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Fluid typing with NMR logging

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Hydrocarbon typing and prediction of fluid properties by nuclear magnetic resonance (NMR) logs is predicated on reliable laboratory correlations between NMR measurements (i.e., relaxation times and diffusion) and fluid properties, for example:

  • Specific gravity
  • Viscosity
  • Gas/oil ratio (GOR)

Early studies were limited to investigations at ambient conditions; [1] however, using the standard correlations derived from these studies may result in seriously underestimating viscosity. More-recent studies have expanded these correlations to oils and mud filtrates at reservoir conditions.[1][2][3]

Characterizing fluid properties

The NMR T2-porosity relationship in which T2 is a function of pore size (i.e., S/V ratio, see Eq.1) holds for water-saturated rocks. The presence of hydrocarbons in water-wet rocks alters the T2 distribution, thus affecting the porosity interpretation. Despite the variability in the NMR properties of fluids, the locations of signals from different types of fluids in the T2 distribution can often be predicted or, if measured data are available, identified (Fig. 1).

  • Fig. 1 – NMR-oil typing. The position and spread of the oil component in the T2 distribution depends on oil viscosity and formation wettability. Oil typing is easiest in water-wet formations because of the moderate breadth and distinct positions of the different oil components in the T2 distribution. Oil typing is most difficult in mixed-wet formations because the oil and water components are broad and overlap one another.

    Fig. 1 – NMR-oil typing. The position and spread of the oil component in the T2 distribution depends on oil viscosity and formation wettability. Oil typing is easiest in water-wet formations because of the moderate breadth and distinct positions of the different oil components in the T2 distribution. Oil typing is most difficult in mixed-wet formations because the oil and water components are broad and overlap one another.

NMR logging uses specialized Carr-Meiboom-Purcell-Gill (CMPG) pulse-acquisition sequences to exploit these differences in pore-fluid NMR properties to achieve specific objectives. The CMPG parameters—wait time (TW), echo spacing (TE), the number of echoes (NE), and the number of sequence repetitions—are selected to take advantage of the wide variation in NMR fluid properties (Fig. 2) for estimating total porosity and for hydrocarbon typing. Table 1 illustrates the range of NMR-related properties of fluids for Gulf of Mexico sandstones, for example. Table 2 lists variations in CMPG pulse sequences for different objectives using time-domain analysis (TDA) and Enhanced Diffusion Method (EDM). The multifrequency tools now in service permit the simultaneous acquisition of multiple measurements on the same rock volume using different acquisition sequences during the same logging run. The general approach is to log in a mode that allows gathering the full spectrum of data. Specialized applications, including some direct fluid-identification methods, involve customized-acquisition sequences that require slower logging and acquisition of more echoes.

  • Fig. 2– Typical NMR properties for pore fluids. Differences in these properties between fluids are exploited by methods that use specific acquisition sequences for hydrocarbon typing buy use of T1 or diffusion-weighting mechanisms.

    Fig. 2– Typical NMR properties for pore fluids. Differences in these properties between fluids are exploited by methods that use specific acquisition sequences for hydrocarbon typing buy use of T1 or diffusion-weighting mechanisms.

  • Table 1

    Table 1

  • Table 2

    Table 2

Determination of the appropriate interpretation method is largely based on the estimated viscosity of the anticipated hydrocarbons (see Fig. 3). These methods will be explained in the following paragraphs.

  • Fig.3 – Graph indicating the current viscosity limits for NMR-interpretation techniques. The resistivity-density technique is limited to gas reservoirs, and the SSM method has been effectively replaced by the newer DSM method. The y-axis is the relaxation time.

    Fig.3 – Graph indicating the current viscosity limits for NMR-interpretation techniques. The resistivity-density technique is limited to gas reservoirs, and the SSM method has been effectively replaced by the newer DSM method. The y-axis is the relaxation time.

