The subject disclosure relates to methods for separating water and oil in multidimensional nuclear magnetic resonance (NMR) maps. More particularly, the subject disclosure relates to methods for processing NMR data and separating water and oil and optionally gas on a multidimensional map such as a Diffusion (D)-T2 relaxation time map. The subject disclosure has particular application to the hydrocarbon industry, although it is not limited thereto.
Nuclear magnetic resonance (NMR) is a useful tool in the determination of the nature of geological formations. More specifically, NMR tools in boreholes traversing earth formations are able to generate fields that result in signals indicating the presence of water and hydrocarbon in the formation. If the signals from water and hydrocarbons can be separated, hydrocarbon-bearing zones may be identified. Various methods have been proposed for separately identifying water and hydrocarbon signals.
The differential spectrum (DSM) and shifted spectrum (SSM) methods proposed by Akkurt et. al. in “NMR Logging of Natural Gas Reservoirs” Paper N. Transactions of the Society of Professional Well Log Analysts (SPWLA) Annual Logging Symposium, 1995, compare T2 distributions derived from two Carr-Purcell-Meiboom-Gill (CPMG) measurements performed with different polarization times (DSM) or echo-spacings (SSM). A modification to these methods, known as time domain analysis (TDA), was later introduced by Prammer et al. in “Lithology-Independent Gas Detection by Gradient-NMR Logging,” SPE paper 30562, 1995. In TDA, “difference” data are computed directly in the time domain by subtracting one set of the measured amplitudes from the other. The difference dataset is then assumed to contain light oil and/or gas. In TDA, relative contributions from light oil or gas are derived by performing a linear least squares analysis of the difference data using assumed NMR responses for these fluids. Both DSM and TDA assume that the Water signal has substantially shorter Tl relaxation times than those of the hydrocarbons. This assumption is not always valid, however. Most notably, this assumption fails in formations where there are large pores or where the hydrocarbon is of intermediate or high viscosity. The SSM method and its successor, the enhanced diffusion method (EDM) proposed by Akkurt et. al. in “Enhanced Diffusion: Expanding the Range of NMR Direct Hydrocarbon Typing Applications”, Paper GG. Transactions of the Society of Professional Well Log Analysts (SPWLA) Annual Logging Symposium, 1998, separate gas, oil and water contributions based on changes in the T2 distributions that result from changes in the echo spacing of CPMG measurements. A strategy for combining and selecting these different NMR methods has been described by Coates et al. in U.S. Pat. No. 6,366,087 which is hereby incorporated by reference herein in its entirety.
The diffusion-editing (DE) pulse sequence by Hurlimann et al. provides a different approach. See M. D. Hürlimann et al., “Diffusion-Editing: New NMR Measurement of Saturation and Pore Geometry,” paper presented at the 2002 Annual Meeting of the Society of Professional Well Log Analysts, Osio, Japan, June 2-5; see also, U.S. Pat. No. 6,570,382 to Hürlimann which is hereby incorporated by reference herein in its entirety.
In addition to DE sequences, specialized interpretation methods have been developed for NMR data in order to further enhance hydrocarbon detection. These methods typically apply forward modeling to suites of NMR data acquired with different parameters. The suite of NMR data are typically acquired with different echo spacings (Te) or polarization times (WT), and sometimes acquired with different magnetic field gradients (G). DE sequences are one example of such data acquisition. Two example methods include: the MACNMR proposed by Slijkerman et al., SPE paper 56768, “Processing of Multi-Acquisition NMR Data” (1999), and the Magnetic Resonance Fluid characterization (MRF) method disclosed in U.S. Pat. No. 6,229,308 to Freedman which is hereby incorporated by reference herein in its entirety.
The MRF method is capable of obtaining separate oil and water T2 distributions. The MRF method may use a Constituent Viscosity Model (CVM), which relates relaxation time and diffusion rates to constituent viscosities whose geometric mean is identical to the macroscopic fluid viscosity. With the MRF method, estimates for water and hydrocarbon volumes are obtained by applying a forward model to simulate the NMR responses to a suite of NMR measurements acquired with different parameters. Specifically, the MRF technique is based on established physical laws which are calibrated empirically to account for the downhole fluid NMR responses. By using realistic fluid models, MRF aims to minimize the number of adjustable parameters to be compatible with the information content of typical NMR log data. Since the model parameters are by design related to the individual fluid volumes and properties, determination of the parameter values (i.e. data-fitting) leads directly to estimates for petrophysical quantities of interest.
