33
Intelligent Oilfield Operations Intelligent Oilfield Operations
Intelligent Oilfield OperationsIntelligent Oilfield Operations
2
Objective of this presentationObjective of this presentation
To review current petroleum production issues regarding real time decision making and,
To present the vision of a intelligent oilfield operations
To Promote the use of technologies for intelligent oilfield operations
To present the results of a continuous self-learning optimization strategy to optimize field-wide productivity
3
ContentsContents
1. The Reservoir Management Challenges 2. Management and Decision Making processes3. Operators Vision & Strategies
• Integration of measurement-models-control• Rapid front end project planning• Collaborative knowledge and application sharing• Rapid technology adaptation
4. What are the opportunities for Intelligent Oilfield Operations?
5. Why don’t we use more Intelligent Oilfield Operations?
6. Case Study
4
Real Time Asset Management Challenge for AdaptationReal Time Asset Management Challenge for Adaptation
NewData
FieldDevelopment
Planning
Drilling and
Construction
ProductionOperations
IntegratedReservoirModeling
Market
Valid Models Current
Performance
Investment Plan
Fleet Availability
Producing Wells
WorkoverCandidates
Forecast Performance
CurrentModel
Field Target Rate
Current Performance
Prospects
Market
5
Reservoir Management is about a careful orchestration of technology, people & resources
The Reservoir Management ChallengeThe Reservoir Management Challenge
InjectionFacilities
Compression &Compression &Treatment PlantsTreatment PlantsProductionProduction
Well & FacilitiesWell & Facilities
DrainageDrainageAreaArea
Drill, build & OperateDrill, build & Operate
SubsurfaceCharacterizationSubsurfaceCharacterization Update ModelUpdate Model
ControlControl
MonitorMonitor
Establish or reviseOptimum Plan Establish or reviseOptimum Plan
Exploitation PlanWell location & numberRecovery mechanismSurface facilitiesWell intervention
6
RealReal--time has different meanings at different levelstime has different meanings at different levels
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
lTime-scale
Slower cycle
Fast cycle
SPE Paper 77703
7
Volumetric Success Volumetric Success 750 Worldwide Samples750 Worldwide Samples
0%
2%
4%
6%
8%
10%
-200% -87.5% -62.5% -37.5% -12.5% 12.5% 37.5% 62.5% 87.5%0%
20%
40%
60%
80%
100%
% S
ucce
ss C
umul
ativ
e D
istr
ibut
ion
% S
ucce
ss F
requ
ency
Dis
trib
utio
n
Mean –29%, st.dev. 64%
Only 15% of the wells lied in ±12.5% range
33% of the wells lied in ±25% range
48% had success < -25%
0 %
2 %
4 %
6 %
8 %
1 0 %
-2 0 0 % - 8 7 .5 % - 6 2 .5 % - 3 7 .5 % - 1 2 .5 % 1 2 .5 % 3 7 .5 % 6 2 .5 % 8 7 .5 %0 %
2 0 %
4 0 %
6 0 %
8 0 %
1 0 0 % ( )V olum etric S ucess 100p lan actua l
p lan
q qq−
= ×
8
Volumetric Success DeviationVolumetric Success Deviation
WellInstability
Circulation Losses
Cementing OperationalProblems
InadequateCompletion
StimulationRock& Fluid
Characterization
SandingFormationDamage
Fracturing
33%
12% 12% 9% 9% 7% 7% 7% 5%
0% 10% 20% 30% 40% 50% 60%
Operational DrillingDisfunction
ReservoirUncertainty 57%
43%
PDVSA, 1999
9
Hydrocarbon production system suffering major technical problems
MotivationMotivation
Traditional ProblemsComplex & risky operations
(Drilling, Workover, Prod.)
Poor reservoir prediction &
production forecasting
Limited resources: injection
volumes, facilities, people.
Unpredictability of events:
gas or water, well damage.
Poor decision making ability
to tune systems, thus, not
optimized operations
More front-end engineering
and knowledge sharing
Integrated Characterization &
Modern visualization tools
Multivariable optimization,
reengineering.
Monitoring & control devices,
Beyond well measurements
Isolated optimization trials
with limited success.
