Contactless measurement of rock wettability by photonic techniques

A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:

• determining rock and oil electromagnetic baselines of rock samples in at least a section of an electromagnetic spectrum ranging from ultraviolet to long terahertz radiation;
• conducting an aging process on each rock sample, initially starting with the rock and oil electromagnetic baselines;
• repeating the aging process using spectrometry on the rock sample and measured wettabilities of the rock sample until changes in spectra are less than a predetermined threshold;
• storing aging information, including the spectra and wettabilities, in a machine learning database;
• obtaining spectra from an unknown rock sample;
• mapping the spectra to clusters in the machine learning database;
• and
• determining wettability ranges for the unknown rock sample based on a mapping of the spectra of the unknown rock sample to clusters in the machine learning database.

A computer-implemented system, comprising:

• one or more processors;
• and
• a non-transitory computer-readable storage medium coupled to the one or more processors and storing programming instructions for execution by the one or more processors, the programming instructions instructing the one or more processors to perform operations comprising:
• determining rock and oil electromagnetic baselines of rock samples in at least a section of an electromagnetic spectrum ranging from ultraviolet to long terahertz radiation;
• conducting an aging process on each rock sample, initially starting with the rock and oil electromagnetic baselines;
• repeating the aging process using spectrometry on the rock sample and measured wettabilities of the rock sample until changes in spectra are less than a predetermined threshold;
• storing aging information, including the spectra and wettabilities, in a machine learning database;
• obtaining spectra from an unknown rock sample;
• mapping the spectra to clusters in the machine learning database;
• and
• determining wettability ranges for the unknown rock sample based on a mapping of the spectra of the unknown rock sample to clusters in the machine learning database.