International Journal of Petroleum and Geoscience Engineering Volume 04, Issue 02, Pages , 2016

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1 International Journal of Petroleum and Geoscience Engineering Volume 04, Issue 02, Pages , 2016 ISSN: Production Optimization for One of Iranian Oil Reservoirs Using Non- Linear Gradient Method Mehdi Talebpour a, Mahdi Rastegarnia b and Ali Sanati c, * a Department of Petroleum Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran. b Department of Petrophysics, Pars Petro Zagros Engineering and Services Company, Tehran, Iran. c Faculty of Petrochemical and Petroleum Engineering, Hakim Sabzevari University, Sabzevar, Iran. * Corresponding author. Tel.: ; address: ali.sanati@yahoo.com A b s t r a c t Keywords: Optimization, Objective Function, Controlling Function. Accepted: 15 Jun 2016 Today optimization is the principal part of every engineering design such that it exists in almost all engineering software products as a black box to optimize the simulated parameters. Optimization methods in general are divided into two main groups: gradient and non-gradient methods. In this work a non-linear non-gradient method was applied to determine the reservoir optimized parameters such as production rate and bottom-hole pressure. Also, maximizing the total production from reservoir regarding constraints like gas oil ratio and water production was considered as the objective function. For this, different scenarios with different well numbers were investigated to obtain the optimum scenario. Moreover, sensitivity analysis is applied on different parameters like daily production and bottom-hole pressure as the controlling parameters. Finally, cumulative production was obtained from optimized production rate and bottom-hole pressure. Academic Research Online Publisher. All rights reserved. 1. Introduction Optimization methods generally are divided into two main groups: gradient and non-gradient methods. Non-gradient methods like simulated annealing are used just for simple models. Increasing the simulation parameters will increase the optimization process s run time. So, using gradient methods in reservoir simulation is of great importance. In this work a non-linear non-gradient method was applied to determine the reservoir optimized parameters such as production rate and bottom-hole pressure and maximizing the total production from reservoir regarding constraints like gas oil ratio and water production, was considered as the objective function. In this method, the objective function s gradient is calculated by an adjoint technique and production constraints like gas oil ratio and water production are included by lagrangian formulations into the optimization process. Moreover, hydrocarbon production will be optimized drastically with the suitable well placement. [1, 2] Well placement is an important part of any filed development which is considered as a non-linear problem. Generally, two approaches are used to 78 P a g e

2 solve this problem. First one is the empirical approach which is common practice in the industry nowadays. This approach is obviously suitable for small scale reservoirs with few numbers of wells. Second one is the mathematical modeling approach which is based on mathematics and computer to determine the well places. This approach is divided into three categories. First; direct methods which are based on finite differences and adjoint methods. Second; random algorithms like Simulated Annealing, Particle Swarm Optimization, Simultaneous Perturbation Stochastic Algorithm and some evolutionary algorithms like Genetic Algorithms, Evolutionary Programming and Evolution Strategies. Third; hybrid methods which are combinations of direct and random methods. [1-7] Optimization is increasingly involved in almost all software programs today. Most simulators use a black box for analyzing the objective function to get the optimized parameters. Eclipse 300 is one of these softwares which uses the objective function s gradients instead of production profiles. These gradients are calculated using the adjoint methods, after that the production constraints are included with a lagrangian formulation [8]. In this study, different scenarios with different well numbers were investigated to obtain the optimum scenario. Moreover, sensitivity analysis is applied on different parameters like daily production and bottom-hole pressure as the controlling parameters. A reservoir sector was modeled and different parameters were change repeatedly in the optimization process. Finally, cumulative production was obtained from optimized production rate and bottom-hole pressure. For the sake of comparison, production resulted from natural depletion is also studied and considered as a base. 2. Methodology and Results 2.1. An Introduction to the Field In this study, Azadegan field was investigated which is located 80 kilometers to the west from Ahwas city near Iran-Iraq barrier. Oil in place is estimated to be around 33 billion barrels and the field area is about 911 kilometers squared. This field comprises of five layers named as Kazhdomi, Gadvan, Fahlian, Sarvak and Ilam Cumulative Production Optimization In order to optimize the production from Azadegan field, different scenarios were investigated with different well numbers in a sector of one of the field s reservoirs. Sensitivity analysis was also performed on different parameters like daily production rate and bottom-hole pressure. We used Eclipse 300 simulation software for simulation process where compositional model were selected. For the sake of comparison, production resulted from natural depletion is also studied and considered as a base for each scenario Well Fluid s PVT Table 1 shows the original composition the well fluid which is derived from a differential separation test at elevated pressure more than the bubble point pressure. Based on this test the bubble point pressure of the sample at 191 F was 2959 psi. 79 P a g e

