MXPA06000042A - System and methods of deriving fluid properties of downhole fluids and uncertainty thereof - Google Patents

Abstract

Methods and systems are provided for downhole analysis of formation fluids by deriving fluid properties and associated uncertainty in the predicted fluid properties based on downhole data, and generating answer products of interest based on differences in the fluid properties. Measured data are used to compute levels of contamination in downhole fluids using 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, in real-time, robust 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 FLUID PROPERTIES OF WELL-BACKGROUND FLUIDS AND THE UNCERTAINTY OF THE SAME RELATED APPLICATION DATA The present 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), naming L. Venkataramanan, et al., As inventors, and filed on January 11, 2005, which is hereby incorporated by reference in its entirety for all processes. FIELD OF THE INVENTION The present invention relates to the analysis of formation fluids to evaluate and test a geological formation for purposes of exploration and development of hydrocarbon producing wells, such as oil or gas wells. More particularly, the present invention is directed to system and methods for deriving fluid fluid properties from forming downhole spectroscopy measurements. 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 the 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 complex mixture of fluids, such as oil, gas and water, is found downhole in reservoir formations. The bottom ground fluids, which are also referred to as forming fluids, have characteristics, including pressure, live fluid color, dead oil density, 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 samples of formation fluids for purposes of characterizing the fluids. Tools have been developed that allow samples to be taken from a training in a registration test or during drilling. The Deposit Training Tester (RFT) and Schlumberger Modular Training Dynamics (MDT) tools are examples of tools of sampling to extract samples of formation fluids for surface analysis. Recent developments in DFA include techniques for bottomhole characterization training fluids in a drilling or drilling well. In this, the Schlumberger MDT tool can include one or more fluid analysis modules, such as the Composition Fluid Analyzer (CFA) and Schlumberger Live Fluid Analyzer (LFA), to analyze the sampled drilling bottom fluids. by the tool while the fluids are still at the bottom of the well. In DFA modules of the above-mentioned type, formation fluids to be analyzed at the bottom of the well flow past the sensor modules, such as spectrometer modules, which analyze flowing fluids by near-infrared absorption spectroscopy. (NIR), for example. The Patents of E.U.A. of the same owner Nos. 6,476,384 and 6,768,105 are examples of patents related to the prior art, the contents of which are hereby incorporated 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. Drilling rod test (DST) is downhole technology used to determine pressure, permeability, skin or productivity of hydrocarbon deposits. The downhole pressure measurements are used in reservoir characterization and the DST string design provides deposit information of multiple zones in the same test to model the reservoir. As a technical solution, DST in a conventional method to test for formation of compartments in exploration wells. However, in deep water or similar settings, DST may be non-economic with the cost often being comparable to the cost of a new well. In addition, DST, in certain applications, could have environmental effects. As a consequence, DST, in some cases, is not a preferred approach to characterize hydrocarbon deposits. Currently, compartments in hydrocarbon deposits are identified by pressure gradient measurements. In this, pressure communication between layers in geological formations is supposed to establish the existence of flow communication. However, the characterization of deposits for compartment formation based solely on pressure communication imposes problems and unacceptable results are frequently obtained as a consequence. In addition, hydrocarbon deposits also need to be analyzed to grade the fluid composition. COMPENDIUM OF THE INVENTION In consequence of the background discussed above, and other factors known in the field of downhole fluid analysis, applicants discovered methods and systems for real-time analysis of formation fluids deriving fluid properties from the fluids and response products of interest based on the predicted fluid properties. In preferred embodiments of the invention, the data from downhole measurements, such as spectroscopic data, is used to compute contamination levels. An oil-based mud contamination monitoring (OCM) algorithm is 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 contamination levels. The uncertainties of predicted fluid properties are derived from the uncertainty in measured data and predicted contamination uncertainty. A statistical structure for fluid comparison is provided to generate strong, real-time response products related to formation fluids and deposits. Applicants developed modeling methodology and systems that 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 derivative purposes. Analytical results based on comparative properties of fluids. Applicants recognized that quantifying levels of contamination in training fluids and determining uncertainties associated with quantified levels of contamination for fluids would be advantageous steps towards deriving response products of interest in oil field exploration and development. The applicants also acknowledged that the uncertainty in measured data and in quantified levels of contamination could be propagated to corresponding uncertainties in other fluid properties of interest, such as live fluid color, dead oil density, gas-oil ratio (GOR) , fluorescence, among others. The applicants further recognized that quantifying the uncertainty in predicted fluid properties of formation fluids will provide an advantageous basis for real time comparison of fluids, and less sensitive to systematic errors in the data. Applicants also recognized that reducing or eliminating systematic errors in measured data, through the use of novel sampling methods of the present invention, would lead to strong and accurate comparisons of formation fluids based on predicate fluid properties that are less sensitive to errors. in downhole data measurements. According to the invention, a method for deriving fluid properties from downhole fluids and providing downhole spectroscopy data response products includes receiving fluid property data for at least two fluids with the property data of fluid of at least one fluid being received from a device in a drilling bottom. In real time with the reception of the fluid property data of the drilling bottom device, derive respective fluid properties from the fluids; quantify the uncertainty in the derived fluid properties; and provide one or more answer products related to the evaluation and testing of a geological formation. The fluid property data may include optical density of a spectroscopic channel of the downhole device and the present embodiment of the invention includes receiving data of uncertainty with respect to the optical density. In one embodiment of the invention, the device at the bottom of the well is placed in a position based on a fluid property of the fluids. In preferred embodiments of the invention, the fluid properties are one or more of living fluid color, dead crude density, GOR and fluorescence and the response products are one or more of compartment formation, composition gradients and sampling process optimum related to the evaluation and testing of a geological formation. A method for deriving the fluid property response products from one or more downhole fluids includes receiving fluid property data for the downhole fluid from at least two sources; determine a fluid property corresponding to each of the data sources received; and quantify the uncertainty associated with the determined fluid properties. The fluid property data can be received from a methane channel and a color channel from a downhole spectral analyzer. A level of contamination and uncertainty of the same can be quantified for each of the channels for the downhole fluid; a linear combination of contamination levels for the channels and uncertainty with respect to the combined levels of contamination can be obtained; the composition of the downhole fluid can be determined; GOR for the downhole fluid can be predicted based on the composition of the downhole fluid and the combined levels of contamination; and the uncertainty associated with the predicted GOR can be derived. In a preferred embodiment of