Recurrent Neural Network based control of an Oil Well

in Brazilian Symposium on Intelligent Automation, Porto Alegre 1-4 October 2017 (2017, October)

Echo State Networks (ESN) are dynamical learning models composed of two parts: a recurrent network (reservoir) with fixed weights and a linear adaptive readout output layer. The output layer’s weights are ... [more ▼]

Echo State Networks (ESN) are dynamical learning models composed of two parts: a recurrent network (reservoir) with fixed weights and a linear adaptive readout output layer. The output layer’s weights are learned for the ESN to reproduce temporal patterns usually by solving a least-squares problem. Such recurrent networks have shown promising results in previous applications to dynamic system identification and closed-loop control. This work applies an echo state network to control the bottom hole pressure of an oil well, whereby the opening of the production choke is manipulated. The controller utilizes a network to learn the plant inverse model, whose model input is the plant output and the vice-versa, and another network to compute the control action that induces a desired plant behavior. Despite the nonlinearities of the well model, the ESN effectively learned the inverse model and achieved near global setpoint tracking and disturbance rejection, with little setpoint deviation in the latter case. These results show that echo state networks are a viable tool for the control of complex dynamic systems by means of online inverse-model learning. [less ▲]

An Echo State Network-based Soft Sensor of Downhole Pressure for a Gas-lift Oil Well

in Iliadis, Lazaros; Jayne, Chrisina (Eds.) Engineering Applications of Neural Networks (2015)

Soft sensor technology has been increasingly used in indus- try. Its importance is magnified when the process variable to be estimated is key to control and monitoring processes and the respective sensor ... [more ▼]

Soft sensor technology has been increasingly used in indus- try. Its importance is magnified when the process variable to be estimated is key to control and monitoring processes and the respective sensor ei- ther has a high probability of failure or is unreliable due to harsh environ- ment conditions. This is the case for permanent downhole gauge (PDG) sensors in the oil and gas industry, which measure pressure and tempera- ture in deepwater oil wells. In this paper, historical data obtained from an actual offshore oil well is used to build a black box model that estimates the PDG downhole pressure from platform variables, using Echo State Networks (ESNs), which are a class of recurrent networks with power- ful modeling capabilities. These networks, differently from other neural networks models used by most soft sensors in literature, can model the nonlinear dynamical properties present in the noisy real-world data by using a two-layer structure with efficient training: a recurrent nonlinear layer with fixed randomly generated weights and a linear adaptive read- out output layer. Experimental results show that ESNs are a promising technique to model soft sensors in an industrial setting. [less ▲]