subsurface fluid characterization using downhole and

SPWLA 56th Annual Logging Symposium, July 18-22, 2015

SUBSURFACE FLUID CHARACTERIZATION USING DOWNHOLE AND CORE NMR T1T2 MAPS COMBINED WITH PORE-SCALE IMAGING TECHNIQUES Margaret Lessenger, Newfield; Dick Merkel, Denver Petrophysics; Rojelio Medina, Sandeep Ramakrishna, Songhua Chen, Ron Balliet, Halliburton; Harry Xie, Core Lab; Pradeep Bhattad, Anna Carnerup, Mark Knackstedt, FEI-Lithicon Copyright 2015, held jointly by the Society of Petrophysicists and Well Log Analysts (SPWLA) and the submitting authors. This paper was prepared for presentation at the SPWLA 56th Annual Logging Symposium held in Long Beach, California, USA, July 18-22, 2015.

the variable wettability of GMBU sandstone reservoirs. We found that the sandstone reservoirs are mixed-wet at the micro- and macro-pore scales, including presence of oil-wet clays. Mixed-wettability complicates estimation of fluid types and volumes from NMR data using standard interpretation techniques. An analysis protocol involving pore-scale imaging, core-flood NMR experiments, and 2.5D NMR processing and analysis permit reduction of interpretation ambiguity of the NMR data.

ABSTRACT Characterization of the subsurface fluid types, porosity, saturations, and wettability are critical for understanding the type and volumes of fluids that will be produced during primary completions and secondary waterflood recovery. The oil reserves within the Green River Formation of the Uinta Basin (Utah, USA) in the Greater Monument Butte Unit (GMBU) have variable fluid volumes, saturations, and wettability. Within a potential pay section of over 2000 feet are over twenty defined producing sandstone reservoir intervals within the Green River Formation with variable depositional environments, mineralogy, and rock quality. Traditional core analyses for saturations and wettability are time-consuming and expensive because of variable reservoir properties within discontinuous sands and highparaffinic oil containing asphaltenes and resins. Similarly, the variable wettability complicates standard analyses of NMR (nuclear magnetic resonance) data for fluid type and volume estimations.

INTRODUCTION Greater Monument Butte Field (GMBU) is located in the Uinta Basin in Utah, USA (Figure 1) and produces from highly discontinuous tight lacustrine sandstones within the Green River Formation (Burton et al., 2012; Ramakrishna, et al., 2012). Paraffinic Green River oil is semi-solid at surface conditions because of the temperature-dependence of its viscosity. This property of the oil complicates core analyses which must be run at reservoir temperature. Green River reservoir intervals could contain hydrocarbons in the solid, liquid, and gas phases. Reservoir sands pose challenges in interpreting log data to determine hydrocarbons in-place and reservoir-flow characteristics. GMBU sand reservoirs are highly variable in rock quality, mineralogy, clay types and volumes, framework grain composition, authigenic minerals, and wettability (Ramakrishna, et al., 2012; Lessenger et al., in press). Advanced logging suites including nuclear magnetic resonance (NMR) and dielectric have been useful in separating solid from liquid phases and estimating oil saturations (Ramakrishna et al.,

Newfield has approximately a dozen wells with NMR T 1 , T 2 , and diffusion data in the producing section of Monument Butte Field. We have identified patterns of NMR, dielectric, and standard triple-combo log data that are associated with differences in estimation of clay-bound water volumes from NMR and XRD data. We present results from pore-scale imaging, NMR core-flood experiments, USBM (United States Bureau of Mines) measurements of wettability, and 2.5D inversion of NMR data used to characterize 1

SPWLA 56th Annual Logging Symposium, July 18-22, 2015

2012; Lessenger et al., 2013).

GMBU LOG AND CORE DATA MISMATCH

Variable wettability has posed a particularly difficult problem in characterizing reservoir flow properties. Sandstone reservoirs range from water-wet to strongly oil-wet. Variable wettability controls the types and volumes of produced fluids and strongly controls residual oil saturations (Merkel and Lessenger, 2014). GMBU produced oil has abundant polar hydrocarbon components with 6% or more asphaltenes and 14% or more resins. Carbonate lithic framework grains and authigenic Fe-calcite and Fe-dolomite are abundant, and are known to be oil-wet in reservoirs (Lessenger et al., 2013). The NMR log responses within GMBU are affected by the highly variable wettability, leading to ambiguous interpretations of those data because of changes in surface relaxation. In combination with pore-scale imaging, we designed a series of core-flood NMR experiments to reduce ambiguity and better characterize the effect of wettability. In this paper we show results of core-flood experiments analyzed with NMR T 1 , T 2 and T 1 T 2 measurements and integrated with log NMR and dielectric data, XRD, pore-scale imaging, and USBM measurements.

Sand reservoirs in GMBU vary in quality as determined by porosity, permeability, capillary pressure data, and waterflood response (Lessenger et al., 2015). Variations in clay volumes, types and pore morphology are associated with rock quality types as determined by XRD and SEM imaging. Better quality reservoir sands have more chlorite and very little mixed-layer illite-smectite (I/S). Authigenic chlorite rosettes line large, open macro-pores (Figure 2A). In poorer quality reservoir sands, I/S is more abundant than chlorite, and I/S is pore-bridging reducing original permeability (Figure 2B).

Fig.1 Location of the Uinta basin and the study area, GMBU Field (modified from Burton et al., 2012).

Fig.2 SEM of pores in a higher quality reservoir sand (A) and a poorer quality reservoir sand (B). In A authigenic chlorite (CHL) forms rosettes in open macro-pores. Mixed layer illite-smectite (I/S) is absent or a minor constituent. In B I/S is more abundant and bridges pores reducing the macro-porosity and permeability. 2

SPWLA 56th Annual Logging Symposium, July 18-22, 2015

We developed a multi-mineral and resistivity-based saturation model in GMBU calibrated to core XRD, routine core analyses, and log NMR and dielectric data. These models are highly consistent with the exception of our estimation of clay-bound water. NMR T1 and T2 data measure clay-bound water volumes in T2 relaxation times less than 2.8 ms (Martin and Dacy, 2004). Based on the cationexchange content (CEC) of clay minerals, we calculate estimated clay-bound water volumes (VCBW) from XRD (Dick Merkel, personal communication, 2012). Reservoir sands in GMBU typically show a mismatch in these two measurements, even accounting for different measurement volumes.

Fig.3 GMBU log and core data in a sand with low VCBW in the reservoir sand. The T 1 and T2 signal is high in the adjacent shale (below 5075 ft.), but disappears abruptly in the sand (“T2 ARRAYS” and “T1 ARRAYS” tracks). XRD volume of wet clay (red dots) matches the mineral model (“MINERAL MODEL” track) and dielectric data match the saturation model (“FLUIDS” track). Laterolog resistivity is greater than 100 ohmm (“RESISTIVITY’ track). VCBW from XRD (blue dots) and the mineral inversion is much higher than estimated from T 1 and T2 data (“VCBW” track).

Typically, NMR T1 and T2 data measure abundant VCBW in shales adjacent to sandstone reservoirs. But this signal disappears abruptly in many sands (Figure 3). We have a mineral model well-calibrated to XRD that matches core volumes of wet clay from XRD as seen in Track 1 in Figure 3. Even though clays are present in these sands, we often calculate more VCBW from XRD than observed in the NMR data. Laterolog resistivities can be in the hundreds of ohmm and dielectric data indicate measurable water volumes in the flushed zone. In other sands, VCBW from XRD and NMR is very low, but more closely matched (Figure 4 Track 5). Laterolog resistivities are in the thousands of ohmm, and dielectric data measure very little water in the flushed zone in these sands. The reservoirs shown in Figures 3 and 4 are associated with better rock quality. Reservoir sands with poorer rock quality can have NMR VCBW much higher than estimated from XRD (Figure 5). In these sands, laterolog resistivities range from the tens to hundreds of ohmm and dielectric data measure water volumes that match the saturation model.

