Development an Easy-to-Use Simulator to Thermodynamic Design of Gas Condensate Reservoir’s Separators

Separator design in petroleum engineering is so important because of its important role in the evaluation of optimum parameters and also to achieve to maximum stock tank liquid. However, no simulator exists that simultaneously and directly optimizes the parameters “pressure”, “temperature”, and so on. On the other hands, Commercial simulators fix one parameter and vary another parameter to achieve the optimum conditions. So, they need long-time simulation. Moreover, gas condensate reservoirs , like another reservoirs , have this problem as well. In present paper, a self-developed simulator applied in the optimized design of gas condensate reservoir’s separators by determining optimized pressure, temperature, and number of separators in order to obtain maximized tank liquid volume and minimized tank liquid density utilizing Matlab software and other commercial simulators such as Aspen-Plus, Aspen-Hysys, and PVTi to do a comparison. Also, each software was separately tested with one, two, and three separators to obtain the optimum number of separators. Additionally, Peng-Robinson equation of state (PR EOS) has been applied in the simulation. For simulation input, a set of field data of gas condensate reservoir has been utilized, as well. The results show a good compatibility of this simulator with other simulators but in so little runtime (this simulator calculates the optimum pressure and temperature in a wide range of pressures and temperatures with the help of a simultaneous optimization algorithm in one stage) and the highest stock tank liquid is calculated with this simulator in comparison to other simulators. Also, with the help of this simulator , we are able to obtain the optimum pressure, temperature, and the number of separators in the gas condensate reservoir’s separators with any desired properties. Finally, this simulator optimizes the temperatures for each separator and obtains very good results despite the other simulators that fix temperatures for all separators in most times.

• Separator Design
• Matlab Software
• Simultaneous Algorithm
• Optimum Condition
• Gas Condensate Reservoir

1. Introduction

Gas condensate reservoirs mostly produce gas, with some liquid dropout, frequently occurring in the wellhead separators. The phase diagram shows the retrograde gas must have a temperature higher than the critical temperature. Also, the phase diagram shows the phase changes in the reservoir, while the curve line shows these changes as the fluid cools going up the wellbore and into the separator. In both cases, liquids drop out as the pressure drops below dew point pressure [1] [2] .

Modeling for optimization of the conditions (pressure, temperature, and number of separators) of separators in multistage separators causes to reduce the amount of gas produced with condensate to a minimum [1] . In gas condensate reservoirs, large amounts of condensate and gas will produce in wellhead that we like to reduce amounts of gas and to obtain optimum conditions. In wellbore fluids or gas reservoirs, we face a high range of compositions that the quantity and characteristic of all of them are not known to us. Therefore, the optimized conditions of separators have to be specified by a combination of laboratory or field data and modeling. By leaving the gas phase from the liquid phases, the separator and stock tank gases have a minimum quantity. The pressure and temperature of this minimum point are referred to as the optimized pressure and temperature of the separator [2] .

The separator will be modeled with the help of phase equilibrium calculations. In phase equilibrium calculation, a thermodynamic model and an optimization algorithm must be chosen. A thermodynamic model gives the relation between pressure, molar volume, and temperature for pure components and mixtures. The thermodynamic model is usually nonlinear and nonconvex and therefore, an optimization method must be utilized to find phase equilibrium [3] [4] . The method proposed by Adewumi for solving isothermal flash calculations was recommended as an optimized solving algorithm [5] . Initially, a converging sequence of upper and lower bound on the global minimum through the convex relaxation of the original problem was proposed [6] . However, the deterministic global optimization algorithm α-based branch was applied in the fluid phase equilibrium problems as well as bound to find chemical equilibrium [7] . On another hand, an enhanced simulated annealing algorithm was proposed to verify phase stability analysis and obtain the true solution of the phase equilibrium problems in multi-component systems at high pressures [8] . But, two direct and indirect algorithms solve the phase equilibrium problem to increase the flexibility of solving algorithm in the fluid phase equilibrium systems [9] . Also, a global optimization method called Tunneling was suggested that is able to escape from local minima and saddle points, and it’s a suitable method for many problems associated with the mathematical issues such as local minimum or/and saddle points [10] .

