huldra initial experiences in real-time multiphase pipeline modelling

7/23/2019 Huldra Initial Experiences in Real-Time Multiphase Pipeline Modelling

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Huldra: Initial Experiences in Real-Time MultiphasePipeline Modelling

Willy Postvoll, Gassco ASSvein Birger Thaule, Gassco AS

Gunnar Flaten, Statoil AS

Olav Urdahl, Statoil AS

Richard Spiers, Energy Solutions International

Jonathan Barley, Energy Solutions International

Abstract

Operation of the Huldra field commenced on November 2001. It is a gas condensatefield, which has been developed with a not normally manned wellhead platform remotely

controlled from the existing, manned Veslefrikk B platform. A first stage separationprocess is installed at the platform to separate gas and liquid.

Two pipelines emanate from the Huldra field a 93.2-mile [150 Km] 20 inner diameterpipeline transporting wet gas from Huldra to the Heimdal platform and an 8 innerdiameter 9.9-mile [16 Km] pipeline transporting unstabilised condensate, including water,to the Veslefrikk platform.

Due to the hydrocarbon mixture, the flow through these pipelines exhibits multiphasebehaviour and therefore requires a multiphase real-time simulation model for surveillanceand optimised control.

Statoil was responsible for development of the Huldra field and is the field operator. The

transient multiphase code OLGA has been an important tool in all phases of the projectfrom the decision to develop the field and into the production phase. The design of theHuldra wet gas pipeline was a significant improvement in multiphase flow transporttechnology due to the long large diameter pipeline which should be tied-in to the existingHeimdal platform with restricted liquid processing capacity. For the condensate lineefficient and good environmental hydrate and wax control methods are selected.

This paper describes the requirements of the simulation engine and the pipelinemodelling system. This leads to a clear split between the functionality that is necessarilyencapsulated in the simulation engine and the functionality that can be incorporatedwithin the wider On-Line System environment developed by Energy Solution Internationalfor gas pipeline modelling. The aim is to provide a common level of functionalitythroughout the system. Functions for multiphase flow pipelines, such as: model tuning,leak detection, liquid accumulation, and composition tracking need to be considered aswell. A brief description of the approach to these issues is given. The strategy foroperating the pipeline and its impact on extending the functionality of the existing OLSsystem is presented.

This paper gives a brief description of the initial operational experiences with the real-time model.


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1 Introduction

In November 2001, the Huldra field commenced production. Design and start-up of thetransport system for wet gas and condensate from Huldra is an extension of current

experience. Statoil, as the operator of the Huldra field, awarded Gassco contracts foroperational support for the condensate and wet gas pipelines. Valuable experiencerelated to operational aspects and real-time modelling of large-scale multiphase flowsystems has been gained after start-up of production.

Huldra is a gas/condensate field located in the North Sea at a water depth of 410 ft [125meters]. After a first stage separation the rich gas containing water and MEG is routed93.2 miles [150 km] to the Heimdal platform for further processing. Unstabilisedcondensate is routed 9.9 miles [16 km] to the Veslefrikk platform for further processing. ADirect Electrical Heating (DEH) system is employed in the condensate pipeline to avoidhydrate formation during shut-ins. Figure 1-1 displays an illustration of the infrastructure.

The Huldra field originally had an estimated in place gas reserve of 685 GSft3 [19.4

GSm

3

] and a condensate reserve of 46.5 MBBLS [7.4 MSm

3

]. Measured reservoirconditions are 9,795 psi and 277 degrees Fahrenheit [675.5 barg/136 degrees Celsius].

The field is developed as a not normally manned wellhead platform. As from July 2002,Huldra has become remotely controlled from Veslefrikk. The well streams are routed to aproduction separator. Gas from the production separator is cooled and liquids areremoved in a scrubber. The gas saturated with water is fiscally metered, inhibited withMEG and routed to Heimdal for further treatment. Liquid production, i.e. condensate andproduced water, from the production separator and the scrubber are mixed andtransported to Veslefrikk, for further treatment.

At Heimdal the flow from Huldra is received by a three-phase free water knock outvessel. Capacity of this vessel is limited to handle 1413 ft

3/h [40 m

3/h] condensate and

35.3 ft3/h [1 m

3/h] water. Liquid storage capacity of the vessel is 247 ft

3[7 m

3]. Further on,

gas from Huldra, the Vale field and the Heimdal reservoir are processed together tocomply with the sales gas specification.

The Veslefrikk platform receives and separates the condensate and gas arriving fromHuldra. The condensate pipeline is equipped with direct electric heating to avoid hydrateand wax formation at low production rates or during shutdowns.

It was considered necessary to install a real-time multiphase pipeline model as anoperational support tool. Statoil in cooperation with Energy Solution Internationaldesigned, developed and set the model in operation.

Gassco AS was established the 14th May 2001 under the provisions of a Norwegian

White Paper. Operational responsibility in the new, regulated gas transport regimecommenced on January the 1st 2002. Gassco is assigned all the operatorsresponsibilities warranted in the Norwegian Petroleum Law and related Regulations.Gassco is the operator of about 3,853 mile [6200 km] of large diameter, high-pressuregas pipelines, gas processing facilities and terminals. Up to 7,770 MSft/d [220 MSm3]gas is delivered to the gas buyers daily. Further, on behalf of Statoil, Gassco havecontracts for operational support for 416 mile [670 km] of oil/condensate and multiphaseflow pipelines, including the pipelines from Huldra. The pipelines operated by Gasscoconnect 60 installations including 4 locations where the gas flows can be blended to


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ensure correct gas qualities. About 6000 telemetry signals are transmitted to the Gasscocontrol centre twice a minute.

Figure 1-1 Huldra Field Infrastructure

2 Design Requirements

Functional requirements for the real-time multiphase model were established to providefor safe and stable operational conditions. The following parameters were embraced bythe functional assessment of the multiphase flow pipelines to Heimdal and Veslefrikk:

Wet gas pipeline

Accumulation of liquids in the pipeline related to gas flow rate

Liquid transport in the pipeline and into the Heimdal process duringproduction turn-down, production start-up and production increase

Flow characteristics at maximum and minimum operating pressures

Gas quality tracking

Pigging characteristics and requirements

Instrument accuracy


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Condensate pipeline

Hydrate formation and wax precipitation conditions

Cool-down time to hydrate forming conditions during shut-ins

Slugging conditions at low operational conditions

Instrument accuracy

A phase diagram for the wet gas transported to Heimdal was established to determinethe liquid fraction at various pressures and temperatures, as shown in Figure 2-1.The gasthat leaves Huldra is saturated and located at the dew point of the phase envelope, andthe condensate transported to Veslefrikk leaves Huldra in the liquid phase. In thepipelines the flowing conditions give two HC phases as the pressure and temperaturedecrease along the lines.

