Plantwide control of an oil production network

Esmaeil Jahanshahi,Dinesh Krishnamoorthy,Andrés Codas,Bjarne Foss,Sigurd Skogestad

doi:10.1016/j.compchemeng.2020.106765

Esmaeil Jahanshahi, Dinesh Krishnamoorthy + Show 3 more

Open Access

https://doi.org/10.1016/j.compchemeng.2020.106765

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Abstract

In this paper, we consider Real-Time Optimization (RTO) and control of an oil production system. We follow a systematic plantwide control procedure. The process consists of two gas-lift oil wells connected to a pipeline-riser system, and a separator at the topside platform. When the gas injection rates are low, the desired steady flow regime may become unstable and change to slug flow due to the casing-heading phenomenon. Therefore, a regulatory control layer is required to stabilize the desired two-phase flow regime. To this end, we propose a new control structure using two pressure measurements, one at the well-head and one at the annulus. For the optimization layer, we compare the performance of nonlinear Economic Model Predictive Control (EMPC), dynamic Feedback-RTO (FRTO) and Self-Optimizing Control (SOC). Based on dynamic simulations using the realistic OLGA simulator, we find that SOC is the most practical approach.

Highlights

Modeling, estimation, control, and optimization methodologies are becoming exceedingly important in the upstream petroleum industries
In addition to economic model predictive control (MPC) and the feedback Real-Time Optimization (RTO) approach, we study the performance of self-optimizing control, by keeping the wellhead gas rate constant at its nominal optimal value
Constructing the surrogate models based on simulation data involves extensive offline computations to obtain the steady-state data. This approach is becoming a viable solution with the availability of faster computers and using cloud computing with high processing power. This is the first publication on the complete control structure design applied to an oil production network and tested on the Olga simulator

Summary

Introduction

Estimation, control, and optimization methodologies are becoming exceedingly important in the upstream petroleum industries. One standard approach for control and optimization of multiinput multi-output processes is centralized model-based control (e.g., Nonlinear Model Predictive Control, NMPC) which simultaneously uses all the inputs and outputs of the system (Engell, 2007). In theory, such a control scheme can optimally handle the dynamic interactions between different input/output pairings, and provide inputs for optimal operation of the system. Campos et al (2015) says that many numerical issues need to be addressed before dynamic optimizers can be widely used in the offshore oil and gas production. Fast local controllers can be used for stabilization while slower centralized optimizers may be used for long-term optimization (Skogestad, 2004)

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