Deep Offshore Well Metering and Permutation Testing

7/30/2019 Deep Offshore Well Metering and Permutation Testing

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Copyright 2002, Offshore Technology Conference

This paper was prepared for presentation at the 2002 Offshore Technology Conference held inHouston, Texas U.S.A., 69 May 2002.

This paper was selected for presentation by the OTC Program Committee following review of

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AbstractAs production in very deep waters becomes a crucial

challenge for many oil companies, a better management of the

production is constantly required. This paper presents two

complementary methodologies for operation support and

improvement of the production conditions. The first one is

based on data reconciliation between process measurements

and flow modelling. It brings an additional level of

information to the problem of continuous metering of deep-

water subsea wells. As periodic well testing is required toachieve this predictive metering, the second methodology

provides the optimal test sequences of well permutations. It

involves flow process simulation and algorithmical sorting,

according to production constraints and operating strategies.

Finally, comparisons between numerical simulations and plant

data demonstrate the ability of these two methodologies to

provide strong and reliable information for deep offshore

producers.

IntroductionThe knowledge of the phase flow rates coming from each

individual well of an oil field is mandatory for a better

production and reservoir management. Generally, thisinformation comes from a series of direct well testing, where a

single well flows directly to a test separator.

In deep offshore, this procedure turns out to be

inappropriate: production developments are based on

gathering network, where manifolds merge the production

from several wells into a single flowline. This is the case on

the Girassol oil field in Angola, see Fig. 1. Moreover, direct

well testing implies deferred production, valve reliability and

flow assurance issues: hydrate formation in dead branches,

slugging at low flow rates, etc.

Whereas conventional solutions, such as hardware

multiphase metering, supply a limited information, our

methodology works as an overall field supervisor for:

estimating individual well production with respect toappropriate pressure and temperature measurements;

detecting abnormal behavior (sensor drift forinstance);

validating hardware measurements, and replacingthem in case of failure. Typically, in deep offshore

production, hardware sensors are not replaced in case

of failure for financial and feasibility reasons.

This methodology is based on data reconciliation. It

assumes that measurements are not necessarily correct and can

be corrected within a confidence interval. Meanwhile,

unmeasured variables derive from redundancy between flow

modelling and field data.

Data reconciliation has been already successfully applied

to a small production network, see Ref. 1. Our paper intends to

go further in the study of this innovative technology andpresents its application to the Girassol field.

Well monitoringGiven a set of temperature and pressure measurements, our

methodology aims to provide an estimate of the phase flow

rates produced by each individual well of an oil field.

Problem modelling. Real-time plant data are completed with

a global steady state simulation of the production network

involving:

mass, force, and heat balance equations;

thermodynamic calculations;

hydrodynamic modelling.For instance, assuming process data at both ends of a

choke and an estimate of the fluid composition, one can derive

a local estimate of the liquid and gas mass flow rates from

hydrodynamic and thermodynamic calculations.

Combination between physical modelling and plant data is

applied to the whole production network, leading to multiple

estimates of the same information. This multiplicity derives

from our uncertain interpretation of the reality: this statement

is precisely the main strength of the data reconciliation

technology.

OTC 14009

Deep Offshore Well Metering and Permutation TestingErich Zakarian, RSI; Arnaud Constant, TotalFinaElf Exploration & Production Angola; Lionel Thomas, TotalFinaElf; MartinGainville, Institut Franais du Ptrole; Pierre Duchet-Suchaux, TotalFinaElf; and Philippe Grenier, RSI


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2 E. ZAKARIAN, A. CONSTANT, L. THOMAS, M. GAINVILLE, P. DUCHET-SUCHAUX, AND P. GRENIER OTC 14009

Whatever the complexity of a model, one should be awarethat it always remains an approximation. However, the latter

can be restricted to a small number of modelling parameters

embedded in residual equations. Again, any hardware sensor

contribution is a residual equation weighted by vendor

uncertainty.

Both hardware measurements and modelling equations are

involved on the same level of analysis through a global datareconciliation and parameter estimate, leading to an

optimization problem. Conversely, experience feedback is

expected to get optimal values of model uncertainties.

