@article{MTMT:35071311,
	title = {Predicting scale deposition in oil reservoirs using machine learning optimization algorithms},
	url = {https://m2.mtmt.hu/api/publication/35071311},
	author = {Khodabakhshi, Mohammad Javad and Bijani, Masoud},
	doi = {10.1016/j.rineng.2024.102263},
	journal-iso = {RESULT ENGIN},
	journal = {RESULTS IN ENGINEERING},
	volume = {22},
	unique-id = {35071311},
	issn = {2590-1230},
	year = {2024},
	eissn = {2590-1230},
	pages = {102263}
}

@article{MTMT:34871021,
	title = {Prediction of barium sulfate precipitation in dynamic tube blocking tests and its inhibition for waterflooding application using response surface methodology},
	url = {https://m2.mtmt.hu/api/publication/34871021},
	author = {Khormali, Azizollah and Ahmadi, Soroush},
	doi = {10.1007/s13202-023-01679-2},
	journal-iso = {J OF PETROLEUM EXPLORATION AND PROD TECH},
	journal = {JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGIES},
	volume = {13},
	unique-id = {34871021},
	issn = {2190-0558},
	abstract = {Scale precipitation is one of the major problems in the petroleum industry during waterflooding. The possibility of salt formation and precipitation should be monitored and analyzed under dynamic conditions to improve production performance. Scale precipitation and its dependence on production parameters should be investigated before using scale inhibitors. In this study, the precipitation of barium sulfate salt was investigated through dynamic tube blocking tests at different injection rates and times. For this purpose, the pressure drop caused by salt deposition was evaluated at injection rates of 1, 2, 3, 4, and 5 mL/min. The software determined the worst conditions (temperature, pressure, and water mixing ratio) for barium sulfate precipitation. Moreover, during the experiments, the pressure drop caused by barium sulfate precipitation was measured without using scale inhibitors. The pressure drop data were evaluated by the response surface method and analysis of variance to develop a new model for predicting the pressure drop depending on the injection rate and time. The novelty of this study lies in the development of a new high-precision correlation to predict barium sulfate precipitation under dynamic conditions using the response surface methodology that evaluates the effect of injection rate and time on the possibility of salt precipitation. The accuracy and adequacy of the obtained model were confirmed by using R 2 statistics (including R 2 -coefficient of determination, adjusted R 2 , and predicted R 2 ), adequate precision, and diagnostic charts. The results showed that the proposed model could fully and accurately predict the pressure drop. Increasing the time and decreasing the injection rate caused an increase in pressure drop and precipitation of barium sulfate salt, which was related to the formation of more salt due to the contact of ions. In addition, in a short period of the injection process, the pressure drop due to salt deposition increased sharply, which confirms the need to use a suitable scale inhibitor to control salt deposition. Finally, the dynamic tube blocking tests were repeated in the presence of two well-known scale inhibitors, which prevented salt deposition in the tubes. At the same time, no pressure drop was observed in the presence of scale inhibitors at all injection rates during a long period of injection. The obtained results can be used for the evaluation of salt precipitation during oil production in the reservoirs, in which barium sulfate is precipitated during waterflooding. For this purpose, knowing the flow rate and injection time, it is possible to determine the amount of pressure drop caused by salt deposition.},
	year = {2023},
	eissn = {2190-0566},
	pages = {2267-2281},
	orcid-numbers = {Khormali, Azizollah/0000-0002-9100-2604}
}

@article{MTMT:26616074,
	title = {Performance evaluation of water control with nanoemulsion as pre-pad fluid in hydraulically fracturing tight gas formations},
	url = {https://m2.mtmt.hu/api/publication/26616074},
	author = {Lou, ML and Si, XD and Zhang, Y and Yuan, ZH and Yang, DY and Gong, J},
	journal-iso = {ENERG FUEL},
	journal = {ENERGY AND FUELS},
	volume = {31},
	unique-id = {26616074},
	issn = {0887-0624},
	year = {2017},
	eissn = {1520-5029},
	pages = {3698-3707}
}