Optimizing Production Using Design of Experiment and Reservoir Modeling

摘要

Historical information about a significant input parameter is stored in a data analytics model of a hydrocarbon reservoir. A historical deep recursive neural network (RNN) model is built based on time-series production data from the hydrocarbon reservoir as a function of the significant input parameter in the data analytics model. The historical deep RNN neural network model is stored on a data storage device. An experiment using the historical deep neural network model is designed to predict the significant input parameter. The experiment is run to produce a significant experimental input parameter. The significant experimental input parameter is compared to the significant input parameter stored in the data analytics model to determine a difference. The data analytics model is adjusted to reduce the difference.