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Collaborating Authors
Fluid Characterization
Abstract Thermal compositional simulation can be challenging when narrow-boiling behavior is involved. The term โnarrow-boilingโ is used in the literature to refer to enthalpy that is sensitive to temperature. This paper presents an analysis of narrow-boiling behavior on the basis of multiphase isenthalpic-flash equations, where energy and phase behavior equations are coupled through the temperature dependency of K values. The Peng-Robinson equation of state is the thermodynamic model used in the analysis. The general condition for narrow-boiling behavior is that the interplay between energy balance and phase behavior is significant. This is realized in engineering computations, such as flash calculations and reservoir simulation, as the sensitivity of K values to temperature. Two subsets of the condition are derived by analyzing the convex function whose gradient vectors consist of the Rachford-Rice equations; (i) the overall composition is near an edge of composition space, and (ii) the solution conditions (temperature, pressure, and overall composition) are near a critical point, including a critical endpoint. A special case of the first specific condition is the fluids with one degree of freedom, for which enthalpy is discontinuous in temperature. Case studies are given to confirm the narrow-boiling conditions for water-containing hydrocarbon mixtures. Narrow-boiling behavior tends to occur in thermal compositional simulation likely because water is by far the most dominant component in the fluid systems formed in the simulation. K values can be sensitive to temperature for those fluids with skewed concentration distributions. Decoupling of temperature from the other variables is confirmed to be robust in isenthalpic flash for narrow-boiling fluids.
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Phase behavior and PVT measurements (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Fluid modeling, equations of state (1.00)
Abstract Adaptive implicit petroleum reservoir simulations result in huge, often very ill-conditioned linear systems of equations. The full system contains characteristics of both hyperbolic and nearly elliptic sub-systems. Traditional single stage solvers, such as variable degree ILU, are often not efficient at converging nearly elliptic, such as pressure, components. Therefore, multi-stage preconditioning methods, such as the constrained pressure residual (CPR) method, are a popular approach to โdivide and conquerโ coupled systems. An algebraic multigrid (AMG) method provides a technique to efficiently solve suitably extracted, nearly elliptic sub-systems as a first stage of CPR. An ILU technique can then be used as a second stage. The primary objective of an efficient two-stage preconditioning strategy consists of extracting nearly elliptic sub-systems that are suitable for an efficient AMG solution while simultaneously ensuring a fast overall convergence of the two-stage approach. This research aims to modify and apply the dynamic row sum preconditioner (DRS) to adaptive-implicit black oil simulation. This extends the original DRS preconditioner (Gries, et al., 2014), which was developed in consideration of individual matrices. Physical weighting is incorporated into the DRS technique using additional information determined during Jacobian building. The adaptive-implicit formulation produces explicit saturation terms that are fully coupled to pressure from the same location. The DRS method is modified to approximately decouple such saturations so that they may be excluded from the iterative matrix solution. The multi-coloured ordering of variables is also considered. Such methods have proved efficient by reducing the number of unknowns from the iterative matrix solution. A parallel algorithm using domain decomposition and red-black ordering for the two-stage approach is examined. The effect of increasing the degree of ILU on the boundaries between domains selectively is investigated for the two-stage approach and a single-stage approach. The results of full simulation show significant acceleration using the preconditioned two-stage approach.