A major innovative aspect of this work is the systematic

computation of the accuracy of any estimated variable. We

notice that the accuracy of a measured variable can be slightly

increased since data reconciliation works as a global

computation where any piece of information is likely to be

improved by the other ones.

We also emphasize that information on the solution

uncertainty is as important as the solution itself: whenever

redundancy remains, a physically incorrect solution can satisfy

the problem. Therefore, a supervisor checks the consistency ofthe solution against intuitive expectations.

Example. Let us consider the following scenario. Given one

well tubing followed by a choke, six sensors are installed

upstream and downstream these equipment to measure the

pressure and the temperature of the fluid, see Fig. 2. We derive

six equations:

Pi - Pmi = 0,.....................................................................(1)

Ti - Tmi = 0, .................................................... .................(2)

where i = 1, 2, and 3 refer respectively to the following

positions: upstream the well tubing, downstream the welltubing, and downstream the choke.

Calibrated values of the water-liquid ratio and gas-oil ratiogive two additional equations:

BSW-BSWm = 0,............................................................(3)

GOR - GORm = 0............................................................(4)

A well tubing model provides a pressure drop relation and

a heat balance between positions 1 and 2:

fWP (P1, T1, P2, T2, Flow,BSW, GOR, WP) = 0,..............(5)

fWT(P1, T1, P2, T2, Flow,BSW, GOR, WT) = 0. ..............(6)

A choke model gives the same kind of relations betweenpositions 2 and 3:

fCP (P2, T2, P3, T3, Flow,BSW, GOR, CP) = 0,...............(7)

fCT(P2, T2, P3, T3, Flow,BSW, GOR, CT) = 0................(8)

Every measurements involved in this system are not

necessarily correct since hardware sensors are subject to errors

and the flow is not perfectly stable in the entire production

system. As far as BSW and GOR, their a priori values

(calibrated) might be trusted with a relative confidence as thefluid composition may change between two consecutive

measurements performed on a test separator. Therefore,

equations (1) to (4) are replaced by residual equations and

weighted by the standard deviation (uncertainty):

ePi = (Pi - Pmi)/Pi, ........................................................ .. (9)

eTi = (Ti - Tmi)/Ti,........................... .............................. (10)

eBSW= (BSW-BSWm)/BSW, ......................................... (11)

eGOR = (GOR - GORm)/GOR, ........................................ (12)

where i = 1, 2, and 3.

As reminded before, an additional parameter calibrates

each modelling equation with respect to a reference value

(tuned from test measurements) and an uncertainty. Four

residuals complete the system:

eWP = (WP - WP,ref)/WP, ............................................. (13)

eWT= (WT- WT,ref)/WT, .............................................. (14)

eCP = (CP - CP,ref)/CP, ............................................... (15)

eCT= (CT- CT,ref)/CT,........ ........................................ (16)

We finally get 16 equations, namely (5) to (16), and 13

variables: BSW, GOR, Flow, WP, WT, CP, CT, Pi, and Ti,where i = 1, 2, and 3. The system seems redundant. However,

redundancy can only be ensured from a detailed analysis: to

avoid any singularity, the rank of the jacobian must be equal to

the number of the variables at any operating point. Note: the

jacobian of the system is the matrix given by the partial

derivatives of the equations (modelling equations andresiduals) with respect to the variables.

This condition is necessary to find a solution, which is a

minimization of an objective function given as a sum of the

squares of the residuals. Meanwhile, the modelling equations

are exactly satisfied (optimization constraints).

Three phase flow modelling. Several models are involved in

the simulation of a Girassol production loop, see Table 1. As a

mixture of oil, gas and water is expected during the field life,

intensive efforts are required to get an acceptable physical

representation, due to the complexity of three-phase flows.

A gridded modelling is used for long tubings (well, sea-

line, riser) since local effects such as slope changes maystrongly impact pressure and thermal profiles. A complex

three-phase hydraulic module ensures a correct representation

of the different flow regimes. Local flash and thermal

calculations improve the physical modelling as well.

