Automating Dynamometer Charts Interpretation with Machine Learning

Abstract

Abstract This work objective is to develop a comprehensive methodology for constructing a mathematical model capable of automating the interpretation of Dynamometer Charts from Sucker Rod Pumps (SRPs). The methodology describes the complete data-handling pipeline that allows for the proper ingestion, normalization, transformation, and evaluation of charts, comparing the predictive performance of multiple well-known Machine Learning models. The methodology delivers a properly tuned model with a high predictive performance that can interpret any chart shown to it, as well as quantifying the similarity of any given chart to that of pre-defined behavioral classes. To develop the methodology, a sample of 293 Dynamometer Charts was evaluated to determine the equipment status, based on known patterns described in the existing bibliography. The resulting dataset was used to train and test around 1,000 different models. These models include Support Vector Machines, Decision Trees, Nearest K-Neighbor, Neural Networks, among others. Each model was tested multiple times to account for bias and overfitting. This way, a best-performing model was found based on performance scores, allowing to determine model family and hyperparameters required for optimal performance. The study demonstrated that many Machine Learning models can be properly trained for performing interpretations of SRPs operational status. The best performing models were those based on the XGBoost algorithm, although Random Forest algorithms proved to be significantly accurate in certain circumstances. Results were differentiated between surface dynamometer and bottom-hole dynamometers, with the intention of demonstrating the potential implementation of models with no data manipulation in between, such as the one caused by using the wave-function transformation. This paper not only shows how Machine Learning algorithms can be used to automate some workflows of the production engineering discipline, but also provides a methodology for validating model performance and determining the best performing model among many candidates. Finally, it shows practical ways in which these models results can be used to generate visual tools to improve the decision-making process of engineers and operators.