eni-mitei annual meeting, s. donato m., 29 june 2010
DESCRIPTION
ENI-MITEI Annual Meeting, S. Donato M., 29 June 2010. Multiscale Reservoir Science for Enhanced Oil Recovery: Technology Development and Field Applications. Rob van der Hilst, Steve Brown, Dan Burns, Michael Fehler, Brad Hager, Tom Herring, Ruben Juanes, Dennis McLaughlin - PowerPoint PPT PresentationTRANSCRIPT
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Multiscale Reservoir Science for Enhanced Oil Recovery: Technology Development and Field
Applications
Rob van der Hilst, Steve Brown, Dan Burns, Michael Fehler, Brad Hager, Tom Herring, Ruben Juanes, Dennis McLaughlin
Earth Resources LaboratoryMIT
ENI-MITEI Annual Meeting, S. Donato M., 29 June 2010
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New fields (e.g., deep off-shore, near/beneath complex structures, arctic region) Enhanced Oil Recovery (EOR) from existing fields (global average < 40%) Unconventional oil/gas (heavy oils, tar sands, tight gas reservoirs, hydrates)
To meet demand:
Overall Motivation:
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Challenge: Increase production from reservoirs that are complex and
strongly heterogeneous (both for new and existing fields)
Reservoir management:Predict reservoir performance to enable optimal operation:
– Maximize reservoir sweep– Best well placement and completion design
Integration of geophysical description with reservoir models more reliable prediction of performance
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For example: fractured reservoirs
deformation during passage of a compressional wave
Carbonate cliffs
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Seismic Data
geology/geophysics ↔ flow modeling ↔ enhanced production
Will
is e
t al
(20
06)
Water InjectionOil Production?
Oil
Water Front
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What do we want to know?• Where are the fractures?• What are the fracture orientations?• What are the fluid-flow properties of fractures
(that is, how do fluids flow through them)?
Approach:• Joint analysis of geophysical response (e.g.,
scattering from fractures and heterogeneity, deformation) and flow
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Using Geophysics to Constrain Flow Model
Response (e.g. well rate)
Qwell
time
model
data
Geophysics-constrainedreservoir description Geophysics-constrained
permeability model
Kfrac
Reservoir description from geophysics
Model updated with new data
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1: Reservoir Structure and
Response
– Fracture Characterization (e.g., seismics)
– Flow Simulation
– Data assimilation & real-time control
– Quantitative integration
INTEGRATED RESERVOIR SCIENCE
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INTEGRATED RESERVOIR SCIENCE
– Surface deformation (GPS & InSAR)
– Coupled geomechanical/reservoir modeling
2:Reservoir Evolutionand Performance
1: Reservoir Structure and
Response
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Integration of Geophysics &
Reservoir performance modeling
3:Application of New Concepts
(Field Case Study)
2:Reservoir Evolutionand Performance
1: Reservoir Structure and
Response
INTEGRATED RESERVOIR SCIENCE
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Data
Model
•Surface seismic•Fracture characterization
Geophysicalinterpretation
CTRW-RTT joint inversion methodology
• Surface deformation - tiltmeters - InSAR, GPS• Wellbore breakouts• Induced seismicity
Geomechanicalmodeling
• Production data• Well logs• Analogue reservoirs• 3D seismic
Flow models
Clearly insufficientcoupled
3-way data assimilation methodologyMain outcomes:• Better forecasts• Optimal production to maximize recovery while controlling subsidence
Different Levels of Integration
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Data
Model
•Surface seismic•Fracture characterization
Geophysicalinterpretation
• Surface deformation - tiltmeters - InSAR, GPS• Wellbore breakouts• Induced seismicity
Geomechanicalmodeling
• Production data• Well logs• Analogue reservoirs• 3D seismic
Flow models
3-way data assimilation methodologyMain outcomes:• Better forecasts• Optimal production to maximize recovery while controlling subsidence
coupled
coupled
Different Levels of Integration
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Data
Model
•Surface seismic•Fracture characterization
Geophysicalinterpretation
CTRW-RTT joint inversion methodology
• Surface deformation - tiltmeters - InSAR, GPS• Wellbore breakouts• Induced seismicity
Geomechanicalmodeling
• Production data• Well logs• Analogue reservoirs• 3D seismic
Flow models
3-way data assimilation methodologyMain outcomes:• Better forecasts• Optimal production to maximize recovery while controlling subsidence
coupled
coupled
3-way data assimilation methodology
Different Levels of Integration
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Data
Model
•Surface seismic•Fracture characterization
Geophysicalinterpretation
CTRW-RTT joint inversion methodology
• Surface deformation - tiltmeters - InSAR, GPS• Wellbore breakouts• Induced seismicity
Geomechanicalmodeling
• Production data• Well logs• Analogue reservoirs• 3D seismic
Flow models
coupled
coupled
3-way data assimilation methodologyMain outcomes:• Better forecasts• Optimal production to maximize recovery while controlling subsidence
Different Levels of Integration
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Numerical and Laboratory Modeling of Scattering from Fractures
• Understand seismic response of fractures and fracture systems– Develop new field-data analysis approaches– Platform/data for testing & evaluation of new
