Unlocking Sub-Seismic Resolution: How integrationAI Uses Machine Learning to Enhance Seismic Interpretation

3-D seismic data is the geoscientist’s best window into the subsurface—but it comes with a catch. While seismic images give us broad spatial coverage, they fall short on resolution, especially when it comes to resolving thin-bed reservoirs. Meanwhile, well logs offer detailed vertical data, but only in narrow slices of the earth.

This mismatch in resolution and coverage has long posed a challenge in reservoir characterization. But what if you could merge the two, and extract high-resolution insights across entire seismic volumes? That’s exactly what the integrationAI package inside SubsurfaceAI does using machine learning (ML).

In this post, we’ll walk through how our platform bridges the gap between seismic and well data to deliver detailed, sub-resolution reservoir predictions using real-wavelet spectral decomposition and ML—turning what was once invisible into actionable insight.

The Resolution Gap: Seismic vs. Well Data

Conventional seismic interpretation hits a hard limit when beds fall below a certain thickness—typically on the order of tens of meters, depending on the dominant frequency. Anything thinner than a quarter of wavelength tends to blur into the background. In contrast, well logs like gamma ray and sonic measurements can detect changes at sub-meter scales. Unfortunately, these logs are only available at sparse well locations.

The key, then, is integration. By intelligently combining high-resolution well logs with wide-coverage seismic data, it becomes possible to predict thin-bed reservoir properties across the entire seismic volume.

integrationAI Workflow for Thin Bed Prediction

subsurfaceAI’s integrationAI package enables this fusion with a carefully engineered machine learning workflow. Here’s how it works:

1. Define the Reservoir Interval

The process starts with identifying the target reservoir intervals in the well logs. This typically involves selecting key log curves—like sonic and gamma ray—and setting thresholds that define the reservoir. For example, a low gamma ray and high sonic velocity might indicate clean sand.

This definition serves as the ground truth against which ML predictions will be trained and evaluated.

2. Generate Iso-Frequency Spectral Amplitudes

Next, we generate iso-frequency spectral amplitude volumes using real-wavelet spectral decomposition. Unlike traditional analytic methods (like Morlet or continuous wavelet transforms), real-wavelet decomposition captures high-fidelity spectral features that align more accurately with true subsurface events.

These spectral amplitudes are crucial because they can highlight subtle, frequency-dependent responses associated with thin beds—essentially providing a fingerprint of features that lie below seismic resolution.

3. Train ML Models on Spectral Data and Logs

Now we bring in machine learning. The spectral amplitudes from seismic data are paired with well log values (gamma ray and sonic) around the reservoir interval. These serve as the inputs and targets for ML training.

Critically, our workflow includes rigorous model validation using blind wells—data not seen during training—to ensure that predictions generalize well. Our platform’s explainable ML tools also help interpret which spectral frequencies and seismic patterns contribute most to the model’s predictions.

4. Predict Logs Across the Seismic Volume

Once trained, the ML model is applied across every seismic trace—essentially predicting the gamma ray and sonic logs at each trace location, and at a resolution close to the original well logs. This produces log-like volumes that span the entire seismic dataset.

You now have a synthetic gamma ray and sonic log at every location, which opens the door to high-resolution interpretation everywhere, not just at well points.

5. Generate Geobodies and Isochore Maps

Finally, we use the same log thresholds that defined the reservoir to extract geobodies from the predicted volumes. These can be used to create isochore (thickness) maps, showing the spatial extent and variability of the reservoir—even in cases where the actual thickness is below seismic resolution.

In a recent project, integrationAI was able to map reservoirs as thin as 1-13 meters, using data from just 13 wells. That’s well below the resolution of conventional seismic interpretation—and it’s only possible through the power of ML-based integration.

Why This Matters

By applying machine learning to spectral seismic attributes and well data, integrationAI helps geoscientists:

• Resolve thin beds invisible to traditional interpretation.
• Extend well log insights across entire fields.
• Quantify uncertainty with explainable models.
• Accelerate reservoir delineation with fewer wells.

This technology isn’t just about making pretty maps—it’s about making better drilling decisions, reducing risk, and getting more out of your data.

Ready to See More?

If you’re working with sparse wells, 3-D seismic data, or complex stratigraphy, we’d love to show you what our ML-powered seismic-well integration can do. Reach out to the subsurfaceAI team to schedule a demo or discuss how to apply this workflow in your next project.

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