Advanced well stimulation selection with gradient boosting

Abstract

To ensure the required level of production of brown fields, it is necessary to plan and implement an efficient program of well stimulation in a timely manner. This program has a significant impact on the further development of the oil field in terms of its productivity and economics. Nowadays, the decisions about well stimulations are made by experts mostly based on their experience and the company’s heuristics. In the work, a novel approach to fast, accurate, and computationally efficient selection of candidates for well stimulations was presented. We explored the ability of Machine Learning algorithms to solve the problem. The predictive pipeline was built on the basis of production and pressure time series of historical data. We developed and applied a comprehensive prepossessing workflow to a real field data to prepare a training data set. Finally, two Gradient-Boosting models were developed, tuned, trained and validated. The first model was used for prediction of the necessity for the well stimulation and the second model—for identification of the required treatment type. Blind test of the models was provided. It resulted in a 0.80 recall score and 0.79 balanced accuracy score.