methods
• Develop models to test relationships between fracture compliance, roughness, permeability, and seismic scattering
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• Seismic response– Numerical
• Single and multiple fractures• 2D and 3D• P-to-P and P-to-S scattering• Finite difference; semi-analytical; boundary element• Static models to estimate compliance
– Experimental• Multiple fracture model• Incorporate flowing fractures
Numerical and Laboratory Modeling of Scattering from Fractures
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wave length fracture seismic response
homogeneous anisotropy zone
(1)
(2)
(3)
Focus Area
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Linear-slip Fracture Model (Schoenberg, 1980)Fracture Compliance
“zero” thickness
u1 u2
fracture
displacement compliance
2 1u u u Z T
tractionlength/stress [m/Pa]
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2D P-to-P FractureResponse Function (FRF)
P-wave
P scattered waves
Numerical Model 1
single fracture
(NB we can do this also in 3D)
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Numerical Model
Fracture Spacing 50 mAperture 5 m
Fracture Zone 50 m thick
Multiple Fractures
Numerical Model 2
Multiple (parallel) Fractures)
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0.5 10
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az=90
az=80
az=70
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az=0
Time (sec)
Ra
0.5 10
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az=80
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Time (sec)
Tr
0.5 10
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az=90
az=80
az=70
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Time (sec)
VzpsNMOfracture
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New approach to analyzing scattering in field data?
Transverse componentshows strong amplitude near 45 degrees
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• Seismic acquisition geometries– Iso-Offset acquisition at different azimuths– Common source gathers at different azimuths– CDP gathers at different azimuths
• Comparison with numerical models• Move towards joint seismic-flow experiments
Laboratory Experiments: Current Status
30 cm
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00
100
900
Offset = 6 cmP Wave SourceP, S Receiver
Laboratory Experiments: Acquisition Geometry
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PP Fracture Tip
P-S Converted
SS Fracture Tip
PP 2nd interface
90
80
70
60
50
40
30
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10
0
Transverse componentshows strong amplitude near 45 degrees(similar to numerical result)
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Conclusions Modeling
The insight thus obtained can be used to infer fracture compliance from seismic field data
• The amplitude of scattered-waves scales with compliance (Z)
• Radiation patterns depend mostly on ratio of normal to tangential compliance (ZN/ZT)
• On the transverse component, P-S Converted wave shows maximum amplitude at about 40-500 possible new orientation attribute
• On the inline component, P-S Converted wave shows systematic increase in amplitude towards 900 (not shown) possible new orientation attribute
• Stacking enhances signal in a direction parallel to fracture orientation (consistent with Scattering Index - Willis et al., 2006)
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– Elastic compliance is a key parameter influencing seismic scattering in fractured rocks.
– We want to know more about compliance values, scaling, and relation to permeability
– We are conducting numerical studies based on realistic fracture roughness statistics
Compliance (e.g., from seismics) Permeability
Fehler, Burns, Brown
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Compliance (e.g., from seismics) Permeability
1/compliance (relative)
Empirical Relationship (from fracture modeling)
Brown
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– Elastic compliance is a key parameter influencing seismic scattering in fractured rocks.
– We want to know more about compliance values, scaling, and relation to permeability
– We are conducting numerical studies based on realistic fracture roughness statistics
– We find:• Large fractures have much larger compliance
• Clear relationships between permeability, compliance, and stress
Compliance (e.g., from seismics) Permeability
Brown
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Fracture Response Function (FRF)
• Can be obtained directly from (multi-component) seismic data
• Methodology validated with numerical and laboratory data
• Provides information about fracture orientation, spacing, and relative compliance (& permeability)
Now: Preliminary Application to data from Emilio Field
Fehler, Burns, Brown
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Emilio Field
Seismic profile across Emilio Field
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Geometry of the top of reservoir & wells
Vp~4km/s
Fehler, Burns, Brown
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ConfidenceFracture Orientation
Confidence
Fehler, Burns, Brown
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Fracture Spacing Fracture Response Function
Fehler, Burns, Brown
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scattering strength~ fracture compliance x fracture density
Relative Compliance
Relative Compliance
With constraints from geodetic data (below) and with (empirical) scaling relationships from modeling this can be used to estimate permeability (and flow)
?
