... architecture, the Spatial Pairwise Interaction Network (SPINet) with independent and contextual neural pathways (NPs), designed to capture the primary well characteristics, and its complex interactions with the neighboring wells. For the contextual NP, we explore using the popular Attention mechanism...

... deep learning machine learning artificial intelligence architecture transformer prediction dataset efficiency multi-scale learning segmentation mask semantic segmentation task smoke detection smoke mask segmentation conference flare smoke detection smoke region relu-based linear...

... learning reservoir characterization figure 4 borehole wellbore grid stability architecture equation formulation concentration geologist artificial intelligence geological subdiscipline simulation comparison jaeger wellbore pressure wellbore wall internal wellbore pressure result...

... information in characterizing reservoir distribution and overburden for assessing containment integrity and storage capacity. A deep learning inversion method for simultaneous estimation of porosity and Vclay was applied and tested in Carbonate Field 1. UNet architecture, chosen for its ability to preserve...

... specialized domains for companies. artificial intelligence deep learning natural language neural network machine learning llm knowledge management information application large language model sequence architecture database petroleum engineering requirement probability fine-tuning arxiv...

... learning flow in porous media reservoir simulation artificial intelligence neural network scaling method geology rock type machine learning upscaling pcu-dl module accuracy architecture physics-constrained upscaling permeability value reservoir characterization pore segmentation equation...

... blocks in a single software user interface. Each functional block can be assigned to execute in multiple remote locations. Furthermore, each functional block can be parametrized to support unique properties of each rig. This architecture makes it significantly simpler and faster to build, deploy...

... performance results presented. The current work showcases the mechanical component architecture employed to ensure the functional capabilities of the new motorized pump were achieved, similar to conventional ESPs but with shorter overall length. The new motorized pump has a 5.62-inch housing diameter. One...

... by utilizing an efficient gradient-based method. The reservoir surrogate model is based on the multi-model Embed-to-control Observe (E2CO) architecture, consisting of four blocks of neural networks: encoder, transition, transition output, and decoder. In this work, the surrogate model is coupled...

...-making and field development. A novel Hybrid Data-Physics ( HDP ) architecture is proposed that integrates data-driven models with physics equations embedded in a deep neural network ( DNN ). This approach optimizes both network and physical parameters simultaneously to predict short and long-term...

... management quantification geological subdiscipline accuracy probability machine learning architecture risk and uncertainty assessment reservoir characterization prediction uncertainty quantification petroleum engineer attention mechanism neural network application seismic stratigraphy...

... monitoring application society reservoir characterization architecture noise operator dataset cnn operation information pipeline machine learning cold lake accuracy petroleum engineer alberta Introduction Cyclic Steam Stimulation (CSS) recovery process is employed to produce...

... that the drilling process state could be estimated and published in real-time on a drilling data hub. The data hub acts as the central repository where all signals are exchanged, following the blackboard system architecture. This includes real-time signals but also more complex data structure such as capability...

... network production control reservoir surveillance dataset tulsa university erosion corrosion research center particle erosion diameter wasserstein gan erosion production monitoring discriminator cwgan pipe diameter architecture generator conganergy sa shirazi generative adversarial...

... such as injection rate and pressure data as input and multiphase production rates as output. We combine reduced physics models into a neural network architecture by utilizing two different approaches. In the first approach, the reduced physics model is used for pre-processing to obtain approximate solutions...

.... The process of seismic interpretation relies on expert interpretation and is generally time consuming. In the current work, we develop a new network architecture for automatic seismic facies class with attention mechanism for improved classification results. geology upstream oil & gas geologist...

... history matching deep learning flow in porous media asia government artificial intelligence neural operator complex reservoir prediction gravity drainage modeling & simulation numerical simulation society sagd reservoir simulation petroleum play type heavy oil play architecture...

..., most of them focused on experimental methods. In this work, we proposed a new approach for recovering geometrical information of the stylolite zone (including its size and location) based on neural network architectures including convolutional neural network (CNN), recurrent neural network (RNN...

.... The term Data Mesh has been introduced as an architectural concept in 2019 by Zhamak Dehghani. A Data Mesh is defined as a decentralized sociotechnical approach to share, access and manage analytical data data in complex and large-scale environments – within or across organizations. (Zhamak Dehghani, Data...

... al., 2014 ) architecture, operating directly on raw seismic data, and is capable of capturing broader contexts, thus ensuring the continuity and complexity of the reflectivity series. Additionally, it minimizes the reliance on prior low-frequency models, enhancing the model's generalization...