Figure 2-1 Predicted Phase Diagrams of the Well Fluid and Wet Gas

The main challenge for operation of the transport lines from Huldra is to keep control ofthe liquid accumulation in the wet gas pipeline, and to control the liquid transport into the

Heimdal process when gas production rates are changed. Figure 2-2 shows the liquidcontent in the transport line when stable conditions have been reached as predicted bythe simulator (not tuned based on operational experiences). It was seen that for flowrates below about 6 MSm3/d the liquid accumulated in the pipeline would be difficult to

Phase Diagram for Rich Gas in the Huldra

Well

0

2000

4000

6000

8000

10000

-400 -200 0 200 400 600 800

Temperature [oF]

Pressure[psi]

Vap/liq frac = 0.999 Vap/liq frac = 0.998

Vap/liq frac = 0.995 Vap/liq frac = 0.990

Vap/liq frac = 0.980

Phase Diagram for the Gas in the HuldraProcess Stream

0

500

1000

1500

2000

2500

-150 -100 -50 0 50 100 150 200

Temperature [oF]

Pressure[psi]

Vap/ liq f rac = 1.000 Vap/ liq f rac = 0.999




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handle by Heimdal for production increases. A real-time simulator is therefore crucial tooptimise the operation of this line to ensure high regularity for the Huldra production.

Liquid Content and Conden sate Rate in the Huldra-Heimdal Pipeline

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

0 2 4 6 8 10 12

Gas flow rate [MSm3/d]

Liquidcontentinpipe[m

3]

0

5

10

15

20

25

30

35

40

45

50

Condensaterate[m

3/h]

Liquid content

Condensate rate

Figure 2-2 Predicted Liquid Content and Condensate Accumulation Rate in theHuldra-Heimdal Pipeline for Various Gas Flow Rates

2.1 Simulation and Operating Environment Requirements

Gassco Transport Control Centre (TCC) is equipped with state-of-the-artdata handling,surveillance systems and pipeline modelling systems. Currently, 24 pipeline systems aremodeled and are running real-time. The TCC computer network is shown in Figure 2.3.

The gas, oil and condensate pipelines are modeled in real-time using the single-phasetransient simulation engine TGNET embedded in the Energy Solutions On-Line systemenvironment (OLS). For real-time modeling the multiphase lines it was required to use amultiphase transient simulation engine. The transient multiphase simulation engineselected was OLGA.

The OLGA simulator facilitates transient multiphase simulation features. The governingequations describe mass balances for each of the phases, momentum balances and

energy balances. Interactions between the phases are computed based on semi-empirical models. Physical properties of the fluids are pre-tabulated using the PVT-package PVTSIM. OLGA also features a flow regime estimation function thatautomatically computes the transition between various flow regimes, depending on thepipeline conditions. Flow regimes accommodated by OLGA are the following: stratifiedflows, annular flow, slug flow and bubble flow.


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Figure 2-3 TCS Computer Network Overview

The TCC Operators are very familiar with the look and feel of the OLS environment.Hence it was decided that the real time multiphase on-line system for Huldra should beintegrated into the existing OLS. However, it is obvious that the OLS, which was

developed for single-phase modelling systems, would need to be extended to encompassthe additional functionality of the multiphase simulator. The benefits of this approach are:

Known Functionality

Consistent Look and Feel

Reduced Training Time

Facilitate maintenance

3 Software Design and Integration

As noted above, a prime requirement was the integration of a multiphase simulation toolwithin the existing on-line modelling capability. The current installation already possessedthe proven ability to model single-phase gas and liquid pipelines. The models are used bythe operation personnel within the Bygnes Control Centre to monitor and control thetransport of hydrocarbon liquids and gases within the pipeline network. New fields, suchas Huldra, have been linked to the existing network using multiphase transportation.Hence, the operators require multiphase modelling facilities in their on-line models. Theywould prefer to view the results from such models through their existing screens ratherthan through new and additional displays. An integrated system must be able to display


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data from both single and multiphase models without the need to specifically identify themodel type.

3.1 Design Basis

Given that the existing system is modular, a number of possible approaches to the

integration were considered:

Using an independent modelling system receiving its own data from SCADAand its own independent suite of displays.

An independent modelling system using the existing interface to SCADA butwith an independent suite of displays.

An integrated simulation engine using the existing interface to SCADA andusing the existing display system.

The first was discounted, as it did not meet the conditions for providing an integratedenvironment for the operators.

The second would use the existing SCADA interface and instrument data processingtools, but would provide a separate suite of displays and display management for themultiphase pipelines. Further, the existing modelling system displays provide access toall aspects of the model from the initial instrument data processing to the simulationresults. Thus, two sets of display management would be involved for accessing themodel data. To the operators, this would not appear as an integrated system.

The third approach allows the system to be run as an integrated unit, provided that themultiphase simulation engine can be run alongside the single-phase engine. It alsorequires that similar data structures be adopted for both engines. This further enablessimilar displays and display management to be adopted for both simulation engines.However, there remains the problem of displaying single and multiphase data in identical

layouts so that switching between the models in the system remains transparent to theuser. This problem was resolved by adopting a preferred phase for each of themultiphase models as follows:

Existing multi-model displays would display the preferred phase values.

Displays would be provided for the total flows.

Separate displays would be provided for each of the gas, hydrocarbon liquidand water phases.

Thus, this third approach was adopted as the design basis.

By adopting this design basis, the following advantages and disadvantages could beseen. Advantages were:

Single SCADA interface and hence no changes required

Common instrument value processing no changes required

Common display management


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Common displays, although new displays were required for the individualphase and total flow data.

Independent processes for each real-time, look-ahead and predictive model.The OLGA simulation engine is also single threaded, i.e., it does not permitmultiple concurrent simulations within the same process.

Disadvantages were:

All models, regardless of simulation engine type, required the same datastructures.

New data structures were required for handling individual phase information.

To enable the above, the configuration of the pipeline models themselveshad to be made similar.

Existing simulation processes were designed to use a single engine.

4 Integration within OLS

4.1 OLGA Client-Server Model

The transient multiphase simulator, OLGA, can be run in two modes:

Batch, where the user executes a simulation of his model for a given set ofparameters. The parameters are provided as part of the input data. This istypical of offline processing.