the invention, the probability that two downhole fluids are different can be determined based on the predicted GOR and associated uncertainty for the two fluids. In another preferred embodiment of the invention, a downhole spectral analyzer is positioned to acquire first and second fluid property data. The first fluid property data being received from a first station of the downhole spectral analyzer and the second fluid property data being received from a second station of the spectral analyzer. In another aspect of the invention, a method for comparing two downhole fluids with the same or different contamination levels and generating real-time bottomhole fluid analysis based on the comparison includes acquiring data for the two background fluids of Well are equal or different pollution levels; determine the respective pollution parameters for each of the two fluids based on the acquired data; characterize the two fluids based on the corresponding contamination parameters; statistically compare the two fluids based on the characterization of the two fluids; and generate bottomhole fluid analysis indicative of a geological hydrocarbon formation based on the statistical comparison of the two fluids. A system of the invention for characterizing formation fluids and providing response products based on characterization includes a drilling tool with a flow line with an optical cell, a pump coupled to the flow line for pumping the formation fluid through of the optical cell, and a fluid analyzer optically coupled to the cell and configured to produce fluid property data with respect to the formation fluid pumped through the bond; and at least one processor, coupled to the drilling tool, which has means to receive fluid property data from the well tool and, in real time with the reception of the data, to determine the fluid data properties of the fluids and uncertainty associated with the fluid properties determined to provide one or more answer products related to geological formations. A computer-usable medium having computer-readable program code thereon, which when run by a computer, adapted for use with a drilling system for real-time comparison of two or more fluids to provide response products derived from The comparison includes receiving fluid property data for at least two downhole fluids, where the fluid property data of at least one fluid is received for the drilling system; and calculating, in real time with the reception of the data, respective fluid properties of the fluids based on the received data and the uncertainty associated with the fluid properties calculated to provide one or more response products related to geological formations. Additional advantages and novel features of the invention will be set forth in the description that follows or can be learned by those skilled in the art 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 following description, 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 training fluids in accordance with the present invention. Figure 3 is a schematic representation of a fluid analysis module apparatus for comparing formation fluids in accordance with the present invention. Figures 4 (A) to 4 (E) are flow charts illustrating preferred methods for comparing downhole fluids according to the present invention and deriving answer products thereof. Figure 5 is a graphic representation of optical absorption spectra of three fluids obtained in the laboratories. The formation fluids A and B are shown in blue and red, respectively, and a mud filtrate is shown in green. Figures 6 (A) and 6 (B) graphically illustrate the results of Simulation A with fluids A and B, referred to in Figure 5 above. Figure 6 (A) shows the actual contamination (black) and the calculated contamination (blue) as time functions for fluid A and Figure 6 (B) shows the actual (black) and calculated (red) contamination as functions of time for fluid B. Figure 7 is a graphic illustration of a comparison of live fluid colors for fluids A (blue) and B (red), also mentioned in Figures 5 and 6 (A) - (B) previous. The dashed lines indicate the measured data and the solid lines show the predicted live fluid color, with the calculated uncertainty, for the two fluids. The two fluids are statistically different. Figures 8 (A) and 8 (B) graphically illustrate the results of Simulation B with fluids C (blue) and D (red) showing the actual contamination (black) and the calculated contamination (blue / red) as time functions . Figure 9 is a graphical representation for comparison of live fluid colors for fluids C (blue) and D (red), also referred to in Figures 8 (A) - (B) above. The dashed lines indicate the measured data and the solid lines show the color of live fluid with error bars for the two fluids. Statistically, the two fluids are similar in terms of color of living fluid. Figure 10 (A) shows graphically an example of spectra of measured crude oil (line of dashes) and predicted (solid line) of a hydrocarbon and Figure 10 (B) represents an empirical correlation between cut wavelength and crude spectrum dead. Figure 11 (A) graphically compares spectra of dead oil (dashed lines) and predicted (solid lines) of fluids A (blue) and B (red) and Figure 11 (B) compares spectra of dead crude oil measured (lines of scripts) and predicted (solid lines) of fluids C (blue) and D (red). The fluids were previously mentioned in the foregoing. Fluids A and B are statistically different and fluids C and D are statistically similar. Figure 12 illustrates, on a graph, the variation of GOR (in scf / stb) of a retrograde gas as a function of volumetric contamination. At small contamination levels, GOR is very sensitive to volumetric contamination; Small uncertainty in pollution can result in large uncertainty in GOR.

Figure 13 (A) graphically shows GOR and corresponding uncertainties for the fluids A (blue) and B (red) as a function of volumetric contamination (fluids A and B were previously mentioned above). The final contamination of fluid A is pR = 5% while the final contamination for fluid B is pB = 10%. Figure 13 (B) is a graphic illustration of the distance K-S as a pollution function. The GOR of the two fluids is better compared to% -ñt where the sensitivity to distinguish between the two fluids is maximum, which can be reduced compared to the optical densities of the two fluids when the contamination level is trD. Figure 14 (A) graphically shows GOR as a pollution function for fluids A (blue) and B (red); the fluids are statistically very different in terms of GOR. Figure 13B shows GOR as a pollution function for C (blue) and D fluids (Red); the fluids are statistically identical in terms of GOR. The fluids are also mentioned above. Figure 15 graphically shows the optical density (OD) from the methane channel (at 1650 nm) for three stations A (blue), B (red) and D (magenta). The adjustment from the contamination model 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 16 illustrates graphically a comparison of measured ODs (dashed traces) and live fluid spectra (solid traces) for stations A (blue), B (red) and D (magenta). The fluid in station D is darker and is statistically different from stations A and B. Fluids in stations A and B are statistically different with a probability of .72. The fluids are mentioned in Figure 15 above. Figure 17 graphically shows the comparison of live fluid spectra (dashed dashes) and predicted dead crude spectra (solid traces) for the three fluids at stations A, B and d (also mentioned above). Figure 18 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 wavelength. Figure 19 is a graph showing the density of dead oil for all three fluids at stations A, B and D (also mentioned above) is about 0.83 g / cc.

Figure 20 (A) graphically illustrates that GOR for fluids at stations A (blue) and B (red) are statistically similar and Figure 20 (B) illustrates that GOR for fluids at stations B (red) and D ( magenta) are also statistically similar. The fluids were previously mentioned above. Figure 21 is a graphical representation of optical density data of station A, corresponding to fluid A, and the data of station B, corresponding to fluids and B. Figure 22 represents a graphical data of the color channel for fluid A (blue) and fluid B (red) measured at stations A and B, respectively (also mentioned in Figure 21). The black line is adjusted by the oil-based mud contamination (OCM) monitoring algorithm 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 23 (A) graphically illustrates the leading edge of the data in station B (see Figures 21 and 22) corresponding to fluid A and figure 23 (B) graphically illustrates the leading edge for one of the channels in Station B , shows that the measured optical density is almost constant (within the noise scale in the measurement).

