An economic evaluation of a new oil well is often required, and this evaluation depends heavily on how accurately production of the well can be estimated. Unfortunately, this kind of prediction is extremely difficult because of complex subsurface conditions of reservoirs. The industrial standard approach is to use either curve-fitting methods or complex and timeconsuming reservoir simulations. In this study, we attempted to improve upon the standard techniques by using a variety of neural network and data mining approaches. The approaches differ in terms of prediction model, data division strategy, method, tool used for implementation, and the interpretability of the models. The objective is to make use of the large amount of data readily available from private companies and public sources to enhance understanding of the petroleum production prediction task. Additional objectives include optimizing timing for initiation of advanced recovery processes and identifying candidate wells for production or injection.
Digital Twin of the Oil Well, Based on Data Mining Technologies