Fehler, Burns, Brown
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(sub)Surface deformation (GPS, InSAR) Fault (re-)activation Induced seismicity
seismic activity and subsidence
Surface subsidence due to reservoir pumping observed by GPS monitoring
• effect on wells/production
• impact of fault activation
• potential seismic risk
Geophysical monitoring of sub-surface reservoirs (Hager, Herring)
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Geodetic Characterization of Fractures:fractures change surface deformation resulting from
pressure changes at depth
Vertical (color) and horizontal (vectors, max = 3) surface displacements for the same point source volume change at unit depth. For the fracture, the maximum horizontal displacement is greater than the vertical displacement.
Isotropic porosity NW-SE oriented vertical fracture
Hager and Herring
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Example of Observed Fracture Response: In Salah CO2 Injection
Observations (Onuma & Ohkawa, 2009) Model (Vasco et al., 2010)
Isotropic δv/v ~ 0.5%
Fracture opening ~ 7 cm
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bb
Sensitivity to fracture properties
• Geodesy– Assume n cracks with width change δb– Displacement ~ nδb
• Only the product is resolvable• Assume δb ~ b• Displacement is then proportional to nb
• Flow studies ( permeability k)– k ~ nb3
Joint inversion of displacement and flow data can resolve n and b
b+δb
Hager and Juanes
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Objective:
Develop efficient and robust framework for the reconstruction of geologic facies from reservoir data.
Facies Identification in Petroleum ReservoirsFacies Identification in Petroleum Reservoirs
D
D
Reservoir:
high permeability
( red region )
Problem Statement:
Given production data from wells, we are interested in the following inverse problem: find the region Ω (the facies) corresponding to the high permeability of the reservoir.
McLaughlin group
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Identification of Absolute Permeability given production data from wellsData: Flow rates from 9 production wells and 4 injection wells.
ReferenceInitial guess 1
(with known facies at the well locations)
Synthetic Experiment: Initial guess 1
McLaughlin group
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Identification of Absolute Permeability given production data from wells
ReferenceReconstruction
Gradient-based(180 iterations)
Synthetic Experiment: Initial guess 1
McLaughlin group
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Coupled flow and geomechanics Computational aspects: discretization, staggered solution Reservoir modeling: response of fractures / faults
Direct numerical simulation of flow in fractured reservoirs
Continuous-time random walk (CTRW) modeling of flow in fractures
Inversion / data assimilation Towards joint seismic-flow inversion: joint CTRW-RTT paradigm Towards 3-way inversion: flow, seismic, geomechanics
Flow Modeling – Research thrusts
Viscous fingering in a Hele-Shaw cell
Juanes
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A deterministic multiscale approach is not attractive for inversion, optimization, and control:
Amount of data is insufficient to obtain a well-posed problem Resolution of data is insufficient to locate individual fractures
Need a stochastic multiscale approach and, in particular: Parsimonious flow model (fewer parameters) Capture anomalous (non-Gaussian) behavior of transport Allows assessment of predictability
Flow in fractured media – why a stochastic approach?
(Photograph by Jon Olson)Juanes
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A simple fracture network – particle tracking Two sets of fractures (constant orientation and density)
Power-law distribution of velocities (uncorrelated)
Develop model of expected transport (mean) and its confidence (variance)
Juanes
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A simple fracture network – effective model
The mean behavior is exactly described by CTRW
The variance is exactly described by a novel two-particle CTRW
Juanes
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“Continuous time random walk” and fractured reservoirs
CTRW can model fast paths (fractures) and their directionality along with slow paths (background matrix)
Parameters for (s,t) can be related to fracture orientation, spacing, connectivity and transmissivity
Juanes, Fehler, Burns, Brown
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Concluding Remarks
– Progress in several areas
– Fracture modeling and laboratory experiments are catalysts for development of new field data analysis methods
– Seismic-to-permeability is helping to bridge transition to reservoir modeling
– Numerical simulation and laboratory experiments
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Concluding Remarks
– Inversion methodologies will be used to combine geophysical and reservoir modeling approaches
– Reservoir analysis developing on many fronts
– Attempt to find approach that makes best overlap with geophysics