... vector machines, and neural network models were explored in this study and their performances were evaluated by several regression error metrics. Analysis of model results showed the potential of machine learning approach for predicting the FBHP especially in multiphase flow regime. The model results...

... of oil and gas equipment. alert buildup workflow neural network operation auto-encoder upstream oil & gas artificial intelligence predictive maintenance tool threshold deep learning machine learning offshore annulus well a-annulus hyperparameter sequential probability ratio test...

.... upstream oil & gas complex reservoir machine learning reservoir optimization problem taranaki basin neural network reservoir characterization architecture deep learning artificial intelligence co2 storage fracture co2 injection geothermal energy production new zealand co2 storage...

... equipment installation time, decreasing unplanned downtime, and reducing the potential for human error. It also provides operators and field service teams with greater confidence that the wellhead equipment has been properly installed. neural network wellhead installation completion installation...

.... Computer vision applied at the shaker will increase overall ROP, greatly improve the speed and accuracy of wellbore instability detection, reduce the exposure in a hazardous zone and improve the detection of pending downhole problems. neural network upstream oil & gas annular pressure drilling...

... iris knowledge assessment expert system representation neural network safeguard application scenario computational knowledge graph knowledge-based artificial intelligence approach operation information knowledge representation hazardous scenario knowledge graph risk management...

.... A framework for artificial neural networks (ANN) is designed to assess the eight outputs and generate a two-dimensional map of the pipe cross-section. The ANN is trained using a finite-difference time-domain electromagnetic forward solver. The designed prototype is tested and validated through simulations...

... intelligence production control prediction neural network co 2 workflow dataset upstream oil & gas north america government production monitoring akhil datta-gupta saturation field-scale application validation dataset realization plume migration application optimization multiplier 0...

... using a hybrid neural network for detecting CO 2 leakage based on bottom-hole pressure measurements. The proposed workflow includes the generation of train-validation samples, the coupling process of training-validating, and the model evaluation. This work solves the diffusivity equation for pressure...

... for pipeline design and operation with corrosion mitigation and management. midstream oil & gas subsurface corrosion upstream oil & gas well integrity corrosion neural network riser corrosion machine learning modeling tsai spe annual technical conference pipeline corrosion application...

... Regression, Random Forest Regression, XGB Regression, and Artificial Neural Network as well as Grey Wolf Genetic Algorithm, to study the permeability and porosity stress dependency for Bakken Formation in Williston Basin, those methods were compared based on their performance by applying the statistical...

... structural geology air emission history matching pressure transient testing fluid dynamics sustainability waterflooding reservoir simulation gas injection method flow in porous media neural network subsurface storage drillstem testing social responsibility upstream oil & gas united states...

... Analysis (MRA), ANN, and SVM to two case studies. Wang et al. (2020) predicted production at ultra-high water cut stage by means of Recurrent Neural Network (RNN) using existing oil field production data. They conducted experimental verification and application effect analysis to compare the RNN...

... dynamics in porous media. An optimization process that exploits Geo-Net speed and finds solutions that minimize the error between observed measurement in the reservoir and the obtained by the simulator. Geological Neural Networks (GEO-NET) and petrophysical characterization The architecture...

..., and the ratio of minimum to mean apertures. We then construct hydraulic aperture surrogates using an Artificial Neural Network (ANN). At the field scale, we deploy Long Short-Term Memory (LSTM) to capture the recovery factor at field-scale. The final results are the time-varying recovery factor and its...

...://doi.org/10.2118/201459-MS Yu , Y. , Si , X. , Hu , C. , and Zhang , J. 2019 . A review of recurrent neural networks: LSTM cells and network architectures . Neural computation , 31 ( 7 ), 1235 – 1270 . Zhu , J. , Forrest , J. , Xiong , H. , and Kianinejad...

... cluster efficiency issue led by the current geometric completion design. Last, using the newly proposed smart sampling algorithm, a 200-critical-case database was built and fed into the Neural Network algorithm for training proxy models. After running the proxy models in a random-search algorithm...

... artificial neural network (ANN) was used as a machine learning approach. To train the neural network, the well data were divided into two sections where the ANN model was optimized on the upper well data interval and tested in the lower interval. During the rock physics analysis, the lower interval...

...’ may be highly subjective, thus, prone to inconsistencies. In our work, we propose to automate the well correlation workflow by using a Soft- Attention Convolutional Neural Network to predict well markers. The machine learning algorithm is supervised by examples of manual marker picks...

... artificial intelligence psia machine learning regressor predictor prediction neural network lightgbm algorithm coefficient artificial proxy feature dew point pressure reservoir temperature relative error dataset upstream oil & gas correlation elsharkawy pseudocritical...