Using pilot wells to integrate geological modelling and history matching : applied to the Norne Benchmark case

The inherent uncertainties in numerical reservoir simulation can lead to models with significant differences to observed dynamic data. History matching reduces these differences but often neglects the geological consistency of the models, compromising production forecasting reliability. To address this issue, this work proposes a geological modelling workflow integrated within a probabilistic, multi-objective history-matching workflow, using the concept of pilot points. The pilot-point method is a geostatistical parameterization technique that calibrates a pre-correlated field, generated from measured values, and a set of additional synthetic data at unmeasured locations in the reservoir, referred to as pilot points. In this study, the synthetic data correspond to synthetic wells; henceforth referred to as pilot wells. The methodology is applied to a real dataset, the Nome Field benchmark case. The flexibility of the pilot-well method is the principal advantage, while a key challenge is to optimize the pilot-well configuration. The configuration includes production data, the preferred fluid-flow paths and the geological framework. The flexibility of the method is demonstrated in the two case studies presented here: generating specific sedimentary features (G-segment) and finding the best location for the cemented stringers responsible for the fluid behaviour (C-segment)52188206This work was carried out in association with the project registered as ‘BG-07 Redução de incertezas através da incorporação de sísmica 4D na modelagem de reservatório’ (UNICAMP/BG Brasil/ANP) funded by BG Brazil under the ANP R&D levy as ‘Compromisso de Investimento com Pesquisa e Desenvolviment

Effect of reservoir and production system integration on field production strategy selection

In petroleum engineering studies, the integration of reservoir and production system models can improve production forecasts. As the integration increases computation time, it is important to assess when this integration is necessary and how to choose a suitable coupling methodology. This work analyzes the influence of this integration, for a petroleum field in the development phase, evaluating the effects on the production strategy parameters. We tested a benchmark model based on an offshore field in Brazil so we could validate the solution in a reference known model. This work continues the research of Von Hohendorff Filho and Schiozer (2014, 2017) and aims to improve step 11 of the 12-step reservoir development and management methodology by Schiozer et al. (2015). The solution is tested in a reference model. Using the integrated production system and reservoir models from step 11 of the methodology, we re-optimize the production strategy of a standalone production development, while evaluating net present value as the objective function. We adapted an assisted workflow to include the optimization of new variables, such as pipe diameters of the well systems and gathering systems, platform positions, and artificial lift application, and compared these with the production strategy obtained from the same benchmark in a standalone approach. Comparing the integrated standalone and integrated production strategies, we observed important changes that indicate the need to integrate reservoir and production models. The optimized integrated systems resulted in significantly increased net present values, maintaining the same oil recovery factor while requiring lower initial investment. We implemented the best integrated production strategy and the integrated production strategy derived from the standalone case in the reference model which, in this case, represents a real field (emulating a real situation). Integration in the implementation step impacted the production forecast more than the optimization step, demonstrating the benefits of integrating reservoir and production systems to increase project robustness

Effect of reservoir and production system integration on field production strategy selection

International audienceIn petroleum engineering studies, the integration of reservoir and production system models can improve production forecasts. As the integration increases computation time, it is important to assess when this integration is necessary and how to choose a suitable coupling methodology. This work analyzes the influence of this integration, for a petroleum field in the development phase, evaluating the effects on the production strategy parameters. We tested a benchmark model based on an offshore field in Brazil so we could validate the solution in a reference known model. This work continues the research of Von Hohendorff Filho and Schiozer (2014, 2017) and aims to improve step 11 of the 12-step reservoir development and management methodology by Schiozer et al. (2015). The solution is tested in a reference model. Using the integrated production system and reservoir models from step 11 of the methodology, we re-optimize the production strategy of a standalone production development, while evaluating net present value as the objective function. We adapted an assisted workflow to include the optimization of new variables, such as pipe diameters of the well systems and gathering systems, platform positions, and artificial lift application, and compared these with the production strategy obtained from the same benchmark in a standalone approach. Comparing the integrated standalone and integrated production strategies, we observed important changes that indicate the need to integrate reservoir and production models. The optimized integrated systems resulted in significantly increased net present values, maintaining the same oil recovery factor while requiring lower initial investment. We implemented the best integrated production strategy and the integrated production strategy derived from the standalone case in the reference model which, in this case, represents a real field (emulating a real situation). Integration in the implementation step impacted the production forecast more than the optimization step, demonstrating the benefits of integrating reservoir and production systems to increase project robustness