- North America > United States > Texas (0.47)
- North America > United States > California (0.28)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Phase behavior and PVT measurements (0.94)
Abstract Simulation technology is constantly evolving to take advantage of the best available computational algorithms and computing hardware. A new technology is being jointly developed by an integrated energy company and a service company to provide a step change to reservoir simulator performance. Multiscale methods have been rapidly developed over the last few years. Multiscale technology promises to improve simulation run time by an order of magnitude compared to current simulator performance in traditional reservoir engineering workflows. Following that trend, an integrated energy company and a service company have been working in collaboration on a multiscale algorithm that significantly increases performance of reservoir simulators. The numerical accuracy of a reservoir simulation depends on the accuracy of each stage in the simulation algorithm. However, not all internal computations require the same amount of work to maintain stability and reach the necessary level of accuracy. In particular, pressure and transport calculations are governed by different physics and tend to have quite different numerical characteristics. A multiscale black-oil reservoir simulation technology has been recently developed and implemented in a reservoir simulator used by the industry. It is based on a sequential fully implicit formulation. Compared to the regular fully implicit formulation, this formulation adds flexibility in the choice of solution strategy for reservoir pressure and transport. The reservoir pressure is computed by a multiscale algorithm that uses coarsening combined with reconstruction of fine-scale mass-conservative fluxes. The transport of fluid is computed using a Schwarz overlapping method based on the coarsening for the pressure solution. The multiscale method has proved to be accurate and reliable for large real-data models. The new solver is capable of solving very large models an order-of-magnitude faster than the current commercial version of the solver.
- North America > United States > Texas (0.68)
- Europe (0.68)
- Reservoir Description and Dynamics > Reservoir Simulation > Scaling methods (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Phase behavior and PVT measurements (0.95)
Abstract The emergence of sophisticated integrated asset simulation software places radically new demands on our fluid modeling capability. Part of this challenge arises from the different modeling objectives and priorities within the broader group of users of such extended systems. For instance, a reservoir engineer will typically focus on detailed resolution of geological features for accurate flow simulation, requiring large grids while using a simple, isothermal fluid model to capture gross volumetric behavior. A production engineer on the same project team, by contrast, will be concerned with thermal losses in wells and flowlines and the attendant effect on fluid flow properties, as well as the impact of fluid blending in common production facilities. Common to both disciplines is the desire for a simple, fit-for-purpose conceptual Black Oil model that makes use of available data without mandating detailed experimental analyses at differing producing conditions. To facilitate fluid model sharing across discipline boundaries we have developed a Universal Black Oil formalism. In this approach, Black Oil correlations are tuned to existing data in a guided workflow, providing a Black Oil model capable of being used in all parts of an asset model. The problem of fluid blending in co-mingled production systems is treated by converting Black Oil models into analogous two-component compositional representations, and by exploiting weaving under the assumption of ideal mixing. The presence of multiple fluid models within an interconnected asset introduces the need for a set of heterogeneous fluid transformation models that must to be solved simultaneously along with reservoir and production system equations. The validity and limitations of the Black Oil blending approach are discussed in the context of an EOS model characterized to actual PVT data and the flexibility of the overall approach is demonstrated in an integrated simulation example.
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Fluid modeling, equations of state (1.00)
Abstract Black-oil fluid properties are determined by lab measurements or can be calculated through flash calculations of the reservoir fluid. Allowing for a variable bubble-point pressure in black- or volatile-oil models requires a table of fluid properties be extended above the original bubble-point. Reservoir simulation accuracy, however, may be affected by discontinuities in the input data and poor predictions of extrapolated fluid properties. Common practice is to add surface gas to the original oil in the lab and increase the pressure to reach a new bubble-point. Another approach is to use linear extrapolation of oil and gas K-values with pressure on a log-log plot, where K-values are equal to 1.0 at a pseudo-critical or convergence pressure. The latter approach results in discontinuities in the phase behavior. We calculate continuous black-oil fluid properties above the original bubble-point by adding a fraction of the equilibrium gas at one bubble-point pressure to achieve a larger bubble-point pressure. This procedure continues until a critical point is reached at the top of the pseudocomponent pressure-composition diagram. Unlike other methods commonly used or recently proposed, the approach provides a smooth and continuous pressure-composition curve to the critical point. The model further allows for reinjection of produced gas, methane, or CO2 to increase oil recovery for both volatile and black oils. We show how to tune the models to the MMP by matching the appropriate critical point pressure. Further, the approach allows the use of black-oil or volatile-oil properties for tight rocks where capillary pressure alters the saturation pressures by decreasing the bubble-point pressure or increasing the dew-point pressure. Bubble-point pressure in the new model is a function of both capillary pressure (effective pore radius) and gas content. The phase behavior is also described on ternary diagrams for up to four components (water, oil, gas, and CO2 or CH4) and three phases (aqueous, oleic, gaseous) to allow for miscible and immiscible injection (or soaking) of various gases. The new phase behavior could be easily incorporated in a compositionally-extended black- or volatile-oil simulator. The approach could also be extended to model gas condensate reservoirs with or without gas injection and capillary pressure.