Fig.4 GMBU log and core data in a sand with very low VCBW in the reservoir sand and very little measured with NMR. Tracks are described in Figure 2. Sample 8 is at 4949 ft. marked with the horizontal blue line.

The association of patterns of mismatch between VCBW from NMR T1 and T2 data and log responses such as estimated volumes of water from dielectric data and deep resistivities led us to consider variable wettability as a source for these patterns (Figures 35). Beginning in 2013 we started a series of log and core analyses to unravel these patterns and better understand and characterize wettability in the sand reservoirs.

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SPWLA 56th Annual Logging Symposium, July 18-22, 2015

surface relaxation, and T2diffusion is the relaxation time of the pore fluid as induced by diffusion in the magnetic field gradient. T2intrinsic is the T2apparent relaxation time with T2diffusion removed. The bulk T1 and T2 relaxation times depend on the fluid type, temperature, and pressure. The surface T1 and T2 relaxation responses require that the fluid is in contact with a pore wall, and depend on the fluid and pore surface properties. Diffusion-induced T2 decay depends on the molecular diffusion coefficient, D, the magnetic field gradient G of the logging tool, and the tool activation settings (Coates et al., 1999). Using simultaneous inversion of T1 and T2 data, T1T2 maps show the relationship between T1 and T2 attributes and have been used to identify fluid types and volumes in different pore sizes (Droeven et al., 2009). Wettability variations shift T1T2 map responses from interpreted water-wet standards (Freedman and Heaton, 2004; Flaum et al., 2005). Wettability modifies the intrinsic T2 relaxation times through surface effects because by definition a fluid that wets a pore surface is in contact with that surface. In a water-wet pore, the intrinsic T2 of water is reduced from the bulk T 2 of water and the intrinsic T2 of oil will be equal to the bulk oil response. Similarly, in oil-wet pores, the intrinsic T2 of oil is reduced from the bulk T 2 of oil and the intrinsic T2 of water will be equal to the bulk T2 of water.

Fig.5 GMBU log and core data in a sand with VCBW from XRD (blue dots) is much lower than estimated from T1 and T2 NMR data (“VCBW” track). Tracks are described in Figure 2. Sample 34 is at 5492 ft. marked with the horizontal blue line.

ANALYSIS PROTOCOL NMR Log Data First, we analyzed the available NMR data in reservoir sands to identify recurring patterns of observed log responses. The NMR response depends on the volumes, types and locations of reservoir fluids in the pore system. The NMR tool activation settings used in GMBU logging permit inversion for NMR T 1, T2apparent, and T2intrinsic data (Equations 1-3; Coates, et al., 1999). The contribution of each fluid to the NMR response depends on bulk, surface and molecular diffusion properties: 1/T1 = 1/T1bulk + 1/T1surface

(1)

1/T2apparent = 1/T2bulk + 1/T2surface + 1/T2diffusion

(2)

1/T2intrinsic = 1/T2bulk + 1/T2surface

(3)

Molecular diffusivity, D, depends on the fluid type, temperature, and pressure. For a given subsurface depth, gas has a higher diffusivity relative to water and oil. Except for very light oil, the diffusivity for oil is less than water and decreases with increasing oil viscosity (Mardon et al., 1996). Inversion for the diffusion coefficient can result in significantly lower values if pores are very small and molecular diffusion is restricted (Coates et al., 1999). Measurements of diffusivity along with relaxation time by NMR are wellestablished methods to investigate fluid molecular motions (Mutina and Hurlimann, 2008). The combined presentation of T2 and diffusivity, often called a T 2D map, provides additional means for hydrocarbon typing.

where T1 is the measured longitudinal relaxation time of the pore fluid, T 1bulk is the T1 relaxation time of the pore fluid as it would be measured with negligible surface effects, T1surface is the T1 relaxation time of the pore fluid resulting from surface relaxation, T 2apparent is the transverse relaxation time of the pore fluid, T 2bulk is the T2 relaxation time of the pore fluid as it would be measured with negligible surface effects, T 2surface is the T2 relaxation time of the pore fluid resulting from

Modern NMR logging tools can acquire a large number of echo trains with a variation of the data acquisition parameters. In principle, a global inversion method (Sun and Dunn, 2005) or similar approaches can be used to invert all echo train data together, and the result can be plotted in three dimensions to show the correlations between key NMR attributes: T 1, T2, and D. In the most general form of NMR 3D inversion, the echo decay functions are expressed by 4

SPWLA 56th Annual Logging Symposium, July 18-22, 2015

𝑀

𝑁

𝑃

When this constraint is applied, Equation 4 is rewritten as a modified 3D model, which is often called a 2.5D model,

𝐸(𝑖, 𝑗, 𝑘) = ∑ ∑ ∑ 𝐸0,𝑚𝑛𝑝 [1 − exp(− 𝑡𝑊𝑘 ⁄𝑇1,𝑚 )] 𝑚=1 𝑛=1 𝑝=1

× exp (− 𝑖 ∙ 𝑡𝐸𝑗 ⁄𝑇2,𝑛 )

𝐸(𝑖, 𝑗, 𝑘)

× exp (−𝛾 2 𝐺𝑙2 𝑖 ∙ 𝑡𝐸3𝑗 𝐷𝑝 ⁄12)

𝑀′

𝑁

𝑃

= ∑ ∑ ∑ 𝐸0,𝑚𝑛𝑝 [1 − exp(− 𝑡𝑊𝑘 ⁄𝑅𝑚 𝑇2,𝑛 )]

(4)

𝑚=1 𝑛=1 𝑝=1

× exp (− 𝑖 ∙ 𝑡𝐸𝑗 ⁄𝑇2,𝑛 ) × exp (−𝛾 2 𝐺𝑙2 𝑖 ∙ 𝑡𝐸3𝑗 𝐷𝑝 ⁄12)

where 𝑖 ∙ 𝑡𝐸𝑗 is the time of the ith echo in an echo train

(7)

acquired with the jth interecho time 𝑡𝐸𝑗 and 𝑡𝑊𝑘 is the kth

where M’ is often a much smaller number than M, which significantly reduces the matrix size. The solution of the inversion of Equation 7 with the nonnegative constraint (Equation 5) is M’ number of T2-D maps; each with a distinctive R. Often, a single combined T2-D map is computed by co-adding, pixelby-pixel, the intensity of the individual T 2-D maps:

,

,

wait time, and the echo signals are contributed from the lth sensitive volume in which the field gradient strength the spins experience is 𝐺𝑙 . The solution 𝐸0,𝑚𝑛𝑝 can be obtained by solving the linear equation sets in the form of Equation 4. Often the non-negative constraint of 𝐸0,𝑚𝑛𝑝 ≥ 0

(5)

∑𝑀′ 𝑚=1 𝐸0,𝑚𝑛𝑝 = 𝐸0,𝑛𝑝

is imposed.

In the present study, the 2.5D inversion used 𝑀′ = 3 and the R values are 1, 3, and 5, respectively.

Directly solving the non-negative 𝐸0,𝑚𝑛𝑝 is timeconsuming, and the large number of unknowns (𝑀 × 𝑁 × 𝑃) may exceed the number of echoes, causing the under-determined problem. In general, increasing the matrix size by a factor of 10 increases the computation time by a factor of 100. For a case of a large number of unknowns and a large number of data, the matrix can be very large, thus becoming expensive in computation time, especially because such inversion has to be performed at each depth interval, in the order of 4-8 samples/ft.

Aside from the benefit of computational efficiency of using 2.5D, the comparison among the fluid distributions in the maps associated with different R (T1/T2 ratio) provides additional insight for helping interpreting fluid types and their wetting characteristics. We compare the correlations between molecular diffusion, D, and T2intrinsic for T1/T2 ratios of 1, 3, and 5. NMR log data presented in this paper was acquired with echo-spacing times ranging from 0.6 to 6 ms. The magnetic gradients ranged from 13.4 to 20.34 gauss/cm. the magnetic gradient depends on temperature and measurement frequency resulting in the slight changes and spread in magnetic gradients.

