High-temperature surfactant foams are simulated by modifying gas-phase mobility in a conventional thermal simulator. Both surfactant-alternating-gas (SAG) and gaslliquid-coinjection processes are modeled. Foam generation by leave-behind and snap-off as well as foam coalescence and trapping mechanisms are incorporated in the model by an equation for the number density of foam bubbles; gas-phase relative permeability and apparent viscosity are modified according to the bubble density. Pressure and saturation data of laboratory corefloods are successfully history matched with simulation results. Field-scale sensitivity studies of the steam-foamdrive process demonstrate how the coalescence rate affects the extent of steam diversion. IntroductionGases (such as steam, CO 2 , and nitrogen) are injected into oil reservoirs as drive fluids in some EOR processes. Early gas breakthrough can occur at producing wells owing to override and channeling, resulting in low oil-recovery efficiency. Injecting surfactant to create foam can reduce gas mobility and improve volumetric sweep efficiency in oil reservoirs. Foams used in both mature and infant steamdrives have resulted in incremental oil production in California heavy-oil reservoirs. 1-4The behavior of foam in porous media is complex, and the mechanisms governing its flow are not yet fully understood. Laboratory and theoretical studies have investigated foam generation,5-7 bubble coalescence,8 and the effect of oil on foam stability. 9, 10 A few investigators have begun to develop a comprehensive model of foam flow in porous media, but only few experiments have been modeled. 6, II, 12This paper offers a mathematical model that includes the principal mechanisms that govern foam displacement in porous media. The effect of foam on gas-phase relative permeability and apparent viscosity is included in the model. Both static or continuousgas foams and "strong" or discontinuous-gas foams are modeled. 5 ,6 In the first case, static foam lamellae block pore throats for gas flow, decreasing the gas-phase relative permeability. In the second case, foam bubbles are displaced through the pore network and the flow behavior is controlled by the rheology and the generation, trapping, and coalescence of the flowing foam bubbles. As proposed by Falls et al. 6 and Patzek, II an equation is incorporated to calculate the flowing-foam-bubble density, which, in turn, dictates how the flowing-foam mobility is modified.Chaser SD1OOO™, a surfactant developed for steam-foam applications, was the sole chemical used in this study. Model parameters are obtained from corefloods and by history matching nitrogen foam floods in Berea sandstone cores. Two laboratory corefloods are compared to simulations with the chosen parameters. Field-scale simulations of the steam-foam-drive process are then presented for a range of bubble coalescence rates.

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oil-in-placeremains bypassed either in the deepeat regions of the resexvoir (gravityoverride case) orExperiments were run to Investigatethe effect in the lower permeabilityzones (channelingcaae). of surfactant concentration, injection rate, foam quality, oil saturation, and rock absolute perme-Because of the high fuel coat to generate the ability on the formation and propagation of foama injected steam, research efforts by the oil indusin Berea aandstoneaand Ottawa sandpacks. try have recently been directed toward overcoming First, both gravity override and channeling effects.One constant pressure unsteady-stategaaliquid relative periueability experiments were con-of tha moat promisingmethods being investigatedis ducted in Berea sandstone for varying residual the injection of surfactantswith steam to form a saturations of synthetic oil. It was found that resistive foam which can divert steam into bypassed high oil saturation can hinder the formation of zones.foam (no substantialdecreaae in the gas phaae relative permeability). In such casea, some of the When a mixture of surfactant,steam, and nonoil must firat be displaced from the core before a condensablegas is injected into a well, a foam is foam can form. generated either in the tubing ox at the sand face. In order to be effective in the diversion of steam, Other experiments were parformed by flowing the foam must not only penetrate into the stes?= preformed foams into cores at varioua conditions. swept zones, but must also propagate away from the The foama were preformed at high ratea repreaentawellbore. This is necessary to prevant the followtive of tubing or sandface rates. They were injecup steam from flowing back into the swept zone ted into the porous medium at J.ower ratea repreaen-beyond the foam plug a few feet away from the welltativa of near wellbore conditions (1.5-5m from bore. tha wellbore) but not of in-depth reservoir conditions. Raaults showed that the foam propagation Foattta are gaa-liquid emulaiona that exhibit a rate was significantlyaffected by rock permeabil-VISCOUS behavior in porous media.2 Several authors ity and injected foam texture. In high permeability have proposed mechanisms or obeened flow patterna sandpacks (40-50 darciaa), the foam propagated at pertinent to the viacoua behavior of foams in the same rate as the liquid phasa. Foam propagaporous media. Fried3 proposed that foam moves tion rates decreased substantiallyin lower parme-through pore spaces aa a body. Helm proposed that ability media. gaa flows aa a discontinuous phase separated by liquid lamellaeQ and that the lamellae break and INTRODUCTION reform as gaa passea through pore channels. Hiraaaki and Lawaon5 concluded that a foam's flow Foama are currently used to improve steam regime and its viscous behavior in capillary tubea aweep efficiency in both cyclic and drive oparationa.l 'ho types of reservoirproblems raduce the can be correlated to the ratio of bubble sfze to capillary radiue. This ratio determines whechar a effectiveness of staam applicationsgravity bulk ...

