SPG 13th Biennial International Conference and Exhibition

Kochi

February 23 - 25, 2020 - Kochi, India

Category: Tradeshows

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CGG's global geoscience technologies enable you to build comprehensive earth models that support the discovery and responsible management of the Earth's natural resources.

Learn more about our broad range of leading products and expert services that help unlock the secrets of the earth through a variety of geoscience disciplines.

Come share our passion.

Our Technical Papers

[04 –Pid-250] Application of Deep Machine Learning for Direct Estimation of Hydrocarbon Pore Thickness
Jyoti Malik
4:30 Sunday February 23, Session TS 02: Machine Learning, Artificial Intelligence & Big Data Analysis

[02 –Pid-123] Revealing Deep Geological Structures and Pay Zones through Advanced Broadband PSDM Processing on an Offshore India Field
Brajesh Kumar
2:30 Monday February 24, Session TS 09: Seismic Data Processing & VSP - I

[Technical Keynote] Advanced Reservoir Characterization through Geostatistical Inversion
Jimmy Ting
4:30 Monday February 24, Session TS 16: Seismic Modeling, AVO & Inversion-I

[Technical Keynote] Uncertainty Quantification in Seismic Inversion
Ranjit Shaw
2:00 Tuesday February 25, Session TS 21: Seismic Modeling, AVO & Inversion-II

 

Pre-Conference Training

Course Number: CE-03
Course Title: Seismic Inversion for Reservoir Characterization
Instructor: Ranjit Shaw
Date: February 20, 2020
Venue: Lulu Bolgatty International Convention Centre, Kochi

We are pleased to announce that Ranjit Shaw, Principle Geoscience Advisor, GeoSoftware will be delivering an SPG Continuing Education course in advance of the conference. This course is aimed at providing participants with a broad exposure to seismic inversion technology currently used for quantitative reservoir characterization.

Spread over five distinct sessions, the course will cover i) Foundation of seismic inversion, ii) Deterministic inversion techniques, iii) Geostatistical inversion and iv) Case studies followed by an v) Interactive review and discussion session. Emphasis will be on the concepts, workflows and applications rather than theoretical/ mathematical treatment of the subject.

Our Booth Presentations

 

2:15 Machine Learning for Geoscience
Jyoti Malik and Priyanka Bhattacharjee
3:00 Multicomponent and Time Lapse Seismic Inversion in Native Domain
Robert Porjesz
3:45 Advanced Seismic Inversion for Reserve Estimation
Jimmy Ting
5:30 Geostatistical Seismic Reservoir Property Modeling for Accurate Subsurface Depiction
Ranjit Shaw
10:30 Multicomponent and Time Lapse Seismic Inversion in Native Domain
Robert Porjesz
11:15 Reservoir Characterization using Advanced HampsonRussell Software
Jyoti Malik
12:15 Advanced Seismic Inversion for Reserve Estimation
Jimmy Ting
2:15 Machine learning for Geoscience
Jyoti Malik and Priyanka Bhattacharjee
3:00 Geostatistical Seismic Reservoir Property Modeling for Accurate Subsurface Depiction
Ranjit Shaw
3:45 Application of Deep Learning for Direct Estimation of Hydrocarbon Pore Thickness
Jyoti Malik
4:30 Static Model Update – Integration of Seismic and Dynamic Data into Static Model Building Workflow
Robert Porjesz
10:30 Static Model Update – Integration of Seismic and Dynamic Data into Static Model Building Workflow
Robert Porjesz
11:15 Application of Deep Learning for Direct Estimation of Hydrocarbon Pore Thickness
Jyoti Malik
12:00 Advanced Seismic Inversion for Reserve Estimation
Jimmy Ting
2:00 Machine learning for Geoscience
Jyoti Malik and Priyanka Bhattacharjee
Subsurface Imaging - FWI

Full Waveform Inversion

Reveal intricate velocity details for the clearest images. Harness seismic low frequencies from BroadSeis, StagSeis, seabed and land data.

GeoSoftware - Machine learning

Machine Learning

Machine learning enables clearer reservoir understanding and faster, more efficient data analysis so you can predict curves based on existing log data.
GeoSoftware - Deterministic Inversion

Deterministic Inversion

Extract rock properties from seismic data using seismic inversion for pre-stack or full-stack analysis.
CGG

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