Another approach based on a maximum entropy principle (MEP) involves a general model-independent method to analyze complex fluids data acquired with NMR logging instruments and present the results in a visually attractive and easy-to-understand format, hereby referred to as Diffusion-Relaxation maps, or D-T2 maps. These maps have been used to understand cases where model-based analysis gives unsatisfactory results because of deviations of NMR properties from the “ideal” behavior assumed in the models. These situations can arise due to anomalous fluid/rock interactions such as restricted diffusion, mixed-Wettability and internal gradients. Deviations from the default properties have also been observed for certain crude oils, leading to inaccurate predictions in the model analysis. Through the use of D-T2 maps, the MEP approach provides a simple graphical representation of the data that can be used to identify fluid responses in different environments. Diffusion-Relaxation maps are further described in U.S. Pat. No. 6,570,382 to Hürlimann et al., and U.S. Pat. No. 6,462,542 to Venkataramanan et al., which are both hereby incorporated by reference herein in their entireties.
In U.S. Pat. No. 7,388,374 to Minh et al., which is hereby incorporated by reference herein in its entirety, a method is disclosed for interpretation of multi-dimensional nuclear magnetic resonance data taken on a sample of an earth formation. Specifically, a set of NMR data is acquired for a fluid sample located either in a borehole or in a laboratory environment. From the set of NMR data, a multi-dimensional distribution is calculated using a mathematical inversion that is independent of prior knowledge of fluid sample properties. The multi-dimensional distribution is graphically displayed on a multi-dimensional map. Each fluid instance or artifact visible on the graph is identified as representing a probable existence of a detected fluid. One or more quantitative formation evaluation answers for one or more fluid instances are computed based on the multi-dimensional distribution associated with the respective fluid instance. In one aspect, quantitative formation evaluation answers may be determined from the multi-dimensional distribution of NMR data by initially determining a set of model parameters which represent aspects of the multi-dimensional distribution. A model dependent inversion may then be applied to compute the fluid properties. In another aspect, quantitative formation evaluation answers may be determined from the multi-dimensional distribution of NMR data by determining a mean diffusion value across a region of a diffusion-T2 relaxation distribution. The mean diffusion may then be used to determine properties of the fluid associated with the selected region.
This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter.
Methods are provided for separating oil and water in multidimensional nuclear magnetic resonance (NMR) maps. In one embodiment, a method for separating oil and water in multidimensional NMR maps includes generating individual multidimensional NMR maps for the oil and the water. In one embodiment, a method for separating oil and water in multidimensional NMR maps includes showing separate oil and water domains on a D-T2 map.
In one embodiment, individual D-T2 maps for oil and water are generated by processing NMR data in order to generate separate T2 intensity graphs for the oil and water (e.g., using magnetic resonance fluid (MRF) processing), processing the NMR data to obtain a multidimensional D-T2 crossplot map, generating diffusion (D) intensity values for each T2 value, summing a plurality of D intensity values until the sum equals a corresponding value for that T2 for each of oil and water as set forth, e.g., in the MRF processing, and using that summation for each T2 value to generate the individual D-T2 maps.
In one embodiment, the summing of a plurality of D intensity values is accomplished for each T2 value of interest, by starting with a lowest diffusion bin, and integrating the D signal from that lower limit to a higher limit which is determined when the integrated signal equals the value for oil obtained by, e.g., MRF processing, thereby generating a D-T2 plot for oil. Then, for water, for the T2 values of interest, the D intensity value signal is integrated from a lower limit equal to the higher limit of oil to a higher limit for the water which is determined when that integrated signal equals the value for water obtained by, e.g., MRF processing, thereby generating a D-T2 plot for water. In one embodiment, the D-T2 plots for water and oil may be applied to the D-T2 map representing the NMR data.