A rather sophisticated modelling is used for the chokes

since flow criticity and gas expansion may seriously affect

pressure and temperature variations. The choke discharge

coefficient must be initially calibrated and periodically

validated against field data. Three-phase flow meters could be

used. Meanwhile, test separator instruments provide sufficient


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OTC 14009 DEEP OFFSHORE WELL METERING AND PERMUTATION TESTING 3

and accurate information for calibration.This tuning procedure is described in a further section. In

terms of computation, tuning is a particular use case of this

well metering methodology.

Thermodynamic issues. Physical phenomena such as

vaporization in wells or expansion in chokes require accurate

thermodynamic calculations. Therefore, the mixturecomposition is tracked along the flow line, and the whole unit

operations perform local vapor/liquid/liquid equilibrium

calculations to get an estimate of the phase properties.

However, it is an illusion to believe that an accurate estimate

of the reservoir fluid molar composition can be found: neither

the system, nor the available sensors are able to catch the

effect of a composition change (at constant phase properties)

between C9 and C10 cuts, for instance. Conversely, gas

coning or water breakthrough does affect sensor data.

Therefore, the fluid composition is corrected with BSW and

GOR variations by addition or removal of water and/or gas

from the first stage separator.

Software issues. The basic domains involved in an industrial

well monitoring tool are:

compositional thermodynamic calculation;

hydrodynamic modelling in pipes and wells;

thermal modelling in pipes and wells;

valve modelling;

reservoir PI modelling;

data reconciliation;

real-time process data recording and analysis.For each domain, several approaches have to be tested and

selected. For example, a particular thermodynamic server

might be suitable for certain operating conditions butunacceptable to other cases. Note: a server is a software

component that provides services for other software

components (client components) through defined interfaces.

Therefore, it should be easy to combine different modules

with the minimum effort. In addition, many pieces of software

from different sources might fulfill our requirements. For

these reasons, we decided to build our well monitoring

simulator as an open software, using the CAPE-OPEN

(Computer-Aided Process Engineering) standard, see Ref. 2.

The compliance to this standard is another major and

innovative aspect of this work. It provides high model

flexibility for the end user, it makes implementation of new

features much easier and faster: plug and play integration ofany CAPE-OPEN compliant component is carried out with the

minimum effort.

Built on a component-based architecture, see Fig. 3, our

well monitoring simulator includes:

CAPE-OPEN compliant components: unit operations,thermodynamic servers;

external components: solver, man machine interface,data interface, supervisor.

CAPE-OPEN standard interfaces ensure the

communication between components through a simulator

executive framework. The latter runs the whole application

and its components, like the configuration of a production

network or the use cases of a well monitoring application.

Sensor failure detection. The data interface component

makes the connection between the simulator executive

framework and external components that provide hardware

measurements: Distributed Control System, database.To avoid any undesirable effect on data reconciliation, the

data interface performs a preliminary analysis to detect any

possible failure (unlikely value, excessive variation) or

unsteady behavior when the average of the measurements

depends significantly on time.

A sensor is declared invalid in case of failure and its

contribution is removed from the system. It does not

participate to the data reconciliation, leaving the determination

of the measurement to the optimizer. This preliminary

detection is as important as the data reconciliation itself. Let

us present an example to confirm this statement.

We consider a network with a well producing in a single

flowline through a choke and a manifold, see Fig. 4. Pressureand temperature sensors are located upstream and downstream

these equipment. Assuming an initial calibration of the system,

we define a particular scenario with hardware sensor failures,

see Fig. 5: at a fixed period of time (five minutes), the data

interface sends sensor measurements to the simulator

executive framework and a new solution is computed. Forty-

five minutes after starting up, the pressure sensor at the bottom

hole returns zero, which is of course an unacceptable value.

This failure lasts half an hour. Two hours after starting up,

both pressure sensors at the bottom hole and the manifold

return zero again.

First, let us consider the case where the data interface does

not perform a detection of sensor failure. At the beginning of

the run, the simulator computes reconciliated data, see Fig. 6.

