Sensitivity Study of The Effect Polymer Flooding Parameters to Improve Oil Recovery Using X-Gradient Boosting Algorithm


  • Tomi Erfando Universitas Islam Riau
  • Rizqy Khariszma Universitas Islam Riau



X- Gradient Boosting Algorithm, Polymer Flooding, Oil Recovery, energy


Implementation of waterflooding sometimes cannot increase oil recovery effectively and requires additional methods to increase oil recovery. Polymer flooding is a common chemical EOR method that has been implemented in the last few decades and provides good effectiveness in increasing oil recovery and can reduce the amount of injection fluid injected into the reservoir. Seeing the success of polymer flooding in increasing oil recovery, it is necessary to know the parameters that influence the success of polymer flooding so that it can be evaluated and taken into consideration in creating a new scheme to increase oil recovery with polymer flooding. The parameters tested in this study include Injection Rate, Injection Time, Injection Pressure, Adsorption, Inaccessible Pore Volume, Residual Resistance Factor. This research uses the X-Gardient Boosting Algorithm to look at the most influential parameters in polymer flooding. The parameters that most influence the performance of polymer flooding on the value of oil recovery with the importance level of each parameter in this study are injection time of 0.452632, injection rate of 0.430075, injection pressure of 0.064662, Adsorption of 0.025564, RRF of 0.021053, IPV of 0.006014 and produce accurate predictive modeling using x-gradient boosting where with 3 variations of the comparison ratio of training and testing data obtained at a ratio of 0.7 : 0.3 obtained an R2 train of 0.9886 and an R2 test of 0.9645, a ratio of 0.8 : 0.2 obtained an R2 train of 0.9891 and an R2 test of 0.9579, and a ratio of 0.9: 0.1 obtained R2 train of 0.9890 and R2 test of 0.9660.


Download data is not yet available.


Al-Saadi, F. S., Amri, B. A., Nofli, S., Van Wunnik, J., Jaspers, H. F., Harthi, S., Shuaili, K., Cherukupalli, P. K., & Chakravarthi, R. (2012). Polymer flooding in a large field in South Oman - Initial results and future plans. Society of Petroleum Engineers - SPE EOR Conference at Oil and Gas West Asia 2012, OGWA - EOR: Building Towards Sustainable Growth, 1(September), 493–499.

AlSofi, A. M., & Blunt, M. J. (2014). Polymer flooding design and optimization under economic uncertainty. Journal of Petroleum Science and Engineering, 124, 46–59.

Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13, 281–305.

Cenk, T., Dike, P., Henny, A., & Raul, M. (2017). Economic comparison of hydrocarbon recovery under injection of different polymers. Society of Petroleum Engineers - SPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition 2017, 2017-Janua.

Chang, P. C., Wang, Y. W., & Liu, C. H. (2007). The development of a weighted evolving fuzzy neural network for PCB sales forecasting. Expert Systems with Applications, 32(1), 86–96.

Daoud, J. I. (2018). Multicollinearity and Regression Analysis. Journal of Physics: Conference Series, 949(1).

Davino, C., Romano, R., & Vistocco, D. (2022). Handling multicollinearity in quantile regression through the use of principal component regression. Metron, 80(2), 153–174.

Delamaide, E., Moe Soe Let, K., Bhoendie, K., Paidin, W. R., & Jong-A-Pin, S. (2016). Interpretation of the performance results of a polymer flood pilot in the Tambaredjo Oil Field, Suriname. Proceedings - SPE Annual Technical Conference and Exhibition, 2016-Janua.

Douarche, F., S, D. V., Feraille, M., Enchéry, G., Touzani, S., & Barsalou, R. (2014). Sensitivity Analysis and Optimization of Surfactant-Polymer Flooding under Geosciences Numerical Methods Modélisation numérique en géosciences. February 2015.

Ekenel, H. K., & Stiefelhagen, R. (2006). Analysis of local appearance-based face recognition: Effects of feature selection and feature normalization. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006.

Erfando, T., Rita, N., & Ramadhan, R. (2019). The Key Parameter Effect Analysis Of Polymer Flooding On Oil Recovery Using Reservoir Simulation. Journal of Geoscience, Engineering, Environment, and Technology, 4(1), 49.

Farrar, D. ., & Glauber, R. . (1964). Multicollinearity in Regression Analysis: The Problem Revisitied. Massachusetts Institute of Technology.

Firozjaii, A. M., & S, M. (2018). Sensitivity Analysis and Optimization of the Effective parameters on ASP Flooding Compared to Polymer Flooding Using CMG-STARS. Journal of Petroleum & Environmental Biotechnology, 09(01).

Fox, J., & Weisberg, S. (2018). Visualizing Fit and Lack of Fit in Complex Regression Models with Predictor Effect Plots and Partial Residuals. 87(9).

Freund, Y., & Schapire, R. E. (1997). On the neutron absorption properties of basic and ultrabasic rocks: The significance of minor and trace elements. Journal of Computer and System Sciences, 55, 119–139.

