Forecasting Field Production Using Machine Learning Time Series

Abstract

Accurate forecasting of field production is an essential ingredient for effective planning, management, and control of construction resources. Traditional forecasting methods often rely on historical data and on the subjective experience of project managers that fail to account for the dynamic and unique nature of construction operations. As such, this paper presents a generic framework to collect, mine, and analyze ongoing field production data. It incorporates developing a time series machine learning based model to forecast production for the upcoming days. The framework was tested on excavation activities of an infrastructure project for eight months. The developed machine learning model showed a satisfactory performance with a good model fit R2 of 0.94 and an RMSE on the training set of 548 cubic meters, 563 on the validation set, and 652 on the test set. Therefore, the proposed framework can assist project managers to accurately forecast field production in order to make data-driven resource allocation decisions.