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Plug-in Details
IPLAB Sweet Spot Analysis Prediction2D
By
IPLAB LLC
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14Days - Evaluation
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Sweet spot analysis results
Result of well log parameter predictions using seismic attributes based on machine-learning algorithms (neural network). Twenty-five realizations using 70% randomly selected well log points on each of these realizations were applied for this prediction. Several maps (average, standard deviation, P10, P50, and P90) were calculated using these realizations. The size of the points reflects the well logs parameter.
Input parameters example
For prediction, it is possible to use several seismic attributes or waveforms in the layer from several seismic cubes.
Quality of the prediction
Multirealization approach with randomly removed learning points allows estimating the quality of predictions.
Correlation table
Correlation table and factor analysis allow estimating influence of each input parameter.
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Plug-in Attributes
Platform:
Petrel
Lifecycle
Domain:
Geophysics
|
Geology and Modelling
Challenges:
Unconventional Heavy Oil | Carbonate and Fractures | Deepwater Exploration and Production | Unconventional Shale
ECCN:
Russia origin, EAR99
Version
2018 | 2019 | 2020
Supporting Documents
Installation Guide
2018
|
2019
|
2020
|
Release Notes
2018
|
2019
|
2020
|
User Manual
2018
|
2019
|
2020
|
Others
Overview
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.
Specifications
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
Features
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)
Associated Plug-ins
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