Single or multiple time-step mode, where the parameters/set-points areprovided from some external source that may or may not be under usercontrol. This is typical of on-line processing.

For the first mode, the user need only provide a single input file, while the results areavailable in a series of output reports or graphical presentations.

For the second mode, OLGA is provided as a server process. Communication betweenthe client and the server is provided through a standard TCP/IP protocol. Control, inputand output data are passed to the simulator via a sequence of messages using a simplerequest/reply paradigm. The content of the messages may range from a single commandto a complete picture of the current state of the simulation and this allows the OLGAsimulator to be integrated into an existing on-line system.

The OLGA server provides the following functionality:

Simulation control, e.g., start, stop

Single time-step simulation

Initialisation

Setting inlet/outlet flows, pressures, temperatures


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Return of simulation results as single values, profiles, or trends.

Return of errors or warnings

Thus, to set up a real-time simulation using the OLGA server, it is necessary to provide aclient program that communicates with the server process using the TCP/IP protocol and

the correct sequence of messages to achieve the desired result. Such a process hasbeen designed and built to integrate the OLGA simulation engine into the existing On-Line System (OLS).

A further advantage of the use of a client-server approach to the simulation engine is thatit allows independent updating of the server software, providing that no changes aremade to the messaging structures.

4.2 The Model Processes

A major advantage of the OLS system when considering the integration of the OLGAserver is its use of independent processes for each real-time, look-ahead and predictivemodel. One, and only one, process being used to execute each model. Such a structure

enables a real-time process to be replaced with a client-server pair. This has the addedadvantage that future engine developments can be readily incorporated into the OLSsystem.

With the requirement to provide an integrated simulation engine, the opportunity wastaken to revise the management of the simulation processes within OLS. In previous OLSsystems, it was a requirement that a real-time model process be available before, duringand after execution of the real-time simulation itself. There was a similar requirement forthe look-ahead simulation. This normally required the system administrator to ensure thatsuch processes were started before the model configurations or simulation tasks werestarted. It was a requirement that equal numbers of real-time and look-ahead processeswere started at the same time. If additional processes were required, then they had to bestarted manually. Where a large number of simulation processes were required the

management of such a system required a considerable amount of care.

With the requirement to be able to execute different simulation engines within the OLSstructure, it becomes necessary for the correct process be available for eachconfiguration. Such a requirement could be accomplished manually, but would requireintense system management. Alternatively, provided that the simulation enginerequirements could be identified a priori, it would a relatively simple matter to start therequired engine for the task demanded. Thus, original OLS type configurations wouldrequest the original simulation engine; OLGA type configurations, an OLGA engine.

Having established the correct simulation engine for the model, the correct processesmust be started and monitored. For an OLGA type simulation, both the client and OLGAserver processes must be started and communication established between the pair.

These two processes must then act as a co-operating pair. The pair must be present andcorrectly initialised before a model can be loaded and run.

The sequence for starting a model process becomes:

Identify the engine type required

Request the start of the necessary process(es)


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Start the necessary process. For OLGA simulations, both the client andserver processes must be started.

Initialise the process. For the OLGA client-server pair, this includesestablishment of communications.

On successful initialisation, set a system flag to indicate that the process isnow available for simulation.

Load the simulation data

In the event of the simulation being stopped or a failure occurring and resulting in theshutdown of a simulation process, the system flag is unset to indicate loss of the process.For the OLGA client-server pair, the failure of either must result in the shutdown of both.

An overview of the integrated system including the process control watchdog is given inFigure 4-1, Integrated System Overview.

SCADA InterfaceInstrument Data

Validation

RTM1

RTM2

(client)

RTM3

RTMn

Instrument

Data for

RTM2

SCADA

Data

Instrument

Data for

RTM1

Instrument

Data for

RTM3

Instrument

Data for

RTMn

OLGA Server for

RTM2

ProcessManagement

DisplayManagement

Figure 4-1 Integrated System Overview

4.3 Data Mapping

One of the key areas enabling the integration of the OLGA engine into the OLSenvironment is the data mapping between the different systems. The OLS has been


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developed over many years using a single (single phase) simulation engine. Theconfiguration data and the data structures for storing state and other variables within OLSare centred on the (single phase) simulation engine. For the integration of the OLGAengine, both of these areas needed to be addressed.

4.3.1 OLGA Configuration Data

Both the OLGA simulation engine and the OLS environment require configurationinformation, such as pipeline geometry. However, the configuration information that iscommon to OLGA and OLS is limited to:

Geometrical System Definition

Boundary and Initial Conditions

Process Equipment

Both OLGA and OLS use a similar approach to entering configuration data into thesystem i.e. they both use an ASCII input file that has a keyword type structure. Indeed,

the keyword structure in OLGA is similar, though not identical, to that employed by OLS.

The OLS configuration input file provides much more information than just theconfiguration data. Many on-line parameters and application options are defined withinconfiguration input file. In particular, the connectivity and location of the instrumentationnecessary to drive the Real-Time Model are defined. For this reason it was required tomerge the configuration data from the OLGA input file into the OLS configuration file priorto processing the OLS configuration file. However, the integrity of the OLGA input filemust be maintained in the above process as both sides of the client/server interface usethis file to define the pipeline geometry. This file merging approach is alsoadvantageous in terms of system maintenance as it means that changes to theconfiguration need be applied to one file only.

4.3.2 OLS/OLGA Server Data Transfer

During the execution of the multiphase real-time simulation, data are passed between theclient and server processes. Typically control data are passed to the server and statedata are returned from the server. Data passing in either direction requires manipulationof the data for the following reasons:

1. OLGA uses a different set of internal physical units to OLS.

2. OLGA data distributions are by a contiguous, ordered set of pipe legs(BRANCH). However, OLS data distributions are by a non-contiguous, non-ordered set of pipe legs (profile line).

3. OLGA data are generally cell-centred with flux based data defined at the cellboundaries. OLS data are always defined at computational cell boundaries andtherefore some redistribution of OLGA data is required to map OLGA data intoOLS data structures.

4. OLGA uses a slightly different set of primary variables to OLS. However, theprimary variables used in OLS may be derived directly from OLGAs primaryvariables.


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5. Many OLS data structures are derived from knot-based structures dynamicallyduring the course of simulation. For OLGA data returned to OLS thesederivations are required to be performed explicitly to populate the non knot-basedOLS data structures.

