Numerical modeling of Slug flows in multiphase pipeline system of lion offshore oil fields

Oil and gas transportation by the pipelines among different production wells from one or more reservoirs is one primary part of an oil field development plan. When multiple pipelines transporting oil and gas from different fields are collected on the same Central Processing Platform (CPP) or Floating Production Storage Offloading (FPSO), however, the fluid behavior in multiphase flow pipelines become more complicated and often cause slugging problems that badly impact on downstream facility performance. It is, therefore, necessary to investigate the slug flow to control and/or improve flow stability in the pipeline systems. In this paper, the workflow for building and calibrating a multiphase flow model are described. The numerical model is then applied for the pipeline system of Lion oilfields in Cuu Long Basin, Southern Vietnam. Sensitivity analysis have been performed to investigate the influences of various factors on the slug flow in the pipeline system. The results from this work would be useful for tracking and controlling the slugging effect on the separator performance.


INTRODUCTION
The tie-in development planning is one of the most effective solutions to reduce the cost needed to construct the treatment and storage facilities and/or transportation of petroleum products from small or marginal reservoirs in harsh offshore environment.With this solution, the oil & gas gathered to the wellhead systems from different reservoirs will be transported through subsea pipeline systems to a processing and treatment facilities system at Central Processing Platform (CPP) or Floating Production Storage and Offloading (FPSO).However, there always existsthe problems associated withflow in the pipeline include transient slugging, wax deposition, and hydrates.The task for building the reliable model to predict the impact of these phenomenon on operating offshore production systems is, therefore, essential.
N.E.Burke et all (1993) presented anapproach for history matching the startup conditions measured for a burried offshore North Sea oil flowlineand evaluated effects of PVT fluid, thermal properties in match.The wellhead and platform arrival temperature, pressure, and flow rates were predicted as the production rate varied during startup.These type of datastudy is useful for designing treatment and prevention programs for hydrate and wax deposition in offhore flowlines.In the paper (Y.Tang, T. Danielson, 2006), based on the combination of the slug tracking model with separator gas/liquid PID controllers, the model with a remakably good match of pressure variations, slugging frequency and liquid level was achieved and used for solving the slugging problems at Alpine facility, on the Alaskan North Slope.S.C. Omowunmi et all ( 2013) also described a methodology for characterising slugs based on OGLA slug tracking module and applied this in studies related to dynamic slug control in the Egina deepwater project, West African.
In this study, based on the theory of multiphase flow together with the dynamic multiphase flow simulator, the thermo-hydraulic model for subsea pipeline tie-in system amongLionoil fields at Block 15.1 in Cuu Long Basin, offshore Southern Vietnam is built.Also, the history matching exercise is conductedby tunning model to match the slugging behavior as observerd in the field.

The Multiphase Flow Model Theory
The framework for this study is a twophase flow model developed by (Kjell H.Bendiksen, Dag Maines, Randl Moe, and Sven Nuland, 1991).The model is based on fundamental physics of multiphase flow systems and has the capacity of predicting hydrodynamic slug formation and propagation in two-phase flow by solving five coupled mass-conservation equations, three momentum-conservation equations, and one energy balance equation for a three-phase system.
Mass-Conservation Equations.For gas phase, For liquid phase at pipe wall, For phase transfer between phases, For interfacial mass-transfer rate, SCIENCE & TECHNOLOGY DEVELOPMENT, Vol 19, No.K1-2016

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Where Momentum-Conservation Equations.For gas phase, For liquid phase at pipe wall, For liquid droplets, Where va=vL for Ψg>0 (and evaporation from the liquid film), va=vDfor Ψg>0 (and evaporation from the liquid droplets), va=vg for Ψg<0 (condensation).

Mixture Energy Conservation Equation.
An energy conservation equation for the mixture is derived as follows: Where E is the internal energy per unit mass, H is the enthalpy, h is the elevation, Hsis the enthalpy from mass source, and Q is the heat transfer from the pipe walls.

Modeling Of the Pipeline Connection
System at Block 15.1

A Brief Subsea Pipeline Connection System Description
A typical subsea pipeline connection system at Block 15.1 scheme, as shown in Figure 1, is used in this study.It consist of eight Wellhead Platform, STN-N, STN-S, SDNE, SDSW, SVNE, SVSW, STT, STV which were tied-in through subsea pipelines system and transfer to a Central Processing Platform.

Work Flow for Building Thermo-Hydraulic Model
The typical work flow in the model building began with the understanding of fluids properties.The basis of design document is developed to identify and summarize the design inputs of the facilities.The steps modeling of the subsea pipeline connection system at Block 15.1 using OLGAis performed, as show in Figure 2.
Step 1 -Collection Basic of design data: Collecting the following data and building the model in OLGA as shown in Figure 3.
-Bathymetry data for production pipeline from WHPs to CPP.
-Fluid Properties (e.g.Fluid composition, viscosity, GOR, and water cut).The fluid properties must be defined by PVTsim before input to OLGA.
-Material Properties for the pipeline (e.g.Thermal conductivity, material density, thermal capacity).
-Coating thickness for each pipeline section.
-Environment data (e.g.Seawater temperature, air temperature, seawater and air velocity).
Step 2 -Data Validation &Calibration: Some data from basic of design has changed during production operation, for instance, fluid composition, GOR, and water cut… Therefore, the data need to be corrected or tuned to current condition.

