Kerogenet

Kerogenet is a term used in geology and energy research to describe a theoretical network-based framework for integrating data on kerogen-rich source rocks, their maturation, and the generation and migration of hydrocarbons within sedimentary basins. It is intended as a conceptual envelope for modeling rather than a single technology.

The term blends kerogen, the insoluble organic matter in sedimentary rocks, with net, suggesting a connected system or network. Kerogenet is not a universally defined standard but a concept that researchers use to discuss interoperability among data and models across disciplines such as geochemistry, petrology, and basin modeling.

Components of the concept include data standards for kerogen characterization, maturation indices, and basin-scale models, along with software that links source-rock quality to predicted hydrocarbon yield. Kerogenet emphasizes the integration and interoperability of geochemical data, petrographic parameters, and geophysical constraints to support cross-study comparisons.

Methods associated with kerogenet commonly involve probabilistic and network-based modeling that combines vitrinite reflectance, Tmax, Hydrogen Index, Rock-Eval data, and other maturation indicators with basin dynamics, hydrology, and uncertainty quantification. Machine learning may be used to identify patterns across basins and to refine predictive capabilities.

Applications of the concept include evaluating hydrocarbon potential, understanding maturation regimes, and informing energy-resource planning by highlighting kerogen-rich systems with favorable characteristics. It also serves as a framework for data sharing and collaborative research among academic, governmental, and industry groups, while remaining exploratory and subject to regional data limitations.

Related topics include kerogen, shale oil, basin modeling, hydrocarbon generation, and geochemical data standards.