Static engine and neural network for a cognitive reservoir system

A method for developing a reservoir, the method comprising:

• receiving static data at a static modeler, the static data including a set of observed data points in a reservoir volume along a well trajectory and well logs corresponding to the set of observed data points;
• generating a set of feature vectors to learn neural networks, the set of feature vectors defined based on a distance between each of the observed data points and a set of randomly selected points in the reservoir volume;
• generating a three-dimensional populated log by propagating well log values of the set of feature vectors across the reservoir volume;
• quantifying uncertainty by applying the learned neural networks to generate a plurality of realizations including the three-dimensional populated log, wherein the set of randomly selected points is changed by the static modeler such that each of the plurality of realizations is different and equally probable;
• clustering the reservoir volume into a plurality of clustered three-dimensional volumes using a learned clustering algorithm alongside randomly selected three-dimensional log data from the plurality of realizations;
• populating core data in the reservoir volume using a learned k-nearest neighbor algorithm alongside the three-dimensional populated log;
• and
• generating a plurality of static models, each of the static models comprising a random sample of three-dimensional populated log data of the plurality of realizations, one of the clustered three-dimensional volumes, and a three-dimensional volume of the core data.