US 20050216196 A1 Abstract Methods and systems are described for estimating of the level of contamination of downhole fluid using physical property measurements, and mathematical modeling of contamination functions and fluid property mixing laws. The proposed approaches enable computation of estimates of the pumping time needed to achieve a certain contamination threshold level.
Claims(34) 1. A method for estimating levels of contamination in borehole fluids, comprising the steps of:
(a) providing a first mathematical contamination function model which expresses a time behavior of one or more fluid properties of a mixture of formation fluids and contamination fluids drawn from a borehole, where said one or more fluid properties are sensitive to the fraction of contamination fluids in the mixture; (b) providing a second mathematical mixing law function model expressing at least one of said one or more fluid properties of a fluid mixture in terms of corresponding properties of formation fluids and contamination fluids in the mixture; (b) drawing fluids from the borehole into a fluid analyzer; (c) measuring at least one of said one or more properties of the drawn fluids by the analyzer; and (d) estimating the level of contamination in the fluids drawn from the borehole at one or more points in time based on the at least one measured fluid property and the provided mathematical models in step (a) and step (b). 2. The method of 3. The method of 4. The method of 5. The method of 6. The method of _{1 }and spin-spin relaxation time T_{2}. 7. The method of _{1 }and/or T_{2 }relaxation times. 8. The method of _{1 }spectrum. 9. The method of _{1 }spectrum. 10. The method of where aj are the amplitudes of a given T
_{1 }spectrum, and j is the number of T_{1 }bins used in the inversion. 11. The method of 12. The method of 13. The method of 14. The method of 15. A method for downhole formation testing, comprising the steps of:
(a) providing measurement signals corresponding to a mixture of formation fluids and contamination fluids drawn from a borehole, the mixture entering a downhole fluid analyzer; (b) based on the provided measurement signals, determining parameters of a contamination function, which expresses the time behavior of one or more fluid properties of the fluid mixture drawn from the borehole; (c) computing from the determined contamination function of a value estimate of said one or more fluid properties for at least one low level of contamination and for at least one high level of contamination; and (d) computing a contamination index for the mixture of fluids drawn from the borehole at different time instants based on the computed value estimates and a fluid mixing law relating properties of the drawn fluids in terms of the corresponding properties of the formation fluids and the contamination fluids. 16. The method of 17. The method of 18. The method of 19. The method of 20. The method of 21. The method of 22. The method of 23. The method of 24. The method of 25. The method of 26. The method of 27. The method of 28. The method of 29. An apparatus for estimating levels of contamination of formation fluids in a borehole, comprising:
(a) means for providing a mathematical model comprising a contamination function which expresses the time behavior of one or more fluid properties of a mixture of formation fluids and contamination fluids drawn from the borehole, where said one or more fluid properties are sensitive to the fraction of contamination fluids in the mixture, and a mixing law describing at least one of said one or more fluid properties of the drawn fluids in terms of the corresponding properties of the formation fluids and the contamination fluids; (b) means for drawing fluids from the borehole into a fluid analyzer; (c) means for measuring at least one of said one or more properties of the drawn fluids by the analyzer; and (d) means for estimating the level of contamination in the fluids drawn from the borehole at one or more time intervals based on the at least one measured fluid property and the provided mathematical model. 30. A computer-usable medium having computer-readable program code thereon for use with fluid analyzers, the program code including code structured to:
(a) collect measurement signals corresponding to a mixture of formation fluids and contamination fluids drawn from the borehole and entering a downhole fluid analyzer; (b) determine parameters of a contamination function, which expresses the time behavior of one or more fluid properties of a the mixture drawn from the borehole, based on the collected measurement signals; and (c) based on a fluid mixing law for the one or more fluid properties determined in (c) provide an estimate of at least one of (i) the level of contamination, or (ii) the pumping time for reaching a given level of contamination. 31. The computer-usable medium of 32. The computer-usable medium of 33. The computer-usable medium of 34. The computer-usable medium of Description This application claims priority from provisional application Ser. No. 60/532,502, filed Dec. 24, 2003, the content of which is incorporated by reference for all purposes. This invention relates to systems and methods for determining the level of mud filtrate contamination in formation fluids. Many oil industry applications require the analysis of downhole fluids. In the prior art this was typically done by bringing samples to the surface using sealed containers, and sending the samples for laboratory measurements. A number of practical limitations are associated with this approach. The main concern usually is that the samples taken to the surface may not be representative of the downhole geologic formation. This is due to the fact that only limited sample material from a limited number of downhole locations can be extracted and taken to the surface, and thus laboratory testing provides only an incomplete picture of the downhole conditions. Furthermore, samples are often contaminated with mud filtrate, and therefore are not truly representative of the native formation fluids. More recently, fluid analysis became possible using pumpout formation testers that provide downhole measurements of certain fluid properties and enable the collection of a large number of representative samples stored at downhole conditions. Three families of such tools have been introduced