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In the literature, improvement of oil recovery in smart-water-injection schemes has been shown to be mediated by wettability alteration. This process reduces residual oil saturation, which, in turn, affects microscopic sweep efficiency and leads to subsequent enhancement of overall waterflood performance. Currently, there are few studies on smart waterflooding in tight and very tight oil reservoirs. This work examines smart-waterflood opportunities in such reservoirs.
Residual-oil-saturation reduction improves microscopic sweep efficiency and, therefore, overall waterflood performance. Furthermore, decreasing endpoint water relative permeability diminishes mobility of the water phase such that water production is similarly reduced. Though these circumstances improve oil-production behavior, the primary parameters that lead to this improvement are still not well-understood.
Among the statistically significant parameters that can influence smart-waterflood performance is clay content. One plausible explanation for the strong correlation between clay content and oil recovery is the positive correlation between cation-exchange capacity (CEC) and clay content. With higher CEC values, more rock surface may be charged. This results in either the expansion or compression of the double layer, which also induces a wettability alteration. Although tight oil reservoirs have limited flow capability, high CEC values in these reservoirs facilitate the wettability-alteration process. The objectives of the complete paper are to examine the smart-waterflood potential in tight and very tight oil reservoirs, and to identify the CEC effect on smart-water-injection performance. The complete paper provides a discussion of the methodology (procedures and strategies) of the study.
The literature contains evidence of smart-waterflood performance, with some works demonstrating that smart water improves oil recovery by reducing residual oil saturation. Furthermore, it decreases the endpoint water relative permeability. Smart water also improves microscopic sweep efficiency, leading to overall waterflooding efficiency.
There is a distinct effect of porosity mean and porosity variance on CEC; there is furthermore a profound effect of CEC upon smart-water performance. Simulation properties relevant to the CEC effect are shown in Table 3 of the complete paper. There are two main types of fluid composition: in-situ fluid composition, which is the initial fluid in the reservoir at timestep zero, and the injected-fluid composition, which is the fluid that is forced into the reservoir at timestep greater than zero. Three homogeneous-reservoir cases with varying CEC values are considered.
Oil recovery, water cut, and mineral changes of calcite, dolomite, and feldspar at the end of the production period are summarized in Table 5 of the complete paper. Even though the static model is a homogeneous reservoir, there is some variation in oil recovery and water cut. The mineral changes of calcite, dolomite, and feldspar, however, illustrate significant variation. All three cases have a simulation material-balance error of less than 10–5. Mineral changes (moles) can affect the porosity and permeability.
The interpolant, aqueous-phase Ca2+ concentration, alters smart-waterflood performance by changing relative permeability. The interpolant value directly affects the smart-waterflood process because it alters the effective permeability of each phase. This is one possible explanation for the results in Table 5 of the complete paper, which prove that CEC value affects smart-waterflood behavior. Incorporating these results with those indicating that the porosity mean and variance influence CEC value, the authors conclude that the porosity mean and porosity variance can also alter smart-waterflood performance.
From results of further experimental scenarios, it can be seen that smart water improves production behavior in three ways: by increasing oil recovery, decelerating water production, and requiring less water injection. All three of these developments will improve project revenue while concomitantly decreasing environmental impact by limiting water usage. Thus, a smart-water-injection scheme has strong potential in tight oil reservoirs. However, rapid pressure drop and the multiphase flow that the pressure drop incurs must also be considered.
Microports Result. In a different scenario involving three cases, a microports reservoir was used. The situation presents a porosity distribution with mean of 7% and variance of 0.01. The flooding pattern was line drive, with two producers and two injectors. In this scenario, there are two low-salinity-water cases: the low-salinity case and the optimized low-salinity case. Fig. 11 of the complete paper displays wettability alteration from more oil-wet to more water-wet with decreasing salinity.
The high-salinity-water case has the lowest oil recovery, while the optimized low-salinity-water case has the highest. These results align with the mesoports-reservoir scenario. Residual oil saturations of two low-salinity cases are reduced by 4 to 20%, which improves the microscopic sweep efficiency of the waterflood process. Furthermore, smart water decelerates the water-cut trend by reducing endpoint water relative permeability by 15 to 35%. As a result, water cut for the optimized low-salinity case develops much more slowly than for the high-salinity-water case.
Interestingly, water cut is not developed until 3,000 to 4,000 days (more slowly than in the mesoports scenario), which means that pressure support is unproductive at the beginning of the pressure-maintenance scheme. To consider this observation, the average reservoir pressure is analyzed. All cases have a material-balance error of less than 10×10–4, which indicates reliable simulation results.
Reservoir pressure drops sharply, from 1,430 to 700 psi, in the first 500 days. As stated previously, only the oleic and aqueous phases exist in the reservoirs of this hypothetical model. However, this pressure drop may release the dissolved gas from the oleic phase in a real situation, resulting in three-phase flow and significantly reduced liquid relative permeability. There exists a contradiction between the mesoport and microport outcomes regarding average reservoir pressure. In the microport scenario, the optimized low-salinity-water case has the same average reservoir pressure as the high-salinity-water case by the end of the simulation and requires the highest cumulative water injection. In the complete paper, mineral-precipitation and -dissolution results are used to explain this behavior.
These results show that smart water improves production behavior through three mechanisms: the increase of oil recovery, the deceleration of water production, and the improvement of effective porosity and permeability. Two of the three mentioned benefits can improve project revenue. Higher water-injection requirements may affect profit and environmental effect negatively. This could be mitigated by fine-tuning the injection/production pressure and controlling the intake fluid volume. Thus, while a smart-water-injection scheme has strong potential in very tight oil reservoirs, the pressure-drop-induced multiphase-flow problem and the water-injection requirement still must be considered.
To determine oil-recovery-improvement opportunities in tight and very tight oil reservoirs, a smart-waterflood numerical study was conducted. The effect of rock property (porosity variation related to clay content) on smart-waterflood behavior was described and shown. Furthermore, the factor of smart water that causes wettability alteration, residual-oil-saturation reduction, and endpoint water relative permeability reduction was investigated, because change in residual oil saturation directly affects microscopic sweep efficiency and overall waterflood performance. Finally, a smart-water-injection scheme in tight and very tight oil reservoirs was presented. The following conclusions were drawn from the investigation:
Enhanced Sweep Efficiency by Use of Smart Water in Tight Oil Reservoirs
01 October 2017