3 Table 1: Differential Separation Test at Elevated Pressure Higher Than the Bubble Point Pressure Components ZI (percent) Weight fraction (percent) Molar weight Specific Gravity INER C C C C C C Natural Depletion Scenario In this step, a reservoir sector was simulated using the rock and fluid properties to get the natural production from reservoir. 8 wells were producing from reservoir without any scenarios for improving the recovery. Figures 1 to 4 show the daily production rate, cumulative oil production, reservoir pressure and gas oil ratio respectively Increasing Well Numbers as a Production Optimization Scenario In this scenario, we increased the number of wells while considering constraints like well spacing and well interferences. Surface production limitations were also considered. Optimization performed with 8, 10 and 13 wells using bottom-hole pressure and daily production rate as the controlling parameters Daily Production Rate as the Controlling Parameter To perform this scenario, the keyword OPTPARS was used to investigate the daily production rate as the controlling parameter to get the optimum cumulative production. Figures 5 to 7 show this fact for different well numbers. As you can see from these figures, cumulative oil production before applying any optimization process were 2.9, 3 and 3.1 million barrels for 8, 10 and 13 wells respectively. After the optimization process based on daily production rate these numbers turned to be 3.2 million barrels for each number of wells. 80 P a g e

4 Fig. 1: Daily Oil Production in Natural Depletion Scenario. Fig. 2: Cumulative Oil Production in Natural Depletion Scenario. 81 P a g e

5 Fig. 3: Reservoir Pressure in Natural Depletion Scenario. Fig. 4: Producing Gas Oil Ratio in Natural Depletion Scenario. 82 P a g e

6 Fig. 5: Cumulative Oil Production for 8 Wells. Fig. 6: Cumulative Oil Production for 10 Wells. 83 P a g e

7 Fig. 7: Cumulative Oil Production for 13 Wells. Figures 8 to 10 show daily production rate for 8, 10 and 13 wells respectively. As you can see from these figures, daily production rate before applying any optimization process were 80, 100 and 130 thousand barrels per day for 8, 10 and 13 wells respectively. After the optimization process based on daily production rate these numbers turned to be 200 thousand barrels per day for each number of wells. 84 P a g e

8 Fig. 8: Daily Oil Production Rate for 8 Wells. Fig. 9: Daily Oil Production Rate for 10 Wells. 85 P a g e

9 Fig. 10: Daily Oil Production Rate for 13 Wells. Figures 11 to 13 show producing gas oil ratio for 8, 10 and 13 wells respectively. As you can see from these figures, producing gas oil ratio before applying any optimization process and just like natural depletion scenario were between 1 to 1.2 thousand cubic feet per day for 8, 10 and 13 wells. After the optimization process based on daily production rate, these numbers turned to be 6 thousand cubic feet per day for each number of wells. 86 P a g e

10 Fig. 11: Producing Gas Oil Ratio for 8 Wells. Fig. 12: Producing Gas Oil Ratio for 10 Wells. 87 P a g e

11 Fig. 13: Producing Gas Oil Ratio for 13 Wells. Figures 14 to 16 show reservoir pressure for 8, 10 and 13 wells respectively. As you can see from these figures, reservoir pressure before applying any optimization process was 3940 psi for each well numbers. After the optimization process, reservoir pressure declined with respect to natural depletion scenario. One possible reason may be the increase in cumulative oil production and also increase in daily production rate. 88 P a g e

12 Fig. 14: Reservoir Pressure for 8 Wells. Fig. 15: Reservoir Pressure for 10 Wells. 89 P a g e

13 Fig. 16: Reservoir Pressure for 13 Wells. Figures 17 to 19 show gas cumulative production for 8, 10 and 13 wells respectively. As you can see from these figures, gas cumulative production before applying any optimization process were 300, 320 and 340 million cubic feet per day for 8, 10 and 13 wells respectively. After the optimization process based on daily production rate these numbers turned to be 440 million cubic feet per day for each number of wells. 90 P a g e

14 Fig. 17: Cumulative Gas Production for 8 Wells. Fig. 18: Cumulative Gas Production for 10 Wells. 91 P a g e

15 Fig. 19: Cumulative Gas Production for 13 Wells Bottom-hole pressure as the Controlling Parameter To perform this scenario, the key word OPTPARS was used to investigate the bottom-hole pressure as the controlling parameter to get the optimum cumulative production. Figures 20 to 22 show this fact for different well numbers. As you can see from these figures, cumulative oil production before applying any optimization process were 2.9, 3 and 3.1 million barrels for 8, 10 and 13 wells respectively. After the optimization process based on daily production rate these numbers turned to be 3.2 million barrels for each number of wells. These numbers are in complete agreement with the optimization scenario based on daily production rate as expected. 92 P a g e

16 Fig. 20: Cumulative Oil Production for 8 Wells. Fig. 21: Cumulative Oil Production for 10 Wells. 93 P a g e

17 Fig. 22: Cumulative Oil Production for 13 Wells. Figures 23 to 25 show daily oil production rate for 8, 10 and 13 wells respectively. As you can see from these figures, daily production rate before applying any optimization process were 80, 100 and 130 thousand barrels per day for 8, 10 and 13 wells respectively. After the optimization process based on bottom-hole pressure, these numbers turned to be 200 thousand barrels per day for each number of wells. 94 P a g e