in the past—the modular dynamic formation tester (MDT) by Schlumberger, the Reservoir Characterization Instrument (RCI) by Baker Atlas, and most recently the Reservoir Description Tool (RDT) by Halliburton. These tools are generally designed to obtain representative formation fluid samples and provide key petrophysical information to determine the reservoir volume, producibility of a formation, type and composition of the movable fluids, and to predict reservoir behavior during production. One of the remaining problems encountered in the operation of such tools is to avoid contamination of the fluid naturally present in the formation with other fluids, in particular the various types of drilling muds used in drilling operations. Drilling mud, also known as drilling fluid, is typically pumped down the center of the hollow drill stem to emerge again at the surface of the borehole. It lubricates the drill shaft, cools the borehole, carries away the drilling detritus, and serves as a wetting-phase, which facilitates the flow of hydrocarbons from the formation and into the borehole. Various types of drilling muds are generally classified based on the type of filtrate used therein. The mud filtrate chosen dictates the mud's function and performance, as well as formation invasion effects. There are two major types of mud filtrates: water-based and oil-based. Water-based mud (WBM) filtrates include, but are not limited to, freshwater, seawater, saltwater (brine) and others, or a combination of any of these fluids. In the oil-based mud (OBM), the filtrate is an oil product, such as diesel or mineral oil. More generally, oil-based mud is characterized as any type of non-aqueous fluid. For the purposes of the present disclosure, oil-based mud also includes the recently developed variety of oil mud that is also referred to as synthetic-based muds. These synthetic-based muds include, without limitation, olefinic-, naphthenic-, and paraffinic-based compounds. Dependent on the type of mud used in the drilling process, different factors affect the ability of the tool the accurately estimate the contamination levels at a given point during pumpout. For WBM drilling, mixing with the formation fluid is considered an immiscible process, and determination of the degree of contamination of the fluid using is relatively straightforward. More challenging is the problem of estimating the degree of contamination in OBM drilling when attempting to obtain high quality formation fluid samples, because these mud filtrate fluids are mixed in the formation oil. This mixing can be immiscible or miscible, but either way complicate the determination of the degree of contamination (with immiscible invasion the fluids to not dissolve with each other but mix, with miscible mixing the fluids dissolve in a diffusion process). For example, the presence of even small volumes of oil-base filtrate in the sample can significantly alter the properties of the formation oil. As a result, in-situ quantification of the oil-base material contamination in the formation oil is difficult, and poorly quantified samples may not be representative of the formation fluid of interest. Additional difficulties are presented in differentiating oil-based mud from connate oil when oil-based filtrate invades the formation. One method for differentiating oil-based mud from connate oil is disclosed in U.S. Pat. No. 6,107,796, owned by the assignee of the present invention, which is incorporated herein by reference. However, no reliable method has been provided for determining the level of contamination of mud filtrate in formation fluids. The most frequently used prior art approach to estimating contamination has been based on the optical properties of the fluids entering a tool. Schlumberger provides for in-situ contamination estimation using an Optical Fluid Analyzer (OFA). Baker-Atlas also offers a service similar to the OFA. The OFA exploits the differences in the absorption spectrum (i.e., color contrast) between the OBM contaminant and the formation fluid to deconvolute the spectrum from a fluid measurement (see, e.g., U.S. Pat. Nos. 6,178,815, 6,274,865, 6,343,507 and 6,350,986, which are incorporated herein by reference for background). The OFA measures the optical density (OD) of the flowing fluid and uses empirical relationships to transform the OD into data on contamination by determining the composition of the measured absorbed light spectrum from the sample. Based on this absorption spectrum one can estimate the types of materials present in the fluid and the proportion of each material in the fluid. While the industry has learned how to interpret OFA data over the years, it still is not robust in certain applications where the color contrast is small, or is masked, as is frequently the case in light oils and condensates. One problem with this approach is that it assumes that the measured property is directly linked to the contamination, which may not necessarily be the case. Another approach to contamination estimation is to use electrical resistivity methods, which involve the measurement of the apparent resistivity of fluids entering the tool. While these measurements are straightforward to implement and, for example, can easily distinguish between oil and water it cannot reliably distinguish contaminants in OBM situations. Various other sensors measuring optical properties, resistivity, capacitance and others within formation sampling tools have been used to estimate levels of fluid contamination during the pump-out phase, but no robust solution has been found yet. A more recent approach to contamination estimation is provided by the use of nuclear magnetic resonance (NMR) measurements. NMR measurements of geologic formations may be done using, for example, the MRIL® tool made by NUMAR, a Halliburton company, and the CMR family of tools made by Schlumberger. Details of the structure of the MRIL® tool and the measurement techniques it uses are discussed in U.S. Pat. Nos. 4,710,713; 4,717,876; 4,717,877; 4,717,878; 4,939,648; 5,055,787; 5,055,78; 5,212,447; 5,280,243; 5,309,098; 5,412,320; 5,517,115, 5,557,200; 5,696,448; 5,936,405; 6,005,389; 6,023,164; 6,051,973; 6,107,796; 6,111,408; 6,242,913; 6,255,819; 6,268,726; 6,362,619; 6,512,371; 6,525,534; 6,531,868; 6,541,969; 6,577,125 and 6,583,621, all of which are commonly owned by the assignee of the present application. The CMR tool is described, for example, in U.S. Pat. Nos. 5,055,787 and 5,055,788 to Kleinberg et al. and further in “Novel NMR Apparatus for Investigating an External Sample,” by Kleinberg, Sezginer and Griffin, J. Magn. Reson. 