Fan, C. et al. Scale prediction and Inhibition for oil and gas production at high temperature/high pressure. SPE J. 17 (02), 379–392 (2012).
Google Scholar
Awan, M. & Al-Khaledi, S. Chemical treatments practices and philosophies in oilfields. in SPE international oilfield corrosion conference and exhibition. SPE. (2014).
Demadis, K. D., Stathoulopoulou, A. & Ketsetzi, A. Inhibition And Control Of Colloidal Silica: Can Chemical Additives Untie The Gordian Knot Of Scale Formation? in NACE CORROSION. NACE. (2007).
Nassivera, M. & Essel, A. Fateh field sea water injection-water treatment, corrosion, and scale control. in SPE Middle East Oil and Gas Show and Conference. SPE. (1979).
Nwonodi, C. Prediction and Monitoring of Scaling in Oil Wells44 (Undergraduate Project, University of Port Harcourt, Rivers State, 1999).
Bijani, M., Behbahani, R. M. & Moghadasi, J. Predicting scale formation in wastewater disposal well of Rag-e-Safid desalting unit 1. Desalination Water Treat. 65, 117–124 (2017).
Google Scholar
Moghadasi, J. et al. Scale Formation in Oil Reservoir and Production Equipment during Water Injection Kinetics of CaSO4 and CaCO3 Crystal Growth and Effect on Formation Damage. in SPE European Formation Damage Conference. (2003).
Bijani, M., Khamehchi, E. & Shabani, M. Optimization of salinity and composition of injected low salinity water into sandstone reservoirs with minimum scale deposition. Sci. Rep. 13 (1), 12991 (2023).
Google Scholar
Bijani, M., Khamehchi, E. & Shabani, M. Comprehensive experimental investigation of the effective parameters on stability of silica nanoparticles during low salinity water flooding with minimum scale deposition into sandstone reservoirs. Sci. Rep. 12 (1), 16472 (2022).
Google Scholar
de Angelo, J. F. & Ferrari, J. V. Study of calcium carbonate scaling on steel using a high salinity Brine simulating a pre-salt produced water. Geoenergy Sci. Eng. 233, 212541 (2024).
Google Scholar
Bijani, M. & Khamehchi, E. Optimization and treatment of wastewater of crude oil desalting unit and prediction of scale formation. Environ. Sci. Pollut. Res. 26 (25), 25621–25640 (2019).
Google Scholar
Jordan, M. M., Johnston, C. J. & Robb, M. Evaluation Methods for Suspended Solids and Produced Water as an Aid in Determining Effectiveness of Scale Control both Downhole and Topside21p. 7–18 (SPE Production & Operations, 2006). 01.
Schmid, J. et al. Mitigating Downhole Calcite and Barite Deposition in the Montney: A Successful Scale Squeeze Program. in SPE Canadian Energy Technology Conference. SPE. (2024).
Khormali, A. & Petrakov, D. G. Laboratory investigation of a new scale inhibitor for preventing calcium carbonate precipitation in oil reservoirs and production equipment. Pet. Sci. 13, 320–327 (2016).
Google Scholar
Khormali, A., Petrakov, D. G. & Moein, M. J. A. Experimental analysis of calcium carbonate scale formation and Inhibition in waterflooding of carbonate reservoirs. J. Petrol. Sci. Eng. 147, 843–850 (2016).
Google Scholar
Zhang, Z. et al. Laboratory Investigation of co-precipitation of CaCO3/BaCO3 Mineral Scale Solids at Oilfield Operating Conditions: Impact of Brine Chemistry75p. 83 (Oil & Gas Science and Technology–Revue d’IFP Energies nouvelles, 2020).
MacAdam, J. & Jarvis, P. Water-formed Scales and Deposits: Types, Characteristics, and Relevant Industries, in Mineral Scales and Depositsp. 3–23 (Elsevier, 2015).
Tungesvik, M. The Scale Problem, Scale Control and Evaluation of Wireline Milling for Scale Removal (University of Stavanger, 2013).
Yap, J. et al. Removing iron sulfide scale: a novel approach. in Abu Dhabi International Petroleum Exhibition and Conference. SPE. (2010).
Li, X. et al. Study on the scale Inhibition performance of organic chelating agent aided by surfactants on CaCO3 at high salinity condition. Tenside Surfactants Detergents, 2024(0).
Qing, G. Scaling Formation Characteristics of Ca~(2+)/Mg~(2+)/Si~(4+)/Ba~(2+) in ASP Flooding (Oilfield Chemistry, 2012).