Assisted process for design optimization of oil exploitation strategy

The decision-making processes to select an oil exploitation strategy can be complex due to many variables to be optimized and, it may be unfeasible to simultaneously evaluate multiple variables. In these cases, assisted methods involving engineering analyses and mathematical optimization algorithms may address the problem. In the literature, while several methodologies optimize specific parts of the design infrastructure of the exploitation strategy, methodologies treating the whole process are less common. Since scientific attention focuses on the solution to specific parts of this problem, methodologies that deal with the whole problem remain less clearly developed and require more tests and studies. This paper presents an assisted method to optimize a set of variables of an oil exploitation strategy in a deterministic approach. The methodology proposes to hierarchically order variables, group them according to their nature and importance and combine different optimization procedures with practical engineering analysis. The optimization variables are separated into three groups: (1) design variables group; to be decided before field development, representing the choice of configuration and equipment, (2) control variables group; these determine the operation of the oil field and (3) design future variables group, applicable at later stages such as infill drilling. It is required the estimation of a maximum number of simulation runs based on the available time, also considering interconnections between the groups. We applied the methodology to a reservoir model based on a Brazilian offshore oil field in the pre-development phase, the period before the well development drilling, when little information is available. Results indicate that this is an efficient procedure to evaluate deterministic scenarios, suggesting optimization procedures for each decision variable and achieving good quality solutions within a reasonable number of simulation runs. This is useful in many practical cases, mainly those that require long simulation time runs. Although this work considers one scenario, a deterministic approach is the first stage for optimizing uncertain scenarios in the probabilistic optimization process. In conclusion, applying an adequate assisted process can significantly reduce the number of required simulations. We also observed that the order of variables was an important aspect to be analyzed before starting the process146473488The authors wish to acknowledge the support of PETROBRAS - Research Network SIGER (Grant Agreement No. 0050.0022715.06.4), and CEPETRO/UNICAMP. We also thank the Computer Modelling Group Ltd. for their software license and technical suppor

Risk management in petroleum development projects: Technical and economic indicators to define a robust production strategy

In this study, we consider robustness as a risk management method in the development of complex petroleum fields, complementing the well-known techniques of acquiring new information and adding flexibility to the production system. To create a robust production strategy we aim to reduce sensitivity to uncertainty. Our methodology is based on the analyzed performance of an optimized production strategy, covering all possible scenarios. We use technical and economic indicators to objectively identify and quantify refinements in this strategy to assure good performance across possible scenarios. We focus on the robust number and placement of wells, and robust platform processing capacities. We consider the robustness of net present value and of the recovery factor, computed using Multi-Attribute Utility Theory. We quahtify the risk through semi-deviation, instead of standard deviation, to focus on the downside volatility. Refining an optimized production strategy significantly improved the optimization process by increasing the expected value of each objective and, dramatically reduced the downside risk151116127COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DO AMAPÁ - FAPEAPsem informaçã

Selection of representative models for decision analysis under uncertainty

The decision-making process in oil fields includes a step of risk analysis associated with the uncertainties present in the variables of the problem. Such uncertainties lead to hundreds, even thousands, of possible scenarios that are supposed to be analyzed so an effective production strategy can be selected. Given this high number of scenarios, a technique to reduce this set to a smaller, feasible subset of representative scenarios is imperative. The selected scenarios must be representative of the original set and also free of optimistic and pessimistic bias. This paper is devoted to propose an assisted methodology to identify representative models in oil fields. To do so, first a mathematical function was developed to model the representativeness of a subset of models with respect to the full set that characterizes the problem. Then, an optimization tool was implemented to identify the representative models of any problem, considering not only the cross-plots of the main output variables, but also the risk curves and the probability distribution of the attribute-levels of the problem. The proposed technique was applied to two benchmark cases and the results, evaluated by experts in the field, indicate that the obtained solutions are richer than those identified by previously adopted manual approaches. The program bytecode is available under request. (C) 2015 Elsevier Ltd. All rights reserved.The decision-making process in oil fields includes a step of risk analysis associated with the uncertainties present in the variables of the problem. Such uncertainties lead to hundreds, even thousands, of possible scenarios that are supposed to be analyz886782sem informaçãosem informaçã