A ubiquitous method for predicting underground petroleum deposits based on satellite data.

Area of Science:

• Geophysics
• Petroleum Geoscience
• Remote Sensing

Background:

• Petroleum exploration is challenged by high costs and low accuracy.
• Accurate prediction of petroleum deposit locations is crucial for energy security.

Purpose of the Study:

• To develop and validate a novel, cost-effective method for predicting petroleum deposit locations.
• To apply the method to Iraq, utilizing publicly available satellite data.

Main Methods:

• Utilized data from the Gravity Recovery and Climate Experiment (GRACE) satellite.
• Calculated the earth's gravity gradient tensor over Iraq.
• Employed machine learning, graph-based analysis, and the novel OR-nAND method for prediction.

Main Results:

• Successfully predicted 25 out of 26 existing petroleum deposits in the study area.
• Identified several prospective petroleum deposit locations for future exploration.
• Demonstrated a generalized approach applicable globally.

Conclusions:

• The proposed method offers a significant improvement in accuracy and cost-effectiveness for petroleum exploration.
• GRACE satellite data combined with advanced analytical techniques can reliably predict hydrocarbon reserves.
• The generalized methodology can be adapted for petroleum exploration worldwide.