Nonlinear Model Predictive Control of

Managed Pressure Drilling Based on Hammerstein-Wiener piecewise linear models

2015 Fall AIChE Meeting by Junho Park

Outline

• Overview the Managed Pressure Drilling Process

- Motivation and Challenges

• Downhole Pressure Control using Lower Order Model

• Downhole Pressure Control using Hammerstein-Wiener Model

• Conclusion and future work

Managed Pressure Drilling

Reservoir

Weight on Bit

Rotation

Speed

Drill String

Annulus

Back Pressure Pump

Main Mud Pump

Choke

Valve

Gas InfluxDownhole

Surface

Measurable variables:

- Surface measurements

- Downhole pressure (mud pulse / wired pipe)

Unmeasurable variables in annulus:

- Downhole pressure (non wired pipe)

Why Automate MPD?

• Average of 4 uncontrolled well situations in

the Gulf of Mexico each year (Morris,

2014)

• MPD Automation Project objectives:

• Regulate downhole pressure with a

controller that changes chokes and pumps

• Combine pressure control with ROP

maximization with a controller that changes

chokes, pumps, RPM, and WOB

• Identify and attenuate unexpected gas influx

• Detect and automate cleaning of cuttings

buildup

Deepwater Horizon 2010

[1] Morris, D., Analysis of Well Control Incidents 2007-2013, U.S. Dept. of the Interior, Presented by Dan Fraser at the 2014 SPE ATCE Meeting, Amsterdam, The Netherlands

Types of Models for MPC

First Principles Model - High Fidelity Model (WeMod, SINTEF, OLGA)- Lower Order Model (Stames et al.)

Empirical Model - Linear Empirical Model (e.g. Transfer function, State-space, ARX, OE etc)

- Nonlinear Empirical Model(e.g. Nonlinear ARX, Hammerstein-Wiener Model etc)

Fitting data with Steady State Values

𝒑𝒑 =𝜷𝒅𝑽𝒅

𝒒𝒑𝒖𝒎𝒑− 𝒒𝒃𝒊𝒕

𝒑𝒄 =𝜷𝒂𝑽𝒂

𝒒𝒃𝒊𝒕 + 𝒒𝒃𝒂𝒄𝒌 − 𝒒𝒄𝒉𝒐𝒌𝒆+ 𝒒𝒓𝒆𝒔 − 𝑹𝑶𝑷 × 𝑨𝒂

𝒒𝒃𝒊𝒕 =𝟏

𝑴𝒑𝒑 − 𝑭𝒅 𝒒𝒃𝒊𝒕 𝒒𝒃𝒊𝒕 + 𝝆𝒅𝒈𝒉𝒃𝒊𝒕 − 𝒑𝒃𝒊𝒕

𝒉 = 𝑹𝑶𝑷

𝒑𝒃𝒊𝒕 = 𝒑𝒄 + 𝝆𝒂𝑭𝒂 𝒒𝒃𝒊𝒕 + 𝒒𝒓𝒆𝒔 𝒒𝒃𝒊𝒕 + 𝒒𝒓𝒆𝒔 𝒉 + 𝝆𝒂𝒈𝒉𝒃𝒊𝒕

𝒑𝒊 − 𝒑𝒊+𝟏 = 𝝆𝒂,𝒊𝑭𝒂,𝒊 𝒒𝒃𝒊𝒕 + 𝒒𝒓𝒆𝒔 𝒒𝒃𝒊𝒕 + 𝒒𝒓𝒆𝒔 𝒉𝒊 − 𝒉𝒊−𝟏 + 𝝆𝒂,𝒊𝒈 𝒉𝒗,𝒊 − 𝒉𝒗,𝒊−𝟏

Estimate the initial parameters in Lower Order Model

Not a good agreement at low pressure rangeIt results a model mismatch when pipe connection takes place

For Non-wired drillpipe rigs

• Estimate the downhole pressure using surface measurements via MHE

• MHE employs the Lower Order Model

Downhole pressure

Mud pump pressure

Choke valve pressure

Choke valve flowrate

PID and Model Predictive Control

Performance comparison for Downhole pressure control

Conventional (PID) Advanced (MPC)

Choke opening

Mud pump flowrate

Choke opening

Mud pump flowrate

Downhole pressure Downhole pressure

Applying Piecewise Linear to MPC

Nonlinearity Piecewise Linear

Nonlinear static element (Gain) and Linear dynamic element (Time Constant) Assume that the variation of the dynamics is negligible.Time constant of Downhole pressure response is ~50 second.

