Implementation Time of Chemical Flood and Its Impact on Ultimate Recovery Authors: Dr. Ali M. AlKhatib and Dr. Amar J. Alshehri ABSTRACT INTRODUCTION During waterflooding processes, injected water can disconnect oil droplets from the pores/throats of the reservoir as it flows through. These disconnections are a consequence of capillary effects, and the mobilization of oil through those pores/throats is hindered afterward. This capillary trapping makes mobilizing the remaining oil in place by any enhanced oil recovery (EOR) process very challenging. Chemical flooding has been identified as an effective EOR method. It is usually implemented in tertiary mode, where field development has reached a mature level. At this stage, the efficiency of waterflooding processes in terms of mobilizing any remaining oil has declined, due to the above described capillary trapping. Chemical EOR processes, such as surfactant flooding, are used to reduce this trapping and mobilize the remaining oil. Surfactants reduce the interfacial tension (IFT), which consequently reduces the capillary pressure effects responsible for trapping the oil. Although most EOR processes are implemented in tertiary mode, earlier implementation is more desirable because that makes capillary trapping less prominent. This study investigates the impact of the timing of surfactant flood implementation after waterflooding, specifically on ultimate recovery and net present value (NPV), given this capillary trapping. A series of numerical experiments was conducted to test this effect while accounting for operating expenses associated with both flooding options. Capillary pressure curves for the waterflood case and the chemical flood case were added to the model to incorporate capillary trapping effects. Then the chemical flood implementation time was varied to evaluate its impact on the ultimate oil recovery. These experiments were performed on a number of stylized reservoir models with varying field size: a 1D coreflood model, the PUNQ-S3, the SPE10 reservoir model and a synthetic fractured reservoir model that is analogous to a Middle Eastern carbonate fractured reservoir. The implementation used an algorithm that was written in MATLAB and coupled with a commercial reservoir simulator. Results show that the sooner chemical EOR is implemented, the higher the ultimate recovery. Due to the relatively large initial investment and operating expenses associated with chemical flooding, however, waterflooding remains more attractive from an NPV perspective. FALL 2015 SAUDI ARAMCO JOURNAL OF TECHNOLOGY Conventional recovery methods, e.g., waterflooding, usually recover about 50% of the oil in place1. To increase oil recovery, more sophisticated engineering techniques are implemented, including advanced reservoir management practices and/or enhanced oil recovery (EOR) methods. Though it is common to implement such recovery enhancing techniques after exhausting the waterflood option, it has been suggested in the literature that their earlier implementation might lead to higher ultimate recovery. The objective of this work is to investigate, using reservoir simulation, the impact of the implementation time of enhanced techniques on ultimate recovery and net present value (NPV). Surfactant floods were selected as the advanced method to be tested while varying the injected pore volumes (PVs) of the preceding waterflood. Two main dynamic effects were investigated: relative permeability and capillary pressure. Using surfactants to reduce oil-water interfacial tension (IFT) and consequently improve oil recovery has been discussed in the literature since the 1920s2. Surfactant floods were selected for this investigation because they impact both of the dynamic effects of interest: relative permeability and capillary pressure. When IFT approaches zero, relative permeability curves become straight lines with no residual saturations, indicating the complete miscibility of the involved phases, e.g., oil and water3, 4. Capillary pressure curves are reduced relative to the magnitude of IFT reduction1. The impact of implementation time on ultimate recovery has been reported in the literature on several occasions. In Smart Waterflooding research, the ionic composition of injected water is tuned to alter rock wettability toward more favorable conditions. It was observed in coreflood experiments that injecting the Smart Waterflooding slug as early as possible increases the ultimate recovery. The cores used in these experiments were carbonate cores5, 6. Similar observations have been made in fractured carbonates7, 8, where surfactant flood experiments were conducted in secondary mode and tertiary mode. The secondary mode experiment led to low recoveries initially, but ultimately it recovered 7% more than the tertiary case. AlSofi and Blunt (2014)9 used a streamline-based simulator to optimize polymer flood design. They found that higher recov- eries are obtained at the earlier implementations of the polymer flood; a continuous polymer slug was used in this work. Upon oil reservoir