- North America > United States > North Dakota > Sanish Field > Bakken Shale Formation (0.99)
- North America > United States > Montana > Williston Basin > Elm Coulee Field > Bakken Shale Formation > Middle Bakken Shale Formation (0.99)
- North America > United States > South Dakota > Williston Basin > Bakken Shale Formation (0.98)
- (2 more...)
Abstract Large-scale reservoir simulation is still a big challenge due to the difficulty of solving linear systems resulted from the Newton methods. For black oil simulation, more than 90% of running time is spent on the solution of linear systems. The problem is getting worse when developing parallel reservoir simulators using parallel distributed systems with tens of thousands of CPUs. Efficient linear solvers and preconditioners are critical to the development of parallel reservoir simulators. Here we introduce our work on developing parallel preconditioners for highly heterogeneous reservoir simulations. A family of new Constrained Pressure Residual (CPR)-like preconditioners and advanced matrix pre-processing techniques are developed, including two new three-stage preconditioners and one four-stage preconditioners. A pressure system is solved by an algebraic multi-grid method, and the entire linear system is solved by the restricted additive Schwarz (RAS) method (one of the domain decomposition methods). To overcome a convective issue in reservoir simulation, a parallel potential-based matrix reordering method is employed to stabilize our preconditioners. Matrix decoupling methods, such as an alternative block factorization (ABF) strategy and a Quasi-IMPES (implicit pressure explicit saturation) strategy, are also applied. With the restricted additive Schwarz and algebraic multi-grid methods, our preconditioners have good scalability for parallel computers. Our preconditioners have been applied to oil-water and black oil benchmark simulations. For the SPE 10 project, which is a big challenge for a linear solver because of highly heterogeneous permeability and porosity, our preconditioners with the GMRES linear solver are stable and efficient. When using 64 CPUs, the number of iterations of our linear solvers is less than 40. When applying our method to a standard black oil simulation with 100 millions of grid blocks, the number of iterations of our linear solvers is only two using 3,072 CPU cores. Our numerical experiments show that our preconditioners and linear solvers are stable with a large number of CPUs and are efficient for highly heterogeneous simulations.
- Europe (0.67)
- Asia > Middle East (0.67)
- North America > United States > Texas > Harris County > Houston (0.28)
- Europe > Norway > North Sea > Tarbert Formation (0.99)
- Europe > Germany > North Sea > Tarbert Formation (0.99)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Phase behavior and PVT measurements (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Waterflooding (0.67)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (0.66)
Abstract Low-temperature oil displacement by enriched-gas or carbon-dioxide (CO2) can exhibit multiphase flow of three hydrocarbon phases; the oleic (L1), solvent-rich liquid (L2), and gaseous (V) phases. The L2 phase can play a significant role in these oil recovery processes. Recently, oil displacement by three hydrocarbon phases was explained on the basis of interphase mass transfer on phase transitions in multiphase flow. However, systematic investigation into the complex interplay between phase behavior and mobilities has been hindered by issues in multiphase compositional flow simulation, such as incorrect phase identification. This paper presents the effect of relative permeability on oil displacement by three hydrocarbon phases. A new method for robust phase identification is developed and implemented in a 1D convective flow simulator with no volume change on mixing. The new method uses tie triangles and their normal unit vectors tabulated as part of the simulation input information. The extensions of the limiting tie triangles at the upper and lower critical endpoints (UCEP and LCEP) define three different regions in composition space; the super-UCEP, super-LCEP, and sub-CEP regions. The method can properly recognize five different two-phase regions surrounding the three-phase region; the two two-phase regions that are super-CEP, and the three different two-phase regions that originate with the corresponding edges of the three-phase region in the sub-CEP region. Multiphase behavior calculations are conducted rigorously by use of the Peng-Robinson equation of state with the van der Waals mixing rules during the flow simulation. Simulation case studies are presented with a quaternary model and the West Sak oil model with 15 components. Results show that the phase-identification method developed in this research can correctly solve for phase identities in three-hydrocarbon-phase flow simulation. The method can quantify the relative location of the current overall composition to the three-phase region in composition space. Simulation results are analyzed by use of the distance parameters that describe interphase mass transfer on multiphase transitions in oil displacement. In the case study for the West Sak oil displacement, the analysis confirms that the miscibility level of oil displacement increases with increasing methane dilution. The effect of relative permeability diminishes as the miscibility level increases owing to methane dilution. The distance parameters can properly represent the interaction of phase behavior and mobilities since they are derived from mass conservation, not only from thermodynamic conditions.