In order to improve the efficiency, a common practice is to apply a valid physical constraint based on the intrinsic T2 and T1 relationship of fluids in porous media. In general, bulk T1 and intrinsic T2 of liquid water and light or medium viscous oils and hydrocarbon gases are substantially close to unity. When affected by the pore surface, the ratio of T 1/T2 may increase somewhat, and generally, in the range of 1 to 5. Heavy oil and tar, on the other hand, is also expected to have a higher T1/T2 ratio. For the range observable by NMR logging instruments, it is generally true that 1 ≤ (𝑅 ≡ 𝑇1 ⁄𝑇2 ) ≤ 10

(8)

Pore-Scale Imaging Concurrent with our analysis of NMR data, in 2013 we initiated a study using pore-scale imaging to better characterize reservoir quality and wettability. Representative 3 mm sub-plugs from sands with different reservoir quality were imaged using micro-CT scanning in as-received, cleaned, and brine-saturated states.

(6)

The plug-scale samples were wax-preserved, and care was taken to minimize damage and drying while extracting the sub-plug. The sub-plug was 5

SPWLA 56th Annual Logging Symposium, July 18-22, 2015

immediately stored in a humidity controlled container with minimum head space and imaged in micro-CT. This was followed by staining using iodine gas for 6 weeks, and again care was taken to minimize the free air space and prevent the sample from drying.

temperature. 1. Measure T1, T2, and T1T2 of the core plug as received (native state). 2. Flood the core plug with produced oil until reaching irreducible water saturation. 3. Measure T1, T2, and T1T2 of the oil-flooded core plug. 4. Flood the core plug with synthetic brine until reaching residual oil saturation. 5. Measure T1, T2, and T1T2 of the brineflooded core.

In the as-received state, hydrocarbon phases present in the pore space are not visible in the Xray micro-CT images. In order to selectively increase X-ray attenuation of oil without moving or disturbing pore fluids, ‘as received state’ samples were doped with iodine gas at room temperature over a period of 6 weeks (Dodd et al., 2014). Iodine gas interaction with the hydrocarbons leads to an increased attenuation of oil and its visibility in an X-ray CT image. In the as-received samples, little to no oil was observed in the plugs, and this could be due to expansion of dissolved gas displacting oil from macro-pores during core extraction, and not an artifact of sample preparation.

The native state NMR results simulate residual oil. The oil-flooded NMR results simulate NMR responses in a fully-charged reservoir. The brineflooded NMR results simulate NMR responses measured in the flushed zone and are most comparable to downhole NMR log data. NMR data of core samples were acquired using a 2MHz MARAN instrument (Resonance Instruments, Oxford, UK), with a reservoir condition Temco core holder (CoreLab Instruments, Tulsa, OK). The interecho spacing (TE) of the standard CPMG pulse sequence for T2 measurements is 0.3 ms. The Inversion Recovery pulse sequence with 31 incremental time spacing steps was used for T 1 measurements. The T1T2 map data were acquired using the combination of the above T1 and T2 measurement settings.

After cleaning and drying, the plugs were imaged in a dry state to show available pore space. The plugs were then saturated with X-ray attenuating brine to map the connected porosity. 3D image registration and calculated differences in these successive images permit 3D measurement of asreceived residual oil and connected porosity (Sok et al., 2010). Back-scattered SEM (BSEM) images and EDX (energy-dispersive x-ray) mineralogy was registered onto the micro-CT images to associate the pore morphology, residual oil, and connected porosity to mineral phases and for comparison with micro-CT images of fluid phases (Golab et al., 2010; Knackstedt et al., 2011). SEM imaging of asphaltene deposition on mineral surfaces combined with EDX spot analysis was used to determine the wettability of specific minerals at the pore scale (Marathe et al., 2012).

USBM measurement of wettability (Anderson, 1987) was done concurrently with the core floods. In addition, we ran XRD and routine porosity and permeability on the experimental core plugs. We measured the bulk T2 response of the produced oil at 150 degF. Four sample plugs were carefully selected to match identified log patterns. End-member Samples

NMR Core-Flood Experiments

In this paper we present log data, pore-scale imaging, and core-flood NMR representing end-member log responses and rock quality. Results for the two plugs not discussed in this paper are intermediate to the two end-member plugs.

After identifying recurring patterns of NMR log data, we then designed a series of core-flood experiments combined with core NMR measurements to determine the fluid types and volumes represented by the identified patterns on log T1T2 map data. All experiments were conducted at a reservoir temperature of 150 degF because of the high paraffin content of Green River oil and measured bulk T2 variations with

Core plug Sample 8 represents a high quality reservoir sand with a log response indicating an oilwet reservoir (Figure 4). We interpreted this sand as oil-wet because deep resistivity is more than 1000 ohmm and dielectric measures extremely low water 6

SPWLA 56th Annual Logging Symposium, July 18-22, 2015

volumes. USBM data in other sands in the field with similar log responses indicate these sands are oil-wet. This sand has higher porosity and permeability (Table 1). XRD is typical for higher quality sands with predominately quartz, plagioclase, carbonates, chlorite, and illite (Tables 2 and 3). Log responses, core porosity, permeability, and XRD correspond to reservoir sands with pore morphology as shown in Figure 2A.

types and fluid properties, T1T2 maps can be used to estimate liquid porosities of different fluid types located in different pore sizes (Droeven et al., 2009). The maps are plots of liquid porosity as a function of T1 and T2apparent relaxation times (Figures 6 and 7). The vertical red line at T2apparent approximately equal to 30 ms is considered the approximate cut-off for bulk volume irreducible water (BVI). The horizontal line at elevated T1 is the “gas line.” Lines of equal ratios of T1 to T2 are the diagonal green and gray lines. The green line is at T1/T2 equal to one and gray lines mark increasing ratios of T1/T2 (Coates et al., 1999).

Core plug Sample 34 represents a poor quality reservoir sand with a log response indicating a more water-wet sand (Figure 5). Deep resistivity is lower and the dielectric data indicate variable water saturation that is higher than for plug 8. Porosity for poorer reservoir may be as high as high quality reservoir, but permeability is much lower (Table 1). XRD indicates quartz, plagioclase, carbonates, illite, chlorite, and much higher volumes of I/S (Tables 2 and 3). Log responses, core porosity, permeability, and XRD correspond to reservoir sands with pore morphology as shown in Figure 2B.

The location of liquid porosity volumes measured on log T1T2 maps show consistent patterns associated with the discrepancies in estimated VCBW and reservoir quality. Better quality reservoirs typically have four dominant porosity locations on T1T2 maps (Figure 6). We have considered the effect of changes in T2apparent due to different echo times (TE) during data acquisition. Different TE acquisition values could shift the NMR signal slightly, but would not change our interpretation. The liquid porosities in the VCBW bins of Zone A are generally weak, and have variable locations corresponding to variable T1/T2 ratios and T2apparent values. Zones B through D have variable liquid porosities. Zone B is located at higher T1/T2 ratios near the bulk volume irreducible T2 line (BVI) at approximately 30 ms. Zone C has a high T1with variable T1/T2 ratios. Zone D is close to the T1/T2 unity line. Poorer quality reservoirs have reduced liquid porosity in Zones C and D and increased porosity in Zones A and B (Figure 7).

Table.1 Porosity and permeability data for samples in the NMR core-flood experiments. Core porosity and permeability is at net confining stress.

