Extracting Valuable Information from Big Data for Machine Learning Control: An Application for a Gas Lift Process

Ana Carolina Spindola Rangel Dias,Maurício Bezerra De Souza,Felipo Rojas Soares,José Carlos Pinto,Johannes Jäschke

doi:10.3390/pr7050252

Ana Carolina Spindola Rangel Dias, Maurício Bezerra De Souza + Show 3 more

Open Access

https://doi.org/10.3390/pr7050252

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Abstract

The present work investigated the use of an echo state network for a gas lift oil well. The main contribution is the evaluation of the network performance under conditions normally faced in a real production system: noisy measurements, unmeasurable disturbances, sluggish behavior and model mismatch. The main pursued objective was to verify if this tool is suitable to compose a predictive control scheme for the analyzed operation. A simpler model was used to train the neural network and a more accurate process model was used to generate time series for validation. The system performance was investigated with distinct sample sizes for training, test and validation procedures and prediction horizons. The performance of the designed ESN was characterized in terms of slugging, setpoint changes and unmeasurable disturbances. It was observed that the size and the dynamic content of the training set tightly affected the network performance. However, for data sets with reasonable information contents, the obtained ESN performance could be regarded as very good, even when longer prediction horizons were proposed.

Highlights

Control structures allow connections between different equipment and processes and the implementation of reference values, reducing the variability and minimizing the effect of disturbances throughout the process
Neural Networks, highlighting the Echo State Network; in Section 3 we describe the mathematical models used to represent the gas-lift well considered in the present work; in Section 4 we design the esn model and use it to perform some open-loop simulations; in Section 5 we present the results concerning the possible implementation of the proposed model in a real production environment
Echo State Networks (ESN) were used to represent the operation of gas-lift oil wells, using data generated by two distinct models

Summary

Introduction

Control structures allow connections between different equipment and processes and the implementation of reference values, reducing the variability and minimizing the effect of disturbances throughout the process. When satisfactorily designed, they ensure the efficient and safe operation. Classical Proportional Integral Derivative (PID) controllers are the ones used most often in the industry, representing at least 95% of the regulatory control loops in operation [1]. These controllers exhibit many advantages, like robustness and simple design. PID controllers require few tuning parameters (three when all modes are available: proportional, integral and derivative), with well-known effects on the control system, which allows the operator to have complete knowledge of the system responses for simple applications (Single-Input Single-Output (SISO) , linear processes).

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