1 2014 AASPI Workplan Kurt J. Marfurt Kurt J. Marfurt Jamie Rich Vikram Jayaram Marcilio Matos OU AASPI Team Attribute-Assisted Seismic Processing and.

Slides:



Advertisements
Similar presentations
Signal Estimation Technology Inc. Maher S. Maklad Optimal Resolution of Noisy Seismic Data Resolve++
Advertisements

Applications of one-class classification
4. Spectral Decomposition
INTRODUCTION TO Machine Learning 2nd Edition
Statistical Characterization Using Automatic Learning Gaussian Mixture Models in Diamond M Field, TX David Lubo*, The University of Oklahoma, Simon Bolivar.
Data Mining Classification: Alternative Techniques
Workplan Kurt J. Marfurt Tim Kwiatkowski Marcilio Matos Attribute-Assisted Seismic Processing and Interpretation AASPI.
An Overview of Machine Learning
AASPI Attribute-Assisted Seismic Processing and Interpretation
Software Quality Ranking: Bringing Order to Software Modules in Testing Fei Xing Michael R. Lyu Ping Guo.
4. Spectral Decomposition
1 Welcome and overview of AASPI accomplishments and software development Kurt J. Marfurt Attribute-Assisted Seismic Processing and Interpretation AASPI.
Chapter 17 Overview of Multivariate Analysis Methods
SAMPLE IMAGE Shale Gas Development: Integrated Approach Hemant Kumar Dixit Mumbai, India 18 January-2013.
Multivariate Methods Pattern Recognition and Hypothesis Testing.
Supervised and Unsupervised learning and application to Neuroscience Cours CA6b-4.
x – independent variable (input)
Earth Study 360 Technology overview
Review Rong Jin. Comparison of Different Classification Models  The goal of all classifiers Predicating class label y for an input x Estimate p(y|x)
Geologic Analysis of Naturally Fractured Reservoirs 2nd Edition, R. A
Projection methods in chemistry Autumn 2011 By: Atefe Malek.khatabi M. Daszykowski, B. Walczak, D.L. Massart* Chemometrics and Intelligent Laboratory.
4. Spectral Decomposition
New Directions and the Importance of Geology Susan Smith Nash, Ph.D. AAPG.
Multisource Least-squares Reverse Time Migration Wei Dai.
U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 2011 Predicting Solar Generation from Weather Forecasts Using Machine Learning Navin.
Overview of AASPI accomplishments and software development Kurt J. Marfurt Kurt J. Marfurt Attribute-Assisted Seismic Processing and Interpretation AASPI.
International Shale Development Optimization
Attribute Expression Using Gray Level Co-Occurrence Sipuikinene Angelo*, Marcilio Matos,Kurt J Marfurt ConocoPhillips School of Geology & Geophysics, University.
1 Welcome and overview of AASPI software and documentation Kurt J. Marfurt Attribute-Assisted Seismic Processing and Interpretation AASPI.
Correntropy as a similarity measure Weifeng Liu, P. P. Pokharel, Jose Principe Computational NeuroEngineering Laboratory University of Florida
Tao Zhao*, Vikram Jayaram, Bo zhang and Kurt J. Marfurt,
AASPI Workplan Kurt J. Marfurt Kurt J. Marfurt Jamie Rich Marcilio Matos Bo Zhang Brad Wallet OU AASPI Team Attribute-Assisted Seismic Processing.
Kui Zhang* and Kurt J. Marfurt 2008 AASPI Consortium annual meeting Migration-driven velocity analysis for anisotropy/stress analysis.
Statistical measures of instantaneous spectra Kui Zhang* and Kurt J. Marfurt 2008 AASPI Consortium annual meeting Not Gaussian!
Fractures play a major role in many tight reservoirs such as shale, carbonate, and low permeability sand by providing fluid flow conduits, for this reason.
1 RPSEA Project – Facies probabilities from seismic data in Mamm Creek Field Reinaldo J Michelena Kevin Godbey Patricia E Rodrigues Mike Uland April 6,
Multivariate Data Analysis Chapter 1 - Introduction.
Bell Laboratories Intrinsic complexity of classification problems Tin Kam Ho With contributions from Mitra Basu, Ester Bernado-Mansilla, Richard Baumgartner,
Joint seismic attributes visualization using Self-Organizing Maps
CS558 Project Local SVM Classification based on triangulation (on the plane) Glenn Fung.
Lecture 2: Statistical learning primer for biologists
Overview of Stark Reality Plugins for OpendTect Coming soon to a workstation near you.
A New Method for Crater Detection Heather Dunlop November 2, 2006.
Using multidimensional scaling and kernel principal component analysis to interpret seismic signatures of thin shaly-sand reservoirs Piyapa Dejtrakulwong1,
Jamie Rich Kurt Marfurt University of Oklahoma ConocoPhillips School of Geology and Geophysics.
CS Statistical Machine learning Lecture 12 Yuan (Alan) Qi Purdue CS Oct
Tao Zhao and Kurt J. Marfurt University of Oklahoma
Pitfalls in seismic processing : The origin of acquisition footprint Sumit Verma, Marcus P. Cahoj, Bryce Hutchinson, Tengfei Lin, Fangyu Li, and Kurt J.
4. Spectral Decomposition
Image Quality Measures Omar Javed, Sohaib Khan Dr. Mubarak Shah.
Data Summit 2016 H104: Building Hadoop Applications Abhik Roy Database Technologies - Experian LinkedIn Profile:
Data statistics and transformation revision Michael J. Watts
SEISMIC ATTRIBUTES FOR RESERVOIR CHARACTERIZATION
4. Spectral Decomposition
3-D Karoo basin reconstruction from ambient passive seismic noise
4. Spectral Decomposition
4. Spectral Decomposition
Modeling of free-surface multiples - 2
MONITORING AND INTERPRETATION OF MICROSEISMS INDUCED BY FLUID INJECTION N. R. Warpinski Sandia National Labs.
Machine Learning for High-Throughput Stress Phenotyping in Plants
Support Vector Machines
What is Regression Analysis?
4. Spectral Decomposition
10. Inversion for acoustic and elastic impedance
Comparative analysis of attributes and Post-stack P-impedance in time and depth domain in a Naturally-Fractured Carbonated reservoir for dolomitic facies.
David Lubo-Robles*, Thang Ha, S. Lakshmivarahan, and Kurt J. Marfurt
Machine Learning Support Vector Machine Supervised Learning
Machine Learning – a Probabilistic Perspective
What is Artificial Intelligence?
Evgeny Tolstukhin*, Reidar Midtun, Pål Navestad, ConocoPhillips Norway
Presentation transcript:

AASPI Workplan Kurt J. Marfurt Kurt J. Marfurt Jamie Rich Vikram Jayaram Marcilio Matos OU AASPI Team Attribute-Assisted Seismic Processing and Interpretation AASPI

2 AASPI 2014 Workplan

Shale dewatering – west Africa (t=3.2 s) Texture 1 Texture 2 Texture 3 20 km AASPI 2014 Workplan: Large scale texture analysis

4 AASPI 2014 Workplan

Correlation of present day stress vector and faults (plate boundaries) 5 Africa

6 “Vector” correlation between anisotropy, a, and curvature c

7 AASPI 2014 Workplan

Microseismic source characterization and correlation with stratigraphy/seismic attributesMicroseismic source characterization and correlation with stratigraphy/seismic attributes Fault plane solutions  Full moment tensors?Fault plane solutions  Full moment tensors? Quantifying value of multiple monitor wellsQuantifying value of multiple monitor wells –Error as a function of number of monitors and SNR –Effect of Double Difference locations vs. number of monitors and SNR Uniqueness of VTI velocity models, we all know that the correct VTI model gives us better locations, but what about incorrect VTI models?Uniqueness of VTI velocity models, we all know that the correct VTI model gives us better locations, but what about incorrect VTI models? A new approach for attribute based ‘productivity maps’A new approach for attribute based ‘productivity maps’ AASPI 2014 Workplan – Integrating Microseismic

9 Productivity Productivity Relative Curvature Cutoff

10 3D Supervised Facies Classification (Tao Zhao, Atish Roy, V. Jayaram)3D Supervised Facies Classification (Tao Zhao, Atish Roy, V. Jayaram) Applications : Sweet Spot Identification, Applications : Sweet Spot Identification, Generating Classification maps Generating Classification maps EUR prediction & Mapping Production Data EUR prediction & Mapping Production Data Techniques Investigated : Techniques Investigated : Distance Metrics in GTM, SOM (Atish Roy) Distance Metrics in GTM, SOM (Atish Roy) Support Vector Machines (Tao Zhao) Support Vector Machines (Tao Zhao) Transformations or Blind Source Separation Techniques (V. Jayaram)Transformations or Blind Source Separation Techniques (V. Jayaram) Applications : Dimensionality Reduction & Cluster Analysis Applications : Dimensionality Reduction & Cluster Analysis Techniques Investigated : Noise Adjusted Principal Components (Atish Roy) Noise Adjusted Principal Components (Atish Roy) Independent Component Analysis Independent Component Analysis Emerging trends & Algorithm development

11 Statistical Parameterization of Well Data (David Lubo, V. Jayaram)Statistical Parameterization of Well Data (David Lubo, V. Jayaram) Applications : Reservoir Characterization, Applications : Reservoir Characterization, Correlating Well data to Seismic Attributes Correlating Well data to Seismic Attributes Techniques Investigated : Gaussian Mixture Models (David Lubo) Techniques Investigated : Gaussian Mixture Models (David Lubo) Markov Models Markov Models Non-Linear Regressors (Melia da Silva, V. Jayaram)Non-Linear Regressors (Melia da Silva, V. Jayaram) Applications : Establishing quantitative relationships between Applications : Establishing quantitative relationships between fracture intensity and curvature, fracture intensity and curvature, Correlating Production Data to Seismic Attributes Correlating Production Data to Seismic Attributes Techniques Investigated : Gaussian Mixture Regression Kernel Regression Kernel Regression Texture Analysis (Marcus Cahoj, V. Jayaram)Texture Analysis (Marcus Cahoj, V. Jayaram) Applications : Geology Interpretation, Fault Identification Applications : Geology Interpretation, Fault Identification Techniques Investigated : Gabor Energy Vs. GLCM Techniques Investigated : Gabor Energy Vs. GLCM Emerging trends & Algorithm Development

10-12 Average fracture intensity vs. curvature (White, 2013) Elastic deformation Fracture generation Fracture saturation Faulting This is NOT a linear correlation

13 AASPI 2014 Workplan

14 AASPI 2014 Workplan Diffraction imaging CLSM with cosθ obliquity factor Time- processed shot gathers Migration velocities Migrated gathers dip3d Dip and azimuth cosθ CLSM with (1-cosθ) obliquity factor Diffraction image? Demigrated shot gathers stack

15 AASPI 2014 Workplan Suggestions from the floor?