Severe slug flow (Le., terrain-dominated slug flow) was studied in a simulated offshore pipeline riser-pipe system. Severe slug flow is characterized by extremely long liquid slugs generated at the base of the vertical riser. This phenomenon occurs at low gas and liquid flow rates and for negative pipeline inclinations. Slugging in some offshore platforms has required the use of operating procedures that drastically curtail production. Losses in flow capacity up to 50% have been reported.A hydrodynamic model has been developed for severe slug flow. The model's predictions agree with experimental data. The model can be used to design new pipeline riser-pipe systems or to adjust the operation of existing systems to prevent the occurrence of severe slug flow. Also, a flow-regime map is presented for predicting the severe slug flow regime, where the boundaries are determined analytically. Finally, additional methods are proposed to prevent the flooding of separation facilities by riser-pipe generated slugs. This study is an extension of Ref. 1, in which severe slug flow was introduced and was only partially modeled.
fu a major oilfield in Saudi Arabia, detailed information on individual well performance is required for operating and planning purposes. Complete design data can only be obtainec through the addition of extensive facilities for periodic testing of all production in the field. Three different test pipeline configurations were considered. Variables included pipe diameter, pipeline length, oil production rate, gas/liquid ratio, and water cut. Lengths up to approximately 10 miles (16.1 km) were considered.Three types of analyses were undertaken. The first analysiS included steady state pressure losses in not only the horizontal segments but also the vertical risers and downcomers at the platforms. The second set of calculations involved transient simulations for both increasing and decreasing flow rates. Finally, slug length and period predictions for both normal and "severe" slugging were performed. Severe slugging occurs only at very low flow rates and negative pipeline inclination angles.Pressure drop calculations based on the Beggs & Brill correlation, with a rough pipe friction factor, showed that 10 in. (25.4 cm) pipelines were adequate for most cases. Transient simulation results indicated that, in general, less than two hours were required to reestablish steady state flow for both increasing and decreasing rates. Normally occurring slugs were found to vary in average length from about 200 to 600 ft (61 to 183m)with a maximum possible slug length of approximately 2500 ft (762 m). Liquid slug lengths for severe slugging varied from about 100 to 800 ft (30.5 to 244 ill depending on the pipeline length and riser height.
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^
Analytical methods for determining the optimal injection rate in a well operating under gas-lift are based on balancing the buoyancy improvements from the additional gas in the production stream with the corresponding increase in the flowline back-pressure and its negative impact on the overall deliverability. Such an approach, while useful in determining the maximum production improvement achievable for an individual well, ignores a number of practical and relatively significant aspects that tend to complicate the real-world application. One of the key aspects to the gas-lift problem is the interaction between the wells in the gathering network. The back-pressure from the additional gas in the flowline adversely affects production from all the other connected wells, whether they be on gas-lift or naturally flowing. As such, the "optimal" gas-lift injection rate determined from existing methods is invariably optimistic. Another major complication arises from the limitations associated with the available compressors. Typically, these facilities are designed to handle the requirements identified when gas-lift is first introduced. As the field depletes, the requirement for lift gas increases. Inevitably, the operator is challenged with the classical gas-lift allocation problem: how does one allocate limited available injection gas to maximize overall field production? Other factors that influence the economics of a field-wide gas-lift strategy include the reservoir depletion behavior, the effect of varying water-cut, the impact of both capital and operating costs, and the detailed performance characteristics of the compressor units. This paper presents a novel approach to the simulation and optimization of the overall gas-lift allocation problem, using a rigorous pressure-balance based multiphase flow network solving technique, coupled with a robust sequential quadratic programming (SQP) approach for the non-linear, constrained optimization. The new technique is applied to typical fieldwide problems, and the results obtained are compared with conventional analyses methods. Introduction Continuous gas-lift is one of the more common forms of artificial lift in oil production. Gas-lift is effective over a wide range of operating conditions, is relatively inexpensive and simple to install, and requires less maintenance especially when compared to alternatives such as electrical submersible pumps. The mechanism of gas-lift is fairly straightforward. Gas at a relatively high pressure in the casing is injected into the tubing string to lighten the fluid column by aeration, until the reduction in the flowing bottom hole pressure creates the pressure differential across the sand face needed to achieve the desired production rate. This paper first analyzes the flow mechanism of a single-well gas-lift system, evaluating the effect of various operating and design parameters. The analysis then proceeds to a two-well system with a common flowline to highlight its significant impact on single well performance parameters. The interaction effects are next extend to a production network with multiple wells sharing a system of common gathering lines leading to separation and treatment facilities at a fixed pressure. Next, the paper addresses the problem of limited gas allocation. The unavailability of adequate injection gas is a common problem with older gas-lift installations. P. 685^
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