symposium, Abu Dhabi, 15–19 April. SPWLA-MERS-2007-W.
Alexeyev, A., Ostadhassan, M., Mohammed, R. A. et al. 2017. Well Log Based Geomechanical and Petrophysical Analysis of the Bakken Formation. Presented at the 51st US Rock Mechanics/Geomechanics Symposium, San Francisco, 25–28 June. ARMA-2017-0942.
Ao, Y., Li, H., Zhu, L. et al. 2018. Logging Lithology Discrimination in the Prototype Similarity Space With Random Forest. IEEE Geosci Remote Sens Lett 16 (5): 687–691. https://doi.org/10.1109/LGRS.2018.2882123.
Asoodeh, M. and Bagheripour, P. 2012. Prediction of Compressional, Shear, and Stoneley Wave Velocities From Conventional Well Log Data Using a Committee Machine With Intelligent Systems. Rock Mech Rock Eng 45 (1): 45–63. https://doi.org/10.1007/s00603-011-0181-2.
Baines, V., Bootle, R., Pritchard, T. et al. 2008. Predicting Shear and Compressional Velocities in Thin Beds. Presented at the SPWLA 49th Annual Logging Symposium, Austin, Texas, 25–28 May. SPWLA-2008-I.
Beale, M. H., Hagan, M. T., and Demuth, H. B. 2012. Neural Network ToolboxTM 7 User’s Guide. Natick, Massachusetts: The MathWorks, Inc.
Birant, D. and Kut, A. 2007. ST-DBSCAN: An Algorithm for Clustering Spatial–Temporal Data. Data Knowl Eng 60 (1): 208–221. https://doi.org/10.1016/j.datak.2006.01.013.
Chumney, E. C. G. and Simpson, K. N. 2006. Methods and Designs for Outcomes Research. Bethesda, Maryland: American Society of Health-System Pharmacists.
Elkatatny, S. M., Zeeshan, T., Mahmoud, M. et al. 2016. Application of Artificial Intelligent Techniques to Determine Sonic Time From Well Logs. Presented at the 50th US Rock Mechanics/Geomechanics Symposium, Houston, 26–29 June. ARMA-2016-755.
Friedman, J. H. 1991. Multivariate Adaptive Regression Splines. Ann. Statist. 19 (1): 1–67. https://doi.org/10.1214/aos/1176347963.
Greenberg, M. L. and Castagna, J. P. 1992. Shear-Wave Velocity Estimation in Porous Rocks: Theoretical Formulation, Preliminary Verification and Applications. Geophys Prospect 40 (2): 195–209. https://doi.org/10.1111/j.1365-2478.1992.tb00371.x.
Handwerger, D. A., Keller, J., and Vaughn, K. 2011. Improved Petrophysical Core Measurements on Tight Shale Reservoirs Using Retort and Crushed Samples. Presented at the SPE Annual Technical Conference and Exhibition, Denver, 30 October–2 November. SPE-147456-MS. https://doi.org/10.2118/147456-MS.
Heuer, H. 2016. Text Comparison Using Word Vector Representations and Dimensionality Reduction. arXiv preprint arXiv:1607.00534.
Huang, A. and Wu, R. 2016. Deep Learning for Music. arXiv preprint arXiv:1606.04930.
Iverson, W. P. and Walker, J. N. 1988. Shear and Compressional Logs Derived From Nuclear Logs. In SEG Technical Program Expanded Abstracts 1988, 111–113. Tulsa: Society of Exploration Geophysicists. https://doi.org/10.1190/1.1892237.
Jain, V., Gzara, K., Makarychev, G. et al. 2015. Maximizing Information Through Data Driven Analytics in Petrophysical Evaluation of Well Logs. Presented at the SPE Annual Technical Conference and Exhibition, Houston, 28–30 September. SPE-174735-MS. https://doi.org/10.2118/174735-MS.
Keys, R. G. and Xu, S. 2002. An Approximation for the Xu-White Velocity Model. Geophysics 67 (5): 1406–1414. https://doi.org/10.1190/1.1512786.
Kim, S.-J., Koh, K., Lustig, M. et al. 2007. An Interior-Point Method for Large-Scale l1-Regularized Least Squares. IEEE J Sel Top Signal Process 1 (4): 606–617. https://doi.org/10.1109/JSTSP.2007.910971.
Kohonen, T. 1990. The Self-Organizing Map. Proc. IEEE 78 (9): 1464–1480. https://doi.org/10.1109/5.58325.
Li, H. and Misra, S. 2017a. Prediction of Subsurface NMR T2 Distribution From Formation-Mineral Composition Using Variational Autoencoder. SEG Technical Program Expanded Abstracts 2017: 3350–3354. Tulsa: Society of Exploration Geophysicists. https://doi.org/10.1190/segam2017-17798488.1.
