... and explain contributing factors. This paper presents application of a unique Deep-Learning algorithm, to automate well-integrity assessment by extracting entire knowledge from an existing database of millions of integrity-tests, using a complex Deep Neural Network (DNN), and transfer this knowledge...

..., and audio sensor. This paper will present the case study of a comprehensive overview of computer vision base CCTV surveillance system and to review the current approaches to each of its processing steps. upstream oil & gas machine learning deep learning neural network smart solution system...

... climate change gravity injection gravity gradient neural network sustainable development taranaki basin monitoring co2 migration sustainability social responsibility machine learning new zealand subsurface storage quantum gravity ai framework application reservoir characterization...

... with the commercial reservoir simulator as a final validation step. Abstract Leveraging the recent developments in the Machine Learning (ML) technology, the objective of this work was to use Artificial Neural Networks to build proxy models to classical reservoir simulation tools for two distinct chemical EOR...

... is user/field/reservoir specific and is by nature very flexible. Also, the source of the input attributes can vary depending upon the availability. For example, Figure 4 shows how the POS is aggregated. drilling operation artificial intelligence upstream oil & gas neural network machine...

... methods being unreliable and inaccurate, an improvised way can be used to predict MMP using computational techniques such as Artificial Neural Networks (ANN). ANN has proven to be highly reliable due to its adaptability, primarily from being able to learn from various input data and compute more...

... stacks inspection and monitoring. The implementation of such system is expected to lower the cost and minimize the human resource risks of flare stack inspection processes. upstream oil & gas deep learning machine learning neural network uav detection operation inspection existence...

... based on machine learning algorithms. The model's predictive accuracy can be improved over time by adding information and further improving the model components. neural network deep learning data mining artificial intelligence upstream oil & gas reservoir surveillance machine learning...

... artificial intelligence production control production monitoring dataset gradient machine learning source neural network reservoir surveillance validation accuracy validate engineer temperature measurement validate pressure johnson operation k-nearest neighbor introduction predictor...

.... upstream oil & gas saudi aramco saudi arabia government asia government artificial intelligence pdt machine learning process digital twin grossmann optimization operation application accuracy knowledge neural network data mining constraint dashboard simplification implementation...

... network (DFN)—was history matched, confirming the successful application of the proposed methodology. upstream oil & gas hydraulic fracturing neural network fracture artificial intelligence hydraulic fracture reservoir characterization simulation fracture geometry gaussian mixture model...

... measurements. All data were evaluated and the noises and outliers were removed. Different types of artificial intelligence methods were examined to come up with the best determination model. Artificial neural network (ANN) technique, support vector machine (SVM) approach, and adaptive fuzzy logic (AFL) systems...

... oil & gas neural network anomaly sensor deep learning wellbore integrity leakage algorithm wellbore design data mining incident model prediction poc detection synthetic case lstm autoencoder historical event artificial intelligence machine learning prediction lstm classifier...

... an application of ML to develop surrogate reservoir models that are based on simple neural networks. The authors used inputs such as well identifiers, static reservoir properties, and dynamic data to predict annual oil rate production. These ML models were then used to replace a numerical reservoir simulator...

.... neural network deep learning reservoir machine learning blunt simulation upstream oil & gas artificial intelligence heterogeneity algorithm dependency correlation workflow mohaghegh application permeability journal physics saturation recognition integration database...

... are expensive and, hence, it is also not economically viable to do more frequent well tests. decision tree learning deep learning neural network artificial intelligence production monitoring prediction machine learning uplift prediction recommendation information conference reservoir...

... prancorr tolerance well integrity subsurface corrosion riser corrosion neural network data mining prediction pipeline integrty management corrosion predictive analytic materials and corrosion flowline corrosion electrolyte assessment prancorr database guideline visualization Introduction...

... data. Beyond data mining, which focused on the data cleanup and visualization, the typical machine learning methods, including analysis of variance, linear discriminating analysis (LDA), and artificial neural network (ANN), were used to improve the logging evaluation, to define the effective sand body...

... a novel machine-learning-based approach as an alternative to conventional random walk simulation for rapid estimation of NMR magnetization signals. This work aims to establish a "value-to-value" model using artificial neural networks to create a nonlinear mapping between the input of Minkowski...

... artificial intelligence neural network upstream oil & gas saudi arabia government deep learning inspection machine learning anomaly cui defect insulation inspection dataset classification detection dataset inspect corrosion corrosion figure 5 temporal thermography pipe multi-class...