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Integrated modeling of multi-field assets, from subsurface to market, is challenging due to the complexity of the problem. This paper is an extension of the SPE 121252, model based integration and optimization gas cycling benchmark [Juell, et al., 2009], extending two gas-condensate fields to two full-field multi-well models. Additionally, a full-field model is added to the Juell benchmark, introducing an oil field undergoing miscible WAG injection, where most data are taken from the SPE 5 Reservoir Simulation Comparative Project. All reservoir models are compositional, but using different EOS representations. A base case scenario is defined with fixed numbers and locations of producers and injectors. A common field-wide surface processing facility is modeled with emphasis on water handling, NGL extraction, sales-gas spec, and gas reinjection. The surface process model interacts with the three reservoir models through two main mechanisms-(1) water-and gas-handling constraints, and (2) distribution of available produced gas for reinjection into the three reservoirs. The field asset model provides long-term production forecasts of gas, oil, and NGL revenue. Cost functions are introduced for all major control variables (number of wells, surface facility selection and operating conditions, injection gas composition). Net present value is used as the target objective function. This paper will evaluate optimal production strategies for the base case benchmark problem, using several key control variables and field operational constraints. Optimization performance will be tested with a few solver algorithms. The benchmark will be provided to the industry through application data files, network infrastructure, and results from our integrated optimization model.

Carbon Dioxide (CO2) injection is a common process used to increase hydrocarbon production in oil and gas fields, which is known as the enhanced oil recovery (EOR) process. This paper presents modeling of a carbon capture, utilization and storage (CCUS) project using an integrated operation and optimization approach. In this case study, the subsurface system consists of two different reservoirs, a gas condensate and an oil reservoir. The products from the two reservoirs are processed in a single surface processing facility. The CO2 will be captured from both reservoirs and injected into the oil reservoir after being processed in the surface facility. The CO2 from various capture processes will produce different types of residual components, and the effects of different compositions of the injected CO2 stream on field economic revenue will be studied. The verification of extra cost by increasing the CO2 mole fraction in the recycle system is modeled in the economic cost equation. The final surface separation products will be condensate (oil), natural gas liquid (NGL), and sales gas. Various derivative-free optimization methods will be used to maximize the net present value (NPV) of the CO2 EOR project. The decision variables are the multi-stage separator pressure conditions (surface parameters) and the CO2 injection rate (sub-surface parameter). Optimization results using different optimization methods will be compared to determine the best field operating parameters. The possibility of combination with water injection will also be investigated to determine the best injection scenarios. An integrated analysis of the CO2 injection process provides a thorough and comprehensive understanding of the performance of the complete CO2 injection value chain. Optimization at the field scale provides insights into the subsurface response to field scale parameter variations. The integrated approach presented in this paper will provide a basis to evaluate CO2 EOR scenarios compared to other injection strategies, such as surfactant or polymer injections.

Oil and gas production systems are complex and usually consist of several production elements and corresponding models: (1) reservoirs modelled with reservoir simulators using geological and fluid data, (2) wells and surface production networks modelled with flow assurance applications, (3) surface processing facilities modelled in process simulators and (4) economic models. The traditional approach ("silo" approach) consists of modelling each part of the system independently from the others without considering upstream and/or downstream interactions. Integrated Asset Modelling (IAM) is a maturing solution incorporating effects of all the elements of an asset. This paper shows the benefits of successful IAM implementations in four highly complex and technically challenging assets around the globe. IAM aims to bring together all models of the value chain, from the reservoir to the point of sales. It enables us to perform numerous sensitivity analysis by changing any parameter across the value chain and investigate its influence on the entire system. The presentation concludes with guidelines and best practices for IAM implementation. It especially focuses on three very important issues faced when dealing with IAM: (1) software and model integration, (2) PVT consistency across the value chain and (3) optimization. Several case studies from the industry are used as illustration: diluent injection optimization for a heavy oil field in the North Sea, integration of reservoir and process models for a complex offshore multi-field asset in Indonesia, production allocation for an onshore multi-field asset in South America and API blending optimization for a multi-field asset in Middle East. The different case studies show that benefits of implementing an IAM approach can be significant and immediate: higher production, lower OPEX or better information for further CAPEX. In the current market situation, IAM approach is a cost-effective solution to optimize oil and gas production. By bringing together existing information and models from all parts of the production system, IAM breaks barriers between disciplines and enables an asset-scale overview that leads to more informed decision-making and ultimately higher profits for operators.