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The UBC Geophysical Inversion Facility
EM geophysics for hydrocarbons: Inversion applications and current research at UBC-GIF Scott Napier Doug Oldenburg Jamin Cristall May 2005
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Acknowledgments AGIP Anglo American BHP Billiton EMI Falconbridge INCO
This research was sponsored by NSERC and: AGIP Anglo American BHP Billiton EMI Falconbridge INCO Kennecott MIM Muskox Minerals Newmont Placer Dome Teck Cominco Thanks to UBC-GIF personnel: Colin Farquharson Eldad Haber Roman Shekhtman
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Outline Electrical conductivity and hydrocarbons
Introduction to forward modelling Introduction to inversion methodology Case Studies Shallow gas Oil sands Current research Marine CSEM Conclusions
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Electrical conductivity and hydrocarbons
Marine CSEM TEM FDEM DC Resistivity Can we detect hydrocarbons with EM measurements at the surface? Resistivity GR/SP SP GR Deep Med Hydrocarbons are resistive Measurements of ρ are common in the borehole 0.2 Ωm 2000 Ωm
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Outline Electrical conductivity and hydrocarbons
Introduction to forward modelling Introduction to inversion methodology Case Studies Shallow gas Oil sands Current research Marine CSEM Conclusions
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Forward Modelling: The airborne FDEM survey
Waveform: Frequency domain I Time Recorded Data: Amplitude and Phase or Real and Imaginary parts Boundary conditions at z =
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Forward Modelling EM data in 1D:
The EM skin depth Divide the earth into stack of layers fixed thickness constant internal conductivity F[m] Tx Rx z1 z2 . z3 . zn Basement half-space 5 frequencies from 385Hz to 102kHz with real and imaginary parts
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Outline Electrical conductivity and hydrocarbons
Introduction to EM methods Introduction to inversion methodology Case Studies Shallow gas Oil sands Current research Marine CSEM Conclusions
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? The inverse problem: Unconstrained Optimization d
Geophysical data are: F[m] + = d m: model --- unknown F: forward mapping operator : errors d: observations (data) Given: data, errors, a forward modelling method Find: the model that generated measurements. Major Difficulty: Non-uniqueness ? F-1 d
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Wd, W : Data error, model weighting
Inversion as an optimization problem Define Model objective function. Misfit function. Minimize = d + m Subject to d < Tol. : Regularization parameter : Observed data : Model and Reference model Wd, W : Data error, model weighting
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where sensitivity matrix
Minimizing the Model Objective Objective function Differentiate where sensitivity matrix
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Linearize F[m+m] = F[m] + J m
Gauss-Newton method Iterate Linearize F[m+m] = F[m] + J m Update the model mk+1=mk + α(δm) Repeat until convergence
![Linearize F[m+m] = F[m] + J m](http://slideplayer.com/slide/16482481/96/images/12/Linearize+F%5Bm%2B%EF%81%A4m%5D+%3D+F%5Bm%5D+%2B+J+%EF%81%A4m.jpg "Gauss-Newton method. Iterate. Linearize F[m+m] = F[m] + J m. Update the model. mk+1=mk + α(δm) Repeat until convergence.")
