The fluorescence characteristics of intracellular fluorescein, formed by hydrolysis of fluorescein diacetate in peripheral human lymphocytes, were studied by fluorometry on cell suspensions and compared to those of albumin bound and free fluorescein in solution. The absorption and fluorescence spectra of both intracellular fluorescein and fluorescein in aqueous solutions of albumin and glycerol were red shifted by 2-10 nm as compared to the spectra of fluorescein in phosphate buffered saline. The fluorescence polarization (P) of both intracellular fluorescein and a mixture of albumin-bound and free fluorescein showed a decrease towards low emission wavelengths and an increase toward high excitation wavelengths. The results were found to be consistent with a simple model assuming that part of the intracellular fluorescein is dissolved in the aqueous phase of the cytoplasm, giving P < 0.1, while the rest is bound to macromolecules, giving P = 0.33. The fraction of bound intracellular fluorescein was estimated to be about 70%. Fluorescein was found to bind with higher affinity and more rigidly (P = 0.43) to albumin than to intracellular macromolecules in general. In order to elucidate the state of intracellular fluorescein further, we have studied the fluorescence characteristics of intracellular fluorescein and compared them to those of fluorescein in phosphate buffered saline (PBS) and in solutions of glycerol and albumin in PBS, glycerol serving as a medium of lower polarity than water and albumin as a model for fluorescein binding proteins. The results are consistent with the assumption that part of the intracellular fluorescein is bound to macromolecules while the rest is dissolved in intracellular water. TheoryTo make a simple model for intracellular fluorescein binding, we assume that the fluorescence from the cells during FDA hydrolysis consists of two components: ( a ) fluorescence from unbound fluores-272
Summary The objective of the present paper is to communicate the basic knowledge needed for estimating the uncertainty in reservoir fluid parameters for prospects, discoveries, and producing oil and gas/condensate fields. Uncertainties associated with laboratory analysis, fluid sampling, process description, and variations over the reservoirs are discussed, based on experience from the North Sea. Introduction Reliable prediction of the oil and gas production is essential for the optimization of development plans for offshore oil and gas reservoirs. Because large investments have to be made early in the life of the fields, the uncertainty in the in-place volumes and production profiles may have a direct impact on important economical decisions. The uncertainties in the description of reservoir fluid composition and properties contribute to the total uncertainty in the reservoir description, and are of special importance for the optimization of the processing capacities of oil and gas, as well as for planning the transport and marketing of the products from the field. Rules of thumb for estimating the uncertainties in the reservoir fluid description, based on field experience, may therefore be of significant value for the petroleum industry. The discussion in the present paper is based on experience from the fields and discoveries where Statoil is an operator or partner, including almost all fields on the Norwegian Continental Shelf,1,2 and all types of reservoir oils and gas condensates except heavy oils with stock-tank oil densities above 940 kg/m3 (below 20° API). Fluid Parameters in the Reservoir Model The following parameters are used to describe the reservoir fluid in a "black oil" reservoir simulation model:densities at standard conditions of stabilized oil, condensate, gas, and water;viscosity (?O) oil formation volume factor (B O) and gas-oil ratio (RS) of reservoir oil;viscosity (?G) gas formation volume factor (B G) and condensate/gas ratio (RSG) of reservoir gas;viscosity (?W) formation volume factor (BW) and compressibility of formation water; andsaturation pressures: bubblepoint for reservoir oil, dew point for reservoir gas. The actual input is usually slightly more complex, with saturation pressure given as a function of depth, with RS and R SG defined as a function of saturation pressure, and with oil and gas viscosities and formation volume factors given as a function of reservoir pressure for a range of saturation pressure values. However, minor changes in saturation pressure versus depth are usually neglected, and the oil dissolved in the reservoir gas can also be neglected (RSG=0) when the solubility is small. Uncertainties in the modeling of other fluid parameters (interfacial tension may for instance be of importance, because of its effect on the capillary pressure), or compositional effects like revaporization of oil into injection gas, are not discussed here. Uncertainties in viscosity, formation volume factor and compressibility of formation water, and density of gas at standard conditions, are judged to be of minor importance for the total uncertainties in the reservoir model. The uncertainty in the salinity of the formation water is discussed here instead, because it is used for calculations of water resistivity for log interpretation, and therefore, affects the estimates of initial water saturation in the reservoir. In a compositional reservoir simulation model, the composition of reservoir oil and gas (with, typically, 4 to 10 pseudocomponents) is given as a function of depth, while phase equilibria and fluid properties are calculated by use of an equation of state. However, the uncertainties in the fluid description can be described in approximately the same way as for a "black oil" model. Quantified uncertainty ranges in the present paper are coarse estimates, aiming at covering 80% of the probability range for each parameter (estimated value plus/minus an uncertainty estimate defining the range between the 10% and 90% probability values3). Prospect Evaluation Assessments of the uncertainties in the reservoir description, as a basis for economic evaluation, are made in all phases of exploration and production. Of course, the complexity in the fluid description increases strongly from prospect evaluation through the exploration phase and further into the production phase, but the main fluid parameters in the reservoir model are the same. The prediction of fluid parameters in the prospect evaluation phase, before the first well has been drilled, is based on reservoir fluid data from discoveries near by, information about source rocks and migration, and empirical correlations. The uncertainties vary strongly from prospect to prospect. The probability as a function of volume for the presence of reservoir oil and gas is usually the most important fluid parameter. The probability for predicting the correct hydrocarbon phase varies from 50% (equal probability for reservoir oil and gas) to 90% (in regions where either oil or gas reservoirs are strongly dominating, or when the reservoir fluid can be expected to be the same as in another discovery near by). For formation volume factors, gas/liquid ratios, viscosities, and densities, an estimate for the most probable value as well as for a high and low possible value is commonly given. The range between the high and low value is often designed to include 80% of the probability range for the parameter, but accurate uncertainty estimates can seldom be made. The ratio of the high and low value is, typically, 1.5 to 50 for R SG 1.1 to 1.5 for B G 1.1 to 2.5 for ?G 1.2 to 3 for RS 1.1 to 2 for BO 1.5 to 5 for (?O and 1.03 to 1.1 for densities of stabilized oil and condensate. From Discovery to Production After a discovery has been made, the fluid description is based on laboratory analyses of reservoir fluid samples from drill-stem tests, production tests, and wireline sampling (RFT, FMT, MDT) in exploration and production wells. Pressure gradients in the reservoirs from measurements during wireline and drill-stem tests, analysis of residual hydrocarbons in core material from various depths, measurements of gas/oil ratio during drill-stem and production tests, and measurements of product streams from the field, give important supplementary information.
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