Our hypothesis was that the liquid porosities in these zones were predominantly associated with different fluid types. We interpreted Zone A as clay-bound water and Zone D as movable oil and water. We were puzzled by Zone C and speculated it could be high GOR oil in reservoirs near or just below bubble-point. We typically see more porosity in Zone C in under-pressured sands that are below bubble point. We interpreted Zone B as residual oil in smaller pores and with higher asphaltene content than the oil in Zone D. Oils with higher asphaltene content have higher T1/T2 ratios (Birdwell and Washburn, 2015) and Zone B is generally near a T1/T2 ratio between 3 and 5. Oil in small pores has been demonstrated in porosity bins near the BVI line in shale oil reservoirs (Chen et al., 2013).

Table.2 XRD data for non-clay minerals for samples in the NMR core-flood experiments.

Table.3 XRD data for clay minerals for samples in the NMR core-flood experiments. RESULTS Log NMR T1T2 Patterns When T1 and T2 data are collected, inversion of the combined T1 and T2apprent data result in T1T2 maps. Based on information about the estimated fluid 7

SPWLA 56th Annual Logging Symposium, July 18-22, 2015

Pore-scale Imaging of Wettability Pore-scale imaging of a high-quality reservoir sand shows residual oil and connected macro- and micro-porosity (Figure 8). Log characteristics of the sand for this sample are comparable to the reservoir sands shown in Figures 3, 4, and 6. In this plug, residual oil saturation is much less than estimated flushed zone oil saturation because of significant loss of oil during core recovery. Most residual oil resides in micro-pores within areas of abundant chlorite (Figures 8 and 9, A) and clayrich lithic fragments (Figures 8 and 9, B). Although not commonly seen on images due to oil evacuation during recovery, some residual oil is seen in connected macro-pores of this sample (Figure 8 and 9, C). These images indicate that there are at least two predominant pore sizes, macro- and micro- pores. Residual oil saturation is associated with chlorite, chlorite- and illite-rich lithic fragments, and open macro-pores.

Fig.6 Log NMR T1T2 map of liquid porosity volumes as a function of T1 and T2apparent in a high quality reservoir sand. Distinct porosity zones are labeled A-B. This T1T2 map is near Sample 8.

Pore-scale imaging of poorer quality reservoir sand also shows residual oil and connected macroand micro-porosity, but porosity is predominantly micro-porosity (Figures 10 and 11). This plug is at a depth of 5490 ft. with log data shown in Figures 5 and 7. The residual saturation imaged in this plug is very close to the invaded zone estimated oil saturation indicating that much less oil was evacuated during core recovery than for the high quality plug. Surprisingly, no residual oil was imaged in the macro-pores even though porosity in the macro-pores is connected. Nearly all of the residual oil resides in micro-porous and clay-rich lithic fragments (Figures 10 and 11, A). The porebridging I/S can be seen in the SEM-registered micro-CT images in the open macro-pores. Some residual oil resides in the pore-bridging I/S, but not all I/S contains residual oil (Figures 10 and 11, B). For both high and poor quality samples, residual oil in the micro-pores is consistent with oil-wet pores within argillaceous lithic grains and authigenic chlorite. When there was no connected porosity in the lithic grains or chlorite, there was also no residual oil present. Residual oil was partially present in the pore-bridging mixed-layer illite-smectite (I/S). We interpret the I/S as being partially oil-wet, but tending more towards being water-wet. There was not enough residual oil preserved in the macro-pores to interpret their wettability with this analysis.

Fig.7 Log NMR T1T2 map of liquid porosity volumes as a function of T1 and T2apparent in a poorer reservoir quality sand. Distinct porosity zones are labeled A-C. There is a faint porosity zone labeled D. This T 1T2 map is near Sample 34.

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SPWLA 56th Annual Logging Symposium, July 18-22, 2015

Fig.10 2D slice of a 3D micro-CT image with registered SEM with residual oil (left; brown), and connected porosity (right; blue). EDX mineralogy is shown in Figure 11. This image is from a poorer quality reservoir sand. A. Lithic fragment composed primarily of muscovite/illite/illite-smectite and quartz with residual oil and connected porosity. B. Open pore with pore-bridging I/S partially filled with residual oil. C. Macro-pore with connected porosity but no residual oil in the I/S. Voxel size is 2 microns.

Fig.8 2D slice of a 3D micro-CT image with registered SEM with residual oil (left; brown), and connected porosity (right; blue). EDX mineralogy is shown in Figure 9. This image is from a higher quality reservoir sand. A. Chlorite with residual oil and connected microporosity. B. Argillaceous lithic fragments composed of illite and other minerals with residual oil and connected micro-porosity. C. Macro-pore with residual oil. Voxel size is 2 microns.

Fig.11 2D slice of a 3D micro-CT image with registered EDX mineralogy. This is the same image view as in Figure 10. Pixel size is 2 microns. Wettability was determined using high resolution SEM imaging of adsorbed asphaltene deposits, which are preserved after removal of bulk brine and oil through solvent cleaning described in Marathe et al., 2012. The cleaning process is designed to preserve adsorbed asphaltene deposits in-place and remove any other fluids from the pore space. The method relies on the reduced solubility of adsorbed asphaltenes as compared to other crude oil components in decalin, heptane and methanol solvents. This cleaning process can take more than 3 months to complete in order to minimize artifacts from the sample preparation.

Fig.9 2D slice of a 3D micro-CT image with registered EDX mineralogy. This is the same image view as in Figure 8. Pixel size is 2 microns.

In Figures 12 and 13, ‘bumpy’ asphaltene films can be seen as aggregated asphaltene particles deposited on otherwise smooth mineral surfaces. 9

SPWLA 56th Annual Logging Symposium, July 18-22, 2015

The presence or absence of asphaltene particle deposits reflects a locally oil-wet or water-wet mineral surface respectively. Surfaces with partial coverage of asphaltene films lie in between these two extremes. In Figures 12 and 13, asphaltene films are labelled ‘OW’ to represent a locally oilwet region. Surfaces free of such films in turn are labeled ‘WW’ to reflect a locally water-wet area. Pore-scale images of asphaltene deposition show mixed-wet mineral surfaces at the macro- and micro-scales (Figures 12 and 13). We observe varied thickness of asphaltene films. Thickness could not be correlated with mineralogy in the images analyzed (see Figures 12 and 13). But the wettability tendencies of mineral surfaces are assigned on the coverage of the mineral surface by asphaltene deposits (Table 4). Wettability imaging demonstrated mixed-wet and oil-wet phases in framework grains and clays (Table 4). The pore-scale imaging results revealed a far more complex distribution of mixed-wet pores than we had originally anticipated. Presence of oil-wet clays also could potentially explain some of the anomalous responses of the NMR T1 and T2 log data in these sands. If oil and not water is in contact with clay surfaces, then this could explain the lack of a “clay-bound water” response in the NMR data. Instead, images suggested that some of these sands contain “clay-bound oil.”

Fig.12 SEM images of quartz (top) and carbonate (bottom) showing presence and absence of asphaltene deposits. Asphaltene deposits indicate oil-wet mineral surfaces and are labeled OW. Surfaces that are clean and free of asphaltene deposits are water-wet and are labeled WW respectively.

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SPWLA 56th Annual Logging Symposium, July 18-22, 2015

Core-flood NMR Experiments and Comparison with log NMR T1T2 and 2.5D Data The core-flood experiments were designed to mimic reservoir conditions and downhole log data as much as possible, but there are physical limitations to obtaining reservoir conditions in the lab. These limitations impact how core and log data are compared and interpreted. Core NMR T 2 data have a signal-to-noise ratio of 100. Log NMR data are stacked to increase the signal-to-noise ratio for inversion. Due to the time constraints in maintaining the core plugs at reservoir temperature, the core NMR T1T2 data have a lower signal-to-noise ratio than either core T2 or log NMR data. The lower signal-to-noise ratio of the core data reduces the resolution of the T1T2 inversion. Consequently, to compare the downhole log data to the core NMR, we resampled the downhole NMR T1T2 data to a coarser grid to mimic the reduced resolution of the core NMR T1T2 data. Unlike the downhole data, the core T1T2 does not have an applied gradient, so the core T2 data are by definition T2intrinsic. The T1 lab data have lower relaxation times than the downhole data because produced dead oil is used in the core experiment whereas the in-situ reservoir oil contains dissolved gas.