Figure 4-2provides an overview of the data processing that is required to transfer anOLGA data set into an OLS data set.

Figure 4-2 OLGA-OLS Data Transfer Process

4.3.3 Simulation Control and Transient Boundary Data

The majority of simulation control parameters (such as simulation end time and tuningparameters) were already supported in the OLS environment. For those parameters thatwere not supported it was a simple matter of including storage and access to theparameters within OLS.

The OLGA (transient) simulation is driven by pressure and mass flow instrument data. As

OLS already uses these variables as input to the (single phase) simulator the onlyrequirement is to convert the data from OLS internal units to OLGA internal units prior todata being sent to the OLGA server. As well as boundary flow and pressure variables,the transient control variables for valve opening/closing and wall heating are passed tothe OLGA engine

4.3.4 Data Structures

In order to integrate the OLGA engine within the OLS framework, it is necessary tosupport certain OLGA simulation data within the OLS data structures. This enables thesystem to provide the required ancillary functionality such as displaying (and modifying)simulation parameters through existing MMI screens, instrument connectivity, andapplications such as Inventory Analysis, Leak Detection, etc. Indeed, from a Userperspective, the aim of the integration is to allow data from a configuration to bedisplayed in the same manner on the same screen no matter which engine originated thedata. To accomplish this, the existing OLS data structures were extended to include datathat is specific to multiphase pipeline operations. This extension of the data structuresrequires a set of data mapping functions to enable data conversion, re-ordering andredistribution of data.

OLGAVariable

UnitConversion

Redistribution ofDiscretized Data

Calculate andPopulate AncilliaryKnot Based Data

Structures

Generate Mappingfrom Profile Line to

Knot Array

Populate AncilliaryNon-Knot BasedData Structures

OLSVariable


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5 Tuning and Leak Detection

It was a requirement for the Huldra multiphase pipelines to be provided with leakdetection and tuning in a similar form to that already used by the single phase linesalready modelled using OLS. This required that some form of deviations analysis bedeveloped for these new pipelines for use with the OLGA simulation engine. Leak

detection by model compensated volume balance is also provided.

Leak detection relies on being able to detect differences between field measurementsand a model simulation data that can only be explained as an un-metered loss of fluidfrom the line. The differences can be in both pressures and flows. Natural differencesexist between field measurements and model-calculated values and these differencesarise from several sources.

It is the task of tuning to reduce the errors between field measurements and simulatedresults to a minimum. It is the task of leak detection to interpret unexplained errors in ameaningful way.

Since both leak detection and tuning considers the same differences between field

measurements and simulation results, it is useful to consider both together.

5.1 Error Sources

In all systems the following errors exist:

Errors in the original field measurements.

Loss of accuracy (or data) through telemetry

Approximations used in the solution of the equations of continuity,momentum and energy.

Solution errors resulting from erroneous input data, e.g., field measurements.

Errors in the prediction of fluid properties.

Errors in line pack calculations.

In multiphase flow, similar errors exist but may be considerably larger:

Flow measurements for each phase are required in place of a singlemeasurement three errors replace a single error

Multiphase flow measurement often requires the separation of the phases

leading to further error due to phase carryover, e.g., liquid in gas.

Flow measurement is inherently less accurate due to the complexity of theflow.

Model predictions of fluid properties rely on accurate predictions of theproperties of each phase and the split between phases.


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Complexity of multiphase behaviour reduces accuracy of the model especially in the phase split calculations.

Line pack calculations require knowledge of the phase splits, line pressuresand temperature to accurately predict liquid hold-up, etc. That is, thecalculations rely on the multiphase correlations and the thermodynamic

modelling.

5.2 Leak Detection

Model-based leak detection methods rely on the comparison of measured and calculated(simulated) values from the pipeline. The measured values are obtained from theinstrumentation and consist of pressure, flow, temperature and fluid properties. Theinstrumentation is typically located at the ingress and egress.

The pipeline models provide complete real time profiles of pressure, flow, temperatureand density along the pipeline accounting for variations due to the operation. Thuschanges in operation cause "expected" variations in flow and pressure ideally with nodeviations between calculated and measured values. Leaks, however, cause an

"unexpected" variation and a well-defined pattern of deviations between calculated andmeasured values develops. These patterns can be detected and assessed to determine ifa leak is present.

Unexpected variations in pressure and flow can be calculated depending on the controlsapplied to the model. Where the pressure is applied as the control measurement, anunexpected variation in flow (UF) can be expected. Similarly, where the flow iscontrolling, an unexpected variation in pressure (UP) will be seen.

5.2.1 Leak Detection Thresholds

Before the unexpected flow (UF) and pressure (UP) responses can be used for leak

detection, thresholds must be established. Thresholds are determined for both flow (UF)and pressure (UP) uncertainties. It is important to note that thresholds derived from theUF and UP responses are applicable to steady-state flow. To avoid false alarms duringtransient, it is usually necessary to raise the thresholds. Thresholds must be raisedimmediately on detection of a transient, but can then be returned slowly towards thesteady-state values after the transient has passed. Such behaviour can be achieved bythe use of a threshold filter factor.

5.2.2 Leak Detection Inhibition

Leak detection using pressure and flow discrepancies requires the model to be anaccurate representation of the actual operating pipeline. Such requirements are not metunder the following conditions:

Bad or invalid pressure or flow boundary measurements have been receivedor detected. Bad or invalid data used in the calculation of a discrepancymerely reduces the effectiveness of the leak detection.

All data used in the calculation of the discrepancies (UP and UF responses)are bad or invalid


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The model has been initialized but not yet attained a suitably tuned state the cold start period.

Changes have been made in the configuration of the leak subsections thewarm start period.

Under any one of these conditions leak detection will become unavailable (inhibited). Ifonly some of the leak subsections are affected, then only those leak subsections willhave leak detection inhibited. Once the condition has cleared, leak detection will be re-enabled. If the condition causing inhibition was bad data, the condition must have clearedfor longer than the warm start period.

Leak detection is also inhibited if slugging is detected from the OLGA model. Inhibitionmay be avoided if the leak detection thresholds can be raised to a sufficiently high level.Once slugging has passed, the thresholds would be returned to normal using thethreshold filter factor.

5.2.3 Leak Location

Once the onset of a leak has been determined, the leak location can be determined bycomparing the pressure profiles of models of the pipeline. The two models required arebased on:

1. Measured downstream pressure as downstream pressure set pointMeasured upstream flow (estimated) leak flow rate as upstream flow setpoint

2. Measured upstream pressure as upstream pressure set pointMeasured downstream flow + (estimated) leak flow rate as downstream flowset point

In the presence of a leak the pressure profiles of the above simulations will cross at a

point this point is a good estimate of the location of a leak (see Figure 5-1).

