Risk Reduction Through Seismic Data Mining - Rock Solid Images.pdf

Risk Reduction through Seismic Data Mining The Ibhubesi Field, Orange River Basin Republic of South Africa © 1998-2003 Rock Solid Images, all rights reserved

Seismic Data Mining “Data mining is a process that uses a variety of data analysis tools to discover patterns and relationships in data that may be used to make valid predictions”

Seismic Attributes

Two Crows Corporation.

PHI, Sw, Vcl

Progressive information extraction and knowledge enrichment from seismic and well data © 1998-2003 Rock Solid Images, all rights reserved

Project Goals ƒ Determine whether seismic data contains any information that might be utilized to reduce exploration risk in the future

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Topography & Bathymetry

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Prospect Leads

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Orange River Basin Architecture

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Project Workflow ƒ Precondition amplitude volume for attribute analysis via spectral extension and edge-preserving smoothing. ƒ “Mine” the 3D offset volumes using multiple seismic attributes. ƒ Determine which attributes are the best discriminators of lithology, fluid and geometry. ƒ Combine this attribute suite in an artificial neural network – Kohonen Self-Organizing Map ƒ Produce a volume that is calibrated to rock properties. © 1998-2003 Rock Solid Images, all rights reserved

Preconditioning of Seismic Data ƒ Amplitude Full-stack

ƒ Spectral balancing Increases frequency spectrum

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Preconditioning of Seismic Data ƒ Spectral extension Increases frequency spectrum

ƒ Smoothing Suppresses random noise

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Preconditioning of Seismic Data ƒ Input Data - Amplitude Full-stack

ƒ Output Data - Spectral extension + Smoothing

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Establishing a Structural Framework ƒ Similarity

ƒ Spectrally decomposed Amplitude centered at 8 Hertz

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Visual Data Mining Opacity Sculpting

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Spectral Decomposition Volume centered at 8 hz – frontal view

ƒ Arrows delineate fault trace

ƒ Interpreted fault plane

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Spectral Decomposition Volume centered at 8 hz – reverse view

ƒ Interpreted fault plane

ƒ Arrows delineate fault trace

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Structural Context

4 wells upthrown, 1 well downthrown © 1998-2003 Rock Solid Images, all rights reserved

Amplitude Data in Structural Context

Isolated amplitudes generally do not conform to structural grain © 1998-2003 Rock Solid Images, all rights reserved

Similarity

Meander cutoff © 1998-2003 Rock Solid Images, all rights reserved

Similarity & Relative Acoustic Impedance

Meander lobes © 1998-2003 Rock Solid Images, all rights reserved

Relative Acoustic Impedance

Sand bodies correspond to volumes low in relative AI

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Voxel based seed picking

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Connectivity Analysis

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Project Information ƒ

3 gas wells (AK-1, AK-2 and AV-1) drilled on bright amplitudes.

ƒ

AW-1 well drilled on brightest amplitude in volume – tested wet.

ƒ

Amplitude & AI good lithology discriminator – poor fluid discriminator

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A-W1 well (wet) Sw

VSH

PR

AI PR INCREASE

A-Y1 well (gas) Sw

VSH

PR

AI

PR DECREASE

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AVO Class III Anomalies ƒ Near-angle stack AK-2 AY-1

Brightening occurs over offset at top sand

AK-2 AY-1

ƒ Mid-angle stack

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Elastic Attributes 2 key elastic rock properties: ƒ shear modulus aka rigidity aka μ, ƒ μ is insensitive to fluid change

ƒ bulk modulus aka incompressibility aka K ƒ K is sensitive to fluid change

Lamé’s coefficient λ = K – 2/3 μ and, λρ = Ip2 – 2Is2 In presence of gas – term 1 decreases & term 2 increases © 1998-2003 Rock Solid Images, all rights reserved

Lambda-Rho

AV-1 AK-2 AK-1

Elastic constants

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Fluid Factor

AK-1

Red-green couplets indicate Vp decrease due to gas presence

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Kohonen Self-Organizing Map (SOM) ƒ Artificial neural network featuring unsupervised learning ƒ N-dimensional clustering technology ƒ Non-linear mapping of multi-attribute signature to seismic lithofacies ƒ Volumetric classification

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Mapped Final Calibrated Volume

Gas/Water Contact

wet sand

shale

gas sand

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Final Calibrated Volume – AY-1 (gas)

wet sand

shale

gas sand

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Final Calibrated Volume – AK-1&2 (gas)

wet sand

shale

gas sand

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Final Calibrated Volume – AV-1 (gas)

wet sand

shale

gas sand

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Final Calibrated Volume – AW-1 (wet)

wet sand

shale

gas sand

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Conclusions

ƒ Attribute Analysis reveals that multiple attributes can be combined in a non-linear fashion via an ANN featuring unsupervised learning – Kohonen’s Self-Organizing Map – to effectively discriminate between lithologies and fluids. ƒ Seismic data mining mitigates drilling risk

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Data Mining Summary Data mining provides knowledge discovery while reducing the volume being mined. Latest workflows being developed encompass this concept.

© 1998-2003 Rock Solid Images, all rights reserved

Acknowledgements ƒ Forest Oil ƒ Tim Berge

ƒ Anschutz Corp ƒ Kevin Corbett

ƒ Soekor ƒ Eric Jungschlager

ƒ Rock Solid Images ƒ Matt Carr, Maggie Smith, Tury Taner, Gareth Taylor, Uwe Strecker

© 1998-2003 Rock Solid Images, all rights reserved

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© 1998-2003 Rock Solid Images, all rights reserved