As a result of the optimization technique, the optimization techniques were applied to directly minimize the fluid properties for a specified number of phases [11] . In accordance with the runtime issue of solving algorithms in the fluid phase equilibrium problems, a method was proposed to accelerate convergence rate of Successive substitution algorithm [10] . After that, a two-phase field flow inside an oil-gas separator with software Fluent was simulated. Based on the analysis of the two-phase flow, the authors realized the centrifugal force and the collision plays an important role in the oil-gas separation. The numerical model and the correspondent analysis are proved to be effective in the engineering design of oil-gas separators. The oil carry-over rate is greatly reduced in the modified separator [12] . Then, a new packing and newly designed Crude oil-water separator related to the physical properties of ASP products in Daqing Oilfield was proposed [13] . The orthogonal test is utilized to optimize the design of the new separator included the structure and material of coalescent packing and the new type separation efficiency of higher than 98%. However, a method for optimizing separator pressures in multistage crude oil production was proposed with the help of equation of states [14] . Also, an approach for the minimization of the Gibbs free energy was developed using the linear programming that guarantees to find the global optimum within some level of precision, for any kind of thermodynamic model [15] . Additionally, a criterion for phase equilibrium is defined as: 1) the temperature and pressure of the phases are equal, 2) the chemical potentials of each component in each phase are equal, and 3) the global Gibbs free energy is a minimum [16] . As a result of novel algorithms that are able to describe the multi-phase and multi-component chemical systems such as oil-gas system either in the dissolution or the separation processes, a new model based on adaptive neuro-fuzzy inference systems (ANFIS) is developed for accurate prediction of carbon dioxide gas diffusivity in oil at elevated temperature and pressures. Also, particle swarm optimization (PSO) technique based on the stochastic search algorithms was applied to obtain the optimal ANFIS model parameters as well [17] .

2. Methodology

As mentioned in the previous section, because of the importance of the wellhead separators as well as their parameters optimization due to some problems associated with available commercial simulators, including high cost and time consuming, as well as the lack of a simulator which particularly studies the phase behavior of fluids in gas condensate reservoirs, a new simulator is developed as below.

In this part, we develop a Matlab code to obtain the required optimum parameters with the help of the followed flowchart as Figure 1 and Figure 2.

We applied some simulators to optimize the required parameters as mentioned previously to show the ability of these to optimize the separator parameters in gas condensate reservoirs and also to the comparison of these with the developed easy-to-use the simulator to show the ability of this simulator in decreasing time and cost. The existence of an algorithm that simultaneously applies to calculate the temperature and the pressure and gives an optimum temperature and pressure without manual working causes time decreasing. However, existence an algorithm that leads to higher stock tank liquid causes income increasing or cost decreasing especially in a high amount of produced liquid in surface facilities.

With each simulator, the optimum parameters were obtained and important parameters of separators fluids such as liquid and gas density, liquid and gas flow, liquid and gas enthalpy, liquid and gas entropy, and average molecular

weight were observed.

Finally, by applying some rules that liquid volume must be maximum and liquid density must be minimum in separators, we could calculate optimum pressure and temperature with the help of this easy-to-use the simulator.

3. Results and Discussion

The simulation occurred with the help of the software below:

a) Aspen Plus b) Aspen Hysys c) PVTi d) Matlab

For analysis, we utilized from a data-set of gas condensate reservoir with 370 k temperature and 250 bar pressure and composition like as Table 1. (Note that γC7+ = 0.8 & MWC7+ = 180)

3.1. Aspen Plus Analysis

We did calculations in three parts with the Aspen Plus analysis.

Part 1: Simulation with one separator and one stock tank as Figure 3 and

Table 1

Composition and mole percent of components

Mol percent (−)	Component (−)	No.
0.29	N2	1
1.72	CO2	2
79.14	C1	3
7.48	C2	4
3.29	C3	5
0.51	IC4	6
1.25	NC4	7
0.36	IC5	8
0.55	NC5	9
0.61	C6	10
4.8	C7+	11

analysis results are as Table 2.

Part 2: Simulation with two separators and one stock tank as Figure 4 and analysis results are as Table 3.

Part 3: Simulation with three separators and one stock tank as Figure 5 and analysis results are as Table 4.

As results, we can see that by increasing in the separators number, the stock tank liquid volume is increased and the stock tank liquid density is decreased as shown in Figure 6(a) and Figure 6(b).

As shown in Figure above, by increasing the separator number from one separator to three separators, the stage of separation process is increased and the separation occurs in a high quality situation. Therefore, the stock tank liquid volume is increased and the stock tank liquid density is decreased, respectively.

