A comprehensive workflow for real time injection-production optimization based on equilibrium displacement

Huijiang Chang, Yingxian Liu, Yuan Lei, Qi Zhang

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Abstract


        

Irregular well network with high oil recovery rate is used in the development of offshore oilfield, which usually leads to imbalanced waterflooding and poor development performance. In this paper, according to the Buckley-Leverett Equation and general waterflooding theory, a quantitative relationship between water-cut, liquid production and water injection rate is gained to improve the unbalanced lateral waterflooding of the present well network. All the single-well water-cuts are considered to obtain balanced waterflooding of present well network through liquid production and water injection rate adjustments. A new injection-production adjustment method is proposed, with the corresponding calculation program being compiled to realize real-time optimization and adjustment. This method is applied to the 1-1195-1 sand body of Bohai BZ Oilfield. The daily oil increment is 80 m3/d and the cumulative annual oil increment is 2.6×104 m3 , which is consistent with the expected program. It can therefore contribute to engineers’ optimizing the injection-production strategy of reservoirs, as well as facilitating revitalizing mature water foods and, more importantly, facilitating the design and implementation of an appropriate IOR pilots. The presented reliable method could provide certain significance for the efficient development of offshore oilfields.

Cited as: Chang, H., Liu, Y., Lei, Y., Zhang, Q. A comprehensive workflow for real time injection-production optimization based on equilibrium displacement. Advances in Geo-Energy Research, 2020, 4(3): 260-270, doi: 10.46690/ager.2020.03.04


Keywords


Equilibrium displacement; plane injection and production adjustment; eurytopic water drive curve; real-time optimization; Bohai BZ oilfield

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References


Alhuthali, A., Oyerinde, D., Datta-Gupta, A. Optimal waterflood management using rate control. SPE Reserv. Eval. Eng. 2007, 10(5): 539-551.

Artun, E. Characterizing interwell connectivity in waterflooded reservoirs using data-driven and reduced-physics models: A comparative study. Neural Comput. Appl. 2017, 28(7): 1729-1743.

Baker, R.O., Kuppe, F., Chugh, S., et al. Full-field modeling using streamline-based simulation: Four case studies. SPE Reserv. Eval. Eng. 2002, 5(2): 126-134.

Batycky, R.P., Blunt, M.J., Thiele, M.R. A 3D field-scale streamline-based reservoir simulator. SPE Reserv. Eng. 1997, 12(4): 246-254.

Bostan, M., Kharrat, R., Barjas, A. Injection efficiency and water loss optimization using streamline simulation in water flooding process. Pet. Sci. Technol. 2013, 31(14): 1477-1487.

Brouwer, D.R., Jansen, J.D. Dynamic optimization of waterflooding with smart wells using optimal control theory. SPE J. 2004, 9(4): 391-402.

Cao, F., Luo, H., Lake, L.W. Oil-rate forecast by inferring fractional-models from field data with koval method combined with the capacitance/resistance model. SPE Reserv. Eval. Eng. 2015, 18(4): 534-553.

Cardoso, M.A., Durlofsky, L.J. Use of reduced-order modeling procedures for production optimization. SPE J. 2010, 15(2): 426-435.

Chen, Y., Oliver, D.S., Zhang, D. Efficient ensemble-based closed-loop production optimization. SPE J. 2009, 14(4): 634-645.

Denney, D. Use of streamline simulation in reservoir management. J. Pet. Technol. 2001, 53(4): 82-83.

Guo, Z., Reynolds, A.C., Zhao, H. A physics-based data-driven model for history matching, prediction, and characterization of waterflooding performance. SPE J. 2018a, 23(2): 367-395.

Guo, Z., Reynolds, A.C., Zhao, H. Waterflooding optimization with the INSIM-FT data-driven model. Comput. Geosci. 2018b, 22(3): 745-761.

Guo, Z., Reynolds, A.C. INSIM-FT in three-dimensions with gravity. J. Comput. Phys. 2019, 380: 143-169.

Hong, A., Bratvold, R., Nvdal, G. Robust production optimization with capacitance-resistance model as precursor. Comput. Geosci. 2017, 21(5-6): 1423-1442.

Hu, J., Li, H. Water flooding flowing area identification for oil reservoirs based on the method of streamline clustering artificial intelligence. Pet. Explor. Dev. 2018, 45(2): 328-335.