Selection of the appropriate acquisition sequence and the choice of acquisition parameters depend on the logging objectives and are part of prejob planning. This process considers several factors regarding the anticipated rock and pore-fluid properties. For example, for characterizing large pores (clastics), clean formations, carbonates, and light oil—all of which are associated with long-T2 values[4]—a large number of echoes should be acquired, and a longTW should be used. However, more echoes and longer TW may require reduced logging speeds. In contrast, characterization of small pores (i.e., low permeability), shaly formations, low porosity, and BVI determination involves the short T2 component of the NMR-porosity spectrum and often it can be accomplished using fewer echoes and shorter TW, which may allow normal logging speeds.

Fluid-typing methods fall into two broad categories depending on the NMR properties that are being exploited:

  • T1-weighting mechanisms take advantage of differences in fluid T1 values
  • Diffusion-weighted mechanisms make use of the diffusion-constant differences between oil and water

There are two general types, or sets, of CMPG acquisitions that are associated with each mechanism:

  • Dual TW
  • Dual TE

These two sets cover the range of major fluid-typing objectives; some interpretation techniques can use one or the other, or both. Each serves specific purposes and is optimized to provide data for specialized analysis programs. In general terms, each consists of at least two echo-train acquisitions in which one or more parameters are varied. The total, or the difference between the echo-train signals, provides an estimate or indicator of porosity, light hydrocarbons, or oil ('Fig. 4).

  • Fig. 4– CPMG acquisition types used for hydrocarbon typing: (a) a T1-weighted method is used to differentiate hydrocarbons from water, and (b) a diffusivity-weighted method is used to differentiate viscous oil from water or to differentiate gas from liquids.

    Fig. 4– CPMG acquisition types used for hydrocarbon typing: (a) a T1-weighted method is used to differentiate hydrocarbons from water, and (b) a diffusivity-weighted method is used to differentiate viscous oil from water or to differentiate gas from liquids.

Advanced hydrocarbon-typing objectives can involve customized-acquisition sequences[5] that are run at reduced logging speeds or even in stationary mode.

T1-weighted mechanism (dual-TW acquisition)

The dual-TW acquisition method is used primarily to identify and quantify light hydrocarbons (gas and light oil) by separating them from the water signal through T1 weighting. The dual-TW acquisition can also provide the standard-T2 acquisition dataset for determination of spectral NMR porosity, permeability, and productivity (mobile fluids). The TE is kept the same in both echo-train acquisitions (0.9 or 1.2 ms), and a short TW (1 second) and long TW (8 seconds) are used. The water signal is contained in both acquisitions, but because of the light hydrocarbons (which have long T1 values), the signal is suppressed in the acquisition using the short TW. Thus, the presence of a signal in the difference of the measurements (differential spectrum) is considered a strong indicator of gas or light oils[6] (Fig.5).

  • Fig.5 – Methods using T1-weighting for hydrocarbon typing: (a) in TDA, T2 echo-train subtraction takes place in the time domain, and (b) in the differential-spectrum method (DSM), T2 echo-train subtraction takes place in the T2 domain.

    Fig.5 – Methods using T1-weighting for hydrocarbon typing: (a) in TDA, T2 echo-train subtraction takes place in the time domain, and (b) in the differential-spectrum method (DSM), T2 echo-train subtraction takes place in the T2 domain.

Fluid volumes can be quantified by integrating the difference into T2 spectra and correcting for the polarization difference between long and short wait times. Fluid typing and quantification are performed through the differential-spectrum (DSM) method and the TDA method. Two conditions must be met to ensure successful DSM interpretation:

  • T1 contrast between the hydrocarbon and brine phases (i.e., a water-wet formation containing light hydrocarbons)
  • T2 contrast between the gas and oil phases

DSM assumes that the hydrocarbon phases relax uniexponentially. DSM is commonly used for: (1) hydrocarbon typing in shaly sands, where the the neutron-density crossover may be suppressed because of the high amount of clay minerals in the rock[7][8] to confirm the presence of light oil in fine-grained rock, and (2) for gas detection in the presence of OBM invasion.[9] Ideally, DSM can be used to compensate for the hydrocarbon effects on NMR measurements and thereby enable correction of NMR total porosity and NMR effective porosity; however, because of S/N requirements, TDA is the preferred technique for correcting NMR logs.