Current ApproachMore data for analysis and
integration limitations.
Long-term studies, Ill-posed
tools, simple or incomplete.
Models are not linked among
different layers
Poor Justification, real time
analysis in early stage.
Decisions made only on few
pieces. Lack of Integration
between subsurface-surface
Challenges
10
Intelligent Oilfield Operations’ VisionIntelligent Oilfield Operations’ Vision
To efficiently use of data and information
to generate opportune decisions
in regards to optimum exploitation
• Awareness of Asset Performance • Transform key data into knowledge• Shared across relevant people• Shared across different locations
• How much to inject or produce?• Where to place new wells? • How to troubleshoot problems?• What-if exploitation scenarios?
• Maximum profitability• Safe and healthy operations • Asset Integrity• Environmentally friendly
11
Intelligent Oilfield Operations’ ElementsIntelligent Oilfield Operations’ Elements
Intelligent operations requires • Seamless integration of field hardware with office
decision systems for continuous decision-making in a closed-loop fashion – Permanent sensors and remote activated actuators, – Surface facilities– Integrate subsurface and reservoir models
• Rapid front-end project planning for reduced execution uncertainty;
• Collaborative knowledge and application sharing across multiple disciplines and geographies; and
• Rapid adaptation to technology and to market changes.
Intelligent Oilfield Operation’s VisionIntelligent Oilfield Operation’s Vision
BusinessBusinessExploitationExploitation
UnitUnit
Decision AssertivenessDecision AssertivenessMinimum CostMinimum Cost
• Best Practices KC’s• High Volumetric and Mechanical Efficiency• Less Adapting Time• Project Front-End Loading
Modern and IndependentModern and Modern and IndependentIndependent
Optimum Exploitation PlanOptimum Exploitation Plan• Self-Learning Reservoir Management
• Large Bandwidth Information • Integration Engineer
The Networked Force The Networked Force Command Control Center Command Control Center
and Communicationsand Communications
• Monitoring 4D • Up/Downstream Integration• Real Time Operations Centers
Automated Fluid ExtractionAutomated Fluid ExtractionAcquisition, Modeling and Control with AI
PDVSA, 1996
13
Intelligent Oilfield Operation Strategies
Integrate Cooperative Technologies (data, apps y processes)Integrate Cooperative Technologies (data, apps y processes)
•• Multidisciplinary Reservoir CharacterizationMultidisciplinary Reservoir Characterization•• Data and Application Integration for decision makingData and Application Integration for decision making•• SubsurfaceSubsurface--surface integrationsurface integration•• High Performance Computing High Performance Computing –– System Architecture System Architecture •• ClosedClosed--Loop Reservoir ManagementLoop Reservoir Management
Increment Well Volumetric and Mechanical Efficiency Increment Well Volumetric and Mechanical Efficiency
Simplify operationsSimplify operations•• Permanent InstrumentationPermanent Instrumentation•• Remote ActuationRemote Actuation•• Complex Data MiningComplex Data Mining•• Intelligent agents Intelligent agents
SMART RESERVOIRSSMART RESERVOIRS
Develop & Maintain competenciesDevelop & Maintain competencies
•• Integration EngineerIntegration Engineer•• Self LearningSelf Learning•• Multiple vendors talkingMultiple vendors talking•• Best PracticesBest Practices
PDVSA, 1996
•• Geologically Optimized Well PlacementsGeologically Optimized Well Placements•• Drilling and Completions Operations CentersDrilling and Completions Operations Centers•• Enhanced Well ProductivityEnhanced Well Productivity•• Optimize and Relax Surface ConstraintsOptimize and Relax Surface Constraints
14
Permanent Sensors and Remote Actuated Controls?
