Simulation of Carbonated Water Injection (CWI), Challenges and Solution

Transcription

1 Simulation of Carbonated Water Injection (CWI), Challenges and Solution Foroozesh J., Jamiolahmady M. (Jami), Sohrabi M. Presented by: Jalal Foroozesh 1

2 Outline Introduction Objectives Our modelling approach Governing equations. GA optimisation tool. Experimental conditions. Simulation results and discussions Summary and conclusions 2

3 Introduction - 1 CWI is an augmented water injection (WI) process with great EOR potentials. During CWI, CO2 is transferred from water into oil due to its higher CO2 solubility, resulting in higher oil recovery. Carbonated Water (CO2 + Water) CO2 Oil 3

4 Introduction - 2 CWI can be used when limited source of CO2 is available. CO2 is in solution rather than free, so less risk of early gas breakthrough (EOR aspect). A good technique for safe CO2 storage with less risk of leakage (Environmental aspect). Compared to experimental investigation of the subject, mathematical modelling of CWI has received less attention. 4

5 Introduction - 3 Viscosity reduction, oil swelling and wettability alteration are among the main mechanisms giving more oil recovery during CWI. It is believed that compositional simulation of the process better captures the process. Current compositional reservoir simulators are based on instantaneous equilibrium assumption. 5

6 Introduction - 4 Our experimental studies have shown that CO2 transfer from water to oil is a slow and nonequilibrium based process during CWI. Therefore this multi-physics (fluid flow and mass transfer) process can not be simulated realistically using available simulators. 6

7 Objectives Mathematical modelling/numerical simulation of CWI Process. Develop a compositional simulator that captures CWI multi-physics process realistically. The simulator needs to be based on nonequilibrium conditions by capturing the kinetics of mass transfer. 7

8 Governing Equations φ (ρ os o ω o o ) t = ρ o u o ω o o 1 φ ρ os o ω o co2 t = ρ o u o ω o co2 + U (2) φ (ρ ws w ω w w ) t = ρ w u w ω w w 3 φ (ρ ws w ω w co2 ) t = ρ w u w ω w co2 U 4 Mass transfer term (U=f(concentration, overall mass transfer coefficient, Sw)) controls the amount and the rate of CO2 transfer from water to oil phase. Equations are solved for P, s w, w w co2, w o co2. Fully implicit method was used to solve the coupled equations. Viscosity and density change due to CO2 transfer between oil and water phases. 8

9 Genetic Algorithm (GA) Mass transfer coefficient (MTC) and unknown in our CWI simulation. Kr were In our approach these unknown parameters are determined by history matching the TOP and DP experimental data. To achieve this, a GA based optimization program was developed that could be linked to our simulator. 9

10 Core Flooding Experimental Conditions Water-wet ( WI and CWI ) Mixed-Wet ( WI and CWI ) Decane with well defined properties used as oil in these tests conducted at 2500 psi. CW q=20 cc/hr fully saturated with Decane (S wi =0) Clashach Core: WW: k=850 md, PV=120 cc MW: k=1 D, PV=180 cc CW CO2 content=5% CW-C10, Partition coefficient=3-4 Water + Decane + CO2 10

11 Simulations Results and Discussions 11

12 In-house Simulator - Consistency Check Water injection (WI) was simulated first using our simulator under its black oil mode. A Kro-w curve that simulates the WI test was obtained by history matching of the corresponding production data using GA. Results of our simulator were compared to those of E100 demonstrating good agreement. 12

13 Water-Wet Core - 1 WI and CWI WI 13

14 TOP(cc) Water-Wet Core CWI Tuned MTC value of 1.5 E-5 with oil-water Kr. Exp Model Time(hr)

15 Mixed-Wet Core

16 Mixed-Wet Core

17 Summary and Conclusions - 1 A compositional based simulator has been developed. The simulator is based on non-equilibrium assumption and captures the kinetics of CO2 transfer. A Genetic Algorithm (GA) based optimization program has been developed. Kr and MTC can be tuned by GA. WI or CWI core flood tests of water-wet and mixed-wet cores when they were fully saturated with Decane were simulated. 17

18 Summary and Conclusions - 2 WI tests were simulated first and later the results were used for the simulation of the corresponding CWI tests. For water-wet cores, water-oil kr curve obtained was used and only MTC was tuned to match CWI production data. Main mechanism: Swelling. For mixed-wet cores, kr curve and MTC were tuned to match CWI production data. Main mechanism: Wettability alteration and Swelling. 18

19 Acknowledgement The support of the following industrial partners acknowledged. The UK Department of Energy and Climate Change (DECC), Petrobras, Total Exploration and Production, ADCO, BG Group, and Galp Energia. 19

20 Thank you for your attentions 20