No abstract

Relative permeability and capillary pressure are important parameters in reservoir engineering calculations and numerical simulation of reservoir performance. Heterogeneities are often avoided during core plug screening and selection for relative permeability and capillary pressure measurements. However, sandstone rocks in many depositional environments show significant small-scale laminations that affect the measured relative permeability. This report demonstrates the length scale dependence of relative permeability data that results from cm to mm scale rock laminations and patterns of initial and final oil saturation distribution in a 108-cm long laminated core. It shows in a quantitative way the capillary, trapping of water in low-permeability lamina during primary drainage and of oil in high permeability lamina during water imbibition. Steady-state water-oil imbibition relative permeability data and unsteady-state drainage and imbibition data were collected using linear x-ray and x-ray CT scanning for in situ fluid saturation measurement. Numerical simulations of the core floods show that relative permeabilities and capillary pressures that are correlated with small-scale differences in porosity and permeability are necessary to reproduce the observed saturation distributions. Thus, the relative permeability length-scale dependence, combined with anisotropy data presented elsewhere (SPE 27968), imply that scaled-up effective relative permeability must account properly for heterogeneity. Assignment of core-plug relative permeability to simulator grid blocks may not capture the correct effective fluid flow performance in rocks that are heterogeneous with correlation length greater than the plug dimensions, and thus lead to erroneous fluid flow performance predictions. Using numerical simulation, a number of papers have reported the importance of core-scale geologic heterogeneity to relative permeability and its ultimate impact to the prediction of reservoir-scale flow behavior. Jones et al. showed that within a channel environment the choice of relative permeability scale-up from small-scale to larger scales is very important to accurate prediction of water breakthrough and produced oil volume and is more important than are spatial arrangement of lithofacies within channel sandbodies.

No abstract

No abstract

Preserving inter-actor connectivity is essential in most wireless sensor and actor network (WSAN) applications as nodes have to collaborate and coordinate their actions against the events reported by the sensors. However, failure of a critical (i.e., cut-vertex) node partitions inter-actor network into disjoint segments and thus hinder network operation. The prime objective of this paper is to analyze the performance of reactive connectivity restoration algorithms for delay-tolerant WSAN applications. First, we provide insights to the state-of-the-art reactive connectivity restoration algorithms. Then, we present a Nearest Non-critical Neighbor (NNN) algorithm; a localized and distributed reactive approach for reconnecting network partitions. In NNN, each actor periodically determine its criticality (i.e., cut-vertex or not) based on 2-hop information and exchange with its neighbors. In case of a critical actor failure, the neighbors detect and trigger a connectivity restoration procedure that involves controlled and coordinated node relocation. NNN prefer to displace non-critical nodes during relocation in order to minimize recovery overhead in terms of distance movement and message coordination. We analyze the performance of reactive schemes through theoretical analysis and simulations. Index TermsSensor and actor networks, node segregation, connectivity restoration, reactive schemes, controlled and coordinated relocation. I.INTRODUCTIONRecent emergence of wireless sensor and actor networks (WSAN) [1] have enabled plethora of novel applications that require autonomous and intelligent interaction with the environment. Examples of such application range from military (e.g., battlefield reconnaissance, homeland security) to civilian (e.g., disaster management, fire detection and containment). In these applications, WSAN employ tiny and low-powered sensors to report an event of interest to one or multiple actors. The corresponding actors process the received data and interact with each other on planning an appropriate action against the event. Therefore, actor's stature is pivotal in handling events such as invasion and disaster.In most applications, successful operation of WSAN primarily depends on inter-actor coordination. Therefore, maintaining inter-actor connectivity is indispensable because actors have to collaborate and coordinate with each other on planning an optimal response and synchronize their actions. Nonetheless, harsh operational conditions may cause failure of an actor which may disrupt inter-actor connectivity and hence hinder network operation. An obvious solution is to replace the failed actor with a spare one. However, this solution may not be practical in harsh and inhospitable application environment as mission-critical applications may not tolerate such failure for a long time. Furthermore, autonomous and unattended operation of WSAN necessitates instantaneous self-healing recovery mechanism which can only be provided through existing actor's relocation. However, random movement of nodes may...

