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Research (oil and gas)

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ESN-PNMPC: Efficient data-driven model predictive control of unknown nonlinear processes

on Tue, 03/17/2020 - 19:12

The control of nonlinear industrial processes is a challenging task since the model of the plant may not be completely known a priori. In addition, the application of nonlinear model predictive control may be affected by modeling errors and subject to high computational complexity.

In this work, a new efficient data-driven scheme is proposed that alleviates some known issues in the so called Practical Nonlinear Model Predictive Controller (PNMPC). In PNMPC, the model is only linearized partially: while the free response of the system is kept fully nonlinear, only the forced response is linearized.  The general model for PNMPC proposed in this work consists of an Echo State Network (ESN), a recurrent neural network with very efficient training for system identification. 

The benefit of the proposed ESN-PNMPC scheme is that it allows:

  • fast system identification for nonlinear dynamic systems with arbitrary accuracy; 
  • analytical computation of derivatives from the ESN model for the forced response.  

This last feature assures significantly lower computational complexity for derivative computation when compared to the original finite difference method of PNMPC.

The proposed scheme is also enhanced with a correction filter that provides robustness to unforeseen disturbances during execution time, and compared to an LSTM (Long-Short Term Memory) implementation for the model as well as to a PI controller. The universality of the approach is shown by application to the control of different nonlinear plants.



Figure from J. Jordanou 2020, et al (submitted).

Online recurrent neural network learning for control of nonlinear plants in oil and gas production platforms

on Wed, 10/17/2018 - 12:54

This research line aims at designing adaptive controllers by using Echo State Networks (ESN) as a efficient data-driven method for training recurrent neural networks capable of controlling complex nonlinear plants, with a focus on oil and gas production platforms from Petrobras.

The resulting ESN-based controllers should learn inverse models of the controlled plant in an online fashion by interacting with the industrial plant and observing its dynamical behaviors.

In collaboration with supervised Master Student Jean P. Jordanou.

Well model. Figure by Jahanshahi et al. (2012).          


Manifold connecting two oil wells and a riser. Figure by Jordanou.

Scheme of Adaptive ESN-based controller and nonlinear plant. Figure by Jordanou

Proxy dynamical models of offshore oil production platforms via recurrent neural networks

on Wed, 12/09/2015 - 14:52

Process measurements are of vital importance for monitoring and control of industrial plants. When we consider offshore oil production platforms, wells that require gas-lift technology to yield oil production from low pressure oil reservoirs can become unstable under some conditions. This undesirable phenomenon is usually called slugging flow, and can be identified by an oscillatory behavior of the downhole pressure measurement.

Given the importance of this measurement and the unreliability of the related sensor, this work aims at designing data-driven soft-sensors for downhole pressure estimation in two contexts: one for speeding up first-principled model simulation of a vertical riser model; and another for estimating the downhole pressure using real-world data from an oil well from Petrobras based only on topside platform measurements. Both tasks are tackled by employing Echo State Networks (ESNs) as an efficient technique for training Recurrent Neural Networks.

We show that a single ESN is capable of robustly modeling both the slugging flow behavior and a steady state based only on a square wave input signal representing the production choke opening in the vertical riser. Besides, we compare the performance of a standard network to the performance of a multiple timescale hierarchical architecture in the second task and show that for some periods the latter architecture performs better.