Production tubing
Liner Hanger
Pressure SensorTemperature SensorVenturi Flow Meter
Zone 1Perforations Zone 2
PerforationsZone 3
Perforations
Subsea Safety Valve
Acoustic Sensor
Resistivity Sensor
Internal Control
Valve
0
200
400
600
800
1000
1200
1400
1600
13/1
0/19
99
14/1
1/19
99
16/1
2/19
99
17/0
1/20
00
17/0
2/20
00
20/0
3/20
00
20/0
4/20
00
22/0
5/20
00
23/0
6/20
00
03/0
7/20
00
07/0
7/20
00
07/0
7/20
00
07/0
7/20
00
07/0
7/20
00
07/0
7/20
00
07/0
7/20
00
07/0
7/20
00
02/0
8/20
00
04/0
9/20
00
Pressure (psia)
0
25
50
75
100
125
150
175
200
225
250
275
300
P actual=680 LPC
T actual=227
Wel
l Ope
n At
q2
Wel
l Shu
t In
Wel
l Ope
n at
q1
Wel
l Ope
n A
t q1
Wel
l Shu
t In
Wel
l Shu
t In
Wel
l Ope
n At
q3
Well Open, Variable Rate
Wel
l Shu
t In
Wel
l Shu
t In
Temperature (°F)
Zone 1 Open OnlyStatic Pressure = 1440 psia
Zone 2 Open OnlyStatic Pressure = 1170 psia
Link
RemoteTerminal
Unit
15
Integrate Subsurface and Surface AutomationIntegrate Subsurface and Surface Automation
FTHP
FLPFTHT
Link
Database Server
GLP
Gas LiftManifold
RemoteTerminal
Unit
AdjustableChoke
ProductionManifold
SP
QGP
QOP
DownholeInternalControlValves
ControlAlgorithms
GasFlow
Liquid Rate BPD
Inflow Performance
Dow
n ho
le F
low
ing
Pres
sure
Outflow Performance
ProductivityEnhancement
Less Drawdown
Pres
Liquid Rate BPD
Inflow Performance
Dow
n ho
le F
low
ing
Pres
sure
Outflow Performance
ProductionIncrease
More Drawdown
Pres
Reduced Restrictions
GLR CHP
Gas LiftCompression Gas Lift
Choke Oil Flow
QWP Water Flow
After SPE Paper 77643 & OTC Paper 16162
16
What is Rapid and Smart Front-End-Planning?
ReservoirDefinition
ExploitationPlan
DesignStatus
Project ExecutionVariables
+
+
+
Technology
Knowledge
+
+
Complexity+
Organization+
• Involve all stakeholder at early stages
• Identify and mitigate risk by early planning– Reservoir Uncertainty– Exploitation Options– Project Execution Time– Economic Sensitivities
• Identify, preserve and apply best practices
• Integrate computer aided high intensity design and design optimization techniques
Prediction of
Costs
Production
ExecutionTime
EVAROCI
-118 0 250 500 572
10
20
30
Net Present Value ($ million)
Wells Scenario2Wells Scenario3
Wells Scenario1
17
Knowing input-output relationships, reservoir could seen as a process plant
Reservoir as a Process Control System StructureReservoir as a Process Control System Structure
Agua
Crudo
Gas
Solvent InjectionGas Lift
ESP Speed
Water InjectionHeat InjectionGas Injection
Flow ChokeZone Control
Manipulated Inputs
Controller
BackpressureAmbient Temperature
Flow RestrictionsInjection Fluid Restriction
MeasuredDisturbances
UnmeasuredDisturbances S
Reservoir Rock HeterogeneityReservoir Fluid Distribution
cheduling
Feed forward path
Unmeasured OutputsWell flowing Pressure: pwf
Reservoir Pressure: pres
Reservoir Saturations: So, Sw
Flow Impairment: S, Kr’s
Zone Multiphase Flow: qo, qw, gq
Drainage Area: A
Tubing Head Pressure: pTHP
Tubing Head Temperature: TTHTFeed back pathMultiphase Flow: qo, qw, gq
Solid Production, Water Analysis
Measured Outputs
18
Reservoir Flow and Pressure ModelingReservoir Flow and Pressure ModelingOil, water and gas flow and pressure as linear functions of flowing pressure
