The Strategic Role of Geobody Interpretation in Reservoir Characterization

In modern subsurface workflows, accurately characterizing the spatial distribution of hydrocarbon reservoirs is foundational. Contrary to the traditional assumption that reservoirs always conform to seismic horizons, many geoscientists now recognize that true reservoir geometries often transcend these horizon boundaries. With ongoing improvements in seismic acquisition and processing technologies, higher-resolution seismic data allows geoscientists to identify discrete subsurface features—clusters of connected voxels with similar seismic response—that represent distinct geologic entities. These are referred to as seismic geobodies.

Geobodies may represent a wide variety of depositional or structural elements such as channel complexes, deepwater fans, carbonate build-ups, or stratigraphic traps. Identifying and interpreting these geobodies directly from 3D seismic data has become a core task in reservoir characterization, supporting exploration targeting, well placement, development planning, and static and dynamic modeling. Importantly, geobody interpretation informs volumetric estimates (OOIP/OGIP) and risk quantification.

Traditionally, geobody interpretation has relied on manual, visually guided workflows. Geoscientists use tools like amplitude thresholding, seed tracking, and geobody extraction based on defined attribute cutoffs. While useful, these manual methods are inherently time-consuming, often subjective, and difficult to scale across large 3D surveys.

In recent years, the integration of machine learning (ML) and artificial intelligence (AI) into seismic interpretation workflows has significantly changed the landscape. AI can now automate geobody detection, reduce interpretation cycles from weeks to hours, and introduce consistency across interpreters and projects. At the forefront of this transformation is SubsurfaceAI, a next-generation geoscience software platform that uniquely integrates traditional geophysical tools with AI-driven capabilities.

Why SubsurfaceAI Is Gaining Widespread Adoption

SubsurfaceAI has quickly gained a reputation as a leading software for geobody interpretation. Its growing user base among geophysicists, interpreters, and asset teams can be attributed to several key strengths:

1. Hybrid Workflow: Combining Traditional Methods with AI Automation

One of the most distinctive features of SubsurfaceAI is its dual-mode interpretation environment. Users are not forced to choose between manual and automated approaches. Instead, they are empowered to:

• Begin with traditional tools to explore data visually.
• Apply thresholding or seed tracking to isolate geobodies.
• Gradually incorporate AI when scaling up interpretations or standardizing across interpreters.

This flexible workflow allows geoscientists to tailor their approach based on the data quality, interpretation objective, available labels, and team preferences. A user might begin a project using classical amplitude-based geobody detection and, once a few examples are labeled, quickly train and apply an AI model across the entire volume.

This hybrid approach reduces cognitive load and helps bridge the gap between legacy workflows and modern digital transformation initiatives.

2. Deep Integration with Seismic Attribute Analysis and Machine Learning

In many conventional platforms, geobody interpretation is treated as an isolated step. In contrast, SubsurfaceAI embeds it within a broader analytical framework that includes:

• Multi-attribute cross-plotting: to correlate seismic response with known lithologies or facies.
• Seismic facies classification: using both supervised and unsupervised learning techniques (e.g., K-means, SOM, decision trees, neural nets).

• Spectral decomposition and RGB blending: to enhance geologic feature visibility across frequency ranges.
• Attribute extraction and transformation: for customized preprocessing ahead of geobody detection.

These capabilities ensure that geobody extraction is grounded in quantitative, data-driven interpretation. For example, attribute crossplots can be used to define a multi-dimensional “signature” for a target feature (e.g., high-porosity channel sands), and this signature can then guide both threshold-based and AI-based interpretation methods.

Moreover, machine learning tools for lithology classification and property prediction (e.g., porosity, impedance, brittleness) are available in the same environment, enabling seamless linkage between geobody interpretation and reservoir property modeling.

3. Well Integration and Geobody Validation

A major challenge in seismic interpretation is validating what we see on seismic volumes against ground truth. SubsurfaceAI directly addresses this by integrating geobody interpretation with well data:

• Seismic-to-well ties ensure proper depth/time correlation.
• Well markers and petrophysical logs are overlaid on seismic sections.
• Cross-validation between interpreted geobodies and well penetrations helps confirm internal lithofacies or fluid content.

This validation process ensures that the interpreted geobodies are not just seismic anomalies but are geologically and petrophysically meaningful. The ability to quickly switch between 3D views, inline/crossline slices, and well correlation panels gives interpreters full control over geobody QC and confidence building.

