PVT Modeling of Qaiyarah Oil Field
محتوى المقالة الرئيسي
الملخص
Qaiyarah oil field is characterized by its complexity due to its extra heavy oil reaching 16° API. Thus, building a systematic PVT model for this field at a specific range of temperatures is a powerful challenge for screening such reservoirs. The peng-Robenson equation of state model with up to six pseudo components was developed for the crude sample of the Qaiyarah oil field. This work represents the fingerprint for constructing a dynamic model for the field under study. The model also applies to the heavy oil reservoirs under splitting and lumping scenarios. This work suggests a lumping scheme to enhance the accuracy and CPU performance of compositional reservoir simulations. Therefore, the full components model (13 components) is lumped into a reduced number of pseudo components (6 components) to be utilized in the compositional fluid simulation. This study outlines the Peng-Robinson equation of state (EOS) to tune the data at a certain pressure range up to 400 psi. More specifically, various essential parameters have been trained to match the model results with the experimental data. Splitting processes of C6+ into four pseudo components, namely, HYP01, HYP02, HYP03, and HYP04 is added to the matching picture. Separately, justifying the critical properties introduced a better result of regression. The results showed an acceptable match for Bo with an error percent below 1%, while calculated oil viscosity deviated from measured values in different ranges against pressure variation.
تفاصيل المقالة
كيفية الاقتباس
تواريخ المنشور
الإستلام
النسخة النهائية
الموافقة
النشر الالكتروني
المراجع
Ahmed, M.A., Abdul-Majeed, G.H., and Alhuraishawy, A.K., 2023. Asphaltene precipitation investigation using a screening technique for crude oil sample from the Nahr-Umr formation/Halfaya oil field. Iraqi Journal of Chemical and Petroleum Engineering, 24(1), pp. 41–50. https://doi.org/10.31699/IJCPE.2023.1.6.
Ahmedzeki, N.S., Ridha, I.M., Ali, Y.M., and AbidAlwahab, Z.T., 2012. Development of PVT correlation for Iraqi crude oils using artificial neural network. Iraqi Journal of Chemical and Petroleum Engineering, 13(3), pp. 9–16.
Alali, Y., and Verlaan, M., 2023. Development of a unified PVT model for a large heavy oil field in Kuwait. SPE-215231-MS. https://doi.org/10.2118/215231-MS.
Ali, A.A., Al-Jawad, M.S., and Ali, A.A., 2019. Asphaltene precipitation modeling of sadi formation in Halfaya Iraqi oil field. Journal of Engineering, 25(8), pp. 113–128. https://doi.org/10.31026/j.eng.2019.08.08.
Ali, A.A., Al-Jawad, M.S., and Ali, A.A., 2019. Asphaltene stability of some Iraqi dead crude oil. Journal of Engineering, 25(3), pp. 53–67. https://doi.org/10.31026/j.eng.2019.03.05.
Ali, N.M., and Naife, T.M., 2021. Deasphalting of atmospheric Iraqi residue using different solvents. Journal of Engineering, 27(5), pp. 17–27. https://doi.org/10.31026/j.eng.2021.05.02.
Al-Jaff, L.S., and Hamd-Allah, S.M., 2023. Reservoir characterization of Jeribe and Euphrates formations in Qaiyarah oilfield. The Iraqi Geological Journal, 56 (2F), pp. 204–213. https://doi.org/10.46717/igj.56.2F.13ms-2023-12-19.
Al-Waeli, A.H.A., Sopian, K., M.T., Chaichan, H.A. Kazem, A. Ibrahim, S. Mat, and Ruslan, M. H., 2017. Evaluation of the nanofluid and nano−PCM based photovoltaic thermal (PVT) system: An experimental study. Energy Conversion and Management, 151, pp. 693–708. https://doi.org/10.1016/j.enconman.2017.09.032.
Asaee, S.D.S., Vafajoo, L., and Khorasheh, F., 2014. A new approach to estimate parameters of a lumped kinetic model for hydroconversion of heavy residue. Fuel, 134, pp. 343–353. https://doi.org/10.1016/j.fuel.2014.05.079.