Structure of Hammerstein-Wiener Models

input nonlinearity

F

linear dynamic model

G

output nonlinearity

H

u(t) w(t) x(t) y(t)

Transform the input and output instead of changing Gain directly.Changing Gain will occur the prediction error.Most efficient way to apply piecewise linear concept to MPC algorithm

Nonlinearity in MPD Process

Increasing q_pIncreasing z_choke

Operation Range

Model Identification – Hammerstein Wiener

Random Signal step test

Mud Pump

flowrate

Back pressure

Pump flowrate

Choke opening

- Implementing Multi Variable Identification

3 input nonlinearity blocks3 Linear Model blocks1 output nonlinearity blocks

- Cover the entire operation range of interest to get nonlinearity.

Structure of Hammerstein-Wiener MPC

Model Validation - 1

Linear Model vs Hammerstein Wiener Model

45% 78%

Model Validation - 2

Linear Model vs Hammerstein Wiener Model

45% 78%

Conclusion

• Estimate the downhole pressure by using Lower Order Model

• Validate the control performance

- Maintain pressure within 1 bar during Normal drilling

- Improved Model accuracy up to 80% for Pipe connection

Future work

• Improve the Lower Order Model for downhole estimation

• Performance verification with high fidelity simulator for:

• Kick attenuation

• Cuttings build-up detection and removal

• MPD Rig Testing at NOV Navasota test facility

Acknowledgements

• We gratefully acknowledge the technical support provided by NOV and BYU students including significant contributions from:

• Reza Asgharzadeh

• Mostafa Safdarnejad

• Thomas Webber

• David Pixton

• Tony Pink

• Reza Rastegar

• Adrian Snell

• Andy Craig

• Shantur Tapar

• Alan Clarke

Questions?

Back up slides

Pressure Controller Results

Normal Drilling:

• Estimator adapts within 1.5% error margin in 30 sec

• Drill bit pressure is controlled to set point

Connection Procedure:

• The controller is able to control the drill bit pressure +/- 3 bar

Kick Attenuation:

• Improved response to unexpected gas influx

• Immediate action avoids more intrusive well control efforts

ROP and Downhole Pressure Control

ROP 20%

higher

• Increased ROP due to the

decreased downhole pressure• Improved kick attenuation compared

to single pressure controller

Observe limits for RPM and

WOB from drilling engineer

PID and Model Predictive Control

FUTURE

Input

(eg. Flow rate)

Output

(eg. Valve)

PAST

k

Model Predictive Control “sees” into the future to makean optimal moves of MV

Conventional (PID) Advanced (MPC)

Models Adapt to Changes

Moving Horizon Estimation

Historical Data

Measured Output

Estimated Output

Manipulated Variable

Estimated Variable

24

Model Predictive Control

PID and Model Predictive Control

Multivariable control aspects

Single-Input-Single-Output Multi-Input-Multi-Output

PID MV

SP

CV MPCCV1

CV2

CVn

Set point or Range

MV1

MV2MV3

MVm

Control System Overview

MHE

MPC

Mud Pump Pressure

Choke Valve Pressure

Choke Valve Flowrate

Choke Valve Opening

Mud Pump flowrate

DownholePressure

Inputs and Outputs (Downhole pressure control)

Controller: Normal Operation

Manipulate topside WOB

to regulate the downhole

WOB

Manipulate choke valve

and mud pump to regulate

BHP

Manipulate topside RPM to

regulate the downhole RPM

Find the optimum WOB, RPM and

BHP that lead to the desired ROP

Desired

ROP

• During normal operation, the optimum downhole pressure values are determined to give

the highest possible ROP.

Previous Work in Pressure Control

• Surface measurements (without WDP)

• Delayed response to kick events

• Estimation of fluid properties difficult

• Reactive pressure control vs. proactive

• Most prior work with linear controllers

• Nonlinear viscoelastic mud properties

• Nonlinear ROP effects

• Decreased and more variable ROP

Reservoir

Surface Measurements

Main Mud Pump

Choke

Valve

Back Pressure Pump

Controller Estimator

Estimated Downhole

Pressure

Indirectly Controlling the Downhole Pressure

• Using advanced technology which transmits pressure data to surface can improve pressure control

Previous Work in ROP Control

• Ignore change of WOB and RPM along drill string

• Ignore effect of downhole pressure dynamics

• Potential for ROP improvement

Reservoir

Real Time

Optimization

Cost Function

Multiple Linear

Regression

Weight on Bit

Rotation Speed

Rate of Penetration

Model Components

• Pressure Hydraulics: Lower order model (Stames et al.)