discovery, the oil phase is relatively closely connected. Continuous waterflooding, however, leaves behind disconnected droplets that are unswept due to capillary trapping. As a result, it becomes harder for the tertiary recovery process to displace the remaining oil. Our claim is that the sooner EOR is implemented, the more oil is recovered. The basis of this claim is that it is easier to displace large droplets than small droplets. Assuming a constant pressure gradient, — P, across a reservoir, the pressure drop, ǵ P, across a droplet with length, L, becomes: DP = —P x L (1) Equation 1 shows that a larger pressure drop is achieved in a large droplet than in a small droplet. As a result of that, large droplets are easier to mobilize and more capable of overcoming entry pressures — capillary pressures — throughout the porous media. ness of 0.11 ft. Permeability was assumed to be homogeneous, and surfactant flooding was initiated after 0, 2, 4, 6, 8 and 10 injected PVs from waterflooding. This was done for the different wettability cases previously shown in Fig. 1. 2D Model: SPE10 Layer 1 This reservoir model is based on layer 1 of the SPE10 model10. This layer is discretized into a 60 × 220 grid. More details on the model properties used can be found in Alkhatib et al. (2013)11. A quarter of a five-spot pattern was assumed, Fig. 2. 3D Model: PUNQ-S3 This model uses the permeability field and the geometry of the PUNQ-S3 reservoir model12 with a total number of 3,800 (19 × 25 × 8) grid cells, Fig. 3. This example model uses the same fluid data as the 1D reservoir model. The well pattern used for the PUNQ-S3 reservoir model is based on placing four producers METHOD A series of numerical experiments was performed to investigate the effect of implementation time on ultimate recoveries. The initiation time of surfactant flooding was varied over a range of times with respect to the length of the preceding waterflood. These simulation runs were performed for three sets of relative permeability and capillary pressure curves, based on wettability conditions, Fig. 1. This workflow was applied to five different reservoir models to capture the effects of the same range of implementation times in models having variable sizes and degrees of heterogeneity. All surfactant-related properties were fixed for the different reservoir models, Appendix A. Fig. 2. 2D reservoir model based on layer 1 of the SPE10 model. 1D Model: Surfactant Coreflood This coreflood model is discretized by an 80 × 1 × 1 Cartesian grid with a length of 0.745 ft, a width of 0.11 ft and a thick- Fig. 1. Relative permeability and capillary pressure curves for the water-wet (WW), mixed-wet (MW) and oil-wet (OW) cases. Fig. 3. The 3D PUNQ-S3 model showing the permeability field and well placements. Scale is logarithmic. SAUDI ARAMCO JOURNAL OF TECHNOLOGY FALL 2015 on a structurally higher position — a crestal area — within high permeability streaks and placing one injector at a structurally lower position. The surfactant flooding initiation times were set at 0, 0.2, 0.4, 0.6, 0.8 and 1 injected PV from a preceding waterflood. 3D Model: Modified SPE10 Reservoir Model (SPE10M) This reservoir model is based on an upscaled section of the SPE10 model10. It represents the upper lithology, the Tarbet Formation, of the original reservoir model; the top 35 layers are upscaled to a 20 × 55 × 7 grid with a total number of 7,700 grid cells. The original SPE10 model reservoir and fluid data are used. The well placement pattern used is an inverted five-spot pattern, Fig. 4. Surfactant flooding initiation times were set at 0, 0.2, 0.4, 0.6, 0.8 and 1 injected PVs. Simulation constraints are presented in Appendix A. For this reservoir model case study, the NPV was calculated and obtained for all surfactant flooding initiation times. The NPV was obtained based on calculating the net revenue as: (2) where t = 1 is the initial simulation time step and T is the final simulation time step, Ro is the oil revenue, and Cwi, Cwp and Cc are the water injection, water production and chemical costs, respectively. CAPEXC is the additional capital expenditure incurred for the chemical EOR process. Continuous discounting was assumed using a fixed rate of 6%. The oil price was assumed to vary with time and was modeled as a mean reverting stochastic process known as the Ornstein-Uhlenbeck process13. The water production and injection costs were correlated with the oil price — they were assumed to be 2% of the oil price at the current time step. The surfactant cost was assumed to follow a uniform distribution with bounds [2.2,6.6] $/kg. More details on the assumptions and approaches used to model the oil price and the capital expenditure for chemical EOR facilities are found in Alkhatib et al. (2013)11 and Alkhatib and King (2014)14. To produce a statistically convergent result for the NPVs calculated, 1,000 realizations over time were sampled for the oil price and chemical cost, and the results were obtained by taking the mean of the NPV over these realizations for each surfactant