... of unknown duration between different drilling parameters, a properly designed time series analysis model may be able to capture their relationships and make reasonable predictions. Recurrent Neural Network with long short-term memory (RNN-LSTM) architecture is a deep learning algorithm capable of making...

... ; Mahmoud et al., 2021 ) and extra parameters might be included as bite features and mud hydraulics. Through the literature, many techniques were implemented for such purposes as support vector (SVM) machine, artificial neural network (ANN), random forest (RF), gradient tree boosting, convolution neural...

... of the calibration dataset with good accuracy. upstream oil & gas flow metering production monitoring deep learning artificial intelligence well aa neural network reservoir surveillance prediction united states government well test production control equation machine learning well test data...

... false alarms and improve interpretability compared to previous methods. The framework is highly extensible, and further performance improvements can be expected in the future. asia government united states government upstream oil & gas neural network machine learning detection drilling...

... variables such as slug frequency, amplitude, slug volume, etc. The selected model outperformed traditional statistical models such as Non-Parametric Time Series (NPTS) and other deep neural network models such as DeepAR, and Simple Feed Forward. The model was able to match the flow behavior during slugging...

... learning models were built and compared with the baseline, such as Gaussian process regressor, random forest, neural network, and natural gradient boosting, with the purpose of identifying the most accurate one. To solve the overfitting issue caused by the small size of the dataset, several strategies have...

... (BAUV) equipped with video cameras and mobile edge computing devices. We deploy a deep neural network (DNN) specially trained for a variety of underwater image/video processing tasks. The intelligent computer vision processing unit allows us to navigate and track objects even when the visibility is poor...

... reasonably, with a reduced number of reliable measurements. Implementation of these techniques can help to extend the useful life of these TTMS. united states government sub-sea system tendon tension tendon tendon 3 neural network machine learning model input prediction mse bridge input...

... data. Artificial neural network (ANN) is used to predict the fatigue failure of the cement sheath. The dataset is retrieved from 6 papers of One-Petro, which includes 314 cement fatigue failure cases. Inputs of the ANN were selected to be seven cement parameters (cement material, additive type...

... work happens. Knowing how people tick, paired with practical tweaks to systems makes all the difference. ergonomics health & medicine exposure risk management communication artificial intelligence neural network human factors neurology social brain hazard hazard offshore technology...

..., and a comparative analysis is presented. Adaptive Neuro Fuzzy Inference System (ANFIS), along with Artificial Neural Network (ANN) based algorithms are utilized to analyze the data and develop models. A process-based approach is followed starting with systematic data analysis, which includes a selection...

... & gas metals & mining neural network recall score tsim efficiency condenser system sensor fouling state precision score subscale condenser system bundle exchanger condenser accuracy prediction algorithm fouling rapid decarbonization flow rate anti-fouling treatment offshore...

... neural network upstream oil & gas adsorption isotherm prediction cortés scale inhibition adsorption franco algorithm error magnitude asphaltene remediation remediation of hydrates accuracy asphaltene adsorption nanoparticle asphaltene inhibition concentration ai agent asphaltene...

...) using a deep convolutional neural network. The correlation pattern was explored and validated by applying meteorological data in the Gulf of Mexico area from the NOAA WAVEWATCH III system. A wave power hotspot was defined as an event when wave power density over a certain threshold during a period...

... conditions to simulate circulating drilling mud from the wellbore. We used both supervised and unsupervised learning techniques for flow regime identification and kick prognosis. These include an Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Trees, K-Means...

... for the planning of expensive field, laboratory, and FE simulations and to reduce the expenditures on future studies. offshore pipeline regression interaction process neural network displacement artificial intelligence sub-gouge deformation etr model etr 1 etr algorithm memorial university etr 4...

... inhibition neural network upstream oil & gas anomaly threshold xylene soak asphaltene producing deepwater well Introduction Asphaltene deposition is a common complex problem and can occur at various stages in Oil and Gas production. Often referred to as the "cholesterol of petroleum...

... application validation upscale high-fidelity model schiozer neural network permeability scenario offshore technology conference Introduction High-fidelity models of a complex reservoir may contain tens of millions of cells describing the details of structures and rock types. The simulation...

... achieved significant improvement in anomalous sounds detection only for machines that can produce noise in the ultrasound region, while other machines achieved worse results compared to the audible sound region. data mining health & medicine machine learning neural network drilling equipment...

... system based on the coal properties, pressure and temperature, and water salinity without the need for experimental measurements of complicated calculations. climate change well logging log analysis neural network upstream oil & gas coal property co sequestration application enhanced...