Specialised Technical Course
Petrophysical Log Prediction Using AI
Build AI log prediction review skills by connecting available well data, training coverage and checks on predicted log behaviour.
Course Overview
Examines AI-assisted prediction of petrophysical logs from available well data. Participants consider missing data, training coverage and checks on predicted log behaviour.
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 prediction objectives and available data
Prepare log inputs and check alignment
Assess training coverage and validation evidence
Compare predicted logs with reference behaviour
Assess predicted logs and interpretation limits
Key topics
- Log data preparation
- Missing data
- Training coverage
- Prediction checks
Course Modules
Back to Course OverviewLog Prediction Objectives
Log Prediction Objectives
Module overview
Define the prediction task and review available logs, missing intervals and the intended use of predicted information.
Key topics
Log data preparation · Missing data · Training coverage · Prediction checks
Learning outcome
Participants can define prediction objectives and available data.
Learning format
Lectures, log datasets and software exercises
Log Preparation and Alignment
Log Preparation and Alignment
Module overview
Examine depth alignment, data quality and missing information before preparing inputs for a prediction workflow.
Key topics
Depth alignment · Data quality · Input preparation · Missing data
Learning outcome
Participants can prepare log inputs and check alignment.
Learning format
Technical review · diagram exercises
Training Coverage and Validation
Training Coverage and Validation
Module overview
Review the relationship between training coverage and evaluation data, identifying gaps that may limit application to other wells.
Key topics
Evaluation data · Coverage gaps · Application scope · Training coverage
Learning outcome
Participants can assess training coverage and validation evidence.
Learning format
Worked examples · guided analysis
Predicted Behaviour and Interpretation
Predicted Behaviour and Interpretation
Module overview
Compare predicted log behaviour with reference observations and geological context, separating measured information from model outputs.
Key topics
Reference observations · Geological context · Prediction labelling · Prediction checks
Learning outcome
Participants can compare predicted logs with reference behaviour.
Learning format
Case discussion · planning exercise
Log Prediction Assessment Case
Log Prediction Assessment Case
Module overview
Review a log prediction workflow and explain the checks and limitations relevant to using its outputs.
Key topics
Workflow review · Output checks · Use limitations
Learning outcome
Participants can assess predicted logs and identify limits to their use.
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, log datasets and software exercises
Request the Complete Course Information