Fig.13 SEM images of chlorite (top) showing presence and absence of asphaltene on clay flakes. Oil-wet clay flakes are labeled OW and water-wet clay flakes are labeled WW. Platy illite (bottom) is commonly admixed with illite and is indeterminate.

To assist our interpretation of the NMR data for wettability effects, it is necessary to characterize the NMR bulk fluid properties. A fluid T2 much less than the bulk T2 of that fluid indicates some wettability to that fluid in the sample. Measured bulk T2 for the produced dead oil is 300 ms at 150 degF. Based on the Vinegar method (Vinegar, 1995; Coates, et al., 1999) we calculate a bulk T2 for the synthetic brine of 2-3 seconds. Table.4 SEM imaging and spot EDX analysis were performed on numerous pores on samples. Predominant wettability tendencies of observed mineral surfaces in the reservoir sands are tabulated. The presence or absence of asphaltene deposits reflects a locally oil-wet or water-wet mineral respectively. Surfaces with partial coverage of asphaltene films lie in between these two extremes. Mineral surfaces with inconsistent asphaltene deposition from one pore to another within the same sample are labelled as indeterminate.

Core NMR for the core-flood experiments, original log T1T2, coarse-grid-sampled log T1T2, and log 2.5D results for Sample 8 are shown in Figures 14-22. Considering the experimental set-up of the core NMR measurements described above, the asreceived core NMR T1T2 results for Sample 8 (Figure 14) are comparable to Zones A, B and D of the log NMR T1T2 (Figures 6 and 21). This confirms that the fluids in Zones A and B of the T1T2 map are residual immovable fluids. But it also indicates some residual fluids reside in Zone D. Fluids in Zone C are not present in the as-received core.

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Fig.14 As-received T1T2 map for Sample 8 Fig.16 Brine-flooded core T1T2 map for Sample 8.

Flooding Sample 8 with produced oil increases the liquid porosity in Zone D and overlaps the asreceived T1 and T2 data (Figures 14, 17, and 19). When oil is added to the sample, oil fills the largest pores. The T2 peak of the larger pores shifts from approximately 40 ms to 80 ms consistent with oil filling the macro-pores. Because only oil has been added to the sample during this step, the predominant macro-pore oil T2 in this sample is 80 ms, much less than the bulk oil T2 of 300 ms. This indicates some oil-wetness in the macro-pores of Sample 8.

Flooding Sample 8 with brine results in two porosity zones in the T1T2 map, with T2 peaks at 10 ms and 1000 ms (Figures 16, 18, and 20). Except for the expected reduced T1 values in the core data, the brine-flooded core T1T2 map is very similar to the downhole log T1T2 (Figure 21). The T2 lobe that peaks at 1000 ms is directly comparable to Zone C. This indicates that Zone C in the reservoir is water, most likely mud filtrate in the macro-pores. The upper peak T2 for water is also less than the calculated brine bulk T2 indicating weak waterwetness in the macro-pores. Because these samples were not cleaned during any of the experimental steps, each sample possibly contains both oil and water in all measurement steps. To further distinguish the T1T2 correlations associated with oil and water we used the 2.5D processed downhole data (Figure 22). Generally, brine has a T1/T2 ratio equal to 1 or slightly larger. The 2.5D map for T1/T2 ratio equal to 1 contains the high T2 data confirming that Zone C is water. The inversion calculates a high molecular diffusion, in the range expected for gas. In the reservoir, the fluid in Zone C could have a high molecular diffusion due to gas, or the paramagnetic effect caused by porelining chlorite inducing internal magnetic gradients (Figure 2A). The 2.5D map for T1/T2 equal to 1 also indicates that the movable fluids in Zone D are predominantly oil with some formation brine possible (Figure 22, second from top).

Fig.15 Oil-flooded core T1T2 map for Sample 8.

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Fig.17 Core T2 as-received (red) and oil-flooded (green) for Sample 8.

Fig.19 Core T1 as-received (red) and oil-flooded (green) for Sample 8.

Fig.18 Core T2 Oil-flooded (green) and brine-flooded (blue) for Sample 8. The slight loss of total liquid porosity in the brine-flood is due to some liquid evaporation in the sample based on sample weight measurements.

Fig.20 Core T1 Oil-flooded (green) and brine-flooded (blue) for Sample 8. The log NMR, core-flood NMR, deep resistivity, dielectric data, and pore-scale imaging are consistent with a mostly oil-wet tight sand with mixedwettability at micro- and macro-pore scales. The USBM measurement for this sample is -0.3, also consistent with a mostly oil-wet sand. Oil is in contact with clay surfaces reducing the NMR signal in the bins normally associated with clay-bound water. This could also explain the variable results comparing VCBW calculated from XRD and NMR as the signal is likely not related to the clays, but the signal is more likely related to variable wettability in the micro-pores.

The 2.5D map for T1/T2 ratios of 3 and 5 indicate that the in-situ fluids for Zones A, B and D are oil with molecular diffusion less than water (Figure 22, third from top and bottom). The 2.5D map for T1/T2 equal to 3 indicates that the oil in Zone B is oil in micro-pores with either slightly higher viscosity than oil in macro-pores, or reduced molecular diffusion due to restricted diffusion. Higher viscosity and higher T1T2 ratios of this oil is consistent with a possible higher asphaltene content of oil in the oilwet micro-pores. The fluids in Zone A must be oil with very low molecular diffusion. We cannot distinguish between the effect of asphaltenes or restricted diffusion causing this reduced molecular diffusion in Zone A.

The core-flood and NMR 2.5D processing for Sample 34 show very different results exhibited in Figures 24-33. The as-received core NMR for Sample 34 shows residual fluids in Zones A and B and not C (Figures 7 and 23). The log NMR T 1T2 13

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map shows a faint porosity signal in Zone D not detected by the as-received core NMR . Flooding Sample 34 with oil adds more liquid porosity in the T2 bins greater than 3 ms, but also removed porosity in the T 2 bins less than 3 ms (Figures 24, 26, and 28). We would expect oilflooding to introduce oil primarily in the macropores. The T2 signal does show more liquid porosity at T2 greater than 100 ms. Although liquid porosity increases in the larger T2 bins, the peak T2 does not shift and remains at approximately 30 ms. This indicates that the macro-pores in Sample 34 have some oil-wetness because the T2 signal is much less than the bulk oil T2 of 300 ms. Flooding Sample 34 with brine shows a good match to the coarse-grid processing of the log T1T2 (Figures 25 and 30). Flooding with brine reduces liquid porosity in the higher T2 bins except for a small amount at a T2 of approximately 400 ms. This increase in porosity restores the lost liquid porosity in the T2 bins less than 3 ms (Figures 25, 27, and 29). We assume that flooding with brine replaces oil with brine in the macro-pores. The peak at higher T2 bins shifts from 30ms to 10ms when brine replaces oil. This indicates that macro-pores are likely more water-wet than oil-wet. Intermediate T2 liquid porosity associated with Zone B (Figure 7) is not affected by the brine-flood.

Fig.21 Comparison of log T1T2 (top), coarse-grid processing of log T1T2 (middle) and brine-flooded core T1T2 for Sample 8 (bottom).

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Fig.23 As-received T1T2 map for Sample 34.

Fig.24 Oil-flooded core T1T2 map for Sample 34.

