... accuracy. artificial intelligence seismic interpolation pinn twt neural network offset fomel claerbout machine learning differential equation international american association upstream oil & gas interpolation spectrum exploration geophysicist applied geoscience information...

... to the maximum amplitude mode in the waveform. The picked arrival time can be used to reconstruct a borehole structure well. deep learning artificial intelligence neural network waveform upstream oil & gas production logging log analysis machine learning separation production monitoring tie...

... even in the presence of noise despite using only noise-free data for the training. deep learning reservoir characterization cnn prediction neural network machine learning international upstream oil & gas artificial intelligence marchenko equation figure 5 cross-section snieder...

... upstream oil & gas prediction exploration geophysicist neural network machine learning deep learning reservoir characterization implementation artificial intelligence frequency american association international identification applied geoscience norlund society workflow...

... is developed for shale gas production performance analysis. First, a machine learning model is built as the surrogate of shale gas production. Then, Shapley values based on game theory principles are employed on top of the surrogate model for key factor analysis. Finally, bi-objective optimization that jointly...

... The oil and gas (O&G) industry uses machine learning extensively to build innovative workflows and products to address a variety of scientific and technical problems. In recent times, rapid advancement in natural language processing (NLP) has renewed interest in the analysis of massive...

... neural network artificial intelligence machine learning deep learning applied geoscience upstream oil & gas reservoir characterization exploration geophysicist international energy 10 american association presented characterizing geophysics interpretation international...

... estimation, with satisfactory results. upstream oil & gas artificial intelligence deep learning neural network machine learning storseismic reservoir characterization noise alkhalifah frequency extrapolation estimation applied geoscience arxiv preprint arxiv application first-break...

... We propose a hyper-parameter optimization workflow for training the deep learning framework to simulate the effect of boundary conditions for wave propagation. Hyper-parameter selection is a crucial step in model building and has a direct impact on the performance of machine learning models. We...

... An algorithm that combines machine learning and geostatistics to build models based on multiple input data sets with a significantly simplified user workflow is used to provide several scenarios of the distribution of net-to-gross (NTG) and net porosity for the giant Handil oilfield...

... network machine learning deep learning reservoir characterization upstream oil & gas noise suppression artificial intelligence noise pixel attenuation birnie suppression energy 10 applied geoscience self-supervised coherent noise suppression noise attenuation procedure trace-wise...

... A machine learning (ML) method has been developed for frac-hit identification. It utilizes Long Short-Term Memory (LSTM) and Multilayer Perceptron (MLP) neural networks to innovatively identify the frac hits due to hydraulic communications between fracking child wells and producing parent wells...

... identification. Multiple synthetic and field examples show that the network can much more accurately and efficiently predict channels in 3D seismic volumes with complex structure. upstream oil & gas china government asia government machine learning neural network reservoir characterization deep...

... advancements in forward stratigraphic modeling (i.e., computational stratigraphy) provide ideal datasets to quantitatively evaluate element-scale hierarchy using high resolution, fully sampled models of deepwater fan systems. In this work, we apply an unsupervised machine learning workflow to elements...

... but was not presented at IMAGE 2022 in Houston, Texas. upstream oil & gas reservoir characterization reflection deep learning machine learning diffraction separation neural network dacig reflection dacig geophysics international adaptively exploration geophysicist scatterer american association...

... and field data, and proven to be useful to inform structural/depth uncertainty. artificial intelligence green international upstream oil & gas wavefield deep green american association neural network machine learning exploration geophysicist propagation energy 10 migration receiver...

... deep learning reservoir characterization upstream oil & gas artificial intelligence interpretation geophysics american association international neural style transfer figure 3 workflow figure 4 exploration geophysicist detection neural network machine learning joseph jenning...

... machine learning alkhalifah upstream oil & gas reservoir characterization wavefield neural network frequency imaging receiver applied geoscience international differential equation american association pinn equation exploration geophysicist society artificial intelligence distance...

... but was not presented at IMAGE 2022 in Houston, Texas. artificial intelligence autoencoder deep learning machine learning upstream oil & gas reservoir characterization reservoir prediction american association geophysics international information seismic inversion applied geoscience formula...

... spectral coefficient applied geoscience energy 10 upstream oil & gas machine learning exploration geophysicist reflection coefficient classification artificial intelligence matrix international china university society correspond fanchang zhang east china nanying lan information...