Proposed IPR for continuous monitoring
( )( )( )
2
0 1 2 3
2
0 1 2 3
2
0 1 2 3
k k k ko e wf wf
k k k kw e wf wf
k k k kg e wf wf
q a a p a p a p
q b b p b p b p
q c c p c p c p
= + × + × + ×
= + × + × + ×
= + × + × + ×
( ) ( ) ( ) ( )1 2 2
1 2 1 3 1 4 2 5 2
k k k k k kwf wf wf wfp p d d p d p d p d p
−= + + ⋅ + ⋅ + ⋅ + ⋅
Proposed Pressure Modeling for continuous monitoring
( ) ( ) ( ) ( )2 2 2
1 2 3 4 5 6k k k k k k k k
wf th o w g o w gp p l q l q l q l q l q l q− = + + + + +
Proposed Pressure Drop Modeling for Continuous Monitoring
19
Outer loop passes the operating point (decisions) to inner loops
Closed Loop Asset Management
Data DrivenModels
Actual Conditions Actual Behavior Actual Values
Measured Production
MarketMarket
DevelopmentPlanning
ReservoirModel
ResourceBase
ResourceBase
ForecastProduction
NewTarget &
ExecutionPlan
Scheduler& Optimizer
Actual Target & Slower Loop
RegulatoryController
Asset:Wells & Facilities
Fastest loop
SupervisoryController
Fast loop
Real Time Production Optimization
Real Time Reservoir Management
20
Upper optimization layer passes the best operating point to lower layer
Multilevel Reservoir Control ModelMultilevel Reservoir Control Model
MPCController
MPCController
o
w
g
qqq
Reservoir(Simulator)Reservoir
(Simulator)wfp
,
,
,
o sp
w sp
g sp
qqq
oq∆+
-
+
d
Linear ProgrammingOptimizer
Linear ProgrammingOptimizer
Optimization Level
Regulatory Level
Longer TermReservoir Forecasts
Longer TermReservoir Forecasts
EmpiricalModel Structure
EmpiricalModel Structure
ModelParameters
ModelParameters
MalhaRapida
MalhaLenta
Net Present ValueFunction
Net Present ValueFunction
Information
21
Attempt to solve a significant reservoir management challenges
Waterflood Management Problem ResultsWaterflood Management Problem Results
Experimental Base: History-matched Model from El Furrial, HPHT, deep onshore, light oil, 2000 days
Controlled CaseBase Case No control• Water irruption detected and
controlled • Zone shut off permitted better well’s
vertical lift• Recovery accelerated at a minimum
cost
• Early water irruption • High water cut reduced well’s
vertical lift• Further recovery possible at a
greater cost
22
Clear benefits from extra little oil but with a lot less effort.
FieldField--wide life cycle comparison Resultswide life cycle comparison Results
Oil CumulativeOil rate
Self-Learning
Non-Controlled
∆Np=500 Mbbls
∆Rev=$5 Million
Self-Learning
Non-Controlled
5%
Water rate
∆Wp= -18 MMbbls∆Wi= -19 MMbbls
∆Rev= -$92.5 Million
Controlled
Non-Controlled
Wp, Produced Water Cumulative
-78%
-55%
Wp Controlled
Wp Non controlled
W inj Non controlled
Winj Controlled
Winj, Injected Water Cumulative
23
Continuous self-learning optimization decision engine
Self Adaptive Reservoir Performance optimization TechniqueSelf Adaptive Reservoir Performance optimization Technique
( )
{
, , 1
min , max
min max
, , ,
LP Optimization Loop
max , , ,$,
s.t.
ˆ ˆ ˆ, ,
o w g
N
o w gq q q
k p k
k p
o opt g opt w opt
f q q q T
p p pq q q
q q q
+
+
= ∆
≤ ≤ ≤ ≤
⇔
∑NPV
1 1,k kp q+ +
ModelIdentification
ModelIdentificationInterpret
Set point
Set point
Model
( )
( )( )
1
1
2
, 1
1, , 1
1
LS Optimization Loopˆˆ
min
, ... , ,...
