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Seismic Image Resolution Enhancement

Energy & Utilities
case study

Business Impacts

48 to 7

Hour reduction in training time

Inference in Minutes

SSIM>0.9

Improvement in model accuracy

Customer Key Facts

  • Location : North America
  • Industry : Oil & Energy

Problem Context

The customer conducts highly expensive geological surveys for oil exploration and had been facing problems in decision-making due to blurry and bad quality imaging. Their research team had an existing GAN-based machine learning model on-premise that demonstrated promising results for 2D images and 3D volumes.

Although they encountered a few bottlenecks when trying to improve the model convergence and performance, finalizing exploration sites based on the low resolution image continued to pose a risk as billions of dollars could be wasted. Therefore, their research team wanted a solution for Super Resolution of low resolution seismic 2D images and 3D volumes, which in turn would allow Geologists make better informed decisions.

Challenges

 

  • Highly expensive geological surveys with low-resolution seismic images
  • Dataset availability
  • Metric for evaluation
  • Domain-specific knowledge requirement

Technologies Used

GitLab

GitLab

Python

Python

Google Cloud Platform

Google Cloud Platform

TensorFlow

TensorFlow

Providing a Generative Model to Augment the Resolution and Quality of 2D and 3D Seismic Volume Images

Solution

Quantiphi implemented state-of-the-art SR-GAN and Conditional SR-GAN architectures to reconstruct both 2D seismic images and 3D seismic images on Cloud Machine Learning Engine (CMLE) platform. By leveraging hyperparameter tuning in CMLE, an extra 5 to 10 percent performance improvement was achieved. Overall, this solution acheived Structural Similarity Indexes (SSIM) of 0.9 for both 2D and 3D seismic images; equipping the client's research team with a modular training and serving pipeline to enhance their seismic images quality for faster and more accurate analysis.

Result

  • Reduced the risk of uncertainty
  • Faster analysis of seismic imaging
  • Improved image quality without losing the data present on the image

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