18 Fig. 23: Daily Oil Production for 8 Wells. Fig. 24: Daily Oil Production for 10 Wells. 95 P a g e

19 Fig. 25: Daily Oil Production for 13 Wells. Figures 26 to 28 show producing gas oil ratio for 8, 10 and 13 wells respectively. As you can see from these figures, producing gas oil ratio before applying any optimization process was between 1 to 1.2 thousand cubic feet per day for 8, 10 and 13 wells respectively. After the optimization process based on bottom-hole pressure, these numbers turned to be 6 thousand cubic feet per day for each number of wells. 96 P a g e

20 Fig. 26: Producing Gas Oil Ratio for 8 Wells. Fig. 27: Producing Gas Oil Ratio for 10 Wells. 97 P a g e

21 Fig. 28: Producing Gas Oil Ratio for 13 Wells. Figures 29 to 31 show reservoir pressure for 8, 10 and 13 wells respectively. As you can see from these figures, producing gas oil ratio before applying any optimization process and just like the natural depletion scenario was 3940 psi for each well numbers. After the optimization process based on bottom-hole pressure, reservoir pressure declined with respect to natural depletion scenario. The reason for this is described before. 98 P a g e

22 Fig. 29: Reservoir Pressure for 8 Wells. Fig. 30: Reservoir Pressure for 10 Wells. 99 P a g e

23 Fig. 31: Reservoir Pressure for 13 Wells. Figures 32 to 34 show gas cumulative production for 8, 10 and 13 wells respectively. As you can see from these figures, gas cumulative production before applying any optimization process were 300, 320 and 340 million cubic feet per day for 8, 10 and 13 wells respectively. After the optimization process based on daily production rate these numbers turned to be 440 million cubic feet per day for each number of wells. 100 P a g e

24 Fig. 32: Cumulative Gas Production for 8 Wells. Fig. 33: Cumulative Gas Production for 10 Wells. 101 P a g e

25 Fig. 34: Cumulative Gas Production for 13 Wells. Conclusions Sensitivity analysis was performed based on the number and location of the wells regarding the well s drainage radius and other factors like surface facilities. Well location turned to be an important parameter affecting cumulative production. Also with applying a suitable optimization process, target production can be achieved with less number of wells. Cumulative oil production before and after optimization is shown below. Scenario Natural Depletion Before Optimization 2.90E+8 FOPT (STBD) After Optimization 2.90E+8 8 Wells 2.90E E+8 10 Wells 2.90E E+8 13 Wells 2.90E E P a g e

26 Daily production rate before and after optimization is shown below: Scenario Natural Depletion Before Optimization FOPR (STBD) After Optimization 8 Wells Wells Wells Producing gas oil ratio after 34 years of production is shown below: Scenario Natural Depletion Before Optimization 1.00E+00 FGOR (STBD) After Optimization 1.00E+00 8 Wells 6.00E E Wells 8.00E E Wells 8.00E E+01 Reservoir pressure after the optimization process declined more than that of natural depletion which is thought to be the result of the increase in cumulative production and also increase in daily production rate. However, reservoir pressure at late time increases due to the fact that gas oil ratio will be the maximum at that time which in turn will increase the reservoir pressure. Simulation results show that the optimization process can be an important factor in increasing cumulative production. Also based on the simulation results, the target production will be achieved with less number of wells which in turn reduce the operational cost drastically. So we strongly advise optimization as the key to success in oil and gas field development. References [1] Bouzarkouna, Z., Well Placement Optimization, Ph.D. Thesis, Department of Computer Science, University Paris-SUD, Paris, France, [2] Nasrabadi, H., Morales, A., Zhu, D., Wellplacement optimization: a survey with special focus on application for gas/gas-condensate reservoirs, J. Nat. Gas Sci. Eng. 5:6-16, [3] Sarma, P., Chen, W. H., Efficient wellplacement optimization with gradient-based algorithms and adjoint models, SPE Intelligent Energy Conference, Amsterdam, The Netherlands, [4] Zandvliet, M. J., Handels, M., Van Essen, G. M., Brouwer, D. R., Jansen, J. D., Adjointbased well- placement optimization under production constraints, SPEJ, 13(4): , [5] Vlemmix, S., Joosten, G. J. P., Brouwer, D. R., Jansen, J. D., Adjoint-based well trajectory optimization in a thin oil rim, SPE EUROPEC/EAGE Annual Conference, Amsterdam, The Netherlands, [6] Wang, C., Li, G., Reynolds, A. C., Optimal well-placement for production optimization, SPE Eastern Meeting Lexington, Lexington, Kentucky, U.S.A, [7] Zakirov, I. S., Aanonsen, S. I., Zakirov, E. S., Palatnik, B. M., Optimization of reservoir performance by automatic allocation of well rates. Proc., 5th European Conference on Mathematics of Oil Recovery, Leoben, Austria, [8] Asheim, H., Maximization of Water Sweep Efficiency by Controlling Production and Injection Rates, SPE European Petroleum Conference, London, P a g e