In contrast to the DSM approach, the TDA technique works directly with the echo-decay data (i.e., in the time domain rather than the T2 domain; see Fig.6). The measured long- and short-wait-time decay trains are subtracted into an echo difference that indicates the presence of a long-T2 component (usually a hydrocarbon). By working in the time domain, when the T2 inversion is performed, only the hydrocarbon T2 component will be present.

  • Fig.6 – Example of TDA. Track 1 shows the pore volumes for gas(red), OBM filtrate and/or native oil (green), movable water (dark blue), and capillary-bound water (light blue) obtained from TDA quantitative analysis using an MRIL data set. Tracks 2 and 3 show the T2 and T1 values, respectively, of gas and light oil calculated through TDA. The lower part of Track 2 shows the oil/water and gas/oil contacts, while the upper part of the track indicates the presence of gas and oil.

    Fig.6 – Example of TDA. Track 1 shows the pore volumes for gas(red), OBM filtrate and/or native oil (green), movable water (dark blue), and capillary-bound water (light blue) obtained from TDA quantitative analysis using an MRIL data set. Tracks 2 and 3 show the T2 and T1 values, respectively, of gas and light oil calculated through TDA. The lower part of Track 2 shows the oil/water and gas/oil contacts, while the upper part of the track indicates the presence of gas and oil.

TDA—an outgrowth of the DSM technique—is based on the fact that different fluids have different rates of polarization (i.e., different T1 relaxation times).[10] TDA provides the following results:

  • Fluid types in the flushed zone
  • Corrected NMR porosity in gas reservoirs
  • Corrected NMR porosity in light oils
  • Complete fluid-saturation analysis in the flushed zone, using only NMR data

The TDA technique is a more robust method than DSM, in part because it provides better corrections for underpolarized hydrogen and HI effects[11] (Figs.6 and 7).

  • Fig.7 – Log examples showing the results of DSM and TDA analysis. Light hydrocarbons can be identified through the subtraction of echo trains obtained at two polarization times. Track 4 displays the differential spectrum obtained from the subtraction of the two separate T2 distributions derived from echo trains acquired with short- (TWL) times, TWS = 1 second and TWL = 8 seconds. The water signals in each completely cancel, while hydrocarbon signals only partially cancel and remain when the two T2 distributions are subtracted from one another. Track 5 displays the TDA results. TDA is performed in time domain (as opposed to T2 domain), and can quantify up to three phases (gas, light oil, and water; gas and water; or light oil and water).

    Fig.7 – Log examples showing the results of DSM and TDA analysis. Light hydrocarbons can be identified through the subtraction of echo trains obtained at two polarization times. Track 4 displays the differential spectrum obtained from the subtraction of the two separate T2 distributions derived from echo trains acquired with short- (TWL) times, TWS = 1 second and TWL = 8 seconds. The water signals in each completely cancel, while hydrocarbon signals only partially cancel and remain when the two T2 distributions are subtracted from one another. Track 5 displays the TDA results. TDA is performed in time domain (as opposed to T2 domain), and can quantify up to three phases (gas, light oil, and water; gas and water; or light oil and water).

Several factors inherent in the dual-TW acquisition require reduced logging speeds:

  • To achieve full polarization (long TW) in one acquisition channel
  • To acquire the small signal amplitudes associated with the short

TW values in another other channel

  • To meet the requirement for acceptable S/N levels

A new triple-wait-time method addresses these issues and also allows T1 acquisition.[12]

Diffusion-weighted mechanism (dual-TE acquisition)

NMR-logging tools generate relatively large field gradients, which are a function of the operating frequency and tool type. If the pore fluids have high diffusivity constants (D0), these large gradients may cause diffusion to become the dominant T2-relaxation mechanism, even at a reasonably short interecho time. The T2 contrast between the liquid and gas phases can then be exploited for hydrocarbon typing.