4. End-to-End Reservoir Modeling Integration

SubsurfaceAI recognizes that seismic interpretation is not an end in itself. Its ultimate purpose is to inform reservoir modeling and development. Accordingly, geobodies extracted in SubsurfaceAI can be:

• Domain converted (e.g., time-to-depth).
• Exported to modeling platforms or used directly in static modeling workflows.
• Used for volumetric analysis, allowing P10/P50/P90 estimation of OOIP and OGIP.

By positioning geobody interpretation as an integrated step in reservoir characterization, SubsurfaceAI reduces the handoff gaps between teams (e.g., from geophysicist to geomodeler) and accelerates the time to actionable insights.

Traditional Geobody Interpretation Tools in SubsurfaceAI

SubsurfaceAI includes a rich toolkit for conventional geobody extraction methods, which remain indispensable in many scenarios. These include:

Interactive Tracking

• Interpreters define a starting point (seed) and a threshold condition (e.g., amplitude).
• The software tracks the connected voxels that meet the threshold criteria.
• Results are visualized instantly in both 3D and map views.

• Options for tracking directionality, connectivity constraints, minimum body size, and edge smoothing allow users to fine-tune results.

Multi-Attribute Thresholding

• Users can select one or multiple attribute volumes and specify:

• Static thresholds (e.g., amplitude > 1.5).
• Ranges (e.g., coherence between 0.7–1.0).
• Conditional filters between attributes (e.g., high amplitude AND low sweetness).

subsurfaceAI then automatically extracts all voxels meeting these conditions within a user-defined interval (e.g., between top and base horizons). This is especially valuable for isolating bright spots, DHI features, or stratigraphic anomalies.

Seismic Facies Conversion

Facies volumes, created through supervised (e.g., labeled well data) or unsupervised classification, can be transformed into geobodies. Conversely, geobodies can be converted back into facies classifications to enrich model-building processes. This two-way conversion supports iterative interpretation and geologic realism.

AI-Based Geobody Interpretation: A New Era

The real leap forward in SubsurfaceAI lies in its AI-powered geobody interpretation workflow. The process is straightforward but powerful:

Step 1: Training the AI Model

The user manually labels geobodies on a few selected vertical sections or stratal slices. These labeled data serve as the training input for the geobody AI model.

Step 2: Validation and Blind Testing

The trained model is tested on blind sections—areas of the seismic volume not used in training. This ensures that the model has generalized well and isn’t overfitting. Any adjustments (e.g., label refinement, hyperparameter tuning) can be made at this stage.

Step 3: Batch Inference on Full Volume

After validation, the model is applied to the entire seismic cube or a targeted stratal grid. SubsurfaceAI handles the batch processing automatically, generating a new labeled volume where geobodies are highlighted for review.

Step 4: Multi-User Model Sharing

The trained AI models are stored in a centralized, versioned database. Colleagues can apply these models to other seismic datasets—ideal for large organizations or consultancy teams working across multiple basins. A model trained in one turbidite system can often be repurposed (with retraining) for a similar geological setting elsewhere.

This scalability and collaboration feature is a unique strength of SubsurfaceAI, aligning well with digital transformation strategies in upstream organizations.

Beyond Interpretation: Toward Automation and Insight

SubsurfaceAI is more than just another seismic interpretation tool—it represents a shift in how geoscientists work. It enables:

• Faster project turnaround by automating repetitive tasks.
• Greater consistency across interpreters, datasets, and projects.
• Higher confidence in interpretation by integrating seismic, well, and model data.
• Scalability for teams handling multiple basins or large seismic volumes.

It supports companies’ broader digital goals—whether building digital twins of reservoirs, deploying AI-assisted exploration programs, or accelerating field development planning.

Conclusion: The Future of Geobody Interpretation is Here

SubsurfaceAI is reshaping the landscape of seismic interpretation by integrating traditional workflows with powerful, user-friendly AI capabilities. It delivers a truly integrated platform for interpreters who want speed, accuracy, and scalability—without compromising geological rigor.

For geoscientists seeking to maximize the value of 3D seismic data, SubsurfaceAI offers a compelling solution. Whether you’re targeting subtle stratigraphic traps, validating interpretation with well data, or preparing inputs for reservoir models, SubsurfaceAI can help you do it better—and faster.

As geoscience workflows evolve toward greater automation, data integration, and predictive modeling, SubsurfaceAI is emerging as the essential tool for modern interpreters. The future of geobody interpretation is not just digital. It’s intelligent.