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intermediategeology
Quantitative Interpretation: Rock Properties from Seismic
Building on Reservoir Fundamentals and Prospect Evaluation, learn how geoscientists turn seismic amplitudes into rock properties — porosity, lithology, fluid and net-to-gross — through rock physics, fluid substitution with Gassmann, AVO and AVA, and seismic inversion, and how to risk a direct hydrocarbon indicator honestly. Short videos, interactive labs, quizzes and a synthetic case study that looks back at the Ada-1 discovery and de-risks the new Ada North prospect prepare you to explain quantitative interpretation in a graduate interview and work alongside a QI geophysicist.
4-5 hours9 sections20 lessons
Course Content
Section 1: 00 - Introduction
1 lessons
Section 2: 01 - Rock Physics
2 lessons
Section 3: 02 - Forward Modelling
3 lessons
Section 4: 03 - AVO and AVA
2 lessons
Section 5: 04 - Seismic Inversion
2 lessons
Section 6: 05 - Rock Properties from Seismic
2 lessons
Section 7: 06 - Pitfalls and DHI Risking
2 lessons
Section 8: 07 - QI in Practice
2 lessons
What You'll Learn
- Explain the quantitative interpretation (QI) workflow — forward from well logs through rock physics to synthetic seismic and AVA, and inverse from angle stacks through inversion to rock properties and risked volumes — and state a polarity convention
- Describe how mineralogy, porosity, cement, compaction, clay and fluid control Vp, Vs, density, acoustic impedance and Vp/Vs, and read a rock-physics template, including typical Niger Delta sand–shale trends
- Run a Gassmann fluid substitution from brine to oil and gas, explain why fizz gas looks like oil, and build synthetics, well ties and tuning-thickness estimates
- Explain AVO and AVA, compute Shuey's intercept and gradient, classify responses as AVO Class I, IIp, II, III or IV, and read an intercept–gradient crossplot
- Explain what seismic inversion does, why it needs a low-frequency model, and how simultaneous inversion gives AI and Vp/Vs, then turn them into porosity and Bayesian lithology–fluid probabilities with uncertainty
- Recognise QI pitfalls, apply a DHI checklist, update the chance of success with a calibrated DHI, and present a QI case such as Ada North with an honest statement of the remaining risks