97, 466-485, 1992. NMR devices, methods and pulse sequences for use in logging tools are also in U.S. Pat. Nos. 4,350,955 and 5,557,201. The content of the above patents and publications is hereby expressly incorporated by reference for background. A brief discussion of the main NMR measurement parameters follows. Basic NMR Properties and Measurement Parameters NMR measurements are based on exposing an assembly of magnetic moments, such as those of hydrogen nuclei, to a static magnetic field. The assembly tends to align along the direction of the magnetic field, resulting in a bulk magnetization. A magnetic field having direction perpendicular to the static magnetic field is applied to rotate the magnetic moments away from the direction of the bulk magnetization. The rate at which the rotated moments return to the equilibrium bulk magnetization after application of the oscillating magnetic field is characterized by the parameter T Another related and frequently used NMR parameter is the spin-spin relaxation time constant T Another measurement parameter used in NMR is the formation diffusivity. Generally, diffusion refers to the motion of atoms in a gaseous or liquid state due to their thermal energy. The self-diffusion coefficient (D) of a fluid is inversely proportional to the viscosity (η) of the fluid, a parameter of considerable importance in borehole surveys. Stokes' equation yields that:
Viscosity and diffusivity are both related to the translational motion of molecules and therefore are interrelated. At higher temperatures T, a molecule contains more energy and can move faster against a given “friction” η, therefore D is proportional to the temperature. Diffusivity is a property that can be precisely determined by NMR techniques without disturbing or altering the fluid. The relationship D∝T/η has been verified over a wide range of viscosities at different temperatures and pressures by NMR spin-echo experiments. Relationships involving the NMR relaxation times T In a uniform magnetic field, diffusion has little effect on the decay rate of the measured NMR echoes. In a gradient magnetic field, however, diffusion causes atoms to move from their original positions to new ones, which also causes these atoms to acquire different phase shifts compared to atoms that did not move. This contributes to a faster rate of relaxation. Recently, Halliburton introduced MRILab®, a logging tool with the ability to analyze key reservoir fluid properties, including fluid type, viscosity and gas-to-oil ratio (GOR), in real-time at reservoir temperature and pressure. MRILab® is based on NMR measurements and operates as a component of Halliburton's Reservoir Description Tool™ (RDT), making laboratory-quality measurements on reservoir fluids that are necessary for reservoir engineering and completion design. Turning back to the problem of contamination estimation, the fundamental difficulty in NMR-based approaches to such estimation is the lack of models that can predict the relaxation spectrum (i.e., the T The driving idea behind the use of the sharpness parameter was that, while OBM has a narrow distribution (implying a lower S value), distributions associated with native crudes are broader. However, relaxation spectra of low viscosity crudes are also very narrow and in the low viscosity/high GOR case, it may not be possible to distinguish one species from the other. Furthermore, both bulk water and natural gas have narrow distributions. The information from sharpness is relative, in that it is an indicator of the changes taking place, but it may not be sufficient to define end point states quantitatively. Additionally, the sharpness parameter is derived from the T Given the difficulties using the prior art approaches, there exists a need for more accurate and robust methods for determining the level of contamination of mud filtrate in formation fluids. The present invention provides a novel approach for determining the level of contamination in formation fluids. As used herein, a contaminant could be any fluid originating from mud filtrate that invades the reservoir during the drilling of a well; contamination (c) is the volume fraction of the contaminant in a fluid sample, where 0≦c≦1. The following additional terms and expressions are used in the description of the proposed approach: a contamination function is a temporal function that substantially matches the behavior of the contamination fluid fraction while pumping a sample from an invaded zone. More generally, a contamination function is applicable to multiple-component fluid systems including systems comprised of miscible or immiscible liquids, including liquids containing dissolved, suspended or dispersed solids. Unless the context indicates otherwise, the term “fluid mixture” includes in its meaning a mixture of liquids (either miscible or immiscible) or a liquid containing soluble, suspended or dispersed solids. In accordance with the proposed approach, a contamination function is a mathematical model that may be derived, for example, through simulations or observation. A mixing law (or mixing rule), as used in this disclosure, is a mathematical function that describes a property of a mixture in terms of the properties of its constituents. A mixing law would thus allow for the property of the mixture to be predicted if the weight or volume functions for the constituents, and the properties of these constituents, are known. Several examples of mixing laws are provided in the illustrative embodiment in which the physical property of the fluid being monitored is its viscosity. Different mixing laws may apply for other physical properties. In general, all that is required for the mixing law is to provide a mathematical expression that relates a property of a fluid mixture in terms of the constituent components properties (i.e., the values of such property for 0% and 100% contamination, respectively). Using these definitions, the proposed contamination estimation approach is based on the use of a contamination function that describes the time behavior of a particular