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.
TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThe Santa Barbara and Bosque (SB/BSQ) fields, located in northern Monagas State of Venezuela, are in an area of the largest and most prolific oil fields in Venezuela and currently produce nearly 1 million BOPD. The fields are operated by Petroleos de Venezuela, S.A. (PDVSA). The proven SB/BSQ hydrocarbon column is more than 6,000 feet thick. The vertical compositional variation creates a complex system where relatively low yield condensate gas changes to high yield condensate, near critical fluid, volatile oil, and finally, highly under saturated oils. The fluid system is complex and area differences exist because of structural compartmentalization. Original field management strategies identified the high potential of increasing reserves by highpressure injection of produced gas. Modeling of the complex interactions of injected gas is important, thus a full and detailed study of the PVT samples, evaluation of area fluid distributions, and the definition of an equation of state to allow reservoir modeling were performed. This paper documents the effort of 1998 and the ongoing process to define an Equation-of-State (EoS) to meet reservoir modeling and management objectives. The Problem -Santa Barbara and Bosque FieldsThe fields were discovered in 1988 with the drilling of the SBC-001 exploration well. Figure 1 (Santa Barbara Bosque Fields Location) shows the approximate location of the fields in eastern Venezuela. The PIC-001 well was credited for delineating the Bosque area, in the northern
The Orocual field is located in the northern Monagas state of Venezuela and is owned and operated by Petroleos de Venezuela S.A. (PDVSA), the national oil company of Venezuela.Reservoir compartmentalization adds complexity to the field, and structurally equivalent, noncommunicating fluid regions exist. An equation of state (EOS) is needed for reservoir modeling, requiring a review of available data and area fluid distributions.A seven-pseudocomponent EOS with a single characterization defining the compositional gradient of the hydrocarbon column from gas to black oil is defined. The method demonstrates that composition relative to depth can be predicted in those parts of the reservoir in which samples do not exist but in which production and test data must be matched, and thus where a gas-to-oil transition occurs.This paper demonstrates a technique to identify representative samples for use in developing an EOS and for initializing fluids in place. A method is presented to adjust the component composition vs. depth, providing consistent vertical composition distribution and compositional-model stability. This method meets the objectives of matching field production observations. A method is presented to quickly initialize a full-field model using a 1D compositional simulator to give full-field-model stability using the local high-temperature gradient.Results of compositional simulation show a single EOS, and vertical compositional and thermal variation reproduce the complex character of the field hydrocarbon column, matching fieldmeasured observations of saturation pressure (p s ), gas/oil ratio (GOR), and fluid densities.
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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