Described are computer-related techniques for determining rotation direction of an axial fan for use in fluid flow simulations. The techniques involve receiving by a computer processing system digital data of a three dimensional representation of an axial fan having plural fan blade, determining by the computer processing system from the data of three dimensional representation of the axial fan, at least a single centerline of a single blade of the axial fan from a two dimensional projection of the axial fan, and calculating by the computer processing system based on the initial valve of fan rotation, an actual value of fan rotational direction.

Described are computer implemented techniques for simulating elements of a fluid flow. These techniques include storing in a memory state vectors for a plurality of voxels, the state vectors comprising a plurality of entries that correspond to particular momentum states of a plurality of possible momentum states at a voxel, storing in a memory a representation of at least one surface that is sized and oriented independently of the size and orientation of the voxels, perform interaction operations on the state vectors, the interaction operations modelling interactions between elements of different momentum states, perform surface interaction operations on the representation of the surface, the surface interaction operations modelling interactions between the surface and substantially all elements of voxels, and performing move operations on the state vectors to reflect movement of elements to new voxels.

The disclosed embodiments include reservoir simulation systems and methods to dynamically improve performance of reservoir simulations. The method includes obtaining input variables for generating a reservoir simulation of a reservoir, and generating the reservoir simulation based on the input variables. The method also includes determining a variance of computation time for processing the reservoir simulation. In response to a determination that the variance of computation time is less than or equal to a threshold, the method includes performing a first sequence of Bayesian Optimizations of at least one of internal and external parameters that control the reservoir simulation to improve performance of the reservoir simulation. In response to a determination that the variance of computation time is greater than the threshold, the method includes performing a second sequence of Bayesian Optimizations of at least one of the internal and external parameters.

A simulation method predicts a behavior of a curable composition in a process of bringing droplets of the curable composition arranged on first and second members into contact with each other, and forming a film of the curable composition on the first member. The method includes inputting a physical property value of a gas between the first and second members, inputting a movement profile of the second member with respect to the first member when bringing the droplets of the curable composition on the first and second members into contact with each other, obtaining a pressure of the gas between the first and second members based on the physical property value and the input movement profile, and predicting, based on the pressure, an amount of a residual gas confined among the droplets by the contact between the droplets and the second member.

Disclosed are computer implemented techniques for conducting a fluid simulation of a porous medium. These techniques involve retrieving a representation of a three dimensional porous medium, the representation including pore space corresponding to the porous medium, with the representation including at least one portion of under-resolved pore structure in the porous medium, defining a representative flow model that includes the under-resolved pore structure in the representation, and constructing by the computer system fluid force curves that correspond to fluid forces in the under-resolved pore structure in the representation.

A method may include obtaining grid model data for a geological region of interest and well data for various wells in the geological region of interest. A well among the wells may correspond to a simulated well network in a reservoir simulation. The method may further include determining, based on the grid model data and the well data, a first simulation solution for a first constraint rate equation decoupled from the simulated well network and using a first search method. The method may further include determining, based on the grid model data, the well data, and the first simulation solution, a second simulation solution for a second constraint rate equation coupled to the simulated well network and using a second search method. The method may further include performing, based on the grid model data, and the second simulation solution, the reservoir simulation.

Disclosed a method for predicting permeability of a multi-mineral phase digital core based on deep learning. In the present disclosure, a three-dimensional digital core is constructed and a pore structure is randomly generated; after a plurality of multi-mineral digital core images is acquired by performing image segmentation on the three-dimensional digital core, permeability corresponding to each of the multi-mineral digital core images is acquired by simulation using multi-physics field simulation software and a multi-mineral digital core data set is constructed based on the plurality of multi-mineral digital core images and the permeability corresponding to each of the multi-mineral digital core images; an SE-ResNet18 convolutional neural network is constructed and trained with the multi-mineral digital core data set; and an image of a multi-mineral core to be predicted is input into the trained SE-ResNet18 convolutional neural network to determine the permeability of the multi-mineral core.

A method for determining the distribution of a proppant and associated slurry exiting perforations made in a casing, which is placed in a well, includes receiving settling data describing the proppant settling in the casing; receiving a slip parameter describing a casing velocity of the proppant relative to a perforation velocity of the proppant; calculating with a computing device, based on a constant proppant concentration model, (1) initial flow rates Q(i) of the proppant through the perforations and (2) initial flow rates Q.sub.case(i) of the proppant through the casing, wherein i is the number of perforations; and calculating with the computing device, based on (1) a variable proppant concentration model, (2) the settling data, (3) the slip parameter, (4) the initial flow rates Q(i) of the proppant through the perforations, and (5) the initial flow rates Q.sub.case(i) of the proppant through the casing, normalized flow rates Q.sub.s(i) of the proppant through the perforations.

A method for monitoring waterfront movement in a subsurface formation involves performing forward modeling of at least one deep electromagnetic survey of the waterfront movement, and determining locations for installing an electrically insulating spacer between well liners to form an on-demand electromagnetic source electrode. Based on the forward modeling, repeat survey time intervals are predicted. The method involves, during well completion, installing the electrically insulating spacer between the well liners in a reservoir to form at least one on-demand electromagnetic source electrode, and installing the electrically insulating spacer between the plurality of well liners in a reservoir to form an on-demand electromagnetic receiver electrode. A waterfront survey is performed by conveying a production logging tool into a well that temporarily converts the well liners into an on-demand electromagnetic source electrode and an on-demand receiver electrode, and inverse modeling of the waterfront survey is performed to produce a water saturation image.

Systems and methods include a method for using reservoir simulations. Permeabilities are measured from a rock sample at different pressures using single-component gas and bulk gas viscosity values. The rock sample is representative of rock used in a reservoir simulation. For each gas component of reservoir gas, porosities are determined, including determining mean free paths for a range of temperatures and pressures encompassing conditions for both reservoir simulation input and the measured permeabilities. A characteristic pore radius for the rock is determined using the measured permeabilities and the determined porosities. Viscosity adjustment factors for a predefined range of temperatures and pressures are determined using the measured permeabilities. Adjusted gas viscosities for the predefined range of temperatures and pressures and the measured permeabilities are determined using the viscosity adjustment factors. The reservoir simulator is executed using the adjusted gas viscosities.