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

Earth Resources LaboratoryMIT

ENI-MITEI Annual Meeting, S. Donato M., 29 June 2010

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:

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

For example: fractured reservoirs

deformation during passage of a compressional wave

Carbonate cliffs

Seismic Data

geology/geophysics ↔ flow modeling ↔ enhanced production

Will

is e

t al

(20

06)

Water InjectionOil Production?

Oil

Water Front

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

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

1: Reservoir Structure and

Response

– Fracture Characterization (e.g., seismics)

– Flow Simulation

– Data assimilation & real-time control

– Quantitative integration

INTEGRATED RESERVOIR SCIENCE


– Surface deformation (GPS & InSAR)

– Coupled geomechanical/reservoir modeling

2:Reservoir Evolutionand Performance


Response

Integration of Geophysics &

Reservoir performance modeling

3:Application of New Concepts

(Field Case Study)

2:Reservoir Evolutionand Performance


Response


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

Data

Model






Flow models


coupled

coupled


Data

Model







Flow models


coupled

coupled

3-way data assimilation methodology


Data

Model







Flow models

coupled

coupled



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

• 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

wave length fracture seismic response

homogeneous anisotropy zone

(1)

(2)

(3)

Focus Area

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]

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)

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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New approach to analyzing scattering in field data?

Transverse componentshows strong amplitude near 45 degrees

• 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

00

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Offset = 6 cmP Wave SourceP, S Receiver

Laboratory Experiments: Acquisition Geometry

PP Fracture Tip

P-S Converted

SS Fracture Tip

PP 2nd interface

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Transverse componentshows strong amplitude near 45 degrees(similar to numerical result)

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)

– 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


1/compliance (relative)

Empirical Relationship (from fracture modeling)

Brown

– 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


Brown

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


Emilio Field

Seismic profile across Emilio Field

Geometry of the top of reservoir & wells

Vp~4km/s


ConfidenceFracture Orientation

Confidence


Fracture Spacing Fracture Response Function


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)

?


(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)

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

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

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

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

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

Identification of Absolute Permeability given production data from wells

ReferenceReconstruction

Gradient-based(180 iterations)

Synthetic Experiment: Initial guess 1

McLaughlin group

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

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

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

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

“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

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

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

Documents

ENI-MITEI Annual Meeting, S. Donato M., 29 June 2010