The identification of viscous oil in water-wet rock using T1-weighting mechanisms (dual-TW acquisition) is very difficult because there is little T1 contrast between water and viscous oil. However, a significant difference in the diffusion characteristics of water and viscous oil is used to create a T2 contrast that separates the two NMR signals, thereby permitting identification.[13] The inter-echo spacing, TE, is chosen long enough that the water and oil signals are fully separated in the T2-domain (i.e., water is at lower T2 than oil; see Fig.8).

  • Fig.8– EDM example. The existence of a signal on the T2 distribution longer than the T2DW indicates the presence of oil in the formation. In this log display, the T2 distributions from TE = 1.2, 3.6, and 4.8 ms are shown in Tracks 3, 4, and 5, respectively. In the EDM results, in Track 4 and 5, a significant signal to the right of the T2DW line indicates obvious oil zones. Also note the increased definition and separation in the fluid signal that occurs in Track 5 because of the increased TE. This observation is used to recognize pay in EDM-log displays.

    Fig.8– EDM example. The existence of a signal on the T2 distribution longer than the T2DW indicates the presence of oil in the formation. In this log display, the T2 distributions from TE = 1.2, 3.6, and 4.8 ms are shown in Tracks 3, 4, and 5, respectively. In the EDM results, in Track 4 and 5, a significant signal to the right of the T2DW line indicates obvious oil zones. Also note the increased definition and separation in the fluid signal that occurs in Track 5 because of the increased TE. This observation is used to recognize pay in EDM-log displays.

Diffusion-weighting techniques, such as shifted-spectrum method (SSM), enhanced-diffusion method (EDM), and diffusion analysis (DIFAN), use the dual-TE acquisition to create this T2 contrast. TE is chosen such that diffusion, rather than surface relaxation, is the dominant relaxation mechanism, and thus, the upper limit of T2 for pore water is T2 diffusion, designated TDW. The dual-TE acquisition set uses long TW and TE values, typically 0.9 or 1.2 ms and 3.6 or 4.8 ms, respectively. In this acquisition, the fluid with the larger diffusion constant (water) has a spectrum shifted more to earlier times than the fluid with the smaller diffusion constant (viscous oil). The presence in the spectra of a minimally shifted portion identifies high-viscosity oil in the formation (see the lower portion of Fig.9).[13][14] These techniques are primarily used for identifying the presence of viscous oil. They are also used in carbonates in which a reduced surface relaxation (compared to clastics) may also result in a reduced T1 contrast.[15][16]

  • Fig.9 – SSM application in gas (upper) and oil (lower) reservoirs.

    Fig.9 – SSM application in gas (upper) and oil (lower) reservoirs.

Shifted spectrum method

This method was the first developed using the diffusion-weighted mechanism and was originally developed for use in gas reservoirs as a qualitative technique (see the upper portion of Fig.9). However, because of signal-processing difficulties and slower logging speeds, this method was effectively replaced by the EDM.[6]

Enhanced diffusion method

This method is the later variation of SSM. Depending on oil-NMR properties and the job objective, EDM data processing can be done in either the T2 domain or time domain.[13] If the EDM objective is to discriminate pay from nonpay, then a single CMPG measurement with long TW (for full polarization) and long TE (for diffusion enhancement) is sufficient; thus, standard-T2 logging with a long TE can be used. If the EDM objective is to quantify fluids in a pay zone, then dual-TE logging will be required. The short-TE measurements will provide correct MPHI and BVI. If the T2 contrast over the zone of interest is not expected to be large enough to separate the T2 values of water and oil, then dual-TW logging with a single, long TE may be required to obtain data for TDA processing.

One caveat for users of the EDM: the flushed-zone saturation (Sxo) obtained from EDM may not reflect the true volume of residual hydrocarbons in the flushed zone if the faster portion of the hydrocarbon’s T2 spectrum gets fully polarized by both wait-time measurements. A correction to Sxo can be applied if the full-hydrocarbon T1 spectrum can be determined by other means, such as laboratory measurements.

