ANALYTICAL MODELING OF THERMAL OIL RECOVERY BY STEAM STIMULATION AND STEAMFLOODING

The purpose of this research was to study the predictive capabilities of existing analytical steam injection models. Three steamflood models and one steamsoak model were evaluated by comparing their predictions with field data. Each model was found inadequate and was modified to improve its ability to history match field data.; The modified Jones steamdrive model with its empirically determined scaling factors was found to give the best history match of oil production for the Kern River field. Fields with different characteristics will require the adjustment of these scaling factors to achieve an acceptable history match. The modified Farouq Ali steam drive model gives reasonably good history matches without need for empirical scaling factors or adjustable parameters. It is thus recommended for the prediction of steamdrive oil recovery when field production data are unavailable. The modified Miller-Leung, gravity override steamflood model, which contains two adjustable parameters, was found to possess the best overall history matching capabilities and is recommended for this purpose.; The deficiencies of the Gontijo-Aziz steamsoak model which contains four adjustable parameters, prompted the development of an improved analytical steam stimulation model. The model is applicable to homogeneous, gravity drainage, pressure depleted reservoirs. The model gave an excellent history match for oil production and water cut for the Midway Sunset field project while employing only one adjustable parameter.