A similar argument can be used to determine leak location if the underlying pipelinemodel is upstream pressure controlled.


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40

50

60

70

80

90

100

110

120

130

0 10 20 30 40 50 60 70 80 90 100

PipeLine Pressure Profile

QP Model

PQ Model

Figure 5-1 Model Pressure Profile under Leak Conditions

For multiphase flow we have to make the following assumptions:

1. The flow is predominantly gas/mist flow in the wet gas line andstratified/bubble flow in the condensate line

2. Liquid content can be ignored for the hydraulics but can be used as amodifier for the pipe diameter.

3. The pipeline will be operated close to a steady-state

The biggest impact on accuracy using the above methodology will be the presence oftransients in the pipeline as these will not only effect the steady state assumption but willaffect the modified diameter distribution.

A simplistic model for the pressure profile is used for determination of the leak location. Inthis simple model the momentum balance may be written (using the usual notation) as

( )srkkP xx 32221

= (1)

whereA

mk= is the mass flux, sghx = is the pipe gradient, and

02D

fr= is the friction

term.

Assuming that the process is isothermal then we can derive a solution for the pressuredistribution along the pipeline. Equation (1) can then be integrated exactly to provide thedistance (x) along the pipe as a function of pressure, P. Assuming that this function isinvertible then we have implicitly a function for Pin x.

Alternatively, if the phase densities and thermodynamic quality are available for eachpipe segment, the homogeneous density can be calculated, and (1) integratednumerically along the pipe to provide the information above.


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5.3 Imbalances

Imbalances arise from the hydraulic simulation as the differences between measured andcalculated values. The location of the imbalances depends on the choice of boundaryconditions. For a simple point-to-point pipeline model whose pressure is set at theegress, flow at the inlet, and pressure and flow measurements are available at both inlet

and outlet, the following imbalances are present:

1. Pressure imbalance at the ingress

2. Flow imbalance at the egress.

Only when a model has been operating for longer than the characteristic time period ofthe pipeline will the imbalances be truly representative of the actual errors in the pipelineinstrumentation. It is the task of tuning to reduce these imbalances to a minimum whiletrying to recognise potential errors in the instrument values.

By making the assumptions and using the equations for the pressure drop describedabove, it possible to predict inlet pressures and outlet flows (They are also available from

the OLGA solution.). Hence expressions can be derived for the pressure and flowimbalances at the pipeline inlet and outlet respectively. It is now required that a piperoughness be found that minimises the pressure and flow imbalances. The actualroughness to be used is obtained by combining the previous roughness with the newvalue and applying a change limit and a suitable filter factor.

5.4 Tuning

In multiphase flow, there are a number of other factors that are not known exactly andmay be adjusted to ensure matching of measured and calculated pressure drop. TheOLGA Server provides access to the following variables for tuning purposes:

Pipe diameter

Entrainment rate

Liquid-gas interfacial friction factor

Water-oil interfacial friction factor

Oil density in water phase

Total liquid viscosity

Mass transfer rate

Pipe wall roughness

Ambient temperature

Of these, only the Pipe Diameter, Pipe Wall Roughness and Ambient Temperature havebeen considered for estimation and automatic adjustment. The other factors may betuned manually.


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5.4.1 Roughness

The purpose of roughness tuning is to try to improve the modelled pressure drop againstthe measured pressure drop by adjusting the pipe wall roughness.

From Moody friction factor charts, it is clear that, for typical pipeline conditions,

roughness only becomes a significant factor in the friction factor at Reynolds Numbersabove ~10

5

. For liquid pipelines this implies that the friction factor is not greatly influencedby the roughness. Such conditions will apply to the condensate line even if small volumesof gas are present. For the wet gas line, pressure drop will be dependent on the amountof liquid in the pipeline and how it is distributed. Since liquid dropout occurs at low flows,it is likely that roughness will not be a major factor in determining the pressure drop underthese conditions. As the flow increases and the amount of liquid decreases, roughnesswill become a more influential factor.

If we assume that our modelled and measured pressure drops are close and that theaverage pressure in the model is in some sense close to the average pressure in theactual line then we can use (1) to determine the change required in the variable r (relatedto the roughness) to correct the difference in the modelled and measured pressure drop.

IfL

PPx

= is the modelled pressure drop and

L

PPx

= is the observed pressure drop,

then

( )srkkP xx322

2

1

= and ( )( )srrkkP xx

322

2

1

+=

where r+r is the friction factor term above required to match the measured pressuredrop.

Then, the change, r, required in r is by:

( )xx PP

kr =

2

(2)

From equation (2), we can calculate the change in the friction factor required and hence,from Colebrook-White, the required change in the roughness. The maximum change inroughness is bounded and the actual value applied filtered.

At low flows, i.e., low Reynolds Numbers, roughness tuning may become insensitive tochanges in pressure and flow. Under such conditions it may be preferable to switch toanother tuning parameter, such as pipe diameter, or undertake tuning manually. A flowlimit is available, below which automatic tuning is discontinued and any parameter

changes can only be made manually.

5.4.2 Ambient Temperature

Temperature tuning uses those temperature measurements that are not used asboundary conditions within the model, e.g., outlet temperatures. This assumes that suchtemperatures are representative of the temperature as seen by the pipeline.


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For each subsection the temperature imbalance is determined. This may then be used tocalculate the incremental ambient temperature. The applied increment is subjected tofiltering, maximum change, and temperature limits

6 Applications

6.1 Overview

For any on-line modelling system, it is important that simulation results can be analysedand presented in a variety of ways. For OLS this is achieved through the variousapplications modules, which are executed as part of the simulation. These take the basicsimulation results of pressure, flow and temperature and perform further calculations orchecks on the results. Typical applications are:

Over/Under Pressure Analysis

Inventory Analysis

Survival Time Analysis

Scraper/Pig Tracking

Quality Alarming

Two-Phase Alarming

Hydrate Alarming

Since these applications are general, it is not unreasonable to assume that they can beused with almost any pipeline simulation engine. They simply require the necessarysimulation results. However, additional facilities were required in individual applications toaccount for multiple phases in place of a single-phase gas or liquid.

