MXPA06000142A - System and methods of deriving differential fluid properties of downhole fluids - Google Patents

Abstract

Methods and systems are provided for downhole analysis of formation fluids by deriving differential fluid properties and associated uncertainty in the predicted fluid properties based on downhole data less sensitive to systematic errors in measurements, and generating answer products of interest based on the differences in the fluid properties. Measured data are used to compute levels of contamination in downhole fluids using, for example, an oil-base mud contamination monitoring (OCM) algorithm. Fluid properties are predicted for the fluids and uncertainties in predicted fluid properties are derived. A statistical framework is provided for comparing the fluids to generate robust, real-time answer products relating to the formation fluids and reservoirs thereof. Systematic errors in measured data are reduced or eliminated by preferred sampling procedures.

Description

SYSTEM AND METHODS FOR DERIVING DIFFERENTIAL FLUID PROPERTIES OF WELL-BACKGROUND FLUIDS RELATED REQUEST DATA This application claims priority according to 35 U.S.C. S 119 to the Provisional Application of E.U.A. Series No. 60 / 642,781 (Attorney's File No. 60.1601 US) naming L. Venkatara anan et al., As inventors, and filed on January 11, 2005; and in accordance with 35 U.S.C. S 1209 as a continuation of part of the US Non-Provisional Application, Series No. 11 / 132,545 (Attorney's File No. 26,0290 US) appointing L. Venkataramanan et al., As inventors, and filed on May 19 of 2005, now pending, the aforementioned applications being incorporated herein by reference in their entirety for all purposes. FIELD OF THE INVENTION The present invention relates to the analysis of foaming fluids to evaluate and test a geological formation for exploration and development purposes of hydrocarbon producing wells, such as oil or gas wells. More particularly, the present invention is directed to system and methods for deriving flux differential properties of bottomhole measurement formation fluids, such as spectroscopy measurements, which are less sensitive to systematic errors in measurement. BACKGROUND OF THE INVENTION Downhole fluid analysis (DFA) is an important and efficient research technique typically used to ensure the characteristics and nature of geological formations that have hydrocarbon deposits. DFA is used in oil field exploration and development to determine petrophysical, mineralogical and fluid properties of hydrocarbon deposits. DFA is a kind of reservoir fluid analysis that includes composition, fluid properties and phase behavior of downhole fluids to characterize hydrocarbon fluids and reservoirs. Typically, a complete mixture of fluids, such as oil, gas, and water, is found at the bottom of the well in reservoir formations. Downhole fluids, which are also referred to as formation fluids, have characteristics, including pressure. , color of live fluid, density of dead oil, gas-oil ratio (GOR), among other fluid properties, which serve as indicators to characterize hydrocarbon deposits.

In this, the hydrocarbon deposits are analyzed and characterized based, in part, on the fluid properties of the formation fluids in the tanks.

In order to evaluate and test the underground formations surrounding a borehole, it is often desirable to obtain formation fluid samples for purposes of characterizing the fluids. Tools have been developed that allow training samples to be taken in a registration race or during drilling. The Deposit Formation Tester (RFT) and Schlumberger Modular Formulation Dynamics Tester (MDT) tools are examples of sampling tools for extracting fluid samples from formation for surface analysis. Recent developments in DFA include techniques for characterizing bottomhole formation fluids in a borehole or drilling well. In this, the Schlumberger MDT tool can include one or more fluid analysis modules, such as the Composition Fluid Analyzer (CEA) and Live Fluid Analyzer.

(LFA) from Schlumberger, to analyze downhole fluids checked by the tool while the fluids are still at the bottom of the well. In DFA modules of the above-mentioned type, the formation fluids that are to be analyzed at the bottom of the well flow beyond the sensor modules, such as the spectrometer modules, which analyze the fluids flowing through absorption spectroscopy almost infrared (NIR), for example. The US Patents of the same owner Nos. 6,476,384 and 6,768,105 are examples of patents related to the prior art, contents of which are incorporated herein by reference in their entirety. Formation fluids can also be captured in sample chambers associated with the DFA modules, which have sensors, such as pressure / temperature gauges, embedded therein to measure the fluid properties of the captured formation fluids. Downhole measurements, such as optical density of forming fluids using a spectral analyzer, are prone to systematic errors in measurements. These errors may include variations in measurements with temperature, deviations in the electronics leading to deviated readings, interference with other effects such as systematic pump hits, among other systematic errors in measurements. These errors have a pronounced effect on the fluid characterizations obtained from the measured data. These systematic errors are difficult to characterize in advance with tool calibration »COMPENDIUM OF THE INVENTION In consequence of the above-discussed background, and other factors that are known in the field of downhole fluid analysis, the applicants discovered methods and systems for real-time analysis of formation fluids deriving differential fluid properties from fluids and response products of interest based on differential fluid properties that are less sensitive to systematic errors in measured data. In preferred embodiments of the invention, the Downhole measurements, such as spectroscopic data, that have reduced errors in measurements are used to compute contamination levels. A petroleum-based mud contamination monitoring (CMO) algorithm can be used to determine contamination levels, for example, oil-based mud filtration (OBM), in downhole fluids »Fluid properties , such as live fluid color, dead oil density, gas-oil ratio (GOR), fluorescence, among others, are predicted for downhole fluids based on predicted levels of contamination. Uncertainties in the properties of fluid are derived from the uncertainty in measured data and uncertainty in predicted contamination »A statistical structure is provided for comparing fluids to generate strong response products, in real time, related to the formation of fluids and deposits. Applicants developed methodology and modeling systems to allow real-time DFA by comparing fluid properties. For example, in preferred embodiments of the invention, modeling techniques and systems are used to process fluid analysis data, such as spectroscopic data, related to downhole fluid sampling and to compare two or more fluids for the purposes of derive analytical results based on the comparative properties of fluids. Applicants recognized that reducing or eliminating systematic errors in measured data, by using novel sampling and downhole analysis methods of the present invention would lead to strong and accurate comparisons of formation fluids based on predicted fluid properties with reduced errors in downhole data measurements. Applicants also recognized that quantifying levels of contamination in training fluids and determining uncertainties associated with quantified levels of contamination for fluids would be advantageous steps to derive response products of interest in exploration and development of oil fields. The applicants also acknowledged that the uncertainty in measured data and in quantified contamination levels could be propagated to corresponding uncertainties in other fluid properties of interest, such as color of live fluid, density of dead oil, gas-oil ratio (GOR), fluorescence, among others. The applicants further recognized that quantifying the uncertainty in predicted fluid properties of formation fluids would provide an advantageous basis for real time comparison of fluids, and is less sensitive to systematic errors in the data. According to the invention, a method for deriving fluid properties from downhole fluids and providing response products from downhole spectroscopy measurements includes acquiring at least a first fluid and a second fluid and, under substantially the same conditions From bottom of well, analyze the first and second fluids with a device in a drilling to generate fluid property data for the first and second fluids. In one embodiment of the invention, the method further comprises deriving respective fluid properties from the fluids based on the fluid property data for the first and second fluids.; qualify the uncertainty of the fluid properties derived; and compare the fluids based on the fluid properties derived and the uncertainty in fluid properties. The fluid properties derived by one or more color of living fluid, dead crude density, GOR and fluorescence. In one embodiment of the invention, the method can include providing response products comprising sampling optimization by the drilling device based on the respective fluid properties derived for the fluids. In another embodiment of the invention, the fluid property data comprises optical density of one or more spectroscopic channels of the device in the perforation and the method further comprises receiving data of uncertainty with respect to the optical density data. In yet another embodiment, the The method may include placing the device in the piercing in a position based on a fluid property of the fluids. Another embodiment of the invention may include quantifying a level of contamination and uncertainty thereof for each of the two fluids. Still other embodiments of the invention may include providing response products, based on fluid property data, related to one or more of compartment formation, composition gradients and optimal sampling process with respect to evaluation and testing of a geological formation. . A method of the present invention includes decolorizing the fluid property data; determine the respective compositions of the fluids; derive the volume fraction of light hydrocarbons for each of the fluids; and provide training volume factor for each of the fluids. The fluid property data for each fluid can be received from a methane channel and a color channel from a downhole spectral analyzer. Other embodiments of the invention may include quantifying a level of contamination and uncertainty thereof of each of the channels for each fluid; obtaining a linear combination of pollution levels for the channels and uncertainty with respect to the combined level of contamination for each fluid; determine the composition of each fluid; predict the GOR of each fluid based on the corresponding composition of each fluid and the combined level of contamination; and derive the uncertainty associated with the predicted GOR of each fluid. The fluids can be compared based on the predicted GOR and the uncertainty derived from each fluid. In one aspect of the invention, comparing fluids comprises determining the likelihood that the fluids will be different. A method of the invention may include acquiring at least one of the first and second fluid of a land formation traversed by the bore. Another aspect of the invention may include acquiring at least one of the first and second fluid from a first source and another from the first and second fluid from a second, different source. The first and second sources may comprise different locations of a land formation traversed by the borehole. At least one of the first and second sources may comprise a stored fluid. The first and second sources can include fluids acquired at different times in the same location of a land formation traversed by drilling. In still another embodiment of the invention, a method for reducing systematic errors in downhole data comprises acquiring downhole data in sequence for at least a first and a second fluid substantially in the same bottomhole conditions with a device in a well of sounding. Still another embodiment of the invention provides a downhole fluid characterization apparatus having a fluid analysis module; a flow line for fluids removed from a formation to flow through the fluid analysis module; a device selectively operable, structured and arranged with respect to the flow line to alternately flow at least a first and a second fluid through the fluid analysis module; and at least one sensor associated with the fluid analysis module for generating fluid property data for the first and second fluids at substantially the same bottomhole conditions. In one embodiment of the invention, the selectively operable device comprises at least one valve associated with the flow line. The valve may include one or more check valves in an outside pumping module and a perforation outlet valve associated with the flow line. In one aspect of the invention, the selectively operable device comprises a device with multiple storage containers for selectively storing and discharging fluids removed from the array. In still another aspect of the invention, a system for characterizing forming fluids and providing response products based on characterization, comprises a drilling tool having a flow line with at least one sensor for sensing the at least one fluid parameter in the flow line? and a selectively operable device associated with the flow line for flowing at least a first and a second fluid through the flow line so as to be in communication with the sensor, wherein the sensor generates fluid property data with with respect to the first and second fluids with the first and second fluids substantially under the same bottomhole conditions. At least one processor coupled to the drilling tool may include means for receiving fluid property data from the sensor and the processor may be configured to derive respective fluid properties from the first and second fluid based on the fluid property data. In other aspects of the invention, a computer-usable medium having computer readable program code therein, which when run by a computer, adapted for use with a drilling system to characterize downhole fluids, comprises receiving fluid property data for when • less first and second downhole fluids, wherein the fluid property data of the first and second fluids are generated with a device in a borehole with the first and second fluids substantially at the same bottomhole conditions; and calculating respective fluid properties of the fluids based on the received data.