Step 3 -OLGA Validation & Calibration:
This step is done through quality checking on operational conditions such as, the boundary, source, and initial conditions.The boundary conditions specify the actual boundary conditions and any mass sources or sinks along the pipe.The source is a location where the fluid enters the system.The initial conditions group specifies the initial values for pressure, gas volume fraction, total mass flow, and fluid temperature for each section of the pipeline, as shown in Table 1.In this model, a fixed pressure of 296 psig and 140 psig were used as the boundary pressures for the STN and CPP separator gas outlet line, respectively.
Step 4 -OLGA Transient Modeling: In this case study,the Central Processing Platform of Lion fields has recently experienced slugging problems severe which enough to trip the high-high inlet separator level, as shown in Figure 4, cause frequent plant shutdowns and loss production of 80kbbl/d.
To ensure the model has ability capture the mechanisms of slug growth, decay, and merging of slugs, also, reduce the simulation time, the OLGA Slug Tracking model must be appliedsuitably for each flowline.OLGA slug tracking model uses the delay constant to determine the required time delay between generations of slugs in a particular section.Time delay t between new slugs is determined by: Note that the delay constant should be defined based on the actual liquid velocity instead of the superficial fluid velocity.The time delay is inversely related to the slug frequency (FS) and the above equation be rearranged as follows: .
In the OLGA simulation, a default value 150 is used for the delay constant.Shea et al. suggested use the following empirical correlation to check the OLGA predicted frequency to make sure it falls in the reasonable range: 1.2 0.55 0.47 Where, d = pipeline diameter, m; VL = real liquid velocity, m/s; t = time delay, sec; DC = delay constant; Fs=slug frequency, slug/sec.Before using Slug Tracking model, OLGA dynamic simulation is run until a steady-state solution is reached and the flow regime indicator, ID, is examined.If ID=3, indicating slug flow regime, the slug tracking option would be run, if ID=1, indicating stratified flow regime, slug tracking option is not required.Through the results, as shown in Figure 5, the OLGA Slug Tracking model should be usedfor the following flowline such as, WHP_B_TO_WHPA_FLEM (Figure 5B), WHPA_FLEM_TO_CPP (Figure 5C), SVNE_CPP (Figure 5D), and SVSW_CPP (Figure 5E).With the slug tracking option turned on, the simulation is run for additional time (5 hours simulation in this case) and a default DC value of 150 is used for delay constant to check the confidence level model and predict results close to field data.The temperature and pressure trend results, as shown in Figure 6 and Figure 7, is different with the measured data, as shown in Figure 10 and Figure 12, respectively.Therefore, the history matching step need to be performed to confirm the validity and accuracy of the OLGA model.

HISTORY MATCHING
The purpose of the history matching is to validate the models as closely imitating the condition in the field.This work is performed by tuning the models to match field pressures and temperature in the system.An iterative simulation workflow for history matching is shown in Figure 8, with step 1 to step 4 is carried out similarly as presented in section 2.2.2.The step Field Matching will be described in detail below.The parameters which is considered for the history matching include production rates (shown in Table 1, the actual environmental data (shown in Table 2), the pressure and temperature at boundary (shown in Table 3), with the boldedvalue is fixed input data, and italicvalue is used for sensitivity studies to obtain a good match.The delay constant DC in the OLGA slug tracking module is adjusted in order to match the pressure fluctuation as observed in the field.Although the OLGA default delay constant of 150 usually gives reasonable prediction for a single system, it was found that this value gives a too much high slugging frequency for measured system in this study.A delay constant of 2000 to match the measured field with slug frequency (Fs) of 4 slugs/hr (shown in Figure 4).Table 4 shows the comparison between the results from the simulation and the actual field data.From the simulation, it is observed the results obtained from the simulation (after tuning) are well within the error margin (i.e., less than 10%) of the OLGA simulator.Figure 9 to Figure 10illustrate the comparison of temperature trends for the history matching simulations and field data.The temperature variation is approximately 0.2% to 3.5% difference.Meanwhile, the matching results of the pressure is more difficult to obtain than temperature, but it is still in the acceptable range 6% to 8.4% difference (as shown in Figure 11 and Figure 12).

CONCLUSION
On the basis of the multiphase flow model theory, also,understanding of governing factor influencing slugging behavior in operation system, the stepsmodeling and calibrating thermo-hydraulic modelwere described and appliedfor Lion fields in this study.
The boundaries pressure, and delay constant of slugging process is used as the key factors which influence on the slug flow in the pipeline system and quality of model in sensitivity analysis.The results showed that the boundary pressure of STN-S (230 psig), WHP-B inlet (230 psig), and the delay constant of slugging process (2000) are vital to obtaining a good match model.The average difference (i.e., less than 5%) in temperature and (i.e., less than 10%) pressure are considered well within the error limit of the OLGA simulation.This mean that the thermal-hydraulic model developed for the subsea connection system of Lion fields can be used to assess the impact of slugging on surface facility production operations and evaluate the pipeline's thermal and hydraulic performance in the future.

Figure 1 .Figure 2 .
Figure 1.A generic subsea pipeline connection system at Block 15.1

Figure 8 .
Figure 8. Work flow modeling and history matching model

Table 1 .
Input Operational Conditions for Model

Table 4 .
Pressure & Temperature for History Matching