OLGA also performs a number of detailed calculations in addition to the basic multiphase

hydraulic simulation. Typical of these calculations are:

Scraper/Pig movement prediction

Full flow regime prediction, including single phase

Hydrate formation predictions

6.2 Two-Phase Applications

From an operational point of view it is of great importance to detect the presence of slugflow. The OLGA Server provides information in the form of an index indicating the flowregime at each calculation point. The simple application generates alarms or events ifslug flow is indicated in any part of the network. A profile plot of the flow regime indicator

together with the elevation profile is provided in the MMI. This is annotated with theinterpretations of the flow regime indicator. The condensate pipeline is designed tooperate outside the slug flow region, and warning must be given if slug flow conditionsare predicted in the pipeline.

OLGA contains an optional slug-tracking module. If this has been activated and slug flowis present, the OLGA slug-tracking module calculates the numbers, lengths and positionsof the slugs. Of particular concern are the approach of slugs to the risers and thepossibility of slug flow therein. An extension of the alarming application considers the


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approach of slugs to the riser base or the presence of slugs in the riser itself. Using theslug front velocity the time to reach the riser base can be estimated.

6.3 Hydrate Alarming

The prediction of the formation of hydrates within the pipelines is considered as follows:

Alarming of the temperature at which hydrates will be predicted to form. Alsoalarming of the approach to such limits by means of a pre-definedtemperature offset.

Hydrate prevention by the use of electrical heating and the injection ofhydrate inhibitor. Calculations are required to optimise the use of electriccurrent and the use of hydrate inhibitors during pipeline shutdown and restartand periods of low flow.

6.3.1 Hydrate Formation Temperature

The OLS is already provided with a hydrate alarming function based on either calculatedhydrate formation temperatures using the gas composition or the provision of tables ofhydrate dissociation. Alarms are generated for any part of the network where the fluidtemperature falls below or to within a pre-defined limit of the hydrate dissociationtemperature.

6.3.2 Hydrate Prevention

The condensate line from Huldra to Veslefrikk is provided with electrical heating for thepipeline section between the spool pieces in order to avoid hydrates and wax. The aim isto maintain the fluid temperature above the hydrate formation temperature duringunplanned shutdowns and low flow conditions. Insulation is provided to minimise the useof electricity. As the heating system does not cover the end zones, hydrates will be

prevented in these sections by using traditional inhibitors during shutdowns.

The OLS hydrate prevention application calculates the electrical power requirementsbased on fluid temperature and hydrate formation temperature. The calculations areperformed in the Real-Time Model using current temperature profiles and hydrateformation temperatures.

Following a shut-in, the velocities in the pipeline will rapidly decay to zero. Under suchconditions, it can be assumed that cooling of the pipe and contents will be mostly byconduction. If it is also assumed that the heat flow from segment to segment due to thelongitudinal profile is small, each segment can be considered of uniform temperature. Ifthe fluid is considered to be a single homogenous phase, then, prior to hydrate formation,the temperature in the shut-in pipe will cool exponentially. The initial segment

temperatures reflect the flowing conditions immediately prior to shut-in. Such a model,using the flow heat transfer model for the transfer of heat from the fluid to the pipe wall,provides reasonable estimates of the cool-down and heating periods under all flowingconditions also. Thus, temperature approaches and estimates of when to turn electricalheating on/off and the power requirements can be determined.

Although primarily concerned with the initiation of electrical heating, the model alsopredicts when electrical heating can be discontinued following the resumption of flow inthe pipeline.


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Warnings or alarms are issued to alert the operator to the time when hydrate inhibitorinjection and/or electrical heating is required. Warnings or alarms on the time remainingto the start of operation of these features are given also.

7 On-line Simulator Experiences During Start-up of

ProductionHuldra commenced production November 2001 with two wells. Another two wells wenton-stream in December 2001. Some operational challenges experienced in the start-upphase that corrupted the system modelling and verification were:

Difficulties with process stability at Heimdal, e.g. compressors, gas alarmsand unstable liquid levels in separators.

Hydrate formation at Heimdal

Inaccurate metering of liquid rates at Heimdal

No total liquid rate metering for the flow into the condensate line at Huldra

One of the aspects that were followed closely is the liquid content and distribution in theHuldra-Heimdal pipeline. A view of these features together with the process diagram andthe corresponding measured and modelled instrument values can be seen in Figure 7-1.

Figure 7-1 A Snapshot of Operational Conditions in the Huldra-Heimdal Pipeline


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The transport conditions for the period March to May 2002 can be viewed in figures 7.2and 7.3. Figure 7.3 displays the monitored pressure drop and the corresponding gas flowrate in the wet gas pipeline to Heimdal, and Figure 7.3 displays the monitored pressuredrop and the corresponding liquid flow rate in the condensate pipeline to Veslefrikk.

Figure 7-2 Monitored Pressure Drop and Flow Rate in the Huldra-Heimdal Pipeline

Figure 7-3 Monitored Pressure Drop and Flow Rate in the Huldra-VeslefrikkPipeline

For the wet gas pipeline the measured and modeled pressure drops during stableproduction periods are within 10% as seen from figure 7-4.

With regard to prediction of liquid accumulation in the wet gas line improved meteringaccuracy at Heimdal is required to verify the predictions.

Huldra-Veslefrikk Delta Pressure and Flow Rate

-40

-30

-20

-10

0

10

20

30

40

01.03 11.03 21.03 31.03 10.04 20.04 30.04 10.05 20.05 30.05

2002

Pressure[barg]

0

50

100

150

200

250

300

350

400

VeslefrikkFlow

Rate[ton/h]

Ves le fr ikk Pressure Ves le fr ikk Flow Rate

Huldra-Heimdal Delta Pressure and Flow Rate

0

5

10

15

20

25

30

1-mar 11-mar 21-mar 31-mar 10-apr 20-apr 30-apr 10-mai 20-mai 30-mai

2002

DeltaPressure[barg]

0

5

10

15

20

25

30

35

Flow

Rate[Msm

3/d]

Huldra P ressure Huldra F low Rate Heimdal F low Rate


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The pressure and temperature drop estimates in the condensate have fairly largeinaccuracy. The flow measurement accuracy must be verified before tuning of themodel can be performed.

Figure 7-4 Comparison of simulated and measured pressure drop across the wetgas pipeline.

Scandpower has extensively verified OLGA against field data. Moreover, Statoil hasverified the predictions using in-house field data. The pressure drop predictions will formost field data be within 10-20%. For liquid accumulation predictions for largediameter pipelines the predictions are within +-20-30% for frictional dominated flow. Forgravity dominated flow the uncertainty in the predictions is large; +- 40-50%.

