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

Claim 0:
1. A computer implemented method of evaluating a subsurface region of the earth for hydrocarbon exploration, development, or production, the method comprising:
receiving, into a processing system, measured information for a plurality of wells within the subsurface region, the measured information including
core log data,
basic well log data, the basic well log data comprising neutron log data, total gamma ray log data, and 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, wherein the basic well log data and spectral gamma ray log data respectively measured for each of the training wells defines training information;

generating, a model comprising a neural network trained to predict synthetic spectral gamma ray log data from the basic well log data, wherein synthetic spectral gamma ray log data comprises predicted measurements of potassium, thorium, and uranium isotope levels, wherein training the model includes,
defining model parameters, the step of defining the model parameters comprising,
defining main inputs and associate inputs to the model, 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
assigning a respective weight to each of the three main inputs, and

training the model by analyzing the training information according to the model parameters using machine learning and multi resolution graph clustering (MRGC) techniques and generating a-the neural network 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;

applying the model to predict synthetic spectral gamma ray log data for a given well 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 comprising predicted levels of potassium, thorium, and uranium isotopes, respectively, for the given well;

quantifying, with the processing system based on the synthetic spectral gamma ray log data generated by the model for the given well, a clay content for the given well.