Abstract: A system can receive a grid-less point cloud model of a geological formation, the grid-less cloud point model that includes data points. The system can determine, by a machine-learning model for clustering data points, clusters for the data points according to a heterogeneity index. The system can determine an outline for each cluster. The system can generate a grid corresponding to the geological formation, the grid comprising a plurality of cells for each cluster of the plurality of clusters, each cluster having cell properties. The system can output the grid for the geological formation to a graphical user interface, the grid usable for executing a flow simulation at the graphical user interface.

Abstract: A method of predicting rock properties at a selectable scale is provided, including receiving coordinates of locations of respective sample points, receiving measurement data associated with measurements or measurement interpretations for each sample point, receiving for each sample point a scale that indicates the scale used to obtain the measurements and/or measurement interpretations, wherein different scales are received for different sample points. A deep neural network (DNN) is trained by applying the received coordinates, measurement data, and scale associated with each sample point and associating the sample point with a rock property as a function of the coordinates, measurement data, and scale applied for the sample point. The DNN is configured to generate rock property data for a received request point having coordinates and a selectable scale, wherein the rock property data is determined for the request point as a function of the coordinates and the selectable scale.

Abstract: A method for processing a well data log may comprise adding one or more boundary areas to the well data log, dividing the well data log into one or more segments using the one or more boundary areas, processing each of the one or more segments on one or more information handling systems, and reforming each of the one or more segments into a final simulation. A system for processing a well data log may comprise one or more information handling systems in a cluster. The one or more information handling systems may be configured to perform the method for processing the well data log.

Abstract: A reservoir model for values of a formation property is simulated using a turning bands method with distributed computing. A distributed computing system simulates the reservoir on separate machines in parallel in several stages. First, line distributions are simulated independently on turning bands. The reservoir model is partitioned into tiles and unconditional simulations are run on each tile in parallel using the corresponding simulated turning bands. The unconditional simulations within each tile are conditioned on known formation values to generate conditional simulations. Conditional simulations are aggregated across tiles to create the simulated reservoir model.

Abstract: A neural network trainer trains neural networks to estimate secondary data at locations throughout a geological formation where secondary data is unknown. The neural networks are trained to estimate secondary data using locations in the geological formation as input. Subsequently, the secondary data is deleted from memory using the trained neural network as a proxy representation to reduce memory footprint and allow for estimation of secondary data at locations where it is unknown.

Abstract: A method for processing a well data log may comprise adding one or more boundary areas to the well data log, dividing the well data log into one or more segments using the one or more boundary areas, processing each of the one or more segments on one or more information handling systems, and reforming each of the one or more segments into a final simulation. A system for processing a well data log may comprise one or more information handling systems in a cluster. The one or more information handling systems may be configured to perform the method for processing the well data log.