Prediction of reservoir brine properties using radial basis function (RBF) neural network

Abstract

Aquifers, which play a prominent role as an effective tool to recover hydrocarbon from reservoirs, assist the production of hydrocarbon in various ways. In so-called water flooding methods, the pressure of the reservoir is intensified by the injection of water into the formation, increasing the capacity of the reservoir to allow for more hydrocarbon extraction. Some studies have indicated that oil recovery can be increased by modifying the salinity of the injected brine in water flooding methods. Furthermore, various characteristics of brines are required for different calculations used within the petroleum industry. Consequently, it is of great significance to acquire the exact information about PVT properties of brine extracted from reservoirs. The properties of brine that are of great importance are density, enthalpy, and vapor pressure. In this study, radial basis function neural networks assisted with genetic algorithm were utilized to predict the mentioned properties. The root mean squared error of 0.270810, 0.455726, and 1.264687 were obtained for reservoir brine density, enthalpy, and vapor pressure, respectively. The predicted values obtained by the proposed models were in great agreement with experimental values. In addition, a comparison between the proposed model in this study and a previously proposed model revealed the superiority of the proposed GA-RBF model.

Keywords

Reservoir brine / Intelligent method / Density / Enthalpy / Vapor pressure / Radial basis function neural network

References

[1] T. Ahmed, Reservoir Engineering Handbook, Gulf Professional Publishing, 2006.

[2] M. Arabloo, H. Ziaee, M. Lee, A. Bahadori, Prediction of the properties of brines using least squares support vector machine (LS-SVM) computational strategy, J. Taiwan Inst. Chem. Eng. 50 (2015) 123-130.

[3] A. Ogunberu, M. Ayub, An analytical model for brine-induced oil recovery, Pet. Sci. Technol. 25 (2007) 1571-1581.

[4] K. Zeidani,A. Bahadori, Analysis of Crude Oil Electrostatic Desalters Performance.

[5] B. Bailey, M. Crabtree, J. Tyrie, J. Elphick, F. Kuchuk, C. Romano, L. Roodhart, Water control, Oilfield Rev. 12 (2000) 30-51.

[6] A. Bahadori, H.B. Vuthaluru, Prediction of silica carry-over and solubility in steam of boilers using simple correlation, Appl. Therm. Eng. 30 (2010) 250-253.

[7] T. Austad, A. Rezaeidoust, T. Puntervold,Chemical Mechanism Of Low Salinity Water Flooding in Sandstone Reservoirs, Society of Petroleum Engineers.

[8] L.F. Silvester, K.S. Pitzer,Thermodynamics of electrolytes. 8. Hightemperature properties, including enthalpy and heat capacity, with application to sodium chloride, J. Phys. Chem. 81 (1977) 1822-1828.

[9] S. Bagci, M.V. Kok, U. Turksoy, Effect of brine composition on oil recovery by waterflooding, Pet. Sci. Technol. 19 (2001) 359-372.

[10] M. Arabloo, A. Shokrollahi, F. Gharagheizi, A.H. Mohammadi, Toward a predictive model for estimating dew point pressure in gas condensate systems, Fuel Process. Technol. 116 (2013) 317-324.

[11] S. Al Ghafri, G.C. Maitland, J.P.M. Trusler,Densities of Aqueous MgCl2(aq), CaCl2(aq), KI(aq), NaCl(aq), KCl(aq), AlCl3(aq), and (0.964 NaCl + 0.136 KCl)(aq) at temperatures between (283 and 472) K, pressures up to 68.5 MPa, and molalities up to 6 mol$kge1, J. Chem. Eng. Data 57 (2012) 1288-1304.

[12] A. Kumar, Densities of aqueous strontium chloride solutions up to 200 degree C and at 20 bar, J. Chem. Eng. Data 31 (1986) 347-349.

[13] J.A. Gates, R.H. Wood,Densities of aqueous solutions of sodium chloride, magnesium chloride, potassium chloride, sodium bromide, lithium chloride, and calcium chloride from 0.05 to 5.0 mol kg^-1 and 0.1013 to 40 MPa at 298.15 K, J. Chem. Eng. Data 30 (1985) 44-49.

[14] R. Crovetto, S.N. Lvov, R.H. Wood, Vapor pressures and densities of NaCl (aq) and KCl (aq) at the temperature 623 K and CaCl 2 (aq) at the temperatures 623 K and 643 K, J. Chem. Thermodyn. 25 (1993) 127-138.

[15] M. Obšil, V. Majer, G.T. Hefter, V. Hynek, Densities and Apparent molar volumes of Na₂SO₄ (aq) and K₂SO₄ (aq) at temperatures from 298 K to 573 K and at pressures up to 30 MPa, J. Chem. Eng. Data 42 (1997) 137-142.

[16] R.C. Phutela, K.S. Pitzer, Densities and apparent molar volumes of aqueous magnesium sulfate and sodium sulfate to 473 K and 100 bar, J. Chem. Eng. Data 31 (1986) 320-327.

[17] A.V. Sharygin, R.H. Wood, Densities of aqueous solutions of sodium carbonate and sodium bicarbonate at temperatures from (298 to 623) K and pressures to 28 MPa, J. Chem. Thermodyn. 30 (1998) 1555-1570.

[18] S.L. Phillips, A Technical Databook for Geothermal Energy Utilization, Lawrence Berkeley National Laboratory, 1981.

[19] R. Busey, H. Holmes, R. Mesmer, The enthalpy of dilution of aqueous sodium chloride to 673 K using a new heat-flow and liquid-flow microcalorimeter. Excess thermodynamic properties and their pressure coefficients, J. Chem. Thermodyn. 16 (1984) 343-372.

