Machine Learning-Based Completions Sequencing for Well Performance
  Optimization
Anjie Liu Jinglang W. Sun Anh Ngo Ademide O. Mabadeje Jose L. Hernandez-Mejia
Establishing accurate field development parameters to optimize long-term oil
production takes time and effort due to the complexity of oil well development,
and the uncertainty in estimating long-term well production. Traditionally, oil
and gas companies use simulation software that are inherently computationally
expensive to forecast production. Thus, machine learning approaches are
recently utilized in literature as an efficient alternative to optimize well
developments by enhancing completion conditions. The primary goal of this
project is to develop effective machine-learning models that can integrate the
effects of multidimensional predictive variables (i.e., completion conditions)
to predict 12-Month Cumulative Production accurately.
  Three predictive regression machine learning models are implemented for
predicting 12-month cumulative oil production: Random Forest, Gradient
Boosting, and Long Short-Term Memory Models. All three models yielded
cumulative production predictions with root mean squared error (RMSE ) values
ranging from 7.35 to 20.01 thousand barrels of oil. Although we hypothesized
that all models would yield accurate predictions, the results indicated a
crucial need for further refinement to create reliable and rational predictive
tools in the subsurface. While this study did not produce optimal models for
completion sequencing to maximize long-term production, we established that
machine learning models alone are not self-sufficient for problems of this
nature. Hence, there is potential for significant improvement, including
comprehensive feature engineering, and a recommendation of exploring the use of
hybrid or surrogate models (i.e., coupling physics reduced models and machine
learning models), to ascertain significant contribution to the progress of
completion sequencing workflows.