• 4 state equations:

• Mud pump pressure (pp)

• Choke valve pressure (pc)

• Drill bit flow rate (qbit)

• Drilling height (h)

• ROP: Bourgoyne & Young model

• 8 functions:

• Formation strength

• Pressure differential of bottom hole

• Formation compaction

• Bit diameter and weight

• Rotary speed

• Tooth wear

• Hydraulics

𝑅𝑂𝑃 = 𝑒𝑥𝑝 𝑎1 +

𝑖=2

8

𝑎𝑖𝑥𝑖

Single Pressure Controller MHE

• Within 30 seconds of operation, the estimator accurately determines the friction factor

and the annulus density within 1.5% of the actual value.

Pressure Control with Normal Drilling

• Within 30 seconds, the drill bit pressure has been controlled within 0.72% of its set point of 345 bar

Kick Attenuation

• Improved Response to Unexpected Gas Influx.

• Immediate action avoids more intrusive well control efforts.

Connection Procedure

• The controller is able to control the drill bit pressure within 1% error margin.

Estimator Performance

20 40 60 80 100 120 140 1600

0.005

0.01

0.015

0.02

0.025

0.03

Time, s

Annulu

s M

ud D

ensi

ty,

10

-5 k

g/m

3

Cle

an

Da

ta

20 40 60 80 100 120 140 1600

0.005

0.01

0.015

0.02

0.025

0.03

Time, s

Annulu

s M

ud D

ensi

ty,

10

-5 k

g/m

3

20 40 60 80 100 120 140 160 1800

0.005

0.01

0.015

0.02

0.025

0.03

Time, s

Annulu

s M

ud D

ensi

ty,

10

-5 k

g/m

3

20 40 60 80 100 120 140 160 1800

0.005

0.01

0.015

0.02

0.025

0.03

Time, s

Annulu

s M

ud D

ensi

ty,

10

-5 k

g/m

3

OutlierDrift OutlierDrift

Co

rru

pte

d D

ata

5.93% dev. 0.82% dev. 1.67% dev. 0.41% dev.

Squared Error ℓ1-norm

MPD Controller Performance

0 20 40 60 80 100 120 140 160290

300

310

320

330

340

350

360

370

Time, sD

rill B

it P

ressure

, bar

Drill Bit Pressure

Choke Valve Pressure

0 20 40 60 80 100 120 140 16020

25

30

35

40

45

50

55

60

Choke V

alv

e P

ressure

, bar

0 20 40 60 80 100 120 140 160250

300

350

400

Time, s

Drill B

it P

ressure

, bar

Drill Bit Pressure

Choke Valve Pressure

0 20 40 60 80 100 120 140 1600

20

40

60

Choke V

alv

e P

ressure

, bar

Squared Error ℓ1-norm

MPD Manipulated Variables

0 20 40 60 80 100 120 140 1600%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Time, s

Choke V

alv

e O

penin

g

0 20 40 60 80 100 120 140 16010%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Time, s

Choke V

alv

e O

penin

g

0 50 100 150 2000

1000

2000

3000

Time, sP

um

p F

low

Rate

, L

/min

0 20 40 60 80 100 120 140 1600

2000

4000

6000

Time, s

Pum

p F

low

Rate

, L/m

in

Squared Error ℓ1-norm

ROP Benefit

ROP 20% higher

• Increased ROP due to the decreased downhole pressure.

Kick Attenuation

• Comprehensive controller is able to attenuate kick more effectively and faster compared with single pressure controller.

Kick Attenuation

• Drilling continues with consistent ROP during kick.

• Avoids cutting loading issues due to ROP fluctuations.

Surge/Swab Induced Kick

0 50 100 150 200 250-500

0

500

1000

1500

Time (sec)

Gas I

nflux (

L/m

in)

No communication loss

Interrupt 20 sec

Interrupt 60 sec

Interrupt 100 sec

• Clear benefit in reducing the time that communication is lost with downhole pressure measurements.

• Automatic model-based controller is able to reject significant events of unexpected gas influx.

