A new heuristic model for estimating the oil formation volume factor

Abstract

The necessity of oil formation volume factor (B_o) determination does not need to be greatly emphasized. Different types of reservoir oil have specific conditions which impart the hydrocarbon's major properties, among which is the oil formation volume factor. Therefore, it seems imperative to construct a model capable of estimating the value of oil formation volume factor. Previous studies have resulted in a number of correlations for oil formation volume factor estimation; however, a large portion of them do not provide an acceptable accuracy (at least in some range of data) and cause a huge error at these points. Some others are not flexible enough to be tuned for a specific type of reservoir oil and a comprehensive piece of work does not exist as well in order to compare the applicability of the new models for estimating the oil formation volume factor. In this research, a model based on simulated annealing (SA) has been built in terms of temperature, solution gas-oil ratio, and gravity of oil and gas to predict the oil formation volume factor. This model is compared with the models proposed in the most recent studies, which shows the greater performance of the new method. In addition, in this paper the models of the recent years were compared with each other and their applicability were discussed. Aiming to compare the models, 420 data points were selected and the estimated values of each model for oil formation volume factor were compared with their experimental ones.

Keywords

Artificial intelligence / PVT properties / Modelling / Temperature / Solution gas oil ratio / Gas gravity / Oil gravity

References

[1] P. Dey, P.K. Deb, S. Akhter, D. Dey,Reserve estimation of saldanadi gas field, Int. J. Innovat. Appl. Stud. 16 (no. 1) (2016).

[2] M.R. Mahdiani, E. Khamehchi, A novel model for predicting the temperature profile in gas lift wells, Petroleum 2 (4) (2016) 408-414.

[3] E. Osman, O. Abdel-Wahhab, M. Al-Marhoun, Prediction of oil PVT properties using neural networks, in: SPE Middle East Oil Show, Manama, Bahrain, 2001.

[4] H. Asheim, Criteria for gas-lift stability, J. Petrol. Technol. 40 (11) (1988) 1-452.

[5] M.R. Mahdiani, E. Khamehchi, Preventing instability phenomenon in gas-lift optimization, Iranian Journal of Oil & Gas Science and Technology 4 (1) (2015) 49-65.

[6] E. Khamehchi, M.R. Mahdiani, The fitness function of gas allocation optimization, in: Gas Allocation Optimization Methods in Artificial Gas Lift, Springer International Publishing, 2017, pp. 7e23.

[7] J.P. Brill, H.D. Beggs, Two Phase Flow in Pipes, Gulf Publishing, 1991.

[8] D. William, J. Mccain, The Properties of Petroleum Fluid, PennWell, United States of America, 1990.

[9] M.R. Mahdiani, E. Khamehchi, Stabilizing gas lift optimization with different amounts of available lift gas, J. Nat. Gas Sci. Eng. 26 (2015) 18-27.

[10] D. William, J. Mccain, The Properties of Petroleum Fluid, PennWell, United States of America, 1990.

[11] M.R. Mahdiani, E. Khamehchi, A new method for building proxy models using simulated annealing, Middle East J. Sci. Res. 22 (3) (2014) 324-328.

[12] M.R. Mahdiani, E. Khamehchi, R. Soltan Mohammadi, B. Azkayi, A new proxy model, based on meta heuristic algorithms for estimating gas compressor torque,in: 11th International Industrial Conference, Tehran, 2015.

[13] M. Norouzi, H. Panjalizadeh, F. Rashidi, M.R. Mahdiani, DPR polymer gel treatment in oil reservoirs: a workflow for treatment optimization using static proxy models, J. Petrol. Sci. Eng. 153 (2017) 97-110.

[14] M.R. Mahdiani, E. Khamehchi, A modified neural network model for predicting the crude oil price, Intellect. Econ. 10 (2) (2017) 71-77.

[15] D. Katz, Prediction of shrinkage of crude oils, in: Drill. Prod. & Prac, API, Dallas, 1942.

[16] A.A. Al-Shammasi, A review of bubblepoint pressure and Oil Formation volume factor correlations, SPE Reservoir Eval. Eng. 4 (2) (2001) 146-160.

[17] M. Standing, A pressure-volume-temperature correlation for mixtures of California oils and gases, in: Drilling and Production Practice, Dallas, 1947. API-47-275.

[18] M. Standing, Oil-system Correlation, McGraw-Hill Book Co, New York City, 1962.

[19] C. Cronquist, Dimensionless PVT behavior of gulf coast reservoir oils, JPT (1973) 538.

[20] M.E. Vasquez, H.D. Beggs, Correlations for fluid physical property prediction, JPT 32 (06) (1980).

[21] O. Glaso, Generalized pressure-volume-temperature correlations 785 (1980).

[22] M. Al-Marhoun, “PVT Correlations for Saudi Crude Oils,” Manama, Bahrein, 1985.

[23] M. Al-Marhoun, PVT correlations for Middle East crude oils, JPT 40 (5) (1988) 650-666.

[24] G. Abdul-Majeed, N. Salman, An empirical correlation for FVF prediction, J. Cdn. Pet. Tech. 27 (6) (1988) 118.

[25] R. Labedi, Use of production data to estimate volume factor density and compressibility of reservoir fluids, J. Petrol. Sci. Eng. 4 (357) (1990).

[26] F. e. a. Farshad, Empirical PVT correlations for colombian crude oils, in: SPE 36105 Presented at the 1996 SPE Latin American and, Port of Spain, Trinidad and Tobago, 1996.

