IMPROVEMENT INFORMATION-CONTROL SYSTEM FOR ENSURING SAFETY OF TUBE FURNACES OIL AND GAS PRODUCTION USING GENETIC ALGORITHMS

Currently, there is a need to improve the systems and control of pumping equipment in the oil and gas production and oil and gas transport industries. Therefore, an adaptive neural network control system for an electric drive of a production well was developed. The task of expanding the functional capabilities of asynchronous electric motors control of the oil and gas production system using the methods of neural networks is solved. We have developed software modules of the well drive control system based on the neural network, an identification system, and a scheme to adapt the control processes to changing load parameters, that is, to dynamic load, to implement the entire system for real-time control of the highspeed process. In this paper, based on a model of an identification block that includes a multilayered neural network of direct propagation, the control of the well system was implemented. The neural network of the proposed system was trained on the basis of the error back-propagation algorithm, and the identification unit works as a forecaster of system operation modes based on the error prediction. In the initial stage of the model adaptation, some fluctuations of the torque are observed at the output of the neural network, which is associated with new operating conditions and underestimated level of learning. However, the identification object and control system is able to maintain an error at minimum values and adapt the control system to a new conditions, which confirms the reliability of the proposed scheme.

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Using Neural Network Predictive Control for Riser-Slugging Suppression

Chemical Product and Process Modeling ◽

10.2202/1934-2659.1315 ◽

2009 ◽

Vol 4 (4) ◽

Cited By ~ 1

Author(s):

Reza Eslamloueyan ◽

Elham Hosseinzadeh

Keyword(s):

Neural Network ◽

Control System ◽

Predictive Control ◽

Oil And Gas ◽

Neural Model ◽

Nonlinear Process ◽

Economic Benefits ◽

Gas Production ◽

Ann Model ◽

Oil And Gas Production

Riser-slugging is a flow regime that can occur in multiphase pipeline-riser systems, and is characterized by severe flow and pressure oscillations. Reducing undesired slugging effects can have great economic benefits. Recently, control methods have been proposed to conquer slugging flow problems in pipeline risers. The advantages of using a control system are that it can be installed on existing oil and gas production facilities with no need for expensive equipment and no significant pressure drop is imposed to the system.In this work, a predictive control system based on Neural Network (NN) model of process is developed for handling and suppressing riser-slugging. An ANN model of the plant is used to predict future response of the nonlinear process. Storkaas dynamic model (Storkaas and Skogestad,2002) is employed for the process simulation. Comparing the results of this research to that of others, indicates that the proposed neural model predictive controller makes a significant improvement in the setpoint tracking especially for higher step change in the setpoint value.

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Study of Slug Control Techniques in Pipeline Systems

Volume 6A: Pipeline and Riser Technology ◽

10.1115/omae2014-23493 ◽

2014 ◽

Author(s):

P. C. C. Monteiro ◽

L. Loureiro Silva ◽

J. L. A. Vidal ◽

Theodoro A. Netto

Keyword(s):

Control System ◽

Flow Rate ◽

Oil And Gas ◽

Gas Production ◽

Low Flow ◽

Flow Loop ◽

Two Phase ◽

Automated Control ◽

Oil And Gas Production ◽

Automated Control System

Severe slugging may occur at low flow rate conditions when a downward inclined pipeline is followed by a vertical riser. This phenomenon is undesirable for offshore oil and gas production due to large pressure and flow rate fluctuations. It is of great technological relevance to develop reliable and economical means of severe slugging mitigation. This study aims to develop an automated control system to detect and mitigate the formation of severe slugging through a choke valve and a series of sensors. As a first step, an overall flow map is generated to indicate the region within which severe slugging may occur based on Boe’s criterion [1] and Taitel’s model [2, 3]. It was possible to obtain different flow patterns by controlling the rate of water and gas injection. The aim of this paper is, however, the formation of severe slugs and study of mitigation techniques. In the control part, we used a choke valve controlled by software which is in feedback with data from a system with pressure, temperature, flow, which are able to measure even small changes in the relevant parameters to the model. A two-phase flow loop was built for the study of severe slugging in pipeline-riser system with air and water as work fluids. The inner diameter of riser and flowline is 76.2 mm. The riser is 20 meters high and the flowline is 15 meters long and could be inclined upward or downward up to 8-degree. It has been shown by experiments how riser slugging can be controlled by automated control system.

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