Patent ID: 11892581
Assignee: SAUDI ARABIAN OIL COMPANY
Field: Measurement (Instruments)
Classification: CPC G  E | IPC E  G

Claim 12:
13. A method for characterizing a geological formation comprising:
a) obtaining induction log data of the formation, the data obtained by positioning an induction tool at one or more locations within a well penetrating the formation;
b) providing a total clay volume fraction and amounts or concentrations of a predefined set of clay minerals in the formation as input to a first computational model and, in response, receiving an apparent cation exchange capacity (CEC) value of the formation as output from the first computational model;
c) providing the induction log data of the formation as input to a second computational model and, in response, receiving a calculated CEC value of the formation as output from the second computational model; and
d) determining amounts or concentrations of a predefined set of clay minerals in the formation based on apparent cation exchange capacity (CEC) value and calculated CEC value of the formation, the method further comprising:
obtaining resistivity log data of the formation, formation total porosity log data of the formation, and gamma ray log data of the formation; and
providing the well log data of the formation as input to at least one trained artificial neural network (ANN) of a machine learning system, wherein the trained ANN is configured to output the total clay volume fraction in the formation in response to receiving, as input, the resistivity log data of the formation, the formation total porosity log data of the formation, and the gamma ray log data of the formation, wherein, before providing the well log data of the formation as input to the at least one trained ANN of the machine learning system, the method comprises:
training at least one ANN of the machine learning system using training data collected from well log data of the plurality of other formations to yield the trained ANN, wherein, for each of the plurality of other formations, the training data includes resistivity log data, gamma ray log data, formation porosity log data, and volume fractions of total clay.