... learning upstream oil & gas artificial intelligence voskov equation neural network simulation compositional simulation figure physics estimation tchelepi approximation deep learning operator application differential equation fraction conservation equation lebesgue integral loss...

... results have been achieved on the field level and good matches have also been observed for key producers. In addition, the history matching consumes mere 4 hours to finish 1,100 simulation jobs. The successful application of the GPSNet to this waterflood field demonstrates a promising workflow that can...

... to distinct reservoir scenarios. Here, we study the application of Stein Variational Gradient Descent (SVGD) method, originally proposed by Liu & Wang (2016) , in reservoir history matching problems. The rationale and mechanics of SVGD method is discussed and the adaptation of this method...

... calculation. Previous models used the conventional Peng-Robinson equation of states (PR EOS), which becomes inaccurate when the pore diameter reduces to the scale of nanometers. With PR-C EOS, fluid characteristics can be accurately modeled under nano-scale conditions, making our model applicable to shale...

... the application of distance-based localization during data assimilation with the Ensemble Smoother with Multiple Data Assimilation (ES-MDA). The performance of the proposed method is investigated in a synthetic benchmark problem with realistic settings. We evaluate the methodology with three variants...

..., and also requires detailed geologic model. Reduced physics models provide an efficient simulator free workflow, but often have a limited range of applicability. Pure machine learning models lack physical interpretability and can have limited predictive power. We propose a hybrid machine learning...

... neural network united states government reservoir geomechanics upstream oil & gas artificial intelligence machine learning journal differential equation deep learning approximation reservoir characterization physics-informed neural network simulation application wang accuracy porous...

..., maintainable code with applicability across a wide range of models. Different parts of a reservoir simulator benefit from different approaches. A modern, object-oriented simulator requires components to handle initialization, property calculation, linearization, linear solver, well and aquifer calculations...

... representations, and special noisy objective function handling protocols are implemented. However, applying such methods to large-scale problems may still be impractical because the gradients of the objective function may be too expensive to compute for realistic applications in the absence of the implementation...

.... The proposed simulation framework is validated against a set of numerical and experimental benchmark tests, demonstrating the efficiency and accuracy of the modeling framework for CCUS-related subsurface applications. Important physical phenomena resulting from the complex thermodynamic interactions of CO 2...

..., followed by application of the method to a travel-time tomography inverse problem to investigate its model updating performance. neural network resolution architecture united states government artificial intelligence autoencoder application spatial adaptivity deep learning machine learning...

... machine learning neural network artificial intelligence reservoir simulation fluid dynamics prediction pressure distribution piml method physics pde numerical example application fracture hydraulic fracturing upstream oil & gas boundary condition analytical solution partial...

.... This paper is to present a new method to speed-up the calibration process of well performance models with multi-million cells and its two applications in the Wolfcamp reservoir in the Delaware Basin. To study well performance and completion effectiveness of 3000 horizontal wells over University Lands...

... Abstract We present our progress on the application of physics informed deep learning to reservoir simulation problems. The model is a neural network that is jointly trained to respect governing physical laws and match boundary conditions. The methodology is hereby used to simulate a 2-phase...

..., and cross-derivative terms for both flow and mechanics equations. The resulting system, though flexible and accurate, can lead to excessive computational costs for field-relevant applications. To resolve this limitation, a scalable parallel solution algorithm is developed and presented. Several proof...

... be adaptable. An Operator Overloading AD design is proposed and tested to provide scalability and computational efficiency seemlessly across memory- and compute-bound applications. This is achieved by 1) use of portable and standard programming language constructs (C++17 and OpenMP 4.5 standards), 2) adopting...

... 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...

... Abstract We present a framework of the application of Bayesian Optimization (BO) to well management in geological carbon sequestration. The coupled compositional flow and poroelasticity simulator, IPARS, is utilized to accurately capture the underlying physical processes during CO 2...

... stabilized at 12 vs. 30 iterations), and a lower computational cost (150-250 vs. 1,300 runs to achieve the same NPV) over the reference methods. Similar performance improvement was also observed in another application to a North Sea type reservoir. This demonstrated the general applicability of the proposed...

... (e.g., CFL) features to build a predictive ML model. This can be pre-trained or dynamically trained to generate an inference model. The trained model can also be reinforced as new data becomes available and efficiently used for transfer learning. We present the application of these workflows...