Advanced Deep Regression Models for Forecasting Time Series Oil
  Production
Siavash Hosseini Thangarajah Akilan
Global oil demand is rapidly increasing and is expected to reach 106.3
million barrels per day by 2040. Thus, it is vital for hydrocarbon extraction
industries to forecast their production to optimize their operations and avoid
losses. Big companies have realized that exploiting the power of deep learning
(DL) and the massive amount of data from various oil wells for this purpose can
save a lot of operational costs and reduce unwanted environmental impacts. In
this direction, researchers have proposed models using conventional machine
learning (ML) techniques for oil production forecasting. However, these
techniques are inappropriate for this problem as they can not capture
historical patterns found in time series data, resulting in inaccurate
predictions. This research aims to overcome these issues by developing advanced
data-driven regression models using sequential convolutions and long short-term
memory (LSTM) units. Exhaustive analyses are conducted to select the optimal
sequence length, model hyperparameters, and cross-well dataset formation to
build highly generalized robust models. A comprehensive experimental study on
Volve oilfield data validates the proposed models. It reveals that the
LSTM-based sequence learning model can predict oil production better than the
1-D convolutional neural network (CNN) with mean absolute error (MAE) and R2
score of 111.16 and 0.98, respectively. It is also found that the LSTM-based
model performs better than all the existing state-of-the-art solutions and
achieves a 37% improvement compared to a standard linear regression, which is
considered the baseline model in this work.