Rapid Reservoir Characterization with Multiple Seismic Attributes

A Complete Workflow Solution Powered by AI and Machine Learning from subsurfaceAI

In today’s energy industry, subsurface professionals face the dual challenge of working with increasingly complex datasets and delivering faster insights with fewer resources. As exploration targets become more subtle and development projects shift to unconventional or mature reservoirs, the pressure on geoscientists to efficiently characterize reservoirs with high accuracy has never been greater.

A significant source of value lies in seismic attributes—derived datasets that enhance interpretability beyond conventional amplitude volumes. However, the rapid expansion of available attributes each year adds to the complexity. Which attributes matter most? How do they relate to actual reservoir properties? How can we interpret them collectively, rather than in isolation? These are questions every geoscientist must answer to succeed in today’s data-rich, time-constrained environment.

SubsurfaceAI delivers a complete AI and machine learning-powered workflow that allows users to extract the full value of seismic attributes. Our integrated platform empowers geoscientists to visualize, analyze, calibrate, and act on multi-attribute data with ease. In doing so, we help eliminate bottlenecks and accelerate the journey from seismic data to production-ready insights.

Key Industry Challenges

1. Information Overload and Limited Time Today’s geoscientists manage a deluge of data: 3D seismic volumes, dozens of attributes, well logs, petrophysical models, production data, and more. Despite this, the teams themselves are leaner. The traditional manual workflows used for interpreting and integrating these data are no longer viable for high-speed decision-making. Productivity—and ultimately project success—is at risk.

2. Too Many Attributes, Not Enough Clarity Every year, new seismic attributes are introduced—ranging from instantaneous phase and curvature to spectral decomposition and deep learning-based features. While each brings unique value, they often carry redundant or overlapping information. Understanding how to distill these into a manageable set of relevant indicators is critical.

3. Linking Attributes to Reservoir Properties Seismic attributes are powerful proxies, but they are not reservoir properties themselves. The real challenge lies in calibrating them with well logs and petrophysical data to build predictive models of porosity, lithology, fluid type, or other reservoir characteristics.

4. Identifying and Mapping “Sweet Spots” Efficiently Reservoir heterogeneity is real—and finding the best areas for drilling or development (the "sweet spots") often requires simultaneous interpretation of multiple seismic attributes. Doing this quickly and reliably across large datasets is nearly impossible without automation and AI assistance.

The SubsurfaceAI Solution: Integrated Intelligence for Reservoir Characterization

SubsurfaceAI offers a comprehensive solution to these challenges by combining high-end visualization, multivariate analysis, machine learning, and well calibration tools into one platform. Here’s how our software works across the full workflow:

1. Visualize What You’ve Been Missing

SubsurfaceAI’s visualization engine is designed for interpreters. It allows users to work interactively with seismic volumes and attributes to uncover patterns that are difficult to detect using conventional tools.

• Volume Rendering with Opacity Control Adjust the opacity of each attribute in real-time to focus on key subsurface features.
• Co-Rendering of Two Attributes Overlay two volumes with independent opacity curves to identify how attributes interact spatially.
• RGB Color-Blending for Three Attributes Use RGB channels to blend three seismic attributes, providing powerful insights into facies, lithology, or structural features. Add a fourth attribute to control transparency for depth perception.
• Unlimited Attribute Overlay Combine as many seismic attributes as needed, even over RGB displays. This enables complex patterns—like fracture zones or fluid contacts—to emerge clearly.

2. Analyze Relationships Across Multiple Attributes

Understanding how seismic attributes relate to one another is key to reducing data redundancy and extracting meaningful signals.

• 2D and 3D Crossplots with Probability Density Maps Visualize correlations between attributes and reservoir properties using crossplots enhanced by statistical density overlays.
• Multivariate Data Exploration Perform in-depth analysis of the attribute space to detect non-linear relationships and clusters.
• Principal Component Analysis (PCA) Identify the most informative combinations of attributes using PCA. The first and second components often explain the majority of variability, helping reduce dimensionality while preserving essential information.
• Self-Organizing Maps (SOM) Use unsupervised machine learning to group multiple attributes into seismic facies maps. This provides a direct way to interpret depositional environments, reservoir zones, or structural compartments.

3. Calibrate Seismic Attributes with Well Data

Machine learning in SubsurfaceAI bridges the gap between seismic and well logs by building predictive models that work across 3D space.

• Well Training and Attribute Screening Train models using well log data (e.g., gamma ray, sonic, porosity) against seismic attributes to identify which ones are relevant and which are not. Eliminate noise and bias early.
• Pseudo Log Volume Generation Once trained, apply models across the 3D volume to generate synthetic well logs (e.g., gamma ray or sonic) from seismic attributes. These pseudo-log volumes help map subsurface properties far from well control.

This calibration step transforms seismic attributes from abstract data into quantifiable geological and petrophysical models, enabling better volumetric estimates and reservoir management.

4. Deliver Results That Drive Decisions

SubsurfaceAI is more than an interpretation tool—it delivers outputs that support critical business decisions:

• AI-Driven Map Generation Rapidly generate maps of reservoir facies, net-to-gross, porosity, or other properties using trained models and multi-attribute integration.
• Geobody Extraction Extract 3D geobodies that represent your reservoir sweet spots—high-permeability zones, structural traps, or stratigraphic features.
• Volumetric Estimation (OOIP/OGIP) Use attribute-trained property volumes to estimate original oil/gas in place (OOIP/OGIP) distributions and support development planning.

These outputs are delivered in formats ready for integration into reservoir models, simulation platforms, or decision dashboards.

From Data Overload to Reservoir Insight—Faster Than Ever

With SubsurfaceAI, geoscientists no longer need to manually sift through dozens of attribute volumes or spend weeks generating maps and geobodies. Our AI-enhanced platform streamlines the entire workflow—from seismic visualization and analysis to well calibration and property prediction.

Whether you’re working in exploration or development, conventional or unconventional plays, SubsurfaceAI helps you:

• Increase interpretation speed and accuracy
• Reduce uncertainty through calibration
• Leverage AI without being a data scientist
• Deliver value to asset teams and management faster

Let’s Redefine Subsurface Workflows—Together

Seismic attributes are here to stay. But only with the right tools can we transform them into actionable insights. At SubsurfaceAI, our mission is to empower geoscientists with the power of AI while respecting the workflows and intuition that make interpretation a science and an art.

To learn more or schedule a personalized demo, visit: 👉 https://subsurfaceai.ca

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SubsurfaceAI Inc

Art Valdez

Cindy Luo

David Gray

Rongfeng Zhang

Renjun Wen