physical property of the mixture of fluids entering a tool, and a mixing-law or rule that is used to estimate the volume fractions of the constituent fluids given information about or measurements of the bulk physical property. In a specific illustrative embodiment, the physical property is viscosity, which is monitored indirectly using relaxation measurements (i.e., T In particular, the determined values of one or more of the above fluid properties over time are fit to a parameterized contamination function. By matching a contamination function to the fluid property measured over a period of time, the variables of this time function are determined, for example, through regression. Once the variables of the contamination function are determined, this function can predict how the fluid property changes over time. Then, the mixing law function that relates fluid fractions to the bulk fluid property can be used to estimate the actual contamination over past or future time periods. In one type of embodiments, the contamination function used to predict the fluid property is either independent or substantially independent from the fluid fractions in the mixing law. These embodiments are referred to as uncoupled or loosely coupled contamination models. In other embodiments, the mixing laws are taken into account in tracking the bulk fluid property over time, resulting in a coupled contamination modeling. Different examples of such models are illustrated in the specific embodiments described below. In specific embodiments monitoring viscosity using NMR measurements, several empirical contamination functions can be used in accordance with the proposed approach, including the exponential, the error and several versions of the arctan contamination functions, as defined below. In another aspect of this disclosure, the log-mean value of a NMR spectrum, in particular the log-mean T It will be appreciated that the mathematical modeling approach based on the use of contamination function and mixing law can be used to estimate the level of contamination of a sample taken at a particular time, to compute the time required to reach certain contamination level during pumping, or to derive other parameters of interest during oil exploration. Various modifications of this general approach can be used in practice. For example, the contamination function used in a particular experiment may be determined in advance through prior knowledge. Further, the accuracy of prediction can be improved if one has a priori knowledge about the monitored property value at 0% and 100% contamination. In alternative embodiments, several contamination functions corresponding to different physical properties can be used to monitor the time behavior of the fluid mixture entering the tool. These embodiments are based on the observation that in certain fluid mixtures one property may be more sensitive than others to the contaminant. Accordingly, an array of devices can be used in such embodiments to measure different fluid properties. Using this approach, for example, different contamination estimates can be combined into a single average contamination estimate. Individual contamination estimates may be weighted, possibly using nonlinear regression techniques. As is the case with MRILab® estimates of viscosity, the formation fluid properties can be more accurately predicted because the end points are used to determine the in-situ sample properties. Accordingly, it is an object of this disclosure to provide methods for estimation of the level of contamination in the fluids flowing through an analyzer tool. Another object of the disclosure is to provide methods for estimating the pumping time needed to achieve a certain contamination threshold level. Yet another object of the disclosure is to provide methods for monitoring at least one physical property of the fluid entering the tool, such as its viscosity, and based on mixing laws that are known or can be derived use the monitored property to derive an estimate of the contamination level. Additional objective is to provide a computationally efficient algorithm for contamination estimation that can be implemented substantially in real time. Accordingly, in one aspect, the invention is a method for estimating levels of contamination of formation fluids in a borehole, and a corresponding system implementing the steps of: (a) providing a first mathematical contamination function model which expresses a time behavior of one or more fluid properties of a mixture of formation fluids and contamination fluids drawn from a borehole, where said one or more fluid properties are sensitive to the fraction of contamination fluids in the mixture; (b) providing a second mathematical mixing law function model expressing at least one of said one or more fluid properties of a fluid mixture in terms of corresponding properties of formation fluids and contamination fluids in the mixture; (b) drawing fluids from the borehole into a fluid analyzer; (c) measuring at least one of said one or more properties of the drawn fluids by the analyzer; and (d) estimating the level of contamination in the fluids drawn from the borehole at one or more points in time based on the at least one measured fluid property and the provided mathematical models in step (a) and step (b). In another aspect, the invention is a method for downhole formation testing, comprising the steps of: (a) providing measurement signals corresponding to a mixture of formation fluids and contamination fluids drawn from a borehole, the mixture entering a downhole fluid analyzer; (b) based on the provided measurement signals, determining parameters of a contamination function, which expresses the time behavior of one or more fluid properties of the fluid mixture drawn from the borehole; (c) computing from the determined contamination function of a value estimate of said one or more fluid properties for at least one low level of contamination and for at least one high level of contamination; and (d) computing a contamination index for the mixture of fluids drawn from the borehole at different time instants based on the computed value estimates and a fluid mixing law relating properties of the drawn fluids in terms of the corresponding properties of the formation fluids and the contamination fluids. Other aspects of the disclosure are