The development of giant fields of extra-heavy crude oil (XHO), through the massive application of thermal EOR processes, involves investments of several millions of dollars. It is a technical challenge to evaluate, by means of numerical simulation, and in a reasonable period of time, the behavior of EOR processes in giant fields. This paper describes a semi-analytical methodology to evaluate thermal processes of EOR and to provide, in a suitable time, the input data required by the investment decision makers. The methodology follows 4 main steps. 1) The field is divided in sectors using a special defined EOR-Quality Index. 2) Six representative Model-sectors are selected in each reservoir and used to perform numerical simulations of all EOR processes to be evaluated in the field. 3) Extrapolation of simulation results to all the individual sectors of the field (Field-sectors), by using a defined extrapolation function. 4) Synchronization of injection and production profiles for each EOR technology, in each individual Field-sector following the field development plan. The methodology was successfully applied to a giant-XHO field, in the Orinoco Oil Belt, where primary production followed by combinations of different sequences of Cyclic Steam Stimulation (CSS), Steam Flooding, Steam Assisted Gravity Drainage and Horizontal Alternate Steam Drive were evaluated at fullfield scale. The proposed methodology allows analyzing many more production strategies without losing the vision of the global impact of EOR operations in the whole field. Two validation tests were performed to check the robustness of the method. The first test checked the extrapolation function. A set of random selected Field-sectors in which EOR recovery performances were evaluated through both numerical simulation, and also with the extrapolation function. Comparable results were obtained by both methods. A final validation test was done performing a fullfield simulation study for a CSS application. The simulation model had 23MM active blocks, about 1000 horizontal wells, thermal PVT formulation, and 40 years of production. Comparable results in terms of production potential and plateau duration were obtained, which validates the proposed methodology. The numerical fullfield test took around one week, and it was also expensive in resources. The presented semi-analytical methodology overcomes the challenges of expensive and long numerical simulations, by an innovative way to characterize the dynamic response of Model-sectors and to extrapolate it into the all Field-sectors. The methodology is flexible enough to consider different kind of EOR processes configuration sequences, as well as, the evaluation of the processes at different scales as: sector, reservoir units and fullfield.

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This paper presents a new methodology developed in the Repsol Technology Center (CTR), to perform the prescreening of Enhanced Oil Recovery (EOR) processes in an innovate and integrated way. The methodology consists in four stages adapted to different levels of available information: 1) an analogous reservoir identification step, where it is analyzed the experience in terms of EOR applications in similar reservoirs; 2) a go/no go pre-screening scheme of EOR technologies based on average reservoir and fluid properties; 3) a more detailed screening using reservoir and fluid properties distribution defined in 3D geological model, where areas of EOR application are identified and the volumetric impact is estimated; and 4) a qualitative ranking of the pre-selected EOR technologies, considering three main factors: the oil recovery, the success factor and the estimated cost of the process including the environmental impact. This methodology has been successfully applied in real fields. In this paper, we will present four different cases showing the flexibility of CTR workflow. Case A and Case B are examples of offshore, brown, light- medium oil fields, of sandstone and carbonates respectively. Cases C and D are illustrations of onshore, sandstone, heavy and extra-heavy oil fields correspondingly.