Cuttings Buildup Protection

15 20 25 30 35 40 45 50 55 60 65 701200

1250

1300

1350

1400

1450

1500

1550

1600

1650

Time, s

Density,

kg/m

3

2

3

4

5

6

7

8

9

10

Density Difference at Location 5

• Early detection of cuttings buildup can improve the operational strategy for cuttings removal.

• The higher density estimate from location 5 (middle point in the axial direction of the annulus) is an

indication of cuttings buildup.

0 10 20 30 40 50 60 700

50

100

Time, s

Choke V

alv

e O

penin

g

Choke Valve Opening

0 10 20 30 40 50 60 70500

1000

1500

2000

Time, s

Pum

p F

low

Rate

, L/m

in

Pump Flow Rate

10 20 30 40 50 60 700

50

100

150

200

250

300

Time, s

Annula

r P

ressure

s,

bar

P1

P2

P3

P4

P5

P6

P7

P8

P9

P10

• Slight increase in pressure at location 5

relative to the other pressure sensors

would be difficult for an operator to

identify.

• The density estimates are fed to the

predictive controller to update the

underlying model used in the forward

looking optimization

Cuttings Buildup Protection

Distributed Pressure Measurements

0 5 10 15 20 25 30 35 400

10

20

30

40

50

60

70

80

90

100

Time, s

Influx R

ate

, L/m

in

Single Sensor

Multiple Sensors

• The pressure sensor closer to the kick location is able to sense the change earlier and with

a higher degree of accuracy.

• The comprehensive controller manipulates the RPM, pump flow, and choke valve opening

simultaneously to attenuate the kick.

Managed Pressure Drilling

Reservoir

Weight on Bit

Rotation

Speed

Drill String

Annulus

Back Pressure Pump

Main Mud Pump

Choke

Valve

Gas InfluxDownhole

Surface

Known variables:

- Surface measurements

- Downhole RPM, WOB

- Annulus pressure (mud pulse / wired pipe)

Unknown variables in annulus:

- Density (annulus)

- Friction Factor (annulus)

- Downhole drilling fluid and gas influx flow rate

Previous Work in MPD

Optimization: Maximize ROP Automation: BHP Regulation

System

f(Zchoke,qpump,friction)BHP

Zchoke

qpump

Two

Controllers

System

f(RPM,WOB,BHP)ROP

RPM

WOB

Improve Economics Improve Safety

Is it possible to design a comprehensive controller which considers both tasks simultaneously?

• Surface measurements (without WDP)

• Delayed response to kick events

• Estimation of fluid properties difficult

• Reactive pressure control vs. proactive

• Most prior work with linear controllers

• Decreased and more variable ROP

• Ignore change of WOB and RPM along drill string

• Ignore effect of downhole pressure dynamics

• Potential for ROP improvement

Proposed Comprehensive Controller

• Comprehensive controller which controls ROP

and BHP simultaneously.

• Considers the interaction between drill string

and hydraulics.

• Kick attenuation methodology based on the

direct BHP measurements using wired pipe

telemetry.

• Designing multivariate controller using multiple

sensor measurement along annulus.

Comprehensive Controller:

Maximize ROP + Regulate BHP

ROPRPM

WOB

System

BHP

Zchoke

qpump

Single Controller

Considers

Multivariate Effects

• Higher ROP and less fluctuations in that.

• Improved Kick attenuation with adding extra

manipulated variable.

• Earlier detection of cuttings loadings during

drilling.

• Improved safety and reduced drilling cost.

Expected Advantages Features of Comprehensive Controller

Model Components

𝑓𝑎 = 𝑎 𝑅𝑒𝑎𝑥𝑖𝑎𝑙𝑏 + 𝑐 𝑅𝑒𝑎𝑛𝑔𝑢𝑙𝑎𝑟

• Rotation Speed (RPM) effect on Friction Factor

Drill String Dynamics

• Drill String Dynamics

• Multiple mass-spring-damper pendulums in

series

• Johannessen, M.K. and T. Myrvold

• WOB Dynamics

• First order plus dead time model

• Surface WOB -> Downhole WOB

http://wwtinternational.com/index.php/torque_reduction

Pressure Hydraulics

• Lower order model (Stames et. al)

Interaction Between Drill String and Hydraulics

• ROP also depends on the downhole pressure

• ROP, Bourgoyne & Young Model

• Friction factor in the annulus depends both on

axial and rotational flow of the mud

Model Components (Cont.)