discussed in the following detailed description, and are defined in the attached claims. FIGS. The principal objective of contamination estimation is to secure fluid samples with low levels of contamination for proper Pressure Volume Temperature (PVT) analysis. Another objective is to predict when to stop pumping with a high level of confidence as to not waste rig time on unnecessary clean up pumping. Improved contamination estimates increase the accuracy of the log analysts' data interpretation, and help to provide better estimates of permeability and anisotropy from the pumpout data. The proposed contamination estimation approach described in different embodiments below is based on the use of one or more contamination functions describing the time behavior of a particular physical property of a mixture of fluids entering a tool; and one or more mixing-law models that can be used to estimate the volume fractions of the constituent fluids given information about or measurements of the physical property. In a specific illustrative embodiment considered below, the physical property is viscosity, which is monitored using indirect measurements, i.e., T In the more general case, however, the mixing law is non-linear and the end point property values are unknown. Consider for example viscosity. As set forth in Eq. (2), viscosity is inversely proportional to the T In accordance with one important aspect of the disclosure, the approaches in this application are based on the observation that the log-mean value of an NMR parameter varies with contamination over time. Previous research has shown that the echo amplitudes in NMR measurements of a fluid entering a fluid analyzer change with time, and as a result have developed time dependent models where an empirical relationship was used to curve fit the echo amplitude changes and to relate the combined time decays to contamination. The embodiment(s) discussed below are based on the observation that log-mean T In particular, the log-mean T Calculation of Log-Mean T In one embodiment, the contamination estimation models are applied to a log-mean value derived from one or more NMR measurements. The derivation of the log-mean value of a T As noted, in a preferred embodiment, the contamination estimation models are applied to log-mean T The typical range for the T Accordingly, in one embodiment, contamination estimation is based on the use of one or more of the bands of a subdivided T In another embodiment, contamination estimation is based on a combination of the bands of the subdivided T Viscosity Estimates Using NMR Measurements The viscosity of a crude oil can be estimated from the T The viscosity relationship of Eq. (6) is an approximation for use mainly with “dead” oils. For “live” oils, which contain gas components, a better viscosity estimate is:
Viscosity Mixing Rules As noted, a mixing law or rule is a mathematical expression that describes a property of a mixture in terms of the properties of its constituents. This allows for the property of the mixture to be predicted if the weight or volume fractions or functions for the constituents, and the properties of the constituents are known. It has been concluded that no single mathematical expression could represent the viscosities of all hydrocarbon mixtures. See Shu, W. R., “A Viscosity Correlation for Mixtures of Heavy Oil, Bitumen, and Petroleum Fractions”, SPEJ, June, 277-282 (1984). This accounts for the variety of mixing rules that are known or can be developed, all of which may be used in accordance with different embodiments. Several empirical viscosity mixing rules exist that relate the measured viscosity of a miscible mixture to the volume fractions of the end-point viscosities, i.e., to the viscosity of each individual component. The four most commonly used relations are listed in the following table:
In the above equations, η The value of n is typically 4 in the viscosity mixing rule in Eq. (8), developed by Todd, M. R., et al. “The Development, Testing and Application of a Numerical Simulator for Predicting Miscible Flood Performance,” Journal of Petroleum Technology (1972). In Eqs. (9) or (10), s The fourth mixing rule in Eq. (11), is a modified version of the classic Arrhenius expression, which was originally proposed by Lederer, E. L., Proc. World Pet. Cong., vol. 2, pp. 526-28, London (1933). The constant α is found empirically and has values between 0 and 1. The volume fractions s As described below, in different embodiments one or more of the viscosity mixing rules can be applied to the log-mean value of an NMR spectrum, or the log-mean value of one or more subdivided bands of the NMR spectrum, for estimating the viscosity of a formation fluid contaminant and/or a hydrocarbon phase in a formation fluid. The contamination estimation methods in accordance with the approach proposed herein will vary according to the chosen viscosity mixing rule. Several variations of the contamination estimation models are described below. Modeling the Contamination Function In addition to the mixing rules considered above in illustrative embodiments, the contamination estimation approach in this application is based on the use of a contamination function, which is a temporal function that substantially matches the behavior of the contamination fluid fraction while pumping a sample from an invaded zone. As a result of a large number of simulations with miscible and immiscible mud systems, as well as studying the behavior of MRILab® field results, the following models have been developed to describe the time dependent behavior of the contamination function c(t), in accordance with different embodiments:
In the models above, a The idea in accordance with the proposed approach is to fit the determined values of one or more fluid properties, such as viscosity, over time to a parameterized contamination function as shown in the table. By matching a contamination function to the fluid property measured over a period of time, the variables of this time function are determined, for example, through regression. Matching could be done over the individual functions listed. For example, the error function in Eq. (17) is a solution for well-defined cases of invasion and these curves have a strong resemblance to the form of a sample quality function given by ƒ -
- 0.50≦a
_{1}≦100, and - 0.50≦a
_{2}≦0.75.