Additional advantages and novel features of the invention will be set forth in the description that follows or can be learned by experts in the field through reading the materials herein or practicing the invention. The advantages of the invention can be achieved through the means mentioned in the appended claims. BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings illustrate preferred embodiments of the present invention and are a part of the specification. Along with the description that follows, the drawings demonstrate and explain principles of the present invention. Figure 1 is a schematic cross-sectional representation of an exemplary operating environment of the present invention. Figure 2 is a schematic representation of a system for comparing formation fluids in accordance with the present invention. Figure 3 is a schematic representation of a fluid analysis module apparatus for comparing comparison fluids in accordance with the present invention. Figure 4 is a schematic illustration of a fluid sampling chamber according to an embodiment of the present invention, for capturing or trapping formation fluids in a fluid analysis module apparatus. Figures 5 (A) to 5 (E) are flow charts illustrating preferred methods for comparing downhole fluids according to the present invention and deriving response products therefrom. Figure 6 (A) graphically shows an example of measured (dashed line) and predicted spectra (solid line of dead oil from a hydrocarbon and Figure 6 (B) represents an empirical correlation between cut-off wavelength and spectrum of crude oil, Figure 7 illustrates, in a graph, several of the GOR (in scf / stb) of a retrograde gas as a function of volumetric contamination.At small levels of contamination GOR is very sensitive to volumetric contamination, the small Contamination uncertainty can result in large uncertainty in GOR Figure 8 (A) graphically illustrates GOR and corresponding uncertainties for fluids A (blue) and B (red) as functions of volumetric contamination. The final contamination of fluid A is ?? = 5% while the final contamination for fluid B is? B = 10%. Figure 8 (B) is a graphic illustration of the distance K-S as a pollution function. The GOR of the two fluids is better compared to? Br where the sensitivity that distinguishes between the two fluids is maximum, which can be reduced compared to the optical densities of the two fluids when the level of contamination of? . Figure 9 graphically shows the optical density (OD) of the methane channel (at 1650 nm) for three stations A (blue), B (red) and D (magenta). The pollution model adjustment is shown in black dashed lines for all three curves. The contamination just before the samples were collected for stations A, B and D are 2.6%, 3.8% and 7.1%, respectively. Figure 10 graphically illustrates a comparison of measured ODs (dashes in dashes) and live fluid spectra (solid plots) for stations A (blue), B (red) and D (magenta). The fluid in station D is darker and statistically different from stations A and B. Fluids in stations A and B are statistically different with a probability of 0.72. Reference was made to the fluids in Figure 9 above. Figure 11 graphically shows the comparison of live fluid spectra (dashed traces) and predicted dead crude spectra (solid traces) for the three fluids at stations A, B and D (also mentioned above). Figure 12 shows graphically the cut wavelength obtained from the dead oil spectrum and its uncertainty for the three fluids at stations A, B and D (also mentioned above). The three fluids at stations A (blue), B (red) and D (magenta) are statistically similar in terms of cut-off wavelength. Figure 13 is a graph showing the dead oil density for all three fluids in stations A, B and D (also mentioned above) is close to 0.83 g / cc. Figure 14 (A) illustrates graphically that GOR of fluids in stations A (blue) and B (red) are statistically similar and Figure 14 (B) illustrates that GOR of fluids in stations B (red) and D (magenta) ) are also statistically similar. The fluids were previously mentioned in the foregoing. Figure 15 is a graphical representation of optical density data of Station A, corresponding to fluid A, and data of Station B, corresponding to Fluids A and B. Figure 16 represents a graph data of color channel for fluid A (blue) and fluid B (red) measured at stations A and B, respectively (also see figure 15). The black line is the adjustment by the observation algorithm of petroleum-based mud contamination (CMO) to the measured data. At the end of the pumping, the contamination level of fluid A was 1.9% and of fluid B was 4.3%. Figure 17 (A) graphically illustrates the leading edge of data in Station B corresponding to fluid A and Figure 17 (B), which graphically illustrates the leading edge of data for one of the channels in Station B, shows that the The measured optical density is almost constant (within the noise scale in the measurement). Figure 18, a graphical comparison of live fluid colors, shows that the two fluids A and B can not be distinguished based on color. Figure 19, a graphical comparison of dead oil spectra, shows that the two fluids A and B are indistinguishable in terms of dead oil color. Through the drawings, identical reference numbers indicate similar elements, but not necessarily identical. While the invention is susceptible to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and will be described in detail herein. However, it should be understood that the invention is not intended to be limited to the particular forms described. Rather, the invention should cover all modifications, equivalents and alternatives that fall within the scope of the invention as defined in the appended claims. DETAILED DESCRIPTION OF PREFERRED MODALITIES Illustrative modalities and aspects of the invention are described below »In the interests of clarity, not all the particularities of an actual implementation are described in the specification. Of course, it will be appreciated that in the development of any such modality, numerous specific implementation decisions must be made to achieve the specific goals of the developers, such as compliance with system-related and business-related constraints, which will vary from one implementation to another. to another. Furthermore, it will be noted that said development effort could be complex and time-consuming, but in any case it would be a routine obligation for those of ordinary experience in the field who have the benefit of the present disclosure. The present invention is applicable to oil field exploration and development in areas such as downhole fluid analysis of wire line and log while drilling (LD) using fluid analysis modules, such as the Fluid Analyzer of Composition (CFA) and / or Modules of Live Fluid Analyzer (LFA) of Schlumberger, in a training test tool, for example. Modular Training Dynamics Tester (MDT). As used in this, the term "real time" refers to processing and analysis of data that are substantially simultaneous with the acquisition of a part or all of the data, such as while a drilling rig is in a well or in a well site coupled with logging or drilling operations; the term "response product" refers to the intermediate and / or final products of interest with respect to oil field exploration, development and production, which are derived from or acquired by processing and / or analyzing bottomhole fluid data; the term "compartmentalization" refers to lithological barriers to fluid flow to prevent a hydrocarbon reservoir from being treated as a single producing unit, the terms "contamination" and "contaminants" refer to unwanted fluids, such as filtering of petroleum-based mud, obtained while sampling for deposit fluids; and the term "uncertainty" refers to a calculated amount or percentage by which an observed or calculated value may differ from the true value. The understanding of applicants for formation of compartments in hydrocarbon reservoirs provides a basis for the present invention. Typically, pressure communication between layers in a formation is a measure used to identify compartmentalization. However, pressure communication does not necessarily translate into flow communication between layers and, an assumption that it does, can lead to a lack of flow compartments. It has been recently established that pressure measurements are insufficient when calculating compartment compartments deposit and gradients of composition. Since pressure communication occurs through geologic ages, it is possible for two scattered sand bodies to be in pressure communication, but not necessarily in flow communication with each other. Applicants recognized that a fallacy in identifying compartment formation can result in significant errors that are made in production parameters such as drainage volume, flow rates, well placement, installation size and completion equipment, and prediction errors of production. The applicants also recognized a current need for applications of strong and precise modeling techniques and novel sampling procedures for the identification of compartment formation and composition gradients, and other features of interest in hydrocarbon deposits.