initiation time. Discrete Fracture Network Model Fig. 4. The modified portion of the SPE10 model, showing permeability and well placement. Scale is vlogarithmic. The discrete fracture network (DFN) model was designed to be analogous to a naturally fractured carbonate reservoir. It was built using corner point geometry and is comprised of 72 × 78 × 6 layers. The model dimensions were assumed to be 7.2 km × 7.8 km with a reservoir thickness of 14 m, Fig. 5. A fracture network was modeled using DFNs. This was performed using the PETREL software package15. The matrix permeability was generated using a two-point geostatistical method known as the moving average method16. Once the DFN is generated in this model, the permeability, shape factor and porosity are upscaled using the analytical Oda approach17. This upscaling Fig. 5. The DFN reservoir model showing the well placement pattern. Injector well symbols are marked in white and the producer well symbols are marked in black. FALL 2015 SAUDI ARAMCO JOURNAL OF TECHNOLOGY method is used because it provides reasonable approximations quickly compared to flow-based methods. It is important to note, however, that this method is not accurate for grid cells with low fracture intensity18. The fracture density was defined as the P32 in the literature, which is the fracture area divided by the volume. A value of 0.06 was used18. The fracture length was stochastically generated using a Power law with a shape factor of 2.1 and a scale of 25. The maximum fracture length was constrained to 1,000 m. The fracture orientation was also stochastically generated and assumed to follow the Fisher model with a mean dip of 90°, a mean dip azimuth of 40° and a concentration of 8. Finally, the aperture was also assumed to be random and followed a lognormal distribution of mean 300 μm and a standard deviation of 50 μm. The production strategy for the carbonate reservoir modeled here is based on a number of peripheral horizontal water injectors and producers, while a series of infill inverted five-spot patterns are used to produce the higher structural region. The surfactant flooding once initiated was maintained at a constant concentration of 1 wt%. The injection rate for the field was controlled at a constant water injection rate of 15,900 m3/day. RESULTS AND DISCUSSION Recovery curves were obtained for the different geologic models previously discussed at three sets of relative permeability and capillary pressure curves. The results are discussed here. 1D Model: Surfactant Coreflood Figure 6 shows the recovery curves of the 1D model. 2D Model: SPE10 Layer 1 Figure 7 shows the recovery curves of the 2D model. Several curves are plotted for each wettability condition where each curve represents a different injected PV from the preceding waterflood. The highest recoveries were achieved in the water-wet case, with lower recoveries in the mixed-wet case and lowest recoveries in the oil-wet case. The ultimate recovery for each wettability state remains constant regardless of the implementation time, but the recovery behavior changes with the implementation time. In this model, more oil is recovered faster with an earlier implementation of surfactant flood. This behavior is clearly visible in the oil-wet case, as seen by the separation between the recovery curves. Fig. 6. Recovery factor plots for different surfactant flooding initiation times for all wettability cases obtained from the 1D coreflood model. Fig. 7. Recovery factor plots for different surfactant flooding initiation times for all wettability casesobtained from the 2D SPE10 Layer 1 model. SAUDI ARAMCO JOURNAL OF TECHNOLOGY FALL 2015 3D Model: PUNQ-S3 Figure 8 again shows the recovery curves for the different wettability cases. Ultimate recovery behaved as observed previously, where recoveries were highest for the water-wet case and lowest for the oil-wet case. Ultimate recoveries achieved for the different initiation times were similar as the simulation continued. While more oil is also recovered faster with an earlier implementation of the surfactant flood, this behavior is not as visible as it was in the preceding SPE10 model. 3D Model: Modified SPE10 Reservoir Model (SPE10M) Figure 9 shows the results for the modified SPE10 3D model. Behavior similar to that in the previous models was observed, where ultimate recoveries were highest for the water-wet case and lowest for the oil-wet case. The ultimate recoveries for the set of initiation times in a given wettability case converged after a long simulation horizon. The separation of the curves once more indicated faster recovery with the early implementation; this behavior is similar in all wettability conditions. The fact that faster recoveries are obtained with earlier implementation seems very rewarding in terms of NPV. But this was not the case with the NPV model used for the simulations, Fig. 10. At all wettability conditions, the greater the delay in the implementation, the higher the NPV. DFN Model Figure 11 shows the recovery results of the DFN model. Similar behavior was