Fig.25 Brine-flooded core T1T2 map for Sample 34. Fig.22 Log T2D map with 2.5D processing for the log depth of Sample 8. a T1/T2 ratio = 1 for the log depth of Sample 8. Full T1/T2 ratios combined (top), T 1/T2 ratio = 1 (second from top), T 1/T2 ratio = 3 (third from top), and T1/T2 ratio = 5 (bottom). 15

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In our study, we conducted core-flood NMR experiments on four samples, representing different patterns of log data. Of these four samples, only Sample 34 has pore-bridging I/S (Figure 2B). Porescale imaging shows that the fibrous I/S is open to connected macro-pores and can contain small volumes of residual oil (Figures 10 and 11). Because the fibers of I/S are open to the macro-pores, flowthrough of liquids in this sample can affect the liquids associated with the pore-bridging I/S. In all the samples without I/S, there was effectively no change in the T2 response for bins less than 3 ms during both the oil and brine floods (e.g., Figures 17 and 18). In contrast, during the oil-flood of Sample 34, porosity was reduced in the T2 bins less than 3 ms (Figure 26). After flooding Sample 34 with brine, porosity in the bins less than 3 ms was restored to the same value as for the as-received measurement (Figure 27). One hypothesis for this result is that fibrous I/S tend to be more water-wet. Flooding with oil could remove residual water open to the macropores, placing oil on the clay surfaces with increased T2 times. Flooding with water resulted in water in the macro-pores and water on the pore-bridging I/S. This would increase true clay-bound water porosity.

Fig.26 Core T2 as-received (red) and oil-flooded (green) for Sample 34.

The 2.5D processing gave further insight into the insitu reservoir fluids in the sand containing Sample 34 (Figure 31). Like for Sample 8, the fluid in porosity Zone C is likely mud filtrate or a mixture of mud filtrate and formation brine in the macro-pores as indicated with the 2.5D processed data for a T 1/T2 ratio of 1 and 3 (Figure 31, second and third from top). The molecular diffusion for this fluid is closer to the estimated value for water than for Sample 8. This reservoir type has very little chlorite lining the macro-pores and consequently is not as affected by paramagnetic effects (Figure 2B).

Fig.27 Core T2 Oil-flooded (green) and brine-flooded (blue) for Sample 34.

The 2.5D processing shows the fluid in porosity Zone B is mostly oil with a T1/T2 ratio closer to 1 (Figure 31 second from top) but also some water with a T1/T2 ratio of 3 or 5 (Figure 31 third from top and bottom). Generally, formation brine has a T 1/T2 ratio close to 1. However, if the formation brine contains iron cations, there can be a paramagnetic affect resulting in a T1/T2 ratio greater than 1 for formation brine (Daigle et al., 2014). GMBU produced waters typically contain iron cations, and so it is possible for the formation brine to have elevated T1/T2 ratios. Presence of oil in the micropores is consistent with the residual oil pore-scale imaging (Figure 10).

Fig.28 Core T1 as-received (red) and oil-flooded (green) for Sample 34.

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Fig.29 Core T1 Oil-flooded (green) and brine-flooded (blue) for Sample 34. The 2.5D processing indicates the fluid in Zone A is likely oil and possibly some water (Figure 31). Calculated molecular diffusion for this fluid is extremely low as seen on the 2.5D data for T1/T2 equal to 3 and 5. Even though the sensitivity of diffusivity in the short T2 region is reduced, the combination of an elevated T1/T2 ratio and reduced D suggest the signal is more like oil than water. This small molecular diffusion is likely due to either oil in very small oil-wet pores or reduced T2 and D caused by oil interacting with asphaltene molecules. Possibly some of this fluid is actual clay-bound water given the high estimated molecular diffusion as shown for T1/T2 equal to 1 and 3 (Figure 31 second and third from top). The core-flood data indicate very little exchange of fluids during oil-flooding and then brine-flooding within Sample 34 consistent with low permeability (Table 1). Like in Sample 8, the fluids in Zone B of Sample 34 are primarily residual oil, but there is also some formation brine with elevated T1/T2 ratios). Also like Sample 8, the fluid in Zone C of Sample 34 is likely mud filtrate or mud filtrate mixed with formation brine in the macro-pores. Surprisingly, the fluids in Zone A are likely oil with some clay-bound water. These sands typically show more NMR T 2 porosity for T2 bins less than 2.8 ms than estimated clay-bound water from XRD data. This suggests that some of this liquid porosity is likely oil and not water. Either the oil resides in very small pores, or it has a reduced T2 caused by the interaction of oil and asphaltenes in the micro-porosity. The fluids in the faint porosity Zone D are likely oil and formation brine in the few macro-pores of this reservoir sand (Figure 10).

Fig.30 Comparison of log T1T2 (top), coarse-grid processing of log T1T2 (middle) and brine-flooded core T1T2 for Sample 34 (bottom). 17

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Originally, we interpreted this particular reservoir sand as water-wet because deep resistivity values are typically less than 100 ohmm, and dielectric data and our resistivity saturation model indicate formation brine is present. The core-flood NMR experiments show little exchange of fluids which is consistent with a small proportion of macro-pores. Surprisingly, USBM data for this plug was -0.2 indicating a more oil-wet rock. The USBM measurement of wettability is a bulk property of the rock. But wettability in Sample 34 is segregated with more oil-wet micropores and more water-wet macro-pores. The presence of oil in micro-pores combined with a dominance of micro-porosity for this plug is consistent with a dominant oil-wetness as measured by USBM measurement of wettability. In contrast, reservoir macro-pores contain water. The wettability of this sample is segregated into micro- and macropores, but the bulk USBM measurement responds to the dominant pore scale of wettability. If formation brine resides predominantly in mixed-wet connected macro-pores, this may provide a connected electrical path thus explaining low deep resistivity data. Consequently, pore-scale segregation of wettability explains the seemingly incongruous results of an oilwet rock with low deep resistivity. DISCUSSION AND CONCLUSIONS Based on field production, log and core data, we understood that the tight sand reservoirs in GMBU have highly variable mineralogy, rock quality, and wettability. Downhole conventional and specially log data showed consistent patterns likely related to these variable rock and fluid reservoir properties. In particular, we observed that downhole NMR data in some reservoir sands measured no claybound water volumes. Dielectric data measures very low water volumes in these sands. But our multi-mineral model and core XRD data indicate that these sands contain clay minerals and should have measureable clay-bound water. In other sands, NMR measures clay-bound water volumes significantly greater than core XRD indicated. NMR data have a different measurement volume than either standard logging suites used in our multi-mineral model or core XRD. Yet, there were consistent patterns in these inconsistencies, and often the differences were larger than measurement uncertainties.

Fig.31 Log T2D map with 2.5D processing for the log depth of Sample 34. a T1/T2 ratio = 1 for the log depth of Sample 34. Full T1/T2 ratios combined (top), T 1/T2 ratio = 1 (second from top), T 1/T2 ratio = 3 (third from top), and T1/T2 ratio = 5 (bottom). 18

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To better understand these patterns, we initiated a study integrating pore-scale imaging, core-flood NMR experiments, and downhole T1T2 and 2.5D NMR processing. In this paper, we present results from the two end-member reservoir sands. The results indicate a far more complex pore-scale view of wettability and locations of oil and water in these reservoirs than we initially assumed. Nearly all reservoirs have some mixed-wet pores at micro- and macro-scales. The reservoirs vary in their proportions of macro-porosity and oilwettability related to rock quality and mineralogy. These variable properties can be characterized in the field using downhole NMR T1T2 and 2.5D processing. Identification of patterns in these data permits extension of this characterization to other wells where we do not have NMR data.

oil-wet pores with oil residing in smaller pores such that the oil response is in the fast relaxation bins of T1 and T2 log data but interpreted as claybound water. The possibility of oil-wet clays or oil-wet microporosity also has implications for development of petrophysical models. Given the possibility of oil volumes interpreted as water volumes in either the assumed movable or immovable relaxation times of NMR data, petrophysical models calibrated to NMR measurements may lead to an inaccurate physical description of the reservoir petrophysical properties such as fluid total and movable volumes for oil and water. Petrophysical models using effective porosity, but calibrated to NMR VCBW bins, may be inaccurate if the fast NMR is responding to oil and not water. Similarly, petrophysical models that estimate movable water using NMR data from the BVI bins may give inaccurate estimates if the NMR response in the BVI bins is due to oil.

