Author: subsurfaceAI

Rock Physics for Seismic Interpreters – Volve Case Study Published: December 17, 2025Author: subsurfaceAI Validating Dynamic Reservoir Simulation Using Rock Physics Modeling in SubsurfaceAIWhen Equinor and its partners released the full Volve field dataset to the public, they provided something unique: a complete, real-world North Sea oil field, from seismic...

Rock Physics for Seismic Interpreters – Part 2 Published: December 17, 2025Author: subsurfaceAI Validate Static and Dynamic Reservoir Models Through Rock Physics Modeling(Revised and expanded version, replacing your original Part 2 article.)Rock Physics Modeling-Part 2Introduction: From Concept to Physics-Based Reservoir ValidationIn Part 1 of this series, we introduced the core...

Technical Supplement: AVO Theory, Derivations, Angle Treatment, Stability, and Examples for SubsurfaceAI Partial-Stack AVO Published: December 5, 2025Author: subsurfaceAI This document provides the mathematical and geophysical foundations behind SubsurfaceAI’s AVO calculation using partial stacks. It elaborates the equations and assumptions behind estimating intercept and gradient from angle/offset-limited stacks, demonstrates the...

Rock Physics for Seismic Interpreters: Part 1 Published: December 4, 2025Author: subsurfaceAI Bringing Rock Physics Into Everyday Seismic Interpretation With SubsurfaceAIRock physics is the scientific foundation that links subsurface reservoir properties—porosity, lithology, fluids, pressure—to the seismic responses we observe in the field. Yet for most seismic interpreters, rock physics has...

AVO Analysis in SubsurfaceAI from Partial Stacks Published: December 4, 2025Author: subsurfaceAI Easy, Integrated Workflow for Interpretation Geophysicists – and How It Compares to Pre-Stack GathersAmplitude Variation with Offset (AVO) is a powerful way to connect seismic amplitude behavior to changes in rock and fluid. In many teams, though, AVO...

Rule-Based Reservoir Modeling – Part 3: Dynamic Testing of Rule-Based Point-Bar Reservoir Models Published: December 4, 2025Author: subsurfaceAI AbstractIn the first two parts of this series, we introduced the motivation and methods behind rule-based reservoir modeling. Part 1 presented the core philosophy—representing geological heterogeneity that controls fluid flow—while Part 2...

Opening the Black Box: How SubsurfaceAI Makes Machine Learning Predictions Explainable for Porosity Prediction Published: November 12, 2025Author: subsurfaceAI The Promise and Problem of Machine Learning in GeoscienceMachine learning (ML) has transformed the way geoscientists interpret subsurface data. From seismic attribute volumes to well logs and core data, algorithms can...

Predicting Well Top Properties from Seismic Attributes Using Machine Learning in SubsurfaceAI Published: October 29, 2025Author: subsurfaceAI Machine learning (ML) is reshaping how geoscientists interpret the subsurface. Traditionally, seismic interpreters rely on manual correlation between well data and seismic attributes — a time-consuming, subjective process that often depends on individual...

Rule-Based Reservoir Modeling: Capturing Reservoir Heterogeneity That Matters Most to Fluid Flow – Part 2 Published: October 14, 2025Author: subsurfaceAI In Part 1 of this series, we introduced the principles of rule-based reservoir modeling and explained how geological-rule–driven geomodels differ from conventional stochastic approaches. By embedding depositional rules, stacking patterns,...