Currently decisions about compartment formation and / or composition gradients are derived from a direct comparison of fluid properties, such as the gas-oil ratio (GOR), between two neighboring zones in a formation »Evaluation decisions, such as possible GOR inversion or density inversion, which are markers for compartment formation, are made based on the direct comparison of fluid properties. The applicants recognized that these methods are appropriate when two surrounding zones have a marked difference in fluid properties, but a direct comparison of fluid properties of nearby zones in a formation is less satisfactory when the fluids therein have varying levels of contamination and The difference between fluid properties is small, however, significant when analyzing the deposit. Applicants also recognized that frequently, in certain geological establishments, fluid density inversions may be small and project through small vertical distances. In facilities where the density inversion, or equivalently the GOR gradient, is small, the current analysis could misidentify a deposit formed in compartments as a single flow unit with costly production consequences as a result of misidentification. Similarly, inaccurate determinations of spatial variations of fluid properties can propagate into significant inaccuracies in predictions with respect to fluid production forcing. In view of the above, the applicants understood that it is critical to determine and quantify small differences in fluid properties between adjacent layers in a geological formation containing hydrocarbon deposits. Additionally, once a deposit has started production, it is often essential to monitor the recovery of hydrocarbons from sectors, such as layers, failure blocks, etc., within the deposit. The key data to accurately monitor hydrocarbon recovery are hydrocarbon compositions and properties, such as optical properties, and differences in fluid compositions and properties, for different sectors of the oil field. Accordingly, the applicants' understanding of the factors discussed herein, in the present invention provides systems and methods for comparing downhole fluids using strong statistical structures, which compare the fluid properties of two or more fluids that the fluids have. same or different fluid properties, for example, equal or different levels of contamination by sludge filtrates. In this, the present invention provides systems and methods for comparing downhole fluids using cost effective and efficient statistical analysis tools. Real-time statistical comparisons of fluid properties that are predicted for downhole fluids are made with a view to characterizing hydrocarbon deposits, such as identifying the compartment formation and / or composition gradients in the deposits. The applicants recognized that fluid properties, eg, GOR, fluid density, as measured depth functions provide advantageous markers for deposit characteristics. For example, if the GOR derivative as a function of depth is step-like, it is To say, it does not continue, the formation of compartments in the deposit is probable. Similarly, other fluid properties can be used with or indicators of compartment formation and / or composition gradients. In one aspect of the invention, downhole measurements, such as spectroscopic data from a downhole tool , such as the MDT, are used to compare two fluids having equal or different levels of sludge filtrate contamination. In another aspect of the invention, downhole fluids are compared by quantifying uncertainty in various predicted fluid properties. The systems and methods of the present invention utilize the concept of sludge filtration fraction that decreases asymptotically over time. The present invention, in preferred embodiments, uses optical density coloration measurement and near infrared measurement (NIR) of gas-oil ratio spectroscopic data (GOR) to derive contamination levels in two or more spectroscopic channels with respect to the fluids being sampled. These methods are discussed in more detail in the following patents, each of which is incorporated herein by reference in its entirety: US Patents. Nos. 5,939,717; 6,274,865; and 6,350,986. The techniques of the present invention provide strong statistical structures for comparing fluid properties of two or more fluids with equal or different levels of contamination. For example, two fluids, identified A and B, can be obtained from Stations A and B, respectively. Fluid fluid properties, such as live fluid color, dead crude density, fluorescence and gas-oil ratio (GOR), can be predicted for both fluids based on the measured data. The uncertainty in fluid properties can be computed from the ncextídumbre in the measured data and uncertainty in contamination, which is derived for the fluids of the measured data. Both random and systematic errors contribute to the uncertainty in the measured data, such as optical density, which is obtained, for example, by means of a downhole fluid analysis module or modules. Once the fluid properties and their associated uncertainties are quantified, the properties are compared in a statistical structure. The differential fluid properties of the fluids are obtained from the difference in the corresponding fluid properties of the two fluids. The uncertainty in the quantification of differential fluid properties reflects both random and systematic errors in the measurements, and can be quite large. Applicants discover new and advantageous fluid sampling and bottomhole analysis procedures that allow data acquisition, sampling and corresponding analysis of two or more fluids so that differential fluid properties are less sensitive to systematic errors in measurements . In conventional downhole sampling procedures, the formation fluids analyzed or sampled in a first station are not trapped and taken to the next station. Consequently, uncertainty computations in differential fluid properties reflect both random and systematic errors in the measured data, and can be significantly large. In contrast, with the preferred sampling methods of the present invention, systematic errors in measurements are minimized. Consequently, the differences derived in fluid properties are stronger and more accurately reflect the differential fluid properties. FIG. 1 is a schematic cross-sectional representation of an exemplary operating environment of the present invention. Although Figure 1 illustrates a land-based operating environment, the present invention is not limited to land and has applicability to water-based applications, including development of deep water in petroleum reservoirs. Also, even when the description in the present uses an adjustment of exploration and production of oil and gas, it is contemplated that the present invention has applicability to other facilities, such as underground water tanks. In Figure 1, a service vehicle 10 is located at a well site having a bore 12 with a drilling tool 20 suspended therein at the end of a wire line 22. In this, it is further contemplated that techniques and systems of the present invention are applicable in L D processes. Typically, the bore 12 contains a combination of fluids such as water, sludge, forming fluids, etc. The drilling tool 20 and the wire line 22 are typically structured and arranged with respect to the service vehicle 10 as shown schematically in Figure 1, in an example layout. Figure 2 describes an exemplary system 14 in accordance with the present invention for comparing downhole fluids and generating analytical products based on the comparative fluid properties, for example, while the service vehicle 10 is located at a well (see Figure 1). The drilling system 14 includes a drilling tool 20 for testing land formations and analyzing the composition of fluids that are extracted from a formation and / or drilling. In a grounding installation of the type illustrated in Figure 1, the drilling tool 20 is typically suspended in the bore 12 (see Figure 1) from the lower end of a multiconductor recording wire or wire line 22 on a bobbin in a bore. crank (note again Figure 1) on the forming surface. In a typical system, the registration cable 22 is electrically coupled to a surface electrical control system 24 having electronics and processing systems suitable for control of the drilling tool 20. Also referring to Figure 3, the piercing tool 20 includes an elongate body 26 housing a variety of electronic components and modules, which are schematically depicted in Figures 2 and 3, to provide the necessary and desirable functionality to the string. of drilling tool. A selectively extendable fluid intake assembly 28 and a selectively extensible tool anchoring member 30 (note Figure 2) are disposed respectively on opposite sides of the elongated body 26. The fluid intake assembly 28 is operable to selectively seal or isolate the selected portions of a piercing wall 12 so that pressure or fluid communication with adjacent ground formation is established. In this, the fluid intake assembly 28 may be a single probe module 29 (illustrated in Figure 3) and / or a packer module 31 (also shown schematically in Figure 3). One or more fluid analysis modules 32 are provided in the tool body 26. The fluids obtained from a formation and / or drilling flow through a flow line 33, through the fluid analysis module or modules 32, and can then be discharged through a port of a pumping module 38 out (Note Figure 3). Alternatively, the formation fluids in the flow line 33 can be directed to one or more fluid collection chambers 34 and 36, such as sample chambers of 3,785, 10,409 or 22.71 liters (1, 2-3 / 4 or 6). gallons) and / or six modules of multiple samples of 450 ce, to receive and retain the fluids obtained from the formation for transport to the surface. The fluid intake assemblies, one or more fluid analysis modules, the flow path and the collection chambers, and other operating elements of the drilling tool string 20 are controlled by electrical control systems, such as the electrical surface control system 24 (note Figure 2). Preferably, the electrical control system 24, and other control systems located in the tool body 26, for example, include processor capability to derive fluid properties, compare fluids, and perform other desirable or necessary functions with respect to training fluids in the tool 20, as described in more detail below. The system 14 of the present invention, in its various embodiments, preferably includes a control processor 40 operatively connected to the drill string 20. The control processor 40 is illustrated in Figure 2 as an element of the electrical control system 24. Preferably, the methods of the present invention are modalized in a computer program running on the processor 40 placed, for example, in the control system 24. In operation, the program is coupled to data, for example, from the fluid analysis module 32, through the wire line cable 22, and to transmit control signals to operating elements of the drill string 20. The computer program may be stored in a computerizable storage medium 42 associated with the processor, or it may be stored in a storage medium 44 usable by an external computer, and electronically coupled to the processor 40 for use as necessary. The storage medium 44 may be any one or more of the currently known storage media, such as a magnetic disc accessory fitted in a disk drive, or an optically readable CD-ROM, or a readable device of any other kind, including a remote storage device coupled via a switched telecommunications link, or future storage means appropriate for the purposes and objectives described herein. In preferred embodiments of the present invention, the methods and apparatus described herein can be modalized in one or more Schlumberger formation tester tool fluid analysis modules, the Modular Formation Dynamics Tester (MDT). The present invention advantageously provides a forming tester tool, such as the MDT, with improved functionality for downhole analysis and collection of formation fluid samples. In this, the training tester tool can be advantageously used to sample training fluids in conjunction with downhole fluid analysis. The applicants recognized the potential value, in downhole fluid analysis, of an algorithmic approach to compare two or more fluids that have different or equal levels of contamination. In a preferred embodiment of a method of the present invention, a level of contamination and its associated uncertainty are quantified in two or more fluids based on spectroscopic data acquired, at least in part, from a fluid analysis module 32 of an apparatus. of drilling, as shown as an example in Figures 2 and 3. Uncertainty in spectroscopic measurements, such as optical density, and predicted contamination uncertainty are propagated to uncertainties in fluid properties, such as live fluid color, crude density dead, gas-oil ratio (GOR) and fluorescence. The meta fluids are compared with respect to the properties predicted in real time. The response products of the invention are derived from the predicted fluid properties and the acquired differences thereof. In one aspect, the response products of interest can be derived directly from the predicted fluid properties, such as formation volume factor (BO), dead oil density, among others, and their uncertainties. In another aspect, the response products of interest can be derived from differences in the predicted fluid properties, in particular, in cases where the predicted fluid properties are computationally close, and the uncertainties in the calculated differences. In yet another aspect, the response products of interest may provide inferences or markers with respect to meta and / or deposit formation fluids based on the calculated differences in fluid properties, ie, likelihood of compartment formation and / or composition gradients derived from the comparison fluid properties and their uncertainties.

