Specialised Technical Course
Petrophysical Evaluation Using AI
Build AI-assisted petrophysical evaluation skills through measurement preparation, model review and interpretation of predictions in geological context.
Course Overview
Examines AI-assisted petrophysical evaluation using subsurface measurements. Participants review data preparation, model validation and the interpretation of predictions in geological context.
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 evaluation objectives and measurement requirements
Prepare measurements and review input features
Review model assumptions and validation arrangements
Interpret predictions alongside geological reference evidence
Assess an AI-assisted petrophysical evaluation workflow
Key topics
- Measurement preparation
- Feature selection
- Model validation
- Petrophysical interpretation
Course Modules
Back to Course OverviewEvaluation Objectives and Measurements
Evaluation Objectives and Measurements
Module overview
Define the evaluation task and review available subsurface measurements, reference information and data quality questions.
Key topics
Measurement preparation · Feature selection · Model validation · Petrophysical interpretation
Learning outcome
Participants can define evaluation objectives and measurement requirements.
Learning format
Lectures, petrophysical datasets and software exercises
Data Preparation and Features
Data Preparation and Features
Module overview
Examine measurement alignment, missing values and feature choices that affect the quality of model inputs.
Key topics
Measurement alignment · Missing values · Feature choices · Feature selection
Learning outcome
Participants can prepare measurements and review input features.
Learning format
Technical review · diagram exercises
Model Development and Validation
Model Development and Validation
Module overview
Review model assumptions and validation arrangements, including how evaluation data represent the intended application.
Key topics
Model assumptions · Evaluation coverage · Comparison criteria · Model validation
Learning outcome
Participants can review model assumptions and validation arrangements.
Learning format
Worked examples · guided analysis
Geological Interpretation and Limits
Geological Interpretation and Limits
Module overview
Connect predictions with geological context and reference evidence to identify interpretation limits and questions needing review.
Key topics
Geological context · Reference comparison · Prediction limits · Petrophysical interpretation
Learning outcome
Participants can interpret predictions alongside geological reference evidence.
Learning format
Case discussion · planning exercise
Petrophysical Evaluation Case
Petrophysical Evaluation Case
Module overview
Review an AI-assisted workflow, assess validation evidence and document how predictions could support the proposed evaluation.
Key topics
Workflow review · Validation evidence · Interpretation record
Learning outcome
Participants can outline an AI-assisted evaluation workflow with appropriate validation checks.
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, petrophysical datasets and software exercises
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