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Keywords: neural network
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Proceedings Papers

Paper presented at the SPE Canada Heavy Oil Conference, September 28–October 2, 2020
Paper Number: SPE-199917-MS
... chamber. Different dimension reduction and parameterization strategies are formulated and tested to represent the solvent chamber locations and interfaces. Convolutional neural network is implemented to dynamically track the solvent chamber positions by correlating the extracted inputs and outputs...
Proceedings Papers

Paper presented at the SPE Canada Heavy Oil Conference, September 28–October 2, 2020
Paper Number: SPE-199938-MS
... recovery flow metering borehole imaging society of exploration geophysicists steam breakthrough completion design downhole sensor artificial intelligence neural network society of petroleum engineers upstream oil & gas liquid rate november 30 fiber optic technology conference...
Proceedings Papers

Paper presented at the SPE Canada Heavy Oil Technical Conference, March 13–14, 2018
Paper Number: SPE-189735-MS
... that data-driven models can capture the non-linear relationships between 3D shale barrier configurations and SAGD production time-series data, and they are useful for inferring shale barrier distribution directly from production profiles. Artificial neural network ( McCulloch and Pitts, 1943 ), or ANN...
Proceedings Papers

Paper presented at the SPE Canada Heavy Oil Technical Conference, June 7–9, 2016
Paper Number: SPE-180716-MS
... barrier, and the production would rise again. The positions and shapes of these decline patterns are retrieved. Next, artificial neural network (ANN) is constructed to calibrate a relationship between the retrieved production pattern parameters (inputs) and the corresponding geologic parameters describing...
Proceedings Papers

Paper presented at the SPE Canada Heavy Oil Technical Conference, June 7–9, 2016
Paper Number: SPE-180715-MS
... of heterogeneous reservoir features from the profiles of SAGD field production data. Artificial Intelligence thermal method SAGD machine learning neural network enhanced recovery steam-assisted gravity drainage oil production prediction Reservoir Heterogeneity proxy model SAGD production basic...
Proceedings Papers

Paper presented at the SPE Canada Heavy Oil Technical Conference, June 9–11, 2015
Paper Number: SPE-174460-MS
... neural network (ANN) is employed to facilitate the production performance analysis. Predicting (input) variables that are descriptive of reservoir heterogeneities and operating constraints, including log-derived petrophysical parameters, dimensionless shale index, effective numbers of producers...
Proceedings Papers

Paper presented at the SPE Heavy Oil Conference-Canada, June 10–12, 2014
Paper Number: SPE-170144-MS
... learning tasks such as classification and non-linear function approximation can be well suited for ANN training. The first neural model was introduced by McCulloch and Pitts (1943) . After some major improvements and developments of ANN in recent decades, many formulations of neural network utilizing...
Proceedings Papers

Paper presented at the SPE Heavy Oil Conference-Canada, June 10–12, 2014
Paper Number: SPE-170088-MS
... recovery neural network es-sagd process thermal method Modeling & Simulation reservoir simulation steam-assisted gravity drainage reservoir simulator thermal compositional simulator equilibrium ratio k-value approach eos equation grid block solubility oil production rate injection...
Proceedings Papers

Paper presented at the SPE Heavy Oil Conference-Canada, June 10–12, 2014
Paper Number: SPE-170113-MS
... of machine learning methods for system forecast, provide an attractive alternative. In this paper, artificial neural network (ANN) is employed to predict SAGD production in heterogeneous reservoirs, an important application that is lacking in existing literature. Numerical flow simulations are performed...
Proceedings Papers

Paper presented at the SPE Heavy Oil Conference-Canada, June 10–12, 2014
Paper Number: SPE-170101-MS
.... It is noted that among numerous parameters that influence the ultimate recovery, remaining bypassed oil, chamber advancement, and heat loss, continuity and position of these features in relation to the well pair play a particularly crucial role. We subsequently employ neural network modeling for constructing...
Proceedings Papers

Paper presented at the SPE Heavy Oil Conference-Canada, June 10–12, 2014
Paper Number: SPE-170085-MS
... for the time horizon of t = 10 years. The time step of each simulation can be different. Obtain the production output training data from the simulations, particularly: entire field Cumulative Oil Production, Oil Production Rate, Water Injection Rate and SOR Cumulative. Build a RBF Neural Networks time...
Proceedings Papers

Paper presented at the SPE Heavy Oil Conference-Canada, June 11–13, 2013
Paper Number: SPE-165557-MS
... The most common algorithm for estimating the unknown network parameters (weights and biases) is the Feedforward Backpropagation Neural Network or Backpropagation Neural Network (BPNN) model. BPNN is a gradient-based minimization technique that utilizes a supervised learning process...
Proceedings Papers

Paper presented at the SPE Heavy Oil Conference-Canada, June 11–13, 2013
Paper Number: SPE-165482-MS
... technology aimed at optimizing the production of 50 wells through timely identification of underperformance occurrences. This was achieved using: Automated data gathering and integration Automated daily production rate estimation using operational data and artificial neural networks (ANNs...

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