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Transcript of Copyright © 2005 POSC Integrated Operations Update David Archer, POSC Integrated Operations SIG...
Copyright © 2005 POSC
Integrated Operations Update
David Archer, POSC
Integrated Operations SIG MeetingStavanger, Norway18 November 2005
Oil fields of the future: real-time oil and gas operations
Onshore Onshore FacilitiesFacilities
Decision Decision CentersCenters
Offshore Offshore FacilitiesFacilities
new capabilities – much more data; much more exposure
Copyright © 2005 POSC
Stavanger
Bergen
Trondheim
Aberdeen
Integrated operations
External experts
Real t
ime
data
Real time data
Service Company’sonshore operation centre
Control room offshore
External experts
Remotecollaboration room
Operator’sonshore operation centre
* Knowledge
* Decisions
* Actions
* Data integration
* Information
* Visualisation
Copyright © 2005 POSC
Copyright © 2005 POSC
Smart SystemsThe basic approach of all “smart technology” is measure-model-control
– measure system properties– model actual vs desired behaviour– derive required correction parameters (adaptive control)– implement control
∆IntOPS
Acquire
Model
Analyze
Control
Source: Shell
IntOPS = Integrated Operations
Copyright © 2005 POSC
Business Headquarters
Capacity Planning Design [months/years]
Operational Planning [months/years]
Scheduling [days/months]
Supervisory Control [minutes/hours]
Regulatory Control [sec/minutes]
Well & Surface facilities
-Flow, pressure and temperature in wells and separator-Fuel injection to produce heat out of a boiler
-SCADA systems for coordinating flow stations and pipelines-Gas distribution/optimization on a pipeline network-Monitoring wellheads, multiples and flow stations
-Scheduling of injection/production plan and resources-Opening and closing wells or partial completions
-Adjusting well operating parameters
-Planning of injection/production plan and resources-Planning drilling and workover resources
- Supply Chain Management & Market and customer demands
-Asset life cycle and installed based maintenance or growth- Supply Chain Management & Market and customer demands
Aut
omat
ion
leve
lTim
e-scale
Fast cycle
Slower cycle
Source: Saputelli SPE 83978
Time Scales (106 range)IntOPS
Acquire
Model
Analyze
Control
Copyright © 2005 POSC
The Problem?• Theorem 1: 50% of the problems in the world result from people
using the same words with different meanings• Theorem 2: The other 50% of the problems results from people
using different words with the same meaning
Stan Kaplan, Risk Analysis, Vol. 17, No. 4, 1997
Copyright © 2005 POSC
Board
Economics
ReservoirEngineering
ExplGeology
Petrophysics PetroleumEngineering
Drilling
Engineering
ProductionGeology
Production
Engineering
Facilities
Engineering
ProductionOperationsGeophysics
DrillingOperations
Completion &Workover
E&P Catalogue Standards
XML exchange standards, design guidelines, profiles
Data Store Solutions SIG
IntOPS SIG
Reference Data Standards
Industry Dictionary
Business Objects
eRegulatory SIG
Practical Well Log Standards
WellHeader, WellPath, WellLog, LogGraphics, Production Reporting, DTS, …
NDR Meetings
POSC Standards / SIGs
Epicentre™ Data Model
Board
Economics
ReservoirEngineering
ExplGeology
Petrophysics PetroleumEngineering
Drilling
Engineering
ProductionGeology
Production
Engineering
Facilities
Engineering
ProductionOperationsGeophysics
DrillingOperations
Completion &Workover
E&P Subject Areas: WITSML Coverage
Can we use this image to portray the growth in WITSML coverage?
General Board
Economics
ReservoirEngineering
ExplGeology
Petrophysics PetroleumEngineering
Drilling
Engineering
ProductionGeology
Production
Engineering
Facilities
Engineering
ProductionOperationsGeophysics
DrillingOperations
Completion &Workover
E&P Subject Areas: WITSML Coverage
Enhancements for raw, calculated and planned Well Path data,coordinate systems, and units.
WellPath
Well Wellbore Trajectory
CRS, ProjectionsUnits of Measure
General Board
Economics
ReservoirEngineering
ExplGeology
Petrophysics PetroleumEngineering
Drilling
Engineering
ProductionGeology
Production
Engineering
Facilities
Engineering
ProductionOperationsGeophysics
DrillingOperations
Completion &Workover
E&P Subject Areas: WITSML Coverage
Enhancements for wireline as well as LWD log data.
Log,WellLog
General Board
Economics
ReservoirEngineering
ExplGeology
Petrophysics PetroleumEngineering
Drilling
Engineering
ProductionGeology
Production
Engineering
Facilities
Engineering
ProductionOperationsGeophysics
DrillingOperations
Completion &Workover
E&P Subject Areas: WITSML Coverage
Enhancements for Production beginning with Volume and Activity Reporting, continuing with PRODML towards Optimization.