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Outline Electrical conductivity and hydrocarbons
Introduction to EM methods Introduction to inversion methodology Case Studies Shallow gas Oil sands Current research Marine CSEM Conclusions
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Case Study: EM methods for shallow gas exploration in NW Alberta
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Shallow Gas: Geologic Background
gas well: W6 ~ 10 m Sand / Gravel/ Conglomerate Glacial Till Clays -Clay till with recent narrow gravel channels -Quaternary Lacustrine clays -Oligocene/Miocene braided river channels, gas or water charged -Cretaceous shale with possible deeply incised gravel/sand channels -Cretaceous shale < 5 m ~ < 5 m m water or gas charged
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Proof of Concept: Surveys over existing shallow gas field
Airborne frequency domain EM (FEM) 2D DC resistivity
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FDEM: Advantages Disadvantages Covers large areas at low cost
low ecological impact 3d images from densely sampled data Disadvantages Inductive method Not particularly sensitive to resistors Shallow depth of investigation (maximum m)
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FDEM Inversion: results
data 5 20 Ωm 200
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FDEM: Result Gas saturated areas are detectable with FDEM data
Forward modelling indicates resistivity will be underestimated gas field could have benefited from this survey depth = 46 m
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DC Resistivity: Advantages Disadvantages galvanic method
sensitivity to resistors good depth of investigation Wenner Array a spacing maximum 400m Disadvantages Ground based slower more expensive acquisition 2D interpretation Data quality based on good electrical contact with ground Suffers in swampy terrain Difficult to penetrate conductive layers
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DC resistivity: Result
Observed Data Predicted Data Recovered Model 5400
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Comparing results: Challenging environment for DC resistivity surveying 5400
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Western Canada Oil Sands Regions
Wabasca Calgary Edmonton Cold Lake Fort McMurray Athabasca Peace River -There are four major oil sands deposits in Canada- Peace River, Wabasca, Cold Lake, and Athabasca. -The Athabasca oil sands are by far the largest and this is where Petro-Canada has the largest interest so they will be the focus of this presentation. Source: Mark Savage, “Oil Sands Characteristics - Geology,” 9 April 2002
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The McMurray Formation
Source: David R. Taylor, “McMurray Fm. Geological Model,” 28 May 2003
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Airborne Time Domain EM Surveying: The GEOTEM system
Waveform: Time Domain Inverted Data: Time Domain I dB/dt Time Time
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Time Domain EM: The GEOTEM system
Advantages No primary field during recording stage secondary fields only Depth of investigation (maximum m) Large areas at low cost low environmental impact Disadvantages Inductive method not particularly sensitive to resistors
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Inversion of Field Data
7 km 10 km
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Comparison to Conductivity-Depth Transform
CDT m Inversion
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Outline Electrical conductivity and hydrocarbons
Introduction to EM methods Introduction to inversion methodology Case Studies Shallow gas Oil sands Current research Marine CSEM Conclusions
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Introduction: The marine CSEM survey
Towed Transmitter horizontal electric dipole Seafloor Receivers record Ex , Ey possible to record Ez , Hx and Hy
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Why Marine CSEM? Reduce risk for expensive deep water wells
Recover reservoir resistivity Recover reservoir geometry Why might it work? Galvanic source sensitive to resistors Seawater provides shielding from EM noise sources can detect signals of extremely low amplitude
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Forward Modelling: Theory
FD Maxwell’s equations (e-it ) Boundary condition
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3D Forward Modelling: Introduction
A Helmholtz decomposition with Coulomb gauge System equations for A and where Discretize on a staggered grid
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Forward Modelling: Response of a large reservoir
Tx 6 km 850 m 100 m 0.3 Ωm 1 Ωm 50 Ωm 1150 m Amplitude of E-field (1 Hz) Reservoir 10 -11 No Reservoir 10 -12 10 -13 |E| [V/m] 10 -14 10 -15 10 -16 1 2 3 4 5 6 7 8 Offset [km]
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Forward Modelling: The Reservoir Model
profile view plan view 1650m depth σ (S/m) Key model parameters water depth depth of burial thickness horizontal extent 1000 m 600 m 100 m 2000 m Transmitter parameters orientated in x direction 100 m long 300m east of center of the reservoir
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Forward Modelling: Reciprocity
Practical surveys consist of few Rx and many Tx Each Tx requires a separate forward model time consuming processing A B M N V I Reciprocity solves this problem 20000 20000 13000 20000 13000 20000
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Forward Modelling: Ex and Ey
Real Ex Real Ey Imag Ex Imag Ey
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Inversion: Results - 2 frequencies (1Hz & 5Hz)
σ (S/m) Z= m z=-1600 m Isosurface at 0.2 S/m Recovered z=-1600 m Isosurface at 0.2 S/m True
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Inversion: Observed and predicted data
Ex Ey
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Conclusion: Frequency Domain EM, DC resistivity inversions could be very important in exploration in production 5400
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Conclusion: Airborne TEM in conjunction with an inversion code can clearly locate oil sand channels Oil sands are a growing proportion of Canada’s hydrocarbon production Source: David R. Taylor, “McMurray Fm. Geological Model,” 28 May 2003 m
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Conclusion: Marine CSEM can significantly reduce risk in expensive offshore exploration Potential to help define reservoir geometry
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Questions?
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