A Data Mining Perspective on the Confluent Ions` Effect for Target Functionality

Babak Fazelabdolabadi, Mostafa Montazeri, Peyman Pourafshary


The production of hydrocarbon resources at an oil field is concomitant with challenges with respect to the formation of scale inside the reservoir rock – intricately impairing its permeability and hindering the flow. Historically, the effect of ions is attributed to the undergone phenomenon; nevertheless, there exists a great deal of ambiguity about its relative significance compared to other factors, or the effectiveness as per the ion type. The present work applies a data mining strategy to unveil the influencing hierarchy of the parameters involved in driving the process within major rock categories – sandstone and carbonate – to regulate a target functionality. The functionalities considered evolve around maximizing the oil recovery, minimizing permeability impairment/ scale damage. A pool of experimental as well as field data was used for this sake, accumulating the bulk of the available literature data. The methods used for data analysis in the present work included the Bayesian Network, Random Forest, Deep Neural Network, as well as Recursive Partitioning. The results indicate a rolling importance for different ion species - altering under each functionality – which is not ranked as the most influential parameter in either case. For the oil recovery target, our results quantify a distinction between the source of ion of a single type, in terms of its influencing rank in the process. This latter deduction is the first proposal of its kind – suggesting a new perspective for research. Moreover, the machine learning methodology was found to be capable of reliably capturing the data – evidenced by the minimal errors in the bootstrapped results.


Doi: 10.28991/HIJ-2021-02-03-05

Full Text: PDF


Big Data; Machine Learning; Bayesian Networks; Random Forest; Formation Damage; Oil Recovery.


Frenier, W. W., & Ziauddin, M. (2008). Formation, removal, and inhibition of inorganic scale in the oilfield environment (p. 808). Richardson, TX: Society of Petroleum Engineers.

Hajirezaie, S., Wu, X., & Peters, C. A. (2017). Scale formation in porous media and its impact on reservoir performance during water flooding. Journal of Natural Gas Science and Engineering, 39, 188–202. doi:10.1016/j.jngse.2017.01.019.

Azizi, J., Shadizadeh, S. R., Khaksar Manshad, A., & Jadidi, N. (2018). Effects of pH and temperature on oilfield scale formation. Iranian Journal of Oil and Gas Science and Technology, 7(3), 18-31. doi:10.22050/ijogst.2017.58038.1350.

Al-Hajri, N. M., Al-Ghamdi, A., Tariq, Z., & Mahmoud, M. (2020). Scale-Prediction/Inhibition Design Using Machine-Learning Techniques and Probabilistic Approach. SPE Production & Operations, 35(04), 0987–1009. doi:10.2118/198646-pa.

Kelland, M. A. (2011). Effect of Various Cations on the Formation of Calcium Carbonate and Barium Sulfate Scale with and without Scale Inhibitors. Industrial & Engineering Chemistry Research, 50(9), 5852–5861. doi:10.1021/ie2003494.

Zhang, P., Zhang, Z., Liu, Y., Kan, A. T., & Tomson, M. B. (2019). Investigation of the impact of ferrous species on the performance of common oilfield scale inhibitors for mineral scale control. Journal of Petroleum Science and Engineering, 172, 288–296. doi:10.1016/j.petrol.2018.09.069.

Ahmadi, M. A. (2015). Developing a Robust Surrogate Model of Chemical Flooding Based on the Artificial Neural Network for Enhanced Oil Recovery Implications. Mathematical Problems in Engineering, 1–9. doi:10.1155/2015/706897.

Baghernezhad, D., Siavashi, M., & Nakhaee, A. (2019). Optimal scenario design of steam-assisted gravity drainage to enhance oil recovery with temperature and rate control. Energy, 166, 610–623. doi:10.1016/j.energy.2018.10.104.

Giro, R., Filho, S., Ferreira, R., Engel, M., & Steiner, M.B. (2019) Offshore Technology Conference Brasil - Artificial Intelligence-Based Screening of Enhanced Oil Recovery Materials for Reservoir-Specific Applications. Offshore Technology Conference Offshore Technology Conference Brasil - Rio de Janeiro, Brazil, October 31. doi:10.4043/29754-ms.

Siavashi, M., & Doranehgard, M. H. (2017). Particle swarm optimization of thermal enhanced oil recovery from oilfields with temperature control. Applied Thermal Engineering, 123, 658–669. doi:10.1016/j.applthermaleng.2017.05.109.

Vo Thanh, H., Sugai, Y., & Sasaki, K. (2020). Application of artificial neural network for predicting the performance of CO2 enhanced oil recovery and storage in residual oil zones. Scientific Reports, 10(1). doi:10.1038/s41598-020-73931-2.

Cheraghi, Y., Kord, S., & Mashayekhizadeh, V. (2021). Application of machine learning techniques for selecting the most suitable enhanced oil recovery method; challenges and opportunities. Journal of Petroleum Science and Engineering, 205, 108761. doi:10.1016/j.petrol.2021.108761.

You, J., Ampomah, W., & Sun, Q. (2020). Development and application of a machine learning based multi-objective optimization workflow for CO2-EOR projects. Fuel, 264, 116758. doi:10.1016/j.fuel.2019.116758.

Koroteev, D., & Tekic, Z. (2021). Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future. Energy and AI, 3, 100041. doi:10.1016/j.egyai.2020.100041.

Dias, L. O., Bom, C. R., Faria, E. L., Valentín, M. B., Correia, M. D., de Albuquerque, M. P., … Coelho, J. M. (2020). Automatic detection of fractures and breakouts patterns in acoustic borehole image logs using fast-region convolutional neural networks. Journal of Petroleum Science and Engineering, 191, 107099. doi:10.1016/j.petrol.2020.107099.