Figure 24, a graphical comparison of live fluid colors, shows that the two fluids A and B (see Figures 21-23) can not be distinguished based on color. Figure 25, a graphical comparison of dead oil spectra, shows that the two fluids A and B (see Figures 21-24) are indistinguishable in terms of dead-green color. Through the drawings, the 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 is to cover all modifications, equivalents and alternatives that fall within the scope of the invention as defined by the appended claims. DETAILED DESCRIPTION OF PREFERRED MODALITIES Illustrative embodiments and aspects of the invention are described below. In the interest of clarity, not all the particulars of an actual implementation are described in the specification. Of course, it will be appreciated that in the development of any such real 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 to the other. implementation to another. In addition, it will be noted that such a development effort could be complex and time-consuming, but, nonetheless, 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 using fluid analysis modules, such as Schlumberger Composition Fluid Analyzer (CFA.) And / or Live Fluid Analyzer (LFA) modules, in the training check tool, for example, the Modular Training Dynamics Tester (MDT). As used in the present, the term "real time" refers to the processing and analysis of data that are substantially simultaneous with acquisition of some or all of the data, so that while a drilling rig is in a well, or a well site coupled with logging and drilling operations; the term "answer product" refers to the intermediate and / or final products of interest with respect to the exploration, development and production of oil field, which are derived from or acquired by processing and / or analyzing background fluid data from water well; the term "compartment formation" refers to lstological barriers to fluid flow that prevent a hydrocarbon reservoir from being treated as a single producing unit, the terms "contamination" or "contaminants" refer to unwanted fluids, such as filtrate of petroleum-based mud, obtained while sampling the reservoir 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 the applicants for formation of compartments in hydrocarbon deposits provides a basis for the present invention. Typically, pressure communication between layers in a formation is a measure used to identify the formation of compartments. However, pressure communication does not necessarily translate into flow communication between layers and, an assumption that it makes, can lead to lack of flow compartment formation. It has recently been established that pressure measurements are insufficient when calculating formation of deposit compartments and composition gradients. Since pressure communication occurs through geologic ages, it is possible that two scattered sand bodies are in pressure communication, but not necessarily in flow communication with each other. Applicants recognized that a fallacy in identifying compartmentalization can result in significant errors in production parameters such as drainage volume, flow rates, well placement, installation size and completion equipment, and errors in production prediction. The applicants also recognized a current need for applications of strong and precise modeling techniques and novel sampling procedures to the identification of compartment formation and composition gradients, and other features of interest in hydrocarbon deposits. Currently, decisions about the formation of compartments and / or composition gradients are derived from a direct comparison of fluid properties, such as 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 ecologic 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 the fluid properties is small, however, significant when analyzing the deposit. Applicants also recognized that frequently, in certain geological settings, fluid density inversions may be small and project through small vertical distances. In settings where the density inversion, or GOR gradient equivalence, is small, the current analysis could erroneously identify 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 the production of formation fluid. In view of the above, applicants understand 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 the deposit has started production it is often essential to supervise the hydrocarbon recovery of sectors, such as layers, fault blocks, etc., inside the tank. 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. In accordance with the applicants' understanding of the factors discussed herein, the present invention provides systems and methods for comparing downhole fluids that utilize strong statistical structures, which compare the fluid properties of two or more fluids that have properties of fluid equal or different, for example, same or different contamination levels of mud filtrates. In this, the present invention provides systems and methods for comparing downhole fluids using cost effective and efficient statistical analysis tools. The real-time statistical comparison of fluid properties that are predicted for downhole fluids is made with a view to characterizing hydrocarbon deposits, such as identifying compartment formation and composition gradients in the reservoirs. Applicants recognized that fluid properties, eg GOR, fluid density, as measured depth functions provide advantageous markers for deposit characteristics. For example, if the derivative of GOR as a function of depth is similar to step, that is, not continuous, the formation of compartment in the deposit is probable. Similarly, other fluid properties can be used as indicators for compartment formation and / or composition gradients. In one aspect of the invention, the spectroscopic data of a downhole tool, such as TDM, 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 the uncertainty in various predicted fluid properties. The systems and methods of the present invention utilize the concept of decreasing the sludge filtration fraction asymptotically over time. The present invention, in preferred embodiments, uses optical density coloration measurement and near-infrared (NIR) measurement of gas-oil ratio (GOR) spectroscopic data to derive contamination levels in two or more spectroscopic channels with respect to fluids that are 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. Figure 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 deep-water development of oil deposits. Furthermore, even though the description herein uses an oil and gas exploration and production adjustment, it is believed that the present invention has applicability in other settings, such as water tanks. In Figure 1, a service vehicle 10 is placed in a well site having a bore 12 with a drilling tool 20 suspended therein at the end of a line 22 of wire. Typically, the perforation 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 arrangement. Figure 2 discloses an exemplary system 14 in accordance with the present invention for comparing bottom drilling fluids and generating analytical products based on comparative fluid properties, for example, while the service vehicle 10 is located in an im- ozo { 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 ground setting of the type illustrated in Figure 1, the drilling tool 20 is typically suspended in bore 12 (note the Figure 1) from the lower end of a multiconductor recording cable or line 22 of reel wire in a 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 electronic and processing systems suitable for the control of the drilling tool 20. Also referring to Figure 3, the piercing tool 20 includes an elongated body 267 that houses a variety of electronic components and modules, which are schematically depicted in Figures 2 and 3, to provide necessary and desirable functionality to string 20 of tool. A selectively extensible fluid that admits the assembly 28 and a selectively extensible tool anchoring member 30 (note Figure 2) are respectively disposed on opposite sides of the elongate body 26. The fining inlet assembly 28 is operable to selectively seal or isolate the selected portions of a borehole wall 12, so that pressure or fluid communication with adjacent terrestrial formation is established. In this, the fluid intake assembly 28 can be a single probe module 19 (illustrated in Figure 3) and / or a packer module 31 (also schematically shown in Figure 3). One or more fluid analysis modules 32 are provided in the tool body 2-6. The fluids obtained from a formation and / or drilling flow through a flow line 33, through the fluid analysis module or modules 32, and then can 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, 10,408 or 22,710 liters. (1, 2 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 sets for