In one embodiment, methods are extended to separating oil, water and gas. In one aspect, it is assumed that any signal in excess of the water signal is due to gas.
In one aspect, the D-T2 plots for water and oil when applied to the D-T2 map representing the NMR data may be considered cutoff lines (curves), such that it is implied that there is no oil above the oil cutoff line (curve), and that there is no water above the water cutoff line (curve) and below the oil cutoff line (curve).
In another aspect, straight oil and water cutoff lines are generated by using known fluid saturations, drawing horizontal lines on the D-T2 map, integrating fluid volume below and above the line, and adjusting the line locations until the fluid volumes equal the known saturation levels.
Additional aspects, embodiments, and advantages of the disclosed methods may be understood with reference to the following detailed description taken in conjunction with the provided drawings.
a-1c are respectively a D-T2 cross-plot showing oil, water and gas regions, and first integration of the cross-plot showing total signal intensity as a function of diffusion, and a second integration of the cross-plot showing total signal intensity as a function of T2;
a and 3b are respectively the D-T2 cross-plot of
a-4c are respectively D-T2 maps separately showing the fluid boundary curves shown in
The particulars shown herein are by way of example and for purposes of illustrative discussion of the examples of the subject disclosure only and are presented in the cause of providing what is believed to be the most useful and readily understood description of the principles and conceptual aspects of the subject disclosure. In this regard, no attempt is made to show details in more detail than is necessary, the description taken with the drawings making apparent to those skilled in the art how the several forms of the subject disclosure may be embodied in practice. Furthermore, like reference numbers and designations in the various drawings indicate like elements.
In NMR logging it is common to locate an NMR logging tool down a borehole, generate a sequence of diffusion editing pulses and pulses with particular spacings and echos, and acquire data that is a function of NMR diffusion (D), spin-lattice relaxation time (T1) and spin-spin relaxation time (T2) of the formation under investigation. It is also common to process the obtained data using a 2D or 3D Laplace inversion and to provide a D-T2 map as shown in
In
In
The D-T2 distribution in
Turning to
In one embodiment, and as seen with reference to
Repeating this process at different T2 values, leads to a set of three numbers for each T2 (some may be zero depending on the T2 values). The data obtained, can be plotted as three curves 410, 420, 430 on a single D-T2 plot, as shown in
In the example of
In one aspect, the limits 161, 164 and 156 can be determined from the requirement that the integrals under the curve 382 (that represents fluids volumes) equate to values of points 280, 282 and 284 obtained from MRF processing. In other words, the integral of curve 382 of
Once this exercise is repeated at different T2 values, a set of numbers will be available which can be plotted as the D-T2 map of
According to one aspect, any point on the 510 curve is an cutoff for oil, implying that there is no water signal above that point. Similarly, any point on the 520 curve is a cutoff for water, implying there is no water signal above that point. However, for water, the 520 curve does not mean that any data below curve 520 is water; rather the lower bound for water is set by the oil curve 510. Therefore, the water cutoff is between curves 510 and 520.
In the embodiment of
Turning to
In one aspect, multidimensional maps and/or cut-off values of the oil and water signals may be displayed on paper or on an electronic medium such as a computer screen.
According to one embodiment, the methods described above can be used to separate the oil and water along the diffusion direction with a D-T2 map, but also with respect to other multidimensional maps such as a a D-T1/T2 map, a D-T2-T1/T2 map, and a D-T2-T1 map. This is because T1 information is obtained and preserved during processing and therefore a T1/T2 ratio can be calculated. Since the separation of oil and water according to the described methods is based along the diffusion direction, and T1 information is not used to extract a boundary, there is an implicit assumption that the boundary is not dependent on T1. Under this assumption, the boundary is the same in the slices for different T1/T2 ratios.
In one aspect, the oil-water boundary and water-gas boundary curves, lines or values may be useful in deriving other information regarding the earth formation under investigation. By way of example and not by way of limitation, the oil-water boundary and water-gas boundary curves, lines or values may be used to obtain determinations or estimates of wettability.
