... Abstract The thermodynamic behavior of a fluid in a tight reservoir differs from that in the conventional environment. A new phase equilibrium algorithm with capillary pressure is presented and formulated using the laws of thermodynamics. At a given temperature, volume, and moles with capillary...

... algorithms and demonstrate their performances in three case studies: a simple synthetic deterministic problem (a two-phase waterflooding model), a large-scale deterministic optimization problem, and a large-scale robust optimization problem, both conducted on the Brugge model. Results show that the LS-SQP...

... costs and compares performances of different matrix factorization subroutines in LAPACK with different options and settings, both numerically (measuring CPU time to solve different problems with different sizes) and theoretically (counting floating-point operations, or flops, required in each algorithm...

... characterization problem. The utilization of stochastic approximation of gradients within a gradient-based SVGD is another novelty aspect of this work. The formulated algorithm is benchmarked using synthetic test problems with multimodal known posterior distributions. Also, the application of the proposed...

... calculation deep learning equation of state calculation oil shale complex reservoir artificial intelligence algorithm dataset mlp shale reservoir multi-layer perceptron pr-c eos pr eos application pvt measurement reservoir simulation shale gas equilibrium machine learning pinn node peng...

... extreme-scale geomechanical model united states government europe government node simulator fsai overpressure solver method algorithm applied mechanics implementation Introduction With the increasing availability of large computational resources, scientists and engineers try to simulate...

.... united states government fluid dynamics neural network deep learning artificial intelligence matrix machine learning koopman operator simulation proxy algorithm upstream oil & gas flow in porous media equation prediction architecture trajectory saturation dynamic mode decomposition...

... is to determine the granularity of the network. Herein, we demonstrate how this can be mitigated by using a dynamic graph adaption algorithm to find a good granularity that improves predictability both inside and slightly outside the range of the training data. europe government optimization upstream oil...

... in the loss function. Bayesian optimization is used to determine the optimal hyperparameters. Step 4 : Validate the PcNN model. In this step, we conduct blind validation by comparing it with the iterative PT flash algorithm. united states government asia government upstream oil & gas deep...

... the number of components used in compositional models. pvt measurement artificial intelligence united states government composition upstream oil & gas equation of state characterization partition algorithm component reservoir simulation machine learning regression lump clustering...

... Table 1 The final ensemble average NPV [MM$] results using different algorithms for the 0.1 md permeability case. The total number of simulation runs at termination is also listed. Table 2 The final ensemble average NPV [MM$] results using different...

... Table 1 Model 1. Linear solver parameters for the multiscale multistage preconditioner ( Algorithm 1 ). Description Symbol Value Maximal relative residual norm 1.0e-8 Maximal number of FGMRES iterations 100 Number of ILU0 pre-smother iterations n pre 0 Number...

... machine learning reservoir simulation artificial intelligence history matching probabilistic forecasting algebraic equation best run statistical model dae model dae equation modeling & simulation upstream oil & gas reservoir simulation model algorithm solver differential equation...

... international journal algorithm upstream oil & gas simulation iteration petroleum engineer hydraulic fracturing fracture tip computer method analytical method fracture fluid-driven fracture propagation equation artificial intelligence engineering geomechanics propagation criterion time...

... and portfolio management fluid dynamics linearization gpu implementation module simulator computation reservoir simulation calculation cpu part algorithm petroleum engineer cpu gpu acceleration implementation linear solver storage Introduction HPC (High Performance Computing) is vital...

... to instill physical knowledge to machine-learning algorithms. This alleviates the two most significant shortcomings of machine-learning algorithms: the requirement for large datasets and the reliability of extrapolation. The principles presented can be generalized in innumerable ways in the future and should...

... Linearization (OBL) for the system of discretized nonlinear governing equations. We use a fracture network generation algorithm that uses distributions for length, angles, size of fracture sets, and connectivity as its main input. This allows us to generate a large number of complex fracture networks...

... of the derived coupling-strength criteria is tested using several test cases. The first test is using a gravitational immiscible dead-oil lock-exchange under a unit mobility ratio and large differences in density. For this case, the SFI algorithm fails to converge to the fully coupled solution due to the large...

... fluid dynamics hydraulic fracturing upstream oil & gas discretization artificial intelligence complex reservoir engineering international journal vertex finite element method shape function mechanics algorithm fracture intersection grid harmonic shape function fractured reservoir...

... Abstract The use of deep-learning-based procedures for geological parameterization and fast surrogate flow modeling may enable the application of rigorous history matching algorithms that were previously considered impractical. In this study we incorporate such methods – specifically...