Maximizing oil recovery in sandstone reservoirs through optimized ASP injection using the super learner algorithm
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Abstract
Optimizing the Alkaline-Surfactant-Polymer (ASP) injection process remains a persistent challenge in Enhanced Oil Recovery (EOR), particularly in heterogeneous sandstone reservoirs where traditional reservoir simulators are constrained by high computational demands and limited flexibility. This study introduces a novel application of the Super Learner (SL) ensemble, a stacking-based machine learning algorithm integrating multiple base models (XGBoost, SVR, BRR, and Decision Tree), to systematically predict and optimize ASP injection parameters. Unlike previous approaches, our method blends high-fidelity CMOST simulation data with machine learning precision in which it enables real-time optimization with field-scale relevance. Using 500 simulation scenarios validated by laboratory input, the SL model achieved exceptional predictive performance (R² = 0.988, RMSE = 0.304), outperforming all individual learners. The optimal recovery factor (RF) of 79.49% was obtained with the finely tuned concentrations of surfactant (5483.29 ppm), polymer (2242.61 ppm), SO?²? (5610.15 ppm), CO?²? (7053.59 ppm), and Na? (9939.35 ppm). Remarkably, the SL approach could reduce optimization time from 10 hours (CMOST) to under 1 minute; this underscored its potential for real-time operational deployment. The novelty of this work lies in its integrated use of ensemble learning to capture the complex and non-linear interactions between ionic chemistry and oil mobilization behavior, offering a field-ready AI framework for rapid and adaptive EOR design. This approach paves the way for the intelligent optimization of ASP schemes by minimizing the reliance on computationally intensive simulations while ensuring chemical and economic efficiency in marginal or complex reservoirs.
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References
A. M. Nasution and D. F. Putra, “Investigasi Sifat Ion Na(+) & NH4(+) Pada Hybrid-Alkali ASP Flooding Menggunakan Simulator CMG GEM 2020,” Lembaran Publikasi Minyak dan Gas Bum, 56 (2022) 133–145.
C. Sun, H. Guo, Y. Li, G. Jiang, and R. Ma, Alkali Effect on Alkali-Surfactant-Polymer (ASP) Flooding Enhanced Oil Recovery Performance: Two Large-Scale Field Tests Evidence, J. Chem. (2020) 1–22.
H. Guo, G. Jiang, J. Zhang, J. Hou, K. Song, and Q. Song, Alkali-surfactant-polymer ASP flooding field test using horizontal wells: Design, implementation and evaluation, in Proceedings - SPE Symposium on Improved Oil Recovery, Tulsa Oklahoma: SPE, (2020) 1–15.
A. D. K. Wibowo, Investigating potential application of bio-based polymeric surfactant using methyl ester from palm oil for chemical enhanced oil recovery (CEOR), Commun. Sci. Technol. 8 (2023) 235–242.
A. D. N’diaye, M. S. Kankou, B. Hammouti, A. B. D. Nandiyanto, D. F. Al Husaeni, A review of biomaterial as an adsorbent: From the bibliometric literature review, the definition of dyes and adsorbent, the adsorption phenomena and isotherm models, factors affecting the adsorption process, to the use of typha species waste as a low-cost adsorbent, Commun. Sci. Technol. 7 (2022) 140–153.
H. Zhong, T. Yang, H. Yin, J. Lu, K. Zhang, and C. Fu, Role of Alkali type in chemical loss and ASP-flooding enhanced oil recovery in Sandstone formations, in the SPE Annual Technical Conference and Exhibition, Dallas, Texas (2019) 431–445.
H. Zhong, T. Yang, H. Yin, C. Fu, and J. Lu, The Role of Chemicals Loss in Sandstone Formation in ASP Flooding Enhanced Oil Recovery, Dallas Texas (2018) 1–17.
D. F. Putra et al., Innovating EOR Strategies: Unlocking the Potential of Streaming Potential (Electrokinetic) as Sustainable and Ecofriendly Surveillance Tools for Monitoring ASP Fluid Front, in The SPE Advances in Integrated Reservoir Modelling and Field Development Conference and Exhibition, Abu Dhabi, UAE, (2025) 1–13.
