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Bridge Electromagnetics, EM Inversion Analysis
By Cegal (previously Blueback Reservoir)
Plug-in Attributes
Platform:
Petrel
Lifecycle
Exploration
ECCN:
Norway, EAR99
Version
  • 2018 | 2019 | 2020
Supporting Documents
Installation Guide
Release Notes
Others
Overview
The Bridge Electromagnetics (EM) plug-in consists of a series of modules for visualization, data QC, EM attribute analysis, EM forward modeling and EM inversion analysis. Running a 3D inversion is extremely CPU expensive. The EM Inversion Analysis module provides various tools and functionality to investigate and better understand the EM inversion data.
Specifications
  • Calculates the relative misfit between observed and synthetic EM data
  • Displays the relative misfit in a standardized multi-offset line response plot
  • Calculates the transverse resistance of an EM inversion result fast and accurately
  • Transverse resistance can be computed on (using an interval), or between, surfaces
  • Automatic sampling of the inversion result into a 3D grid
  • Tools for identifying, and separating out, the background-, and target-resistivity, in the EM inversion result
  • Archie’s law tool calculates the resistivity, and resistivity surface for a given range in porosity and water saturation
  • For a given net resistivity (resistivity in the net pay zone) and EM inversion result, the net pay thickness is estimated and a fluid contact surface is calculated
  • By stochastic sampling (e.g. Monte-Carlo) of the parameter space of the net resistivity, the most likely net pay thickness, and hence HC volume, is calculated
Features
Bridge EM Inversion Analysis is divided into three topically different parts: Quality control, Qualitative interpretation, and Quantitative interpretation. The QC part quickly identifies areas where the inversion result can be trusted. The qualitative interpretation part makes the integration of EM inversion results with other HC indicators easy. Finally, the quantitative interpretation uses the inversion result in a Monte-Carlo simulation to get a statistically robust estimate of the associated HC volume.