Method and system for image-based reservoir property estimation using machine learning

A system, comprising:

• a logging system coupled to a logging tool;
• a well system coupled to the logging system and a wellbore;
• and
• a reservoir properties estimator comprising a computer processor, wherein the reservoir properties estimator is coupled to the logging system and the well system, the reservoir properties estimator comprising functionality for:
• obtaining core image data regarding a geological region of interest;
• obtaining well log data regarding the geological region of interest from one or more wells;
• determining a sliding window that corresponds to a predetermined window size;
• determining a plurality of quantitative image attributes using the core image data, the well log data, and the sliding window,
• wherein the plurality of quantitative image attributes are determined in a continuous manner by moving the sliding window along the core image data;
• and
• generating predicted rock data for the geological region of interest using the plurality of quantitative image attributes, a machine-learning algorithm, and a machine-learning model,
• wherein the machine-learning algorithm comprises a K-means clustering algorithm that outputs the machine-learning model, and
• wherein the machine-learning model comprises a plurality of clusters that are organized according to one or more visual rock types (VRTs) that are indexed by depth.

A non-transitory computer readable medium storing instructions executable by a computer processor, the instructions comprising functionality for:

• obtaining core image data regarding a geological region of interest;
• obtaining well log data regarding the geological region of interest from one or more wells;
• determining a sliding window that corresponds to a predetermined window size;
• determining a plurality of quantitative image attributes using the core image data, the well log data, and the sliding window,
• wherein the plurality of quantitative image attributes are determined in a continuous manner by moving the sliding window along the core image data;
• and
• generating predicted rock data for the geological region of interest using the plurality of quantitative image attributes, a machine-learning algorithm, and a machine-learning model,
• wherein the machine-learning algorithm comprises a K-means clustering algorithm that outputs the machine-learning model, and
• wherein the machine-learning model comprises a plurality of clusters that are organized according to one or more visual rock types (VRTs) that are indexed by depth.