Paper 266 of 383
Published June 1, 2026
As geological datasets increase in size and complexity, the limiting factor increasingly becomes interpretation rather than acquisition.
Modern systems may contain seismic information, well logs, geochemistry, structural mapping, basin analysis, remote sensing, production history, and regional geological observations simultaneously.
The challenge becomes computational.
How should observations be organized, weighted, ranked, and compared?
This paper evaluates computational geological intelligence through constraint networks, signal extraction, survivorship analysis, information ranking, and predictive weighting.
Within ABC Sequencing, intelligence is defined as the ability to improve decision quality through structured evaluation of competing observations.
The objective of computational intelligence is not to replace geological expertise.
The objective is to improve the consistency, repeatability, and scalability of geological reasoning.
A successful intelligence system should help experts focus attention on the observations most likely to matter.
This paper establishes computational geological intelligence as a framework for organizing observations, extracting signal, ranking information, and improving decision quality.
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