In another aspect, multidimensional maps showing oil-water boundary and water-gas boundary curves, lines or values may be useful in deriving other information regarding the earth formation under investigation. By way of example and not by way of limitation, the multidimensional maps showing oil-water boundary and water-gas boundary curves, lines or values may be used to obtain determinations or estimates of wettability.
In one aspect, the processing of NMR signals to obtain oil-water boundary and water-gas boundary curves, lines or values, or multidimensional maps showing the same involves the transformation of the NMR signals into a physical representation that may be seen and utilized.
In one aspect, some of the methods and processes described above, such as MRF processing and inverse Laplace transformed are performed by a processor. The term “processor” should not be construed to limit the embodiments disclosed herein to any particular device type or system. The processor may include a computer system. The computer system may also include a computer processor (e.g., a microprocessor, microcontroller, digital signal processor, or general purpose computer) for executing any of the methods and processes described above. The computer system may further include a memory such as a semiconductor memory device (e.g., a RAM, ROM, PROM, EEPROM, or Flash-Programmable RAM), a magnetic memory device (e.g., a diskette or fixed disk), an optical memory device (e.g., a CD-ROM), a PC card (e.g., PCMCIA card), or other memory device.
Some of the methods and processes described above, as listed above, can be implemented as computer program logic for use with the computer processor. The computer program logic may be embodied in various forms, including a source code form or a computer executable form. Source code may include a series of computer program instructions in a variety of programming languages (e.g., an object code, an assembly language, or a high-level language such as C, C++, or JAVA). Such computer instructions can be stored in a non-transitory computer readable medium (e.g., memory) and executed by the computer processor. The computer instructions may be distributed in any form as a removable storage medium with accompanying printed or electronic documentation (e.g., shrink wrapped software), preloaded with a computer system (e.g., on system ROM or fixed disk), or distributed from a server or electronic bulletin board over a communication system (e.g., the Internet or World Wide Web).
Alternatively or additionally, the processor may include discrete electronic components coupled to a printed circuit board, integrated circuitry (e.g., Application Specific Integrated Circuits (ASIC)), and/or programmable logic devices (e.g., a Field Programmable Gate Arrays (FPGA)). Any of the methods and processes described above can be implemented using such logic devices.
In one aspect, the methods described may be applied to geological formations downhole, or uphole on rock or core samples. Where the methods are carried out with downhole NMR tools, the processing of the obtained signals may be carried out downhole and/or uphole.
Although only a few examples have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the examples without materially departing from this subject disclosure. Thus, by way of example only, and not by way of limitation, while various embodiments show the provision of oil-water and water-gas boundaries, the number of boundaries and types of boundaries shown may be different. For example, depending upon the number of fluids that are present and that generate distinct NMR signals, a single boundary (e.g., oil-water, or water-gas, or oil-gas) may be shown, or more than two boundaries may be shown. Also, while methods have been described that involve processing the NMR signals to obtain separate oil and water T2 intensity distributions utilizing MRF processing (which uses global inversion processing of a model having a plurality of components for an oil phase and a water or brine phase), it will be appreciated that other methods of separating the T2 responses from different fluids may be utilized such as by way of example, differential spectrum, shift spectrum, enhanced diffusion, and dual-wait-time dual-echo spacing methods (see Sun, Boqin, and Dunn, Keh-Jim, “NMR Inversion Methods for Fluid Typing”, SPWLA 44th Annual Logging Symposium, Jun. 22-25, 2003). Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the following claims. In the claims, means-plus-function clauses are intended to cover the structures described herein as performing the recited function and not only structural equivalents, but also equivalent structures. Thus, although a nail and a screw may not be structural equivalents in that a nail employs a cylindrical surface to secure wooden parts together, whereas a screw employs a helical surface, in the environment of fastening wooden parts, a nail and a screw may be equivalent structures. It is the express intention of the applicant not to invoke 35 U.S.C. §112, paragraph 6 for any limitations of any of the claims herein, except for those in which the claim expressly uses the words ‘means for’ together with an associated function.
This application claims priority to U.S. Provisional Patent Application No. 61/903,637, filed Nov. 13, 2013, the entire disclosure of which is hereby expressly incorporated by reference herein.
Number | Date | Country | |
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61903637 | Nov 2013 | US |