3.2. Aspen Hysys Analysis

We did calculations in three parts with the Aspen Hysys analysis.

Part 1: Simulation with one separator and one stock tank as Figure 7 and analysis results are as Table 5.

Part 2: Simulation with two separators and one stock tank as Figure 8 and analysis results are as Table 6.

Table 2

Aspen Plus analysis results with one separator and one stock tank

		Feed	G1	G2	L1	L2
C7+ Flow	(kmol/hr)	4.8	3.33E−03	8.30E−04	4.796675	4.795845
N2 Flow	(kmol/hr)	0.29	0.285368	4.62E−03	4.63E−03	9.46E−06
CO2 Flow	(kmol/hr)	1.72	1.539227	0.176727	0.180773	4.05E−03
C1 Flow	(kmol/hr)	79.14	76.17237	2.950285	2.96763	0.017344
C2 Flow	(kmol/hr)	7.48	6.47294	0.974496	1.00706	0.032564
C3 Flow	(kmol/hr)	3.29	2.31863	0.867913	0.971371	0.103458
IC4 Flow	(kmol/hr)	0.51	0.274789	0.180572	0.235211	0.054639
NC4 Flow	(kmol/hr)	1.25	0.588138	0.463662	0.661862	0.1982
IC5 Flow	(kmol/hr)	0.36	0.111993	0.120292	0.248008	0.127715
NC5 Flow	(kmol/hr)	0.55	0.142699	0.166146	0.407301	0.241155
C6 Flow	(kmol/hr)	0.61	0.073492	0.093216	0.536508	0.443292
FlowTOT.	(kmol/hr)	100	87.98297	5.998763	12.01703	6.018266
T	(˚C)	96.85	25	25	25	25
P	(bar)	250	57	1	57	1
FractionVAP.	(−)	0.828596	1	1	0	0
FractionLIQ.	(−)	0.171404	0	0	1	1
FractionSOL.	(−)	0	0	0	0	0
E	(cal/mol)	−22877.4	−19958.1	−24034.8	−50608.3	−74755.1
E	(cal/gm)	−814.732	−1051.42	−762.422	−534.474	−474.193
E	(cal/sec)	−6.35E+05	−4.88E+05	−4.00E+04	−1.69E+05	−1.25E+05
S	(cal/mol-k)	−45.8335	−30.3582	−39.2842	−158.595	−263.467
S	(cal/gm-k)	−1.63227	−1.59931	−1.24616	−1.67492	−1.67125
Ρ	(mol/cc)	8.81E−03	2.73E−03	4.07E−05	7.04E−03	4.90E−03
Ρ	(gm/cc)	0.247364	0.051802	1.28E−03	0.666511	0.772773
MWAV.	(gm/mol)	28.07965	18.98205	31.5243	94.68798	157.647
VL	(cc/min)	111.8395	84.24125	7.268958	27.59826	20.3293