A one-pattern, steam-foam mechanistic field trial was conducted in Section 26C of the Midway-Sunset field (upper Monarch sand). The test objectives were (1) to understand the mechanisms of steam diversion caused by foam under reservoir conditions, (2) to establish whether foam can exist in-depth away from the injection well, and (3) to measure incremental oil that can be attributed to foam. Surfactant was injected with steam and nitrogen continuously, and bottomhole injection pressure (BIHP) increased from 100 to 300 psig, indicating good foam generation. Better steam distribution across the injector's perforations occurred when foam was generated. Improvements in both vertical and areal sweep efficiency of steam were observed. Substantial temperature and gas saturation increases coincided with surfactant breakthrough and local reservoir pressure increases at observation wells. Complementary laboratory corefloods showed that foam generation could occur at low-pressure gradients, which are typical of in-depth conditions. Both laboratory and field data were interpreted as evidence that the in-depth presence of foam was the result of local generation wherever surfactant, steam, and nitrogen were present, rather than propagation of a foam bank generated near the injector. Some oil-production increase was also observed during the test; however, an accurate quantitative estimate of incremental oil owing to foam was difficult to establish.

It is well established that uncertainty exists in simulated recovery forecasts due to the ambiguity in the measurement and representation of the reservoir and geologic parameters. This is especially true for immature projects, such as deep-water reservoirs, where the high cost of data limits the information that is available to build reservoir models. We present two strategies, based on Experimental Design, to quantitatively assess this uncertainty in recovery predictions for primary and waterflood processes. We apply the Experimental Design methodology to channelized sandstone systems because of their relevance to many deep-water projects. We choose to study synthetic geological analogs of channelized systems that are built from panoply of relevant parameters while taking into account the uncertainty that exists in the estimation of their ranges. We use the results of this study to generate type curves with neural networks. The trained neural networks can be used to rapidly predict reservoir performance where field data is very limited. We discuss applications of this methodology on field cases from western Africa. Introduction An irony of contemporary petroleum exploration and development is that although technological advances have been significant, risk has not been reduced in all cases. In fact, risk may actually be greater in many frontier reservoir development projects. For example, in deepwater projects the large initial capital investments due to costs associated with platform design, and well construction, are made with limited knowledge of reservoir architecture and geology. The high cost of drilling, completing and coring wells limits the availability of geological, petrophysical and engineering data, which are needed to build reliable reservoir simulation models to help in the decision process. A method that can identify the key parameters governing uncertainty in production and economic forecast in the early phases of the study will significantly ameliorate the data acquisition program. Simulation is often the tool of choice in the planning and evaluation of sequential reservoir development phases. Typically, earth scientists build the most representative geological model applying expert knowledge using well logs and other geological data. A few geostatistical realizations are generated to sample the uncertainty in geological parameters. A representative combination of geology, fluid and flow parameters, along with well locations constitutes the base case model. This model is then simulated to obtain production profiles and recovery factor for a chosen recovery process. Finally, economic performance indicators (ROI, NPV) are computed for the project. Estimating recovery uncertainty is complicated because it requires an understanding of both the reservoir's static architecture and dynamic behavior during production. Recovery depends on structural, stratigraphic, and per-meability architecture, fluid and engineering properties, drive mechanisms, and spacing/orientation of producing and injecting wells. The uncertainty, which is associated with the measurement and estimation of these parameters, will result in uncertainty in reservoir performance estimates.