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Dynamic Modeling and Advanced Control for Offshore Platforms

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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:


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