Shale oil production predication based on an empirical model-constrained CNN-LSTM
Qiang Zhou, Zhengdong Lei, Zhewei Chen, Yuhan Wang, Yishan Liu, Zhenhua Xu, Yuqi Liu
Abstract
Accurately predicting the production rate and estimated ultimate recovery (EUR) of shale oil wells is vital for efficient shale oil development. Although numerical simulations provide accurate predictions, their high time, data, and labor demands call for a swifter, yet precise, method. This study introduces the Duong–CNN–LSTM (D-C-L) model, which integrates a convolutional neural network (CNN) with a long short-term memory (LSTM) network and is grounded on the empirical Duong model for physical constraints. Compared to traditional approaches, the D-C-L model demonstrates superior precision, efficiency, and cost-effectiveness in predicting shale oil production.