, ... , ,...
k k
k k
N
ia b i
k ko g w T T
k kres n T T
q f p p q q
p f p p q q
−
−
=
−
−
+
⇒
=⇔
=
∑-1T T
Y = Xθ e
e X X X Y
Physical Process
Physical Processsp
wf
spG
p
q Measure
ControlImplementation
ControlImplementation Control
,o optq
( )
[ ][ ]
[ ]
2 2
1 1
min | max
min | max
| 1|
QP Optimization Loop
ˆmin
. .ˆ ; 1,
; 1,
; ,
p mSP
k j k ju j j
k j k
k j k
k i k k m k
y y R u
s ty y y j p
u u u j m
u u i m p
+ +∆= =
+
+
+ + −
− + ∆
≤ ≤ =
≤ ≤ =
= =
∑ ∑
Optimize
Reservoir ValueOptimization
Reservoir ValueOptimization
24
What benefits does collaborative environments bring?
What are Collaborative Environments?They can be either:• Web-based portals • Interactive collaborative environments• Automated Workflow Management• Immersive large scale visualization Rooms• Real-time, just-in-time and remotely enabled
What are the benefits?• Access and visualization of large datasets• Access and visualization of whole asset• Information stays at its original source• Shared across disciplines and geographies
25
Which applications define RTAM?Which applications define RTAM?
Optimization Control
FieldMesurements
Transforming Data into Better Informed Decision
Advise done by providing asset data awareness
Advise done by providing report
on forecasted values
Advise applied automatically
over field actuators
Indirect Mesurements &
Inference Models
Advanced Performance
Models
Monitoring
Visualization
Modeling
Automation
26
How do we rapidly and smartly adapt to changes?
• Plan, Nurture and Protect Knowledge Communities• Tie Knowledge and Technology to Business Value• Promote and Reward Performance Improvement Initiatives• Promote and Reward Culture of Change
Develop and Maintain CoreBusiness Competencies
KnowledgeCommunities
Support
Measure
Lead
Promote
Business Needs
Triggers
ProtectCorporateResearch
Preserve and Divulge Best Practices and Lessons Learned
TechnologyStrategy
TechnologyAdoption
Technology Portfolio
GameChanging
Technologies
Apply &Influence
Open &Engage
AssetBusiness
Units
Materialize Value
External Sources
CapturePlan &Support
Best Practices
Capture
SPE Paper 53759
27
What architecture supports Intelligent Oilfield Operations?
RemoteTerminal
Units
FieldSensors
FieldController
RemoteTerminal
Units
FieldSensors
FieldController
Wells and Subsurface Flow Devices
Surface Facilities & Equipment
HistoryMatch
Multiple ScenarioModeling
Asset ViewPortal
Asset ViewPortal
ProductionSurveillance
ProductionAllocation
NodalAnalysis
Real TimeOptimization
NetworkModeling
IntegratedOptimization Economics Financial Intervention
design
IntegratedProduction Drilling and Engineering
Database
Real TimeExpert Systems
Forecast
History
DynamicAssetModel
SCADA’s Real TimeHistorian
Seamless integration of field hardware with office decision systems …
28
Real Time Optimization Systems from SPE TIGReal Time Optimization Systems from SPE TIG
Com
plex
ity o
f Sol
utio
n X
Mag
nitu
de o
f Dep
loym
ent
Organizational/Enterprise Adoption (People, Process, Ownership)
Circle’s radial = Value Created for the Pilot ImplementationSlotted Circle’s Radial = Further Implementation Forecasted
Value Arrow = Project Direction
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.90.8 1.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.9
0.8
1.0
5
1
1’
1’’2’
3’
4
4
2
3
6PeoplePeople
TechnologyTechnology
ProcessProcess
MeasuringTelemetryDataAnalysisVisualizationControl & OptimizationIntegration
Business MissionIntegrationCollaborationOther Process Issues
Training Cultural
Change Management Other People Issues
0
1
2
3Measuring
Telemetry
Data
AnalysisVisualization
Control System &Actuation
Integration & Automationfor Optimization
Before After
Spider Diagram for Prudhoe Bay Integrated Surface & Subsurface Optimization Example, Before and After
Legend
Project Complexity Adoption Value Comments
1 Low Low Low Initial Pilot with proven value
1' Low Low Medium Increased adoption with increased complexity will increase value
1'' Medium High High Full deployement with large value and large complexity
2 Medium Low Low Initial Pilot with proven value and medium complexity
2' Medium Medium Medium Increased value with increased adoption without more complexity
3 Low Medium Low Initial Pilot with proven value and low complexity
3' Medium Medium Medium Increased value with increased complexity without more adoption
