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Scalable 3D Image Segmentation

Energy & Utilities
case study

Business Impacts

Efficient Visualization of Overlap Results

Improvement in Process Efficiency

Improved UX with Modular Model Selection

Customer Key Facts

  • Location : Europe
  • Industry : Oil & Energy

Problem Context

The client, a French multinational integrated oil and gas company and one of the seven “Supermajor” oil companies in the world, wanted to augment the capabilities of their current software by providing refactoring and additional features that highlight seismic artifacts. The inherent data (geological) characteristics required a deeper customization of the model components.

Challenges

 

  • Hardware memory limitations
  • Latency requirements
  • Technical domain specific processing
  • Custom loss functions

Technologies Used

Cloud Functions

Cloud Functions

Cloud ML Engine

Cloud ML Engine

Cloud Dataflow

Cloud Dataflow

GitLab

GitLab

Building Scalable 3D Image Segmentation Models with ML Pipelines for Training & Prediction

Solution

Quantiphi built a robust multi-functionality application for geoscientists to train seismic machine learning models and predict 2D seismic images and 3D volumes; allowing flexibility when running the models on Cloud Machine Learning Engine (CLME).

Result

  • Scalable and robust ETL pipeline
  • Functional web app to overlap seismic volumes and artifacts
  • Modularity for users to select the relevant models for inference directly from the UI

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