Figure 4 is a schematic illustration of a trapping chamber 40 for trapping and retaining samples of forming fluids in the drilling tool 20. The chamber 40 may be connected to the flow line 33 through a line 42 and check valve 46. Chamber 40 includes one or more bottles 44. If a plurality of bottles 44 is provided, bottles 44 can be structured and arranged as a rotating cylinder 48 so that each bottle can be aligned in sequence with line 42 to receive fluids from training to catch and hold in the lined bottle. For example, when the formation fluids flowing through the flow line 33 reach acceptable contamination levels after cleaning, the check valve 46 can be opened and the formation fluids can be collected in one of the bottles 44 which is aligned with line 42. Trapped fluids can then be discharged from chamber 40 to run or flow past one or more spectroscopy modules and into another sample chamber (not shown) that is positioned beyond the modules of spectroscopy. Analysis of formation fluids can be done at different times during the downhole sampling / analysis process. For example, after the formation fluids of two stations have been collected, the fluids can be flowed past the spectral analyzers one after the other. As another embodiment, the fluids in the same location of the apparatus 20 in the perforation 12 (note Figure 2) can be collected or captured at different times to acquire two or more samples of formation fluids for analysis with the module or modules 32 of fluid analysis, as described in more detail below. In this, the present invention contemplates various and various methods and techniques for collecting and trapping fluids for the purposes of fluid characterization as described herein. It is contemplated that various situations and contexts may arise where it is necessary and / or desirable to analyze and compare two or more fluids substantially under the same downhole conditions using one or more fluid analysis modules. For example, it may be advantageous to allow a sample or samples of fluid to settle over a period of time, to allow separation by gravity, for example, of fines or separate phases in the fluids, before analyzing two or more fluids to substantially the same bottomhole conditions to obtain fluid property data with fewer errors due to measurement errors. As other possibilities, it may be advantageous to vary pressure and volume of fluids by means of a pressure and volume control unit, for example, or to determine the pressure-volume characteristics of two or more fluids at substantially the same bottomhole conditions. These methods are discussed in greater detail in the copending and commonly owned U.S. patent application number 11 / 203,932, entitled "Well Bottom Fluid Analysis Methods and Apparatus," naming T. Terabayashi et al. As inventors. , filed on August 15, 2005, which is incorporated herein by reference in its entirety. Such variations and adaptations in acquiring downhole fluids and in analyzing the fluids for purposes of the invention described herein are within the scope of the present invention. The optical densities of the acquired fluids and the derived response products can be compared and strong predictions of differential fluid properties derived from the measured data. In this, two or more fluids, for example, fluids A and B, can flow past the spectral analyzers alternately and repeatedly so that substantially concurrent data for the two fluids is obtained. Figure 4 shows a schematic representation of an alternate fluid flow past a sensor to sense a fluid parameter. Other flow regimes are also contemplated by the present invention.

In other embodiments of the present invention, appropriately dimensioned sample bottles can be provided for bottomhole fluid comparison. The multiple sample bottles can be filled at different stations using techniques that are known in the art. In addition, the formation fluids whose pressure-volume-temperature (PVT) properties are to be determined can also be collected in other, for example, larger bottles, for analysis of additional PVT in a surface laboratory, for example. In said embodiments of the invention, different formation fluids, ie, fluids collected in different seasons and times, etc. , can be subsequently compared by flowing the fluids beyond the spectral analyzers or other sensors to perceive fluid parameters. After the analysis, the formation fluids can be pumped back into the perforation or collected in other sample bottles or handled as desired or necessary. Figure 4 shows another possible embodiment of the chamber 40 for fluid comparison according to an embodiment of the present invention. Properly sized bottles 44 can be incorporated into a cylinder 48 revolver. The cylinder 48 may be structured and arranged for fluid communication with the flow line 33 through a vertical displacement thereof so that the line 42 of the flow line 33 is connected to a specific bottle 44. The connected bottle 44 can then be filled with forming fluids, for example, by displacing an internal piston 50. The trapped fluids can then be used for fluid comparison according to the present invention. In this, the formation fluids of various depths other than the perforation can be compared by selecting specific bottles from the chamber 40. The check valve 46 can be provided to prevent fluid leakage once the flow line 73 has been disconnected from the flow. the camera 40 while when the camera 40 is connected to the flow line 33, the check valve 46 allows fluid flow in both directions. Figures 5 (A) to 5 (E) represent in flow charts preferred methods in accordance with the present invention for comparing downhole fluids and generating response products based on the comparative results. For purposes of brevity, a description will be provided herein primarily directed to contamination of petroleum-based mud filtrate (OBM). However, the systems and methods of the present invention are readily applicable to water-based mud (WBM) or also synthetic oil-based mud (SBM) filtrates. Quantification of contamination and its uncertainty Figure 5 (A) represents in a flow chart a preferred method for quantifying pollution and uncertainty in pollution in accordance with the present invention. When an operation of the fluid analysis module 32 is started (Step 100), the probe 28 extends out to make contact with the formation (note Figure 2). The pumping module 38 withdraws formation fluid to the flow line 33 and drains it into the sludge while the fluid flowing in the flow line 33 is analyzed by the module 32 (Step 102). A petroleum-based mud contamination monitoring (CMO) algorithm quantifies pollution by monitoring a fluid property that clearly distinguishes the ludo-filtrate from the formation hydrocarbon. If the hydrocarbon is heavy, for example dark oil, the mud-filtrate, which is assumed to be colorless, will discriminate from the formation fluid using the color channel of a fluid analysis module. If the hydrocarbon is light, for example gas or volatile oil, the sludge-filtrate, which is assumed not to have methane, is discriminated from the formation fluid using the methane channel of the fluid analysis module. Described in additional detail below is how the contamination uncertainty can be quantified from two or more channels, v_gr », color and methane channels. The quantification of contamination uncertainty serves three purposes. First, it allows the propagation of uncertainty in contamination towards other fluid properties, as described in more detail below. Second, a linear combination of two-channel contamination, eg, color and methane channels, can be obtained so that a resulting contamination has a lower uncertainty compared to the uncertainty of contamination of either of the two channels. Third, since the CMO applies to all mud filtration cleanings regardless of the fluid flow pattern or formation class, quantifying the contamination uncertainty provides a means to capture model-based error due to CMO. In a preferred embodiment of the invention, data from two or more channels, such as color and methane channels, are acquired (Step 104). In the OCM, the spectroscopic data such as, in a preferred embodiment, measured optical density d (t) with respect to time t is fitted with an energy law model, D (t) = ifj - k2t-5/12 (1. 1) The parameters ki and k2 are computed by minimizing the difference between the data and the fit of the model. Suppose d = [d (l.}. D { 2),,. d (5.}...,? (N) jt, k = [ki k2] 7 '(1.2) and A = 1 -t 12 = YSVT (1.3) where the matrices U, S V are obtained from the singular value decomposition of matrix A and T denotes the transposition of a vector / matrix. The OCM model parameters and 6 their uncertainty denoted by cov (k) are, K = VS ^ V dr cov (k) = szVS ~ 2? (1.4) where s2 is the noise variation in the measurement. Typically, it is assumed that the mud filtrate has omissable contribution to the optical density in the color channels and methane channel. In this case, the volumetric pollution? [t) is obtained (Step 106) as rt? = írr. { 1-5) the two factors that contribute to uncertainty in the predicted contamination are uncertainty in the spectroscopic measurement, which can be quantified by laboratory or field tests, and model-based error in the mud contamination monitoring model Oil-based (OCM) used to compute pollution. The uncertainty in pollution denoted by s? [t) (derived in Step 108) due to uncertainty in the measured data is, The analysis of a number of sets of field data supports the validity of a simple energy law model for pollution as specified in Equation 1.1. However, frequently, the model-based error may be more dominant than the error due to incextity in the xuido. A measure of the model-based error can be obtained from the difference between the data and the adjustment as, This calculation of the variation of Equation 1.7 can be used to replace the noise variation in Equation 1.4. When the model provides a good fit to the data, the variation of Equation 1.7 is expected to coincide with the noise variation. On the other hand, when the model provides a low fit to the data, the model-based error is much larger reflecting a greater value of variation in Equation 1. 1. This results in a greater uncertainty in the parameter k in the Equation 1.4 and consequently a greater uncertainty in contamination? (t) in Equation 1.6. A linear combination of contamination in both color and methane channels can be obtained in the Faso 110) so that the resulting pollution has a lower uncertainty compared to the contamination of either of the two channels. Suppose the contamination and uncertainty of the color and methane channels at any time is denoted fj? (t), sn? . { t) and fj2 (t), s? 2 (?, respectively.) Then, the most "strong" pollution calculation can be obtained as:? (t) = ßiifftif + ßif? i® (1.8) where fí /? _? "Ñ _ s3? L The calculation of contamination is stronger since it is a non-deviant calculation and has a lower uncertainty than either of the two calculations? \ (Fy ?? (). The uncertainty in contamination r \ { T) in Equation 1.8 is , sn. { t) = Jß ?. { t} s2 + ß? (t) s2 * (1.9) A person skilled in the field will understand that the Equations 1.3 to 1.9 can be modified to incorporate the effect of a weight matrix used to weigh the data differently at different times. Comparison of two fluids with contamination levels Figure 5 (B) represents in a flow chart a preferred method for comparing an example fluid property of two fluids in accordance with the present invention. In preferred embodiments of the invention, four fluid properties are used to compare two fluids, namely, live fluid color, dead crude spectrum, GOR and fluorescence. For brevity purposes, a method of comparing fluid properties is described with respect to GOR of a fluid. The described method, however, is applicable to any other fluid property as well. Assume that the two fluids are identified A and B. The magnitude and uncertainty in contamination (derived in Step 112, as described in relation to Figure 5 (A), Steps 10-6 and 108), above) and the uncertainty in the measurement for fluids A and B (obtained by hardware calibration in the laboratory or by field tests) they propagate towards the magnitude and uncertainty of GOR (Step 104). Assume μR, s2B denote the mean and uncertainty in GOR of the fluids A and B, respectively. In the absence of any information about the density function, it is assumed that Gaussian is specified by a medium or uncertainty (or variation), in this way, the underlying density functions £ &; and ± B (or equivalently the cumulative distribution functions Fñ and FB) can be computed by the medium and uncertainty in the GOR of the two fluids. Suppose x and y are random variables of the functions of density fñ and fß, respectively. The probability Pi that the GOR of fluid B is statistically greater than GOR of fluid A is: P? = lfB (and >? WA {?) d? (1 .10) When the density function probability is Gaussian, Equation 1.10 is reduced to; where erfc () refers to the complementary error function. The probability Pi take the value between 0 and 1. If Pi is very close to zero or 1, the two fluids are statistically very different. On the other hand, if Pi is close to 0.5, the two fluids are similar. An alternative and more intuitive measure of difference between two fluids (Step 116) is, P2 = 2 jPa - 0.5 | (1-12) Parameter P2 reflects the probability that the two fluids are statistically different. When it is close to zero, the two fluids are statistically similar. When P2 is close to 1, the fluids are statistically very different. The probabilities can be compared to a threshold to allow qualitative decisions about the similarity between the two fluids (Step 118). Next, four example fluid properties and their corresponding uncertainties are derived, as depicted in the flow charts of Figure 5 (C), by initially determining contamination and contamination uncertainty for the fluids of interest (Step 112 above) . The difference in the fluid properties of the two or more fluids is then quantified? using Equation 1.12 above. Magnitude and uncertainty in Live Fluid Color Assuming that the mud filtrate has no color, the color of fluid alive at any wavelength?, At any instant of time t can be obtained from the optical density (OD) measured S? . { . ), The uncertainty in the tail of the color of living fluid is, 2 s2 ^ (t) Sj (t) ff W = u? + [1 _ ^)] 4 (1.14 The two terms in Equation 1.14 reflect the contributions due to uncertainty in the measurement S¿ (t) and contamination?. {T), respectively. Once < that the color of living fluid (Step 202) and the associated uncertainty (Step 204) are computed for each of the fluids being compared, the two fluid colors can be compared in a number of ways (Step 206). For example, the colors of the two fluids can be compared to a selected wavelength. Equation 1.14 indicates that the uncertainty in color is different at different wavelengths. In this way, the most sensitive wavelength for fluid comparison can be selected to maximize discrimination between the two fluids. Another method of comparison is to capture the color at all associated wavelengths and uncertainties in a parametric way. An example said parametric form is, S? ÍLF = aexp (i / A). In this example, the parameters a, ß and their uncertainties can be compared between the two fluids using Equations 1.10 to 1.12 above to derive the probability that the colors of the fluids are different (Step 206). Dead oil spectrum and its uncertainty A second fluid property that can be used to compare two fluids is the dead oil spectrum or response products derived from part of the dead oil spectrum. The dead oil spectrum essentially equal to the live oil spectrum without the spectral absorption of pollution, methane, and other lighter hydrocarbons. It can be computed as follows.

