Supervised machine learning (ML) projects require data for model training, validation, and testing. However, the confidential nature of field and well production data often hinders the progress of ML projects. To address this issue, we developed a well simulator that generates realistic well production data based on physical, governing differential equations. The simulation models the reservoir, wellbore, flowline, and choke coupled using transient nodal analysis to solve for transient flow rate, pressure, and temperature as a function of variable choke opening over time in addition to a wide range of static parameters for each component. The simulator’s output is then perturbed using the gauge transfer function to introduce systematic and random errors, creating a dataset for ML projects without the need for confidential production data. We then generated a simulated dataset to train a recurrent neural network (RNN) on the task of classifying well on/off times. This task typically requires a significant number of manhours to manually filter and verify data for hundreds or thousands of wells. Our RNN model achieves high accuracy in classifying the correct on/off labels, representing a promising step towards a fully-automated rate allocation process. Our simulator for well production data can be used for other ML projects, circumventing the need for confidential data, and enabling the study and development of different ML models to streamline and automate various oil and gas work processes. Overall, the success of our RNN model demonstrates the potential of ML to improve the operational efficiency of various oil and gas work processes.