Landmark Graphics Corporation

Disclosed are systems and methods for allocating resources for executing a simulation. These include receiving a simulation for execution, calculating an initial runtime of an initial time step of the simulation, determining a total runtime of the simulation based on the initial runtime, selecting a runtime model based on the initial time step, total runtime, or a parameter of the simulation, identifying, based on the selected runtime model, an allocation of a resource providing an increase in runtime speed, allocating the identified resource, and executing the simulation using the allocated resource.

A method for managing integrity of a tubular comprises obtaining fluid transportation system data, wherein the tubular is a component within a fluid transportation system. The method comprises determining, via a mechanistic model, a mechanistic corrosion rate of the tubular based on the fluid transportation system data. The method comprises determining, via a learning machine, a residual corrosion rate of the tubular based on the fluid transportation system data. The method comprises determining, via a hybrid model, a final corrosion rate of the tubular based on the mechanistic corrosion rate and the residual corrosion rate.

A heat flow modeler preprocesses geological and heat flow data for an earth formation for inputting into a plurality of supervised learning models. The heat flow modeler trains the plurality of supervised learning models on the preprocessed geological data to estimate heat flow throughout the earth formation. The heat flow modeler interpolates the estimated heat flow values to a set of desired locations in the earth formation and cosimulates the preprocessed heat flow values with the interpolated heat flow values as auxiliary variables to generate a cosimulated heat flow map. A final heat flow map is generated by rasterizing the cosimulated heat flow map.

An apparatus for generating forecasts from a high-dimensional parameter data space comprising a reservoir model, a model order reduction module, and an assisted history matching module. The reservoir model having input variables, output variables, and an algorithmic model. The input variables, output variables, and the algorithmic model are generated by a flow simulator module and from a formation and reservoir properties database and a field production database. The model order reduction module generates a subset of the original or transformed input variables. The assisted history matching module adjust values of the output variables based on a difference between the at least one of the output variables and dynamic field production data to improve model accuracy.

System and methods for training neural network models for real-time flow simulations are provided. Input data is acquired. The input data includes values for a plurality of input parameters associated with a multiphase fluid flow. The multiphase fluid flow is simulated using a complex fluid dynamics (CFD) model, based on the acquired input data. The CFD model represents a three-dimensional (3D) domain for the simulation. An area of interest is selected within the 3D domain represented by the CFD model. A two-dimensional (2D) mesh of the selected area of interest is generated. The 2D mesh represents results of the simulation for the selected area of interest. A neural network is then trained based on the simulation results represented by the generated 2D mesh.

A method includes selecting a model for a simulation of hydrocarbon recovery from a reservoir having a plurality of fractures during injection of an injected gas into the plurality of fractures. Selecting the model includes determining a flux ratio of a convection rate to a diffusion rate for the reservoir, determining whether the flux ratio is less than a threshold, and in response to the flux ratio being less than the threshold, selecting the model that includes diffusion. Selecting the model includes performing the simulation of the hydrocarbon recovery from the reservoir based on the model.

The disclosure presents processes for improving the design phase of plastic material lined tubular structures used downhole of a borehole. A plastic material lined tubular structure model is utilized for tubular structures that have a metal layer, a grout layer, and a plastic material layer. The model can use a modified wall thickness for the metal layer. A strength model can be applied to the modified critical dimensions, e.g., wall thickness parameters. A thermal model can be applied to the tubular structure to determine pressure and temperature parameters. The strength model and the thermal model outputs can be utilized by a stress analyzer to determine loads, safety factors, and design limit parameters. The plastic material lined tubular structure model can enable more efficient use of tubular structures, designing a longer operational lifetime, such as in acidic environments, or the use of thinner structures while maintaining a satisfactory operational lifetime.

A method includes selecting a model for a simulation of hydrocarbon recovery from a reservoir having a plurality of fractures during injection of an injected gas into the plurality of fractures. Selecting the model includes determining a flux ratio of a convection rate to a diffusion rate for the reservoir, determining whether the flux ratio is less than a threshold, and in response to the flux ratio being less than the threshold, selecting the model that includes diffusion. Selecting the model includes performing the simulation of the hydrocarbon recovery from the reservoir based on the model.

A method includes selecting a model for a simulation of hydrocarbon recovery from a reservoir having a plurality of fractures during injection of an injected gas into the plurality of fractures. Selecting the model includes determining a flux ratio of a convection rate to a diffusion rate for the reservoir, determining whether the flux ratio is less than a threshold, and in response to the flux ratio being less than the threshold, selecting the model that includes diffusion. Selecting the model includes performing the simulation of the hydrocarbon recovery from the reservoir based on the model.

Hybrid computing that utilizes a computer processor coupled to one or more graphical processing units (GPUs) is configured to perform computations that generate outputs related to reservoir simulations associated with formations that may include natural gas and oil reservoirs.