First, the optical density data can be decolorized and the composition of the computed fluids using LFA and / or CFA response matrices (Step 302) by techniques that are known to persons skilled in the art. Next, a state equation (EOS) can be used to compute the density of methane and light hydrocarbons at temperature and -pressed tank pressure. This allows the computation of the volume fraction of the lighter hydrocarbons VLH (Step 304). For example, in the CFA, the volume fraction of light hydrocarbons is: VLS = yiiai + y2m2 + y4m4 (1.15) where i, m2 and m are the partial densities of Ci, C2-C5 and C02 computed using principal component analysis or at least partial squares or an equivalent algorithm. The parameters yi, y2 and y4 are reciprocal of the densities of the three groups at specified reservoir pressure and temperature. The uncertainty in the volume fraction (Step 304) due to uncertainty in the composition is, l y4 | where A is the covariance matrix of the components Ci, C2-C5 and C02 computed using the LFA and / or CFA response matrices, respectively. From the measured spectrum S¿ (t), the dead raw spectrum S¿.dc (t) can be predicted (Step 206) as, The uncertainty in the dead oil spectrum (Step 306) is, s nS? Ude W íñ - [i-Vmg2 (ty? (t? +, [iV sL2 ^ H (t W? (?}. * "*," [l-Vsu ^ fity? ®] 4 p IR l J- • J- °.

The three terms in Equation 1.18 reflect the contributions in uncertainty in the dead crude spectrum due to uncertainty in the measurement S, t (t), the fraction of volume of light hydrocarbon VLH (t) and contamination rj. { t), respectively. The two fluids can be compared directly in terms of the dead raw spectrum at any wavelength. An alternative and preferred approach is to capture the uncertainty at all wavelengths towards a parametric form. An example of a parametric form is, S ^ e = aexp? / A) (1.19) The dead raw spectrum and its uncertainty at all wavelengths can be translated into parameters a and ß and their uncertainties. In turn, these parameters can be used to compute a cut-off wavelength and its uncertainty (Step 308). Figure 6 (a) shows an example of the measured spectrum (dashed line) and predicted dead crude spectrum (solid line) of a hydrocarbon. The dead raw spectrum can be made parameter by cutting wavelength defined as the wavelength at which the OD is equal to l. In this example, the cut-off wavelength is around 570 nm. Frequently, the correlations between the cut wavelength and the density of dead oil are known. An example of a global correlation between cut wavelength and dead oil density is shown in Figure 6 (B). Figure 6 (B) helps translate the magnitude and uncertainty in cut wavelength to a magnitude and uncertainty in density of dead oil (Step 310). The probability that the two fluids are statistically different with respect to the dead oil spectrum, or their derived parameters, can be computed using Equations 1.10 to 1.12 above (Step 312). The computation of the dead raw spectrum and its uncertainty has a number of applications. First, as described herein, it allows easy comparison between two fluids. Second, the CFA uses lighter hydrocarbons as its training set for major component regressions; Tacitly assumes that the C6 + components have a density of -0.68 g / cm3, which is regularly accurate for dry gas, wet gas, and retrograde gas, but is not required for volatile oil and black oil. In this way, a predicted dead crude density can be used to modify the Cs + component of the CFA algorithm to better compute the partial density of the heavy components and thus better predict the GOR. Third, the training volume factor (Bo), which is a valuable response product for users, is a by-product of the analysis (Step 305). The assumed correlation between dead oil density and interruption wavelength can be used additionally to restrict and iteratively compute Bo. This method of computing the training volume factor is direct and surrounds alternative indirect methods to compute the training volume factor using correlation methods. Significantly, the density of light hydrocarbons computed using EOS is not sensitive to small pressure and deposition temperature perturbations. In this way, the uncertainty in density due to the use of EOS is omisibly small. Gas-Oil Ratio (GOR) and its uncertainty GOR computations in LFA and CFA are known by the experts in the field. For brevity purposes, the description herein will use GOR computation for the CFA. The GOR of the fluid in the flow line is computed (Step 3404) of the composition, where the climbers k = 107285 and? = 0.782. The variables x and y denote the weight fraction in the gas and liquid phases, respectively. Suppose that [m2 m m3 m4] denote the partial densities of the four components Ci, C2-C5, C6 + and C02 after the discoloration of the data, that is, eliminating the contribution of color absorption of the NIR channels (Step 402) . Assuming Ci, C2-C5 and C02 are completely in the gas phase and C6 + is completely in the liquid phase, e y = m3 where ai = 1/16, a2 = 1 / 40.1 and a4 = 1/44. Equation 1.21 assumes that C6 + is in the liquid phase, but its vapor is part of the gas phase that has dynamic equilibrium with the liquid. The constants < 2?, A2 «4 and ß are obtained from the average molecular weights of Ci, C2-C5f C6 + and C02 with an assumption of a distribution in the C2-C5 group. If the flow line fluid contamination is small, the GOR of the formation fluid can be obtained by subtracting the contamination from the partial density of C6 +. In this case, the GOR of the formation fluid is provided by Equation 1.21 where p is the known density of the OBM filtering. In fact, the GOR of the fluid in the flow line at any other level of contamination? it can be computed using Equation 1.21 with y = m3-. { ? * -?) p. The uncertainty in the GOR (derived in Step 404) is provided by, (y-ß-) yes ß x-y FGOR -k l < y-pxp. { y-ßc] s? y sy '< y- 2; i.22) where [ai] - [a] (1.23) ? is the covariance matrix of the components i, m2 and m4 and computed from CFA analysis y = si3 + p2s2? (1.24) Cxy = "1 Gm? M3 + 0-2 < Jm2m3 + ?? (Jm3m (1.25) In Equations 1.24 and 1.25, the variable s? ¡, Refers to the correlation between random variables x and y.Figure 7 illustrates an example of GOR variation (in scf / stb) of a retrograde gas with respect to volumetric contamination At small levels of contamination, the measured flow line GOR is very sensitive to small changes in volumetric contamination, therefore, small uncertainty in contamination can result in large uncertainty in GOR Figure 8 (A) shows an example to illustrate an issue solved by applicants in the present invention, namely, what is a strong method for comparing GORs of two fluids with different levels of contamination? A) shows GOR traced as a function of contamination for two fluids After hours of pumping, fluid A (blue trace) has a contamination of? A = 5% with an uncertainty of 2%, while fluid B (trace) red) has a c ontamination of? B = 10%, with an uncertainty of 1%. The known methods of analysis tacitly compare the two fluids predicting the GOR of the formation fluid, projected to zero contamination, using Equation 1.21 above. However, at small pollution levels, the uncertainty in GOR is very sensitive to contamination uncertainty that results from higher error bars for predicted GOR of the formation fluid. A stronger method is to compare the two fluids at an optimized contamination level to discriminate between the two fluids. The level of optimal contamination is as follows. It is assumed that μA (, s2 (? F) and μB (?), S2R (tf) denote the mean and uncertainty in GOR of fluids A and B, respectively, to a contamination? In the absence of any information about the density function, it is assumed that Gaussian is specified by means and variation.Thus, at a specified contamination level, the underlying density functions fA and FB, or equivalently the cumulative distribution functions Fñ and FB? can be computed of medium and uncertainty in GOR of the two fluids The distance of Kolmogorov-Smirnov (KS) provides a natural way to quantify the distance between the two distributions FA and FB d = max { FA - FB] (1.26) An optimum contamination level for comparing the fluid to be selected to maximize the K-S distance. This level of pollution denoted by? (Step 406) is "optimal" in the sense that it is more sensitive to the difference in GOR of the two fluids. Figure 8 (B) illustrates the distance between the two fluids. In this example, the distance is maximum a? - =? B = l?%. The comparison of GOR in this case can be reduced to a direct comparison of optical densities of the two fluids at the pollution level of? B. Once the optimum contamination level is determined, the probability that the two fluids are statistically different from the GOR can be computed using Equations 1.10 to 1.12 above (Step 408). The K-S distance is preferred because of its simplicity and is not affected by reparamereterization. For example, the distance K-S is independent of using GOR or a GOR function such as log (GOR). Those skilled in the art will appreciate that alternative methods for defining distance in Anderson-Darjeeling distance or Kuiper distance can also be used. Fluorescence and its uncertainty Fluorescence spectroscopy is performed by measuring the emission of light on the green and red scales of the spectrum after excitation with blue light. The measured fluorescence is related to the amount of polycyclic aromatic hydrocarbons (PAH) in crude oil. The quantitative interpretation of fluorescence measurements can be challenging. The measured signal is not necessarily linearly proportional to the concentration of PAH (there is no equivalent of Beer-Lambert's law). In addition, when the concentration of PAH is quite large, the performance of how much can be reduced by rapid cooling. In this way, the signal is often a non-linear function of GOR. Even if in an ideal situation only the formation fluid is expected to have signal measured by fluorescence, the surfactants in the OBM filtering can be a contributing factor to the measured signal. In "WBM, the measured data may depend on oil and water flow regimes." In certain geographic areas where water-based mud is used, CFA fluorescence has been shown to be a good indicator of GOR of the fluid, the apparent hydrocarbon density of CFA and mass fractions of Ci and CD + These findings can also be applied to situations with OBM where there is low OBM contamination (< 2%) in the sample that is being analyzed. In addition, the amplitude of the fluorescence signal is seen to have a strong correlation with the dead crude density. In these cases, it is desirable to compare two fluids with respect to the fluorescence measurement. As an illustration, a comparison with respect to the measurement in CFA is described herein. Assume that F0A, FXA, F0B and F? B denote the integrated spectra above 550 and 680 nm for fluids A and B, respectively, with OBM contamination f hf? / Br respectively. When pollution levels are small, the integrated spectra can be compared after correction for contamination (Step 502). In this way, within a scale of uncertainty quantified by uncertainty in contamination and uncertainty in fluorescence measurement (derived in Step 504 by hardware calibration in the laboratory or by field tests). If the measurements are widely different, this should be marked to the operator as a possible indication of difference between the two fluids. Since various other factors such as a stained window or tool orientation or flow rate can also influence the measurement, the operator may select to further prove that the two fluorescence measurements are genuinely reflective of the difference between the two fluids. As a final step in the algorithm, the probability that the two fluids are different in terms of color (Step 206), GOR (Step 408), fluorescence (Step 506), and dead oil spectrum (Step 312) or their parameters Derivatives are provided by Equation 1.12 above. The comparison of these probabilities with a user-defined threshold, for example, as a response product of interest, allows the operator to formulate and make decisions on gradients of composition and formation of compartments in the deposit.