- Research Report > New Finding (0.87)
- Research Report > Experimental Study (0.86)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- (21 more...)
Abstract Oil shale, which comprises abundant organic matter called kerogen, is a vast energy source. Pyrolysis of kerogen in oil shales releases recoverable hydrocarbons. Here we describe the pyrolysis of kerogen using an in-situ upgrading process, which is applicable to the majority of oil shales. The pyrolysis is represented by six kinetic reactions resulting in 10 components and four phases. Expanding Texas A&M Flow and Transport Simulator (TAMU FTSim), which is a variant of TOUGH+ simulator (Moridis and Freeman 2014), we develop a fully functional capability that describes kerogen pyrolysis and accompanying system changes with a minimum of simplifications and assumptions. The simulator describes coupled process of mass transport and heat flow through porous and fractured media and includes all known physics and chemistry of reservoir systems. The simulator involves a total of 15 thermophysical states and all transitions between them and computes a simultaneous solution of 11 mass and energy balance equations per element. The simulator solves the equations in a fully implicit manner by solving Jacobian matrix equations using Newton-Raphson iteration method. To conduct a realistic simulation, we account for geological structure of oil shale reservoirs and physical properties of bulk oil shale rock. In addition, we consider interaction between fluids and porous media, diverse equations of state for computation of fluid properties, and numerical modeling of fractured media. We intensively validate the simulator by reproducing the field production data from Shell In-situ Conversion Process implemented in Green River Formation. We conduct sensitivity analyses of diverse reservoir parameters, such as presence or absence of a pre-existing fracture system, oil shale grade, permeability of the pre-existing fracture system, and thermal conductivity of a reservoir formation. We analyze the effects of the reservoir parameters on productivity and find a model that shows a similar production rate curve to the field production. The simulator is successfully validated and provides a powerful tool to evaluate effectiveness of in-situ upgrading processes and corresponding amount of recoverable hydrocarbons.
- Geology > Rock Type > Sedimentary Rock > Organic-Rich Rock > Oil Shale (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- Government > Regional Government > North America Government > United States Government (0.46)
- North America > United States > Texas > Sabinas - Rio Grande Basin > Colorado Field (0.99)
- North America > United States > Gulf of Mexico > Central GOM > East Gulf Coast Tertiary Basin > Ship Shoal South Addition > Block 359 > Mahogany Field (0.99)
- North America > United States > Gulf of Mexico > Central GOM > East Gulf Coast Tertiary Basin > Ship Shoal South Addition > Block 349 > Mahogany Field (0.99)
- North America > United States > Colorado > Piceance Basin > Williams Fork Formation (0.99)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale oil (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Fluid Characterization (1.00)
- Facilities Design, Construction and Operation > Processing Systems and Design > Heavy oil upgrading (1.00)
A Unified Finite Difference Model for The Simulation of Transient Flow in Naturally Fractured Carbonate Karst Reservoirs
He, Jie (Texas A&M University) | Killough, John E. (Texas A&M University) | Fadlelmula F., Mohamed M. (Texas A&M University at Qatar) | Fraim, Michael (Texas A&M University at Qatar)
Abstract The presence of cavities connected by fracture networks at multiple levels make the simulation of fluid flow in naturally fractured carbonate karst reservoirs a challenging problem. The challenge arises in properly treating the Darcy and non-Darcy flow in the different areas of fractured medium. In this paper, we present a single-phase transient flow model which is based on the Stokes-Brinkman equation and a generalized material balance equation. The generalized material balance equation proves to be exact in both cavities and porous media, and the Stokes-Brinkman equation mathematically combines Darcy and Stokes flow, thus allowing a seamless transition between the cavities and porous media with only minor amounts of perturbation introduced into the solutions. Finite differences are implemented for the solution of the proposed transient flow model. This solution method provides a smooth transition from standard multiple-porosity/permeability reservoir simulators and moreover, it is physically more straightforward, mathematically easier to derive and implement, and more apt to generalization from two-dimensional to three-dimensional cases than alternative techniques. Application of the derived transient flow model is shown by examples of three fine-scale 2-D geological models. The first two models, although simple, provide verification of the proposed transient flow model. The third example presents a more complex and realistic geological model derived from multiple-point statistics simulation technique with the second model used as the training image. The results of the third model form the foundation for future study of multi-phase and 3-D reservoir cases.
