..., contributing significantly to operational efficiency and safety. deep learning neural network artificial intelligence anomaly machine learning solvent pump information threshold anomaly detection detection equipment anomaly detection system lead detect anomaly reconstruction loss...

... depths. These fused datasets train ML and DL models—including Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and ResNet—to predict bathymetry with improved precision. Comparative analysis of ML and DL techniques showed that Sentinel-2 bands 2, 3, 4, and 8 (10 m...

... neural network petroleum geology artificial intelligence reservoir surveillance production monitoring production control machine learning artificial lift system geological subdiscipline economic geology dynamometer card beam pumping accuracy computer vision detection dynamometer card image...

... neural network with two hidden layers in addition to input layer and an output layer. The first layer consists of 1024 neurons, followed by 512 and 256 neurons, respectively. All hidden layers use the "swish" activation function, a newer activation function that often performs better than ReLU...

... in a Mature Waterflood Field in the San Jorge Basin in Argentina . SPE-207897-MS . deep learning neural network artificial intelligence machine learning classification vector model operation fine-tuning violation semantic vector model deep learning-based classification study drilling...

..., advanced models, including artificial neural networks (ANN) and genetic programming (GP), were employed to simulate the complex relationships between reservoir parameters and gas production rates. Key features such as compressibility, bottom-hole pressure, reservoir pressure, percentage of produced working...

... dynamics, and geophysical modeling. fluid dynamics deep learning reservoir simulation flow in porous media neural network natural language machine learning large language model artificial intelligence geological subdiscipline geologist petroleum geology reservoir characterization loss...

... neural network drilling data acquisition hydrocarbon concentration annular pressure drilling lwd drilling measurement sensor geologist drilling operation artificial intelligence architecture estimation machine learning utilization cuttings gamma ray information integration petroleum...

... the real-time predictive capabilities of the functional neural networks (FNNs) for the prediction of the formation tops. Trained on 3162 datasets of six drilling parameters, the FNNs model aims to predict lithology changes and formation tops across the sandstone, anhydrite, carbonate with shale streaks...

... finalized. The dataset is also appended with the optimized dimensionless fracture conductivity and dimensionless productivity index calculated with the classical boundary element routine. This synthetically constructed dataset was then subjected to a feed-forward neural network to generate data-based models...

... explore the potential of using this workflow for other types of infrastructure inspection as well. deep learning inspection accuracy dataset machine learning precision neural network inspector conductor artificial intelligence detection yolov8 arxiv safety yolov5 algorithm learning...

... of pore movement and recovery degree during microscopic oil displacement provides a new method for microscopic image processing. geology geologist neural network rock type deep learning machine learning enhanced recovery accuracy pixel particle displacement u-net waterflooding...

... the Remaining Oil (LTRO)". machine learning upstream oil & gas asset and portfolio management artificial intelligence depletion development well production forecast simulation gharif knowledge management neural network fdp petroleum engineer petroleum development oman case study...

... artificial intelligence model that outperformed other models in predicting drilling troubles. data mining drilling operation real time system neural network hook-load value machine learning operation crew drilling equipment artificial intelligence model artificial neural network data quality...

... to identify the existing gaps and reviews the lessons learned during the two decades of the application of DAS in downhole monitoring. vertical seismic profile production monitoring neural network completion monitoring systems/intelligent wells das production control artificial intelligence...

..., Evaluation and Deployment. This paper is shading lights on these phases, also it will derive the readers through a drilling case-study, where this methodology was applied leading to successful artificial intelligent drilling project. machine learning neural network data mining drilling operation...

... on a quadruple tank system (QTS), which consists of four tanks and two electrical-pumps with two pressure control valves. Two artificial neural networks are constructed the DHP approach, which are the critic network (the provider of a critique/evaluated signals) and the actor-network or controller (the provider...

... very difficult to explore every single possible scenario for a specifc EOR project due to time, computational power, and human power limitations. Artificial neural networks are capable of correlating inputs and outputs and finding nonlinear relations through complicated systems. For that reason, ANNs...