The expanding trend of wind power technology motivates scholars to pursue more investigation on optimising energy extraction from the wind and integrating high-quality power into the utility grid. This paper is aimed at introducing a novel application of the sine cosine algorithm (SCA) which attempts to find the optimal gains of proportional-integral (PI) controllers used to control the power electronic converter (PEC) equipped with the Variable speed Wind turbine (VSWT) such that a maximum power extraction and performance enhancement can be realized. The PEC equipped with the VSWT combines a machine side converter (MSC) and a grid-side inverter (GSI). Both the MSC and GSI are controlled by the proposed SCA-based PI controllers through cascaded vector control schemes. The MSC is responsible for controlling the wind generator's rotational speed, active power, and reactive power. The GSI is used to regulate the dc-link voltage and to keep the terminal voltage at the desired frame set by the operator. To obtain the optimum PI gains, the SCA is applied to minimize the sum of the integral squared error (ISE) of twelve PI controllers error inputs in the control schemes simultaneously. Performances of the proposed SCA-PI control schemes are assessed under severe grid disturbance and random wind speed variation to mimic more realistic conditions. The effectiveness of the proposed SCA-PI is verified in the MATLAB/Simulink environment, and the results are compared to those obtained using a grey wolf optimizer and particle swarm algorithm-based optimal PI controller. The simulation findings confirm the SCA-PI can be regarded as an efficacious way to enhance the performance of the VSWT.INDEX TERMS Wind turbine control, power electronic converter, MPPT, PMSG, PI controller, sine cosine algorithm.

An improved compositional chemical-flood simulator has been used for the study of the low-tension pilot project at the Big Muddy field near Casper, WY. Both the tracer injection conducted before injection of the chemical slug and the chemicalflooding stages of the pilot project were analyzed. Using a compositional simulator, we successfully matched not only the oil recovery but also the tracers, polymer, alcohol, and chloride histories.Simulation results indicate that for this freshwater reservoir, the salinity gradient during preflush and the resulting calcium pickup by the surfactant slug played a major role in project success. In addition, the analysis of the effects of crossflow on the performance of the pilot project indicate that for the well spacing of the pilot, crossflow does not playas important a role as it might for a large-scale project. Brief Description of the SimulatorUTCHEM is an isothermal, slightll. compressible, chemicalflooding compositional simulator. 6-,16 In the simulator, the material-balance equations are solved for up to 19 components: water, oil, surfactant, polymer, anions, divalent cations, Cosurfactant 1, Cosurfactant 2, water tracer, partitioning tracer, oil tracer, sodium dichromate, thiourea, trivalent chromium, gel, hydrogen, carbon, and organic acid species. Monovalent cations are given by electroneutrality condition. These components may form up to three phases-aqueous, oleic, and microemulsion-depending on relative amounts and effective salinity of the phase environment. The major physical phenomena modeled in the simulator are phase density, phase viscosity, phase behavior, dispersion, dilution effects, adsorption, interfacial tension (1FT), relative permeabilities, capillary pressure, capillary phase trapping, cation exchange, alcohol partitioning (constant or variable), and polymer properties such as permeability reduction, inaccessible PV, and shear-thinning effects.The solution scheme used is analogous to implicit pressure, explicit saturation formulation. First, the pressure equation is solved implicitly for the phase pressures and velocities using explicit dating of saturation-dependent terms. Then the conservation equations are solved explicitly for total concentrations. Phase concentrations and saturations are obtained by flash calculations. SPE Reservoir Engineering, February 1989