The results have broader implications for interpreting NMR data in mixed-wet reservoirs. Standard NMR interpretation techniques using T1, T2, T1T2 and T2D data assume a reservoir is waterwet. T1 and T2 data with fast relaxation times are routinely interpreted as a measure of clay-bound water volumes. Liquid porosities for formation brines and variable viscosity oils are interpreted from T1T2 and T2D maps using water-wet assumptions of the NMR responses. Oil- or mixedwet pores complicate these interpretations.

This study shows that NMR results can be used to characterize the pore locations and volumes of different reservoir fluids resulting from wettability variations. This study provides a recipe for data to collect and analyses to perform to utilize NMR data in mixed-wet reservoirs.

Oil-wettability reduces the T2 response of oil from the expected dominant T2 value because of increased surface relaxivity. Interpreting NMR T2 and T2D data assuming water-wet conditions can lead to an over-estimation of water volumes as the oil response appears more like a water response. Core T2 NMR is commonly performed on cleaned plugs to calibrate movable and immovable fluid volumes using log T2 data. Cleaning can render a mixed-wet rock to water-wet, giving calibration results not applicable to reservoir conditions.

The most useful log data are standard triplecombo, dielectric, and NMR data. A dielectric model, using appropriate mineral permitivities, permits estimation of reservoir water volumes in the flushed zone for calibration of saturation models (Merkel, 2006). Triple-combo data can be used for a multi-mineral and saturation models for more field-wide extension of results. For fluid and wettability characterization, NMR data need to be acquired with tool activations for measuring T 1, T2 and diffusion data. Once acquired, inversion of these data can be used to construct T1T2 and 2.5D maps. T1T2 maps show the correlations of T1 and T2 data for fluids in different pore sizes, but can be ambiguous for fluid-typing if there are mixed-wet pores. 2.5D processing and analysis gives insight into fluid types corresponding to the porosity zones of the T1T2 maps.

The possibility of oil-wet clays in a reservoir can explain anomalous log NMR results. Simply put, if there is no clay-bound water response on NMR, it does not necessarily mean the sand lacks clay minerals. It could mean the reservoir has claybound oil instead of water. Similarly, NMR data can indicate more clay-bound water than expected. Sometimes NMR data indicate only clay-bound water porosity with no movable fluids in a reservoir, yet the reservoir inexplicably produces oil. These anomalous results could be the result of

These log data alone can be ambiguous with respect to wettability and fluid typing. Calibration with core data can reduce ambiguity leading to a more field-wide characterization of log data. The 19

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most useful core data are XRD, pore-scale imaging, and, if permeability permits, NMR coreflood experiments and USBM measurements of wettability. XRD data can be used to estimate volumes of clay minerals and associated claybound water. These data can be compared to NMR estimates of clay-bound water to determine any significant differences. SEM imaging of asphaltene deposits combined with EDX mineral identification can be used to understand wettability in the context of pore structure and mineralogy. Micro-CT scans of as-received, cleaned, and brine-filled plugs combined with registered SEM and EDX mineral maps provide additional information about wettability, locations of residual oil, proportions of micro- and macro-porosity, and connected porosity. NMR core-floods compared to log NMR data and integrated with pore-scale imaging and log T1T2 and 2.5D data provide additional insight into the NMR response of oil and water in micro- and macro-pores. USBM measurement of wettability characterizes the bulk rock response and potentially gives insight into the wettability of the dominant connected porosity. Unlike USBM measurements, pore-scale imaging can indicate if wettability is segregated by pore type and scale.

Beyond GMBU, these results have implications for core and log NMR analyses, and development of petrophysical models from NMR data. Typically, core NMR is measured on a clean and dried plug to determine T1 and T2 cutoffs for estimating volumes of irreducible and movable fluids from log NMR data. Clay-bound water volumes are estimated using a 2.8 ms T2 cutoff. In mixed-wet reservoirs, oil or oil and water rather than only water may reside within interpreted claybound and irreducible volumes. When a plug is cleaned and dried, mixed-wet pores can be altered to water-wet which can complicate estimation of cutoffs. Petrophysical models that distinguish between total and effective porosity may be inaccurate if the clay-bound water model is calibrated to NMR data actually containing oil. Similarly, estimation of produced water volumes based on BVI estimation from NMR may be inaccurate if oil resides in the BVI bins in NMR data. The results of this study go far beyond understanding primary fluid production in GMBU. Because Monument Butte is being subjected to a water flood, analysis of the wettability of the various sand facies controls the effectiveness of the flood. Moreover, because of the complex mineralogy and variations in wettability, composition of the flood water as well as injection site locations create an unexpected new set of variables for this field.

Measurements of fluid properties are also useful. Measurement of bulk T2 of produced oil can be compared to the dominant T2 response of oil in the reservoir. Reservoir T2 values much less than the bulk T2 indicate some oil-wetness in the reservoir. SARA (measurements of saturates, aromatics, resins and asphaltenes) of produced oil and extracted residual oil can be used to determine the amount of polar hydrocarbons in the movable and residual oil. Chemical analyses of produced water are useful to determine if cations in the reservoir brine might result in a T1/T2 ratio water response greater than 1.

ACKNOWLEDGEMENTS The authors would like to thank the management of Newfield Exploration Company for the release of these data. Margaret Lessenger thanks Terri Olson and Lyn Canter for fruitful discussions about pore-scale imaging and wettability. REFERENCES

Produced fluid types and volumes are strongly controlled by pore-scale geometries, mineralogy, and fluid types. Because oil can reside in microporosity and on clay surfaces, we cannot assume only water in these locations. Therefore, delineating the pore scales where oil and water reside in a reservoir and potential pore segregation of wettability increases the importance of porescale imaging combined with NMR 2.5D processing for reservoir characterization.

Anderson, W.J., 1986, Wettability literature survey part 2: Wettability measurement, JPT, November 1996, p. 1246-1262 (1986). Birdwell, J.E., Washburn, K.E., 2015, Multivariate analysis relating oil shale geochemical properties to NMR relaxometry, Energy & fuels, (in press). Burton, D., Sullivan B., Adams, S., 2012, Quantifying Low Net:Gross, Fluvial-Lacustrine Reservoirs Using 20

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Proportional Tops and Zonation: Green river Formation, Monument Butte Field, Utah: The Mountain Geologist, v. 49, no. 4.

3D. Characterizing mineralogy, wettability and residual fluid phases at the pore scale, SPE 145093. Lessenger, M., Merkel, R., Sullivan, B., Burton, D., 2013, Application of dielectric and standard logging suites to characterize the stratigraphic and lithologic variations in Archie parameters within the Green River Formation of the Greater Monument Butte Field, Uinta Basin, Utah, USA, SPWLA 54th Annual Logging Symposium, June 22-26, 2013.

Chen, S., Miller, D., Li, L., Westacott, D., Murphy, E., and Balliet, R., 2013, Qualitative and quantitative information NMR logging delivers for characterization of unconventional shale plays: Case studies, SPWLA 54th Annual Logging Symposium, June 22-26, 2013. Coates, G.R., Xiao, L., Prammer, M.G., 1999, NMR Logging Principles and Applications, Halliburton Energy Services, Houston, 233 pages.

Lessenger, M., Sullivan, B., Woolf, K., Burton, D., 2015, Fluid sensitivity in tight sandstones – when rocks behave badly, AAPG Memoir 110, (in press).

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Dodd, N., Marathe, R., Middleton, J., Fogden, A., Carnerup, A., Knackstedt, M., Mogensen, K., Marquez, X., Frank, S., 2014, Pore-scale imaging of oil and wettability in native-state, mixed-wet reservoir carbonates,Paper IPTC 17696 presented at the Internatinal Petroleum Technology conference, Doha, Qatar, 20-22 January.