Diffusion analysis

DIFAN is an empirical model used for quantitative-diffusion analysis. This method uses a dual-TE acquisition and is useful for typing and quantifying oils with viscosities ranging between 0.5 to 35 cp at reservoir conditions (i.e., temperature ≥ 200°F and pressure ≥ 2,000 psi). It is not recommended for light oils and condensates because the D 0 contrast between hydrocarbons and water is too small.

Neither EDM nor DIFAN is recommended for use in high-viscosity oil (heavy oil) because the difference between the T2 values of dead oil and irreducible water is too small. The DIFAN model generates two T2 distributions using the two echo trains generated from dual-TE logging.

Echo ratio method

An apparent diffusion coefficient, calculated from the ratio of long and short TE echo-train data (time domain), serves as a qualitative gas indicator.[17] This method is accurate and provides a direct measurement of gas volume, but requires high HI values for good results.


Multifluid (forward modeling) methods

The dual-TW and dual-TE techniques described above only provide T2 distributions and are considered one-dimensional methods.[18] Oil-saturation determination using these methods may be difficult or even impossible because differential diffusion-weighted methods assume that NMR water and hydrocarbon signals can be cleanly separated by subtracting the acquisitions made at different TE values. In practice, however, partial—or even total—overlap is common.

To avoid these problems, new multifluid-forward models were developed to take advantage of the full suite of data obtained using the latest multifrequency logging tools. These models perform a simultaneous inversion of the NMR fluid properties obtained in multiple parameter-domain dimensions (e.g., T1, T2, and D0).[19][20][21][22][23][24] These methods make use of the enhanced T2 relaxation in a magnetic-field gradient (i.e., incorporate measurement of the diffusion coefficient, D0) and the large contrast between diffusion coefficients of oil and water. In effect, they are simultaneously combining both T1- and diffusion-weighted processing techniques, and thus, provide more accurate and robust results. In addition to porosity and permeability information, the output data from these inversion models are plotted as two-dimensional plots (e.g., D-T2 plot) in which the oil and water signals are clearly separated.[25] Recent two-dimensional NMR techniques include diffusion editing, diffusion mapping, and relaxation-diffusion 2D.[26][22][27][28][29][30]

Multidimensional (2D and 3D) NMR analysis greatly increases the accuracy of fluid typing and saturation determination.[18] These methods are particularly useful for identifying highly diffusive fluids (e.g., gas, condensate, and light oil)[19][30][31][32][33] and for identifying wettability alteration caused by OBM-filtrate invasion or chemical surfactants used in enhanced recovery operations.[22][32][34] This information is essential for reservoir simulation, development, and production.[35]

Nomenclature

D0 = molecular diffusion coefficient, gauss/cm
NE = number of echoes
Sxo = flushed-zone saturation, %
T1 = longitudinal relaxation time, seconds
T2 = transverse relaxation time, seconds
TDW = dual wait time—the value of TE such that diffusion, rather than surface relaxation, is the dominant relaxation mechanism, seconds
TE = CMPG interecho spacing, seconds
TW = polarization (wait) time, seconds