The latter are very close to the real measurements reproduced

on the figure as dotted lines.

At 45 minutes where the first pressure sensor collapses, the

simulator manages to rebuild a measurement at the bottom

hole. The rebuilt value is physically acceptable but likely far

from reality. At the same time, the production is overestimated

(the productivity index remains the same in the whole run) and

the gas-oil ratio decreases, see Fig. 7. The wellhead pressure

is also strongly affected because of its direct dependence onthe bottom hole pressure through the well tubing model. The

pressure drop increases by 106 Pa.

At 75 minutes where the sensor starts running again, the

initial solution is recovered but at 120 minutes, both pressure

sensors at the tubing ends stop to run. The simulator fails to

find a solution.Let us run the same simulation but with detection of sensor

failure. We notice that the data reconciliation works perfectly

in this case, showing its ability to rebuild unmeasured

variables, see Fig. 8, 9. The sensor failure does affect the

redundancy of the problem, which is lower than before, but it

does not affect the results.


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At 120 minutes, both sensor failures are detected at thewell tubing ends. The solution remains acceptable. However,

the manifold pressure drops significantly and its a posteriori

confidence interval as well. In other words, there is not enough

redundancy to trust the reconciliated value of the manifold

pressure.

This second computation confirms the ability of our

methodology to replace hardware sensors in a productionsystem. It also shows its weakness whenever the number of

valid sensors is not sufficient to allow redundancy.

Real-scale validationProduction at the Girassol field started in late 2001. We

propose to use a first series of measurements to validate our

methodology and confirm its ability to provide reliable

information. These measurements were recorded from

December 4th to 24th, 2001.

Calibration. We focus our presentation on a single well

flowing into the right branch of the P10 loop. We consider the

network from the well tubing to the test separator on FPSO(Floating Production Storage and Offloading), see Fig. 10.

Note that riser tubing and riser choke are not simulated in this

presentation.

Modelling parameters derive from a single simulation with

the following configuration:

At the test separator: set the uncertainties of the phaseflow-rate sensors to vendor accuracy (we assume

accurate measurements)

At the upstream equipment and inflow model: removethe residuals of the modelling parameters (GOR,BSW,

choke discharge coefficient, productivity index and

friction factors).

The vendor accuracy of the flow-rate sensors is small.

Therefore, the phase flow-rates computed by the optimizer are

necessarily close to their measured values. Meanwhile, the

phase flow-rates derived from the modelling have to match

these measured values. Assuming a small uncertainty on the

pressure/temperature measurements, the optimizer is forced to

change the values of the modelling parameters (a particular

situation occurs here since the system has no redundancy, and

solution is independent on the sensor accuracy).

Twice a day in December 2001, measurements on a test

separator were carried out at the Girassol field to estimate the

production of the well. We propose to calibrate our system on

one of these tests. Then, after a certain period of time, we will

compare the oil, water, and gas flow-rates predicted by the

simulation and those derived from real testing.

Well metering. A demonstrative test can be found from

December 7th to 12th. During this period of time, the

production of a well was progressively increased with a choke

opening from 36 % to 49 %, see Fig. 11. Meanwhile, the

pressure drop in the well tubing remained approximately

constant and the one through the choke dropped from 5 106 Pa

to 2.5 106 Pa.

We calibrate the simulator with data recorded onDecember 7th, between 02:00 and 08:00 am. Then, we run a

metering, up to December 12th.

With a sampling DCS period of five minutes, production is

estimated every thirty minutes, using data filtered on the past

hour. If we assign the same level of confidence to the models

(choke, inflow, tubings), the system overestimates the

production, but the predicted trend is consistent with reality,see Fig. 12.

A detailed analysis shows that the choke model is

responsible for this deviation: tuning with subsequent tests

shows that the discharge coefficient drops from 0.92 to 0.6 on

December 8th, see Fig. 13. Meanwhile, productivity index and

friction factors remain roughly constant. This observation

reveals some inconsistency between plant data and the choke

modelling. Further analysis will be required to get a better

understanding of the real choke behavior.

