.... For each specific scenario we train and test an Artificial Neural Network (ANN) with position and time as input variables, and axial displacement as output. The Machine Learning (ML) model is designed with 7 hidden layers, 100 the maximum number of neurons per layer and hyperbolic tangent as activation...

.... Artificial Neural network models were developed by Chen, Rahman and Sarma (2014) to predict the interaction between hydraulic fractures and natural fractures to improve treatment design parameters in a gas reservoir. Artificial neural networks were also used to estimate production in hydraulic fractured...

.... As a final step, a feed forward neural network (ANN) model was developed from the mapped data. This model was used to estimate Wolfcamp B production and economics for completion and frac designs. In the performance of this project, it became apparent that the incorporation of reservoir data was essential...

... database linear flow analysis choke strategy neural network service provider flowback data equinor application email smart assistant society of petroleum engineers Attachment optimal choke change machine learning choke change information database flowback data analysis oil and gas...

... during hydraulic fracturing. The new algorithm can assist engineers in monitoring and optimizing the pumping schedule. We explored several neural network models. For each hydraulic fracturing stage, we train a machine learning (ML) model with the data from the first several minutes and predict...

... on a single technique for proppant selection but uses a combination of various data sources, analysis techniques and economic criteria to provide a more holistic approach to proppant selection. fracturing materials hydraulic fracturing Upstream Oil & Gas completion neural network white sand...

... by a fixed-length sliding window and structured as matrices, which resemble the data structure of ML inputs. A Convolutional Neural Network (CNN) is trained for the classification, and each data point is classified as either within a stage's pumping time or otherwise as the data is received, with minimal...

... sand. Data Analysis of Eagle Ford Shale To address the aforementioned issue, a neural network modeling technique is used to evaluate a wide range of wells. Neural network modeling is based on analyzing the data in a system (set of wells in this case) to find connections between the system input...

... in Barnett shale are made. Various aspects of this dataset are examined using data modeling and mining techniques including self-organizing maps (SOM). SOMs are unsupervised artificial neural networks that can cluster large amounts of data into two dimensional maps. Using SOM, frac design parameters...

... network upstream oil & gas fracturing fluid information modeling completion actual production conductivity reservoir boe psi sensitivity assumption bo psi spe 152121 frac treatment artificial neural network evaluation geology eagle ford reservoir quality productivity fracture...