References

  1. ↑ 1.0 1.1 Winkler, M., Freeman, J., and Appel, M.: "The Limits Of Fluid Property Correlations Used in NMR Well Logging; an Experimental Study of Reservoir Fluids at Reservoir Conditions," paper DD presented at the 2004 Soc. of Petrophysicists and Log Analysts Annual Logging Symposium, Noordwijk, The Netherlands, 6–9 June.
  2. ↑ Hirasaki, G.J., Lo, S.-W., and Zhang, Y.: "NMR Properties of Petroleum Reservoir Fluids," Magnetic Resonance Imaging (2003) 21, No. 3–4, 269.
  3. ↑ Chen, S. et al.: "Laboratory Investigation of NMR Crude Oils and Mud Filtrates Properties in Ambient and Reservoir Conditions," paper SPE 90553 presented at the 2004 SPE Annual Technical Conference and Exhibition, Houston, 26–29 September.
  4. ↑ Timur, A.: "Nuclear Magnetic Resonance Study of Carbonate Rocks," The Log Analyst (1972) 13, No. 5, 3.
  5. ↑ Hurlimann, M.D.: "Diffusion and Relaxation Effects in General Stray Field NMR Experiments," J. of Magnetic Resonance (2001) 148, 367.
  6. ↑ 6.0 6.1 Akkurt, R. et al.: "NMR Logging of Natural Gas Reservoirs," The Log Analyst (1996) 37, No. 5, 33.
  7. ↑ Coates, G.R., Gardner, J.S., and Miller, D.L.: "Applying Pulse-Echo NMR to Shaly Sand Formation Evaluation," paper B presented at the 1994 Soc. of Professional Well Log Analysts Annual Logging Symposium, Tulsa, 19–22 June.
  8. ↑ Chitale, D.V., Day, P.I., and Coates, G.R.: "Petrophysical Implications of Laboratory NMR and Petrographical Investigation on a Shaly Sand Core," paper SPE 56765 presented at the 1999 SPE Annual Technical Conference and Exhibition, Houston, 3–6 October.
  9. ↑ Moore, M.A. and Akkurt, R.: "Nuclear Magnetic Resonance Applied to Gas Detection in a Highly Laminated Gulf of Mexico Turbidite Invaded With Synthetic Oil Filtrate," paper SPE 36521 presented at the 1996 SPE Annual Technical Conference and Exhibition, Denver, 6–9 October.
  10. ↑ Buller, D.: "Carbonate Evaluation Using NMR Time Domain Analysis," paper GGG presented at the 2000 Soc. of Professional Well Log Analysts Annual Logging Symposium, Dallas, 4–7 June.
  11. ↑ Prammer, M.G. et al.: "Lithology-Independent Gas Detection by Gradient-NMR Logging," paper SPE 30562 presented at the 1995 SPE Annual Technical Conference and Exhibition, Dallas, 22–25 October.
  12. ↑ Hou, B.L. and Miller, D.: "Determining Fluid Volume in Gas and/or Light Oil Reservoirs—Using a New Triple-Wait-Time NMR Logging Method," paper U presented at the 2000 Soc. of Professional Well Log Analysts Annual Logging Symposium, Dallas, 4–7 June.
  13. ↑ 13.0 13.1 13.2 Akkurt, R. et al.: "Enhanced Diffusion—Expanding the Range of NMR Direct Hydrocarbon-Typing Applications," paper GG presented at the 1998 Soc. of Professional Well Log Analysts Annual Logging Symposium, Keystone, Colorado, 26–27 May.
  14. ↑ Akkurt, R. et al.: "Determination of Residual Oil Saturation by Use of Enhanced Diffusion," SPEREE (June 1999) 303.
  15. ↑ Horkowitz, J.P. et al.: "Residual Oil Saturation Measurements in Carbonates With Pulsed NMR Logs," The Log Analyst (1997) 38, No. 2, 73.
  16. ↑ Howard, J.J.: "Wettability and Fluid Saturations Determined From NMR T1 Distributions," Magnetic Resonance Imaging (1994) 12, No. 2, 197.
  17. ↑ Flaum, C., Kleinberg, R., and Hurlimann, M.: "Identification of Gas With the Combinable Magnetic Resonance Tool (CMR)," paper L presented at the 1996 Soc. of Professional Well Log Analysts Annual Logging Symposium, New Orleans, 16–19 June.
  18. ↑ 18.0 18.1 Sun, B. et al.: "Two-Dimensional NMR Logging and Field Test Results," paper KK presented at the 2004 Soc. of Petrophysicists and Log Analysts Annual Logging Symposium, Noordwijk, The Netherlands, 6–9 June.
