Forecasting of isothermal enhanced oil recovery (EOR) and waterflood processes
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Oil production from EOR and waterflood processes supplies a considerable amount of the world's oil production. Therefore, the screening and selection of the best EOR process becomes important. Numerous steps are involved in evaluating EOR methods for field applications. Binary screening guides in which reservoirs are selected on the basis of reservoir average rock and fluid properties are consulted for initial determination of applicability. However, quick quantitative comparisons and performance predictions of EOR processes are more complicated and important than binary screening that are the objectives of EOR forecasting. Forecasting (predicting) the performance of EOR processes plays an important role in the study, design and selection of the best method for a particular reservoir or a collection of reservoirs. In EOR forecasting, we look for finding ways to get quick quantitative results of the performance of different EOR processes using analytical model/s before detailed numerical simulations of the reservoirs under study. Although numerical simulation of the reservoirs is widely used, there are significant obstacles that restrict its applicability. Lack of necessary reservoir data and time consuming computations and analyses can be barriers even for history matching and/or predicting EOR/waterflood performance of one reservoir. There are different forecasting (predictive) models for evaluation of different secondary/tertiary recovery methods. However, lack of a general purpose EOR/waterflood forecasting model is unsatisfactory because any differences in results can be caused by differences in the model rather than differences in the processes. As the main objective of this study, we address this deficiency by presenting a novel and robust analytical-base general EOR and waterflood forecasting model/tool (UTF) that does not rely on conventional numerical simulation. The UTF conceptual model is based on the fundamental law of material balance, segregated flow and fractional flux theories and is applied for both history matching and forecasting the EOR/waterflood processes. The forecasting model generates the key results of isothermal EOR and waterflooding processes including variations of average oil saturation, recovery efficiency, volumetric sweep efficiency, oil cut and oil rate with real or dimensionless time. The forecasting model was validated against field data and numerical simulation results for isothermal EOR and waterflooding processes. The forecasting model reproduced well (R2> 0.8) all of the field data and reproduced the simulated data even better. To develop the UTF for forecasting when there is no injection/production history data, we used experimental design and numerical simulation and successfully generated the in-situ correlations (response surfaces) of the forecasting model variables. The forecasting model variables were proven to be well correlated to reservoir/recovery process variables and can be reliably used for forecasting. As an extension to the abilities of the forecasting model, these correlations were used for prediction of volumetric sweep efficiency and missing/dynamic pore volume of EOR and waterflooding processes.