Case Study: American Energy Corporation and Prominent Oil Company save time and money in seismic interpretation with SoftServe

Machine Learning Saves Time and Money in Seismic Data Interpretation

American Energy Corporation and Prominent Oil Company, a global petroleum exploration and production company, needed a faster and more accurate way to interpret seismic data. Manual labeling of seismic volumes was taking weeks or months and was prone to interpretation errors, making it difficult for geophysicists and exploration teams to make timely decisions.

SoftServe built a proof of concept using deep learning for seismic segmentation, applying architectures such as U-Net and FPN in PyTorch on GPU-powered GCP infrastructure. The solution automatically processed non-labeled seg-y volumes into labeled cubes, incorporated predictions into an ontology tree, and demonstrated that seismic interpretation could be significantly accelerated while reducing human bias and improving decision-making for future exploration activities.