  19. ↑ 19.0 19.1 Heaton, N. et al .: "Saturation and Viscosity From Multidimensional Nuclear Magnetic Resonance Logging," paper SPE 90564 presented at the 2004 SPE Annual Technical Conference and Exhibition, Houston, 26–29 September.
  20. ↑ Slijkerman, W.F.J. et al.: "Processing of Multi-Acquisition NMR Data," SPEREE (2000) 492.
  21. ↑ Chen, S. et al : "G-Te Correction for Processing Multigradient, Multiple-Te NMR Log Data," paper SPE 84481 presented at the 2003 SPE Annual Technical Conference and Exhibition, Denver, 5–8 October.
  22. ↑ 22.0 22.1 22.2 Freedman, R. et al .: "Wettability, Saturation, and Viscosity Using the Magnetic Resonance Fluid Characterization Method and New Diffusion-Editing Pulse Sequences," SPEJ (2003) 317.
  23. ↑ Fang, S. et al. : "Quantification of Hydrocarbon Saturation in Carbonate Formations Using Simultaneous Inversion of Multiple NMR Echo Trains," paper SPE 90569 presented at the 2004 SPE Annual Technical Conference and Exhibition, Houston, 26–29 September.
  24. ↑ Sun, B. and Dunn, K.J.: "A Global Inversion Method for Multi-Dimensional NMR Logging," J. of Magnetic Resonance (2005) 172, 152.
  25. ↑ Sun, B. and Dunn, K.J.: "Two-Dimensional Nuclear Magnetic Resonance Petrophysics," Magnetic Resonance Imaging (2005) 23, No. 2, 259.
  26. ↑ Freedman, R. et al .: "A New NMR Method of Fluid Characterization in Reservoir Rocks: Experimental Confirmation and Simulation Results," SPEJ (December 2001) 452.
  27. ↑ Bonnie, R.J.M. et al.: "Advanced Forward Modeling Helps Planning and Interpreting NMR Logs," paper SPE 71735 presented at the 2001 SPE Annual Technical Conference and Exhibition, New Orleans, 30 September–3 October.
  28. ↑ Hurlimann, M.D. and Venkataramanan, L.: "Quantitative Measurement of Two-Dimensional Distribution Functions of Diffusion and Relaxation in Grossly Inhomogeneous Fields," J. of Magnetic Resonance (2002) 157, 31.
  29. ↑ Hurlimann, M.D. et al .: "Diffusion Editing: New NMR Measurement of Saturation and Pore Geometry," paper FFF presented at the 2002 Soc. of Professional Well Log Analysts Annual Logging Symposium, Oiso, Japan, 2–5 June.
  30. ↑ 30.0 30.1 Sun, B., and Dunn, K.: "Core Analysis With Two Dimensional NMR," paper SCA 2002-38 presented at the 2002 Soc. of Core Analysts Annual International Symposium, Monterey, California, 23–25 September.
  31. ↑ Hurlimann, M.D. et al .: "Diffusion-Relaxation Distribution Functions of Sedimentary Rocks in Different Saturation States," Magnetic Resonance Imaging (2003) 21, No. 3–4, 305.
  32. ↑ 32.0 32.1 Toumelin, E. et al .: "A Numerical Assessment of Modern Borehole NMR Interpretation Techniques," paper SPE 90539 presented at the 2004 SPE Annual Technical Conference and Exhibition, Houston, 26–29 September.
  33. ↑ Hursan, G., Chen, S., and Murphy, E.: "New NMR Two-Dimensional Inversion of T1/T2app vs. T2app Method for Gas Well Petrophysical Interpretation," paper GGG presented at the 2005 Soc. of Petrophysicists and Well Log Analysts Annual Logging Symposium, New Orleans, 26–29 June.
  34. ↑ Flaum, M., Chen, C., and Hirasaki, G.: "NMR Diffusion Editing for D-T2 Maps—Application to Recognition of Wettability Change," paper JJ presented at the 2004 Soc. of Petrophysicists and Log Analysts Annual Logging Symposium, Noordwijk, The Netherlands, 6–9 June.
  35. ↑ Sun, B. et al .: "NMR Imaging with Diffusion and Relaxation," paper SCA2003-24 presented at the 2003 Soc. of Core Analysts Annual International Symposium, Pau, France, 21–24 September.

