... in borehole image interpretation. Digging geological knowledge from data to gain a more comprehensive understanding of structural features remains un-solved problem. We introduce a dip picking approach based on deep neural networks. Compared to conventional data-driven approaches, like SVM or AdaBoost, deep...

... of lithostratigraphic sequences, a successful adaptation of NLP techniques to solve a geoscientific challenge. lithology geologist plate tectonics upstream oil & gas brazil government deep learning neural network structural geology rock type lithostratigraphy asia government natural language...

.... This paper presents a novel approach using artificial neural networks (ANNs) to predict the discharge pressure of electrical submersible pumps. The proposed model will enable early detection of possible failures and reduce downtime. Also, the effectiveness of the ANN model will be compared against...

... insights for their text analysis endeavors. A data-driven rock typing scheme is necessary for decision-making and optimization to achieve the best ultimate recovery of hydrocarbons in the most efficient way. geologist deep learning geology asia government upstream oil & gas neural network...

... an outstanding 90.5% accuracy addressing OCR failure modes (due to noisy raster images). neural network log analysis deep learning well logging raster segmentation detection asia government artificial intelligence segmentation raster digitization module information extraction dpp binary...

... and can be used to find the optimal parameters almost inexpensively. The full optimization including the training of the metamodels can be completed in a few hours on an average grade laptop without the need of any powerful graphics card. upstream oil & gas neural network artificial...

... fracture characterization upstream oil & gas united states government asia government complex reservoir algeria government israel government detection algorithm iran government reservoir characterization identification application clastic rock fracture neural network artificial...

.... united states government algorithm deep learning north america government asia government health & medicine neural network saudi arabia government available yolov5 computer vision prediction machine learning detection map experiment artificial intelligence accessed architecture...

... above the permitted speed limit. A database was created, supplemented by images from the YOLO convolutional neural network architecture, version 7, for the development of machine learning. The model was then trained, tested, and validated. The software and no-code platform were subsequently developed...

... the potential to be applied to other features in seismic interpretation that would significantly optimize the process. The proposed model also allows the implementation of a greener deep learning model with a lower carbon footprint. upstream oil & gas neural network structural geology...

... estimation allows quantification on the stability of the solutions when varying training dataset. geologist sedimentary rock dataset neural network reservoir geomechanics united kingdom government geology rock type reservoir characterization clastic rock deep learning artificial intelligence...

... network saudi arabia government machine learning classification accuracy representation deep learning drilling fluids and materials reservoir characterization petroleum science automated dataset identification fragment convolutional neural network journal Introduction The task...

... classifier and simple Feedforward Neural Network) trained on the whole set of wells and models trained only on selected wells. The integration of similarity-based wells selection step allowed us to improve computational time of models, while preserving or even outperforming prediction quality...

..., accurate, and easily integrable into existing workflows in the oil and gas industry. The study uses deep-learning techniques, specifically convolutional neural networks (CNN), to develop a model for feature extraction and classification. The model employed is the state-of-the-art, open-source...

... Abstract Accurate mapping of internal depositional architectural elements of a Pliocene-aged gas-bearing turbidite reservoir of the Mediterranean Basin into discrete 3D geobodies has been achieved through applying innovative workflow assisted by the convolutional neural network. The mapped...

... oil & gas lstm petroleum engineer time series forecasting limitation neural network reservoir surveillance production control subsurface spatial machine learning modeling evaluation estimation Introduction Temperature logging plays a crucial role in understanding the behavior...

... and to help mitigate the risks associated with CO2 leakage. geologist deep learning subsurface storage prediction climate change united states government complex reservoir artificial intelligence machine learning geology neural network north america government chemical flooding methods...

... concentration deep learning machine learning upstream oil & gas neural network prediction simulation variation dispersion long short-term memory network differential equation misra geology artificial intelligence scenario pollutant concentration prediction high-fidelity tracking...

... such tools, and this study utilizes artificial neural networks (ANNs) to develop a novel approach for an Omani condensate tight gas field to achieve these objectives. This study adopted a top-down modeling approach, where ANNs were developed, trained, validated, and tested for a tight gas field. Three ANNs...

... method, considering various parameters related to petrophysics, geology, reservoir, and decision-making. These samples generated a comprehensive training dataset of approximately 700 simulation cases, forming the basis for training the UNet model, a type of convolutional neural network. The UNet models...