Xian, W. An Experimental Study on Rock/Water Reactions for Alkaline/Surfactant/Polymer Flooding Solution Used at Daqing (Oilfield Chemistry, 2003).
Li, Z. et al. Formation damage during alkaline-surfactant-polymer flooding in the Sanan-5 block of the Daqing oilfield, China. J. Nat. Gas Sci. Eng. 35, 826–835 (2016).
Trujillo-Chavarro, Y. C. et al. A Novel Integrated Methodology for Predicting and Managing Caco3 Scale Deposition in Oil-Producing Wells. Available at SSRN 4552032.
Tahmasebi, P. & Hezarkhani, A. A fast and independent architecture of artificial neural network for permeability prediction. J. Petrol. Sci. Eng. 86, 118–126 (2012).
Su, X. et al. Research on the scaling mechanism and countermeasures of tight sandstone gas reservoirs based on machine learning. Processes 12 (3), 527 (2024).
Google Scholar
Zabihi, R., Schaffie, M. & Ranjbar, M. The prediction of the permeability ratio using neural networks. Energy Sour. Part A Recover. Utilization Environ. Eff. 36 (6), 650–660 (2014).
Google Scholar
Al-Hajri, N. M. & AlGhamdi, A. Scale Prediction and Inhibition Design Using Machine Learning Techniques. in SPE Gas & Oil Technology Showcase and Conference. SPE. (2019).
Al-Hajri, N. M. et al. Scale-prediction/inhibition design using machine-learning techniques and probabilistic approach. SPE Prod. Oper. 35 (04), 0987–1009 (2020).
Google Scholar
Hamid, S. et al. A practical method of predicting calcium carbonate scale formation in well completions. SPE Prod. Oper. 31 (01), 1–11 (2016).
Google Scholar
Ugoyah, J. C. et al. Prediction of Scale Precipitation by Modelling its Thermodynamic Properties using Machine Learning Engineering. in SPE Nigeria Annual International Conference and Exhibition. SPE. (2022).
Yousefzadeh, R. et al. An Insight into the Prediction of Scale Precipitation in Harsh Conditions Using Different Machine Learning Algorithms38p. 286–304 (SPE Production & Operations, 2023). 02.
Nallakukkala, S. & Lal, B. Machine Learning for Scale Deposition in Oil and Gas Industry, in Machine Learning and Flow Assurance in Oil and Gas Productionp. 105–118 (Springer, 2023).
Khodabakhshi, M. J. & Bijani, M. Predicting scale deposition in oil reservoirs using machine learning optimization algorithms. Results Eng., : p. 102263. (2024).
Hu, Y. & Lv, M. Research on prediction model of scaling in ASP flooding based on data mining. J. Comput. Methods Sci. Eng. 23, 3037–3054 (2023).
Vazquez, O. et al. Optimization of Alkaline-Surfactant-Polymer (ASP) flooding minimizing risk of scale deposition. 2017: pp. 1–20. (2017).
Ness, G. et al. Application of a Rigorous Scale Prediction Workflow to the Analysis of CaCO3 Scaling in an Extreme Acid Gas, High Temperature, Low Watercut Onshore Field in Southeast Asia. in ADIPEC. (2022).
Amiri, M., Moghadasi & J. and The prediction of calcium carbonate and calcium sulfate scale formation in Iranian oilfields at different mixing ratios of injection water with formation water. Pet. Sci. Technol. 30 (3), 223–236 (2012).
de Cosmo, P. Modeling and validation of the CO2 degassing effect on CaCO3 precipitation using oilfield data. Fuel 310, 122067 (2022).
Zhang, Y. et al. The kinetics of carbonate scaling—application for the prediction of downhole carbonate scaling. J. Petrol. Sci. Eng. 29 (2), 85–95 (2001).
Google Scholar
Lai, N. et al. Calcium carbonate scaling kinetics in oilfield gathering pipelines by using a 1D axial dispersion model. J. Petrol. Sci. Eng. 188, 106925 (2020).
Google Scholar
Poletto, V. G. et al. Calcium Carbonate Formation within the Oil and Gas Workflow: A Combined Thermodynamic, Kinetic and CFD Modeling Approach. in Offshore Technology Conference Brasil. (2023).
Jordan, M. I. & Mitchell, T. M. Machine learning: trends, perspectives, and prospects. Science 349 (6245), 255–260 (2015).
Google Scholar
Mohammadpoor, M. & Torabi, F. Big data analytics in oil and gas industry: an emerging trend. Petroleum 6 (4), 321–328 (2020).
Vapnik, V. & Chervonenkis, A. On the one class of the algorithms of pattern recognition. Autom. Remote Control. 25 (6), 250 (1964).