Network Model Volume Report Activity Report
General Board
Economics
ReservoirEngineering
ExplGeology
Petrophysics PetroleumEngineering
Drilling
Engineering
ProductionGeology
Production
Engineering
Facilities
Engineering
ProductionOperationsGeophysics
DrillingOperations
Completion &Workover
E&P Subject Areas: WITSML Coverage
We have a pending request to establish a Geophysical SIG.Such a SIG could lead to WITSML Geophysical data exchange Standards.
General Board
Economics
ReservoirEngineering
ExplGeology
Petrophysics PetroleumEngineering
Drilling
Engineering
ProductionGeology
Production
Engineering
Facilities
Engineering
ProductionOperationsGeophysics
DrillingOperations
Completion &Workover
E&P Subject Areas: WITSML Coverage
At a future time, we could consider the viability of data exchange Standards for facility data, reservoir data, economics data, etc. filling out the E&P space.
General Board
Economics
ReservoirEngineering
ExplGeology
Petrophysics PetroleumEngineering
Drilling
Engineering
ProductionGeology
Production
Engineering
Facilities
Engineering
ProductionOperationsGeophysics
DrillingOperations
Completion &Workover
E&P Subject Areas: WITSML Coverage
This would call for close cooperation with other industry groupsto avoid duplication of effort and ensure smooth transitions.
Copyright © 2005 POSC
PRODML: A Shared Solution for Upstream Oil and Gas Companies to Optimize Their Production
• What?– Project focused on optimization of oil and gas production
• Who?– BP, Chevron, ExxonMobil, Shell, Statoil, eProduction Solutions, Halliburton, Invensys, OSIsoft, Petroleum
Experts, Schlumberger, Sense Intellifield, TietoEnator and POSC
• What?– Production optimization involves integrating real time data from specialty, multi-vendor software applications
and streamlining work processes to enable oil and gas field operational efficiencies– Build on success of WITSML™ - develop the necessary XML-based data exchange solutions as an open
industry standard– Extend the WITSML™ ‘architecture’ to include data needed for field production optimization
• How? When?– Drive towards commercial software products to improve data exchange and work process efficiency in
production optimization - over a 12 month timeframe– POSC will maintain the standard and make it publicly available
Joint Work of Richard Carter, Joint Work of Richard Carter, Todd Dupont and Henry Todd Dupont and Henry
RachfordRachford
Control of Gas Flow in Control of Gas Flow in PipelinesPipelines
Why is it Hard, What Can We Do about it, and Why is it Hard, What Can We Do about it, and Why do We Care?Why do We Care?
Consider a Typical Consider a Typical PipePipe• 60 Miles Long• 47 inches Inside Diameter• 1000 Microinches Roughness• Pressure, p(t), at Inlet• Flow, q(t), at Outlet, where t is time
p(t) = Pin q(t)=Qout
60 miles, 47” inside diameter.Inlet Outlet
Consider a Simple Consider a Simple ScenarioScenario
Settling Settling qq Toward a Toward a New StateNew State
Steady State @ t=0Pin = 850 psiaQout = 1842 MMSCFDTemp = 60 oF
Set Qout = 2090 MMSCFD @t=0Hold boundaries constant for t>0
Relaxation Time,Relaxation Time, t tr r . . LetLet
and δ(x,t) = p(x,t) - p(x,T), for 0 < x < L, to< t < T. From any pipeline state fix p at inlet, q at outlet, to cause a state change.
Then for T >> to, we find
22
2||4,),(),(
πδδ
dcLvf
twhereetxtx ro ==−
−r
o
t)t(t
L = Pipe Length c = Sonic Velocity in gas
f = Friction coefficient t = A time after a change
v = Gas velocity to= A fixed value of t
d = Inside Diameter T= A very large value of t
tr = 46.5 minutes, to = 10 minutes
Introduce Sensors Introduce Sensors [ S(P), S(Q) ][ S(P), S(Q) ]L = Pipe Length c = Sonic Velocity in gas
f = Friction coefficient t = A time after a change
v = Gas velocity to= A fixed value of t
d = Inside Diameter T= A very large value of t
p(t) = Pin q(t)=Qout
Inlet Outlet
S(Pin),S(Qin) S(Pout),S(Qout)
What Does This Mean What Does This Mean
for a Real Pipeline for a Real Pipeline
System?System?
• Response to control takes hours • Controls to Achieve One Goal may Interact
Unexpectedly with Others
Look at an Example
300-Mile 5-Station 300-Mile 5-Station PipelinePipeline
p(t) q(t)
50 mi 50 mi 40 mi45 mi 55 mi 60 mi
SP1(t) SP2(t) SP3(t) SP4(t) SP5(t)
Station # Max HP Max Set Point Min. Suct. p 1 26000 900 psia 500 psia 2 28000 900 500 3 28000 900 500 4 18000 900 500 5 18000 900 500
In Out
Out04q4(t)
A Simple Scenario A Simple Scenario
• Begin @ Steady State with OUT-End Flow 1500, Station 4 Delivery 450 (in MMSCFD) SPk(0)=800 psia, k=1,…,5 (in Control)
• Hold p(t) @ ‘IN’ at 825 psia (0:00 to 24:00). At 09:00 increase Delivery at OUT to 1950 MMSCFD for 5-Hours, return to 1500
• Delivery at 24:00 is Same as at 00:00, so Make End State Same as Start State
• Use Simple Control: All Setpoints stay 800• Call this Scenario 1.
