Patent ID: 11953647
Assignee: SAUDI ARABIAN OIL COMPANY
Field: Computer technology (Electrical engineering)
Classification: CPC G  E | IPC E  G

Claim 10:
11. A system for evaluating a subsurface region of the earth for hydrocarbon exploration, development, or production, the system comprising:
a computing device including:
a non-transitory storage medium,
an interface for receiving measured information for a plurality of wells within the subsurface region, the measured information including core log data and basic well log data, the basic well log data comprising neutron log data, total gamma ray log data, density log data measured for each of the wells respectively, and wherein, for each a plurality of training wells among the wells, the measured information includes spectral gamma ray log data respectively measured for each of the training wells, the basic well log data and spectral gamma ray log data for the training wells defining training information,
a processor, wherein the processor is configured by executing code that is in the form of one or more modules stored in the non-transitory storage medium, wherein the one or more modules configure the processor to:
implement a model comprising a neural network trained for predicting synthetic spectral gamma ray log data from basic well log data, wherein synthetic spectral gamma ray log data comprises predicted measurements of potassium, thorium, and uranium isotope levels, the model having defined model parameters including three main inputs, three associated inputs and weights assigned to each of the three main inputs respectively,
wherein three main inputs comprise total gamma ray log data, neutron log data, and density log data, respectively, and wherein three associated inputs comprise spectral gamma ray log data representing measured levels of potassium, thorium, and uranium isotopes, respectively, and
wherein the model is trained by analyzing the training information according to the model parameters using machine learning and multi resolution graph clustering (MRGC) techniques, and statistically linking patterns in the main inputs to patterns in the associated inputs comprising spectral gamma ray log data representing measured levels of potassium, thorium, and uranium;

apply the model to predict synthetic spectral gamma ray log data for a given well among the wells that is lacking measured spectral gamma ray log data, wherein applying the model includes:
inputting into the model, as the three main inputs, the neutron log data, total gamma ray log data, and density log data measured for the given well, and
generating, by the model based on the three main inputs, the synthetic spectral gamma ray log data for the given well comprising predicted levels of potassium, thorium, and uranium, respectively; and
quantify, based on the synthetic spectral gamma ray log data generated by the model for the given well, a clay content for the given well.