observed, with ultimate recoveries highest for the water-wet case and lowest for the oil-wet case. One difference was that the mixed-wet recovery curves were very similar to those obtained for the water-wet case. The ultimate recoveries for the set of initiation times in a given wettability case converged after a long simulation horizon. The ultimate recovery in all wettability conditions here, however, was much lower than the recoveries achieved in the previous models. This is mainly a result of the fractures in the system, which take up most of the injected fluids with minimal imbibition to the matrix. The implementation time showed no effect on the ultimate recovery, but did have an effect, as previously shown, on the quickness of recovery. This behavior is more visible in the oil-wet case than in the water-wet case, which will significantly impact the NPV calculation. Figure 12 shows the NPV results. The results show a clear Fig. 8. Recovery factor plots for different surfactant flooding initiation times for all wettability cases obtained from the 3D PUNQ-S3 model. Fig. 9. Recovery factor plots for different surfactant flooding initiation times for all wettability cases obtained from the modified portion of the SPE10 model. FALL 2015 SAUDI ARAMCO JOURNAL OF TECHNOLOGY Fig. 10. NPV plots for different surfactant flooding initiation times for all wettability cases obtained from the modified portion of the SPE10 reservoir model. Each NPV curve is obtained as the mean of 1,000 NPV realizations incorporating oil price uncertainty in the time series. Fig. 11. Recovery factor plots for different surfactant flooding initiation times for all wettability cases obtained from the DFN reservoir model. Fig. 12. NPV plots for different surfactant flooding initiation times for all wettability cases obtained from the DFN reservoir model. Each NPV curve is obtained as the mean of 1,000 NPV realizations incorporating oil price uncertainty in the time series. trend favoring later initiation of surfactant flooding, given oil price uncertainty in the time series. The NPV curves obtained for the water-wet and mixed-wet cases were similar, while the NPV curves for the oil-wet case showed much lower values. This is in agreement with the lower oil mobility in the oil-wet case. Based on the experimental results in the literature and on the simulation results of this study, it is clear that there is disagreement with the trends observed for ultimate recovery as a function of initiation time of surfactant flooding. The dynamic effects of the capillary pressure and the relative permeability curves must be taken into consideration to capture the varying SAUDI ARAMCO JOURNAL OF TECHNOLOGY FALL 2015 ultimate recovery behavior observed in the experiments in the literature. Several studies have shown that dynamic capillary curves should be used in simulations, rather than the static curves that are often measured and used19-21. Such variations between static and dynamic measurements will definitely impact the results of fluid-flow simulations. To demonstrate how this behavior can alter the ultimate recovery and NPV of surfactant flooding, a numerical experiment was designed using the DFN reservoir model and assuming that the initial behavior is oil-wet and that surfactant flooding is initiated after 1 PV of water injection. This assumption was used as a proxy for the dynamic behavior of the relative permeability and capillary pressure curves with time as the reservoir is produced. At this point, three different production profiles were obtained by using the water-wet, mixed-wet or oil-wet relative permeability and capillary pressure curves in the reservoir simulation, Fig. 13. The results show that in the water-wet and mixed-wet cases, behavior after surfactant flooding initiation is nearly similar, while if the oil wettability properties are maintained, recovery and NPV are less. Although the differences in NPV among the different wettability systems are more pronounced in Fig. 12 for any surfactant initiation time, with the oil-wet system achieving markedly lower return, the results here for the dynamic wettability systems show that the NPV is dominated by the recovery profile prior to the surfactant flooding initiation point at 1 PV waterflooding, which in this test was based on an oil-wet system. As a result, a sensitivity measure of when the wettability changes as a function of the surfactant flooding timing is required. It is important to mention here that the assumption used is that wettability changes due to waterflooding prior to surfactant flooding and not due to alteration by surfactant adsorption post-surfactant flooding. CONCLUSIONS The objective of this study was to investigate the effect of dynamic capillary pressure and relative permeability on ultimate recovery for chemical EOR. This was performed on a number of simulation models ranging from a 1D coreflood model to a 3D fractured reservoir model. Three sets of relative permeability and capillary pressure curves were used to characterize water-wet, mixed-wet and oil-wet