Field Example CFA was run in a field in three different stations marked A, B and D in the same wellbore. The GORs of the flux line fluids obtained from the CFA are shown in Table 1 in column 2. In this work, the fluid was instantaneously evaporated on the surface to compute again the GOR shown in column 3. In addition, the contamination was quantified using gas chromatography (column 4) and the corrected well site GOR is shown in the last column 5. Column 2 indicates that there may be a gradient of composition in the deposit. This hypothesis is not substantiated by column 3. Table 1 GOR of CFA GOR of well site OBM% GOR of site (scf / stb) (as is) of well corrected A 4010 2990 1 3023 B 3750 2931 3.8 3058 DD 3 3445500 2841 6.6 3033 The data were analyzed by the methods of the present invention. Figure 9 shows the methane channel of the three stations A, B and D (blue, red and magenta). The black trace is the curve fitting obtained by OCM. The levels of final volumetric contamination before the samples were collected were calculated as 2.6, 3.8 and 7.1%, respectively. These contamination levels compare reasonably well with the pollution levels calculated as the well site in Table 1. Figure 10 shows the measured data (dashed lines) with the predicted live fluid spectra (solid lines) of the three fluids It is very evident that the fluid in station D is much darker and different from the fluids in stations A and B. The probability that the fluid in station D is different from A and B is quite high (0.86). The fluid in station B has more color than the fluid in station A. Assuming a conventional noise deviation of 0.01, the probability of the two fluids in stations A and B being different is 0.72. Figure 11 shows the spectra of live fluid and dead oil spectra predicted with uncertainty. The principle shows the training volume factor with its uncertainty for the three fluids. Figure 12 shows the calculated cut-off wavelength and its uncertainty. Figures 11 and 12 illustrate that the three fluids are not statistically different in terms of cut wavelength. From Figure 13, the density of dead oil for all three fluids is 0.83 g / cc.

The similarity or statistical difference between the fluids can be quantified in terms of the probability P2 obtained from Equation 1.12. Table II quantifies the probabilities for the three fluids in terms of live fluid color, dead crude density and GOR. The probability that fluids at stations A and B are statistically different in terms of density of dead oil is low (0.3). Similarly, the probability that the fluids in stations B and D are statistically different is also small (0.5). Figures 14 (A) and 14 (B) show GOR of the three fluids with respect to contamination levels. As before, based on the GOR, the three fluids are not statistically different. The probability that the fluid from station A is statistically different from the fluid from station B is low (0.32). The probability that the fluid in station B is different from D is close to zero. Table II Fluid color Density of crude GOR Dead-alive P2 (A? B) 0.72 0.3 0.32 P2 (B? D) 1 0.5 0.06 Comparing these probabilities with the threshold defined by the user allows an operator to formulate and make decisions about gradients of composition and formation of compartments in the deposit. For example, if a threshold is set to 0.8, it would be concluded that the fluid at station D is definitely different from the fluids at stations A and B in terms of color of living fluid. For current processing, the conventional noise deviation has been adjusted to 0.01 OD. Additional discrimination between fluids in stations A and B can also be made if the conventional deviation of noise in optical density is less. As described above, the aspects of the present invention provide advantageous response products related to differences in fluid properties derived from contamination levels that are calculated with respect to downhole fluids of interest. In the present invention, applicants also provide methods to calculate whether differences in fluid properties can be explained by errors in the CMO model (note Step 120 in Figure 5 (C)). In this, the present invention reduces the risk of reaching an incorrect decision by providing techniques for determining whether differences in optical density and calculated fluid properties can be explained by varying the levels of contamination (Step 120). Table III compares the predicted GOR contamination of formation fluid, and color of live fluid at 647 nm for the three fluids. Comparing the fluids in stations A and D, if the fluid contamination of station A is lower, the predicted GOR of the formation fluid in station A will be closer to D. However, the difference in color between stations A and D will be greater. In this way, decreasing the pollution in station A drives the difference in GOR and the difference in color between stations A and D in opposite directions. Therefore, it is concluded that the difference in calculated fluid properties can not be explained by varying levels of contamination. Table III? GOR of forming fluid Color of live fluid at 647 nm A 2.6 3748 0.152 B 3.8 3541 0.169 D 7.1 3523 0.219 Advantageously, the probabilities that the fluid properties are different can also be computed in real time in order to allow an operator compare two or more fluids in real time and modify a sampling work on the way based on decisions that are enabled by the present invention. Analysis in water-based mud.

The methods and systems of the present invention are applicable to analyze data where the contamination is from water-based sludge filtering. The conventional processing of the water signal assumes that the flow regime is stratified. If the volume fraction of water is not very large, the CFA analysis pre-processes the data to compute the volume fraction of water. The data is subsequently processed by the CFA algorithm. The decoupling of the two steps is controlled by a large amount of the water signal of an unknown flow rate of water and oil flowing past the CFA module .. Under the assumption that the flow regime is stratified, the uncertainty in the partial density of water can be quantified. The uncertainty can then be propagated to an uncertainty in the corrected optical density representative of the hydrocarbons. The processing is valid regardless of the location of the LFA and / or CFA module with respect to the pumping module outside. The systems and methods of the present invention are applicable in a self-consistent manner to a combination of fluid analysis module measurements, such as LFA and CFA measurements, in a station. The techniques of the invention for fluid comparison can be applied to resistivity measurements of the LFA, for example. When the LFA and CFA mount the pump module outside (as is most often the case), the pump module outside can lead to gravitational segregation of the two fluids, ie, the fluid in the LFA and the fluid in the CFA . This implies that the CFA and LFA are not testing the same fluid, challenging the simultaneous interpretation of the two modules. However, both CFA and LFA can be used independently to measure pollution and its uncertainty. The uncertainty can be propagated to magnitude and uncertainty in the fluid properties for each module independently, thereby providing a basis for comparing fluid properties with respect to each module. It is necessary to ensure that the difference in fluid properties is not due to a difference in fluid pressure in the spectroscopy module. This can be done in several ways. A preferred approach for calculating the optical density derivative with respect to pressure is now described. When a sample bottle is opened, adjust a pressure transient in the flow line. Consequently, the optical density of the fluid varies in response to the transient. When the magnitude of the pressure transient e can compute from a pressure gauge, the derivative of the DO with respect to the pressure can be computed. The derivative of the DO, in turn, can be used to ensure that the difference in fluid properties of the fluids tested at different points in time is not due to differences in fluid pressure in the spectroscopy module. Those skilled in the art will appreciate that the magnitude and uncertainty of all fluid parameters described herein are available in closed form. Thus, there is virtually no total computation during data analysis. Quantification of magnitude and uncertainty of fluid parameters can provide a window into the nature of the geochemical charge process in a hydrocarbon reservoir. For example, the relationship of methane to other hydrocarbons can help distinguish between biogenic and thermogenic processes. Those skilled in the art will also appreciate that the methods described above can be advantageously used with conventional methods to identify the formation of compartments, such as observing pressure gradients, performing vertical interference tests through potential permeability barriers, or identifying particularities. lithologies that may indicate potential permeability barriers, such as identifying wireline record stylioliths (such as Micro Formation Trainer records or Elemental Capture Spectroscopy). Figure 5 (D) represents in a flow chart a preferred method for comparing training fluids based on differential fluid properties that are derived from measured data acquired by the preferred data acquisition methods of the present invention. In Step 602, the data obtained in Station A, corresponding to fluid A, is processed to compute volumetric contamination? R and its uncertainty s? ^ Associated. The contamination and its uncertainty can be computed using one of several techniques, such as the petroleum-based mud contamination (CMO) monitoring algorithm in Equations 1.1 to 1.9 above. Typically, when a sampling or exploration work by a training tester tool is considered complete in Station A, the perforation outlet valve is opened. The pressure between the inside and the outside of the tool is equalized so as to avoid the collision and abatement of the tool as the tool moves to the next station. When the perforation outlet valve is opened, the differential pressure between the fluid in the flow line and the fluid in the perforation causes mixing of the two fluids.

