Using ensemble learning techniques based on feature selection to predict shear wave velocity

Authors

1 Shahid Chamran University of Ahvaz

2 Department of Geology, Faculty of Earth Science Shahid Chamran University of Ahvaz

Abstract

Shear wave velocity is one of the important parameters for determining mechanical and petrophysical properties in hydrocarbon reservoirs. Shear waves are usually obtained from dipole sonic imager (DSI) tools or core analysis in the laboratory. However, these methods as common sources for shear wave estimation are time-consuming and costly and thus can only provide information on shear wave in a few drilled wells. To overcome these limitations, different artificial intelligence methods are used to estimate the mentioned parameter through the conventional well logs. In this study, the shear wave velocity was estimated using ensemble learning methods in Asmari Reservoir in the Mansouri oilfield. In this study, shear wave velocity was estimated using ensemble learning methods such as voting, stacking, bagging, and boosting in the Asmari reservoir, and the results were compared with conventional models such as Linear regression (LR), support vector regression (SVR), nearest neighbor algorithm (KNN), decision tree (DT), neural network (ANN) and hybrid methods such as combining neural network with genetic algorithm (ANN-GA) ), particle swarm (ANN-PSO) and fuzzy systems (ANFIS). In order to evaluate and validate the models, correlation coefficient (R2) and root mean square error (RMSE) were used. A comparison of conventional models and hybrid methods with ensemble learning methods showed that ensemble algorithms perform better in shear wave estimation. Considering the testing phase, among the ensemble learning methods, the Catboost model has provided the lowest error (RMSE=0.058) and highest correlation coefficient (R=0.983) can determine shear wave with high accuracy.

Keywords

Main Subjects


Abedi, R., Costache, R., Shafizadeh-Moghadam, H., Pham, Q.B., 2022. Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees. Geocarto International 37, 5479-5496. https://doi.org/10.108 /10106049.2021.1920636.
Al-Dousari, M., Garrouch, A.A., Al-Omair, O., 2016. Investigating the dependence of shear wave velocity on petrophysical parameters. Journal of Petroleum Science and Engineering 146, 286-296. https://doi.org/10.1016/ j.petrol.2016.04.036.
Anemangely, M., Ramezanzadeh, A., Tokhmechi, B., 2017. Shear wave travel time estimation from petrophysical logs using ANFIS-PSO algorithm: A case study from Ab-Teymour Oilfield. Journal of Natural Gas Science and Engineering 38, 373-387. https://doi.org/10.1016/j.jngse.2017.01.003.
Anifowose, F.A., Labadin, J., Abdulraheem, A., 2017. Ensemble machine learning: An untapped modeling paradigm for petroleum reservoir characterization. Journal of Petroleum Science and Engineering 151, 480-487. https://doi.org/10.1016 /j.petrol.2017.01.024.
Anselmetti, F.S., Eberli, G.P., 1993. Controls on sonic velocity in carbonates. Pure and Applied geophysics 141, 287-323. https://doi.org/10.1007/BF00998333.
Breiman, L., 1996. Bagging predictors. Machine learning 24, 123-140. https://doi.org/10.1007/BF00058655.
Castagna, J.P., Batzle, M.L., Eastwood, R.L., 1985. Relationships between compressional-wave and shear-wave velocities in clastic silicate rocks. geophysics 50, 571-581. https://doi.org/10.1190/1.1441933.
Chang, J.F., Dong, N., Ip, W.H., Yung, K.L., 2019. An ensemble learning model based on Bayesian model combination for solar energy prediction. Journal of Renewable and Sustainable Energy 11, 043702. https://doi.org/10.1063/1.5094534.
Chen, T. and Guestrin, C., 2016, August. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining 785-794. https://doi.org/10.1145/2939672.2939785.
Da Silva, R.G., Ribeiro, M.H.D.M., Moreno, S.R., Mariani, V.C., dos Santos Coelho, L., 2021. A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting. Energy 216, 119174. https:// doi.org/10.1016/j.energy.2020.119174.
Eberhart-Phillips, D., Han, D.H., Zoback, M.D., 1989. Empirical relationships among seismic velocity, effective pressure, porosity, and clay content in sandstone. Geophysics 54, 82-89. https://doi.org/10.1190/1.1442580.
Eskandari, H., Rezaee, M.R., Mohammadnia, M., 2004. Application of multiple regression and artificial neural network techniques to predict shear wave velocity from wireline log data for a carbonate reservoir South-West Iran. CSEG recorder 42, 48. https://doi.org/10.4236/ojg.2014.47023.
Guyon, I., Weston, J., Barnhill, S., Vapnik, V., 2002. Gene selection for cancer classification using support vector machines. Machine Learning 46, 389-422. https://doi.org/10.1023/A:1012487302797.
Jabeur, S.B., Gharib, C., Mefteh-Wali, S., Arfi, W.B., 2021. CatBoost model and artificial intelligence techniques for corporate failure prediction. Technological Forecasting and Social Change 166, 120658. https://doi.org/10.1016/j.techfore.2021.120658.
Jafarzadeh, H., Mahdianpari, M., Gill, E., Mohammadimanesh, F., Homayouni, S., 2021. Bagging and boosting ensemble classifiers for classification of multispectral, hyperspectral and PolSAR data: a comparative evaluation. Remote Sensing 13, 4405. https://doi.org/10.3390/rs13214405.
Kavianpor Sangno, M., Namdarian, A., Mousavi-Harami, S.R., Mahboubi, A. and Omidpour, A., 2015. The Study of Role and Texture of Anhydrite in Production Zone of Asmari Formation in Mansuri Oil Field, Zagros, Iran. Scientific Quarterly Journal of Geosciences 24, 203-216. https://doi.org/10.22071/gsj.2015.42659.