Optimization of Surface Network and Platform Location using a Next Generation Reservoir Simulator Coupled with an Integrated Asset Optimizer - An Application to an Offshore Deep Water Oil Field in Brazil

Abstract

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].

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

“…Pipeline network and surface facilities are designed to optimize the combination of oil recovery, CApital EXpenditures (CAPEX), and OPerating EXpenses (OPEX). This requires a reliable, integrated model to calculate injection and predict production and so assess the overall economics of the project (Campozana et al, 2008).…”

“…We used the empirical correlations of tubing multiphase flow by Brill and Beggs (1991) to model all satellite producer wells (Victorino et al, 2016), and the Standing correlations (Standing, 1947) to model fluids. These correlations were included in the multiphase flow simulator Ptube TM 2014 (CMG).…”