1. Dynamic Modeling and Advanced Control for Offshore Platforms
VI Oil and Gas Production Optimization Workshop
Dynamic Modeling and Advanced Control for Offshore Platforms
Argimiro R. Secchi
Chemical Engineering Program – COPPE
Universidade Federal do Rio de Janeiro
Technological Center, Rio de Janeiro – RJ
Rio de Janeiro
26 Apr 2017
Solutions for Process Control and Optimization

2. Outline Challenges for Offshore Production Advanced Process Control
Model Predictive Control
APC in Offshore Platforms
Final Remarks

3. Challenges for Offshore Production
Source: PETROBRAS
Lula
250 km
- Ultra-deep water
- Faraway from coast
- Pre-salt layer
- High CO2 content
- Stringent regulations
- High costs

4. Challenges for Offshore Production
- Complex networks for well allocation, gas/water injection, and gas lift
- Multiphase flow (gas, oil, water, sand, hydrate)
- Lack of instrumentation
- New technologies for subsea processing
- Difficult maintenance
- Automation and Remote operation
Source: FMC Technologies

5. Challenges for Offshore Production
gas lift flowrate
oil flowrate
- Time-varying process (gas-oil-ratio, water cut, gas/water coning)
- Nonlinearities (variable gain, output saturation)
- Severe disturbances (slugs)
- High uncertainties
flow instability
Data from offshore platform

6. Challenges for Offshore Production
Plenty of room for
Modeling and Soft Sensors
Advanced Process Control
Process Optimization

7. Challenges for Offshore Production
Targeting
Maximize Production (oil & gas)
Ensure Quality Specification (oil, water & gas)
Minimize Losses (flare & TOG)
Minimize Energy Consumption (heat & power)
Minimize Operational Costs (maintenance & shutdowns)
Minimize Process variability
Ensure Process Stability and Safety
Flow Assurance (prevent: hydrate, paraffins, asphaltenes, fouling)

8. Process Control Hierarchy
Planning and Scheduling
Plantwide computer
weeks
Lack of room for computer in the platform
Real-Time Optimization
hours
Process computer
Advanced Process Control
DSC
minutes
Regulatory Control
seconds
Process

9. Advanced Process Control
Advanced Process Control (APC): is a term that can include a range of methodologies, including model predictive control (MPC), fuzzy logic, statistical control, etc. The common objective is to find a way to manage complex interactions within a process better than traditional regulatory control.

10. APC & RTO Objectives Constrained optimization problem
Maximize production
Ensure product specifications
Minimize energy and water consumption
Minimize process variability
Minimize loss of products
Respect process constraints
Safeguard environmental laws
Constrained optimization problem

11. Challenges for Offshore Production
Targeting  APC+RTO Objectives
Maximize Production (oil & gas)
Ensure Quality Specification (oil, water & gas)
Minimize Losses (flare & TOG)
Minimize Energy Consumption (heat & power)
Minimize Operational Costs (maintenance & shutdowns)
Minimize Process variability
Ensure Process Stability and Safety
Flow Assurance (prevent: hydrate, paraffins, asphaltenes, fouling)

12. Open-Loop Optimal Control
Controller
Plant
set-point
input
output
r(t)
u(t)
y(t)
measurements
model constraints
path constraints
terminal constraints

13. Model Predictive Control (MPC)
Feedback nature: Implement first “control move” then correct for model mismatch.
(desired output)
(desired output)
Open-loop optimal control problem: Find current and future manipulated inputs that best meet a desired future output trajectory.
next sample time
Major issue: disturbances vs. model uncertainty.
* B.W. Bequette, Process Dynamics. Modeling, Analysis, and Simulation, Prentice Hall.

14. Open questions in MPC Type of model for predictions?
linear: state space, TF, step response, impulse response, ARX
nonlinear: first principles, NN, Volterra, Wiener, Hammerstein, multiple model, fuzzy, NARX
Information needed at step k for predictions?
outputs, state estimates, measured disturbances, model parameters
Objective function and optimization technique?
quadratic (QP), absolute values (LP), economics (EMPC), nonlinear (NLP)
Correction for model error?
additive output, additive input, disturbance estimation (KF, EKF, MHE)

15. Implementation of APC Control structure design
Check instrumentation and retune regulatory control
Pre-tests and design of inferences (soft sensors)
Plant test and identification of dynamic models
Controller configuration and closed-loop simulation
Commissioning and tuning of the controller
Monitoring the APC performance
Training of operators and documentation

16. Retuning Regulatory Control
Regulatory control is essential for the success of APC
Gas Processing Plant
* Campos, M.C.M., Teixeira, A.F., Advanced Control Systems, 7th Int. Conf. on Integrated Operations, Trondheim, Norway.

