Optimum Design and Control of the Production-Injection Operation Systems in Petroleum Reservoirs

Optimum Design and Control of the Production-Injection Operation Systems in Petroleum Reservoirs Daoyong Yang; Daoyong Yang University of Regina Search for other works by this author on: This Site Google Scholar Qi Zhang; Qi Zhang University of Petroleum, China Search for other works by this author on: This Site Google Scholar Yongan Gu Yongan Gu University of Regina Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, Port-of-Spain, Trinidad and Tobago, April 2003. Paper Number: SPE-81036-MS https://doi.org/10.2118/81036-MS Published: April 27 2003 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Yang, Daoyong, Zhang, Qi, and Yongan Gu. "Optimum Design and Control of the Production-Injection Operation Systems in Petroleum Reservoirs." Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, Port-of-Spain, Trinidad and Tobago, April 2003. doi: https://doi.org/10.2118/81036-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Latin America and Caribbean Petroleum Engineering Conference Search Advanced Search AbstractThis paper presents a systems engineering approach to implementing optimum design and control of the production-injection operation systems. At first, a production performance model including flowing and different artificial lifting methods is modified from a well basis to an oil field basis. Secondly, the modified model, the injection models and surface pipeline network are integrated with a reservoir model in which the reservoir geological model is updated by continuous monitoring and surveillance. Finally, either the oil rate or the net present value (NPV) can be chosen as an objective function and optimized by a non-numerical algorithm so that the global optimum parameters for both producers and injectors are obtained. This systematic approach has been successfully applied in more than forty reservoirs. These field applications show that this technique can determine not only the optimum production operation methods for a newly discovered reservoir but also the optimum production- and injection- strategies for an existing reservoir. The success rate is over 85% for determing proper production operation methods among individual wells in a new reservoir. For a reservoir currently under production, the reservoir pressure can be kept in an appropriate range with slight increase in the water-cut and the gas-oil ratio. Thus such an integrated technique can be applied to increase the oil recovery and to extend the reservoir life.IntroductionIt is well known that the production-injection operation systems (PIOS), consisting of injectors, reservoir, producers and surface pipeline network, are important to successful exploitation of an oil field. After an oil reservoir is put into production, it will be converted from a static system into a dynamic system. Generally, this system can be treated as a black box, to which injection fluids including gas and water are considered as the inputs and from which the production fluids are treated as the outputs. Although in principle an underground reservoir system can be controlled properly in terms of the production- and injection-rates, numerous uncertainties can make the whole system extremely complicated1. Therefore, optimum design and control of the PIOS should be implemented with properly chosen decision-making variables throughout the life of an oil field.How to properly exploit a new oil or gas field is among the most perplexing and difficult technical problems as selection of proper production operation methods (POMs) is critical to the long-term profitability of most producing wells2–5. To determine POMs, Brown6 employed the nodal analysis method 7 to design and analyze the production operation system for a single well. Clegg et al.2 compared the main selection attributes of eight major artificial lift methods and proposed some practical guidelines on the performance and operating capabilities of these methods. Bucaram4, Naguib et al.8 and Ramirez et al.9 also discussed the screening criteria for different POMs. It is noted that, nevertheless, so far there have been few comprehensive decision-making models or methods with quantitative parameters available for determining optimum POMs at a field scale.After a development scheme is selected for a reservoir, there are still many possible facility designs, which impose different investment risks and operational costs and thus lead to rather different future production rates and cash returns10. A better understanding of fluid movement and energy distribution is required to implement optimum control of the PIOS. Several methods have been proposed to perform the multivariate optimization of the production systems. However, some are oversimplified or take no account of the reservoir models, while the others do not consider different POMs or economic factor10–17. In addition, usually the reservoir geological model remains unchanged during the process of optimization, though the actual field development is essentially a dynamic process. Recently, some novel integrated models have been developed to optimally adjust the PIOS for a reservoir18. Keywords: artificial intelligence, optimum design, oil field, enhanced recovery, initial development scheme, pressure profile, production operation method, operation system, reservoir, liquid productivity index Subjects: Improved and Enhanced Recovery This content is only available via PDF. 2003. Society of Petroleum Engineers You can access this article if you purchase or spend a download.

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