• Drill String Dynamics

• Multiple mass-spring-damper pendulums in

series

• Johannessen, M.K. and T. Myrvold

• WOB Dynamics

• First order plus dead time model

• Surface WOB -> Downhole WOB

• Rotation Speed (RPM) effect on Friction Factor

• Fluid and cuttings rotational movement

• Affect hydrostatic head downhole

• Ozbayoglu et al. model

0 2 4 6 8 10 12 14 16 18 20100

102

104

106

108

110

112

114

116

118

120

Time (sec)

Rota

tional R

ate

(R

PM

)

Top Drive

Mid-Point in Drill String

Bottom Hole Assembly

𝑅𝑒𝑎 =757 𝜌 𝑣𝑎 𝐷𝑜 − 𝐷𝑖

𝜇𝑎Velocity in Axial Direction

𝑓𝑎 = 𝑎 𝑅𝑒𝑎𝑥𝑖𝑎𝑙𝑏 + 𝑐 𝑅𝑒𝑎𝑛𝑔𝑢𝑙𝑎𝑟

𝑅𝑒𝜔 =2.025 𝜌 𝑅𝑃𝑀 𝐷𝑜 − 𝐷𝑖 𝐷𝑖

𝜇𝜔Rotation Speed of Drill String

Control System Scheme

Dow

nh

ole

RP

M

Dow

nh

ole

WO

B

Reservoir

Main Mud Pump

Choke

Valve

Estimator

Surface Measurements

ρ𝑎, 𝑓𝑎

Gas Influx

Pressure

Controller

Operator Input

, WOB, RPM

ROP

Optimizer

Pressure set point

Back Pressure Pump

Downhole Pressure

RPM MPC

WOB MPC

Downhole RPM SP

Downhole WOB SP

ROP and RPM set point

Kick Attenuation Mode

Change choke valve opening, surface RPM

and pump flow rate

Hold until

𝑞𝑖𝑛𝑓𝑙𝑢𝑥 < specified limit

Normal Drilling Operation

𝑃𝑏𝑖𝑡 > 𝑆𝑎𝑓𝑒𝑡𝑦 𝑀𝑎𝑟𝑔𝑖𝑛 + 𝑃𝑠𝑝 and

𝑞𝑖𝑛𝑓𝑙𝑢𝑥 > 0

Switch the controlled variable from downhole

pressure into choke valve pressure and set

the set point as

𝑃𝐶𝑆𝑃 = 𝑃𝐶

𝑏𝑒𝑓𝑜𝑟𝑒 𝑘𝑖𝑐𝑘+ 𝑘𝑝𝐸 + 𝑘𝐼 𝐸 + 𝑘𝑑

𝑑𝐸

𝑑𝑡

𝐸 = 𝑃𝑏𝑜𝑡𝑡𝑜𝑚𝑐𝑢𝑟𝑟𝑒𝑛𝑡 − 𝑃𝑏𝑜𝑡𝑡𝑜𝑚

𝑠𝑝Calculate

the new

reservoir

pore

pressure

(Pres)

Switch the controlled variable from choke

valve pressure to downhole pressure

Reservoir

Pump flow Rate

Choke Pressure

Set Point

Choke Valve

Set Point

Gas Influx

Downhole Pressure

Surface RPM

• Kick attenuation has higher priority than achieving higher ROP

Less Sensitive to Bad Data

20 40 60 80 100 120 140 1600

0.005

0.01

0.015

0.02

0.025

0.03

Time, s

Annulu

s M

ud D

ensi

ty,

10

-5 k

g/m

3

Cle

an

Da

ta

20 40 60 80 100 120 140 1600

0.005

0.01

0.015

0.02

0.025

0.03

Time, s

Annulu

s M

ud D

ensi

ty,

10

-5 k

g/m

3

20 40 60 80 100 120 140 160 1800

0.005

0.01

0.015

0.02

0.025

0.03

Time, s

Annulu

s M

ud D

ensi

ty,

10

-5 k

g/m

3

20 40 60 80 100 120 140 160 1800

0.005

0.01

0.015

0.02

0.025

0.03

Time, s

Annulu

s M

ud D

ensi

ty,

10

-5 k

g/m

3

OutlierDrift OutlierDrift

Co

rru

pte

d D

ata

5.93% dev. 0.82% dev. 1.67% dev. 0.41% dev.

Squared Error ℓ1-norm

Controller Results

• Drilling continues with consistent ROP during kick.