admitting fluid, 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 means of electrical control systems, such as the electrical surface control system 24 (note Figure 2). Preferably, the electric control system 24, and the other control systems located in the tool body 26, for example, include processor capability for deriving fluid properties, comparing fluids, and performing other desirable or necessary functions with respect to to forming 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 the reception data, for example, of 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 computer-usable storage means 43 associated with the processor 40, 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 can be any one or more of the currently known storage media, such as a magnetic disk that fits 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 the preferred embodiments of the present invention, the methods and apparatus described herein may be odalized in one or more fluid analysis modules of the Schlumberger formation tester tool, the Modular Formation Dynamics Tester (MDT). The present invention advantageously provides a formation testing 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 forming 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 by way of example in Figures 2 and 3. The uncertainty in spectroscopic measurements such as optical density, and predicted contamination uncertainty are propagated in uncertainties in fluid properties, such as color of live fluid, density of dead oil, gas-oil ratio (COR) and fluorescence. The target finishes are compared with respect to the properties predicted in real time. Advantageously, the answer 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 incextíduxes, In another aspect, the products The response 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 still another aspect, the answering products of interest may provide interferences or markers with respect to the meta and / or deposit formation fluids based on the differences calculated in fluid properties, ie, likelihood of compartment formation and / or composition gradients derived from the comparative fluid properties and uncertainties thereof. Figures 4 (A) to 4 (E) represent in flow charts preferred methods in accordance with the present invention for comparing downhole fluids and generating answer products based on the comparative results. For brevity purposes, the description herein will be directed primarily to oil-based side-filtrate (OBM) contamination. However, the systems and methods of the present invention are readily applicable to water-based mud (WBM) or synthetic oil-base mud (SBM) filtrates as well. Quantification of contamination and its uncertainty Figure (A) represents in a flow chart a preferred method for quantifying contamination 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 of contact with the formation (see Figure 2). The outward pumping module 38 attracts 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 pollution monitoring algorithm (CMO) quantifies pollution by monitoring a fluid property that clearly distinguishes the mud filtrate from the formation hydrocarbon. If the hydrocarbon is heavyFor example, dark oil, the sludge filtrate, which is supposed to be colorless, is discriminated 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 metal channel of the fluid analysis module. It is described in further detail below how the contamination uncertainty can be quantified from two or more channels, eg, 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, for example, color and methane channels, can be obtained so that a resulting contamination has a lower uncertainty compared to the contamination uncertainty of either of the two channels. Third, since the CMO applies to all mud filtration cleanings regardless of the fluid flow pattern or training class, quantifying 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, is required (Step 104). In the OCM, the spectroscopic data such as, in a preferred embodiment, optical density measured d (t) with respect to time t is adjusted with an energy law model. The parameters ki and k2 are computed by minimizing the difference between the data and the fit of the model. Suppose d = fd. { l) d. { 2) . . . d. { 2) . . . d (N)] 1, k = [k ± k2] t (1.2) and A = -1-16 = ÜS (1.3) where the matrices U, S and V are obtained from the singular value decomposition of the matrix A and T denotes the transposition of a vector / matrix. The OCM model parameters and their uncertainty denoted by cov (k) are, k = VS ^ UT d, cov (k) = s2 S-2 ^ (1.4) Where sz is the noise variation in the measurement. Typically, it is assumed that the mud filtrate has omissable contribution in the optical density in the color channels and methane channel. In this case, the volumetric contamination p (t) is obtained in Step 106) as 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 supervision model (CMO) of petroleum-based mud contamination used to compute pollution. The uncertainty in pollution denoted by sp (f) (derived in Step 108), due to uncertainty in the measured data is, The analysis of a number of field data sets supports the validity of a simple energy law model for pollution as specified in Equation 1.1. However, frequently model-based error can be more dominant than error due to uncertainty in noise. A measure of model-based error can be obtained from the difference between the data and the fit as, s¿ = N (1-7) This calculation of the variation of Equation 1.7 can be used to replace the noise variation in the 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 variation of noise. On the other hand, when the model provides a low adjustment to the data, the error based on the model is much greater reflecting a greater value of variation in Equation 1.7. This results in a greater uncertainty in the parameter k in Equation 1.4 and consequently a greater uncertainty in the pollution p (t) in Equation 1.6. A linear combination of the contamination of both color channels and methane can be obtained (Step 110) so that the resulting contamination has a lower uncertainty compared to the contamination of either of the two channels. Suppose that the contamination and uncertainty of the color and methane channels at any time is denoted as tc? t), sp? (t) and 7. { t), < Tpi (f) t respectively. Then a more "strong" contamination calculation can be obtained as, p® = ßtf) ptf) + ß2®p2 (t) (1.8) where The calculation of contamination is stronger because it is a non-deviant calculation and has a lower uncertainty than either of the two calculations p? (T) and 2 (- the contamination uncertainty p (t) in Equation 1.8 is, sp ( f) = Jßl (f) s + ß2 (f) s 2 p2 (i .9: A person skilled in the art will understand that 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 levels of contamination Figure 4 (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 , ie, color of live fluid, dead oil 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 Another property of fluid is also assumed.The two fluids are assumed to be labeled A and B. The magnitude and uncertainty in contamination (derived in Step 112, as e described in relation to Figure 4 (A), Steps 106 and 108, above) and measurement uncertainty for fluids A and B (obtained by hardware calibration in laboratory or field tests) are propagated to the magnitude and GOR uncertainty (step 114). Assume μA, cr¿ and μe, ^ B denote the mean and uncertainty in GOR of 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 and uncertainty (or variation). In this way, the underlying density functions fA and ÍB (or equivalently the cumulative distribution functions FA and FB) can be computed from the mean and uncertainty in the GOR of the two fluids. Suppose that x and y are random variables derived from density functions fA and fB, respectively. The probability Pa that GOR of fluid B is statistically greater than GOR of fluid A When the probability density function is Gaussian, Equation 1.10 is reduced to; where erfc () refers to the complementary error function. The probability Pi takes 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 alternate and more intuitive measure of difference between two fluids (Step 116) is, P2 = 2] P? - 0.5! (1.12) Parameter P2 reflects the probability that the two fluids are statistically different. When P2 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 with 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 4 (C), initially determining contamination and contamination uncertainty for the fluids of interest (Step 112 above) . The difference in fluid properties of the two or more fluids is then quantified using Equation 1.12 above. Magnitude of uncertainty in Live Fluid Color Assuming that the filtrate has no color, the color of fluid alive at any wavelength? in • any time t