- 0.50≦a
The other contamination functions in the preceding table (Eqs. 12-16) can be matched to the simulated curves in a similar manner. Experience with field data, the mixing models and the sensor data aids in determining the best contamination model for a particular fluid property. More specifically, in a preferred embodiment, matching can be done by defining a vector ρ of unknown parameters: ρ=[a It will be appreciated that the application of the contamination estimation methods of this disclosure vary according to the contamination model chosen to be applied, for example, i.e., to an NMR spectrum, or to one or more subdivisions of the NMR spectrum. The methods may also change dependent on whether the contamination model is applied in conjunction with one or more mixing rules. Variations of the Miscible Fluid Models (MFMs) The above modeling is applicable to OBM applications, in which it is assumed that contamination is the result of mixing miscible fluids. Accordingly, the proposed models are termed Miscible Fluid Models (MFM). As noted before, in the case of miscible fluids, the challenge in contamination estimation is to find a mixing rule that relates the end-point fluid properties to a measured bulk fluid property as a function of their saturations; and the basic approach to MFM in preferred embodiments is to relate the viscosity of the mixture to the log-mean value of an indirect property measurements, such as the log-mean T This section provides four variations of the contamination estimation methods used in different embodiments, based on four different contamination estimation models. It will be appreciated that other contamination estimation models can be formulated from variations of the approaches discussed herein. While the contamination estimation models are described relative to T Implicit Log-Mean T At present, there is no model that can fully predict the T A first embodiment of the invention, called the Implicit Log-mean T In the ILMT1 model, the T The parameter c(t) is assumed to conform to the shape of one of the contamination function models discussed above. The problem to be solved is an over-determined non-linear least squares problem, where the parameters in the contamination function are solved using well-known nonlinear least-squares (NLLS) solvers. In one embodiment, the level of contamination of a formation fluid or an estimation of the pumping time needed for achieving a given contamination level can be provided based on application of the ILMT1 contamination estimation model. In a specific embodiment, using the ArcTan-Shifted 2 contamination model in Eq. (15), the model is solved (using NLLS techniques) to compute the parameters a In a second aspect of the ILMT1 model, the inverse of the log-mean value (i.e., 1/T Explicit Log-Mean T The Explicit Log-mean T In the ELMT1 model, the T Finally, a viscosity mixing rule, e.g., Eqs. (8) through (11), is applied to compute c(t Coupled Log Mean T In the ELMT1 model, the contamination model and the mixing rule are applied in two discrete steps. However, in a third embodiment of the invention, referred to as the Coupled Log-Mean T In a first aspect according to the third embodiment, the ArcTan contamination model given in Eq. (12) is coupled with the Arrhenius viscosity mixing rule given in Eq. (10), which results in the following non-linear couple system:
In a second aspect according to the third embodiment, the viscosity mixing rule of Todd et al. given in Eq. (8) is coupled with the error function contamination model c(t) given in Eq. (17), which results in the following non-linear couple system:
There are many other possible specific forward models for the CLMT1 model resulting from the coupling of a contamination function model with a viscosity mixing rule, given the four viscosity mixing rules and the six contamination function models described above, and other contamination models or viscosity mixing rules available to one of ordinary skill in the art. It is intended that such alternative contamination function and mixing law models are within the scope of the present invention and the appended claims. Immiscible Fluids Models (IFM) An assumption implicit in the MFM models and methods is that the mixing is miscible. However, in many instances the distinction between miscible and immiscible may disappear, and models specifically designed for immiscible mixing (i.e., in WBM applications) may be necessary for a particular application. Another embodiment in accordance with this disclosure provides the Immiscible Fluid Model (IFM), which is designed for WBM applications. The IFM can be applied for NMR measurements either in the time domain or in the T The first step of the IFM model is to determine the end-point signatures x and y. The major unknowns in this step are the NMR signatures of the two fluids (either in time, or T Equations (26) and (27) are given as discrete functions only for ease of notation, and the IFM is not so limited. The c The parameters can be solved using well-known NLLS solvers. For example, a separable non-linear least squares approach can be used where a bi-linear problem is solved to get x, y first, followed by c The second step of the IFM contamination estimation model includes fitting the c -
- 0.50≦a
_{1}≦100, and - 0.25≦a
_{2}≦0.50.
- 0.50≦a
Another possible contamination model is a logarithmic arctan function:
The IFM method described above can be modified without departing from the teachings of this disclosure as will be appreciated by those of skill in the art. For example, in alternate embodiments, the input data can be weighted based on flow rate, hydrogen index, fit error, noise level, and others. Also, contamination can be estimated in different embodiments either in the time-domain, or T Summary of Contamination Estimation Using NMR Measurements The following algorithm summary is provided for the convenience of the reader in the illustrative embodiment using viscosity as the fluid property being monitored, and indirect NMR measurements to determine these properties. The input curve for the algorithm is T -
- (1) Read T
_{1Lm }data; - (2) Compute the viscosity index of the fluid mixture η
_{m }for each experiment by applying the oil viscosity formula in Eq. (6); - (3) In a least squares fashion, fit η
_{m }to a parameterized viscosity index function c(t) of a given structure (Eq. 12-16). - (4) Compute the viscosity indices of the contaminant: η
_{1}=c(t), t=0; and of the formation fluid η_{2}=c(t), t=∞. - (5) Compute the contamination index for each experiment by applying a fluid mixing model in Eq. (8-11).
