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Miscible flooding is presently the most-commonly used approach in enhanced oil recovery. Miscible flooding is a general term for injection processes that introduce miscible gases into the reservoir. A miscible displacement process maintains reservoir pressure and improves oil displacement because the interfacial tension between oil and water is reduced. A decision to implement a miscible flood in a particular field will usually consist of a sequential approach. First is the screening stage.
This chapter concerns the use of water injection to increase the production from oil reservoirs, and the technologies that have been developed over the past 50 years to evaluate, design, operate, and monitor such projects. Use of water to increase oil production is known as "secondary recovery" and typically follows "primary production," which uses the reservoir's natural energy (fluid and rock expansion, solution-gas drive, gravity drainage, and aquifer influx) to produce oil. The principal reason for waterflooding an oil reservoir is to increase the oil-production rate and, ultimately, the oil recovery. This is accomplished by "voidage replacement"--injection of water to increase the reservoir pressure to its initial level and maintain it near that pressure. The water displaces oil from the pore spaces, but the efficiency of such displacement depends on many factors (e.g., oil viscosity and rock characteristics).
There are many opportunities to modify and improve the waterflood as data are acquired and analyzed. Applying material balance concepts means that initially there is "reservoir fill-up" if the reservoir previously had some years of primary production. During this period, the reservoir is repressured to its original reservoir pressure because the injected-water volumes will be substantially greater than the produced-fluid volumes. Thereafter, the waterflood will be operated as a voidage-replacement process. The earliest waterflood monitoring techniques were developed soon after the first field applications of waterflooding; they were based on simple plots, maps, and calculations.
Ideally, fluid properties such as bubblepoint pressure, solution gas/oil ratio, formation volume factor and others are determined from laboratory studies designed to duplicate the conditions of interest. However, experimental data are quite often unavailable because representative samples cannot be obtained or the producing horizon does not warrant the expense of an in-depth reservoir fluid study. In these cases, pressure-volume-temperature (PVT) properties must be determined by analogy or through the use of empirically derived correlations. This page introduces these correlations and provides links to more in-depth calculations. The calculation of reserves in an oil reservoir or the determination of its performance requires knowledge of the fluid's physical properties at elevated pressure and temperature.
Before computer modeling was common, the 3D aspects of a waterflood evaluation were simplified so that the technical problem could be treated as either a 2D-areal problem or a 2D-vertical problem. To simplify 3D to 2D areal, either the reservoir must be assumed to be vertically a thin and homogeneous rock interval (hence having no gravity considerations) or one of the published techniques to handle the vertical heterogeneity and expected gravity effects within the context of a 2D-areal calculation must be used. The primary areal considerations for a waterflood involve the choices of the pattern style (see Figure 1) and the well spacing. Maximizing the ultimate oil recovery and economic return from waterflooding requires making many pattern- and spacing-related decisions when secondary recovery is evaluated. This has been particularly true for onshore oil fields in the US in which a significant number of wells were drilled for primary production.
This section discusses the impact of vertical variations in permeability and the effect of gravity on simple 2D reservoir situations in which the areal effects are ignored. Gravity effects always are present because for any potential waterflood project, oil always is less dense than water, even more so after the gas is included that is dissolved in the oil at reservoir conditions. The discussion below does not include the Pc effects on vertical saturation distributions. Through countercurrent imbibition, Pc effects help to counteract nonequilibrium water/oil saturation distributions. The mathematics of including Pc effects makes the problems too complicated for inclusion here.
At the pore level (i.e., where the water and oil phases interact immiscibly when moving from one set of pores to the next), wettability and pore geometry are the two key considerations. The interplay between wettability and pore geometry in a reservoir rock is what is represented by the laboratory-determined capillary pressure curves and water/oil relative permeability curves that engineers use when making original oil in place (OOIP) and fluid-flow calculations. This article discusses these basic concepts and their implications for initial water- and oil-saturation distribution, relative permeability, and how initial gas saturation will affect water/oil flow behavior. Figure 1 is a schematic diagram of the water/oil displacement process. Wettability is defined in terms of the interaction of two immiscible phases, such as oil and water, and a solid surface, such as that of the pores of a reservoir rock.
Oil reservoirs are classified according to their fluid type. There are three broad oil classes. In order of increasing molecular weight, they are volatile oil, black oil, and heavy oil. Heavy-oil reservoirs are of minor interest during pressure depletion because they typically yield only marginal amounts of oil because of their low dissolved-gas contents and high fluid viscosities. The distinguishing characteristic between volatile and black oils is the stock-tank-oil content of their equilibrium gases.
If least-squares linear regression is used to compute N in Step 5, an equation analogous to Eq. 17 is used (where Eow is substituted for Eowf). This solution method is iterative because the material-balance error must be minimized. This calculation is carried out with a trial-and-error method or a minimization algorithm. Least-squares linear regression and minimization algorithms have become standard features in commercial spreadsheets.