Table 3

Aspen Plus analysis results with two separators and one stock tank

		Feed	G1	G2	G3	L1	L2	L3
C7+ Flow	(kmol/hr)	4.8	3.33E−03	2.99E−05	5.99E−04	4.796675	4.796645	4.796046
N2 Flow	(kmol/hr)	0.29	0.285368	2.92E−03	1.71E−03	4.63E−03	1.71E−03	4.87E−06
CO2 Flow	(kmol/hr)	1.72	1.539227	0.031015	0.145148	1.81E−01	0.149758	4.61E−03
C1 Flow	(kmol/hr)	79.14	76.17237	1.187863	1.765283	2.96763	1.779767	0.014484
C2 Flow	(kmol/hr)	7.48	6.47294	0.130382	0.837625	1.00706	0.876678	0.039054
C3 Flow	(kmol/hr)	3.29	2.31863	0.046648	0.792982	0.971371	0.924722	0.131741
IC4 Flow	(kmol/hr)	0.51	0.274789	5.28E−03	0.161714	0.235211	0.229926	0.068212
NC4 Flow	(kmol/hr)	1.25	0.588138	0.011132	0.407847	0.661862	0.650731	0.242884
IC5 Flow	(kmol/hr)	0.36	0.111993	1.98E−03	0.099258	0.248008	0.246024	0.146766
NC5 Flow	(kmol/hr)	0.55	0.142699	2.50E−03	0.134063	0.407301	0.404805	0.270743
C6 Flow	(kmol/hr)	0.61	0.073492	1.18E−03	0.070246	0.536508	0.535324	0.465077
FlowTOT.	(kmol/hr)	100	87.98297	1.420939	4.41647	12.01703	10.59609	6.17962
T	(˚C)	96.85	25	25	25	25	25	25
P	(bar)	250	57	38	1	57	38	1
FractionVAP.	(−)	0.828596	1	1	1	0	0	0
FractionLIQ.	(−)	0.171404	0	0	0	1	1	1
FractionSOL.	(−)	0	0	0	0	0	0	0
E	(cal/mol)	−22877.4	−19958.1	−20323	−24992.9	−50608.3	−54557.9	−73795.8
E	(cal/gm)	−814.732	−1051.42	−1036.32	−730.942	−534.474	−520.81	−475.531
E	(cal/sec)	−6.35E+05	−4.88E+05	−8021.61	−30661.2	−1.69E+05	−1.61E+05	−1.27E+05
S	(cal/mol-k)	−45.8335	−30.3582	−29.9179	−43.1607	−158.595	−175.176	−259.43
S	(cal/gm-k)	−1.63227	−1.59931	−1.52558	−1.26228	−1.67492	−1.67223	−1.67173
Ρ	(mol/cc)	8.81E−03	2.73E−03	1.73E−03	4.07E−05	7.04E−03	6.63E−03	4.96E−03
Ρ	(gm/cc)	0.247364	0.051802	0.034017	1.39E−03	0.666511	0.694998	0.770258
MWAV.	(gm/mol)	28.07965	18.98205	19.61078	34.19267	94.68798	104.7559	155.1862
VL	(cc/min)	111.8395	84.24125	1.381344	5.600417	27.59826	26.21692	20.6165

Table 4

Aspen Plus analysis results with three separators and one stock tank

		Feed	G1	G3	G4	L1	L2	L3	L4
C7+ Flow	(kmol/hr)	4.8	3.33E−03	1.15E−04	8.35E−05	4.796675	4.796645	4.796529	4.796446
N2 Flow	(kmol/hr)	2.90E−01	2.85E−01	1.68E−03	3.07E−05	4.63E−03	1.71E−03	3.14E−05	6.41E−07
CO2 Flow	(kmol/hr)	1.72	1.539227	1.25E−01	0.020164	1.81E−01	0.149758	2.48E−02	4.61E−03
C1 Flow	(kmol/hr)	79.14	76.17237	1.689443	0.085201	2.96763	1.779767	9.03E−02	5.12E−03
C2 Flow	(kmol/hr)	7.48	6.47294	0.675978	0.149642	1.00706	0.876678	0.200701	0.051059
C3 Flow	(kmol/hr)	3.29	2.31863	0.454155	0.212735	0.971371	0.924722	0.470568	0.257833
IC4 Flow	(kmol/hr)	5.10E−01	0.274789	0.063909	0.040708	0.235211	0.229926	0.166017	0.12531
NC4 Flow	(kmol/hr)	1.25	0.588138	0.140246	0.095643	0.661862	0.650731	0.510485	0.414842
IC5 Flow	(kmol/hr)	3.60E−01	0.111993	0.024874	0.018816	0.248008	0.246024	0.22115	0.202334
NC5 Flow	(kmol/hr)	5.50E−01	0.142699	0.031085	0.023904	0.407301	0.404805	0.37372	0.349817
C6 Flow	(kmol/hr)	6.10E−01	0.073492	0.013367	0.010629	0.536508	0.535324	0.521956	0.511327
FlowTOT.	(kmol/hr)	100	87.98297	3.219836	0.657556	12.01703	10.59609	7.376254	6.718698
T	(˚C)	96.85	25	25	25	25	25	25	25
P	(bar)	250	57	4	1	57	38	4	1
FractionVAP.	(−)	0.828596	1	1	1	0	0	0	0
FractionLIQ.	(−)	0.171404	0	0	0	1	1	1	1
FractionSOL.	(−)	0	0	0	0	0	0	0	0
E	(cal/mol)	−22877.4	−19958.1	−23499.5	−27201.4	−50608.3	−54557.9	−67255.3	−70822.1
E	(cal/gm)	−814.732	−1051.42	−839.969	−637.115	−534.474	−520.81	−486.402	−479.743
E	(cal/sec)	−6.35E+05	−4.88E+05	−21018	−4968.46	−1.69E+05	−1.61E+05	−1.38E+05	−1.32E+05
S	(cal/mol-k)	−45.8335	−30.3582	−36.0418	−56.9177	−158.595	−175.176	−231.344	−247.065
S	(cal/gm-k)	−1.63227	−1.59931	−1.28828	−1.33313	−1.67492	−1.67223	−1.67312	−1.6736
ρ	(mol/cc)	8.81E−03	2.73E−03	1.66E−04	4.09E−05	7.04E−03	6.63E−03	5.43E−03	5.16E−03
ρ	(gm/cc)	0.247364	5.18E−02	4.63E−03	1.75E−03	0.666511	0.694998	0.751315	0.762066
MWAV.	(gm/mol)	28.07965	18.98205	27.97669	42.69463	94.68798	104.7559	138.271	147.625
VL	(cc/min)	111.8395	84.24125	3.715306	0.948889	27.59826	26.21692	22.50161	21.55272