300-Mile 5-Station 300-Mile 5-Station PipelinePipeline
p(t) q(t)
50 mi 50 mi 40 mi45 mi 55 mi 60 mi
SP1(t) SP2(t) SP3(t) SP4(t) SP5(t)
Station # Max HP Max Set Point Min. Suct. p 1 26000 900 psia 500 psia 2 28000 900 500 3 28000 900 500 4 18000 900 500 5 18000 900 500
In Out
Out04q4(t)
Qout4(t)=450Pin(t)=825 Qout(0-9)=1500; Qout(9-14)=1950;Qout(14-24)=1500
SPk(t)=800
Observations on Observations on Delivery Delivery
• 5-Hour Increase at OUT adds 450 MMSCFD. This Adds Same as Out04 Flow
• 450 Increase Cannot be Supported at Steady State (Max Steady Increase is 150)
• Total Initial Gas in Pipe: 1.07 BCF (Initial State Linepack)• Extra Delivery is 93.7 MMSCF, or 8.7% of Linepack
Initial Pipeline Initial Pipeline StateState
Scenario 1: All Scenario 1: All Stations SP=800Stations SP=800
Scenario 1: Scenario 1: Station 1, 1 DayStation 1, 1 Day
Scenario 1: Station 2 Scenario 1: Station 2
Control InterchangeControl Interchange
Scenario 1: Scenario 1: Station 5, 1 DayStation 5, 1 Day
Scenario 1 Deliver Scenario 1 Deliver OUT-End?OUT-End?
Ouch!
Scenario 1: Final Scenario 1: Final StateStateDay’s End: 2.4% Loss in Day’s End: 2.4% Loss in LinepackLinepack
Scenario 1: % Scenario 1: % Linepack ChangeLinepack Change
Scenario 1 Fails. Scenario 1 Fails. What Should Be the What Should Be the Control? Control? For this, Enumerate Goals:For this, Enumerate Goals:
• Make Deliveries above Minimum p• Do not Violate Maximum Allowable p• Do not Deplete the Pipeline, e.g., Achieve the
End-of-Day Target State• Always Maintain Control, i.e., Never Default
Control to Station Maximum • Minimize Fuel Usage, Emissions
How Can We Find How Can We Find Controls?Controls?
• Recall p(t), q(t), q4(t) are Specified by Users• We Need to find SPk(t), k=1, 2, …, 5• Guess the five SPk(t) functions. Recall 900 PSIA is maximum value for
any SPk(t)• Simulate Results, Compare with Goals• Use Trial and Error to Satisfy Goals
With Experience, guessing and trial and error will improve
Alternatively, Guide Alternatively, Guide the Trial the Trial and Error and Error with Mathematicswith Mathematics
• Define the Control Set S = SPk(tn), k =1,…,5, n = 1,…,23, say, i.e., tn = n, i.e. 115 numbers
• Begin as Before: Take SPk(tn) = 800
• Interpolate in time between Control Values and use Interpolated Values to Simulate the Gas Day
• Quantify Missed Goals as a Functional, J• Calculate Gradient of J with Respect to S• Iterate to revise SPk(tn), so as to Minimize J. • Call Resulting Control Scenario 2
INITIAL STATE THE INITIAL STATE THE SAMESAME
Scenario 2: Scenario 2: External FlowsExternal Flows
Scenario 2: Station 1 Scenario 2: Station 1 PerformancePerformance
Scenario 2: Station 2 Scenario 2: Station 2 PerformancePerformance
Scenario 2: Station 3 Scenario 2: Station 3 PerformancePerformance
Scenario 2: Scenario 2: Station 4 Station 4 PerformancePerformance
Scenario 2: Station Scenario 2: Station 5 5 PerformancePerformance
OUT-End Delivery?OUT-End Delivery?
Scenario 2: End-of-Day Scenario 2: End-of-Day StateState
• Pack only 0.16% less than original
Scenario 2: Flows, % Scenario 2: Flows, % Pack ChangePack Change
Compare Scenario Compare Scenario Results Results 11 22
ConclusionsConclusions
• Mathematical Optimization Finds a Control to Achieve Goals in 1-2 Minutes Clock Time
• If Delivery were Infeasible, this Fact Would have been Found just as Fast
• If Loads Change During Day, Simply find New Controls for the New Schedule again in 1-2 Minutes Clock Time
Questions?David Archer
President / CEOPOSC
24 Greenway PlazaSuite 1315
Houston, TX 77046
+1 832 282-8132 [email protected]