Azinfar, B., Haddadnia, A. Zirrahi, M., Hassanzadeh, H., and Abedi, J., 2018b. A thermodynamic model to predict propane solubility in bitumen and heavy oil based on experimental fractionation and characterization. Journal of Petroleum Science and Engineering, 168, pp. 156–177. https://doi.org/10.1016/j.petrol.2018.04.065.
Azinfar, B., Zirrahi, M. Hassanzadeh, H., and Abedi, J., 2018a. Characterization of heavy crude oils and residues using combined Gel Permeation Chromatography and simulated distillation. Fuel, 233, pp. 885–893. https://doi.org/10.1016/j.fuel.2018.06.110.
Chamgoué, A.C.C.L., Ngankap, Metsebo, M.T., J., and Akoteh, A.A., 2023. Improving the recovery of hydrocarbons in a well in the Gullfaks field by injecting sequestrated CO2. Iraqi Journal of Chemical and Petroleum Engineering, 24(1), pp. 1–4. https://doi.org/10.31699/IJCPE.2023.1.1.
Chen, Z., Zhao, Z., and Yang, D., 2020. Quantification of phase behavior for solvent/heavy-oil/water systems at high pressures and elevated temperatures with dynamic volume analysis. SPE Journal, 25(06), pp. 2915–2931. https://doi.org/10.2118/201240-PA.
Elizalde, I., Rodríguez, M.A., and Ancheyta, J., 2009. Application of continuous kinetic lumping modeling to moderate hydrocracking of heavy oil. Applied Catalysis A: General, 365(2), pp. 237–242. https://doi.org/10.1016/j.apcata.2009.06.018.
Farkha S. A., Zangana, M.H.S., and Shoham, O., 2023. Evaluation of compositional models and PVT correlations for Iraqi light crude oils properties. Energy Science and Engineering, 11(7). https://doi.org/10.1002/ese3.1456.
Fleming, G., and Wong, T., 2015. Efficient compositional simulation with locally lumped EoS characterization. SPE Reservoir Characterisation and Simulation Conference and Exhibition, D031S013R001. https://doi.org/10.2118/175566-MS.
Fouad, W.A., Abutaqiya, M.I.L., Mogensen, K., Yap, Y.F., Goharzadeh, A., Vargas, F.M., and Vega, L.F., 2018. Predictive model for pressure–volume–temperature properties and asphaltene instability of crude oils under gas injection. Energy and Fuels, 32(8), pp. 8318–8328. https://doi.org/10.1021/acs.energyfuels.8b01783.
Ghasemi, M., and Whitson, C.H., 2021. PVT modeling of complex heavy oil mixtures. Journal of Petroleum Science and Engineering, 205, P. 108510. https://doi.org/10.1016/j.petrol.2021.108510.
Ghorayeb, K., Mawlod, A.A., Maarouf, A., Sami, Q., El Droubi, N., Merrill, R., El Jundi, O., and Mustapha, H., 2022. Chain-based machine learning for full PVT data prediction. Journal of Petroleum Science and Engineering, 208, P. 109658. https://doi.org/10.1016/j.petrol.2021.109658.
Hadi, L.I., and Hamd-Allah, S.M., 2020. Estimation of minimum miscibility pressure for hydrocarbon gas injection based on EOS. Association of Arab Universities Journal of Engineering Sciences, 27(3), pp. 9–14. https://doi.org/10.33261/jaaru.2020.27.3.002.
Hameed, A.A., Lazim, S.A., and Hamd-Allah, S., 2018. Estimation of minimum miscibility pressure for [CO] _2 flood based on EOS. Journal of Engineering, 24(4), pp. 89-95. https://doi.org/10.31026/j.eng.2018.04.06.
Izadi, S., and Jafarzadegan, M., 2021. Comparison of asphaltene models in two commercial compositional simulators. Journal of Petroleum Science and Technology, 11(4), pp. 43-54. https://doi.org/10.22078/jpst.2022.4746.1789.
Kerr, E., Venepalli, K.K., Patel, K., Ambrose, R., and Erdle, J., 2020. Use of reservoir simulation to forecast field EOR response - an eagle ford gas injection huff-n-puff application. SPE Hydraulic Fracturing Technology Conference and Exhibition, The Woodlands, Texas, USA, SPE-199722-MS. https://doi.org/10.2118/199722-MS.