The applicants discovered advantageous methods for accurate and strong comparison of fluid properties of forming fluids using, for example, a formation test tool, such as the MDT. When work at Station A is considered complete, the remaining fluid in the flow line is retained in the flow line to be trapped in it as the tool moves from Station A to another Station B. Encroachment Fluid can be achieved in a number of ways. For example, when the fluid analysis module 32 (see Figures 2 and 3) is downstream of the pump module 38 off, the check valves on the pump module 38 outside can be used to prevent the ingress of sludge to line 33 of flow. Alternatively, when the fluid analysis module 32 is upstream of the pumping module 38 off, the tool 20 with the fluid trapped in the flow line 33 can be moved with its perforation outlet valve closed. Typically, downhole tools, such as the MDT, are rated to tolerate high differential pressure so that the tools can be moved with the drill hole closed. Alternatively, if the fluid of interest has already been sampled and stored in a sample bottle, the contents of the bottle can be passed through the spectral analyzer of the tool. Figure 4, discussed above, also describes a chamber 40 for trapping and retaining forming fluids in the drilling tool 20. These embodiments of the invention, and others contemplated by the present disclosure, can be advantageously used for downhole analysis of fluids using a variety of sensors, while the fluids are at substantially the same bottomhole conditions, reducing in this way the systematic errors in data measured by the sensors. In Station B, the measured data reflect the properties of both fluids A and B. The data can be considered in two successive time windows. In an initial time window, the measured data corresponds to fluid A as the fluid is trapped in the flow line from Station A, flows past the spectroscopy module of the tool. In other preferred embodiments of the invention, the fluid A can be flowed beyond a tool sensor from other appropriate sources. The subsequent time window corresponds to the fluid B attracted in Station B, or in alternative embodiments of the invention, from other sources of fluid B. In this way, the properties of the two fluids A and B are measured under the same external conditions. , such as pressure and temperature, and almost at the same time by the same hardware. This allows a quick and strong calculation of difference in fluid properties. Since there is no additional contamination of fluid A, the fluid properties of fluid A remain constant in the initial time window. Using the property that in this time window the fluid properties are invariable, the data can be preprocessed to calculate the conventional deviation of noise < 7ODA in the measurement (Step 604). In conjunction with the contamination of Station A (derived in Step 602), the data can be used to predict fluid properties, such as live fluid color, GOR and dead oil spectrum, corresponding to fluid A (Step 604) using the techniques previously described above. In addition, using the OCM algorithm in Equations 1.1 to 1.9 above, the uncertainty in the measurement s0oA (derived in Step 604) can be coupled together with the contamination uncertainty s ?? (derived in Step 602) to compute the uncertainties in the predicted fluid properties (Step 604). The subsequent time window corresponds to fluid B as it flows past the spectroscopy module. The data can be preprocessed to calculate the noise in the measurement s0DB (Step 606). Pollution ?B and its uncertainty s? B can be quantified using, for example, the OCM algorithm in Equations 1.1 to 1.9 above (Step 608). The data can then be analyzed using the techniques previously described to quantify the fluid properties and associated uncertainties corresponding to fluid B (Step 610). In addition to quantifying the uncertainty in the measured data and contamination, the uncertainty in fluid properties can also be determined by systematically pressing the formation fluids in the flow line. Analyzing the variations of fluid properties with pressure provides a degree of confidence about the predicted fluid properties. Once the fluid properties and associated uncertainties are quantified, the properties of the two fluids can be compared in a statistical structure using Equation 1.12 above (Step 612). The differential fluid properties are then obtained as a difference of the fluid properties that are quantified for the two fluids using the techniques described above. In the process of moving a downhole sampling and analysis tool to a different station, it is possible that the density difference between the OBM filtrate and the reservoir fluid may cause gravitational segregation in the fluid that is retained in the line of flow, or caught or captured in another way for fluid characterization. In this case, the placement of the fluid analysis module at the next station can be based on the type of reservoir fluid being sampled. For example, the fluid analyzer can be placed on the top or bottom of the tool string depending on whether the filtrate is lighter or heavier than the reservoir fluid. Example Figure 15 shows a set of field data obtained from a spectroscopy module (LFA) placed downstream of the pumping module outside. The check valves in the outside pumping module were closed as the tool moved from Station a to Station B, thereby trapping and moving fluid A in the flow line from one station to the other. The initial part of the data up to 5 = 25500 seconds corresponds to fluid A in Station A. The second part of the data after type t = 25500 seconds is from Station B. In Station B, the leading edge of time data 25600 - 26100 seconds corresponds to fluid A and the rest of the data corresponds to fluid B. The different lines correspond to the data of different channels. The first two channels have a large OD and are saturated. The remaining channels provide information about color, composition, GOR and contamination of fluids A and B. The computations of difference in fluid properties and associated uncertainty include the following steps: Step 1: The volumetric contamination corresponding to fluid A is computed in Station A. This can be done in a number of ways. Figure 16 shows a color channel (blue trace) and model adjustment (black trace) by the CMO used to predict pollution. At the end of the pumping process, contamination was determined to be 1.95 with an uncertainty of approximately 3%. Step 2: The leading edge of the data in Station B corresponding to fluid A is shown in Figure 17 (A). The data measured for one of the channels in this time frame is shown in Figure 17 (B). Since there is no additional contamination of fluid A, the fluid properties do not change with time. In this way, the measured optical density is almost constant. The data was analyzed to provide a conventional noise deviation d0OA of around 0.003 OD. The events corresponding to the adjustment of the probe and previous test, seen in the data in Figure 17 (B), were not considered in the computation of the noise statistics. Using the contamination and its uncertainty from Step 1, above, and s0DA = 0.003 OD, the color of live fluid and dead oil spectrum and associated uncertainties are computed for fluid A by the equations previously described above. The results are shown graphically in the blue dashes in figures 18 and 19, respectively. Step 3: The second section of the data in Station B corresponds to fluid B. Figure 16 shows a color channel (red trace) and model adjustment (black trace) using the OCM used to predict contamination. At the end of the pumping process, contamination was determined to be 4.3% with an uncertainty of approximately 3%. The predicted live fluid color and dead oil spectrum for fluid B, computed as previously described, are shown by red dashes in Figures 18 and 19. The conventional noise deviation computed by low-pass filtering of the data and calculating the deviation The conventional high-frequency component is s0DB = 0.005 OD. The uncertainty in the noise and contamination is reflected as uncertainty in the color of the live predicted fluid and the dead oil spectrum (red lines) for fluid B in figures 18 and 19, respectively. As shown in Figures 18 and 19, the live and dead oil spectra of the two fluids A and B overlap and can not be distinguished between the two fluids. In addition to the color of live fluid and dead oil spectrum, the GORs and the associated uncertainties of the two fluids A and B were computed using the equations previously discussed above. The GOR of fluid A in 1 flow line is 392 + 16 scf / stb. With a contamination of 1.9%, the pollution-free GOR is 400 +, 20 scf / stb, The GOR of fluid B in the flow line is 297 + 20 scf / stb. With 4.3% contamination, the pollution-free GOR is 310 + 23 scf / stb. In this way, the differential GOR between the two fluids is significant and the probability that the two fluids A and B are different is close to 1. In contrast, ignoring the leading edge of the data in Station B and comparing the fluids A and B directly from Stations A and B, large uncertainty occurs in the measurement. In this case, s0DA and s0DB would capture both systematic and random errors in the measurement and, therefore, would be considerably larger. For example, when s0DA = s0DB = 0.01 OD, the probability that the two fluids A and B are different in terms of GOR is 0.5. This implies that the differential GOR is not significant. In other words, the two fluids A and B can not be distinguished in terms of GOR. The methods of the present invention provide accurate and strong measurements of differential fluid properties in real time. The systems and methods of the present invention for determining difference in fluid properties of formation fluids of interest are useful and cost effective tools to identify the formation of compartments and composition gradients in hydrocarbon reservoirs. The methods of the present invention include analyzing measured data and computing fluid properties of two fluids, for example, fluids A and B, obtained in two corresponding stations A and B, respectively. In Station A, fluid contamination A. and its uncertainty are quantified using an algorithm discussed above. In one embodiment of the invention, the formation fluid in the flow line can be trapped therein as long as the tool is moved to Station B, where fluid B is pumped through the flow line. The data measured in Station B has unique, advantageous property that allows for improved measurement of difference in fluid properties. In this, the leading edge of the data corresponds to the fluid A and the posterior section of the data corresponds to the fluid B. In this way, the data measured in the same station, ie Station B, reflects fluid properties of both fluids A and B. The differential fluid properties obtained in this way are strong and accurate measurements of the differences between the two fluids and are less sensitive to systematic errors in measurements other than conventional fluid sampling and analysis techniques. Advantageously, the methods of the present invention can be extended to multiple fluid sampling stations and other regimes to flow two or more fluids through a flow line of a fluid characterization apparatus so as to be in communication, in Substantially the same bottomhole conditions, with one or more sensors associated with the flow line. The methods of the invention can be advantageously used to determine any differences in fluid properties obtained from a variety of sensing devices, such as density, viscosity, composition, contamination, fluorescence, amounts of H2S and C02, isotopic ratios and methane ratios. ethane The algorithm-based techniques described herein are easily generalizable to multiple stations and comparison of multiple fluids in a single station. The applicants recognized that the systems and methods described herein allow for real-time decision making to identify compartment formation and / or deposit composition gradients, among other features of interest with respect to hydrocarbon formations. The applicants also recognized that the systems and methods described herein would help to optimize the sampling process used to confirm or disapprove predictions, such as gradients in the deposit which, in turn, would help to optimize the process by capturing the samples from Deposit fluid more representative. Applicants further recognized that the systems and methods described herein would help to identify how hydrocarbons of interest in a tank are being swept by invasion fluids, e.g., water or gas injected into the tank, and / or would provide advantageous data as a if a hydrocarbon deposit is being exhausted in a uniform manner or by compartments. The applicants also recognize that the systems and methods described herein would potentially provide a better understanding of the nature of the geochemical charge process in a deposit. The applicants further recognize that the systems and methods described herein could potentially guide next generation analysis and hardware to reduce the uncertainty in predicted fluid properties. As a result, the risk involved in the decision-making related to the exploration and development of the oil field can be reduced. The applicants further recognized that in a deposit that is assumed to be continuous, some variations in fluid properties are expected at depth in accordance with the composition graduation of the deposit. The variations are caused by a number of factors such as thermal and pressure gradients and biodegradation. A quantification of difference in fluid properties can help provide an insight into the nature and origin of the composition gradients. The applicants also recognized that the modeling techniques and systems of the invention would be applicable in a self-consistent manner to spectroscopic data from different downhole fluid analysis modules., such as CFA and / or Schlumberger LFA.