If we set a lower relative confidence on the choke model

(by increasing the discharge coefficient uncertainty), we verify

that the initial tuning is sufficient to get, five days latter, a

good estimate of the expected oil and gas production, see Fig.14. This implies that the initial tuning of the GOR was good

enough for the five following days of metering. We effectively

notice that the GOR derived from tuning remains constant, see

Fig. 15.

Permutation testing Assistance toolPeriodic calibration is required to keep the modelling close to

the real process. Three-phase flow meters could be used but

test separator measurements through well testing can also

provide sufficient and accurate information.

Production at the Girassol field is based on a loop

configuration where sea-lines are connected to each other

through subsea manifolds, see Fig. 1. Each well of a loop is

routed to a production line, either left or right. There is no

specific line for well testing.

This network architecture and flow assurance issues

strongly impact the well testing strategy:

direct testing leads to increase deferred production;

direct testing at low flow-rates may lead to instabilityin the flowlines (slugging);

direct testing at flow rates below 10 000 bbl/d maylead to a fluid temperature lower than the paraffin

formation temperature (about 40C);

direct testing for the nearest wells leaves the upstream

flowline full of dead fluid during the test, and hydrateinhibition with methanol is required.

Therefore, permutation testing (in addition to direct

testing) has been included in the Girassol well monitoring

strategy:

a direct testing connects a single well to a singleproduction line; estimating the phase flow rates of the

well is straightforward;

a permutation testing connects several wells to bothproduction lines (but a well is necessarily allocated to

a single line). A series of different permutations leads


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to different measurements of the phase flow rates ofeach production line. The phase flow rates of each

individual well derives from solving a set of linear

equations.

Testing strategy. Since the wells of a production loop may

produce into either left or right production line, a permutation

can be considered as a set of two well arrangements, left andright. The number of possible arrangements is necessarily

much greater than the number of wells. For example, let us

consider four producers: WA, WB, WC, WD. Any sequence

involving these four wells can be acceptable. One of them is

shown on Table 2; notation: WA + WB + WD means a test with

WA, WB, and WD.

The number of possible test sequences increases drastically

with the number of wells, see Table 3. This observation and

the complexity of involved phenomena prevent us from

deriving a simple synthetic rule that could be used in operation

to select the best test sequences. The latter have to be

compliant with the whole production constraints.

A permutation testing assistance tool has been specificallydesigned to achieve this work. Basically, a steady state process

simulator is used for network and gaslift computation,

providing well production in any arrangement, see Table 4 for

few examples. Then, sequence sorting is carried out versus

user strategy, see Fig. 16.

Flow modelling. Flow rates and production losses are

estimated from a simplified flow modelling: well performance

curves and pressure loss tables derive from experiments or

simulations performed on predictive multiphase software.

Specific unit operations implement these tables in the process

simulator. Thus, any code can be used to generate the flow

modelling.

Contrary to the well monitoring tool, this permutation

testing assistant is an off-line software. However, a periodic

tuning against real process data is recommended in order to

adjustBSWor GOR of each individual well.

Sequence sorting. After network configuration and

calculations, a sorting service provides an ordered succession

of permutations.

The initial number of possible sequences is Ckj where k is

the length of the sequence and j is the total number of possiblearrangements, see Table 3. Some of them do not comply with

thermal constraints and are removed. The same applies for

singular sequences.

For one sequence of k arrangements, there are k! orders.

Applying an order-dependent criterion, the best order is

selected. The simulator computes the expected test accuracy,namely the accuracy of each individual well production, GOR,

and BSW. Finally, the remaining sequences are sorted with

respect to user strategy, see Fig. 16. Typical simulation results

are shown on Table 5.

Features. Direct testing is intuitively the best solution to reach

the maximal accuracy. For instance, if we consider aproduction loop with four wells, sixteen different sequences of

four direct tests will estimate the production, without any loss

of accuracy. But, only few of them may satisfy operation

constraints.

