To design, modify, and expand surface facilities is a multidisciplinary task which involves substantial financial resources. It can take months or years to be completed, depending on the size and level of detail of the project. Nowadays, the use of Next Generation Reservoir Simulators (NGRS) is the most sophisticated and reliable way of obtaining field performance evaluation since they can couple surface and subsurface equations, thus eliminating the need to generate lengthy multiphase flow tables. Furthermore, coupling a NGRS with an optimizer is the best way to accomplish a large number of simulation runs on the search for optimized solutions when facilities are being modified and/or expanded. The suggested workflow is applied to a synthetic field which reproduces typical Brazilian offshore deepwater scenarios. Hundreds of coupled simulation runs were performed and the results show that it is possible to find optimal diameters for the production lines as well as the ideal platform location. Foreword Because of an ever growing demand for oil in the world market, the high prices for the barrel of crude, and the growing Brazilian domestic demand for gas, there is a need to quickly develop oil and natural gas fields. Therefore, development decisions, which usually involve high risk and a large amount of resource investments, often need to be taken very early and in a swift manner. In such a scenario, the optimization of the decision-making process can become quite a challenging task; especially if the impact of the uncertainty variables on the workflow and on the project goals needs to be assessed. Flow assurance in the presence of asphaltens, parafins, and low WAT (Wax Appearance Temperature) as well as hydrates, inorganic scale, CO2, H2S, and so forth, brings even more complexity to the problem. The process of calculating, modifying, and expanding surface facilities while accounting for all these variables and their impact on reservoir fluid flow is a multidisciplinary task that involves substantial resources and can take several months or even years to be performed. Optimized pipeline network and surface facilities are those which maximize oil recovery while minimizing CAPEX and OPEX. This task requires a reliable, integrated model such that production / injection prediction is possible and the overall economics of the project can be adequately assessed. Currently, the use of numerical reservoir simulators with optimization tools is the most sophisticated and reliable way to obtain the required quality of the results. Optimization tools allow several development scenarios to be sequentially or simultaneously evaluated. Techniques such as parallel simulation, experimental design, genetic algorithms, Monte Carlo simulations, artificial intelligence systems and global optimizers have contributed to characterize uncertainties and optimize drainage strategies, allowing the quantification of the benefits in order to choose the best scenario [1–8]. Optimization methodologies can be used to verify the potential integration of uncertainty and decision variables, considering all project constraints and preset goals. Among the applications for this type of study, the most important are:sensitivity analysis,the analysis of the impact of uncertainties and risks [3, 7],well location and number of wells optimization [1, 5],optimization and distribution of injection rates,production history matching [4], andproduction strategy optimization [3].

The Congro field (Campos Basin, Brazil) contains considerable reserves located in a extremely heterogeneous, very low-permeability carbonate reservoir. For many years after its discovery, this reservoir was considered non-economic. However, a newly drilled horizontal well showed encouraging results. An advanced, integrated reservoir study showed that the exploitation of the reservoir can be economically attractive if non-conventional wells are used. In this work, we demonstrate how cutting-edge technology plus interdisciplinary efforts involving geophysics, geology, and reservoir engineering were the key to make a noneconomic asset into an economic one. Two major problems were faced: the short available time (only two months for the whole study) and the lack of data. The lack of data was overcome by using analogy with a similar, well-sampled reservoir. The time constraint was handled by taking an integrated approach where the geophysical, geological, and numerical simulation models were built almost simultaneously. Stochastic reservoir models were generated using a geostatistical method called truncated Gaussian technique to assess uncertainty on facies distribution. Three different types of wells were considered: vertical fractured, horizontal, and multilateral. As multilateral wells showed better economics, we optimized the number of legs and their length based on numerical simulation. Introduction Technology has more and more made the difference between success and failure of hydrocarbon exploitation projects, especially when the asset is on the economic borderline. New drilling techniques, as well as reservoir description techniques, have allowed the development of fields that had been considered economically unattractive in the past. Throughout the world, many projects have been reviewed and had their economic indicators (net present value, payout, etc.) significantly improved when new technologies were applied to them. Multilateral drilling and seismic interpretation are among the techniques that suffered major breakthroughs in the last few years. The first allows one to greatly improve well productivity index as well as enhance sweep efficiency; furthermore, the use of multilateral wells reduces drilling and equipment costs such as flowlines and christmas trees. The second can give a pretty good idea of where the best-quality rock facies are located and help identify reserves that may not be produced optimally. A third technique that can add value to a project is geostatistics. Multiple equiprobable reservoir models can be generated to assess uncertainty on reservoir description and on reservoir production forecast. Geostatistical models are generally more detailed than conventional mapping techniques, allowing a better description of the reservoir heterogeneity. Another advantage of geostatistics is that it can integrate different types of data independently measured at different scales. In this work, we show how the techniques mentioned above were applied in an integrated way to make the carbonate reservoir of Congro field economically viable. Team integration (geophysicist, geologist, geostatistician, reservoir engineer, drilling engineer, economist, etc.) was another key element to the success of this project. Pressured by time, the team did not take the conventional approach of taking one step at a time to build the reservoir model. That is, to build the geophysical model first, then the geological model, and, finally, build the numerical simulation model. Instead, the geophysicist, the geologist, and the reservoir engineer worked together so that their models were built almost simultaneously. This approach saved time and ensured that the models were coherent with all available data.

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