Liu, Y., Wang, D., Sun, X., Dinh, N., and Hu, R., 2021. Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments. Reliability Engineering and System Safety, 212(107636). https://doi.org/10.1016/j.ress.2021.107636.
Liu, Y., Yuan, Y., Zhou, F., Qi, Z., Zhang, W., Zhang, H., and Wang, Q., 2020. Analysis on PVT test and empirical formula of Bohai heavy oil with different types of dissolved gases. Journal of Petroleum Exploration and Production Technology, 10, pp. 3609–3617. https://doi.org/10.1007/s13202-020-00973-7.
Maes, J., Muggeridge, A. H., Jackson, M. D., Quintard, M., and Lapene, A., 2016. Modeling in-situ upgrading of heavy oil using operator splitting method. Computational Geosciences, 20, pp. 581–594. https://doi.org/10.1007/s10596-015-9495-6.
Meziani, S., Tahir, S., and Al Hashemi, T., 2018. A thorough investigation of PVT data and fluid model for giant onshore field, hidden lateral trends identified. Abu Dhabi International Petroleum Exhibition and Conference, SPE-193154-MS, D041S098R001. https://doi.org/10.2118/193154-MS.
Michelsen, M.L., 1982. The isothermal flash problem. Part II. Phase-split calculation. Fluid Phase Equilibria, 9(1), pp. 21–40. https://doi.org/10.1016/0378-3812(82)85002-4.
Nabipour, N., and Baghban, A., 2019. Rigorous model for determination of PVT properties of crude oil in operational conditions. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 45(3) PP. 8879–8885. https://doi.org/10.1080/15567036.2019.1677823.
Quinones-Cisneros, S.E., Dalberg, A., and Stenby, E.H., 2004. PVT characterization and viscosity modeling and prediction of crude oils. Petroleum Science and Technology, 22(9–10), pp. 1309–1325. https://doi.org/10.1081/LFT-200034092.
Rastegar, R., and Jessen, K., 2009. A flow based lumping approach for compositional reservoir simulation. SPE Reservoir Simulation Conference, SPE-119160. https://doi.org/10.2118/119160-MS.
Ratnakar, R.R., Dindoruk, B., and Wilson, L.C., 2017. Phase behavior experiments and PVT modeling of DME-brine-crude oil mixtures based on Huron-Vidal mixing rules for EOR applications. Fluid Phase Equilibria, 434, pp.49-62. https://doi.org/10.1016/j.fluid.2016.11.021.
Riyahin, M., Montazeri, G.M., Jamoosian, L., and Farahbod, F., 2014. PVT-generated correlations of heavy oil properties. Petroleum Science and Technology, 32(6), pp. 703–711. https://doi.org/10.1080/10916466.2011.604060.
Samba, M.A., Yiqiang, L., Alkhyyali, W.A., Altaher, Y.A., and Hidaya, F., 2023. Evaluates a PVT correlation to estimate dead oil viscosity for Libyan crudes using 104 samples from different reservoirs. Journal of Engineering Research, 11(1B). https://doi.org/10.36909/jer.12229.
Soto-Azuara, L.A., Ramírez-López, R., del Carmen Monterrubio-Badillo, M., and Elizalde, I., 2022. Mathematical modeling of the hydrocracking kinetics of a heavy oil fraction using the discrete lumping approach: the effect of the variation of the lump number. Reaction Kinetics, Mechanisms and Catalysis, 135(2), pp. 655–667.
https://doi.org/10.1007/s11144-022-02156-3.
Zirrahi, M., Hassanzadeh, H., and Abedi, J., 2017b. Experimental and modeling studies of MacKay River bitumen and light n‐alkane binaries. The Canadian Journal of Chemical Engineering, 95(7), pp.1417-1427. https://doi.org/10.1002/cjce.22775.
Zirrahi, M., Hassanzadeh, H., and Abedi, J., 2017a. Experimental and modeling studies of water, light n-alkanes and MacKay River bitumen ternary systems. Fuel, 196, pp.1-12. https://doi.org/10.1016/j.fuel.2017.01.078.