[27] R.B. Gharbi, A.M. Elsharkawy, Neural network model for estimating the PVT properties of Middle East crude oils 2 (3) (1999) 255-265.

[28] A.M. Elsharkway, A.A. Alikhan, Correlations for predicting solution gas/oil ratio, oil formation volume factor, and undersaturated oil compressibility, J. Petrol. Sci. Eng. 17 (3) (1997) 291-302.

[29] A.H. El-Banbi, K.A. Fattah, M.H. Sayyouh, New modified black-oil PVT correlations for gas condensate and volatile oil fluids, in: SPE Annual Technical Conference and Exhibition, Texas, USA, 2006.

[30] R.P. Sutton, An accurate method for determining oil PVT properties using the standing-katz gas z-factor chart, SPE Reservoir Eval. Eng. 11 (2) (2008) 246-266.

[31] E.A. El-Sebakhy, Forecasting PVT properties of crude oil systems based on support vector machines modeling scheme, J. Petrol. Sci. Eng. 34 (no. 1-4) (2009) 25-34.

[32] S. Elmabrouk, A. Zekri, E. Shirif, Prediction of bubblepoint pressure and bubblepoint Oil Formation volume factor in the absence of PVT analysis, in: SPE Latin American and Caribbean Petroleum Engineering Conference, Lima, Peru, 2010.

[33] I.S. Nassar, A.H. El-Banbi, M.H. Sayyouh, Modified black oil PVT properties correlations for volatile oil and gas condensate reservoirs, in: North Africa Technical Conference & Exhibition, Cairo, Egypt, 2013.

[34] M. Karimnezhada, M. Heidarian, M. Kamaric, H. Jalalifar, A new empirical correlation for estimating bubble point oil formation volume factor, J. Nat. Gas Sci. Eng. 18 (2014) 329-335.

[35] F. Torabia, C. Zuntia, C.C. Ma, B.Y. Jamaloeia, The prediction of viscosity, formation volume factor, and bubble point pressure of heavy oil using statistical analysis, artificial neural networks, and three-dimensional modeling: a comparative evaluation, Energy Sources, Part A Recovery, Util. Environ. Eff. 38 (no. 18) (2014).

[36] A.A. Sulaimon, N. Ramli, B.J. Adeyemi, I.M. Saaid, New correlation for Oil Formation volume factor, in: SPE Nigeria Annual International Conference and Exhibition, Lagos, Nigeria, 2014.

[37] A. Shokrallahi, A. Tatar, H. Safari, On accurate determination of PVT properties in crude oil systems: committee machine intelligent system modeling approach, Journal of the Taiwan Institute of Chemical 55 (2015) 17-26.

[38] M.R. Mahdiani, G. Kooti, The most accurate heuristic-based algorithms for estimating the Oil Formation volume factor, Petroleum 2 (1) (2016) 40-48.

[39] E. Khamehchi, M.R. Mahdiani, Gas Allocation Optimization Methods in Artificial Gas Lift, Springer International Publishing, 2017.

[40] E. Khamehchi, M.R. Mahdiani, An introduction to gas lift, in: Gas Allocation Optimization Methods in Artificial Gas Lift, Springer International Publishing, 2017, pp. 1e5.

[41] D. Ghetto, G. Marco, V. Marco,Reliability analysis on PVT correlations, in:SPE European Petroleum Conference, 1994. London.

[42] R. Almehaideb,Improved PVT correlations for UAE crude oils, in: Paper SPE 37691 Presented at the 1997 SPE Middle East Oil Show, 1997. Manama, Bahrain.

[43] S. Godefroy, S.H. Khor, D. Emms,Comparison and validation of theoretical and empirical correlations for black oil reservoir fluid properties, in:Offshore Technology Conference, 2012. Texas, USA.

[44] E. Khamehchi, M.R. Mahdiani, “Optimization algorithms, in: Gas Allocation Optimization Methods in Artificial Gas Lift, Springer International Publishing, 2017, pp. 35e46.

[45] E. Khamehchi, M.R. Mahdiani, “Constraint optimization, in: Gas Allocation Optimization Methods in Artificial Gas Lift, Springer International Publishing, 2017, pp. 25e34.

[46] B. McKay, M. Willis, G. Barton, Steady-state modelling of chemical process systems using genetic programming, Comput. Chem. Eng. 21 (no. 4) (1997).

[47] H. Cao, J. Yu, L. Kang, Y. Chen, The kinetic evolutionary modelling of complex systems of chemical reactions 23 (1999) 123-151.

[48] E. Sakamoto, H. Iba, Inferring a System of Differential Equations for a Gene Regulatory Network by Using Genetic Programming, COEX, World Trade Center, Seoul, Korea, 2001.

[49] S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi, Optimization by simulated annealing 220 (1983) 671-680.

[50] V. Černý, Thermodynamical approach to the traveling salesman problem: an efficient simulation algorithm 45 (1985) 41-51.

[51] M.R. Mahdiani, E. Khamehchi, Using mapping for increasing the speed and quality of meta heuristic optimization (Case Study: Petroleum Engineering),in:11th International Industrial Conference. Tehran, 2015.

[52] P.M. Omar, A. Todd,Development of new modified black oil correlations for malaysian crudes, in:SPE Asia Pacific Oil and Gas Conference and Exhibition, Singapore, 1993.

[53] M.E. Dokla, M.E. Osman, Correlation of PVT properties for UAE crudes 7 (1) (1993).