Nozohour-leilabady, B., & Fazelabdolabadi, B. (2016). On the application of artificial bee colony (ABC) algorithm for optimization of well placements in fractured reservoirs; efficiency comparison with the particle swarm optimization (PSO) methodology. Petroleum, 2(1), 79–89. doi:10.1016/j.petlm.2015.11.004.

Wood, D. A. (2016). Metaheuristic profiling to assess performance of hybrid evolutionary optimization algorithms applied to complex wellbore trajectories. Journal of Natural Gas Science and Engineering, 33, 751–768. doi:10.1016/j.jngse.2016.05.041.

Hassan, A., Elkatatny, S., & Abdulraheem, A. (2019). Application of Artificial Intelligence Techniques to Predict the Well Productivity of Fishbone Wells. Sustainability, 11(21), 6083. doi:10.3390/su11216083.

Panja, P., Velasco, R., Pathak, M., & Deo, M. (2018). Application of artificial intelligence to forecast hydrocarbon production from shales. Petroleum, 4(1), 75–89. doi:10.1016/j.petlm.2017.11.003.

Alatrach, Y., Mata, C., Omrani, P.J., Saputelli L., Narayanan R., & Hamdan M. (2020) Prediction of Well Production Event Using Machine Learning Algorithms. Abu Dhabi International Petroleum Exhibition & Conference, Abu Dhabi, UAE.

Bikmukhametov, T., & Jäschke, J. (2019). Oil Production Monitoring using Gradient Boosting Machine Learning Algorithm. IFAC-PapersOnLine, 52(1), 514–519. doi:10.1016/j.ifacol.2019.06.114.

Hosseini-Dastgerdi, Z., & Jafarzadeh-Ghoushchi, S. (2019) Investigation of Asphaltene Precipitationusing Response Surface Methodology Combined with Artificial Neural Network. Journal of Chemical and Petroleum Engineering 53(2) 153-167. doi:10.22059/jchpe.2019.261438.1238.

Ahmadi, M. A., Mohammadzadeh, O., & Zendehboudi, S. (2017). A cutting edge solution to monitor formation damage due to scale deposition: Application to oil recovery. The Canadian Journal of Chemical Engineering, 95(5), 991–1003. doi:10.1002/cjce.22776.

Ahmadi, M., & Chen, Z. (2020). Machine learning-based models for predicting permeability impairment due to scale deposition. Journal of Petroleum Exploration and Production Technology, 10(7), 2873–2884. doi:10.1007/s13202-020-00941-1.

Rostami, A., Shokrollahi, A., Shahbazi, K., & Ghazanfari, M. H. (2019). Application of a new approach for modeling the oil field formation damage due to mineral scaling. Oil & Gas Science and Technology – Revue d’IFP Energies Nouvelles, 74, 62. doi:10.2516/ogst/2019032.

Li, H., Yu, H., Cao, N., Tian, H., & Cheng, S. (2020). Applications of Artificial Intelligence in Oil and Gas Development. Archives of Computational Methods in Engineering, 28(3), 937–949. doi:10.1007/s11831-020-09402-8.

Ahmadi, S., Hosseini, M., Tangestani, E., Mousavi, S. E., & Niazi, M. (2020). Wettability alteration and oil recovery by spontaneous imbibition of smart water and surfactants into carbonates. Petroleum Science, 17(3), 712–721. doi:10.1007/s12182-019-00412-1.

Fathi, S. J., Austad, T., & Strand, S. (2010). “Smart Water” as a Wettability Modifier in Chalk: The Effect of Salinity and Ionic Composition. Energy & Fuels, 24(4), 2514–2519. doi:10.1021/ef901304m.

Montazeri, M., Fazelabdolabadi, B., Shahrabadi, A., Nouralishahi, A., HallajiSani, A., & Moosavian, S. M. A. (2020). An experimental investigation of smart-water wettability alteration in carbonate rocks – oil recovery and temperature effects. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 1–13. doi:10.1080/15567036.2020.1759735.

Zhao, L., & Zhang, B. (2020) Measurement and correlation of solubility of 2-chloro-3-(trifluoromethyl)pyridine in pure solvents and ethanol + n-propanol mixtures. Journal of Molecular Liquids 298(15), 112103. doi:10.1016/j.molliq.2019.112103.

Genuer, R., & Poggi, J. M. (2020). Random Forests with R. Springer. doi:10.1007/978-3-030-56485-8.

Kelleher, J.D. (2019). Deep Learning. The MIT Press.

Korb, K. B., & Nicholson, A. E. (2010). Bayesian artificial intelligence. CRC Press.

Nagarajan, R., Scutari, M., & Lebre, S. (2013). Bayesian Networks in R with Applications in System Biology. Springer Science.

Fazelabdolabadi, B., & Golestan, M. H. (2020). Towards Bayesian Quantification of Permeability in Micro-scale Porous Structures – The Database of Micro Networks. HighTech and Innovation Journal, 1(4), 148–160. doi:10.28991/hij-2020-01-04-02.

Rahman, R., Dhruba, S. R., Ghosh, S., & Pal, R. (2019). Functional random forest with applications in dose-response predictions. Scientific Reports, 9(1). doi:10.1038/s41598-018-38231-w.

LeDell, E., & Poirier, S. (2020) H2O AutoML: Scalable Automatic Machine Learning. 7th ICML Workshop on Automated Machine Learning (AutoML).

Salimova, R. (2021). Data-driven analysis of low salinity waterflooding in carbonates. M.Sc. Dissertation, Nazarbayev University, Astana, Kazakhstan.

Full Text: PDF

DOI: 10.28991/HIJ-2021-02-03-05


  • There are currently no refbacks.

Copyright (c) 2021 Babak Fazelabdolabadi, Mostafa Montazeri, Peyman Pourafshary