Noteworthy papers in OnePetro

Cao Minh, C., Crary, S. F., Zielinski, L., Liu, C., Jones, S., & Jacobsen, S. J. (2012, January 1). 2D-NMR Applications in Unconventional Reservoirs. Society of Petroleum Engineers. doi:10.2118/161578-MS

Zielinski, L., Ramamoorthy, R., Minh, C. C., Al Daghar, K. A., Sayed, R. H., & Abdelaal, A. F. (2010, January 1). Restricted Diffusion Effects in Saturation Estimates From 2D Diffusion-Relaxation NMR Maps. Society of Petroleum Engineers. doi:10.2118/134841-MS

Chen, J. (Jason), Chen, S., Smith, E., Shao, W., & Itter, D. (2010, June 19). Determination of Gas-Oil Ratio And Live-Oil Viscosity From NMR Log Incorporating Oil-Based Mud Filtrate Invasion. Society of Petrophysicists and Well-Log Analysts.

Chen, S., & Hursan, G. (2010, June 19). A New Method For Estimating Formation Water Rw Using NMR Logging Data. Society of Petrophysicists and Well-Log Analysts.

Boyle, K. S., Zuraidah, Z., & Manescu, A. (2009, January 1). 2D NMR - Quantifying Oil Volume in High Clay Content Low Salinity Reservoirs. Society of Petroleum Engineers. doi:10.2118/123250-MS

Chen, J., & Chen, S. (2008, January 1). A New Mixing Rule of Self Diffusivities in Methane Hydrocarbon Mixtures and the Determination of GOR and Oil Viscosities From NMR Log. Society of Petroleum Engineers. doi:10.2118/115510-MS

Songhua, C., Munkholm, M., Dossan, J., Wei, S., & Begova, A. N. (2006, January 1). Application Of Nmr Logging For Characterizing Movable And Immovable Fractions Of Viscose Oils In Kazakhstan Heavy Oil Fields. Society of Petrophysicists and Well-Log Analysts.

Boqin, S., Olson, M., Baranowski, J., Songhua, C., & Li, W. (2006, January 1). Direct Fluid Typing And Quantification Of Onorico Belt Heavy Oil Reservoirs Using 2D Nmr Logs. Society of Petrophysicists and Well-Log Analysts.

Boqin, S., Olson, M., Baranowski, J., Songhua, C., & Li, W. (2006, January 1). Direct Fluid Typing And Quantification Of Onorico Belt Heavy Oil Reservoirs Using 2D Nmr Logs. Society of Petrophysicists and Well-Log Analysts. Hursan, G., & Chen, S. (2005, January 1). New, NMR Two-dimensional Inversion of T1/T2 apparent vs. T2 apparent Method for Gas Well Petrophysical Interpretation. Society of Petrophysicists and Well-Log Analysts.

Fang, S., Chen, S., Tauk, R., Fornage, P., & Georgi, D. (2004, January 1). Quantification of Hydrocarbon Saturation in Carbonate Formations Using Simultaneous Inversion of Multiple NMR Echo Trains. Society of Petroleum Engineers. doi:10.2118/90569-MS

Freedman, R., Heaton, N., Flaum, M., Hirasaki, G. J., Flaum, C., & Hürlimann, M. (2003, December 1). Wettability, Saturation, and Viscosity From NMR Measurements. Society of Petroleum Engineers. doi:10.2118/87340-PA

Chen, S., Gamin, H., Georgi, D. T., Minetto, C., Olima, O., & Withjack, E. M. (2000, January 1). Estimation Of Oil Viscosity With Multiple Te Dual Wait-time Mril Logs. Society of Petrophysicists and Well-Log Analysts.


External links

Use this section to provide links to relevant material on websites other than PetroWiki and OnePetro

See also

Nuclear magnetic resonance (NMR) logging

NMR petrophysics

Fluid identification and characterization

PEH:Nuclear_Magnetic_Resonance_Applications_in_Petrophysics_and_Formation_Evaluation

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