Reservoir simulation studies allow engineers to identify development strategies that can maximize the economic recovery of hydrocarbon resources. During the life of a field, the reservoir engineer confronts numerous decisions about the placement and operation of wells for production or injection. Traditionally, engineers evaluate the costs and benefits associated with competing development strategies by using reservoir simulation tools in different scenarios. The numerical reservoir simulator is used in forecasting recovery of reserves under existing production schemes and in evaluating the effects of changing operating conditions. This approach limits the overall benefit of a field study because it does not consider the effects that flows in wellbores and surface facilities have on the economic performance of the recovery programs. Yet, the costs of compression, separation, fluid injection, or water treatment can have a significant effect on the success of production strategies. When the goal is to identify an optimal hydrocarbon-recovery scheme, a compelling case arises for the computational integration of subsurfaceand surface-simulation technologies.In this paper, we discuss an integrated computational solution for management of reservoir-production strategies and field development. The approach is implemented by coupling a reservoir simulator (Eclipse) with a surface and production network simulator and optimizer (Netopt). Parallel Virtual Machine (PVM) interface provides the coupling communication mechanism that integrates the two simultaneously running simulators. At each timestep, the network simulator and the reservoir simulator provide a consistent integrated solution after rate and pressure convergence is achieved within a predetermined tolerance.
Summary A theoretical and experimental study was performed to develop a dynamic model for gas-passage performance of a 1.5-in., nitrogen-charged, bellows-operated gas-lift valve. Performance curves were obtained by using air for 0.25 and 0.50-in. ports with flow rates reaching 2.5 MMscf/D. Internal pressures and temperatures were measured during flow-performance tests to develop a dynamic model for both orifice and throttling flow. Introduction To design an efficient gas-lift installation, the production engineer needs reliable information on the performance of all system components, from the outer boundary of the reservoir to the separator. One critical component is the gas-lift valve. In a producing system, the gas-lift valve controls the point of entry of producing system, the gas-lift valve controls the point of entry of compressed gas into the production string and acts as a pressure regulator while the injection gas is controlled at the surface choke. During the unloading process, the behavior of gas-lift valves becomes the primary factor for reaching optimum single-point gas injection depth. Injection pressure-operated valves are the most commonly used continuous-flow gas-lift valves. They consist of a nitrogen-charged dome and bellows assembly connected to a stem and ball that seat on a port (Fig. 1). The performance curves of injection pressure-sensitive valves show two distinct flow regions (Fig. 2). In the orifice flow region, at a constant injection pressure, the flow rate increases as downstream pressure decreases during subcritical flow, but eventually critical flow occurs, where flow rate remains constant despite further decreases in downstream pressure. On the other hand, in the throttling flow region, at a constant injection pressure, the flow rate increases with decreasing downstream pressure, the flow rate increases with decreasing downstream pressure until it reaches a maximum and then decreases with pressure until it reaches a maximum and then decreases with decreasing downstream pressure. For a given port size, the occurrence of orifice or throttling flow depends mainly on the relative magnitudes of the nitrogen pressure in the dome and the injection pressure. One way to obtain reliable data in orifice and throttling flow regions is to perform flow-performance tests on the gas-lift valves currently available with the valve treated as a black box and volumetric flow rates reported as a function of valve-setting parameters and the differential pressure across the valve. This parameters and the differential pressure across the valve. This data-acquisition method is extremely time-consuming because of the combination of parameters affecting gas-passage performance of a valve. Modeling the valve on physics principles allows a significant reduction in the number of tests needed to characterize valve performance. This study investigates pressure and temperature distribution within the valve, internal valve mechanism, and forces acting on internal elements of the valve, The paper explains the nature of the experimental data and results obtained, defines the important parameters that affect valve performance, and provides a model for parameters that affect valve performance, and provides a model for both orifice and throttling flow regions.
Management of field development, and determining optimum operating plan requires reliable information on the pressure and rate behavior of the formation as well as on the performance, stability and deliverability of the surface & production network. The integration of the network simulator and optimizer. NETOPT with the reservoir simulator. ECLIPSE uses a robust convergence procedure in an interface that tightly couples the two simulators. The integrated solution becomes an effective tool for field development and management. Introduction Field-wide planning over the life of a reservoir has been the subject of considerable interest in recent years. The ability to rigorously incorporate the effect of surface multiphase piping networks, as well as changing compression, separation and pumping facilities is critical for the accurate simulation of reservoir behavior for planning purposes. The development of an interface that integrates a production & surface network simulator with a reservoir simulator is the initial step in creating a field-wide planning tool. In the integrated solution, the network simulator determines the behavior of production or injection wells in the wellbore and through surface facilities while accounting for reservoir behavior over time. The reservoir simulator characterizes the fluid flow, saturation and pressure behavior within the formation, and determines the inflow performance (IPR) for each well at its grid block. An equivalent IPR is also incorporated in the network simulator. Individual well controls are implemented on the network side, however additional controls such as a drilling schedule may be imposed by the reservoir model. The network simulator honors production allocations or group controls that currently reside in the reservoir model. Integrated Simulation Approach The two programs are integrated using Parallel Virtual Machine (PVM) interface, eliminating any necessity for file exchange. The network simulator acts as the master program sending and receiving messages that include flow rates and pressures for each well in the integrated simulation model. Reservoir simulator provides local inflow performance data as a function of flow rate, sandface pressure and cell block pressure within the time step, which determine the boundary conditions to simulate the production systems & surface facility performance. Simulation advances a time step after rate & pressure convergence is achieved within the predetermined tolerance. Field Study In this field application, the reservoir simulator has a 20x15x8 grid with 1639 active cells. A total of 19 wells are included in this grid (Fig. 1). 8 wells are water injectors. 1 well is a gas injector, and 10 wells are producers. All producers are included in the network simulation; injectors are not tied into a surface network however they do affect the production network since performance of the producers are tied to the overall flow and pressure behavior within the reservoir. The wells are on a drilling schedule. Initially, there is only one well flowing. The remaining wells come into production in 2 to 6 month intervals. Two of the wells, LU1 and LU2 which are the first two to be producing, have a maximum water-cut limit of 70%. In addition, wells can be shut in by the reservoir simulator due to economic limits. The simulation runs to 1720 days (4.7 years) under surface network control. There are no chokes or other flow constraining devices in production network. (Fig. 2). P. 285^
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