[20] J.E. Mayrath, R.H. Wood,Enthalpy of dilution of aqueous sodium chloride at 348.15 to 472.95 K measured with a flow calorimeter, J. Chem. Thermodyn. 14 (1982) 15-26.

[21] J.E. Mayrath, R.H. Wood, Enthalpy of dilution of aqueous solutions of LiCl, NaBr, NaI, KCl, KBr, and CsCl at about 373, 423, and 473 K, J. Chem. Thermodyn. 14 (1982) 563-576.

[22] H.F. Gibbard Jr., G. Scatchard, R.A. Rousseau, J.L. Creek, Liquid-vapor equilibrium of aqueous sodium chloride, from 298 to 373 deg K and from 1 to 6 mol kg-1, and related properties, J. Chem. Eng. Data 19 (1974) 281-288.

[23] H.F. Gibbard, G. Scatchard,Liquid-vapor equilibrium of aqueous lithium chloride, from 25 to 100 deg and from 1.0 to 18.5 molal, and related properties, J. Chem. Eng. Data 18 (1973) 293-298.

[24] C.-t. Liu,W.T. Lindsay Jr., Thermodynamics of sodium chloride solutions at high temperatures, J. Solut. Chem. 1 (1972) 45-69.

[25] A. Bahadori, M. Al-Haddabi, H. Vuthaluru, The estimation of reservoir brine properties during crude oil production using a simple predictive tool, Petroleum Sci. Technol. 31 (2013) 691-701.

[26] S. Mohaghegh, R. Arefi, S. Ameri, K. Aminiand, R. Nutter, Petroleum reservoir characterization with the aid of artificial neural networks, J. Pet. Sci. Eng. 16 (1996) 263-274.

[27] S. Mohaghegh, R. Arefi, S. Ameri, D. Rose, Design and development of an artificial neural network for estimation of formation permeability, SPE Comput. Appl. 7 (1995) 151-154.

[28] M.A. Ahmadi, Prediction of asphaltene precipitation using artificial neural network optimized by imperialist competitive algorithm, J. Petroleum Explor. Prod. Technol. 1 (2011) 99-106.

[29] M.A. Ahmadi, M. Ebadi, A. Shokrollahi, S.M.J. Majidi, Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir, Appl. Soft Comput. 13 (2013) 1085-1098.

[30] M. Al-Marhoun, E. Osman, Using artificial neural networks to develop new PVT correlations for Saudi crude oils, in: Abu Dhabi International Petroleum Exhibition and Conference, Society of Petroleum Engineers, 2002.

[31] R. Gharbi, A.M. Elsharkawy, Neural network model for estimating the PVT properties of middle East crude oils, in: Middle East Oil Show and Conference, Society of Petroleum Engineers, 1997.

[32] D. Fadare, Modelling of solar energy potential in Nigeria using an artificial neural network model, Appl. Energy 86 (2009) 1410-1422.

[33] H. Rashidi, P. Valeh-e-sheyda, Estimation of vaporeliquid equilibrium ratios of crude oil components: a comparative study, Fuel 140 (2015) 388-397.

[34] S.M.J. Majidi, A. Shokrollahi, M. Arabloo, R. Mahdikhani-Soleymanloo, M. Masihi, Evolving an accurate model based on machine learning approach for prediction of dew-point pressure in gas condensate reservoirs, Chem. Eng. Res. Des. 92 (2014) 891-902.

[35] J.V. Tu, Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes, J. Clin. Epidemiol. 49 (1996) 1225-1231.

[36] R. Santos, M. Ruppb, S. Bonzi, A. Filetia, Comparison between multilayer feedforward neural networks and a radial basis function network to detect and locate leaks in pipelines transporting gas, Chem. Eng. Trans. 32 (2013) e1380.

[37] H. Yu, T. Xie, S. Paszczynski, B.M. Wilamowski, Advantages of radial basis function networks for dynamic system design, Ind Electron IEEE Trans. 58 (2011) 5438-5450.

[38] S.S. Cross, R.F. Harrison, R.L. Kennedy, Introduction to neural networks, Lancet 346 (1995) 1075-1079.

[39] A.G. Bors, Introduction of the radial basis function (rbf) networks,in: Online Symposium for Electronics Engineers, 2001, pp. 1-7.

[40] J. Park, I.W. Sandberg, Approximation and radial-basis-function networks, Neural Comput. 5 (1993) 305-316.

[41] M.J. Orr, Introduction to radial basis function networks, Technical Report, Center for Cognitive Science, University of Edinburgh, 1996.

[42] K.-L. Du, M.N.S. Swamy, Radial Basis Function Networks, Neural Networks in a Softcomputing Framework, Springer, London, 2006, pp. 251-294.

[43] F. Girosi, T. Poggio, Networks and the best approximation property, Biol. Cybern. 63 (1990) 169-176.

[44] T. Poggio, F. Girosi, Networks for approximation and learning, Proc. IEEE 78 (1990) 1481-1497.

[45] Y. Liao, S.-C. Fang, H.L.W. Nuttle, Relaxed conditions for radial-basis function networks to be universal approximators, Neural Netw. 16 (2003) 1019-1028.

[46] C. Micchelli, Interpolation of scattered data: distance matrices and conditionally positive definite functions,in: S. P. Singh, J.W.H. Burry, B. Watson (Approximation Theory and Spline Functions,Eds.), Springer, Netherlands, 1984, pp. 143-145.

[47] A. Tatar, A. Shokrollahi, M. Mesbah, S. Rashid, M. Arabloo, A. Bahadori, Implementing radial basis function networks for modeling CO2-reservoir oil minimum miscibility pressure, J. Nat. Gas Sci. Eng. 15 (2013) 82-92.