Reduce exploration and production costs and increase production accuracy with cognitive subsurface evaluation, machine learning and guided discovery.

How it works

IBM’s subsurface corpus builder and cognitive guided discovery deepen insight about new targets for more accurate properties estimation and improved risk assessment.

The solution features reservoir analogue machine learning engines that enable you to characterize and quantify uncertainty when assessing the potential production or economic value of a reservoir.

Our unique regression models are built to predict horizontal oil and gas production “sweet spots” based on one-dimensional features extracted from neighboring well logs.

Advanced machine learning algorithms combined with computer vision techniques yield seismic and well data to help you identify geological features.

How you benefit

This solution improves exploration efficiency and risk assessment by helping you:

  • Broaden your understanding of prospects and regions by identifying internal best analogy and seismic interpretation history
  • Validate assumptions by using external technical data and analogous worldwide plays
  • Prevent avoidable mistakes, and improve knowledge capture and dissemination using contextual help
  • Reduce risk of “wild bids” by using proven models to calculate return on investment for a set of targets

Speak with an IBM Oil and Gas expert