• Avoids cutting build up issues due to ROP fluctuations.

Publications

• Asgharzadeh Shishavan, R., Hubbell, C., Perez, H.D., Hedengren, J.D., and Pixton, D.S., Combined Rate of

Penetration and Pressure Regulation for Drilling Optimization Using High Speed Telemetry, SPE Drilling and

Completions Journal, accepted for publication / in press.

• Hedengren, J. D. and Asgharzadeh Shishavan, R., Powell, K.M., and Edgar, T.F., Nonlinear Modeling, Estimation

and Predictive Control in APMonitor, Computers and Chemical Engineering, Volume 70, pg. 133–148, 2014.

• Asgharzadeh Shishavan, R, Perez, H., Hedengren, J., Brigham Young University; Pixton, D.S., NOV IntelliServ;

Pink, A.P., National Oilwell Varco, Multivariate Control for Managed Pressure Drilling Systems Using High Speed

Telemetry, Submitted to SPE Journal, under review.

Accepted Journal Articles

• Reza Asgharzadeh Shishavan, David S. Pixton, Ammon N. Eaton, Junho Park, Hector D. Perez, and John D.

Hedengren and Andrew Craig, Addressing UBO and MPD Challenges with Wired Drillpipe, Submitted to SPE Journal,

under review.

Submitted Journal Articles

54

Publications Cont.

55

Peer Review Conference Papers

• Asgharzadeh Shishavan, R., Perez, H., and Hedengren, J.D., Multivariate nonlinear model predictive controller for managed

drilling processes, AIChE Annual Meeting, Atlanta, GA, Nov 2014.

• Asgharzadeh Shishavan, R., Hubbell, C., Perez, H.D., Hedengren, J.D., Pixton, D.S., and Pink, A.P., Multivariate Control for

Managed Pressure Drilling Systems Using High Speed Telemetry, SPE Annual Technical Conference and Exhibition (ATCE),

Amsterdam, The Netherlands: 27-29 Oct 2014.

• Asgharzadeh Shishavan, R., Hubbell, C., Perez, H.D., Hedengren, J.D., and Pixton, D.S., Combined Rate of Penetration

and Pressure Regulation for Drilling Optimization Using High Speed Telemetry, SPE Deepwater Drilling and Completions

Conference, Galveston, TX: 10-11 Sept 2014.

• Asgharzadeh Shishavan, R., Brower, D.V., Hedengren, J.D., Brower, A.D., New Advances in Post-Installed Subsea

Monitoring Systems for Structural and Flow Assurance Evaluation, ASME 33rd International Conference on Ocean, Offshore

and Arctic Engineering, OMAE2014/24300, San Francisco, CA, June 2014.

• Brower, D.V., Brower, A.D., Hedengren, J.D., Asgharzadeh Shishavan, R., A Post-Installed Subsea Monitoring System for

Structural and Flow Assurance Evaluation, Offshore Technology Conference, OTC 25368, Houston, TX, May 2014.

• Pixton, D., Asgharzadeh Shishavan, R., Hedengren, J.D., and Craig, A., Addressing UBO and MPD Challenges with Wired

Drillpipe, SPE/IADC MPD & UBO Conference & Exhibition, Madrid, Spain: 8 - 9 Apr 2014.

• Brower, D., Hedengren, J.D., Asgharzadeh Shishavan, R., and Brower, A., Advanced Deepwater Monitoring System, ASME

32st International Conference on Ocean, Offshore and Arctic Engineering, OMAE2013/10920, Nantes, France, June 2013,

ISBN: 978-0-7918-5531-7.

55

Publications Cont.

• Asgharzadeh Shishavan, R., Memmott, J., Hedengren, J.D, and Pixton, D., Pressure Regulation and Kick Attenuation

with Wired Pipe Technology in Managed Pressure Drilling. AIChE Spring Meeting, New Orleans, LA, April 2014.

Asgharzadeh Shishavan, R. and Hedengren, J.D.,

• Improved Estimator Insensitivity to Outliers, Measurement Drift, and Noise, AIChE Spring Meeting, New Orleans, LA,

April 2014. Brower, D., Brower, A., Memmott, J.A.,

• Hedengren, J.D., Mojica, J.L., Asgharzadeh Shishavan, R., Safdarnejad, S.M., Recent Advances in the Application of

MIDAE Systems, AIChE National Meeting, San Francisco, CA, Nov 2013.

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