Applicants also recognized that the methods and modeling systems of the invention would have applications with formation fluids contaminated with petroleum-based mud (OBM), water-based mud (WBM) or synthetic petroleum-based mud (SBM). Applicants further recognized that the modeling structures described herein would have applicability in comparison to a wide range of fluid properties, eg, live fluid color, dead crude density, dead crude spectrum, GOR, fluorescence, factor of formation volume, density, viscosity, compressibility, hydrocarbon composition, isotropic ratios, methane-ethane ratios, amounts of H2S and C02, among others, and phase envelope, for example, bubble point, dew point, asphaltene, pH, among others. The above description has been presented only to illustrate and describe the invention and some examples of its implementation. It is not intended to be exhaustive or to limit the invention to any precise form described. Many modifications and variations are possible in the light of the previous teaching. The preferred aspects were selected and described in order to better explain the principles of the invention and its practical applications. The above description is intended to allow other experts in the field to better utilize the invention in various modalities and aspects and with various modifications as appropriate for the particular use contemplated. It is intended that the scope of the invention be defined by the following claims.

Claims (3)

1. CLAIMS 1.- A method for deriving fluid properties from downhole fluids and providing response products from downhole measurements, the method comprising: acquiring at least a first fluid and a second fluid; and substantially at the same bottomhole conditions, analyzing the first and second fluids with a device in a bore to generate fluid property data for the first and second fluid.
2. 2. The method for deriving fluid properties from downhole fluids and providing response products according to claim 1, further comprising: deriving respective fluid properties from the fluids on the fluid property data for the first and second fluid; quantify the uncertainty in the derived fluid properties; and to share the fluids based on the properties of fluid derived and uncertainty in fluid properties.
3. 3. The method for deriving fluid properties from downhole fluids and providing response products according to claim 2, wherein the fluid properties are one or more of live fluid color, dead crude density, GOR and fluorescence. 4. - The method for deriving fluid properties from downhole fluids and providing response products in accordance with claim 2, further comprising: providing response products comprising sampling optimization by the drilling device based on the properties of respective fluid derivatives for fluids. 5. The method for deriving fluid properties from downhole fluids and providing response products according to claim 1, wherein the fluid property data comprises optical density of one or more spectroscopic channels of the device in the perforation, the method also comprising: receiving data of uncertainty with respect to the optical density data. 6, - The method for deriving fluid properties from downhole fluids and providing response products according to claim 1, further comprising: placing the device in the borehole in a position based on a fluid property of the fluids . 7. The method for deriving fluid properties from downhole fluids and providing response products according to claim 1, further comprising: quantifying the level of contamination and uncertainty thereof for each of the at least two fluids 8, - The method for deriving fluid properties from downhole fluids and providing response products according to claim 1, further comprising: providing response products, based on the fluid property data, comprising one or more than compartment formation, composition gradients and optimal sampling process with respect to evaluation and testing of a geological formation. 9. The method for deriving fluid properties from downhole fluids and providing response products according to claim 1, further comprising: decolorizing the fluid property data; determine the respective compositions of the fluids; derive volume fraction of light hydrocarbons for each of the fluids; and provide training volume factor for each of the fluids. 10. The method for deriving fluid properties from downhole fluids and providing response products according to claim 1, wherein: the fluid property data for each fluid are received from a methane channel and a channel color of a downhole spectral analyzer. 11. The method for deriving fluid properties from downhole fluids and providing response products in accordance with claim 10, further comprising: quantifying the level of contamination and uncertainty thereof for each of the channels for each fluid. 12. The method for deriving fluid properties from downhole fluids and providing response products in accordance with claim 11, which also includes: obtaining a linear combination of contamination levels for the channels and uncertainty with respect to the combined level of contamination for each fluid. 13. The method for deriving fluid properties from downhole fluids and providing response products according to claim 12, further comprising: determining the composition of each fluid; predict GOR for each fluid based on the corresponding composition of each fluid and the combined level of contamination; and derive uncertainty associated with the predicted GOR of each fluid. 14. The method for deriving fluid properties from downhole fluids and providing response products according to claim 13, further comprising: comparing the fluids based on the predicted GOR and uncertainty derived from each fluid. 15. The method for deriving fluid properties from downhole fluids and providing response products according to claim 14, wherein comparing the fluids comprises determining the likelihood that the fluids are different. 16. The method for deriving fluid properties from downhole fluids and providing response products according to claim 1, wherein: acquiring at least the first and second fluid comprises acquiring at least one of the first and second fluid of a terrestrial formation traversed by perforation. 17. The method for deriving fluid properties from downhole fluids and providing response products according to claim 1, wherein: acquiring at least the first and second fluid comprises acquiring at least one of the first and second fluid from a first source and another from the first and second fluid of a second, different source. 18. The method for deriving fluid properties from downhole fluids and providing response products according to claim 17, wherein the first and second sources comprise different locations of a land formation traversed by the borehole. 19, - The method for deriving fluid properties from downhole fluids and providing response products according to claim 17, wherein at least one of the first and second sources comprises a stored fluid. 20. The method for deriving fluid properties from downhole fluids and providing response products according to claim 17, wherein the first and second sources comprise fluids acquired at different times in the same location of a traversed land formation by piercing. 21 »- A method for reducing systematic errors in downhole data, the method comprising: acquiring downhole data in sequence for at least a first fluid and a second fluid substantially at the same bottomhole conditions with a device in a hole. 22. A downhole fluid characterization apparatus, comprising: a fluid analysis module, the fluid analysis module comprising: a flow line for fluids withdrawn from a formation to flow through the analysis module of fluid; a selectively operable device structured and arranged with respect to the flow line for flowing at least a first and a second fluid through the fluid analysis module; and at least one sensor associated with the fluid analysis module for generating fluid property data for the first and second fluid substantially at the same bottomhole conditions. 23. The downhole fluid characterization apparatus according to claim 22, wherein the selectively operable device comprises at least one valve associated with the flow line, 24.- The bottom fluid characterization apparatus of Well according to claim 23, wherein the valve comprises one or more check valves in an outside pumping module and a perforation outlet valve associated with the flow line. 25. The downhole fluid characterization apparatus according to claim 22, wherein the selectively operable device comprises a device with multiple storage containers for selectively storing and discharging fluids removed from the formation. 26.- A system to characterize 'training fluids and provide response products based on the characterization, the system comprising: a drilling tool that includes: a flow line with an optical cell; a selectively operable device associated with the flow line for flowing at least a first and a second fluid through the optical cell, and a fluid analyzer optically coupled to the cell and configured to produce fluid property data with respect to the first and second fluids flowing through the cell; and at least one processor, coupled to the drilling tool, comprising: means for receiving fluid property data from the drilling tool, wherein the fluid property data is generated with the first and second fluids substantially the same Downhole conditions, the processor being configured to derive respective fluid properties from the first and second fluid based on the fluid property data. 27.- A computer-usable medium that has a computer-readable program code in it, which when run by a computer, adapted for use with a drilling system to characterize downhole fluids, comprises: receiving data from fluid property for at least one first and second bottomhole fluid, wherein the fluid property data of the first and second fluids are generated with a device in a borehole substantially at the same bottomhole conditions; and calculating respective fluid properties of the fluids based on the received data.