- (1) Read T
In the above summary, the time-function and mixing-model are uncoupled. It will be appreciated that both uncoupled and coupled estimates can be provided based on principles discussed above. In a particular embodiment, several tests may be performed to determine the estimation model that optimally fits the data. It will be appreciated that in the embodiments discussed above the end point properties—either apparent viscosity or T Application of the Contamination Estimation Models In another aspect, this disclosure also provides methods for applying the contamination estimation models to the non-ideal conditions encountered while drilling. The equation of the contamination estimation models are developed based on an assumption of ideal behavior of the formation fluids. One assumption of the contamination estimation models is that there are two end point fluids, e.g., the contaminant and the hydrocarbon. However, the fluid that is measured at the beginning of each experiment may not contain either of the end-point materials. The fluid measured in the experiments at earlier times may be the fluid left in the flow line from a previous station, possibly measured at a different depth where the reservoir fluid could be completely different. For the very first measurement using a new analyzer in the well, the fluid in the flowline may be water left in the tool during calibration in the shop. Also, contamination is usually modeled as a monotonic phenomenon, in that contamination is taken to decrease as a function of time as the pump-out time increases. For example, T An example of such behavior can be seen in the top graph of Accordingly, in a preferred embodiment, an inflection-point detection algorithm may be applied to identify the relevant time window that contains data from the fluids of interest. Once the relevant time window is identified, data outside this window can be excluded from the inversion. The application of an inflection-point detection algorithm to a data set is demonstrated in the top graph of Earlier contamination estimation models have been developed based on the assumption that the contamination approaches zero if pumping continues indefinitely (i.e., c(∞)→0). See, e.g., U.S. Pat. No. 6,178,815; 6,274,865; 6,343,507 and 6,350,986, which are incorporated herein by reference. Unless the well is drilled under-balanced, this would generally not be the case. The overbalance pressure that exists in most wells allows a small amount of mud filtrate to continually leak through the mudcake. With sampling, the filtrate leakage near the probe would be diverted and mixed with the formation fluids entering the tool, causing some residual contamination. Factors that can influence this residual contamination include overbalance, permeability, and (to a lesser degree) anisotropy. The permeability influences how quickly the mudcake forms and the flow rate at which the formation tester can pump a sample. As a result, the residual contamination increases with reduced permeability. The residual contamination can be estimated, and in most cases is less than 1%. The present disclosure also provides methods of adjusting the contamination models to take into account the residual contamination. Using additional simulations, it is possible to develop a correlation function, where the residual contamination (related to the overbalance and permeability) is estimated before pumping starts. The limits of the contamination estimation functions (e.g., Eqs. 12-18) used to estimate the contamination while pumping, i.e., the two end points, are then rescaled, so that the projected sample contamination is asymptotic to the residual contamination. Comments and Algorithm Extensions Fluid Properties While the contamination estimation models described in the illustrative embodiment using viscosity and NMR measurements for illustration, the principles of this disclosure can be applied to any other physical parameters or measurements of the formation fluid that are sensitive to contamination. For example, resistivity, capacitance, density, viscosity, HI (hydrogen index), compressibility, speed of sound, and even the pumping pressures can be sensitive to changes in contamination. The specifics of the mixing rule(s) or contamination function models applicable in each case are believed within the scope of knowledge of a person of ordinary skill in the art and will not be considered in further detail. The interested reader is directed to the discussions in [LIST OF REFERENCES] for background information. The use of different fluid properties in accordance with the principles of this disclosure is illustrated using the Implicit LogMean T The elements of the vector of unknowns x=(x The task is then to find the vector x that minimizes the above function, like in any other least squares problem. While the illustrative embodiments using viscosity d In accordance with another aspect of the disclosure it will be appreciated that in certain fluid mixtures one of these properties may be more sensitive than others to the contaminant. Accordingly, an array of instruments can be used in a preferred embodiment to measure individual properties and the approaches disclosed below applied to each measurement. In a specific embodiment, different contamination estimates can be combined into a single average contamination estimate. Individual contamination estimates may be weighted, preferably using nonlinear regression techniques. As is the case with MRILab® estimates of viscosity, the formation fluid properties can be more accurately predicted because the end points are used to determine the in-situ sample properties. Multi-Fluid Modeling In another aspect, it will be appreciated that the mixing rules described in the above illustrative embodiments are applicable to two fluids—generally a contaminant and the native formation fluid. In certain applications, it may be advantageous to consider three different fluid types, for example, in the production of a well, where fluids of different viscosities are produced from different zones. It will be appreciated that in such cases, for example, a gas zone, and two oil zones may be encountered, where the oils have different viscosities. The viscosity mixing rules given for 2 fluids (Eqs. 8 thru 11), have counterparts for 3 or more fluids. For example, the 2-fluid mixing rule, referred to as Todd et al, given by