systems. For each set, different implementation times were tested to see how significant the effect of the surfactant flooding implementation time would be. Results were compared with the experimental results from the literature. It was observed from the simulations that a basic varying of the implementation time does not impact the ultimate recovery, and so does not reflect what several experiments have shown in the literature. There is an indication that early implementation usually leads to more favorable conditions and eventually better recoveries. Therefore, it is highly recommended to reevaluate dynamic properties at different initial conditions and correlate the results to the implementation time. The newly evaluated dynamic properties are now used in a reservoir simulator to the corresponding implementation time. Initiation time had a different effect on the NPV. In all cases, the earlier the implementation time, the lower the NPV. The waterflood scenario by itself is very attractive for several reasons. Waterflooding does not require a large initial investment for its facilities. It provides an excellent means of pressure maintenance and usually leads to reasonable recoveries. It is relatively much easier to design and implement for a green field. It also allows for studying and characterizing the reservoir while it remains in production, and consequently leaves room for improving the injection plan. Fig. 13. Recovery factor and NPV plots for different wettability cases obtained from the DFN reservoir model assuming surfactant flooding initiation after 1 PV of waterflooding. FALL 2015 SAUDI ARAMCO JOURNAL OF TECHNOLOGY Therefore, for a method other than waterflood to be economically successful, several components need to be achieved. The cost of injectant and of injection and handling facilities has to be minimized. Injectants need to be highly effective in mobilizing the remaining oil, reaching values larger than what can be achieved in a waterflood. Finally, improvements in injectant screening procedures for a green field are needed to reduce uncertainties in selecting the most efficient injectant. 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APPENDIX A: CASE STUDIES SIMULATION CONSTRAINTS AND SURFACTANT PROPERTIES Constraint 3 Producer Oil Rate (m /d) Injector Water Rate (m3/d) Water Cut (%) BHP min (bars a) Max Injection Pressure (bars a) PUNQ-S3 SPE10M 32 280 None 17 300 24 280 None 17 500 Table 1. Simulation constraints for PUNQ-S3 and SPE10M case studies T Surfactant Concentration (kg/m3) 0 0.28 0.57 2.24 Surfactant Water Viscosity (cP) 0.4 1.1 1.2 1.3 Table T 2. Surfactant viscosity Surfactant Concentration (kg/m3) 0 0.28 0.56 1.42 Surfactant Water/Oil Surface Tension (dynes/cm) 20 2 0.70 0.064 Table 3. Surfactant/oil surface tension T Log10 (Capillary Number) Miscibility Function -9 0 -3 0 2 0 5 1 Table 4. Capillary desaturation curve T Surfactant Concentration (kg/m3) 0 0.28 0.56 2.8 Surfactant Adsorption (kg/kg) 0 0.00052 0.00052 0.00052 Table 5. Surfactant adsorption T FALL 2015 SAUDI ARAMCO JOURNAL OF TECHNOLOGY BIOGRAPHIES Dr. Ali M. AlKhatib is a Reservoir Engineer currently working with the Reservoir Engineering Technology group of Saudi Aramco’s Exploration and Petroleum Engineering Center – Advanced Research Center (EXPEC ARC). His research interests include enhanced oil recovery, uncertainty quantification and stochastic optimization. Ali is a member of the Society of Petroleum Engineers (SPE) and has served on the steering committees of a number of SPE workshops. He is also a technical reviewer for the Journal of Computational Geosciences and the Journal of Petroleum Science and Engineering. Ali received his B.S. degree in Chemical Engineering with Management from the University of Edinburgh, Edinburgh, U.K., and M.S. and Ph.D. degrees in Petroleum Engineering from Imperial College London, London, U.K. He has authored and/or coauthored more than 15 conference and peer-reviewed technical papers. Dr. Amar J. Alshehri is a Reservoir Engineer working with Saudi Aramco’s Southern Area Reservoir Management Department. He has several years of experience in areas related to enhanced oil recovery (EOR), petrophysics, production engineering, reservoir management and numerical reservoir simulation. Amar has been an active member of the Society of Petroleum Engineers (SPE). He recently served as the chairman of the 2015 SPE Annual Technical Symposium and Exhibition pre-event courses and workshops. Amar is also a technical reviewer for the SPE Journal and the Journal of Petroleum Science and Engineering, and he has reviewed several technical papers related to chemical EOR topics. He has more than 10 technical papers and articles published in several venues. Amar received his B.S. degree in Petroleum Engineering from the Colorado School of Mines, Golden, CO, and his M.S. and Ph.D. degrees in Petroleum Engineering from Stanford University, Stanford, CA. During his time at Stanford, Amar received the Certificate of Achievement in Mentoring and won the William H. Brigham Memorial Award.