Streamlines simulation of barrier fracture as a novel water shutoff technique
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Excessive water production has been a problem in the oil industry for many years. To handle this problem, many research projects have focused on developing conformance control systems. Conformance fracturing, a combination of hydraulic fracturing and water control, has proven to be an effective conformance control technique. Hydraulic fracturing is now the technology of choice for increasing well productivity. The chemistry of relative permeability modifiers has also undergone extensive change; the most notable result of which has been to prolong the life of water control treatments using relative permeability modifier (RPM) polymers. The purpose of this study was to investigate the application of barrier-fracturing using streamline simulation. Barrier-fracturing is a novel idea that involves modifying the flow profile and diverting the displacing fluid by placing a fracture with essentially zero permeability deep into the reservoir. There are many ways to create a zero permeability fracture, examples of which include injection of cement or a conformance fluid into the fracture. In our study, we created several streamline simulation models to show the fidelity and validity of this innovative idea. The streamline simulation models that are presented in this paper range from a simple homogeneous reservoir to a very heterogeneous reservoir. The effect of different barrier-fracture lengths on the reservoir performance was analyzed. We also built streamline models for conventional mechanical and chemical water shutoff techniques (e.g. re-completion and RPM) to compare them with the novel barrier-fracture water shutoff technique. The resulting saturation distribution maps from the longer barrier-fracture clearly show the power of a barrier-fracture to modify flow profile and divert the displacing fluid in comparison to conventional water shutoff techniques. Barrier-fractures helped improve oil recovery by delaying water-breakthrough and eventually improving the volumetric sweep efficiency.
KeywordsStreamlines simulation Sweep efficiency Reservoir Management Water shutoff Barrier fracturing
Conformance control refers to any solution designed to enhance the injection/production profile of a well by controlling the production of unwanted fluid. Conformance treatments may involve mechanical or chemical approaches or a combination of the two. Mechanical control may involve the use of packers or Inflow Control Devices (ICD’s). Chemical approaches may be divided into two broad groups. One involves injecting a sealant into the reservoir to fully stop unwanted fluid flow. The other involves injection of relative permeability modifier (RPM) polymers to significantly reduce the relative permeability to water, while keeping the relative permeability to oil fairly intact.
It is very crucial to define reservoir characteristics and also conformance treatment conditions that could lead to successful chemical treatments and also provide guidelines for the application to actual fields. These might include thief zone temperature, vertical to horizontal permeability ratio, injection concentration and slug size. Tsau et al. (1985) used a simulator to model chemical treatments (polymer gels) and identify reservoir properties that strongly influence a conformance treatment. They found that low level of cross flow helped vertical conformance treatment and that a high ratio of permeability-thickness product between high and low permeability zones resulted in increased recovery. Melo and Aboud (2008) conducted over 100 conformance fracturing operations in Brazil, using conventional as well as lightweight proppants, and relative permeability modifiers. The authors presented a table of different treatments (design, logistics, materials,and equipment) versus obtained results (oil and water production over time), showing the improvements made over time. (Dang Cuong et al. 2011) history matched polymer gel behavior with experimental data and generated parameters for field scale simulation of the Lower Miocene reservoir of the White Tiger field. The field scale simulation results showed that implementing polymer gel treatment reduced excessive water production and improved oil recovery from unswept zones. Herbas et al. (2004) used a mechanistic field simulation to design water conformance treatments in Eastern Venezuelan HPHT reservoirs under different gel treatment scenarios. The simulation results showed water cut reduction from 90 to 30 %, matching trends observed in wells at different locations and gel treatment effects in typical Eastern Venezuelan reservoirs.
Another important design criterion that affects conformance treatments is the temperature near wellbore. So, to design the treatment based on a realistic temperatures rather than a bottom hole temperature, (Hardy et al. 1997) used temperature simulations to predict temperature near wellbore during water shutoff treatments and optimize treatment placement rates, fluid composition and shut-in times. Two North Sea field cases were presented. The first case illustrates how cooldown inside reservoir was used to place a treatment that would otherwise have gelled spontaneously at reservoir temperature. In the second case, the temperature simulations showed that several different activator compositions and concentrations were required for the early, intermediate and final treatment stages.