can be obtained from the measured optical density (OD) S? (? The uncertainty in the color tail of living fluid is, * Su¡Yes :) = [lp (í) P + [lp (/)] 4 (1-14) The two terms in Equation 1.14 reflect the contributions due to uncertainties in the measurement S? (F) and pollution p (t), respectively. Once the color of fluid alive (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 of such a parametric form is, 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). ). Simulation Example 1 Shown in Figure 5 are optical absorption spectra of three fluids obtained in the laboratory: The formation fluids a and B (blue and red traces) with GOR and 500 and 1700 scf / stb, respectively, and a filtrate of mud (green vestige). In the first simulation, the two formation fluids were contaminated with a disinfectant amount of contamination simulating cleaning fluid formation. Different contamination models were used for the two fluids. At the end of a few hours, the true contamination was 20% for fluid A and 2% for fluid B as shown by the black traces in Figures 6 (A) and 6 (B). Next, this simulation will be called "Simulation A" for additional reference. The data was analyzed using the OCM contamination algorithm described above in Equations 1.1 to 1.9. Since the pollution model used during the analysis was very different from that used in the simulation, the final contamination levels calculated by the algorithm are deviated. As shown in Figures 6 (A) and 6 (B), the final contamination for fluids A and B were calculated to be 10% and 2%, respectively, with an uncertainty of approximately 2%. The measured data S? and the SfLF prediction live fluid spectrum for the two fluids are shown in Figure 7. The green and red dashed lines correspond to the measured optical density. The solid blue and red lines with error bars correspond to the spectra of live prediction fluid. At any wavelength, the probability that the two live fluid spectra are different is 1. Thus, even though the pollution algorithm did not correctly produce contamination for fluid A, the predicted live fluid colors are very different for the two fluids and can be used to distinguish them clearly. Simulation Example 2 In a second simulation (mentioned below as Simulation B), two sets of data were simulated for the same training fluid (Fluid B from Simulation A above) with different pollution models. The two new fluids are referred to as fluids C and D, respectively. At the end of a few hours, the true contamination was 9.3% for fluid C and 1% for fluid D as shown by the black dashes in Figures 8 (A) and 8 (B). The data was analyzed using the OCM pollution algorithm described above in Equations 1.1 to 1.9. The final contamination levels for the two fluids were 6.3% and 1.8%, respectively, with an uncertainty of approximately 2%. As before, the pollution model provides deviant estimates for contamination, since the model used for analysis is different from the model used to simulate pollution. The data measured for the two fluids (blue and red dashed traces) and the corresponding predicted live fluid spectrum (blue and red solid traces) and their uncertainty are shown in Figure 9. The live fluid spectra for the two fluids coincide very closely indicating that the two formation fluids are statistically similar. 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 equals the live oil spectrum with 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 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 measured temperature and tank pressure. This allows computation of the volume fraction of the lighter hydrocarbons LH (Step 304). For example, in the CFA, the volume fraction of light hydrocarbons is, where mi, m2, and m4 are the partial densities of Ci, C2-Cd, and C02 computed using principal component analysis or least partial squares or an algorithm equivalent. The parameters y,, and e, and 4 are the reciprocal of the densities of the three groups at the reservoir pressure and temperature specified. The uncertainty in the volume fraction (Step 304) due to the uncertainty in the composition is, I Yl I v = [Yi Yz y <;?] A I Yz I (1 .16) I y4 I where A is the covariation matrix of the components Ci, C2-C5 and C02 computed using the LFA and / or CFA response matrices, respectively. From the measured spectrum S? F, the dead raw spectrum S ^ e (t) can be predicted (Step 306) as, S?, Dc (t) =? -va (t p (i) (1.17) The uncertainty in the dead raw spectrum (Step 306) is, sS AXl) ~ [l-VLH ( {) -p (t? + [lF £ ff (í) -p (/)] * + ll-VLHÍt - (tl -L - JB, The three terms in the Equation 1.18 reflects the contributions of uncertainty in the dead crude spectrum due to the uncertainty in the measurement S, (t), the fraction of light hydrocarbon volume VLH (t) and contamination p (t), respectively. they can compare 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 ¡B = ae? p (ß /?) (1.19) The dead raw spectrum and its uncertainty at all wavelengths can be translated into parameters a and ß and their uncertainties, which in turn can be used to compute a cut-off wavelength. and its uncertainty (Step 308) Figure 10 (a) shows an example of the measured spectrum (dashed line) and the predicted dead crude spectrum (solid line) of a hydrocarbon. The dead raw spectrum can be made parameter by the cut 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, correlations between cut wavelength and dead oil density are known. An example of a global correlation between cut wavelength and dead oil density is shown in Figure 10 (B). Figure 10 (B) helps translate the magnitude and uncertainty in cut-off 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 oil 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 in its training set for major component regressions; Tacitly assumes that 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, the predicted black crude density can be used to modify the C6 + 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 (B0), which is a valuable answer product for users, is a secondary product of the analysis (Step 305), The assumed correlation between dead oil density and cut wavelength can be used additionally to restrict and iteratively compute BQ. This method of computing the training volume factor is direct and circumvents 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 disturbances of reservoir pressure and temperature. In this way, the uncertainty in density due to the use of EOS is omisibly small. Simulation Example 1 Figure 11 (A) compares dead oil spectra of two fluids used in Simulation a previous. It is evident that the two fluids are very different in terms of the dead oil spectra and, therefore, in terms of density. Simulation Example 2 Figure 11 (B) compares dead raw spectra of two fluids used in Simulation B above. The two spectra of dead oil overlap very well and the probability that the two formation fluids have the same dead oil spectrum is close to 1. Gas-Oil Ratio (GOR) and its uncertainty The GOR computations in LFA and CFAs are known to the experts in the field. For purposes of brevity, the description herein will use GOR computing for the CFA. The GOR of the fluid in the flow line is computed (Step 404) from the composition, GOR = k fcsfstb (1.21) where you will scale k-107285 and? = 0.782. The variables x and y denote the weight fraction in the gas and liquid phases, respectively. Suppose [i m2 m3 m] denote the partial densities of the four components Ci, C2-C5, Ce + and C02 after decolorizing the data, ie removing the contribution of color absorption of the NIR channels (Step 402). Assuming that O., C2-C5 and C02 are completely in the gas phase and C6 + is completely in the liquid phase, x = a \ m \ + a2m + a ^ nt ey = m3 where "o.? = 1 / 16, a2 = 1/40, lycn = 1/44 Equation 1.21 assumes C6 + is in the liquid phase, but its vapor is part of the gas phase and has dynamic equilibrium with the liquid, the constants are obtained from the average molecular weight of Ci, C2-Cs, C6 + and C02 with an assumption of a distribution in the group C2-C5 If the fluid contamination of the flow line p * 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 filtrate, in fact, the GOR of the fluid in the flow line in any Another level of contamination p can be computed using Equation 1.21 with y = m3-. {p * - p p) The uncertainty in the GOR (derived in Step 404) is provided by, sGOR = k i (y-ß?) 2 (Y-ßc) 2] [s2] [(y-ßxfi [^ r] d .22) where M | a4 | ? is the covariance matrix of the components mi, m2 and m4 and computed from CFA analysis and s2 = s2m, + p2s2 (1 .24) < T? Y + G. \ < Jm? N + G, 2 < Tmzmi + O.A < Jm¡- m4 (1.25) In Equations 1.24 and 1.25, the variable sxy refers to the correlation between random variables x and y. Figure 12 illustrates an example of variation of GOR (in scf / stb) of a retrograde gas with respect to volumetric contamination. At