Saudi Arabian Oil Company

Osman Hamid

Murtadha J AlTammar

Khalid Mohammed Alruwaili

Weichang Li

Mohammad H Altwaijri

Ivan Deshenenkov

Hamza M Aljamaan

A computer system obtains a first set of micro-computed tomography (micro-CT) data representing a rock sample obtained from a subterranean formation that includes gas-bearing sandstone. The system obtains a plurality of second sets of micro-CT data in a sequence, each representing the rock sample after a performance of a corresponding triaxial shear test on the rock sample. Performing each triaxial shear test includes applying a triaxial load force to the rock sample, and removing the triaxial load force from the rock sample. The system estimates, based on the first micro-CT data and the plurality of second sets of micro-CT data, one or more characteristics of the underground formation, including a permeability of the underground formation and/or a porosity of the underground formation. The system causes one or more resource extraction operations to be performed on the underground formation based on the one or more characteristics of the underground formation.

Assessing characteristics of subterranean formations using micro-computed tomography and rock mechanics testing

A computer-implemented method may include obtaining well logs data pertaining to a well of interest. The method may further include training a physics-constrained machine learning (PCML) model using the obtained well logs data as inputs. The method may further include outputting one or more sonic logs and mechanical properties of interest determined by using the trained PCML model and the obtained well logs data for the well of interest. The method may further include updating the determined sonic logs and mechanical properties of interest based on a breakout model and field breakout data for the well of interest. The method may further include outputting the final sonic logs for the well of interest. The method may further include determining one or more mechanical properties for well planning based on the final sonic logs for the well of interest.

Method and system for correcting and predicting sonic well logs using physics-constrained machine learning

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Dhahran, Saudi Arabia

Osman Hamid