Specialised Technical Course
Permeability Estimation from Petrophysical Properties Using AI
Develop judgement in AI permeability estimation by reviewing input properties, reference measurements, model performance and prediction uncertainty.
Course Overview
Examines AI methods for estimating permeability from petrophysical properties. Participants assess input data, reference measurements and prediction uncertainty when reviewing model performance.
Who This Course Is For
- Reservoir and development engineers
- Petrophysicists and subsurface analysts
- Field evaluation technical teams
- Data specialists supporting reservoir studies
What Participants Will Learn
Define permeability targets and input requirements
Compare input properties with reference measurements
Assess model performance against reference evidence
Identify prediction uncertainty and application limits
Review a permeability prediction validation workflow
Key topics
- Input properties
- Reference permeability
- Model evaluation
- Prediction uncertainty
Course Modules
Back to Course OverviewPermeability Estimation Objectives
Permeability Estimation Objectives
Module overview
Define the estimation task and identify the petrophysical properties and reference measurements available for model assessment.
Key topics
Input properties · Reference permeability · Model evaluation · Prediction uncertainty
Learning outcome
Participants can define permeability targets and input requirements.
Learning format
Lectures, worked examples and data exercises
Reference and Input Data
Reference and Input Data
Module overview
Review measurement alignment and data quality, identifying differences between model inputs and reference permeability observations.
Key topics
Measurement alignment · Data quality · Reference coverage · Reference permeability
Learning outcome
Participants can compare input properties with reference measurements.
Learning format
Technical review · diagram exercises
Model Assessment and Comparison
Model Assessment and Comparison
Module overview
Examine evaluation arrangements and performance comparisons to identify where predictions are supported by the available evidence.
Key topics
Evaluation arrangements · Performance comparison · Evidence coverage · Model evaluation
Learning outcome
Participants can assess model performance against reference evidence.
Learning format
Worked examples · guided analysis
Prediction Uncertainty and Limits
Prediction Uncertainty and Limits
Module overview
Review uncertainty and application limits, including how differences in data coverage affect confidence in permeability predictions.
Key topics
Coverage differences · Confidence questions · Application limits · Prediction uncertainty
Learning outcome
Participants can identify prediction uncertainty and application limits.
Learning format
Case discussion · planning exercise
Permeability Prediction Review Case
Permeability Prediction Review Case
Module overview
Assess a prediction workflow and document model strengths, reference gaps and questions requiring further validation.
Key topics
Workflow assessment · Reference gaps · Validation questions
Learning outcome
Participants can evaluate a permeability prediction workflow against reference measurements.
Learning format
Practical case · group review
Programme Information
Course duration, location, practical components, training-centre information and applicable certification arrangements depend on the selected programme.
Learning format: Lectures, worked examples and data exercises
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