Deep learning based reservoir modeling

A system comprising:

• at least one processor;
• and
• a memory including instructions that, when executed by the at least one processor, cause the at least one processor to:
• receive input data comprising information associated with one or more well logs in a region of interest;
• generate, using the received input data, a training dataset, a validation dataset, and a test dataset, wherein the training dataset, the validation dataset, and the test dataset are mutually exclusive subsets of the received input data;
• determine, based at least in part on the input data, an input feature associated with a first deep neural network (DNN) for predicting a value of a petrophysical property at a location within the region of interest;
• predict, using the first DNN, values of the petrophysical property for a plurality of points of a point cloud, each of the plurality of points corresponding to a different location in the region of interest;
• and
• classify, using a second DNN, types of facies for the plurality of points of the point cloud based at least in part on the predicted values of the petrophysical properties for the plurality of points of the point cloud.

A non-transitory computer-readable medium including instructions stored therein that, when executed by at least one computing device, cause the at least one computing device to:

• send input data to a server, the input data including information associated with one or more well logs in a region of interest, the region of interest corresponding to a geologic volume,
• wherein the input data is used to generate a training dataset, a validation dataset, and a test dataset,
• wherein the training dataset, the validation dataset, and the test dataset are mutually exclusive subsets of the input data,
• wherein values of a petrophysical property for a plurality of points of a point cloud, each of the plurality of points corresponding to a different location in the region of interest, are determined using a first deep neural network (DNN),
• wherein, based at least in part on the values of the petrophysical property for the plurality of points of the point cloud, types of facies for the plurality of points of the point cloud are determined using a second DNN;
• receive, from the server, a second point cloud corresponding to the region of interest, the second point cloud including information for at least the petrophysical properties and facies classification of each point included in the second point cloud;
• and
• provide for display a 3D reservoir model based on the information from the second point cloud.