17. Retuning Regulatory Control
FPSO Control System (offshore oil & gas production plant)
Many transients
Shutdowns
well re-alignments
Flow instabilities
compressors availability
About 90% of PID control loops analyzed could have a better performance
Adaptive tuning
* Campos, M.C.M., Teixeira, A.F., Advanced Control Systems, 7th Int. Conf. on Integrated Operations, Trondheim, Norway.

18. Monitoring the APC Performance
MPC performance can degrade due to:
Changes in the unit operations objectives;
Equipment efficiency losses (e.g., fouling);
Changes in the feed quality;
Problems in instruments and in soft sensors;
Lacks of qualified personnel for the controller's maintenance.

19. MPC in Offshore Platforms
Industrial applications:
Hocking & Caward (1999) – Honeywell RMPCT applied to production control  2% increase in production
Godhavn et al. (2005) – Statoil in-house MPC controller (SEPTIC): slug control  3% increase in production
Simulated:
Plucenio & Djmgab-ah (2006) – NL-DMC applied to pressure control
Willersrud et al. (2011) – NMPC with unreachable setpoint for production control
Cota & Reis (2012) and Ribeiro et al. (2016) – MPC applied to production and quality control
Miyoshi et al. (2012) and Mendes et al. (2012) – MPC applied to slug control
Peixoto et al. (2015) – Extremum seeking for production control.

20. Challenges for MPC Optimization problem - infinite prediction horizon
- multiple objectives
Simplifying the model development process
- plant testing & system identification
- nonlinear model development
- intensive use of dynamic simulators
- model reduction techniques
State Estimation
- Lack of sensors and sensor location for key variables
Reducing computational complexity
- approximate solutions, preferably with some guaranteed properties
- modern computation (sparse matrices, better numerical methods)
Better management of “uncertainty”
- creating models with uncertainty information (e.g., stochastic model)
- on-line estimation of parameters / states
- “robust” solution of optimization
- self-tuning and adaptive MPC

21. APC in Offshore Platforms Conventional Platform
FPSO (Floating Production, Storage and Offloading)
Located on the Marlim Field

22. Conventional Platform
Gas Lift
Exportation
Compression
Fuel Gas
Wells
Oil Treatment
Oil
Produced Water Treatment
Purge

23. Environment for Modeling, Simulation and Optimization
What can we do with EMSO?
Steady-state simulations
Dynamic simulations
Steady-state optimizations (NLP, MINLP)
Steady-state parameter estimations
Dynamic parameter estimations
Steady-state data reconciliations
Process follow-up and inferences with OPC communication
Build bifurcation diagrams (interface with AUTO for DAEs)
Dynamic simulations with SIMULINK (interface with MATLAB)
Add new solvers (DAE, NLA, NLP)
Add external routines using the Plugins resource

24. Environment for Modeling, Simulation and Optimization
EMSO Key Features
Open source library of models
Object-oriented modeling
Built-in automatic and symbolic differentiation
Automatic checking and conversion of units of measurement
Solve high-index problem
Perform consistency analysis (DoF, initial condition)
Integrated Graphical User Interface (GUI)
Building blocks to create flowsheets
Discrete (state and time) event handling
Multitask for concurrent and real-time simulations
Very modular architecture and support to sparse algebra
Multiplatform: win32 and posix
Interface with user code written in C/C++ or Fortran
Automatic documentation of models using hypertexts and LaTeX

25. Conventional Platform

26. Produced Water Treatment
Pre-Salt Platform
Exportation
Injection Wells
Exportation
Compression
Injection
Compression
Fuel Gas
Main Compression
Gas Dehydration
Dew-Point Adjustment
CO2
Removal
CO2
Compression
Wells
Oil Treatment
Vapor Recovery Unit
Produced Water Treatment
Oil
Purge

27. Pre-Salt Platform

28. Thermodynamic Models - VRTherm
High CO2 content at high pressure and temperature
GERG2008
CPA with quadrupole
Mixture 1
Mixture 2
Component
Mixture 1
Mixture 2
Pressure (bar) 28

29. Production well model
Observer Design for Multiphase Flow in Vertical Pipes with Gas-Lift — Theory and Experiments, O.M. Aamo, G.O. Eikrem, H.B. Siahaan, B.A. Foss, Journal of Process Control 15 (2005) 247–257.
Simplified model that aims to capture the casing heading phenomenon
Constant reservoir pressure
Two-phase flow in the pipeline, oil and water treating as a single phase

