A benchmark for computational integration of petroleum operations has been constructed. The benchmark consists of two gascondensate reservoirs producing to a common process facility. A fraction of the processed gas is distributed between the two reservoirs for gas injection. Total project economics are calculated from the produced streams and process related costs. This benchmark may be used to compare different computational integration frameworks, and optimization strategies.The methods of model integration and optimization discussed in this paper are applicable to complex petroleum operations where it is difficult to quantify cause-and-effect without comprehensive model-based integration. A framework for integration of models describing petroleum operations has been developed. An example test problem is described and studied in detail. Substantial gains in full-field development may be achieved by optimizing over the entire production system.All models and data in the benchmark problem are made available so that different software platforms can study the effects of alternative integration methods and optimization solver strategy. The project itself can, and probably should, be extended by others to add more complexity (realism) to the reservoir, process, and economics modeling.

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.

The decrease in oil and gas prices results in a huge impact in the oil and gas company. This paper discusses the importance of integrated asset modeling and optimization of oil and gas fields under various oil and gas price scenarios. The integrated model is defined as simulation runs from the reservoir up to the surface process and continues with an economic evaluation.Various field models under different production strategies are presented in the paper and evaluated based on the integrated asset model. The reservoir models are a gas condensate reservoir under gas cycling and an oil reservoir under water alternating gas (WAG) injection production strategies taken from SPE 3 and SPE 5 comparative projects. Derivative free optimization methods are used to find the best operating conditions for the field. The optimization objective is to maximize economic key performance indicators, such as net present value, and to minimize cost, such as gas injection cost. The decision variables are gas injection fraction, gas injection time, water injection rate and WAG time.Using integrated system modeling, the operating parameters can be determined accurately considering reservoir and surface process capabilities. The optimization results showed that optimum gas injection time is longer during high oil prices compared to low oil prices for the gas condensate reservoir under gas injection. The oil and gas prices are fluctuating in a certain cycle over time, and the dynamic fluctuations are accommodated in the economic model. Many reasons are behind the dynamic fluctuations of oil and gas prices. The fluctuations will have an impact on company production strategies. The economic model will be simulated under very high, high, intermediate, low and very low price scenarios. For each scenario, the model will be run in the integrated modeling and reservoir only modeling. A scenario of varying the ratio of oil to gas price is also included in the analysis. Different oil and gas prices will lead to different management decisions during the field lifetime. A conservative production strategy, meaning when the field is simulated separately part-by-part, could mislead field production decisions. Based on the reservoir simulation, the liquid production might flow easily from the reservoir to the surface. However, based on the surface simulation, the well might be shut᎑down due to flow restriction on the surface. This paper presents the important concept of the integrated model and optimization under dynamic oil and gas prices. The oil and gas prices have an influence in determining production parameters. The integrated model that is used in the paper is flexible and can be replaced with another reservoir model, surface process model, or economic models.

Summary A benchmark for computational integration of petroleum operations has been constructed. The benchmark consists of two gas/ condensate reservoirs producing to a common process facility. A fraction of the processed gas is distributed between the two reservoirs for gas injection. Total project economics is calculated from the produced streams and process-related costs. This benchmark may be used to compare different computational integration frameworks and optimization strategies. Even though this benchmark aims to integrate all parts of a petroleum operation, from upstream to downstream, certain simplifications are made. For example, pipe flow from reservoir to process facility is not included in the integrated model. The methods of model integration and optimization discussed in this paper are applicable to complex petroleum operations where it is difficult to quantify cause and effect without comprehensive model-based integration. A framework for integration of models describing petroleum operations has been developed. An example test problem is described and studied in detail. Substantial gains in full-field development may be achieved by optimizing over the entire production system. All models and data in the benchmark problem are made available so that different software platforms can study the effects of alternative integration methods and optimization solver strategy. The project itself can, and probably should, be extended by others to add more complexity (realism) to the reservoir, process, and economics modeling.