small pollution levels, the measured GOR flow line is very sensitive to small changes in volumetric contamination. Therefore, the small uncertainty in contamination can result in large uncertainty in GOR. Figure 13 (A) master an example to illustrate an expedition results by the applicants in the present invention, to say, what is strong to compare the GORs of two fluids with different levels of contamination? Figure 13 (A) shows GOR plotted as a pollution function for two fluids. After pumping hours, fluid A (blue trace) has a contamination of pA = 5% with an uncertainty of 2% while fluid B (red trace) has a contamination of pR = 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 the contamination uncertainty that results in 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. Suppose μA (t? SA2 (? I) and μB (p), < TB (p) 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 is assumed to be Gaussian specified by means and variation, Thus, at a specified pollution level, the underlying density functions fA and fB, or equivalently the cumulative distribution functions FA 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 two distributions FA and FB, d = max [FA - FB] (1.26) An optimal contamination level for fluid comparison can be selected to maximize the K-S distance. This level of contamination denoted by rf (Step 406) is "optimal" in the sense that it is the most sensitive to the difference in GOR of the two fluids. Figure 13 (B) illustrates the distance between the two fluids. In this example, the distance is maximum a? ~ = FjB =? %. The comparison of GOR in this case can collapse 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 statically different with respect to GOR can be computed using Equations 1.10 to 1.12 above (Step 408). The distance K-S is preferred because of its simplicity and is not affected by repairmethyrization. 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. Example 1 of GOR Simulation and its associated uncertainty for the two fluids in Simulation A above are plotted as a function of contamination in Figure 14 (A). In this case, the two GORs are very different and the probability of P2 that the two fluids are different is close to one. Example 2 of GOR Simulation and its associated uncertainty for the two fluids in Simulation B above is plotted as a pollution function in Figure 14 (B). In this case, the two GOR are very similar and the probability P2 that the two fluids are different is close to zero. Fluorescence and its uncertainty Fluorescence spectroscopy is performed by measuring light emission at 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 to the Beer-Lambert law). In addition, when the concentration of PAH is very large, the performance 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, surfactants in OBM filtering can be a contributing factor to the measured signal. In WBM, the measured data may depend on the oil and water flow rates. In certain geographical areas where water-based mud is used, CFA fluorescence has been shown to be a good indicator of GOR of the fluid, apparent hydrocarbon density of CFA and mass fractions of Ci and C6 +. These findings also apply to situations with OBM where there is low contamination of 0MB (<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. Suppose that FDA, FX ?, F0B and F? B denote integrated spectra above 550 and 680 nm for fluids A and B, respectively, with contamination of OBM? A,? B, 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 laboratory or 0 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 painted 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 0 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 derived parameters is provided by the Equation 1.12 above. The comparison of these 5 • probabilities with a user-defined threshold, for example, as an answering 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 at three different stations marked A, B and D in the same borehole, the GORs of the flow 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 recompute 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 of (scf / st5b) (as is) corrected well A 4010 2990 1 3023 B 3750 2931 3.8 3058 D 3450 2841 6. 6 3033 The data was analyzed by the methods of the present invention. Figure 15 shows the methane channel of the three stations A, B and D (blue, red and magenta). The black trace is the curve adjustment 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 at the well site in Table 1. Figure 16 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 from 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 that the two fluids in stations A and B are different is 0.72. Figure 27 shows the spectra of live fluid and the dead crude spectra predicted with uncertainty. The principle shows the training volume factor with its uncertainty for the three fluids. Figure 18 shows the calculated cut-off wavelength and its uncertainty. Figures 17 and 18 illustrate that the three fluids are not statistically different in terms of cut wavelength. From Figure 19, the dead oil density 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). In a similar way, the probability that the fluids in stations B and D are statistically different is also small (0.5). Figures 20 (A) and 20 (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 a user-defined threshold allows an operator to formulate and make decisions about gradients of composition and formation of compartments in the deposit. For example, if a threshold of 0.8 is adjusted, it would be concluded that the fluid in station D is definitely different from the fluids in stations A and B in terms of color of living fluid. For current processing, the conventional noise deviation has been set 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, aspects of the present invention provide advantageous answer products related to differences in fluid properties derived from contamination levels that are calculated with respect to bottomhole fluids of interest. In the present invention, applicants also provide methods for calculating whether differences in fluid properties can be explained by errors in the OCM model (note Step 120 in Figure 14 (C)). In this, the present invention reduces the risk of reaching an incorrect decision by providing techniques to determine whether differences in optical density and estimated fluid properties can be explained by varying levels of contamination (Step 120). Table III compares the pollution, predicted GOR 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 may 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 difference in color between stations A and D in opposite directions. Therefore, it is concluded that the difference in the calculated fluid properties can not be explained by varying the levels of contamination. Table III? GOR of formation fluid Color of live fluid at 647 nm A 2. 6 3748 0.152 B 3. 8 3541 0.169 DD 7 7.11 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 to compare two or more fluids in real time and to modify the work Sampling in operation based on decisions that are allowed by the present invention. Water Based Mud Analysis The methods and systems of the present invention are applicable to analyzing data where the contamination is from water-based sludge filtration. 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 and an unknown flow rate of water and oil that flows 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. Processing is valid regardless of the location of the LFA module and / or CFA with respect to the pumping module facing out. 