As we stated above, polymer flood works by reducing the mobility of water in highly conductive zones near the wellbore. However, injection of huge quantities of polymer near wellbore results in decreasing the drive fluid throughput and thus reservoir pressure support. To avoid such issues and also divert flow into lower permeability, unswept zones to mobilize bypassed oil, different conformance treatments have been proposed to form a block deep into the thief zone. Benson et al. (2007) proposed a novel deep penetrating mobility control method and used a numerical simulator to model the behavior of pH-sensitive polymer in reactive porous media. The simulation results showed that pH-sensitive polymer slug treatments improved vertical conformance in two layer radial and linear geometry floods. Another in-depth profile modification method was proposed by Garmeh et al. (2011) to use thermally activated polymer (TAP) which is an expandable submicron particulate of low viscosity and developed two simulation approaches to model properties of the thermally activated polymer (TAP) and its interaction with reservoir rock. Results showed that ultimate oil recovery and conformance control depend on thief zone temperature, vertical to horizontal permeability ratio, injection concentration and slug size, among other factors. Along the same line, Tobenna Okeke and Lane (2012) used a numerical simulator to model the potential effectiveness and performance of deep diverting gels (DDG) to plug thief zones deep within the reservoir by considering a wide range of reservoir characteristics and conditions. The authors compared the performance of the DDG to waterflooding and polymer flooding and found that a properly designed polymer flood had the highest NPV in all case comparisons, followed by DDG.
For such complex treatments, it is important to simulate the wellbore heat and pressure loss, reservoir temperature, polymer gelation and polymer adsorption. Unfortunately, these mechanisms have been modeled disparately by researchers. (Ansah et al. 2006) presented the first 3D, three phase, four component, pseudo-compositional, non-isothermal coupled reservoir/wellbore simulator that incorporates all these mechanisms through a rigorous tracking of all fluid concentrations during injection, shut-in and finally back flow of the fluids. Modeling the fourth phase, the conformance fluid, helps in tracking its location anywhere within the wellbore and reservoir during injection and flowback and thus maximizes the return and benefit of the various placements. The authors tested this methodology using coning and channeling examples in addition to two field treatments into two wells operated by Repsol YPF in Ecuador. The simulation results helped predict more accurate post-treatment water and hydrocarbon production and reduced operational and economic risks. Thornton et al. (2010) extended this work to optimize design and initialization of mechanical conformance using inflow control devices (ICD) and simulations helped in the optimum placement of ICD to minimize the water production. Also, Vasquez and Miranda (2010) used the same simulator presented by (Ansah et al. 2006) to evaluate performance of an RPM system under different scenarios and varying parameters. The simulation results showed that RPMs helped improve the injection profile by diverting water flow from high permeability into low permeability zones.
The purpose of this study was to investigate the application of the novel idea of barrier-fracturing using streamline simulation. We created several streamline simulation models to show the power of this innovative idea in modifying flow profile and delaying water breakthrough. The streamline simulation models range from a simple homogeneous reservoir to a heterogeneous reservoir with naturally fractured reservoirs (NFR). Following the introduction, we provide some background about streamline simulation technology and its demonstrated superiority, which made it the modeling tool of choice for us. Next, we discuss the proposed solution and the details of our simulation models. Simulation results and their interpretation are provided in the results section. Finally, our conclusions will be stated.
Streamline simulation is an Implicit Pressure Explicit Saturation (IMPES) type reservoir simulation that solves the pressure equation implicitly and then solves the saturation/conservation equations explicitly. Thus, streamline simulators operate on the principle of decoupling the pressure equation from the saturation equation. This simplification allows a heterogeneous 3D domain to be decomposed into a number of 1D streamlines where all fluid calculations are carried out.
This dual grid approach distinguishes streamline simulators from conventional finite difference simulators.
This simulation process involves many different mathematical calculations on both grid systems to solve the pressure and transport equations. The following references thoroughly explain the mathematical formulations. These references include but are not limited to (Batycky 1997; Ingebrigtsen et al. 1999; Doi and Suzuki 2000; Lolomari et al. 2000; Gautier et al. 2001; Jessen and Orr Jr. 2002; Di Donato et al. 2003; Moreno et al. 2004; Gerritsen et al. 2005; Mallison et al. 2006; Cheng et al. 2006).
These are just some of the advantages of streamlines methods. We would highly recommend that the reader refers to AlNajem et al. (2012) for attaining a better idea about the different advantages of streamlines technology and also the wide range of petroleum engineering applications that symbolize the relevance and validity of streamline simulation in addressing reservoir engineering concerns.
Like FD simulation, streamline simulation has its limitations. Two important ones are mapping between coordinates and modeling of fluid flow complex physics. Streamline simulation contains two separate grids, an underlying physical grid where the pressures and the velocities are calculated, and the streamline time-of-flight grid where the fluid transportation is calculated. Streamlines are re-generated at each pressure update. This means that the saturations from the old set of streamlines must be mapped back to a new set of streamlines. Streamlines transport saturations rather than conserved volumes, which are only implicitly defined in the time-of-flight coordinate. Because of the re-sampling of implicit volume from time-of-flight coordinates to physical coordinates, potential mass balance errors and to some extent, numerical dispersion may be introduced.
