Reservoir Performance Forecasting in Term of Recoverable and Production Life Time in Y Field
Recovery factor is more essential tasks for reservoir engineering to determine the withdrawal rate carried out from the geological subsurface in order to maximize the recoverable as much as possible, because the aims of any oil and gas company is to gain a profit (Onuka & Okoro, 2019). The recovery factor in Y field acquired 1.5% by Natural Flowing, its quite lower recoverable due to pressure depleted in undersaturated conditions. Therefore, the wells equiped Electrical Submersible Pump to improving the oil rate hence to maximize the recoverable. In this study, predicted the reservoir performance to acquire more understanding on production behavior in Y field in term of maximize the recovery factor and determine the production life time in the future. This work presented the the recovery factor are obtained from the tank as big 8.14% by using the pumping system which working at normal speed (60 hz). However, the pressure are declining to 3156.25 psia along with the wells produced at 520.71 stb/d in 2050, the production will be continually yielding because no intersection between the production rate and the limitations rate which 247.45 stb/d (Fig 6), in term of technically evaluation. There is ESP’pump constraint method using to find out the production limitations wich demostrated by Kermiz and Brown statement that production in pumping system should not produced lower than 40% of Absolotu Open Flow Potential in order to prevent the pump thrust. This approach is applicable to demostrate the production limitations using lifting system which means not seem in ecomical limit.
Ahmed, T. (2001). Reservoir Engineering Handbook (second). Butterworth-Heinemann.
Ahmed, T., & McKinkey, P. D. (2005). Advanced Reservoir Engineering. Elsevier Inc.
Akpara, K. (2007). Tuning the analytical model for better prediction of recovery factor. Society of Petroleum Engineers - Nigeria Annual International Conference and Exhibition 2007, NAICE 2007. https://doi.org/10. 2118/111919-ms
Arslan, O., Wojtanowicz, A. K., & White, C. D. (2018). Inflow performance methods for evaluating downhole water sink completions vs. conventional wells in oil reservoirs with water production problems. Canadian International Petroleum Conference 2003, CIPC 2003. https://doi.org/ 10.2118/2003-195.
Azari, M., Soliman, M., & Gazi, N. (1997). Reservoir Engineering Applications to Control Excess Water and Gas Production. Society of Petroleum Engineers.
Bartolomeu, M., & Rahmawaty, S. (2014). Benefits of electrical submersible pumping in a production by gas-alternating-water recovery process. Society of Petroleum Engineers - SPE Middle East Artificial Lift Conference and Exhibition. https://doi.org/10.2118 /173704-ms
Botermans, C. W., Van Batenburg, D. W., & Bruining, J. (2001). Relative Permeability Modifiers: Myth or Reality? SPE - European Formation Damage Control Conference, Proceedings, 429–441. https://doi.org/10.21 18/68973-ms
Brown, K. E. (1977). The Technology of Artificial lift. In PennWell Books (Vol. 1). PennWell Books. 7623701
Clegg, J. D. (1988). High-Rate Artificial Lift. Society Petroleum Engineering, Shell Oil Co, March, 277–282.
Craft, B. C., & Hawkins, M. F. (1991). Applied Petroleum Reservoir Engineering Second Edition. In Prentice Hall PTR (p.432). https://books.google.com/books/about/ Applied_Petroleum_Reservoir_Engineering.html?id=uDFQAQAAIAAJ
da Silva, P. F., Branco, C. C. M., Bampi, D., Silveira, G. E., Nunes, F. P., Faerstein, M., & Tessarolli, F. G. C. (2020). Improving recovery factor in Campos Basin. Offshore Technology Conference Brasil 2019, OTCB 2019. https://doi.org/10.4043/29798-ms
Dake, L. P. (1978). Fundamentals for Reservoir Engineering. In Developments in Petroleum Science. Shell Learning and Development.
