Mini-Review on Petroleum Molecular Geochemistry: Opportunities with Digitalization, Machine Learning, and Artificial Intelligence

Molecular geochemistry plays a vital role in understanding
the
origin of oil and gas, correlating hydrocarbons with their source
rocks, and evaluating the potential of source rocks. However, traditional
molecular geochemistry methods increasingly struggle to meet the demands
of modern exploration due to their complexity and inefficiency. This
challenge is particularly pronounced in the digital era, where petroleum
exploration is characterized by continuous refinement and the growing
prominence of unconventional hydrocarbons. To address these challenges,
various machine-learning techniques, leveraging statistical and chemometric
principles, have emerged as effective solutions. This review analyzes
the application and challenges of machine-learning-based methods in
molecular geochemical data processing, highlighting both unsupervised
techniques (such as hierarchical cluster analysis and principal component
analysis) and supervised approaches (including artificial neural networks).
Additionally, it explores the future development of machine learning
in petroleum molecular geochemistry, emphasizing the creation of integrated
big data systems and intelligent analysis tools. This includes the
use of advanced technologies, such as digitalized chromatograms and
convolutional neural networks, which promise to further enhance data
interpretation and decision-making in petroleum exploration.