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 measurements of LFA and CFA, 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 pumping module outward (as is most often the case), the pumping 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, doing interpretation simultaneous of the two challenging modules. However, both CFA and LFA can be used independently to measure pollution and its uncertainty. The uncertainty can be propagated in magnitude and uncertainty in the fluid properties for each module independently, thus 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 various 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 can be computed from a pressure gauge, the derivative of DO with respect to the pressure can be computed. The OD derivative, in turn, can be used to ensure that the difference in fluid fluid properties tested at different points in time is not due to difference 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. In this way, there is virtually no total computing during the data analysis. The quantization of magnitude and uncertainty of fluid parameters can advantageously provide insight 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 compartment formation, such as observing pressure gradients, performing vertical interference tests through potential permeability barriers, or identifying lithological particulars. which may indicate potential permeability barriers, such as identifying wire line record stolites (such as Formation Micro Imager or Elemental Capture Spectroscopy records). The techniques described above 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, labeled A and B, can be obtained from stations A and B, respectively. The fluid properties of the fluids, such as live fluid color, dead oil density and gas-oil ratio (GOR), can be predicted for both fluids based on the measured data. The uncertainties in fluid properties can be computed from the uncertainty in the measured data and the 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 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 quantification of differential fluid properties reflects both random and systematic errors in the measurement, and can be quite large. Applicants discover novel and advantageous fluid sampling procedures that allow data acquisition, sampling and analysis of corresponding data from two or more fluids so that differential fluid properties are not sensitive to systematic errors in measurements. Figure 4 (D) represents in a flow chart a preferred method for comparing training fluids based on differential fluid properties and arc-derived 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, are processed to compute volumetric contamination? and its associated uncertainty? 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 scanning job is performed by a training tester tool is considered complete at station A, the drill exit valve is opened. The pressure between the inside and the outside of the tool is equalized so that the tool collapse and depressurization of the tool is avoided 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. Applicants discoveries • advantageous methods for accurate and strong comparison of fluid properties of forming fluids using, for example, a training tester 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 therein as the tool moves from station A to another station B. entrapment Fluid can be achieved in a number of ways. For example, when the fluid analysis module 32 (see FIGS. 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 entry of sludge into the pump module 38. line 33 of flow. Alternatively, when the fluid analysis module 32 is upstream of the pumping module 38 off, the tool 20 with 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 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. In station B, the measured data reflects 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 fluid trapped in the flow line of station A flows past the spectroscopy module of the tool. The subsequent time window corresponds to the fluid b attracted in station B. In this way, the properties of the two fluids A and B are measured in 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. By 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 s0DA 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 a 1. 9 above, the uncertainty in the measurement are (derived in Step 604) may be coupled together with the contamination uncertainty s, / A (derived from step 602) to compute the uncertainties in the illustrated fluid properties (Step 604). The last time window corresponds to fluid B as it flows past the spectroscopy module. The data can be preprocessed to calculate the noise in the s0oB measurement (Step 606). The contamination? 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 in 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 a conventional sampling procedure, where the formation fluid of a station is not trapped and taken to the next station, the uncertainty in fluid differences reflects 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, the systematic error in measurement is canceled. Accordingly, the present methods for obtaining differences in fluid properties are stronger and more accurate compared to other methods of sampling and data acquisition.

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 retained in the reservoir. flow line. 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 21 shows the field data set obtained from a spectroscopy module (LFA) placed downstream of the pump module outside. The check valves of the outside pump 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 t = 25500 seconds corresponds to fluid A in station A. The second part of the data after time 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 the 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 22 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 1.9% with an uncertainty of about 3%. Step 2: The leading edge of the data in station B corresponding to fluid A is shown in Figure 23 (A). The data measured for one of the channels in this time frame is shown in Figure 23 (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 deviation of noise s0D? of around 0.003 OD. The events corresponding to the adjustment of the probe and previous test, seen in the data in Figure 23 (B), were not considered with the computation of the noise statistics. Using the contamination and its uncertainty from Step 1, above, s0OA = 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 by the blue dashes in Figures 24 and 25, respectively. Step 3: The second section of the data in station B corresponds to fluid B. Figure 22 shows a color channel (red trace) and model adjustment (black trace) by the CMO 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 described above, are shown by red dashes in Figures 24 and 25. The conventional noise deviation computed by the low pass filtering data and calculate the conventional deviation of the high frequency component is s0oB = 0.005 OD. The uncertainty in noise and contamination is reflected as uncertainty of the predicted live fluid color and dead oil spectrum (red dashes) for fluid B in Figures 24 and 25, respectively. As shown in Figures 24 and 25, 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 the flow line is 392 + 16 scf / stb. With a pollution 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, s0? and O? B could 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 will be 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 properties of two fluid computing fluid, for exa, fluids A and B, obtained in two corresponding stations A and B, respectively. In station A, the contamination of fluid A and its uncertainty are quantified using an algorithm discussed above. Advantageously, the formation fluid in the flow line is trapped therein while the tool is moved to station B, where fluid B is pumped through the flow line. The data measured in station B has an advantageous, unique property, which 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 the measurements than other fluid sang and analysis techniques. Advantageously, the methods of the present invention can be extended to multiple fluid sang stations. The methods of the invention can be advantageously used to determine any differences in fluid properties obtained from a variety of sensor 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 can easily be generalized to multiple stations and the comparison of multiple fluids in a single station. Applicants recognized that the systems and methods described herein allow for real-time decision making to identify the formation of compartments 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 sang process used to confirm or disapprove predictions, such as gradients in the deposit that, in turn, would help optimize the process by capturing sas 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 flushed by invasion fluids, eg, water or gas injected into the tank, and / or would provide advantageous data. as to whether a hydrocarbon deposit is being depleted in a uniform manner or by compartments. The applicants also recognized 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 recognized that the systems and methods described herein could potentially guide next generation analysis and hardware to reduce uncertainty in predicted fluid properties. Consequently, the risk involved in making decisions related to exploration and development of the oil field could be reduced. The applicants further recognized that in a deposit that is assumed to be continuous, some variations in fluid properties are expected with 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 the spectroscopic data of different downhole fluid analysis modules, such as CFA and / or Schlumberger LFA. The applicants also recognized that the methods and modeling systems of the invention would have applications with forming fluids contaminated with petroleum-based mud (OBM), water-based mud (WBM) or synthetic oil-based mud (SBM).