- Europe (1.00)
- North America > United States > Texas (0.69)
- Asia > Middle East (0.68)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Naturally-fractured reservoirs (1.00)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Carbonate reservoirs (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- (3 more...)
Modeling from Reservoir to Export: A Compositional Approach for Integrated Asset Model of Different Gas Fields in North Kuwait Jurassic Carbonate Reservoirs
Torrens, Richard (Schlumberger) | Daoud, Ahmed (Schlumberger) | Amari, Mustafa (Schlumberger) | Sharifzadeh, Ahmad (Schlumberger) | Prakash, Roshan (Schlumberger) | Al-Enzi, Bashayer (Kuwait Oil Company) | Dashti, Qasem (Kuwait Oil Company)
Abstract A project was undertaken to construct an overview to build an integrated asset model (IAM) of an onshore fractured carbonate gas condensate and volatile oil asset in Northern Kuwait that is considered the first gas asset discovered in Kuwait. The asset has the potential to produce from six distributed fields producing from four hydrocarbon-bearing structures. The development strategy calls for extensive drilling and facilities expansion to increase and sustain production with the potential addition of depletion compression to further sustain the plateau. Because the reservoirs are highly compartmentalized, they are split into 19 separate models. Production is through three surface facilities, fluids vary significantly across the field from sour gas condensate to volatile oil, and it is important to consider the impact of reservoir deliverability, facilities capacity, and surface backpressure when evaluating different development scenarios. A novel IAM was constructed that integrates reservoirs, wells, pipelines, and facilities models into an integration platform. The IAM comprises 19 black oil dual porosity reservoir models coupled to a compositional network model via black oil delumping to convert the subsurface rates into six-components composition. A split table (compositional delumping) is then used to convert the six-components composition to 35 surface components to be used in the equation-of-state (EOS) surface network models to estimate the composition at each point at the surface (inlet and outlet of each facility). Then the network model is coupled to surface facilities modeling to estimate the rates and composition at the export level. This idea of mapping the subsurface fluid from black oil at subsurface to compositional at surface reduces the subsurface running time and makes the IAM more feasible from the running time perspective. The IAM has highlighted several differences versus the stand-alone modeling and the coupled modeling at the surface only. First, more accurate accounting for backpressure results in an increase in the plateau. Second, a production forecast for each facility gives a detailed analysis of production and the number of wells for each facility. Finally, detailed compositional information becomes available at all points in the surface network, which is important input to the facilities design.
- Asia > Middle East > Kuwait > Jahra Governorate > Arabian Basin > Widyan Basin > North Kuwait Jurassic (NKJ) Fields > Marrat Formation > Upper Marrat Formation (0.98)
- Asia > Middle East > Kuwait > Jahra Governorate > Arabian Basin > Widyan Basin > North Kuwait Jurassic (NKJ) Fields > Marrat Formation > Sargelu Formation (0.98)