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Oil & Gas Training
and Competency Development
Competency Management system SLB NEXT

Generalized Surface Multiple Prediction (GSMP) with Omega

This class provides a hands-on, workflow-based course on data-driven surface multiple prediction.

At the end of this class, students will have the skills to perform surface multiple prediction using the General Surface Multiple Prediction (GSMP) seismic function module (SFM). These skills include effective testing, quality control and optimum parameterization

Day 1

Understanding the Input Data Prerequisites and Designing a Testing Plan

  • Introduction
  • Brief theoretical background: GSMP key features
  • Input data pre-requisites
  • Choosing targets based on structural complexity
  • Designing a test plan

At the end of the day the student will be able to explain the input data requirements for GSMP and its basic theoretical background, which involves but is not limited to the earth’s convolutional model, multiples’ convolutional model and 3D marine acquisition limitations.

Day 2

GSMP Optimum Parameterization, Testing and Quality Assessment

 

  • Introduction: the Multiple Contribution Gather (MCG) and its value
  • Testing the core parameters: aperture and DRP spacing
  • Optimum parameterization of “on-the-fly” Interpolation:
  • Nearest neighbor search weights
  • Differential moveout velocity

At the end of the day the student will be able to parameterize GSMP and describe the strategies behind the optimization of various parameters by using QC tools such as SeisView/MAD/Petrel etc.

Day 3

Analysis of Results and Introduction to Adaptive Subtraction

 

  • Other GSMP parameters
  • Analysis of results
  • Effective Quality Control
  • Quality Control in different domains
  • X-Correlations
  • Introduction to adaptive subtraction
  • Simultaneous subtraction and the curvelets domain

 

At the end of the day the student will be able to describe the various approaches available for adaptive subtraction. There will also be an enhanced session for various uses of GSMP modelling in different geological settings.

Learning activity mix

Experienced geophysicists who wish to gain a more in-depth understanding of the GSMP technique

  • Input data requirements
  • Understanding the impact of the input data limitations on the quality of data driven multiple prediction
  • Designing an effective test plan for a given geology or survey challenges
  • Optimally parameterizing the GSMP module
  • Effective quality control of the model prediction results

An understanding the theory of data-driven Surface Related Multiple Elimination (SRME) and familiarity with WesternGeco’s scheme of General Surface Multiple Prediction (GSMP), including “on-the-fly interpolation”.

Basic Omega and Petrel skills.

Currently there are no scheduled classes for this course.

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