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IPLAB Sweet Spot Analysis Prediction2D
Plug-in Attributes
Unconventional Heavy Oil | Carbonate and Fractures | Deepwater Exploration and Production | Unconventional Shale
Russia origin, EAR99
  • 2018 | 2019 | 2020
Supporting Documents
Installation Guide
Release Notes
User Manual
The sweet spot analysis plug-in is based on machine-learning algorithms applying a set of maps (seismic attributes) or seismic waveform in the layer. The result will be one or several maps (average, standard deviation, P10, P50, and P90) with predictive parameters per the cross-validation option.
    Learning algorithms allow using cross-validation options
  • Applying of all learning points.
  • applying the defined number of realizations based on random selected well log points on each of these realizations; several maps (average, standard deviation, P10, P50, and P90) were calculated using this realization
    Predictive algorithms
  • Linear regression
  • Alternating conditional expectations (ACE) regression
  • Nearest neighbour
  • Random forest
  • T-neural network
Additional Information
    For predictions, it is proposed to use
  • set of maps—seismic attributes or any other maps arranged like a set of surface attributes
  • seismic waveforms defined in the layer (top and bottom surface) using several seismic cubes (full-angle stacks of seismic attributes)