8 Conclusions

The development of the Huldra pipeline system has been an interesting and technicallychallenging experience.

The pressure drop predicted by OLGA for the wet gas line is within 10% of measuredvalues without parameter tuning. For verification of the liquid predictions for the wet gasline and pressure and temperature drop predictions for the condensate line the flowmeter accuracy must be verified/improved.

8.1 Real-Time Modelling System

The OLGA engine has been successfully integrated to run in parallel withanother pipeline simulator and is providing the operator with criticaloperational information.

There is minimum duplication of input data between the two systems, with noimpact on OLGAs input data requirements.

All displays and other output data requirements have been integrated to acommon basis.

Pressure Drop Hu ldra-Heimd al - Des ign vs . M easurements

0

5

10

15

20

25

30

2 3 4 5 6 7 8 9 10 11

Flow r a te [M Sm

3

/d ]

Pressuredrop[bar]

M easure m ents G ra vita t ion dom ina ted Fric t ion dom ina ted


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The client-server model is ideal for the inclusion of multiple pipelinesimulation engines within a single on-line environment.

Applications specific to multiphase flow have been developed.

8.2 Operational Requirements

Re-evaluate liquid inventory predictions in the Huldra gas pipeline.

The models used for supervision must be tuned using the operational data.

Assess possible improvements of the leak detection ability if the liquidmetering system at Huldra is upgraded.

Installation of a liquid metering system at Heimdal to improve the liquidaccumulation and liquid rate predictions

9 AcknowledgementsThe authors appreciate the permission to publish this paper given by the Huldra license with thepartners Statoil, TotalFinaElf, Conoco, Svenska Petroleum, Petoro and Paladin Resources.


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Authors Biographies

Dr. Svein Birger Thaule

Executive Officer Technology,Gassco AS, NORWAY.

Svein Birger started his professional career with Norsk Hydro Oil and Gas in 1981 where he worked withoffshore field development projects. In 1985 he joined Statoil where he had various positions in developmentprojects, operations and research. From 2002 he has been Executive Officer Technology in Gassco AS.

Current Technical Interests

Sub-sea pipeline technology, gas processing technology and systems engineering.

Education

MSc Fluid Mechanics, University in Trondheim (NTH), Norway

Dr. Ing., Computational Fluid Dynamics, University in Trondheim (NTH), Norway.

Publications

Thaule, S.B. and Herle, E., Etzel Gas-Lager, Storage facilities and preparation for operations, GasProcessors Association, European Chapter 10thContinental Meeting, Paris June 1993.

Thaule, S.B., Etzel Gas Storage, Operational and Predictive Model for Gas Withdrawal, International GasUnion, 19th World Gas Conference, Milan 20/23 June 1994.

Thaule, S.B. and Gentzsch, L., Experience with Thermophysical Modelling of Gas Cavern Operations in Etzel,SMRI-Fall Meeting 1994, Hannover, Germany.

Thaule, S.B., Lagring av Naturgass, Commett Seminar, Haugesund October 1994.

Thaule, S.B., Computational Analysis of Thermophysical and Flow Characteristics in Cylindrical Gas Cavern,Dr. Ing. thesis December 1994 (NTH 1995-15), Trondheim, Norway.

Thaule, S.B., Computational Analysis of Thermophysical and Flow Characteristics in a Cylindrical GasCavern, ASME/JSME joint thermal conference, Hawaii March 1995.

Thaule, S.B. and Fosse, A.P., Experience with large withdrawal rates from Etzel gas storage, SMRI-FallMeeting 1995, San Antonio, TX, U.S.A.

Thaule, S.B. and Lokna, T., Commercial implications of European pipeline developments, Transport & Tradingin UK & European Gas, London September 1996.

Thaule, S.B. and Postvoll, W., Experience with computational analyses of the Norwegian Gas TransportNetwork., PSIG Annual Meeting, San Francisco, CA, U.S.A.

Thaule, S.B. and Postvoll, W., Experience with computational analyses of the Norwegian Gas TransportNetwork, International Gas Union, 20th World Gas Conference, Copenhagen 9/13 June 1997. Also publishedin Oil&Gas Journal, March 23. 1998.

Thaule S.B. Computational analysis of thermophysical and flow characteristics in gas caverns, SMRI-Fall

Meeting 1997, El Paso TX, U.S.A.

Andresen, M. and Thaule, S.B., Vectors Demonstrates How Simulations Save Cost of Gas StorageOperations, SMRI-Fall Meeting 1998, Rome, Italy.


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Willy Postvoll

Specialist, Gas Technology

Gassco AS, Norway

Willy started his professional career with Statoil in 1983 where he worked as a Reservoir Engineer in the Oil

and Gas Field Development division. After spending some time as a Senior Engineer providing technicalsupport for reservoir simulation he joined the Transport Division specializing in Real-Time Systems, TransportControl and Supervision. From 2002 he has been the Real Time Systems Specialist in Gassco AS.


Real-Time modeling and simulation of single and multiphase transport pipelines and systems engineering.

Education:

1983: B.Sc. Petroleum Engineering, University of Rogaland (RDH), Norway

1985: M.Sc. Reservoir Engineering, University of Rogaland (RDH), Norway

Publications:

PSIG San Francisco 1997: Experience with Computational Analysis of the Norwegian Gas Transport Network

IGU WGC Nice 1998: Operational experience with gas transport in Zeepipe

Oil & Gas Journal, March. 1998: Flexibility need prompts installation of Zeepipe modeling system.

BHR Multiphase Technology Cannes 1999: Pig Velocity Control - Method, Simulations and Field Measurements


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Dr. Gunnar Flaten

Flow Assurance Responsible, Kristin project

Statoil AS, Norway

Gunnar joined Elkem in 1989. In 1991 he started to work with multiphase pipeline transport at Institutt for

energiteknikk (IFE). His professional career with Statoil started in 1998 where he worked as Specialist inmultiphase flow technology. In 2002 he started his work as Flow Assurance Responsible in the Kristin project.


Multiphase flow evaluations and fluid control.

Education:

1985: M.Sc. Fluid mechanics, University of Oslo, Norway

1991: Dr.Sc. Fluid mechanics, University of Oslo, Norway

Publications:

M.Sc. thesis 1995: Wave diffraction from two-dimentional bottom elevations.

Dr.Sc. thesis 1991: Wave dissipation and diffraction of surface waves due to permeable obstacles.

Costal Engineering 1991: Dispersive shallow water waves over a porous sea bed.