According to our assistance tool, only one sequence does

not require hydrate inhibition with methanol, see Fig. 17: no

dead branch is created if we consider the first arrangement asthe initial loop configuration. Conversely, if we accept a

relative loss of accuracy, permutation testing will keep the

production at its optimal level, see Fig. 18.

Since both maximal accuracy and minimal production loss

strategies may be required, a global weighted criterion is

actually used to bring all the strategies together and perform

the sequence sorting.

Theoretically, there is no limitation on the number of wells

to consider. However, let us remind that the number of

possible test sequences increases drastically with the number

of wells, see Table 3. In the case of six wells or more, the

computation time can be prohibitive unless one or several

wells are exclusively allocated to a production line.

ConclusionThis paper demonstrates the ability of a well monitoring

software to provide reliable information for producers:

production estimate of each individual well, abnormal

behavior detection, validation of hardware measurements and

replacement in case of failure.

Although the described methodology can be applied to any

type of onshore/offshore development scheme, this work is

mainly intended to deep offshore developments, such as the

Girassol field in Angola.

Based on data reconciliation between field data and flow

modelling, our well monitoring tool requires a periodic

calibration to keep its modelling close to the real process. This

tuning derives from test separator measurements.

Since a combination of direct and permutation well testing

is presently involved at the Girassol field, we also designed a

second tool to compute the optimal test sequences versus usual

production and operating constraints.

Intensive use and positive feedback will confirm the

usefulness and reliability of this work. This will be the main

topic of a second paper.

NomenclatureBSW = Basic Sediment and Water (water volume

flow/liquid volume flow), m3/m3

BSWm = Measured value ofBSW, m3/m3

Flow = Total mass flow rate, kg.s-1

GOR = Gas-Oil Ratio (gas volume flow/oil volumeflow), Sm3/m3

GORm = Measured value ofGOR, Sm3/m3

P1 = Well tubing upstream pressure, Pa

P2 = Well tubing downstream pressure, Pa

P3 = Choke downstream pressure, Pa

Pmi = Measured value ofPi, (i = 1, 2, 3), Pa


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T1 = Well tubing upstream temperature, K

T2 = Well tubing downstream temperature, K

T3 = Choke downstream temperature, K

Tmi = Measured value ofTi, (i = 1, 2, 3), K

WP = Tuning parameter for well tubing pressure drop

WT = Tuning parameter for well tubing heat balance

CP = Tuning parameter for choke pressure drop

CT = Tuning parameter for choke heat balance

WP,ref = Calibrated value ofWP

WT,ref = Calibrated value ofWT

CP,ref = Calibrated value ofCP

CT,ref = Calibrated value ofCT

Pi = Uncertainty ofPmi (i = 1, 2, 3)

Ti = Uncertainty ofTmi (i = 1, 2, 3)

BSW = Uncertainty ofBSWm

GOR = Uncertainty ofGORm

WP = Uncertainty ofWP

WT = Uncertainty ofWT

CP = Uncertainty ofCP

CT = Uncertainty ofCT

References1. Van der Geest, R., Reliability Through Data Reconciliation,

OTC 13000 presented at the 2001 Offshore Technology

Conference held in Houston, Texas (2001).

2. Braunschweig, B., Paen, D., Roux, P., and Vacher, P., The Use of

CAPE-OPEN Interfaces for Interoperability of Unit Operations and

Thermodynamic Packages in Process Modelling, The European

Refining Technology Conference, Paris, France (2001). See also

http://www.colan.org.

Figures

Wellhead Manifold

Fig. 1: Girassol subsea loop

Well tubing

Choke

Pm1, Tm1

Pm2, Tm2

Pm3, Tm3

Fig. 2: Example of a production network

Simulator ExecutiveFramework

Solver

Man MachineInterface

DCS

Unit Operation Thermo Server

CAPE-OPENCOMPONENTS

CAPE-OPEN

SIMULATION

ENVIRONMENT

EXTERNAL

COMPONENTS

Supervisor

Fig. 3: Well monitoring software overview

Fig. 4: Sensor failure simulation


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0.00E+00

5.00E+06

1.00E+07

1.50E+07

2.00E+07

2.50E+07

3.00E+07

0 20 40 60 80 100 120 140 160

Time (min)

