...) 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...

... to expand NDT capabilities for inspection including thickness measurements, corrosion mapping, defect detection (e.g. such as cracks, voids, and pits caused by corrosion), weld inspection and many more. machine learning inspection artificial intelligence attachment neural network marsupial...

... 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...

... models that can be used for accurate and efficient fatigue damage predictions for marine risers subject to VIV. deep learning riser neural network cylinder machine learning upstream oil & gas lstm-modnet artificial intelligence sensor measurement prediction database reconstruction...

... kriging (GEK); radial basis function; support vector machines; space mapping ( Bandler et al., 2004 ) artificial neural networks and Bayesian networks are among the top surrogate methods ( Cardenas, 2019 ). Fourier surrogate modeling ( Bliek et al., 2016 ; Manzoni et al., 2020 ), and random forests...

... drilling recorder (EDR). The software was applied real-time as a well was being drilled in deepwater Gulf of Mexico. Thorough examination and conditioning of EDR and wireline data give way to a training model construction for the artificial neural network (ANN) using full suites of log-data in offset...

... into the task of salt body interpretation. In this study, we introduce a novel machine learning approach of a deep neural network to mimic an experienced geophysical interpreter's intellect in interpreting salt bodies. Here, the benefits of using machine learning are demonstrated by implementing the MultiResU...

... reservoirs to attain for them a similar improved overall reservoir characterization. reservoir characterization neural network machine learning artificial intelligence well logging log analysis al-mudhafar testing data lithofacies adaptive regression spline structural geology lithofacies...

... regression ( Na'imi et al., 2014 ), gradient boosting machine ( Baziar et al., 2018 ), neural network ( Miah et al., 2020 ). When it comes to decision making, it is often necessary to examine inside a model thoroughly. However, it is difficult to achieve interpretation of a model with traditional black...

... component analysis threshold artificial intelligence anomaly score batch training workflow neural network machine learning workflow prediction predict anomalous sanding event offshore operator offshore technology conference data pipeline pipeline erosion ml model principal component analysis...