Martin, P., Dacy, J., 2004, Effective Qv by NMR core tests, SPWLA 45th Annual Logging Symposium, June 6-9, 2004.

Droeven, C., Acuña, C., , Lopez, E., Sarvotham, S., Balliet, R., 2009, San Jorge Gulf Basin complex formation evaluation with 2D NMR T1-T2 data, SPWLA 50th Annual Logging Symposium, June 21-24, 2009.Flaum, M., Chen, J., Hirasaki, G.J., 2005, NMR diffusion editing for D-T2 maps: Application to recognition of wettability change, Petrophysics, v. 46, no. 2, p. 113-123

Merkel, R., 2006, Integrated petrophysical models in tight gas sands, SPWLA 47th Annual Logging Symposium, June 4-7, 2006. Merkel, R., Lessenger, M., 2014, Characterizing the oil reservoirs in the Uinta Basin, SPE Conference Paper SPE-169510-MS. Mutina, A.R., Hurlimann, D., 2008, Correlation of transverse and rotational diffusion coefficient: A probe of chemical composition in hydrocarbon oils, Journal of Physical Chemistry, A112, p. 3291-3301.

Flaum, M., Chen, J., Hirasaki, G.J., 2005, NMR diffusion editing for D? T2 maps: Application to recognition of wettability change, SPWLA 46 th Annual Logging Symposium, June 26-29, 2005.

Ramakrishna, S., Merkel, R., Balliet, R., Lessenger, M., 2012, Mineralogy, porosity, fluid property, and hydrocarbon determination of oil reservoirs of the Green River Formation in the Uinta Basin, SPWLA 53rd Annual Logging Symposium, June 16-20, 2012.

Freedman, R., Heaton, N., 2004, Fluid characterization using nuclear magnetic resonance logging, Petrophysics, v. 45, no. 3, p. 241-250. Golab, A.N., Knackstedt, M.A., Holger, A., Senden, T., 2010, 3D porosity and mineralogy characterization in tight gas sandstones, The Leading Edge, December, 2010, p. 1476-1483.

Sok, R.M., Varslot, T., Ghous, A., Latham, S., Sheppard, A.P., Knackstedt, M.A., 2010, Pore scale characterization of carbonates at multiple scales: integration of micro-CT, BSEM, FIBSEM, Petrophysics, v. 51, no. 6, p. 379-387.

Knackstedt, M., Senden, T., Carnerup, A., Fogden, A., 2011, Improved characterization of EOR processes in 21

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Sun, B.Q., Dunn, K.J., 2005, A global inversion method for multi-dimensional NMR logging, Journal of Magnetic Resonance, v. 40 no. 2, p. 152-160.

engineer before joining FRS in 2010. Rojelio received his B.S. degree in Geology from Texas A&M University – Kingsville in 2005. Rojelio is a member of SPWLA and SPE.

Vinegar, H., 1995, Relaxation mechanisms, chapter 3, in Georgi, D.T., ed., 36th Annual SPWLA Logging Symposium: Nuclear magnetic resonance logging short course notes, variously paginated.

Sandeep Ramakrishna is a petrophysicist for Halliburton in the US Southern Region Wireline and Perforating product service line. He has more than 17 years of experience in the industry. Ramakrishna has been actively involved the development of techniques to analyze unconventional reservoirs. He holds a MS degree in petroleum engineering from the University of Tulsa, Oklahoma and a BSc degree in geology from the University of Pune, India. Ramakrishna is a member of SPWLA, SPE, and AAPG.

ABOUT THE AUTHORS Margaret Lessenger is a petrophysicist for Newfield Exploration Company in Denver. She has over 30 years of experience as a geophysicist, geologist and petrophysicist working in various basins in the Rockies, North Sea and Gulf of Mexico. She has worked for the Superior Oil Company, ARCO Oil and Gas, Platte River Associates, the Colorado School of Mines Department of Geology, and Williams Exploration prior to joining Newfield. Lessenger holds a BS in Geophysical Engineering, MS in Geophysics, and PhD in Geology from the Colorado School of Mines. She is a member of SPWLA, AAPG, SPE and SCA.

Songhua Chen is senior manager of the NMR Sensor Physics at Halliburton. Before joining Halliburton he was Sr. Staff Scientist and Sr. Manager of Integrated Interpretation and Petrophysics in Houston Technology Center of Baker Hughes. He has been involved in various projects on NMR, sensor R&D, petrophysics, and carbonate and shale rock models. Prior to join the industry, he was doing research at Texas Engineering Experiment Station on the application of NMR for multiphase flow in porous media. Songhua received his BS degree in physics from Nanjing Institute of Technology in China and a PhD from the University of Utah in Salt Lake City, Utah, both in physics.

Dick Merkel is President of Denver Petrophysics LLC, which is a consulting firm dedicated to developing core and log analytical techniques for petrophysical models tied to completion and production data in complex reservoirs. As a petrophysicist for Newfield Exploration Company, he worked for six years on teams that developed reservoir models for unconventional oil and gas reservoirs in the Rocky Mountains. Dick also was with EnCana Oil & Gas in Denver where he worked on developing petrophysical models for tight gas sandstone reservoirs. Prior to its closing in 2000, he was a Senior Technical Consultant at Marathon Oil Company’s Petroleum Technology Center in Littleton where he worked on evaluating new logging tools and technology, and developing techniques for their application in Marathon’s reservoirs. Dick has a BS in physics from St. Lawrence University and a MS and Ph.D. in geophysics from Penn State. He is a past president of SPWLA, the SPWLA Foundation, and DWLS.

Ron Balliet is the Global NMR product champion for Halliburton. He joined Numar in 1991 and worked with oilfield NMR in several locations worldwide. Since 1997, he has held various positions with Halliburton in West Africa and the USA, becoming the Global NMR product champion in 2006. Balliet holds BS degrees in geology and geophysics from the University of Minnesota (1984). He is a member of the SPWLA and SPE. Zonghai Harry Xie is NMR Senior Advisor at Core Laboratories in Houston. Before joining Corelab, he had been working for the instrumentation industry for about 20 years. He spent several years working as Product Specialist to develop and support laboratory NMR products (MARAN product line) at Resonance Instruments Ltd. He also spent time at Bruker as the Senior Applications Scientist in the Time Domain NMR division, Technical Director of the Time Domain products for Asia Pacific, and General Manager of Bruker Optics China. He received his PhD in Physics from the University of Kent at Canterbury, UK.

Rojelio Medina works as a consultant for petrophysics and log analysis for Halliburton’s Formation and Reservoir Solutions (FRS) group in Houston, TX. In FRS, Rojelio has worked in many disciplines of petrophysics including NMR interpretations. Rojelio joined Halliburton in 2006 and worked as a field 22

SPWLA 56th Annual Logging Symposium, July 18-22, 2015

Pradeep Bhattad is a Senior Project Manager at FEI Company in Houston. He has over 10 years of experience in digital rock physics, microCT imaging, 3D image analysis and colloids & interface science. Pradeep holds a BE in Chemical Engineering from Bangalore University, India and PhD in Chemical Engineering from Louisiana State University, USA. He is a member of SPWLA, AAPG, SPE, SCA and AIChE. Anna Carnerup is senior lab engineer at FEI Australia, previously Lithicon Australia and Digitalcore. Prior to joining Digitalcore she was conducting research at the University of Warwick and Lund University within physical sciences. Carnerup holds a BS degree in chemical engineering from Malmö University and PhD from the Australian National University in physical sciences. Mark Knackstedt is Director of Technology Development for FEI/Lithicon and a Visiting Professor at the Department of Applied Mathematics at the Australian National University. He is a past (20072008, 2009-2010, 2012-2013) SPWLA distinguished speaker, was awarded the George C. Matson Memorial Award from the AAPG in 2009 and the ENI award for New Frontiers in Hydrocarbon Research in 2010. Knackstedt was awarded a BSc (1985) from Columbia University and a PhD in Chemical Engineering from Rice University (1990).

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