Well injectivity, models

H2S production caused by the growth of sulfate-reducing bacteria in a biofilm in the reservoir rock close to the injection well (biofilm model)... 

In most instances, hydrodynamic dispersion is not great enough to require detailed consideration in hydrogeologic modeling for fate assessment of deep-well-injected wastes. Flowever, regional variations (such as the presence of an USDW in the same aquifer as the injection zone, as is the case in parts of Florida) should be evaluated before a decision is made to exclude it. 

METHODS AND MODELS FOR PREDICTING THE GEOCHEMICAL FATE OF DEEP-WELL-INJECTED WASTES... 

The geochemical interactions possible between an injected waste and the reservoir rock and its associated fluids can be quite complex. Thus a combination of computer modeling, laboratory experimentation, and field observation will inevitably be necessary to satisfy current regulatory requirements for a geochemical no-migration deep-well injection. This section covers the computer methods and models available for predicting geochemical fate. 

Calcium-sodium-chloride-type brines (which typically occur in deep-well-injection zones) require sophisticated electrolyte models to calculate their thermodynamic properties. Many parameters for characterizing the partial molal properties of the dissolved constituents in such brines have not been determined. (Molality is a measure of the relative number of solute and solvent particles in a solution and is expressed as the number of gram-molecular weights of solute in 1000 g of solvent.) Precise modeling is limited to relatively low salinities (where many parameters are unnecessary) or to chemically simple systems operating near 25°C. 

FIGURE 20.8 Relative tradeoffs between physical (microcosm) and mathematical models as affected by effluent complexity. (From U.S. EPA, Assessing the Geochemical Fate of Deep-Well-Injected Hazardous Waste A Reference Guide, EPA/625/6-89/025a, U.S. EPA, Cincinnati, OH, June 1990.)... 

SARIPALLI, P. McGrail, P. 2002. Semi-analytical approaches to modeling deep well injection of C02 for geological sequestration. Energy Conversion and Management, 43, 185-198. 

Unfortunately, it is difficult to do a well flow test on an injection well and thus the standard test to estimate AOF is difficult to perform. A suitable well flow model should be used to model the AOF of the injection well. Some safety factor can be used to increase this flow rate based on the uncertainty of the available models. 

K.P. Saripalli, M.M. Sharma, and S.L. Bryant, Modeling Injection Well Performance During Deep-well Injection of Liquid Wastes, J. Hydrology, 227(1-4), 41-55, Jan. 31 (2000). 

Figure 11.9 Normalised comparison of electron absorption dynamics of N3-sensitised Ti02 films in ethylene/propylene carbonates (1 1) following excitation at different wavelengths. Inset the same data plotted on a shorter time scale. The solid lines are fits using the two-state injection model. The fast component is well described by a <100 fs rise and the slow component is fitted by a stretched exponential function with a 50 ps time constant. Reproduced with permission from J. Phys. Chem. B 107, 7376 (2003) (Asbury et al, 2003). Copyright 2003 American Chemical Society.

A black oil model was built based on matching 35 years of waterflooding history. This model was used to define the injection and production rates, limits of well injection, and flowing pressure so that the injection and production were... 

The one-compartment bolus IV injection model is mathematically the simplest of aU PK models. Drug is delivered directly into the systemic circulation by a rapid injection over a very short period of time. Thus the bolus rV injection offers a near perfect example of an instantaneous absorption process. Representation of the body as a single compartment implies that the distribution process is essentially instantaneous as well. The exact meaning of the assumptions inherent in this model are described in the next section. Model equations are then introduced that allow the prediction of plasma concentrations for drugs with known PK parameters, or the estimation of PK parameters from measured plasma concentrations. Situations in which the one-compartment instantaneous absorption model can be used to reasonably approximate other types of drug delivery are described later in Section 10.7.5. 

Well Injectivity. Barkman and Davidson (2) were among the first to model injectivity decline due to foreign solids invasion. They considered that after a specific period of time, known as the bridging time, the decline in injectivity is due to filter cake formation only. According to the filtration theory, the cumulative volume is proportional to square root of filtration time, provided that the pressure drop across the filter remains constant. Figure 14 shows the variation of the cumulative vol-... 

The black oil model history match simulated a 640 acre area of the expected 2980 acre field polymer process area. The model area represented 1.5 nine-spot patterns or 12.6 percent of the expected field polymer flood area pore volume. The model grid was 13 X 39 X 5 layers (2,535 blocks) and contained 16 active wells. The model represented the light oil zone, the heavy oil zone, and the aquifer in order to match performance. The model area was chosen to exhibit a porosity thickness value, injection withdrawal ratio, and water oil ratio trend that was representative of the field. A... 

To model the injection molding process, a viscosity function is required. A number of well known models are... 

It Is important to know how much each well produces or injects in order to identify productivity or injectivity changes in the wells, the cause of which may then be investigated. Also, for reservoir management purposes (Section 14.0) it is necessary to understand the distribution of volumes of fluids produced from and injected into the field. This data is input to the reservoir simulation model, and is used to check whether the actual performance agrees with the prediction, and to update the historical data in the model. Where actual and predicted results do not agree, an explanation is sought, and may lead to an adjustment of the model (e.g. re-defining pressure boundaries, or volumes of fluid in place). 

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