4 High Low Medium Initial Pilot with proven high value high complexity and low adoption
4' Medium Low Low Project downsized to reduce complexity and value reduced
5 Low High High High value project fully deployed and low complexity. No further grow
6 Medium Low Low Low value initial pilot with medium complexity. Project abandoned.
SPE Paper No. 90213
29
Intelligent Oilfield Operations Intelligent Oilfield Operations -- Technology StatusTechnology Status
0 1 2 3 4 5
Field Telecommunications & NetworksSurface Pressure Meters
Surface Temperature MetersDownhole Temperature Sensors
Downhole Pressure SensorsDownhole Fiber Optic Sensors
Surface Multiphase MetersDownhole Actuators (Smart Well
Surface Wireless Pressure SensorsSurface Wireless Temperature Sensors
Surface Wireless Flow SensorsSurface Actuators
Field Wireless Sensor NetworksTraditional Reservoir Simulation
Reservoir CharacterizationWell Modeling
Pipeline ModelingNetwork Modeling
Production EconomicsProcess Design & Modeling
Network OptimizationDesktop Production Data Capture
Hand Held Production Data CaptureField Surveillance
Well DataBase & VisualizationProduction Allocation
Process Optimization (Steaqdy State)Advance Prod Data Mining / Viz
Advanced Process Control (Transient State)RT Prod Ops Portal Visualization
Best Practice CapturesShutdown Incident Registry
Value Initiatives RegistryAdvance Prod Data Modeling
Integration - MiddlewareIntegration - Data Loader/Mover
Next Generation Reservoir SimulationWorkover Identification
Production EnhancementReal Time Production Optimization
Production Operations OptimizationKnowledge Management
Har
dwar
e
Embrio Stage market not clear
Proof-of-concepts in place
Growing market And Acceptance
Industry Standard Mature, Robust
Beyond Mature
Softw
are
Proc
esse
s
Saputelli et al., 2004
30
There are many ways to propose …
What are the opportunities ?
Integrated Asset Management and Optimization:Well location, scheduling, spacing and quantityWell completion and vertical lift strategySecondary and enhanced oil recovery design operationSurface facilities & total fluid handling capacity target plateauResource allocation (Capex, Opex, Gas Compression)Plant and equipment overhaul schedulesPipeline scheduling availabilityPortfolio Optimization & Resource base planning
Production Operations OptimizationWell profile management (coning, cusping, well management)Field and well level gas lift optimizationHydrocarbon and other fluids transportationSurface de-bottlenecking and continuous field-optimizationCandidate selection for stimulation and intervention
Drilling & Completion Optimization Well construction design (materials, time, resources)Drilling operations (hydraulics, trip time, non-productive time)
SPE Paper 83978
31
What are the blockers for Intelligent Oilfield Operations?
Don’t have the right data: either low quality or insufficient quantity or taken too infrequently.
Don’t have the integrated software toolsto properly model the specific system the way we would like it. “waiting on
common data standards“.
It seems like a good idea, but would probably be too expensive.We cannot handle change management well enough
and so a system will soon be out of date.
Lack of training in automated optimization engineeringPoor communication layers across disciplines involved.Lack of resources (time and financial) to focus on real-time
optimization.Contentment with the past way of doing things.
SPE Paper 83978
32
Why do we think is not used more?
Existing tools are not well understood
Misunderstanding about how emerging technologies fit in with existing field developments.
The inability to build a convincing business case for management
SPE Paper 83978
33
Conclusions
• Intelligent Oilfield Operation’s Vision is to efficiently use of data and information to generate opportune decisions in regards to optimum exploitation
• IOO’s vision capitalizes in these elements:• Integration of measuring, SS models and
actuation• Front-end engineering planning for accurate
prediction• Remote collaboration of experts and data sharing• Rapid adaptation of technologies
• IOO’s Technologies are available, business case fully justified.
• Feasibility of IOO demonstrated through a number of cases studies
