Structuring an artificial intelligence based decision making tool for cyclic steam stimulation processes

The major contribution of this work includes training of the ANN model using a network topology optimization workflow to help the ANN better understand the complex data structures that are encountered in such processes. The developed ANN model successfully incorporates rock-fluid properties such as relative permeability and temperature dependent viscosity as input parameters together with the other relevant data. Last but not least, the network model utilizes a hybrid structure to adapt to the automatic cycle switching scheme that can be encountered in cyclic steam injection processes. The paper shows that the ANN model can be employed both as a classification tool and a nonlinear regression tool. The model is validated via extensive blind tests against high fidelity simulation models and can be used as a powerful screening and process design tool in global optimization of the process.