... models and machine learning models, so that the most appropriate approach can be used depending on resources and reservoir complexity. We have bridged the gap between pure machine learning models and full reservoir simulation. Novel/Additive Information The approach to use multi-variate time series...

... Abstract The Physics Inspired Machine Learning (PIML) is emerging as a viable numerical method to solve partial differential equations (PDEs). Recently, the method has been successfully tested and validated to find solutions to both linear and non-linear PDEs. To our knowledge, no prior studies...

... fields. The model honors mass conservation and the underlying physics laws, which many existing approaches don't take into direct consideration. artificial intelligence fluid dynamics machine learning upstream oil & gas simulation durlofsky 2020 deep learning pod-tpwl liu reduced-order...

... 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...

... numerical model is employed to demonstrate the proposed optimization workflow. Since using MOO requires computationally intensive procedures, machine-learning-based proxies are introduced to substitute for the high-fidelity model, thus reducing the total computation overhead. The vector machine regression...

... a large ensemble of DFM models, which represent complex fracture networks and allow for making decisions under uncertainty using more detailed high-resolution numerical models. machine learning upstream oil & gas intersection reservoir simulation fracture network complex reservoir voskov...

... tuning for fluid characterization. machine learning upstream oil & gas training data monahan clearfork oil artificial intelligence stability analysis composition fraction pvt measurement composition absolute difference peng-robinson eos predictor variable fugacity coefficient proxy...

... be extended to construct generalized dual-porosity, dual-permeability models or include more complex physics, such as capillary and gravity effects. artificial intelligence deep learning equation of state upstream oil & gas scaling method reservoir simulation machine learning neural network...

... delaunay triangulation convergence machine learning upstream oil & gas discretization tpfa transmissibility truncation error artificial intelligence fluid dynamics voronoi grid grid quality measure strata local approximation permeability grid flow in porous media approximation spe...

... random field vassilevski grid lognormal field conditioned problem size covariance lawrence livermore national security petroleum engineer machine learning optimization problem coarse level fine level scalable hierarchical multilevel sampling correlation length Introduction Obtaining...

..., we also highlight the often more severe non-technical pitfalls that cannot be evaluated by measures like R 2 . Thoughts are shared on how they can be avoided, especially during project framing and the three critical transition scenarios. machine learning production forecasting risk...

... in this work. machine learning reservoir simulation fluid dynamics artificial intelligence deep learning geomodel flow in porous media history matching es-mda procedure realization prediction application production well neural network upstream oil & gas time step evaluation...

... sequestration. IPARS is coupled to IBM Bayesian Optimization (IBO) for parallel optimizations of CO 2 injection strategies during field-scale CO 2 sequestration. Bayesian optimization builds a probabilistic surrogate for the objective function using a Bayesian machine learning algorithm, Gaussian process...

...-phase flow in a realistic fracture geometry. The DPDP results match well the DFM reference solution, while being significantly faster than the latter. machine learning complex reservoir artificial intelligence hydraulic fracturing scaling method reservoir simulation simulation dpdp model...

...-MDA are good alternatives and give equivalent results in terms of computational cost and quality predictions. reservoir simulation engineering flow path artificial intelligence upstream oil & gas optimization producer ensemble machine learning history matching flow diagnostic...

.... In addition, it is also an automatic process that fits well with iterative optimization algorithms. machine learning reservoir characterization production monitoring risk management reservoir simulation drillstem/well testing drill sequence evolutionary algorithm enhanced recovery asset...

... Abstract We present a novel reservoir simulator time-step selection approach which uses machine-learning (ML) techniques to analyze the mathematical and physical state of the system and predict time-step sizes which are large while still being efficient to solve, thus making the simulation...

... ( ( ⋅ ^ ) ) are the estimated values. The paths presented with continuous arrows are used in both training and prediction steps, and those with dashed arrows are only used on the training procedures. Abstract Physics-aware machine learning (ML) techniques have been used to endow data-driven proxy models with features...

... efficiency of the calculated statistics for the update step. neural network autoencoder neural network architecture artificial intelligence reservoir simulator representation model realization dataset machine learning upstream oil & gas lsda ensemble latent variable model latent...

... prediction realization machine learning reservoir simulation fluid dynamics optimization algorithm permeability bayesian mcmc lstm petroleum engineer Introduction Accurate modeling of fluid flow in subsurface reservoirs is essential in various environmental and energy applications...