Another major limitation of streamline simulation results from its main advantage defined above: computational speed. When dealing with complex physics like high compressibility, capillary effects and phase behavior, the computational speed decreases. This is due to the need for more frequent re-sampling of streamlines, which means more frequent solving of the pressure equation.
Streamline-based flow simulation has made significant advances in the last 15 years. Today’s simulators are fully three-dimensional and fully compressible and they account for gravity, fracture flow, and non-uniform conditions as well as complex well controls. Most recent advances also allow for compositional and thermal displacements.
Description of the streamline simulation model
One-fourth of five spot pattern streamline model was built using FRONTSIM, the Schlumberger streamline simulator. The base case 3D simulation model is 660 by 660 by 490 ft with a Cartesian grid of 44 × 41 × 13 grid blocks in the x, y and z directions, respectively. The sizes of each grid block in both x and y directions were designed using a geometric gridding coefficient of 1.2; the size of the layers in the z direction varies as shown in Fig. 4. We intentionally designed the grids in this manner to aid the placement and modeling of the barrier fracturing deep in the reservoir. Both injector and producer were completed in the first ten layers and the injection and production rates were maintained constant at 1,000 STB/D. Porosity is 24 % and constant, and the vertical permeability is maintained at 10 % of horizontal permeabilities (i.e. K x and K y ). The simulation was run for 6,000 days (>16 years) and the barrier fracturing is modeled by reducing the grid’s permeability in all directions to a very small number (10e–11 md). Figure 5 compares the oil saturation along the streamlines in the model at the breakthrough time and at the end of simulation (6,000 days) where, after placing the barrier fracture, the streamlines had to go around it to reach the producer, which would delay the breakthrough of water.
Application of the model–numerical cases
In this section, we present five numerical cases to show the use of the streamline simulation to model barrier fracturing. As mentioned earlier, these cases included the modeling of a simple homogeneous reservoir, a homogeneous reservoir with included areas of high permeability, low permeability, and a heterogeneous reservoir. By going with the conventional wisdom, we modeled the barrier fracture right after breakthrough occurs in all of the streamline models. This is done using the “restart” simulation files at the time of breakthrough to initialize the new streamline model where the barrier fracture has been included. We also built streamline models for the conventional mechanical and chemical water-shut off techniques such as re-completion and RPM, respectively, to compare them with the novel barrier fracture water shutoff concept.
Because this innovative concept revolves around modifying the flow profile deep in the reservoir, the placement of the barrier fracture is somewhat contrived. However, the effects of different barrier fracture lengths on reservoir performance and sweep efficiency were analyzed to find the optimum length that would serve the purpose of placing a barrier fracture in the reservoir. This barrier fracture length sensitivity analysis was carried out only with the homogenous reservoir case and since this optimum length gave the maximum reservoir performance, we utilized the same length when modeling the other numerical cases.
Case-1: homogenous reservoir
Comparison of conventional water shutoff and barrier fracturing
In practical field management, operators usually react to the production of water by either re-completing the well to stay away from water producing zones or by injecting relative permeability modifiers (RPM). So, because the “no-action” case that we presented might not be realistic, we built streamline models for the conventional mechanical and chemical water shutoff techniques (i.e. re-completion and RPM) to determine the superiority of the novel barrier fracture water shutoff technique.
Case-2 and 3: Effects of high and low permeabilities
Case-4: heterogeneous reservoir
This is a synthetic heterogeneous and anisotropic model where the permeability was distributed log normally in all directions. Similar procedures as in the homogeneous model were followed to place the barrier fracture after the breakthrough of water and initialize using the “restart” files.
So, we believe it is fair to state that the application of barrier fracture to shutoff excess water production is not limited to specific reservoir type. As a matter of fact, Pirayesh et al. (2012) applied this innovative concept to a wide variety of conditions and various patterns of injection and proved its positive impact in improving sweep efficiency.
Based on the oil and water saturation distributions along streamlines of reservoirs with and without a barrier-fracture, a barrier-fracture has the ability to modify flow profile and divert the displacing fluid.
Barrier-fractures can help to improve recovery by delaying water-breakthrough and improving the volumetric sweep efficiency of a water-flooding project.
Oil production can be increased and water production decreased as a result of introducing a barrier-fracture into a reservoir that was under water-flooding.
Models with longer barrier-fractures show better performance than those with shorter ones.
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