Abstract: An improved reservoir simulator is provided that determines variable convergence criteria used in newton iterations in in a manner that addresses the problem of oversolving while minimizing the adverse effect on the nonlinear system. In the improved reservoir simulator, nonlinear information along with some information from the linear system can be used to determine a variable convergence tolerance. The variable convergeance tolerance can be used to define the variable convergence criteria used in newton iterations performed by the reservoir simulator.

Abstract: A method for performing a modified two point flux approximation scheme is disclosed. The method includes: obtaining a first pressure value for a first neighbor cell and a second pressure value for a second neighbor cell, where the first neighbor cell has a first value of a reservoir property and the second neighbor cell as a second value of the reservoir property; determining a first weight using the first pressure value and a second weight using the second pressure value; calculating a third value of the reservoir property as a weighted average of the first value and the second value; and applying the third value to the first neighbor cell.

Abstract: A method for performing a field operation of a field. The method includes obtaining historical parameter values of a runtime parameter and historical core datasets, where the historical parameter values and the historical core datasets are used for a first simulation of the field, and where each historical parameter value results in a simulation convergence during the first simulation, generating a machine learning model based at least on the historical core datasets and the historical parameter values, obtaining, during a second simulation of the field, a current core dataset, generating, using the machine learning model and based on the current core dataset, a predicted parameter value of the runtime parameter for achieving the simulation convergence during the second simulation, and completing, using at least the predicted parameter value, the second simulation to generate a modeling result of the field.

Abstract: A method for performing a modified two point flux approximation scheme is disclosed. The method includes: obtaining a first pressure value for a first neighbor cell and a second pressure value for a second neighbor cell, where the first neighbor cell has a first value of a reservoir property and the second neighbor cell as a second value of the reservoir property; determining a first weight using the first pressure value and a second weight using the second pressure value; calculating a third value of the reservoir property as a weighted average of the first value and the second value; and applying the third value to the first neighbor cell.

Abstract: A method for performing a modified two point flux approximation scheme is disclosed. The method includes: obtaining a first pressure value for a first neighbor cell and a second pressure value for a second neighbor cell, where the first neighbor cell has a first value of a reservoir property and the second neighbor cell as a second value of the reservoir property; determining a first weight using the first pressure value and a second weight using the second pressure value; calculating a third value of the reservoir property as a weighted average of the first value and the second value; and applying the third value to the first neighbor cell.

Abstract: A method for performing a field operation of a field. The method includes obtaining historical parameter values of a runtime parameter and historical core datasets, where the historical parameter values and the historical core datasets are used for a first simulation of the field, and where each historical parameter value results in a simulation convergence during the first simulation, generating a machine learning model based at least on the historical core datasets and the historical parameter values, obtaining, during a second simulation of the field, a current core dataset, generating, using the machine learning model and based on the current core dataset, a predicted parameter value of the runtime parameter for achieving the simulation convergence during the second simulation, and completing, using at least the predicted parameter value, the second simulation to generate a modeling result of the field.

Abstract: A method for performing a modified two point flux approximation scheme is disclosed. The method includes: obtaining a first pressure value for a first neighbor cell and a second pressure value for a second neighbor cell, where the first neighbor cell has a first value of a reservoir property and the second neighbor cell as a second value of the reservoir property; determining a first weight using the first pressure value and a second weight using the second pressure value; calculating a third value of the reservoir property as a weighted average of the first value and the second value; and applying the third value to the first neighbor cell.

Abstract: Using a CPU and at least one GPU to simulate geomechanical reservoir deformation due to a change in force on the reservoir is presented. An example method includes obtaining a stiffness matrix representing a finite element mesh for a grid of the reservoir, obtaining a load vector representing the change in force, determining a displacement vector representing the deformation of the reservoir, by computing, as a setup process apart from a conjugate gradient solver iteration, and by the CPU and the at least one GPU, at least a portion of a deflation operator and iterating, by the CPU and the at least one GPU, a conjugate gradient solver applied to a system of linear equations defined by at least the deflation operator, the stiffness matrix, the load vector, and the displacement vector, such that a deflated solution corresponding to the displacement vector is produced.