... alternative without compromising accuracy. In this study, we adopt the U-Net enhanced graph convolutional neural network (UGCN) to predict the spatial and temporal evolution of CO 2 plume saturation and pressure buildup in saline aquifers. Utilizing the U-Net architecture, which incorporates skip connections...

... and the complexity of overflow image discrimination, which has not been well studied so far. In this work, we propose an intelligent kick recognition model based on ROV images that couples convolutional neural network (CNN) and generative adversarial network (GAN). It consists mainly of (i) a GAN model for data...

... uncertainties. In this paper, we propose an intelligent inversion method using a Lagrange multipliers-guided physical residual neural network (Lg-PRNN), incorporating nonlinear variations, adaptive parameters, and Lagrange multipliers. The use of Lagrange multipliers eliminates the need to manually adjust...

... 17 2 2025 10 2 2025 12 3 2025 14 5 2025 Copyright © 2025 Society of Petroleum Engineers sedimentary rock deep learning mudrock well logging mudstone geologist neural network rock type log analysis reservoir characterization structural geology lithofacies...

... in reservoir models before field applications. Therefore, this work presents an innovative machine-learning (ML) workflow using convolutional neural networks (CNNs) for the estimation of residual oil saturation ( S or ) based on the generation of partitioning tracer responses in heterogeneous media. To train...

...% of the total global expenditure on artificial lift technologies ( Yang et al. 2022 ). deep learning artificial lift system neural network artificial intelligence current signal machine learning rotational speed frequency spectrum vibration diagnosis current spectrum robustness current data...

...Jodel Cornelio; Syamil Mohd Razak; Young Cho; Hui-Hai Liu; Ravimadhav Vaidya; Behnam Jafarpour Summary Data-driven models, such as neural networks, provide an alternative to physics-based simulations in predicting well behavior within unconventional reservoirs. However, these models struggle...

... significant challenges to accurate prediction. To address these complexities, we present a hybrid neural network model that combines a modified graph attention network (MGAT) with a memory-augmented neural network (MANN). MGAT enhances the extraction of spatial features from shale gas wells, while MANN uses...

... features and identify dependencies between injector and producer wells, ultimately leading to more accurate production forecasting. We developed a novel residual 3D convolutional long short-term memory neural network (residual 3D-CNN LSTM) by incorporating deeper and residual bottleneck structures...

... fault parameters based on well testing theory and transfer learning is proposed. Transfer learning is used to develop a 1D convolutional neural network (CNN) inversion model for obtaining five key fault parameters, including matrix permeability, fault conductivity, fault length , fault distance...

... The Authors. Published by the Society of Petroleum Engineers. This paper is published under the terms of a Creative Commons Attribution License (CC-BY 4.0). fluid dynamics drillstem/well testing wellbore design structural geology drillstem testing neural network hydraulic fracturing equation...

..., artificial neural networks have been used for enumerative analysis of reservoirs from well logs ( Aminian and Ameri 2005 ; Kumar 2012 ; Korjani et al. 2016 ; Saikia et al. 2020 ; Okon et al. 2021 ). The use of artificial neural networks offered a reliable, straightforward, and effective approach...

.... Unlike models such as “you only look once” or mask region-based convolution neural network (R-CNN), which rely on mask generation, the proposed method focuses on different objectives, offering an alternative approach to feature picking. Although trained on limited data and validated through validation...

... algorithm encoder experimental result reservoir characterization diagenetic facies recognition characteristic identification compaction facies attention mechanism feature extraction madeline dimension category neural network diagenetic facies recognition data distribution Diagenetic...

... with the initial model. The proposed method was compared with the previously developed convolutional neural network-principal component analysis (CNN-PCA) and demonstrated similar history-matching performance. However, it qualitatively showed better preservation of the geological style of the trained reservoir...

... dynamics permeability geologist neural network scaling method subvolume reservoir simulation reservoir geomechanics geological subdiscipline accuracy architecture analytical solution geology deep learning artificial intelligence machine learning voxel flow in porous media constraint...

... reservoir simulation rock type scaling method geological subdiscipline clastic rock neural network artificial intelligence mudstone hr image real lr image sedimentary rock deep learning bilinear method image quality bilinear and bicubic method algorithm real hr image super-resolution...

... petroleum geology structural geology artificial intelligence machine learning seep deep learning chemical spill geological subdiscipline algorithm neural network oil seep economic geology node reservoir characterization pixel study area reflectance iraq detection oil seep detection seep...

... and performance optimization. In this study, we propose a pioneering hybrid model that integrates tabular, spatial, and temporal modalities to enhance production forecasting in unconventional shale gas reservoirs. Despite traditional methods, such as artificial neural networks (ANN) and extreme gradient boosting...

... of a Creative Commons Attribution License (CC-BY 4.0). geologist deep learning drilling equipment annular pressure drilling directional drilling geology drilling operation drilling fluid formulation wellbore design drilling fluids and materials neural network drilling fluid property expert...