(371x) Enterprise Wide Optimization of an Integrated Oil and Gas Company: Detailed Transportation and Simplified Refinery Models Integration

Enterprise Wide Optimization of an integrated oil and gas company: Detailed transportation and simplified refinery models integration

Keywords: enterprise wide optimization, pipeline planning, oil dilution, crude oil blending, mathematical optimization, Upstream, Midstream, Downstream

Oscar Neira ^a, Ariel Uribe ^a, Carlos Dallos ^b, Cesar Pereira ^a, Carlos Diaz ^a, Fabio Sanabria ^a, Juan Esteban Torres ^a, Joao Alexander Garcia ^b, Luis Javier Hoyos ^a,c

^a Instituto Colombiano del Petróleo, Sede el Limonal, Piedecuesta, Colombia

^b Pensemossi, calle 40 A No. 13 – 09, Piso 18, Oficina 1801,Edificio Ugi, Bogotá, Colombia

^c corresponding author

The crude oil price collapse during the years 214-2015 and the perception that it is going to be in a medium range in the long term has renewed the interest in Enterprise Wide Optimization (EWO) in the oil and gas sector. As a matter of fact several companies gave losses during several quarters of those years and many investment projects were cancelled or postponed. Many companies then had to review all their processes including planning and scheduling in order to decrease costs, identify new market opportunities and improve their supply chain operations.

An integrated petroleum company represents a complex system, compounded by multiple core business units such as crude oil, natural gas and natural gas liquids production, transport of this commodities to exportation ports or refineries, transformation of hydrocarbon at refineries and petrochemical complexes, delivery of fuels, refinery products and petrochemicals to clients and commercialization of all this products. Each business is characterized has its own characteristics in installed technologies, process operation, infrastructure capacity, commercial contracts and others. Typically the planning tasks of each of the business units are performed independently, especially in major oil and gas companies. However, medium size companies often operate their businesses in the same geographical area and many synergies are lost when the planning activities are done with stand-alone practices. This crisis has shown the need to integrate the planning of all these tasks in only one process to increase competitiveness and the effectiveness of the decision making process. This challenge requires the development of enterprise wide optimization models where all business units are considered in a single system.

Zhang and Grossman (2016) published a review about EWO applied to industrial side management showing how to integrate production and energy management and decision making at several time and space scales and Laínez et al (2012) presented a state of the art about how EWO is applied to the pharmaceutical industry and identified the main challenges is this area of application. Zhao et al (2017) presented a multi-period MINLP model where a petroleum refinery and an ethylene production plant were integrated. The model allowed to study of the synergies and trade-offs of the integration between both plants compared to a sequential approach of optimization. Wassick (2009) developed an enterprise wide optimization model of a chemical complex and study the opportunities that appeared through modeling integration. Menezes et al (2017) showed a model to integrate crude oil refining operations and procurement of raw materials. This model integrates planning and scheduling within days to week periods. The up-to-down sequence of solutions are integrated in a feedback iteration.

This paper describes a mathematical model of an integrated petroleum business group, focused on maximizing operational margin. The model covers crude oil and natural gas liquids production fields, crude oil pipeline networks, crude oil trucking, exportation of crude oils and fuels, purchase of crude oils from third parties in productions fields and transport stations, crude oil refining, local demand satisfaction of fuels, refined products and petrochemicals, multimodal transportation of fuels and refined products (barges, pipelines and trucks), imports of fuels and naphtha to dilute heavy crude oils and movement of intermediate streams and fuels among refineries. The model is able to mix crude oils along the transport networks, exportation and importation ports and refinery tank areas allowing the better valorization of the crude oils available. Additionally as some crude oil producers have small capacities to segregate their owned crude oils, cooperation is allowed among them in such a way that batches belonging to several owners can be planned keeping a track record of proprietary participation. Refineries were modelled using a simplified approach based on metamodeling methods developed from a linearization of previously developed detailed refinery models. A medium sized integrated national oil company was used as a case of study, being compounded by 4 refineries, 574 crude oil wells, 9 blends to be exported, 3 crude oil derivatives available for imports, 319 transportation stations, 1250 routes of multimodal transportation modes and 8 ports. The model developed is a MINLP mathematical programming, characterized by its high non-convexity, high scale and multiple local maxima.

The model has been used to evaluate short and middle term forecasting scenarios where multiple opportunities have been identified, giving new opportunities to be achieved through changes in operational schemes driven for the economic performance of the business group, such as new blending schemes, new routes to be used in logistic for transportation of fluids and also changes in imports and exports in each seaport and new refinery crude oil slates.

To the knowledge of our research and development group this is one of few cases published that establishes an integrated modelling for planning in petroleum industry that involves upstream, midstream and downstream in the same system.

References

Wassick, J. M., 2006. “Enterprise-wide optimization in an integrated chemical complex”. Computers and chemical engineering, vol. 33, 1950 – 1953.

Laínez, J. M.; Schaefer, E. and G.V. Reklaitis, 2012. “Challenges and opportunities in enterprise-wide optimization in the pharmaceutical industry”. Computers and Chemical Engineering, vol. 47, 19 – 28.

Zhang, Q. and I. E. Grossmann, 2016. “Enterprise-wide optimization for industrial demand side management: Fundamentals, advances, and perspectives”. Chemical Engineering Research and Design, vol. 116, 114-131.

Menezes, B. C.; Grossmann , I. E. and J. D. Kelly, 2017. “Enterprise-Wide Optimization for Operations of Crude-Oil Refineries: Closing the Procurement and Scheduling Gap”. Computer Aided Chemical Engineering, vol. 40, 1249 – 1254^.

Zhao, H.; Ierapetritou, M. G.; Shah, N. K. and G. Rong, 2017. “Model of refining and petrochemical plant for enterprise-wide optimization”. Computers and Chemical Engineering, vol. 97, 194 – 207.