Modeling and Scaleup of S†eamflood in a Heterogeneous Reservoir

Abstract

In the numerical modeling of a steamflood process in a highly stratified, thick reservoir with thin interbedded discontinuous permeability barriers, the vertical grid size must be comparable to the length scale of permeability variation in the vertical direction. In a thick reservoir, the smaller length scale of vertical heterogeneities results in an impractically large number of layers for the modeling of a computationally intensive process such as steamflooding. In the first part of this study a series of simulation runs were conducted for different geostatistically derived cross sectional models to study the degree of heterogeneity that is required to properly model steamfloods in the presence of thin diatomite barriers in both the dipping and non-dipping sections of a heavy oil, thick reservoir. These cross sectional models each had a different number of layers and were developed by sequential indicator simulation of log traces. The most detailed models contained 300 layers. In the second part of this work we applied different methodologies for coarsening the most detailed model while still capturing the effects of the geologic features. Two different methods were examined to coarsen the detailed models. In one method, we maintained the impermeable layers and coarsened the sands by averaging each 10 sand layers. In the other method, the 300 layer models were coarsened using a general scale up method. In this method the dominant flow paths in the cross section are first identified through solution of a single phase flow problem. This information is then used to selectively scale up the reservoir properties, leaving detail in regions where required and coarsening in other regions. The results show that the coarse models developed directly from the sequential indicator simulation underpredict the recovery in the non-dipping cross sections and overpredict the recovery in the dipping cross sections. The results also show that the scaled up coarse models predict recoveries in good agreement with the detailed model in both the dipping and non-dipping sections of the reservoir. The results also show that the coarse models which were developed by keeping the shale and averaging the sand layers only provide accurate results in the non-dipping section of the reservoir. Introduction As heat and reservoir management become more important in steamflood operations improved engineering tools are required to cost-effectively manage steam injection projects. Application of reservoir simulation as a tool for steamflood management is growing. However, many steamflood reservoirs have a high amount of geologic heterogeneities, which may impact both actual and simulated project performance. The importance and impact of heterogeneities on the performance prediction of steamflood processes in highly stratified systems have been recognized in the literature.

Application of a Multi-Purpose Compositional Simulator in Designing Steam Injection Projects

“…Effect of vertical grid distribution on oil recovery in a steam injection operation is dramatic, and can not be generalized with simple relations as observed in the numerical experiments conducted with different reservoir thickneses. 4. Numerical experiments conducted with various steam injection rates highlight the difficulty of coming up with a practical rule of thumb for a generic optimum steam injection rate with universal application.…”

“…In the literature there are numerous studies on the modeling of steam injection operations 1,2,3,4 . Comparative studies of solutions from different models for steam injection in oil reservoirs in general indicate good agreement 5 .…”

Experiment on the Influence of Vertical Heterogeneity of Reservoir on the Steam Drive Effect

“…[1][2][3][4][5], that is to conduct an experiment under the initial conditions of the reservoir. However, in China, to develop the heavy oil reservoirs, first we conduct the multi-cycle steam stimulation, and then turn to steam drive, and the reservoir conditions at this time are very different from the initial conditions [6][7][8].…”

Important Modeling Parameters for Predicting Steamflood Performance

“…However, because of recent developments in computer hardware technology, faster CPUs became available. Use of faster CPUs combined with better numerical solvers, and advancements in parallel computing, resulted in significant improvements in the performance of thermal simulators, therefore enabling modeling of larger, multipattern project areas (Dehghani et al 1995;Johnson et al 1992;Kumar 1992;Williams et al 2001).…”