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

Abstract Cyclic steam stimulation (CSS) is one of the more popular EOR techniques due to the existence of giant heavy oil reserves existing in different parts of the world. Numerical reservoir simulation plays a critical role in investigating the mechanisms and optimizing field development strategies of CSS processes. An artificial neural network (ANN) based model is considered to be a powerful subsidiary tool of high fidelity models for its fast computational speed and reliable prediction capability. This work focuses on the development of a robust surrogate model as a screening/design tool for cyclic steam injection processes using artificial neural network technology. 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.