Darvish Sarvestani, A., & Hadipour, A. (2019). artificial lift method selection for mature oil fields: A case study. Society of Petroleum Engineers - SPE Annual Caspian Technical Conference 2019, CTC 2019, October, 16–18. https://doi.org/10.2118/198424-ms
Del Pino, J. J., Martin, J. L., Vargas, H., Maldonado, J. S., Rubiano, E., Núñez, W., Sánchez, L. M., Prada, J., Gómez, S., Sarkis, N., & Gonzalez, A. (2017). Installation of electric submersible pump as artificial lift method in low flow rate wells, a case history. Society of Petroleum Engineers - SPE Electric Submersible Pump Symposium 2017, 171–180. https://doi.org/10.2118 /1851 55-ms
Fraga, R. S., Castellões, O. G. S., Assmann, B. W., Estevam, V., de Moura, G. T., Schröer, I. N., & do Amaral, L. G. (2020). Progressive vortex pump: A new artificial lift pumped method. SPE Production and Operations, 35(2), 454–463. https://doi.org/10.2118/200497-PA
Guo, B., Lyons, W. C., & Ghalambor, A. (2007). Petroleum Production Engineering - A computer Assisted Approach. Elsevier Science & Technology Books.
Henson, W. L., Wearden, P. L., & Rice, J. D. (1961). A Numerical Solution to the Unsteady-State Partial-Water-Drive Reservoir Performance Problem. Society of Petroleum Engineers Journal, 1(03), 184–194. https://doi.org/10.2118/1651-g
Idogun, I., Jeboda, O., Charles, D., & Ufomadu, H. (2015). Material balance modeling and performance prediction of a multi-tank reservoir. Society of Petroleum Engineers - SPE Nigeria Annual International Conference and Exhibition, NAICE 2015. https://doi.org/10.2118/178 344-ms
Karmakar, G. P., Grattoni, C. A., & Zimmerman, R. W. (2002). Relative Permeability Modification Using an Oil-Soluble Gelant to Control Water Production. Proceedings - SPE Annual Technical Conference and Exhibition, 733–740. https://doi.org/10.2523/77414-ms
Nguyen, T. (2020). Artificial Lift Methods: Design, practices, ans applications (G. Oluyemi (ed.)).
Okano, H., & Corp, M. N. (2013). Reservoir Model History-Matching and Uncertainty Quantification in Reservoir Performance Forecast Using Bayesian Framework.
Onuka, A. U., & Okoro, F. (2019). Prediction of oil reservoir performance and original-oil-in-place applying Schilthuis and hurst-van everdingen modified water influx models. Society of Petroleum Engineers - SPE Nigeria Annual International Conference and Exhibition 2019, NAIC 2019. https://doi.org/10.2118/198714-MS
Ounsakul, T., Sirirattanachatchawan, T., Pattarachupong, W., Yokrat, Y., & Ekkawong, P. (2019). Artificial lift selection using machine learning. International Petroleum Technology Conference 2019, IPTC 2019. https://doi.org/10.2523/19423-ms
Oyewole, P. (2017). Artificial-lift selection strategy to maximize value of unconventional oil and gas assets. JPT, Journal of Petroleum Technology, 69(7), 64–66. https://doi.org/10.2118/0717-0064-jpt
Pankaj, P., Patron, K. E., & Lu, H. (2018). Artificial lift selection and its applications for deep horizontal wells in unconventional reservoirs. SPE/AAPG/SEG Unconventional Resources Technology Conference 2018, URTC 2018, Oyewole 2016. https://doi.org/10. 15530/urtec-2018-287
Patron, K. E., Zhang, K., Xu, T., Lu, H., & Cui, S. (2018). Case study of artificial lift strategy selection and optimization for unconventional oil wells in the Williston Basin. Society of Petroleum Engineers - SPE Liquids-Rich Basins Conference - North America 2018, LRBC 2018. https://doi.org/10.2118/191793-ms.
Petroleum Experts. (2010). Petroleum Experts MBAL- User Manual. IPM PROSPER Version 7.5, 37 (January 2010), 79–81.
Qing, X., Shuqin, C., Haiyan, M., Min, Z., & Jinying, W. (2013). Prediction Model of Oil Economic Limit Production and its Comparative Study. 1, 1–9.
Ratcliff, D. E., Gomez, C., Cetkovic, I., & Madogwe, O. (2013). Maximizing oil production and increasing ESP run life in a brownfield using real-time ESP monitoring and optimization software: Rockies field case study. Proceedings - SPE Annual Technical Conference and Exhibition, 5, 3658–3668. https://doi.org/10.2118/166386-ms
Sylvester, O., & Onyekonwu, M. O. (2015). Software for reservoir performance prediction. Society of Petroleum Engineers - SPE Nigeria Annual International Conference and Exhibition, NAICE 2015, August, 4–6. https://doi.org/10.2118/178288-ms.
Copyright (c) 2023 Timor-Leste Journal of Engineering and Science
This work is licensed under a Creative Commons Attribution 4.0 International License.