Table 5

Aspen Hysys analysis results with one separator and one stock tank

		Feed	G1	L1	L3
Flow fractionVAP.	(−)	0.863477	1	0	0
T	(˚C)	96.85	26.15107	26.15107	6.440161
P	(kpa)	25000	7000	7000	100
FlowTOT.	(kmol/hr)	100	88.42481	11.57519	6.276834
FlowTOT.	(kg/hr)	2807.982	1688.545	1119.436	965.2965
VL	(m3/hr)	6.702509	5.085935	1.616573	1.24514
Q	(kj/hr)	9,783,158	7,468,622	2,661,763	2,143,982

Part 3: Simulation with three separators and one stock tank as Figure 9 and analysis results are as Table 7.

As results, we can see that by increasing in the separators number, the stock tank liquid volume is increased and the stock tank liquid density is decreased as shown in Figure 10(a) and Figure 10(b).

Table 6

Aspen Hysys analysis results with two separators and one stock tank

		Feed	G1	G2	G3	L1	L3	L5
Flow fractionVAP.	(−)	0.863477	1	1	1	0	0	0
T	(˚C)	96.85	26.15107	24.30809	8.421311	26.15107	24.30809	8.421311
P	(kpa)	25000	7000	4000	100	7000	4000	100
FlowTOT.	(kmol/hr)	100	88.42481	1.682519	3.453154	11.57519	9.892669	6.439515
FlowTOT.	(kg/hr)	2807.982	1688.545	33.16979	111.4076	1119.436	1086.267	974.859
VL	(m3/hr)	6.702509	5.085935	9.83E−02	0.256468	1.616573	1.518244	1.261776
Q	(kj/hr)	−9,783,158	−7,468,622	−144,383	−352,291	−2,661,763	−2,517,380	−2,165,089

Table 7

Aspen Hysys analysis results with three separators and one stock tank

		Feed	G1	G2	L1	L3	L5	L7
Flow fractionVAP.	(−)	0.863477	1	1	0	0	0	0
T	(˚C)	96.85	26.15107	24.30809	26.15107	24.30809	21.45056	10.80439
P	(kpa)	25000	7000	4000	7000	4000	1500	100
FlowTOT.	(kmol/hr)	100	88.42481	1.682519	11.57519	9.892669	8.452935	6.673252
FlowTOT.	(kg/hr)	2807.982	1688.545	33.16979	1119.436	1086.267	1054.275	988.1731
VL	(m3/hr)	6.702509	5.085935	9.83E−02	1.616573	1.518244	1.428958	1.285273
Q	(kj/hr)	−9783158	−7468622	−144383	−2661763	−2517380	−2386674	−2195199

As shown in Figure above, by increasing the separator number from one separator to three separators, the stage of separation process is increased and the separation occurs in a high quality situation. Therefore, the stock tank liquid volume is increased and the stock tank liquid density is decreased, respectively.

3.3. PVTi Analysis

We did calculations in one part with the PVTi analysis.