Fu, J.-F., Fenton, R. ., & Cleghorn, W. . (1991). A mixed integer-discrete-continuous programming method and its application to engineering design optimization. Engineering Optimization, 17 (4)(ISSN 0305-2154), 263–280.

Gao, S., Jiang, Z., Zhang, K., Liu, H., Fu, Q., Yan, W., & Fu, B. (2016). High concentration polymer flooding field test with well infilling to change fluid flowing direction after polymer flooding. Society of Petroleum Engineers - SPE EOR Conference at Oil and Gas West Asia, OGWA 2016.

Gbadamosi, A. O., Junin, R., Manan, M. A., Agi, A., & Yusuff, A. S. (2019). An overview of chemical enhanced oil recovery: recent advances and prospects. In International Nano Letters (Vol. 9, Issue 3). Springer Berlin Heidelberg.

Gogarty, W. B. (1967). Mobility Control With Polymer Solutions. Society of Petroleum Engineers Journal, 7(2).

Guo, H. (2017). How to select polymer molecular weight and concentration to avoid blocking in polymer flooding? Society of Petroleum Engineers - SPE Symposium: Production Enhancement and Cost Optimisation 2017, 1996.

Gupta, I., Devegowda, D., Jayaram, V., Rai, C., & Sondergeld, C. (2019). Machine Learning Regressors and their Metrics to predict Synthetic Sonic and Brittle Zones. 1–20.

Han, D., Jung, J., & Kwon, S. (2020). Comparative study on supervised learning models for productivity forecasting of shale reservoirs based on a data-driven approach. Applied Sciences (Switzerland), 10(4), 1–19.

Hidayat, W., & ALMolhem, N. (2019). Polymer flooding simulation modeling feasibility study: Understanding key aspects and design optimization. SPE Middle East Oil and Gas Show and Conference, MEOS, Proceedings, 2019-March.

Huggett, J. M. (2006). Geology and wine: A review. Proceedings of the Geologists’ Association, 117(2), 239–247.

Jain, A., Nandakumar, K., & Ross, A. (2005). Score normalization in multimodal biometric systems. Pattern Recognition, 38(12), 2270–2285.

Jain, S., Shukla, S., & Wadhvani, R. (2018). Dynamic selection of normalization techniques using data complexity measures. Expert Systems with Applications, 106, 252–262.

Johnson, E. W. (2020). Discrete Probability Distributions. Forest Sampling Desk Reference, 1974, 195–234.

Juárez-Morejón, J. L., Bertin, H., Omari, A., Hamon, G., Cottin, C., Morel, D., Romero, C., & Bourdarot, G. (2019). A new approach to polymer flooding: Effects of early polymer injection and wettability on final oil recovery. SPE Journal, 24(1), 129–139.

Khan, A. M., Sadiq, A., Khawaja, S. G., Alghamdi, N. S., Akram, M. U., & Saeed, A. (2020). Physical Action Categorization using Signal Analysis and Machine Learning. August.

Kim, J. H. (2019). Multicollinearity and misleading statistical results. Korean Journal of Anesthesiology, 72(6), 558–569.

Kirori, Z. (2019). Hyper-parameter parameter optimization?: towards practical sentiment analysis using a Convolutional Neural Network ( CNN ). Research Journal of Computer and Information Technology Sciences, 7(2), 1–5.

Koh, H., Lee, V. B., & Pope, G. A. (2016). Experimental investigation of the effect of polymers on residual oil saturation. Proceedings - SPE Symposium on Improved Oil Recovery, 2016-Janua.

Komorowski, M., Marshall, D. C., Salciccioli, J. D., & Crutain, Y. (2016). Secondary Analysis of Electronic Health Records. Secondary Analysis of Electronic Health Records, October, 1–427.

Lake, L. W. (1989). Enhanced Oil Recovery Prentice Hall. Enhanced Oil Recovery, 224(4649), 159–186.

Larestani, A., Mousavi, S. P., Hadavimoghaddam, F., Ostadhassan, M., & Hemmati-Sarapardeh, A. (2022). Predicting the surfactant-polymer flooding performance in chemical enhanced oil recovery: Cascade neural network and gradient boosting decision tree. Alexandria Engineering Journal, 61(10), 7715–7731.

Liang, Y., & Zhao, P. (2019). A machine learning analysis based on big data for eagle ford shale formation. Proceedings - SPE Annual Technical Conference and Exhibition, 2019-Septe.

Lüftenegger, M., Kadnar, R., Puls, C., & Clemens, T. (2016). Operational challenges and monitoring of a polymer pilot, matzen field, Austria. SPE Production and Operations, 31(3), 228–237.

Miles, J. (2014). Tolerance and Variance Inflation Factor. Wiley StatsRef: Statistics Reference Online, 1–2.

Moe Soe Let, K. P., Manichand, R. N., & Seright, R. S. (2012). Polymer flooding a ?500-cp oil. SPE - DOE Improved Oil Recovery Symposium Proceedings, 2(April), 1670–1682.

Mortimer, R. G. (2013). Probability, Statistics, and Experimental Errors. Mathematics for Physical Chemistry, 191–206.