Multiphase 97 in Cannes: Evaluation of the dynamic behaviour for the Petroboost system.

Multiphase01 in Cannes: Experiences and improvement work for the bundle from the Gullfaks South field.


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Dr. Olav Urdahl

Lead Process Engineer,Huldra/Veslefrikk Operations

Statoil ASA, NORWAY.

Olav joined Statoil in 1993 and worked 5 years in the R&D division on topics related to multiphase flow, gas hydrates, fluid

characterization and liquid-liquid separation. After that he has spent 4 years in Operations with both Gullfaks and

Huldra/Veslefrikk

His current position is Lead Process Engineer, Huldra Veslefrikk Operations

Education

Cand. Scient, Physical Chemistry, University of Bergen, Norway, 1990

Dr. Scient., Surface and Colloid Chemistry, University of Bergen, Norway, 1993.

Publications

Iun the period 1990 to 2001 Olav has published 37 papers on topics related to multiphase flow, gas hydrates, fluidcharacterization and liquid-liquid separation.

The latest ones are

O. Urdahl, N. Wayth, T. Williams, A. Bailey and H. Frdedal, "Compact Electrostatic Coalescer Technology", inEncyclopedic Handbook of Emulsion Technology (J. Sjblom, Ed.), Marcel Dekker, p. 679-694, (2001).

O. Urdahl, K.H. Nordstad, P. Berry, N.J. Wayth, T. Williams, A.G. Bailey and M.T.Thew, "SPE 69169; Development of anew Electrostatic Coalescer Concept", SPE Production Facilities, February, p. 4-8, (2001)

E.E. Johnsen, H. Frdedal and O. Urdahl, "An improved approach for measuring viscosity of water-in-crude oil emulsionsunder flowing conditions", Journal of Dispersion Science and Technology, 22(1),p. 33-39, (2001).


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Dr. Richard P Spiers

Senior Technical Consultant

Energy Solutions International, UK

Dick joined ESI (formerly LICEnergy, formerly SSI) in April 1984 as a Consultant after leaving BP Engineering

where he provided simulation support within the Central Engineering Department. During his time with ESI hehas worked on development, integration, implementation and support of many Real-Time Pipeline ModellingSystems. In particular, he was responsible for the development and implementation of Gasscos (formerlyStatoil) current Pipeline Modelling System, and to which he has provided support since acceptance.

Education:

BSc(Eng) Chemical Engineering, University College London, 1969

PhD, Chemical Engineering, University of Bradford, 1977

Publications:

1974 "Free Coating of a Newtonian Liquid on to a Vertical Surface"R.P.Spiers, C.V.Subbaraman and W.L WilkinsonChemical Engineering Science, Vol.29, pp 389-396, 1974

1974 "Uniqueness of Film Thickness and Meniscus Profiles in Vertical Withdrawal"R.P.Spiers and W.L WilkinsonChemical Engineering Science, Vol.29, pp 1821-1825, 1974

1975 "Free Coating of a Non-Newtonian Liquid on to a Vertical Surface"R.P.Spiers, C.V.Subbaraman and W.L WilkinsonChemical Engineering Science, Vol.30, pp 379-395, 1975

1977 " The Dynamics of the Weissenberg Rheogoniometer "R.P.SpiersPh.D. Thesis, University of Bradford, 1977

1978 "Free Coating of Viscoelastic and Viscoplastic Fluids on to a Vertical Surface"K.Adachi, R.P.Spiers and W.L WilkinsonJournal of Non-Newtonian Fluid Mechanics, Vol.3, pp 331-345, 1978

1982 "The dynamic performance of the Weissenberg Rheogoniometer

I. Small amplitude oscillatory shearing"W.C.MacSporran and R.P.SpiersRheologica Acta, Vol.21, pp 184-192, 1982

1982 "The dynamic performance of the Weissenberg RheogoniometerII. Large amplitude oscillatory shearing"W.C.MacSporran and R.P.SpiersRheologica Acta, Vol.21, pp 193-200, 1982

1984 "The dynamic performance of the Weissenberg RheogoniometerIII. Large amplitude oscillatory shearing harmonic analysis"W.C.MacSporran and R.P.SpiersRheologica Acta, Vol.23, pp 90-96, 1984


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Dr. Jonathan J Barley

Pipeline Studio Product Manager

Energy Solutions International, UK

Jon joined ESI (formerly LICEnergy, formerly SSI) in November 1992 as a Consultant after leaving BP

Research where he worked as an Applied Mathematician in the High Speed Computing group. During his timewith ESI he has worked on development, integration, implementation and support of many Real-Time PipelineModelling Systems.

He is currently the Pipeline Studio Product Manager and a Senior Technical Consultant within ESI's UKOperations group.


Technical interests include the numerical modelling of gas, liquid and multiphase pipeline networks, applicationof modern software techniques to problems in the oil and gas industry, computer graphics and visualisation andcode optimisation.

Education

BSc (Hons) Mathematics II(i) University of York, 1985

PhD, Computational Fluid Dynamics, University of Reading 1989

Publications:

1989 "'Genuinely 2-D Schemes for the Non-Linear Equation of Gas Dynamics'"J.J.BarleyPh.D. ThesisUniversity of Reading

1990 "High Resolution Simulation of Multidimensional Viscous Fingering"J.J.Barley, A.H.Muggeridge and M.A.ChristieIn "Science and Engineering on Supercomputers"Editor E.J.PritcherCMP Springer Verlag 1990

1992 "3-D Simulation of Viscous Fingering and WAG Schemes"M. A. Christie, A. H. Muggeridge, J. J. Barley

SPE 21238in proceedings of the 11th Symposium on Reservoir Simulation.

1992 "3-D Simulation of Viscous Fingering and WAG Schemes"M. A. Christie, A. H. Muggeridge, J. J. BarleySPE Journal of Reservoir Engineering Volume 8 No.1 Feb 1993.

1993 "Modelling and Visualisation of Three Dimensional Miscible Viscous Fingering"J. J. Barley, D. W. WilliamsIn Animation & Scientific Visualisation: Tools and ApplicationsEditors R. A. Earnshaw and D. Watson

Academic Press 1993

1996 "The Role of Pipeline Monitoring Software in the Allocation and Nomination of North Sea GasJ. J. Barley, H.W. Robinson, A. SpencePSIG9609in proceedings of the 29

thPipeline Simulation Interest Group, 1996

huldra initial experiences in real-time multiphase pipeline modelling

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