Abstract: There is provided a method and apparatus for accelerated in-situ prediction and dynamic visualization of characteristics of a porous medium, including an input source for inputting data from computer-aided simulations and real-time core flooding experiments, an embedded hardware unit comprising of a processor running a prediction model with inferences of porous medium samples, and a human-machine interface comprising a display unit for displaying the estimated plurality of characteristics of the porous medium and an input unit for accepting commands from a user. The input source is in real-time communication with the embedded hardware unit and the display unit and the apparatus reduces a total analysis time taken for characterizing the porous medium. Further, the porous medium is a rock sample and the plurality of properties of the porous medium comprises the relative permeability (Kr) and capillary pressure (Pc) characteristics of the porous medium.

Abstract: A computer-implemented method for generating a trained, graph-based neural network model for use in prediction of a time-evolution of a pressure profile of a hydrocarbon reservoir of a hydrocarbon field is described. Geologic reservoir information and an initial spatial pressure profile are obtained for one or more reservoirs of the hydrocarbon field. Based on the geologic reservoir information and the initial spatial pressure profile, a computational sector model is generated for a selected sector comprising a plurality of modelling cells. Using the computational sector model, simulated spatiotemporal pressure profiles for the selected sector is generated wherein each spatiotemporal pressure profile is generated for a corresponding injection production plan.

Abstract: A method and system for approximating a predicted three-dimensional imbibition phase saturation profile from a measured three-dimensional drainage phase saturation profile, a derived one-dimensional drainage phase saturation profile, a measured one-dimensional imbibition phase saturation profile using a trained machine-learning algorithm are disclosed. A method for training of the machine learning algorithm is also disclosed.

Abstract: There is provided a system and process to predict the petrophysical properties of unclean rock samples using Medical-CT scanned three-dimensional (3D) images at both low and high resolutions. The captured 3D images are passed through machine learning, statistical methods and data lookups to identify the petrophysical properties of rock samples. Also disclosed is the process of measuring phase saturations of a clean rock sample or porous medium using Micro-CT scanned three-dimensional (3D) images.

Abstract: There is provided a method and apparatus for accelerated in-situ prediction and dynamic visualization of characteristics of a porous medium, including an input source for inputting data from computer-aided simulations and real-time core flooding experiments, an embedded hardware unit comprising of a processor running a prediction model with inferences of porous medium samples, and a human-machine interface comprising a display unit for displaying the estimated plurality of characteristics of the porous medium and an input unit for accepting commands from a user. The input source is in real-time communication with the embedded hardware unit and the display unit and the apparatus reduces a total analysis time taken for characterizing the porous medium. Further, the porous medium is a rock sample and the plurality of properties of the porous medium comprises the relative permeability (Kr) and capillary pressure (Pc) characteristics of the porous medium.