Summary. A third-order differencing scheme is described and compared with several alternative methods commonly used to discretize the convection term of the component conservation equations of compositional simulators. This new method can be used at higher cell Peclet numbers than those used with other common convective differencing schemes, resulting in a substantial saving in computation time for the same accuracy. Comparisons are made with analytical solutions and with 2D and 3D simulation results for single-point upstream weighting, two-point upstream weighting, and Chaudhari's method. Introduction Because many EOR methods are slug processes, dispersive mixing and crossflow significantly affect performance. Proper numerical modeling and prediction of these effects are essential for successful design and evaluation of both laboratory corefloods and field-scale projects. In chemical flooding, a slug process where a small amount of oil-mobilizing chemical is injected into the formation, mixing and crossflow phenomena are especially important to performance and need to be modeled accurately. This mixing is characterized by dispersion in both the longitudinal and transverse directions. Interaction of dispersion and phase behavior may cause the chemical slug to lose its effectiveness through several mechanisms (e.g., phase trapping or changes in phase environment). In other cases, dispersion may enhance oil recovery efficiency by increasing the sweep efficiency (e.g., unfavorable mobility ratio and/or flow in layered reservoirs). Dispersion may also lead to early breakdown of the surfactant slug, depending on the relative position of the different banks that may develop during micellar/ polymer flooding. In 3D simulation of tracer studies conducted before and during many EOR projects for reservoir characterization, one main objective is to determine the levels of physical dispersion and heterogeneity, which in practice are coupled on a reservoir scale. In numerical reservoir simulation, however, artificial numerical dispersion can further smear concentration fronts by increasing the level of dispersion, resulting in inaccurate predictions of recoveries and breakthrough times. In 1D simulation of corefloods where recovery mechanisms may be under investigation, the interaction of artificial numerical dispersion and phase behavior may result in erroneous conclusions. Therefore, accurate simulation of physical processes involved in EOR requires that numerical dispersion be essentially eliminated. One would like to simulate physical dispersion accurately in a controllable manner through information entered into the simulator with no additional numerical dispersion, practical grid sizes, and results that are independent of grid orientation. The artificial numerical dispersion encountered in compositional reservoir simulation results primarily from the truncation error of the finitedifference operator used to discretize the first-order spatial derivative of the convection term. On the other hand, for convectiondominated flow, at high Peclet numbers, the hyperbolic nature of the equations can produce oscillatory solutions. It is well known that lower-order methods, such as single-point up weighing, suffer from excessive numerical dispersion and grid orientation effects. For these reasons, other methods have been proposed, such as the method of characteristics and flux-correction schemes. Because of either the complexity of the solution algorithm or the computer requirements (both storage and run time), however, such methods have not been popular in compositional reservoir simulation of EOR processes. A simpler second-order explicit method, proposed by Chaudhari and implemented in the U. of Texas chemical flooding simulator (UTCHEM), produces accurate results for chemical flooding applications when the cell Peclet number is kept below two; this is, unfortunately, a limiting factor in large-scale or convection-dominated applications. At low cell Peclet numbers, the two-point upstream-weighting method produces more accurate results than single-point upstream weighting; however, at higher cell Peclet numbers (convection-dominated flows), it also produces inaccurate results. A third order convective differencing scheme based on Leonard's method is presented that is especially suited for convection-dominated flow problems and that results in a minimal increase m storage or computation time per timestep per gridblock compared with lower-order methods. This method, the "higher-order method," has been generalized to variable velocities in three dimensions with complex phase behavior and even allows for a variable number of phases locally. Comparison of the results of four differencing schemes (single-point upstream weighting, two-point ups weighting, Chaudhari's numerical dispersion control, and the higher-order method) used with UTCHEM for tracer flow, waterflooding, polymer flooding, and micellar/polymer flooding is presented. Mathematical Formulation The following differencing equations are suggested by Leonard: for u >0, deltaC Ci+1-Ci-l Ci+l-3Ci+3Ci-l-Ci-2 u ------ =u ------------------------------- deltaX i 2delta Xi 6delta Xi 1 delta 4C ---------------delta Xi3+H).........................(1a) 12 delta X4 and for u less than 0, delta C Ci+1-Ci-1 Ci+2-3Ci+1+3Ci-Ci-1 U------ =u --------------------------------- delta X i 2delta Xi 6 delta Xi 1 delta 4C +------------- delta Xi3+H)...........................(1b) 12 delta X4 These differencing equations can be thought of as being equivalent to central differencing with an upstream-weighted correction factor. They can be derived by Taylor series expansion for the first-order spatial derivative of concentration. This method is also equivalent to a weighted average of the two-point upstream-weighting method and central-differencing method with weighting coefficients of 1/3 and 2/3, respectively. The above equations are for constant velocity and constant grid sizes. In compositional simulation, the differencing operator for the first-order spatial derivative of the convection term can generally be defined as delta uC Ui+ 1/2 Ci+ 1/2 -Ui-1/2 Ci-1/2 -------- = ---------------------------------..............(2) delta X i delta Xi The component and phase radices are not shown. SPERE P. 623⁁