We describe an oil and gas production optimization field application of a miscible gasflood for a cluster of oil reservoirs in South Oman. A portion of the produced sour gas from different reservoirs will be processed as sales gas and the extracted CO2 and H2S will be blended with the remaining produced gas stream, which will be injected back into selected reservoirs for miscible gas flooding.
An integrated production system modeling (IPSM) methodology with a fully compositional (EOS, equation of state) description is used to couple the various reservoir models to a surface facilities model. A new element is a compositional gas blending optimization model in the gas processing facilities. This is the main source of non-convexity and non-linearity in the overall optimization model and requires a choice of local and global optimizers to uncover high quality solutions. What optimizer to use depends on the structure and targets of the gas blending problem. The produced oil and gas rates could be 10–20% below the optimum, especially when using local optimizers like SLP and SQP. Even for solutions where the fields produce the same oil and gas rates, and the blended sales and injection gas rates are the same, multiple (sub-)optimal solutions are still possible in the gas blending model by creating injection and sales gas with different compositions.
Clear benefits of our IPSM methodology are that long term production forecasts were achieved while honoring all available facilities constraints such as those related to oil and gas production, the gas sweetening unit H2S and CO2 mole rates, injection compressor molecular weight, gas export specifications and facilities uptime. In addition, various forecast scenarios could quickly be generated to study the impact of different optimization objectives, e.g. high gas export vs. high oil production. Very important is also that all the constraints and objectives are contained in a single input file for running the integrated subsurface-surface model. This makes auditing the various forecast scenarios very transparent.
A large number of gas and oil fields are contaminated by significant amounts of CO2 and H2S. Separation of CO2 and H2S from the hydrocarbon gas stream is expensive and leads to disposal problems. However, one way to use these contaminants is to re-inject them back into the oil reservoirs for enhanced oil recovery (EOR) by miscible gas flooding, which can result in very high recovery factors. Depending on the requirements of individual reservoirs for miscible conditions, the extracted H2S and CO2 can be blended in various ratios with produced sour hydrocarbon gas. Such oil production and gas blending optimization problems are challenging when producing from a portfolio of reservoirs with different compositions, pressures and temperatures.
In this paper, we present the integrated oil and gas production optimization of a miscible gasflood in South Oman. The carbonate reservoirs are about 100 m thick and located at a depth of 3.5 to 5.5 km. There are several subsurface uncertainties, such as the presence of (sealing) faults, or "thief zones", which are higher permeable layers in low permeable rocks. These may cause the gasflood not to perform at all, or lead to early gas breakthrough. To minimize the risk, development of the oil reservoirs is done in a phased manner. A miscible gasflood EOR scheme also requires abundant availability of injection gas and this is provided by a nearby gas field. How the gas can be blended is described by Deinum et al.1.
The various subsurface reservoirs are modeled with the in-house MoReS reservoir simulator in compositional mode and linked to a fit for purpose compositional surface facilities model in the Hydrocarbon Field Planning Tool (HFPT)2,3 a Shell proprietary tool for integrated subsurface-surface modeling. The simulations are run efficiently and robustly using an adaptive implicit non-linear solver with a fast PVT flash method. The well equations in MoReS, including those of the separator train, are solved fully implicitly, whereas the coupling to the facilities is treated explicitly. As gas blending problems are strongly non-linear and non-convex, HFPT has a choice of local and global optimization algorithms to deal with the gas production and blending optimization to produce the sales and injection gas.