Mousavi, S. M., Jabbari, H., Darab, M., Nourani, M., & Sadeghnejad, S. (2020). SPE-200752-MS Optimal Well Placement Using Machine Learning Methods?: Multiple Reservoir Scenarios Introduction Method and Model Description.

Needham, R. B., & Doe, P. H. (1987). Polymer Flooding Review. JPT, Journal of Petroleum Technology, 39(12), 1503–1507.

Patro, S. G. K., & sahu, K. K. (2015). Normalization: A Preprocessing Stage. Iarjset, 20–22.

Phankokkruad, M., & Wacharawichanant, S. (2019). Prediction of mechanical properties of polymer materials using extreme gradient boosting on high molecular weight polymers. Advances in Intelligent Systems and Computing, 772, 375–385.

Putatunda, S., & Rama, K. (2018). A comparative analysis of hyperopt as against other approaches for hyper-parameter optimization of XGBoost. ACM International Conference Proceeding Series, 6–10.

Saleh, L. D., Wei, M., Zhang, Y., & Bai, B. (2017). Data analysis for polymer flooding that is based on a comprehensive database. SPE Reservoir Evaluation and Engineering, 20(4), 876–893.

Saqer, F., & Osama, R. (2016). Oil Recovery by Flooding?: Sensitivity Analysis To Technical Parameters. 151.

Shaik, A. R., AlAmeri, W., AlSumaiti, A., Muhammad, M., & Thomas, N. C. (2019). Application of supervised machine learning technique to investigate the effect of brine hardness on polymer bulk rheology. Society of Petroleum Engineers - Abu Dhabi International Petroleum Exhibition and Conference 2019, ADIP 2019.

Sheng, J. J. (2013). Polymer Flooding-Fundamentals and Field Cases. In Enhanced Oil Recovery Field Case Studies (First Edit). Elsevier Inc.

Sheng, J. J., Leonhardt, B., & Azri, N. (2015). Status of polymer-flooding technology. Journal of Canadian Petroleum Technology, 54(2), 116–126.

Shrestha, N. (2020). Detecting Multicollinearity in Regression Analysis. American Journal of Applied Mathematics and Statistics, 8(2), 39–42.

Sieberer, M., Jamek, K., & Clemens, T. (2017). Polymer-flooding economics, from pilot to field implementation. SPE Economics and Management, 9(3), 51–60.

Skauge, T., Vik, B. F., Ormehaug, P. A., Jatten, B. K., Kippe, V., Skjevrak, I., Standnes, D. C., & Uleberg, K. (2014). Polymer flood at adverse mobility ratio in 2D flow by X-ray visualization. Society of Petroleum Engineers - SPE EOR Conference at Oil and Gas West Asia 2014: Driving Integrated and Innovative EOR, June 2017, 820–834.

Tadjer, A., Bratvold, R. B., Hong, A., & Hanea, R. (2021). Application of machine learning to assess the value of information in polymer flooding. Petroleum Research, 6(4), 309–320.

Teeuw, D., Rond, D., & Martin, J. H. (1983). Design of a Pilot Polymer Flood in the Marmul Field, Oman. Society of Petroleum Engineers of AIME, (Paper) SPE, 513–524.

Thompson, C. G., Kim, R. S., Aloe, A. M., & Becker, B. J. (2017). Extracting the Variance In flation Factor and Other Multicollinearity Diagnostics from Typical Regression Results. Basic and Applied Social Psychology, 39(2), 81–90.

Vishnyakov, V., Suleimanov, B., Salmanov, A., & Zeynalov, E. (2020). Chemical EOR. Primer on Enhanced Oil Recovery, 141–159.

Wang, D., Wang, G., & Xia, H. (2011). Large scale high viscous-elastic fluid flooding in the field achieves high recoveries. Society of Petroleum Engineers - SPE Enhanced Oil Recovery Conference 2011, EORC 2011, 2, 831–837.

Wassmuth, F. R., Arnold, W., Green, K., & Cameron, N. (2009). Polymer flood application to improve heavy oil recovery at East Bodo. Journal of Canadian Petroleum Technology, 48(2), 55–61.

Wassmuth, F. R., Green, K., Hodgins, L., & Turta, A. T. (2007). Polymer flood technology for heavy oil recovery. Canadian International Petroleum Conference 2007, CIPC 2007.

Wu, W., Xing, E. P., Myers, C., Mian, I. S., & Bissell, M. J. (2005). Evaluation of normalization methods for cDNA microarray data by k-NN classification. BMC Bioinformatics, 6, 1–21.

Yang, J., & Guan, Ji. (2022). A Heart Disease Prediction Model Based on Feature Optimization and Smote-Xgboost Algorithm.




How to Cite

Erfando, T., & Khariszma, R. . (2023). Sensitivity Study of The Effect Polymer Flooding Parameters to Improve Oil Recovery Using X-Gradient Boosting Algorithm . Journal of Applied Engineering and Technological Science (JAETS), 4(2), 873–884.