Vapnik, V. N. Pattern recognition using generalized portrait method. Autom. Remote Control. 24 (6), 774–780 (1963).
Al-Anazi, A. F. & Gates, I. D. Support vector regression for porosity prediction in a heterogeneous reservoir: A comparative study. Comput. Geosci. 36 (12), 1494–1503 (2010).
Google Scholar
Breiman, L. Classification and Regression Trees (Routledge, 2017).
Patel, N. & Upadhyay, S. Study of various decision tree pruning methods with their empirical comparison in WEKA. Int. J. Comput. Appl., 60(12). (2012).
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
Guo, L. et al. Relevance of airborne lidar and multispectral image data for urban scene classification using random forests. ISPRS J. Photogrammetry Remote Sens. 66 (1), 56–66 (2011).
Google Scholar
Rodriguez-Galiano, V. F. et al. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J. Photogrammetry Remote Sens. 67, 93–104 (2012).
Google Scholar
Breiman, L. Bagging predictors. Mach. Learn. 24, 123–140 (1996).
Google Scholar
Dietterich, T. G. An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach. Learn. 40, 139–157 (2000).
Hemmati-Sarapardeh, A. et al. Applications of Artificial Intelligence Techniques in the Petroleum Industry (Gulf Professional Publishing, 2020).
Geurts, P., Ernst, D. & Wehenkel, L. Extremely Randomized Trees Mach. Learn., 63: 3–42. (2006).
Cao, L. et al. Interpretable Soft Sensors Using Extremely Randomized Trees and SHAP56p. 8000–8005 (IFAC-PapersOnLine, 2023). 2.
Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat., : pp. 1189–1232. (2001).
Friedman, J. H. Stochastic gradient boosting. Comput. Stat. Data Anal. 38 (4), 367–378 (2002).
Google Scholar
Available from: https://www.geeksforgeeks.org/what-is-data-normalization/
Wang, F. et al. Study on Offshore Seabed Sediment Classification Based on Particle Size Parameters Using XGBoost Algorithm149p. 104713 (Computers & Geosciences, 2021).
Wang, C. C., Kuo, P. H. & Chen, G. Y. Machine learning prediction of turning precision using optimized Xgboost model. Appl. Sci. 12 (15), 7739 (2022).
Google Scholar
Fix, E. Discriminatory Analysis: Nonparametric Discrimination, Consistency PropertiesVol. 1 (USAF school of Aviation Medicine, 1985).
Cover, T. & Hart, P. Nearest neighbor pattern classification. IEEE Trans. Inf. Theory. 13 (1), 21–27 (1967).
Google Scholar
Brownlee, J. Data Preparation for Machine Learning: Data Cleaning, Feature Selection, and Data Transforms in Python (Machine Learning Mastery, 2020).
Ali, P. J. M. et al. Data normalization and standardization: a technical report. Mach. Learn. Tech. Rep. 1 (1), 1–6 (2014).
Liu, H. & Zhang, S. Noisy data elimination using mutual k-nearest neighbor for classification mining. J. Syst. Softw. 85 (5), 1067–1074 (2012).
Yang, J., Rahardja, S. & Fränti, P. Outlier detection: how to threshold outlier scores? in Proceedings of the international conference on artificial intelligence, information processing and cloud computing. (2019).
Zanjani, M. S., Salam, M. A. & Kandara, O. Data-driven hydrocarbon production forecasting using machine learning techniques. Int. J. Comput. Sci. Inform. Secur. (IJCSIS). 18 (6), 65–72 (2020).
Eyitayo, S. I., Ekundayo, J. M. & Mumuney, E. O. Prediction of Reservoir Saturation Pressure and Reservoir Type in a Niger Delta Field using Supervised Machine Learning ML Algorithms. in SPE Nigeria Annual International Conference and Exhibition. SPE. (2020).
Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data. in Proceedings of the SAS global forum. SAS Institute Inc. Cary, NC, USA. (2017).
Probst, P., Boulesteix, A. L. & Bischl, B. Tunability: importance of hyperparameters of machine learning algorithms. J. Mach. Learn. Res. 20 (53), 1–32 (2019).
Google Scholar
Group, F. A. Available from: https://forensicreader.com/grid-search-method/
Pandey, Y. N. et al. Machine Learning in the Oil and Gas Industry (Mach Learning in Oil Gas Industry, 2020).
Tariq, Z. et al. A systematic review of data science and machine learning applications to the oil and gas industry. J. Petroleum Explor. Prod. Technol. 11 (12), 4339–4374 (2021).
Google Scholar
