Introduction
Rock physics forms the critical foundation of Quantitative Interpretation (QI) workflows. enabling the accurate seismic reservoir characterization of for confident exploration and production (E&P). Most subsurface interpretation errors originate from poor data Quality Assurance and Consistency (QAC), inconsistent seismic petrophysics and rock physics model calibration, inadequate seismic conditioning, or inappropriate selection and application of rock-property estimation techniques such as seismic inversion or machine-learning (ML) algorithms.
This course provides a unified value-proposition approach that fills a critical skills gap by teaching participants how to integrate well logs, apply rock physics diagnostics, achieve consistent seismic petrophysical and rock physics modeling, and confidently perform seismic amplitude inversion or machine-learning–based property estimation to significantly enhance subsurface reservoir prediction.
Growing reliance on QI, inversion, and ML makes rock physics essential for reducing uncertainty, improving rock property and lithofacies estimation, and enabling confident exploration, appraisal, and development decisions.
Duration
8 hours
CEUs
.8
Intended Audience
The course is suitable for geophysicists, geologists, petrophysicists, reservoir engineers, seismic interpreters, and technical professionals involved in exploration, appraisal, or development workflows.
Level
Intermediate and Advanced
Prerequisites
A basic geoscience or engineering background is recommended, along with familiarity with well logs, rock physics and seismic data interpretation.
Prior experience in exploration, petrophysics, or quantitative interpretation is helpful but not required.
Learner Outcomes
- Perform well-log Quality Assurance and Consistency (QAC) and seismic data conditioning (SDC) to ensure reliable inputs for QI & reservoir characterization workflows
- Produce consistent seismic petrophysics and calibrate site-specific rock physics models for clastic, unconventional, and fractured reservoirs Petro-elastic properties relationships
- Generate and analyze synthetic seismograms to calibrate seismic data with well properties and improve seismic-to-well ties for more reliable reservoir characterization
- Lithofacies estimation using seismic petrophysics, rock physics analysis, fluid-substitution modeling, and AVO/AVAz forward modeling and interpretation
- Apply deterministic, probabilistic, and ML-assisted seismic inversion techniques to derive robust subsurface reservoir property predictions for quantitative reservoir characterization