It can be seen from the above that extension of the other mixing rules, for 3 or more fluids, in many cases is straightforward. NMR Measurements of Viscosity The relationship between viscosity and T It should be noted, however, that in not knowing f(GOR) does not adversely impact on the contamination estimates obtained in accordance with this disclosure, because whether the oil is “live” or “dead,” the values for T Effects of Flow on T If the fluid is viscous, in which case the T In accordance with another aspect, the contamination algorithms presented herein may function well without flow corrections to the data even in the worst case of long T The results of the contamination estimation for data shown in In the top report section, several parameters and answers are listed. The results of the inversion, for the end-point viscosity indices, as well as the parameters a The second section contains a plot of the estimated contamination vs. time. The estimated contamination is about 2 percent towards the end of the measurement. The third section is a plot of the input data (solid curve) vs. the fit (connected diamonds). The flow rate is shown in the bottom plot, for reference. In the third section, one can see the T The top graph of The top graph of While the IFM is not suitable for OBM, it nevertheless gives results comparable to the MFM. The contamination estimation results using the IFM for the data shown in The lower plot of The upper plot shows the contamination values as a function of experiment number. The monotonically decaying smooth curve is the contamination curve. Towards the end of the measurement, contamination values are on the order of 2 percent. The results shown here are obtained by constraining the contamination values between 0 and 1, which explains the abundance of points occurring at 0 and 1. The applicability of an IFM model to an OBM system is probably due to the nature of the fluids and the type of the data collected. Basically, when the two end points are very different, and one has waited long enough for clean up as in this example, any monotonically decaying function will show that contamination is low. The following description summarizes equipment and methods used in the illustrative embodiment of the application, which relies on NMR measurements. The interested reader is directed for additional detail on NMR to the disclosure of the patents and publications set forth in the background section of the invention. NMR Fluid Analyzer For a saturation-recovery T The magnetic field in the measurement volume of the device shown in The electronics used in the NMR fluid analyzer, which is illustrated in a block diagram in In a transmit mode, the controller The MRILab® described above determines hydrogen density, self-diffusion rates and NMR relaxation rates of fluids during the pump-out phase, from which one can compute sample viscosity and GOR. By design, fluid samples are analyzed under true reservoir conditions and results are available substantially in real time. In particular, the MRILab® measures the hydrogen index and the NMR polarization time constant (T The MRILab® can also be switched to T Relaxation Time Measurements The T More specifically, the T As noted, following the saturation pulse, a variable delay is inserted. Preferably, consecutive measurements with delay values of 1 ms, 2 ms, 4 ms, . . . , up to 16384 ms are used. During these intervals, the nuclear magnetization builds up again to its equilibrium value. Also during this time, depending on the flow rate, fluid volume moves into the receiver coil volume, while unprepared fluid enters the resonance volume. As long as the flow rate is not high enough to allow unprepared fluid from the polarization section to enter the receiver coil section, it will be appreciated that the measurement is independent of the actual flow rate. After the saturation recovery delay, the instantaneous value of the nuclear magnetization is determined. This is done with a short pulse sequence, consisting of a π/2 pulse, followed by a π pulse. The RF phase of these pulses is shifted by 90° against each other to cancel the effects of B Examples of T The data points illustrated have been acquired by circulating different fluids through the analyzer. Shown from top to bottom are: water (mild brine) with a single relaxation peak in the “water window” at 2 seconds; next a simple hydrocarbon (diesel) with a single relaxation peak in the “oil window” at 0.5-1 second; and a complex hydrocarbon (crude), which shows a characteristic asymmetric distribution that starts in the few tens of milliseconds and extends to the “oil window.” These samples were under atmospheric conditions at ambient temperature. At elevated temperatures, Eq. (2) predicts an increase in T The determination of long relaxation times no longer depends on how long an echo train persists. In the implementation discussed above, small perturbations in the applied field have relatively limited effect. Additionally, the saturation pulse prepares a much larger sample volume than what is actually used for the readout portion. Therefore, as long as the flow rate is low enough, and the readout is based on a fluid sample that was present anywhere within the resonance regions during the saturation pulse, the measurement is valid. In contrast to T Hydrogen Density Measurements The hydrogen density or the total number of hydrogen atoms within the measurement volume is a by-product of any T Hydrogen density can be automatically converted to hydrogen index (HI), which is the hydrogen density of a material relative to that of water at ambient conditions. The spin density of the fluid is proportional to the hydrogen index. Under the assumption that the oil contains only hydrogen and carbon atoms, the mass density ρ See, for example, Zhang et al., (1998), “Some Exceptions to Default NMR Rock and Fluid Properties, Paper FF: SPWLA.” presented at the 39 It has been reported that most saturated hydrocarbon liquids have relative hydrogen indices of 1 within ±5%. The hydrogen density in gases is significantly lower due to the overall lower density. Thus, a depressed hydrogen index serves as a first-order alert to the presence of gas and a change in the relationship between T Diffusion Measurements Diffusion measurements can be performed using the NMR fluid analyzer using steady-gradient spin-echo (SGSE) experiments. The experiments require that the fluid flow is temporarily stopped. The concept of using the fringes of a uniform field volume for diffusometry derives from so called SSF-SGSE methods. The main advantage of the SGSE method over pulsed-field gradient spin-echo (PFGSE) diffusometry is instrumental simplicity and superior stability. The main drawback is a limit on sensitivity, which, for the downhole implementation, is approximately 10 The sensitive volume of the apparatus can be divided into an interior, homogeneous region and an exterior gradient region. The field in the fringe volume, which makes up about ⅓ of the total volume, can be approximated by a single field gradient value G In particular, two CPMG sequences with a short echo spacing (typically 0.25 ms) and a long spacing (T For a formation fluid with a given relaxation time T The relations also apply for an arbitrary distribution of times T The system parameter K This curve is fit to a uni-exponential model plus an offset. In the upper graph of The two curves in the upper graph of In a preferred embodiment, viscosity is determined as follows:
In this expression, the viscosity η is measured in cp, the temperature T in Kelvin and the diffusivity D in cm Although the present invention has been described in connection with the preferred embodiments, it is not intended to be limited to the specific form set forth herein, but on the contrary, it is intended to cover such modifications, alternatives, and equivalents as can be reasonably included within the spirit and scope of the invention as defined by the following claims. Referenced by
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