Oil and Gas–Smart Exploration through Machine Learning and AI
Oil and Gas–Smart Exploration through Machine Learning and AI

Oil and Gas

Oil and Gas – Smart Exploration through Machine Learning and AI

Why Machine learning and AI in Oil and Gas Exploration

O&G drilling has become extremely competitive in recent years

  • Entry of newer players like Shale operators has impacted the demand supply balance, forcing down market prices
  • Lowering breakeven points through better prospecting and cost effective operations is imperative to maintain competitiveness
Crude Oil Brent
  • Geological features are extremely complex and often contain non-linear relationships between variables of interest
  • Similarly, drilling equipment contain several expensive and critical moving parts with complex failure scenarios that need to be predicted realtime
  • New advanced algorithms like deep neural networks are essential to model such non-linearities
Conventional and Unconventional Reservoir Types

Visualizing Oil Field Surveys

  • Modern geological surveys capture vast amounts of raw seismic trace data

  • Deep learning is required to create accurate 3D maps of underground faults and other subsurface features to effectively characterize reservoirs

Without deep learning, the required accuracies are not feasible

  • Features like natural porosity, permeability, etc. have quite non- linear relations with the outcome of interest, the oil volume in a potential reservoir

Accurate visualization leads to several business advantagese

  • Effective lease bidding
  • Higher service revenues for operators
Visualizing Oil Field Surveys

Extracting value from shale formations

  • Every shale formation is somewhat unique in terms of geological characteristics and requires customized drilling
  • To improve well productivity and increase ROI, it is important to find effective fracture recipes for each potential site
  • AI solutions can help find the right mix of spacings, depth of horizontal bores, proppants, and pressure patterns for each well, allowing efficient fracking
  • AI driven fracking becomes especially important in the mature stage of a well, to extend the lifetime and maintain a more consistent throughput
Extracting value from shale formations

Maintenance Analytics for Drilling Equipment

  • Massive amounts of sensor data is collected during drilling such as pump pressures, RPMs, flow rates and subsurface temperatures; yet detecting equipment failure is a difficult problem
  • Failure scenarios are often chaotic and complex and advanced machine learning is required to generate real time accurate predictions

Predicting equipment health leads to actionability

  • Preventive Maintenance to extend equipment life
  • 3D view of equipment problems like metal fatigue, rust, corrosion, etc. to allow quicker repair
  • Temporarily pausing operations to prevent chaotic machine breakdowns and damage to the well
Maintenance Analytics for Drilling Equipment