The applicants further recognized that the modeling structures described herein would have applicability in comparison to a wide variety of fluid properties, eg, live fluid color, dead crude density, dead crude spectrum, GOR, fluorescence, factor formation volume, density, viscosity, compressibility, hydrocarbon composition, isotropic ratios, methane-ethane ratios, amounts of H2S and CO2 among others, and phase envelope, for example, bubble point, dew point, asphaltene principle , 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 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 embodiments 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 (23)

1. CLAIMS 1.- A method to derive fluid properties from downhole fluids and to provide downhole spectroscopy data response products, the method comprising: receiving fluid property data for at least two fluids, where the fluid property data of at least one fluid is received from a device in a borehole; in real time with the reception of the fluid property data of the drilling device, derive respective fluid properties from the fluids; quantify the uncertainty in the fluid properties derived; and provide one or more response products related to the evaluation and testing of a geological formation.
2. 2. The method for deriving fluid properties from drilling fluids and providing response products according to claim 1, wherein the fluid property data includes optical density of a spectroscopic channel of the device in the drilling; the method also comprising. receive the data of uncertainty with respect to optical density.
3. 3. 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 .
4. 4. The method for deriving fluid properties from downhole fluids and providing response products according to claim 1, wherein the fluid properties are one or more color of live fluid, density of dead oil, GOR and fluorescence.
5. 5. The method for deriving fluid properties from downhole fluids and providing response products according to claim 1, further comprising. quantify a level of pollution and uncertainty of the same for each of the two fluids.
6. 6. The method for deriving fluid properties from downhole fluids and providing response products according to claim 1, wherein the response products are one or more of compartment formation, composition gradients and sampling process optimum related to the evaluation and testing of a geological formation.
7. 7. - The method for deriving fluid properties from downhole fluids and providing response products, according to claim 1, further comprising bleaching 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.
8. 8. The method for deriving fluid properties from downhole fluids and providing response products, according to claim 1, wherein the response products include sampling optimization by the drilling device based on the fluid properties respective derivatives for fluids.
9. 9. A method for deriving fluid property response products from one or more downhole fluids, the method comprising: receiving fluid property data for the downhole fluid from at least two sources; determine a fluid property corresponding to each of the data sources received; and quantify the uncertainty associated with the determined fluid properties.
10. 10. The method for deriving response products according to claim 9, wherein the fluid property data is received from a methane channel and a color channel of a downhole spectral analyzer.
11. 11. The method for deriving response products according to claim 10, further comprising quantifying a level of contamination and uncertainty thereof for each of the channels for the downhole fluid.
12. 12. The method for deriving response products, according to claim 11, which further comprises obtaining a linear combination of contamination levels for the channels and uncertainty with respect to the combined levels of contamination.
13. 13. The method for deriving response products, according to claim 12, which further comprises determining the composition of the downhole fluid; predict the GOR for the downhole fluid based on the composition of the downhole fluid and the combined levels of contamination; and derive uncertainty associated with the predicted GOR.
14. 14. The method for deriving response products, according to claim 13, further comprising: quantifying a level of contamination and uncertainty thereof for each of at least two data sources for another downhole fluid, obtain a linear combination of pollution levels for the two data sources for the other downhole fluid and uncertainty with respect to the combined levels of contamination; determine the composition of the other downhole fluid; predict the GOR for the other downhole fluid over the composition of the other downhole fluid and the combined levels of contamination; derive the uncertainty associated with the predicted GOR of the other downhole fluid; and determine the probability that the downhole fluids are different.
15. 15. - The method for deriving response products, according to claim 9, wherein the fluid property data includes first fluid property data for the downhole fluid and second fluid property data for another bottom fluid of well.
16. 16. The method for deriving response products, according to claim 15, further comprising placing a downhole spectral analyzer to acquire the first and second fluid property data, wherein the first fluid property data it is received from a first station of the downhole spectral analyzer and the second fluid property data is received from a second station of the spectral analyzer.
17. 17.- A method to compare two downhole fluids with equal or different levels of contamination and generate real-time downhole fluid analysis based on the comparison, the method comprising: acquiring data for the two background fluids of well with equal or different levels of contamination; determine the respective pollution parameters for each of the two fluids based on the acquired data; characterize the two fluids based on the corresponding contamination parameters; compare statistically the two fluids based on the characterization of the two fluids; and generate downhole fluid analysis indicative of a geological hydrocarbon formation based on the statistical comparison of the two fluids.
18. 18. The method for comparing two downhole fluids, according to claim 17, wherein: characterizing the two fluids includes deriving GOR and uncertainty in GOR for the two fluids; and which further comprises: determining an optimum level of contamination to discriminate between the two fluids, wherein the two fluids are compared at the optimum contamination level.
19. 19. The method for comparing two downhole fluids, according to claim 17, wherein: acquiring data for the two downhole fluids includes acquiring first downhole fluid data with a first analysis module of fluid and second bottomhole fluid data with a second fluid analysis module; determining respective pollution parameters includes determining the contamination and uncertainty in contamination for each module, characterizing the two fluids including determining the fluid properties and uncertainty of the same for each module; and comparing the two fluids includes comparing the fluid properties determined for each module.
20. 20.- A method to analyze fluids of an underground formation, using a drilling tool that has a fluid analyzer, the method comprising: making downhole measurements of the formation fluids; receive data for training fluids from at least two sources, where at least one of the two sources comprises the downhole measurements, use the data received to determine levels of contaminants in the formation fluids; derive the uncertainty associated with the determined levels of pollutants; and provide real-time fluid property analysis for training fluids based on the determined levels of contaminants and the derived uncertainty.
21. 21. The method for analyzing fluids of an underground formation, according to claim 20, wherein making downhole measurements of forming fluids includes making spectroscopic measurements at a wavelength that responds to the presence of at least one of methane and petroleum; and receiving data includes receiving the spectroscopic measurements with respect to at least one of the methane and oil.
22. 22. A system for characterizing training fluids and providing response products based on characterization, the system comprising: a drilling tool that includes: a flow line with an optical cell, a pump coupled to the flow line for pumping fluid formation through the cell; and at least one processor, coupled to the drilling tool, which includes: means for receiving fluid property data from the drilling tool and, in real time with receiving the data, determining the fluid fluid data properties and the uncertainty associated with the fluid properties determined to provide one or more response products related to the geological formations.
23. 23. A computer-usable medium having a computer-readable program code thereon, that when executed by a computer, adapted for use with a drilling system for real-time comparison of two or more fluids to provide products of response derived from the comparison, comprises: receiving the fluid property data for at least two downhole fluids, wherein the fluid property data of at least one fluid is received from the drilling system; and calculating, in real time with the reception of the data, respective fluid properties of the fluids based on the received data and the uncertainty associated with the fluid properties calculated to provide one or more response products related to the geological formations.