Table 8

PVTi analysis results with three separators and one stock tank

		Sep.1	Sep.2	Sep.3	S.T.
Mol fractionVAP.	(−)	0.888	0.9039	0.928	0.928
Mol fractionLIQ.	(−)	0.112	0.0961	0.072	0.072
VV	(Sm3)	21.0368	21.4149	21.9865	21.9865
VL	(m3)	0.0158	0.0148	0.0131	0.013
GOR	(Sm3/m3)	1328.286	1442.772	1677.038	1677.038
BO	(Rm3/Sm3)	1.2144	1.1382	1.0053	1.0053
ρV	(kg/m3)	54.6194	34.7169	12.375	0.8276
ρL	(kg/m3)	704.8399	724.8136	757.6122	761.6396
MWAV.V	(kgm/Kmol)	19.0365	19.148	19.54	19.54
MWAV.L	(kgm/Kmol)	99.6357	111.975	138.0461	138.0461
T	(k)	298.15	298.15	298.15	288.7056
P	(bar)	60	40	15	1.0132

Simulation with three separators and one stock tank was done and the simulation results are as Table 8.

3.4. Matlab Analysis

We did the calculations with the help of the two parameters Peng-Robinson equation of state (PR EOS) as shown in Equations (1) through (8) [18] :

P = R T V − b − a c α V ( V + b ) + b ( V − b ) (1)

a c = 0.457235 R 2 T c 2 P c (2)

b = 0.077796 R T c P c (3)

m = 0.3796 + 1.485 ω − 0.1644 ω 2 + 0.01667 ω 3 (4)

A = a P ( R T ) 2 (5)

B = b P R T (6)

z 3 − ( 1 − B ) z 2 + ( A − 2 B − 3 B 2 ) z − ( A B − B 2 − B 3 ) = 0 (7)

ln ∅ = ( z − 1 ) − ln ( z − B ) + A 2 B 2 ln z + ( 1 − 2 ) B z + ( 1 + 2 ) B (8)

where P, V, T, R, a_c, b, α, P_c, T_c, ω, φ, and z are the pressure, volume, temperature, universal gas constant, real gas correction factor due to the intermolecular forces, real gas correction factor due to the gas molecular size, temperature-dependent parameter, critical pressure, critical temperature, acentric factor, fugacity coefficient and compressibility factor, respectively.

Equilibrium ratio (k_i) was calculated with the help of the Wilson Correlation as shown in Equation (9) [19] [20] :

k i = ( P c i P ) exp ( 5.37 ( 1 + ω i ) ( 1 − T c i T ) ) (9)

Subscript “i” is related to i-component in the two-phase solution.

Flash calculations were calculated with the flash calculations equations as shown in Equations ((10) and (12)):

x i = z i 1 + ( k i − 1 ) n v (10)

y i = z i k i 1 + ( k i − 1 ) n v (11)

f ( n v ) = ∑ i = 1 n ( y i − x i ) = ∑ i = 1 n z i ( k i − 1 ) 1 + ( k i − 1 ) n v = 0 (12)

where x_i, y_i, z_i, and n^v are the mole percent of i-component in the liquid phase, mole percent of i-component in the gas phase, mole percent of i-component in the two-phase solution, and volume percent of gas (vapor) phase, respectively.

We developed a code that is able to calculate equilibrium calculations for any specific data set and also to obtain the optimum parameters with the help of the algorithms as shown in Figure 1 and Figure 2. Input feed was considered as 100 kmol/hr.

Simulation with three separators and one stock tank was done and simulation results are as Table 9 and mole fraction of each component in both liquid and

Table 9

Code analysis results with three separators and one stock tank

		Sep.1	Sep.2	Sep.3	S.T.
Liq. output	(kmol/hr)	11.69	10.47	9.13	8.72
T	(˚C)	31.85	22.85	30.85	25
P	(bar)	63	38	13	1

Table 10

Code analysis of flash calculation for each separator stage

		Sep.1	Sep.2	Sep.3	S.T.	Sep.1	Sep.2	Sep.3	S.T.
Component	zi (−)	xi (−)	xi (−)	xi (−)	xi (−)	yi (−)	yi (−)	yi (−)	yi (−)
N2	0.29	0.02	0.01	0	0	0.33	0.15	0.05	0.01
CO2	1.72	1.44	1.33	0.83	0.49	1.75	2.19	4.73	2.95
C1	79.14	15.28	8.53	1.97	0.43	87.54	72.41	53.18	12.31
C2	7.48	9.2	9.01	6.71	0.466	7.23	9.73	24.59	18.29
C3	3.29	11.63	12.47	12.65	11.99	2.15	3.02	10.87	11.03
IC4	0.51	2.82	3.07	3.35	3.41	0.2	0.27	1.09	1.18
NC4	1.25	7.7	8.42	9.31	9.62	0.37	0.51	2.09	2.31
IC5	0.36	2.64	2.9	3.27	3.45	0.05	0.07	0.029	0.32
NC5	0.55	4.16	4.57	5.17	5.46	0.06	0.08	0.35	0.39
C6	0.61	4.93	5.43	6.18	6.58	0.02	0.03	0.13	0.15
C7+	4.8	40.18	44.26	50.55	53.93	0	0	0	0

gas phases were calculated for each separator stage too as Table 10.

