This paper presents the development of a method to provide decision support in the feasibility studies and concept planning phases of oil and gas field development. The objective in developing the methodology was to provide an easy-to-use facility to integrate the production-governing elements of oil and gas fields that capture the integrated production and economic performance of the system. This in a modular and scalable manner includes numerical optimization and uncertainty analyses needed to support engineering decisions. The method follows a series of steps that allow determining the optimal field production profile, drilling schedule, type of offshore structure, pressure support method and selection of artificial lift. The first step consists of creating efficient (low running time) proxy models of the production performance of the field and the costs figures associated with the project. The proxy model of the production performance is based on curves of maximum production rates versus cumulative production and contains all relevant field design features and computation of the most relevant performance indicators to consider in the evaluation. The proxy model to estimate the costs associated with the project is based on linear equations function of production rates and number of wells. The second step is to perform numerical optimization to find optimal production profile and drilling schedule that maximize the net present value of the specific development strategies considered. For the last step, an evaluation of the effect of uncertainties on the results of the numerical optimization using probabilistic methods is performed. The method was applied in a synthetic production system based on public data of Wisting field (currently under development). The field is a remote low-energy oil reservoir located in the Barents Sea. Nine strategies, obtained from the combination of three recovery support methods and three processing facilities, were compared using the net present value as decision factor. The best strategy consists of using a tension leg platform as processing facility and multiphase boosting plus water injection as recovery support method. This strategy generated the highest production and required the lowest costs, resulting in the highest profitability. It was demonstrated that the methodology successfully finds optimal field design features while quantifying the effect of uncertainties.
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.
Peregrino is a field offshore Brazil with a FPSO and 2 fixed platforms currently producing close to 100 000 stb/d of oil. Production wells are equipped with electric submersible pumps (ESP) and all produced water is reinjected back into the reservoir using 6 injectors for pressure support. The viscosity of the crude is high (163 cp at reservoir conditions). The present work explores the merits and provides the development details of a model-based production optimization scheme to advice on the best frequency settings of the ESPs in each well. This to ensure the maximum amount possible of oil is produced when water injection capacity is a bottleneck. Furthermore, it studies how the optimal operating conditions change with time. The optimization formulation considers maximization of total oil production, the maximum allowable water produced given by the available injection capacity and the operational constraints of the ESPs. The optimization was formulated as a Mixed-Integer Linear Problem (MILP). The performance of the wells is represented with piecewise linear tables generated from a commercial simulator. For the cases tested, the proposed optimization scheme works successfully: It has low running times suitable for real time production optimization, handles successfully multiple operational constraints, and guarantees global optimality. Optimization results are presented for future times. By using piecewise linear tables to represent the well performances the fidelity of the original model is maintained, while ensuring a robust and fast optimization of the problem. Moreover, it is suitable for frequent model updates without requiring changing the optimization formulation. In summary, this work proposes a method to handle part of the operational complexities of the Peregrino field using a digital twin.
The most affected parts of the electrical power system in Germany are the distribution grids due to the rapid expansion of renewable energies. Most of these micro power plants e.g. photovoltaic systems feed into the low (LV) and medium (MV) voltage level. The voltage stability in these levels is thus much more complex and is partially violated in moments of strong feed-in. To reduce this negative impact the directive VDE-AR-N 4105 was specified that PV-inverters must provide reactive power instead of pure active power. This directive should however only be the beginning. In a smart grid, the asset inverter should be used much more intensively.In this paper it is demonstrated by power flow simulations with the data of a real distribution grid including PV-system feed-in that there are more possibilities but providing the reactive power for the distribution and voltage stability. It is illustrated that the use of the complete apparent power of the inverter is a more appropriate way to obtain the stationary stability of distribution grids. With this strategy in addition, the power loss can be influenced in LV-and MV-level and the power factor of the feed-in of the HV/MV-supplying transformer can be adjusted.Index Terms-Reactive power control, smart grids, photovoltaic cells, power system management.
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