Hu, G. A new method for calculating volumetric sweep efficiency in a water-flooding oilfield. Pet. Explor. Dev. 2013, 40(1): 111-114.

Jansen, J.-D., Brouwer, D., Naevdal, G., et al. Closed-loop reservoir management. First Break 2005, 23(1): 43-48.

Lerlertpakdee, P., Jafarpour, B., Gildin, E. Efficient production optimization using flow-network models. SPE J. 2014, 19(6): 1083-1095.

Liu, M., Zhang, S., Yan, W., et al. How to make injection more effective and get production more optimum-A good case from China. Paper SPE 170996 Presented at SPE Oilfield Water Management Conference and Exhibition, Kuwait city, Kuwait, 21-22 April, 2014.

Liu, Y. A new calculationg method of theoretical decline law for water flooding standstone reservoir. China Offshore Oil and Gas 2016, 28(3): 97-100. (in Chinese)

Mamghaderi, A., Bastami, A., Pourafshary, P. Optimization of waterflooding performance in a layered reservoir using a combination of capacitance-resistive model and genetic algorithm method. J. Energy Resour. Technol. 2013, 135(1): 013102.

Nguyen, A.P., Lasdon, L., Lake, L.W., et al. Capacitance resistive model application to optimize waterflood in a west texas field. Paper SPE 146984 Presented at SPE Annual Technical Conference and Exhibition, Denver, USA, 30 October-2 November, 2011.

Omara, E.A., EI hawary, A.F., Nosseir, M., et al. Identifying opportunities in a complex mature oil reservoir; a company. Paper Presented at International Petroleum Technology Conference, Doha, Qatar, 19-22 January, 2014.

Park, H.Y., Datta-Gupta, A. Reservoir management using streamline-based flood efficiency maps and application to rate optimization. J. Pet. Sci. Eng. 2013, 109: 312-326.

Sajjadi, S.A., Nasriani, H.R., Dailami, K., et al. Optimizing volumetric sweep efficiency in water flooding by streamline simulation. Energy Sources Part A-Recovery Util. Environ. Eff. 2017, 39: 1-8.

Sayarpour, M., Zuluaga, E., Kabir, C.S., et al. The use of capacitance-resistance models for rapid estimation of waterflood performance and optimization. J. Pet. Sci. Eng. 2009, 69(3-4): 227-238.

Thiele, M.R., Batycky, R.P., Blunt, M.J., et al. Simulating Flow in Heterogeneous Systems Using Streamtubes and Streamlines. SPE Reserv. Eng. 1996, 11(1): 5-12.

Van Essen, G., Zandvliet, M., Van den Hof, P., et al. Robust waterflooding optimization of multiple geological scenarios. SPE J. 2009, 14(1): 202-210.

Wen, T., Thiele, M.R., Ciaurri, D.E., et al. Waterflood management using two-stage optimization with streamline simulation. Comput. Geosci. 2014, 18: 483-504.

Yousef, A.A., Gentil, P.H., Jensen, J.L., et al. A capacitance model to infer interwell connectivity from production and injection rate fluctuations. SPE Reserv. Eval. Eng. 2006, 9(6): 630-646.

Zhang, J., An, R., Xu, J., et al. Analyzing the applicability of an eurytopic water-drive curve and its extensible applications. China Offshore Oil and Gas 2013, 25(6): 55-60. (in Chinese)

Zhang, J., Yang, R. A further study on Welge equation. Energy Explor. Exploit. 2018, 36(5): 1103-1113.

Zhao, H., Kang, Z., Zhang, X., et al. A physics-based data-driven numerical model for reservoir history matching and prediction with a field application. SPE J. 2016, 21(6): 2175-2194.

Zhao, H., Xu, L., Guo, Z., et al. A new and fast waterflooding optimization workflow based on INSIM-derived injection efficiency with a field application. J. Pet. Sci. Eng. 2019, 179: 1186-1200.

Zhao, H., Xu, L., Zhang, Q., et al. Flow path tracking strategy in a data-driven interwell numerical simulation model for waterflooding history matching and performance prediction with infill wells. SPE J. 2020, 25(2): 1007-1025.




DOI: https://doi.org/10.46690/ager.2020.03.04

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