... capability to create generic models for the presented problem. drilling fluid formulation drilling fluids and materials neural network chemical treatment upstream oil & gas fluid compatibility production chemistry viscosity available drilling fluid selection and formulation oilfield...

.... It is implemented using deep neural networks with 3D Fourier neural operator. We verify this image morphing operator with both synthetic data and field data experiments. The goal of this study is in speeding up the velocity model scenario tests. operator machine learning deep learning reservoir...

... Artificial Neural Network in order to predict the emulsion dynamic viscosity of thirty (30) oils from Brazilian basins. The Artificial Neural Network used as inputs rheological data considering different values of temperature, shear rate, oil °API and water fraction in order to predict the dynamic viscosity...

... drone offshore technology conference machine learning fracture structural geology neural network algorithm identify igneous rock complex reservoir deep learning technique dyke artificial intelligence Bemis , S.P. , Micklethwaite , S. , Turner , D. , James , M.R...

... of the output model. The best combination was the one using the PReLU activation function with the Adamax optimizer. machine learning deep learning variation seismic modeling seismic data rmsprop leaky relu relu upstream oil & gas neural network activation function velocity model...

... systems and equipment drilling parameter drilling performance workflow modeling sensitivity analysis bha drilling operation mud motor drilling equipment artificial intelligence neural network upstream oil & gas simulation of human behavior reliability physics-based simulation data...

... Abstract Artificial Neural Networks (ANN) applications have grown exponentially in all areas of science and technology. The advantages are its versatility, speed and ability to aggregate information, perform predictions of a given set of data. These attributes attract the petroleum industry...

... if measured production noticeably deviates from the forecast. Two case studies were done, and the results indicate that a Long Short-Term Memory Network-based proxy is able to forecast production with a remarkably low error, validating the methodology and supporting its use. neural network forecasting...

... and 10-year return period. The acceleration data is used to get the modal frequencies and calibrate the FE model. Also, the measured stress value is compared with the FE model generated stress value, and the FE model is further calibrated. Machine Learning Algorithm (Recurrent Neural Network) is used...

... corpus machine learning representation natural language neural network application upstream oil & gas artificial intelligence offshore technology conference natural language processing algorithm vector space model Ashton , W. B. , and Stacey , G. 1995 . Technical...

... Abstract This paper proposes an unsupervised neural network model for facies pattern recognition and formation characterization using borehole images. The goal is to create an automated workflow for rock fabric identification using high resolution acoustic or electrical borehole images...

... step towards reducing the impact of incidents and accidents. Using computer vision, deep neural networks, and video footage, we created a web solution for analyzing the imagery in real-time and issuing alerts when a violation happens. For this specific domain, we accomplished the best results by using...

... offshore. A data set of 448 MICP from the Pre-Salt carbonates of Barra Velha Formation was used to build the FZI-RRT model. The optimal number of RRTs, five in total, is determined by using an unsupervised neural network with capillary pressure parameters as inputs, permeability, effective porosity...

... mean std geological modeling data assimilation principal component analysis artificial intelligence facies model autoencoder realization neural network reservoir characterization C. C. Agbalaka and D. S. Oliver . Application of the EnKF and localization to automatic...

... unhealthy states. maintenance activity unhealthy condition availability sensor value compressor neural network deviation machine learning mathematical representation equipment malfunction health condition monitoring offshore technology conference artificial intelligence information...

... texture initial development phase resistivity machine learning upstream oil & gas porosity trajectory nmr log analysis pilot well net pay reservoir characterization spectroscopy artificial intelligence neural network otc 22738 permeability reservoir well logging horizontal...

..., following the success for MeOH-salt systems, MEG-salt systems, and several different KHIs-salt systems. An artificial neural network correlation has been developed for aqueous sample solutions containing ethanol and salts. This correlation covers a salt concentration from 0 to 10 mass% and ethanol...