Utilization of machine learning for the estimation of production rates in wells operated by electrical submersible pumps

Abstract

In this study, a neural network model is developed for the prediction of oil flow rates in wells lifted by electrical submersible pumps (ESPs). Three attributes of the model in this work make this study unique. First, the knowledge on the computational cost of models has been presented, a rarity in most neural network models on this subject; second, the models have been explicitly presented, a feature uncommon in published ANN predictive modelling studies; and third, it includes a sensitivity analysis of input variables. The dataset utilized for the model development comprises 275 data points collected from ESP-lifted wells in the Middle East. Statistical evaluation of the model’s performance using the metrics such as mean square error, root mean square error and coefficient of determination demonstrates high predictive accuracy with respective values of 0.0000201861, 0.00449 and 0.999. In order to ascertain the parametric importance of the inputs, Garson’s algorithm was utilized. In this regard, choke size and upstream pressure had the highest influence (19% and 16%, respectively), while casing head pressure had the least effect (4.8%) on oil flow rate. In terms of memory requirements and processing speed for software applications, the model had a memory footprint of 888 bytes and required 191 multiply and accumulate operations to give an output. By utilizing the proposed models, the time-consuming separator tests measurements of flow rate would no longer be necessary and real-time results could be provided in the field. This work would be useful to production engineers who seek a quick and accurate means of estimating oil flow rate from ESP wells in real time.