Code analysis in the optimum parameters calculations shows that output liquid volume and density from the third separator or input liquid volume and density into stock tank calculated from the code is higher and lower than calculated from other simulators that are very important issue in petroleum engineering surface facilities. According to the algorithm, calculations of the third separators in a range of pressures and temperatures shown in Figure 11 that is obvious that in what pressure and temperature we have the highest liquid volume and the lowest liquid density, these quantities are optimum quantities.

Finally, we concern on the optimum parameters calculated with the different simulators to do a comparison. Optimum pressure, temperature, and liquid output volume calculated from the different simulators are as Figures 12(a)-(c).

The liquid output from the third separator is very important that is maximum in the code calculations in comparison to other simulators as Figure 13.

4. Conclusion

A computer simulator is written to optimize the pressure, temperature, and the number of separators of gas condensate reservoir’s separators using Matlab software and other commercial simulators such as Aspen-Plus, Aspen-Hysys, and PVTi to do a comparison. This simulator is in good agreement with other

simulators to predict the required parameters.

Also, this simulator is an easy-to-use simulator that the required parameters are directly obtained from it with the help of a simple algorithm.

Additionally, this simulator considers temperature variation with pressure variation simultaneously, and also this simulator is able to show optimum pressure and temperature between any ranges of pressures and temperatures that the user enters into this simulator. So, calculations and optimizations are done without any manual working. Finally, we can see the effect of various parameters on the optimum parameters in a so little runtime.

By considering the effect of both the pressure and the temperature in the optimum parameters (the stock tank liquid volume and the density), this simulator gives the highest amount of liquid volume into the stock tank in comparison to the other commercial simulators.

Also, by considering high amount of produced fluid in the wellhead, if the increased produced liquid volume which is predicted by the simulator is so little, the increased produced liquid volume which is practically predicted is so much in volume, because of the difference in the units. Therefore, it has very economical advantages.

Eventually, this simulator can be coupled with the other simulators to separator analysis with high accuracy.

Acknowledgements

The acknowledgments are for the Shiraz University for supporting this research.

Appendix (A)

Economic Analysis of the Developed Simulator

Simulator’s feed is calculated as kmol/hr (100 kmol/hr), but field’s feed is calculated as bbl/day (5000 bbl/day for example). So, 100 kmol/hr is equivalent to 5000 bbl/day. If stock tank liquid calculated from various simulators is different (CODE and ASPEN HYSYS) and this difference was 0.68 kmol/hr (9.13 kmol/hr −8.45 kmol/hr), so it is equivalent to a high amount of bbl liquid in several years by applying the appropriate conversion factor.

0.68   kmol / hr × 147.625   kgr / kmol × 1 0.762066   lit / kgr × 1 160 bbl / lit × 24   hr / day ≈ 20   bbl / day

For 5 years:

5   year × 365   day / year × 20   bbl / day ≈ 36500   bbl

Economical view:

36500   bbl × 40   $ / bbl ≈ 1460000   $

Nomenclature

T Temperature

P Pressure

Fraction_VAP. Vapor fraction in input flow to separators

Fraction_LIQ. Liquid fraction in input flow to separators

Fraction_SOL. Solid fraction in input flow to separators

E Enthalpy

S Entropy

ρ Average density

ρ_L Liquid density

ρ_V Vapor density

MW_AV. Average molecular weight

MW_AV.L Liquid average molecular weight

MW_AV.V Vapor average molecular weight

V_L Liquid volume

V_V Vapor volume

Q Heat rate

GOR Gas oil ratio

B_o Oil formation volume factor

z_i Mole percent of i-component in two phase flow

x_i Mole percent of i-component in liquid phase

y_i Mole percent of i-component in vapor phase

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