Pressure

(Pa)

Bottom hole pressure

Wellhead press ure

Manifold pressure

Fig. 5: Pressure sensor measurements

0.00E+00

5.00E+06

1.00E+07

1.50E+07

2.00E+07

2.50E+07

3.00E+07

0 20 40 60 80 100 120 140 160

Time (min)

Pressure

(Pa)

Reconciliated bottom hole pressure

Reconciliated wellhead press ureReconciliated manifold press ure

Fig. 6: Reconciliated pressure sensor measurements (no

preliminary failure detection)

0.00E+00

2.00E+01

4.00E+01

6.00E+01

8.00E+01

1.00E+02

1.20E+02

1.40E+02

1.60E+02

0 20 40 60 80 100 120 140 160

Time (min)

Gas-Oil Ratio (Sm3/m3)

Total mass flow rat e (kg/s)

Fig. 7: Production estimate (no preliminary failure detection)

0.00E+00

5.00E+06

1.00E+07

1.50E+07

2.00E+07

2.50E+07

3.00E+07

0 20 40 60 80 100 120 140 160

Time (min)

Press

ure

(Pa)

Reconciliated bottom hole pressure

Reconciliated wellhead press ureReconciliated manifold press ure

Fig. 8: Reconciliated pressure sensor measurements (activated

failure detection)

0.00E+00

2.00E+01

4.00E+01

6.00E+01

8.00E+01

1.00E+02

1.20E+02

1.40E+02

1.60E+02

0 20 40 60 80 100 120 140 160

Time (min)

Gas-Oil Ratio (Sm3/m3)

Total mass flow rat e (kg/s)

Fig. 9: Production estimate (activated failure detection)

Fig. 10: Typical Girassol production line


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Time

Bottom hole pressure

Wellhead pressure

Choke downstream pressure

Choke opening

Fig. 11: Pressure measurements and choke opening (%) at a

Girassol well

Fig. 12: Girassol well: production simulation (first case)

Fig. 13: Girassol well: calibration of the modelling parameters

Fig. 14: Girassol well: production simulation (second case)

Fig. 15: Girassol well: GOR calibration

Production

loss Oil production

uncertainty

Methanol

consumption

Subsea valve

operation

Dead branch

creation

Water production

uncertainty

Gas production

uncertainty

Fig. 16: Various strategies for well permutation sequence sorting


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Right

P1021

P1022

Left

P1011

P1012

Right

P1021

P1022

Left

P1012

P1011

Right

P1021

P1022

Left

P1011

P1012

Right

P1021

P1022

Left

P1011

P1012

TEST

TEST

TEST

TEST

M102

M102

M102

M102

M101

M101

M101

M101

Fig. 17: direct testing sequence

Right

P1021

P1022

Left

P1012

P1011

Right

P1021

Left

P1012

P1012

Right

P1021

P1022

Left

P1012

Right

P1022

P1021

Left

P1012

TEST

TEST

TEST

TEST

M102

M102

M102

M102

M101

M101

M101

M101

P1022

P1011

P1011 Fig. 18: Permutation testing sequence

TablesUnit operation Description

Block valve Routing valve (open/closed)

Choke Three-phase model

Fluid source Gas-lift model

Manifold Connection between wells and production loop

PipelineThree-phase flow model. Gridded model.Used for well tubing, sea-line, and riser

Piping Simulation of small scale piping networks

Sensor Hardware sensor model

InflowDefinition of fluid composition and ProductivityIndex relation

Table 1: Well monitoring unit operations

Test number Well arrangement1 WC

2 WA + WB+ WD

3 WB+ WC

4 WB+ WD

Table 2: Example of a well test sequence

Number ofwells

Number of wellarrangements

Number of well testingsequences

3 12 220

4 28 20475

5 60 2.12E+06

6 124 1.52E+09

Table 3: maximal number of well testing sequences

Table 4: Well permutation tests

Table 5: Test sequences (production loss minimization)

Deep Offshore Well Metering and Permutation Testing

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