Production Optimization in an Oil Producing Asset – The BP Azeri Field Optimizer Case

Jalilova, Nigar; Tautiyev, Abekir; Forcadell, J.; Rodriguez, J.C.; Sama, S.

doi:10.2118/118454-ms

Cited by 7 publications

(2 citation statements)

References 4 publications

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“…Applied online-optimization of industrial plants has become an increasingly prominent topic. Several examples of successful implementation of model-based optimization from the petro-industry such as (Sarabia et al, 2012) for hydrogen networks, (Jalilova et al, 2008) and (Ramdial et al, 2009) for refineries, and (Müller et al, 2012b) for the chemical industry are presently available. The success of these model-based optimizers is due to the utilized models with two important characteristics: very high to reasonable accuracy regarding the process and acceptable computational effort regarding the optimization calculation.…”

Section: Motivationmentioning

confidence: 99%

Systematic approaches for model derivation for optimization purposes

Esche

Müller

Kraus

et al. 2014

Chemical Engineering Science

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One of the main problems in process optimization lies in the non-linearity, nonconvexity, and sheer size of existing process models. In this contribution, a systematic workflow for process systems engineers developing models suitable for optimization purposes is presented. Hereby, three fundamentally different cases are discussed: the availability of a complex, highly accurate model; the existence of a simplifying, so called short-cut model; and the non-existence of a model of any kind. Furthermore, a focus is lain on the systematic model reduction for complex systems by means of linearization and convexification. Afterwards, two case studies are presented showing how this workflow can be applied to a reactive absorption system and to a multi-phase separation process. The presented systematic leads to a successful implementation of process models applicable for optimization, which are both reduced in size, non-linearity, and non-convexity.

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Section: Motivationmentioning

confidence: 99%

Systematic approaches for model derivation for optimization purposes

Esche

Müller

Kraus

et al. 2014

Chemical Engineering Science

View full text Add to dashboard Cite

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“…Over the last several years, online and continuous model driven optimisation and/or operational guidance systems have also demonstrated their value (12) (13) (14) (15). Many of these integrate the effects of both well delivery and facility handling constraints.…”

Section: Introductionmentioning

confidence: 99%

Experiences Using Lagosa to Improve Production Operations and Gas Marketing Performance

Hudson

2009

All Days

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The promise of improved production performance and consistent delivery against market and regulatory commitments is driving the need for better decision making within daily operations. In addition, tighter integration between EP sources and markets forces broader consideration of factors from across the scope of business. At the same time, market maturity is forcing more dynamic and complex nomination and delivery processes within the EP business. Addressing these challenges requires better, faster and more collaborative decision-making, driven initially by improved leveraging of engineering expertise into operations and further by integration of both engineering and operations expertise into EP business processes.A key aspect of Shell's Smart Fields program has been to drive performance around Smart Fields value loops, concisely given as measure-model-decide-execute. This paper illustrates several examples where Shell have implemented Lagosa, an office domain, collaborative work process solution that encompasses a scope from source to sales. The paper will show how the solution engages various roles to contribute to better performance, as demonstrated by sustained improvements in value delivery. Learning points will be offered, particularly regarding implementation, embedment and sustainability.

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Real‐Time Optimization in the Chemical Processing Industry

Müller

Dercks

Nabati

et al. 2017

Chemie Ingenieur Technik

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The advancements of computation capabilities and efficient flowsheet simulators have led simulation based real-time optimization (RTO) of continuous processes to become a prominent topic in the last years. This contribution discusses prerequisites to help guarantee the economic viability of an RTO project as well as the deliberations that should be performed prior to its initiation. Additionally, considerations regarding collaborations with various technical fields are highlighted and approaches to alleviate some challenges associated with the implementation of RTO are discussed. Motivation and IntroductionAn ever increasing global competition and tightening of product requirements has continuously fortified the necessity of improving production processes [1]. Over the last decades multiple advancements in the field of mathematics, thermodynamics, process systems engineering, and computational sciences have promoted the development of online model-based real-time optimization (RTO). Among these advancements are the availability of efficient flowsheet simulators, the development of improved property calculations methods, the advent of equation oriented modeling, and improved optimization algorithms for nonlinear systems. Due to these advancements, RTO has received a significant amount of attention in both the industrial and academic literature, thus, becoming an ever more prominent topic. An abundance of successful RTO implementations can be found in numerous industrial fields ranging from [2 -4] in the petrochemical industry, [5 -7] in the chemical industry, to [8,9] in the energy and water supply sector. Adding to this, concepts and overviews of RTOtopics were written repeatedly over the last decades, such as contributions by Naysmith and Douglas [1] or by Darby et al. from 2011 [10].Even though the topic is well known and many successful RTO implementations can be found, (~250 to 300 using commercially available techniques [10]) opposing opinions can still be found when it comes to an investment decision concerning RTO. In most cases, the benefits are seen and a consensus exists to carry out an investment. In other cases, however, it was concluded that RTO is not viable. This history as well as experiences of the last years affords us the opportunity to deliberate the success of RTO technology in the chemical industry and to carry out a discussion of when RTO truly becomes viable for a company. Therefore, the aim of this article is to shed light on the following arising questions from an industrial perspective: -The time when a company should continue to invest in RTO applications. -Deliberations performed prior to the implementation of RTO. -Considered collaborations to successfully realize an RTO project. -Considered factors and challenges to increase the likelihood of success of an RTO project. As background information, a brief overview of RTO's role in the control and decision making hierarchy of a plant as well as the main steps in the RTO functionality cycle is given.www.cit-journal.com

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Production Optimization in an Oil Producing Asset – The BP Azeri Field Optimizer Case

Cited by 7 publications

References 4 publications

Systematic approaches for model derivation for optimization purposes

Systematic approaches for model derivation for optimization purposes

Experiences Using Lagosa to Improve Production Operations and Gas Marketing Performance

Real‐Time Optimization in the Chemical Processing Industry

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