30. Riser model
A low-dimensional dynamic model of severe slugging for control design and analysis, Espen Storkaas, Sigurd Skogestad, John-Morten Godhavn, Multiphase'
Simplified model that aims to reproduce severe slugs, capturing the main pressure dynamics in the pipeline

31. Three-phase separator model
Black Oil model
Mass balances:
Oil chamber
Separation chamber
Gas space

32. Compression cycle model
1 turbine
1 turbo compressor per stage
1 heat exchanger per stage
1 flash drum per stage
1 heat exchanger at the end of the cycle

33. CO2 removal model Gas-separation membrane modules Cocurrent operation
Permeation flowrate of each compound as function of the log-mean square of its partial pressure differences and permeability.
Feed
Permeate
Retentate

34. Regulatory control Oil level control Oil-water interface level control
Flash drums level control
Compressors capacity control
Anti-surge control
Flowrate control
Pressure control
Temperature control
Anti-slug control

35. Anti-slug control
Topside choke valve: Havre & Dalsmo (2001), Eikrem et al. (2004), Godhavn et al. (2005), Sinegre et al. (2005), Storkaas & Skogestad (2008), Scibilia et al. (2008), Di-Meglio et al. (2012), Jahanshahi et al. (2012), Stasiak et al. (2012), Bendia (2013), Oliveira et al. (2015), Campos et al. (2015)
Gas-lift control: Asheim (1988), Hu (2004), Plucenio et al. (2012), Krima et al. (2012)
MIMO control: Pagano et al. (2008), Miyoshi et al. (2012), Abardeh (2013), Jahanshahi et al. (2013)

36. Topside choke opening (u)
Anti-slug control
Variable gain and unstable region
Topside choke opening (u)
Wellhead pressure (P1)
Stable s.s.
Min. pressure
Max. pressure
Unstable s.s.
Average pressure
Hopf bifurcation point

37. Anti-slug control PI controller with adaptive tuning (Bendia, 2013)
topside choke
well
separator
PI controller with adaptive tuning (Bendia, 2013)

38. Anti-slug control

39. Wellhead pressure (bar)
Anti-slug control
Wellhead pressure (bar)
controller on
Time (s)
Topside choke opening
Time (s)

40. Anti-slug control
PI proportional gain
Time (s)
Proportional gain

41. MPC in Offshore Platform
MPC for production and quality control (Ribeiro et al., 2016)
Anti-slug regulatory control

42. MPC in Offshore Platform
Goals
Setpoint tracking of oil production flowrate
TOG ≤ TOGmax = 1000 ppm (Total of Oil and Grease)
BSW ≤ BSWmax = 20% (Basic Sediments and Water)

43. 43 model identification
MPC in Offshore Platform
43 model identification
EMSO-MATLAB/Simulink interface:

44. sampling points (sampling time = 10s)
MPC in Offshore Platform
Model fit = 95.4%
Identification with overlaped inputs signals
sampling points (sampling time = 10s)
Planta (EMSO)
Modelo
Output 1: oil flowrate

45. Produced oil flowrate setpoint change
MPC in Offshore Platform
Produced oil flowrate setpoint change

46. MPC in Offshore Platform
Increase of water content due to reservoir aging, represented by a linear increase of BSW in well #91 from 30% to 40% during 4 hours

47. MPC in Offshore Platform
Disturbance rejection (BSW increase in well #91)

48. Nonlinearity

49. NMPC in Offshore Platform
(Simões, 2017)
Controlled Variables
Description
Unit
HP liquid level
Manipulated Variables
Description
Unit
LP liquid level
Control valve of HP liquid level
TO interface level
Control valve of LP liquid level
HP pressure
Control valve of TO int. level
LP pressure
Control valve of HP pressure
Control valve of LP pressure
Compression system
Compression system booster
Exportation oil
Production
header
Produced water treatment 49

50. NMPC in Offshore Platform
Disturbance
Description
Unit
Disturbance
Description
Unit
HP inlet liquid flowrate
LV01 outlet pressure
HP inlet gas flowrate
PV01 outlet pressure
LP inlet liquid flowrate
LV02 outlet pressure
LP inlet gas flowrate
PV02 outlet pressure
LV03 outlet pressure
Pump outlet pressure
HP temperature
LP temperature
Compression system
Compression system booster
Exportation oil
Production
header
Produced water treatment 50

51. NMPC in Offshore Platform
Oil Treatment Plant
NMPC model language
HYSYS
Simulation
BRNMPC
OPC 51

52. NMPC in Offshore Platform
Response to slug flow disturbance
TO level control
LP pressure control

53. NMPC in Offshore Platform
Step response to setpoint change in the TO interface level

54. NMPC in Offshore Platform
NMPC presented better performance;
Level control:
MPC and NMPC: effects of the upstream vessels were taken into account;
MPC and NMPC: presented similar results;
Pressure control:
Nonlinear behavior;
NMPC: superior performance than MPC and PID, with anticipatory actions;
5 PID controllers were replaced by one (N)MPC (safety with watchdog).