Li, H. and Misra, S. 2017b. Prediction of Subsurface NMR T2 Distributions in a Shale Petroleum System Using Variational Autoencoder-Based Neural Networks. IEEE Geosci Remote Sens Lett 14 (12): 2395–2397. https://doi.org/10.1109/LGRS.2017.2766130.
Lingjaerde, O. C. and Christophersen, N. 2000. Shrinkage Structure of Partial Least Squares. Scand Stat Theory Appl 27 (3): 459–473. https://doi.org/10.1111/1467-9469.00201.
Maleki, S., Moradzadeh, A., Riabi, R. G. et al. 2014. Prediction of Shear Wave Velocity Using Empirical Correlations and Artificial Intelligence Methods. NRIAG-JAG 3 (1): 70–81. https://doi.org/10.1016/j.nrjag.2014.05.001.
Moghaddas, H., Habibnia, B., Ghasemalaskari, M. K. et al. 2017. Lithofacies Classification Based on Multiresolution Graph-Based Clustering Using Image Log in South Pars Gas Field. Presented at the 2017 SEG International Exposition and Annual Meeting, Houston, 24–29 September. SEG-2017-17724469.
Møller, M. F. 1993. A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning. Neural Netw 6 (4): 525–533. https://doi.org/10.1016/S0893-6080(05)80056-5.
Pedregosa, F., Varoquaux, G., Gramfort, A. et al. 2011. Scikit-Learn: Machine Learning in Python. J Mach Learn Res 12 (February): 2825–2830.
Petriello, J., Marino, S., Suarez-Rivera, R. et al. 2013. Integration of Quantitative Rock Classification With Core-Based Geologic Studies: Improved Regional-Scale Modeling and Efficient Exploration of Tight Shale Plays. Presented at the SPE Unconventional Resources Conference and Exhibition-Asia Pacific, Brisbane, Australia, 11–13 November. SPE-167048-MS. https://doi.org/10.2118/167048-MS.
Qin, X., Xu, Y., Yan, H. et al. 2017. Unsupervised Well Clustering: Pattern Recognition in Overpressure Mechanisms. Presented at the 2017 SEG International Exposition and Annual Meeting, Houston, 24–29 September. SEG-2017-17797818.
Rezaee, M. R., Ilkhchi, A. K., and Barabadi, A. 2007. Prediction of Shear Wave Velocity From Petrophysical Data Utilizing Intelligent Systems: An Example From a Sandstone Reservoir of Carnarvon Basin, Australia. J Pet Sci Eng 55 (3–4): 201–212. https://doi.org/10.1016/j.petrol.2006.08.008.
Shi, X., Cui, Y., Guo, X. et al. 2017. Logging Facies Classification and Permeability Evaluation: Multi-Resolution Graph Based Clustering. Presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 9–11 October. SPE-187030-MS. https://doi.org/10.2118/187030-MS.
Tan, P.-N., Steinbach, M., and Kumar, V. 2006. Introduction to Data Mining. Boston, Massachusetts: Pearson.
Tariq, Z., Elkatatny, S., Mahmoud, M. et al. 2016. A New Artificial Intelligence Based Empirical Correlation to Predict Sonic Travel Time. Presented at the International Petroleum Technology Conference, Bangkok, 14–16 November. IPTC-19005-MS. https://doi.org/10.2523/IPTC-19005-MS.
Tibshirani, R. 1996. Regression Shrinkage and Selection via the Lasso. J R Stat Soc Series B Stat Methodol 58 (1): 267–288. https://www.jstor.org/stable/2346178.
van der Maaten, L. and Hinton, G. 2008. Visualizing Data Using t-SNE. J Mach Learn Res 9 (November): 2579–2605.
Vesanto, J. and Alhoniemi, E. 2000. Clustering of the Self-Organizing Map. IEEE Trans Neural Netw 11 (3): 586–600. https://doi.org/10.1109/72.846731.
Wang, B., Zhou, F., Zou, Y. et al. 2019. Quantitative Investigation of Fracture Interaction by Evaluating Fracture Curvature During Temporarily Plugging Staged Fracturing. J Pet Sci Eng 172 (January): 559–571. https://doi.org/10.1016/j.petrol.2018.08.038.
Wattenberg, M., Vie´gas, F., and Johnson, I. 2016. How to Use t-SNE Effectively. Distill (13 October), https://doi.org/10.23915/distill.00002.
Wold, H. 2004. Partial Least Squares. In Encyclopedia of Statistical Sciences, second edition, ed. S. Kotz, C. B. Read, N. Balakrishnan, et al., Vol. 9. Hoboken, New Jersey: Wiley.
Yegnanarayana, B. 2009. Artificial Neural Networks. New Delhi, India; Prentice Hall of India Private Limited.
Zou, H. and Hastie, T. 2005. Regularization and Variable Selection via the Elastic Net. J R Stat Soc Series B Stat Methodol 67 (2): 301–320. https://www.jstor.org/stable/3647580.