H. Guo, Y. Li, Y. Li, D. Kong, B. Li, and F. Wang, Lessons learned from ASP flooding tests in China, in The SPE Reservoir Characterisation and Simulation Conference and Exhibition, Abu Dhabi UAE (2017) 226–248.
H. Guo et al., ASP Flooding: Theory and Practice Progress in China, J Chem, (2017) 1–11.
F. Abadli, Simulation Study of Enhanced Oil Recovery by ASP (Alkaline, Surfactant and Polymer) Flooding for Norne Field C-segment, Norwegian University of Science and Technology, (2012).
M. A. Ahmadi and M. Pournik, A Predictive Model of Chemical Flooding for Enhanced Oil Recovery Purposes: Application of Least Square Support Vector Machine, Petroleum, 2 (2016) 177–182.
D. Steineder, G. Vanegas, T. Clemens, and M. Zechner, Deriving Alkali Polymer Parameter Distributions from Core Flooding by Applying Machine Learning in a Bayesian Framework to Simulate Incremental Oil Recovery, in The 82nd EAGE Conference and Exhibition, Virtual: SPE, (2020) 1–21.
F. Hadavimoghaddam, M. Ostadhassan, M. A. Sadri, T. Bondarenko, I. Chebyshev, and M. Semnani, Prediction of Water Saturation from Well Log Data by Machine Learning Algorithms: Boosting and Super Leaner, J. Mar. Sci. Eng. 9 (2021) 1–23.
A. Larestani, S. P. Mousavi, F. Hadavimoghaddam, M. Ostadhassan, and A. Hemmati-Sarapardeh, Predicting the Surfactant-Polymer Flooding Performance in Chemical Enhanced Oil Recovery: Cascade Neural Network and Gradient Boosting Decision Tree, Alex. Eng. J. 61 (2022) 7715–7731.
M. M. Al-Dousari and A. A. Garrouch, An Artificial Neural Network Model for Predicting the Recovery Performance of Surfactant Polymer Floods, J. Pet. Sci. Eng. 109 (2013) 51–62.
L. Van Si and B. H. Chon, Artificial Neural Network Model for Alkali-Surfactant-Polymer Flooding in Viscous Oil Reservoirs: Generation and Application, Energies 9 (2016) 1–20.
J. Hou, Z. Guan Li, X. Long Ca, and X. Wang Song, Integrating Genetic Algorithm and Support Vector Machine for Polymer Flooding Production Performance Prediction, J. Pet. Sci. Eng., 68 (2009) 29–39.
E. C. Polley and M. J. Van Der Laan, Super Learner In Prediction, Berkeley, 266 (2010).
M. J. Van Der Laan, E. Polley, and A. Hubbard, Super Leaner, Berkeley, 222 (2007).
E. Khamehchi, M. R. Mahdiani, M. A. Amooie, and A. Hemmati Sarapardeh, Modeling viscosity of light and intermediate dead oil systems using advanced computational frameworks and artificial neural networks. J. Pet. Sci. Eng. 193 (2020) 107388.
F. Hadavimoghaddam et al., Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations, Energies (Basel), 14 (2021) 1–6.
S. Munadi, Seismo-Electric Phenomena from Granite Containing Crude Oil, Science Contribution Oil and Gas, 30 (2007) 43–46.
A. Novriansyah, W. Bae, C. Park, A. K. Permadi, and S. S. Riswati, Optimal Design of Alkaline – Surfactant – Polymer Flooding under Low Salinity Environment, Polymers (Basel), 12 (2020) 1–11.
E. Effendi, R. Purwaningsih, Pujiarko, E. M. Adji, L. Notman, and J. Ewing, Plan of Field Development of Tilan Field, Pekanbaru, 2003.
Q. Sun, T. Ertekin, M. Zhang, and T. On, A Comprehensive Techno-Economic Assessment of Alkali–Surfactant–Polymer Flooding Processes Using Data-Driven approaches, Energy Reports, 7 (2021) 2681–2702.