55. MPC in Compression System
1st train y01-y05
1st header u09
2nd train y06-y10
Dehydration
2nd header u10
1st relief valve
u12
Feed
d01-d02
Heat exchanger u01
Heat exchanger u02
Heat exchanger u03
Heat exchanger u04
Sweetening
y21
2nd relief valve
u13
1st train y11-y15
2nd train y16-y20
Heat exchanger u05
Heat exchanger u06
1st train
2nd train
Heat exchanger u07
Heat exchanger u08
3rd header u11
Exportation
d03
output
recycle
u15
u14
u16
u17
u18
u19
(Thomaz, 2017)

56. MPC in Compression System
nz = 305
Original system: 22  21 = 462 transfer functions
Not considering functions with small static gains (< 10-3): result in 305 transfer functions.
Controlled variables
Manipulated variables

57. Interface EMSO-MATLAB®
EMSO: virtual plant with regulatory control
Simulink®: MPC
measured disturbance
unmeasured disturbance d
EMSO
(regulatory control and process model)
reference trajectory
MPC
Toolbox u y
output
feedback

58. MPC in Compression System
Step response to +5% disturbance in feed flowrate
Main Compressor
Less work
with MPC
Less work
with MPC
Less energy
with MPC

59. MPC in Compression System
Step response to +5% disturbance in feed flowrate
Export Compressor
Fast response and constraint satisfaction
Less work
with MPC
Less work
with MPC
Less energy
with MPC

60. MPC in Compression System
Step response to +5% disturbance in feed flowrate
CO2 removal
Less impact in separator efficiency
with MPC

61. MPC in Compression System
Main compressor
Step response to 3% disturbance in 2nd train compressor efficiency
1st train
2nd train

62. MPC in Compression System
Significant economic gains were obtained with MPC, constraints were respected increasing the uptime of the plant and less maintenance of the equipment;

63. Final Remarks Lots of work for Process System Engineers!!
Offshore plants are becoming more complex, requiring APC and RTO strategies
MPC and RTO are mature industrial technologies
Robustness and remote operation are still very demanding for offshore APC/RTO
Monitoring and diagnosis are open issues for feedback information
First principles models are even more needed
Formation and training of engineers in these advanced tools are crucial
Lots of work for Process System Engineers!!

64. Team Professors: Adilson E. Xavier Argimiro R. Secchi Bruno Capron
Frederico W. Tavares
Maurício B. de Souza Jr.
Paulo L. C. Lage
Príamo A. Melo Jr.
Pós-Docs:
José Mauel G. T. Perez
Leonardo S. Souza
Rodrigo G. D. Teixeira
Simone C. Miyoshi
Engineers:
Carlos R. Paiva
Jéssi C. Heck
Rafael M. Bendia
Secretariat: Rosemary Cezar
PhD students:
Ana K. Muniz
Alex F. Teixeira
Ataíde S. Andrade
Caio F. C. Marcellos
Daniel M. Thomaz
Eliza H. C. Ito
Felipe C. Cunha
Guilherme A. A. Gonçalves
Gustavo V. K. Campos
Jacques Niederberger
Jeiveison G. S. S. Maia
Leonardo D. Ribeiro
Rafael B. Demuner
Rafael R. L. Britto
Reinaldo C. Spelano
Roymel R. Carpio
Sergio A. C. Giraldo
Thamires A. L. Guedes
MSc students:
André F. F. Souza
Joaquin Lujan
Mario G. Neves Nt.
Maria Rosa R. T. Goes
Otávio F. Ivo
Pedro C. N. Ferreira
Saul Simões Nt.
Thiago C. Dávila
Undergrad. students:
Carlos M. M. Fonseca
Isabella Q. Souza
Lucas Marques
Luis A. V. Carapeto
Pedro Delou
Rodrigo Moyses
Silvio Cisneiros Nt.
Thales S. M. Gama
Victor C. Gomes
Vinícius C. C. Plácido

65. ... thank you for your attention!
Solutions for Process Control and Optimization
Process Modeling, Simulation and Control Lab
Prof. Argimiro Resende Secchi, D.Sc.
Phone: