2nd SPWLA, India Regional Symposium, November 19-20, 2009

1

NMR Applications in Fresh Water Reservoirs

Jaipal Singh, Sairam Prasad (ONGC)

T.Klimentos, V.Srivastava, M.Tayal, A. Mathur, A. Pal (Schlumberger)

Abstract In two recent Assam wells, conventional triple combo logs did not differentiate between oil and water bearing zones (salinity ~1-4ppk). Deep resistivity logs read approximately 5 to 7 ohm.m in both the oil and water bearing zones. NMR logs and station measurements for magnetic resonance fluid characterization were run to assist in the log interpretation. The main NMR acquisition objectives were to differentiate between oil and water bearing zones with low resistivity contrast and determine accurate porosity, permeability and hydrocarbon saturation. This information would assist in the DST zone selection. NMR depth log and several magnetic resonance fluid characterization stations were recorded along with modular formation dynamic tester tool pretests and sampling. The magnetic resonance fluid characterization station analysis identified the presence of light oil at several depths. The NMR oil detection was in agreement with the formation tester tool pressure gradients and fluid samples, which also indicated presence of light oil over the same zones. Moreover, the NMR depth log, provided measurements for the determination of accurate porosity, permeability and irreducible water saturation, which were used for enhanced formation evaluation in these unconventional reservoirs.

Introduction Formation water salinity is a critical parameter in the formation evaluation of fresh water Assam reservoirs, and a major cause of the low resistivity contrast between pay zones and wet sands. Some of the largest fields are located in a fairway of fresh water (<1-4 ppk). At such low formation water salinities, and in the presence of clay minerals and silt in the formation, true formation resistivity Rt is often insensitive to the formation water resistivity Rw and the excess conductivity term is dominant in all commonly used shaly sand equations. From an operational perspective, low resistivity contrast pay poses a serious limitation on real time fluid-type prediction, particularly in wells where logging suites are limited. Thus, presence of formation fresh water and the complex lithology of Assam reservoirs often cause many challenges in formation evaluation. In two recent Assam wells, conventional resistivity logs read approximately 5 to 7 ohm.m over most of the intersected sands, as shown in Fig.1 and 2. Thus, using the resistivity was not adequate in differentiating oil and water bearing zones. NMR logs and magnetic resonance fluid characterization station measurements (MRF, Freedman et al., 2001, 2002) were also run. The NMR data was used as an aid in differentiating between low resistivity contrast oil and water bearing zones. Moreover, with NMR data we determined accurate porosity (independent of lithology), permeability and flushed zone hydrocarbon saturation; the continuous permeability log

2nd SPWLA-India Symposium, November 19-20, 2009

WV

2nd SPWLA, India Regional Symposium,

information provided at well-site, assisted in optimizing the dynamic formation tester acquisition. The following sections first describe the resistivity contrast problem; then the measurement and MRF principles explained; finally, the results from the studied wells are presented, followed by conclusions and recommendations Resistivity response and saturation evaluation challenges in fresh water complex lithology (shaly and silty) reservoirs

Assam sandstones reservoirs are often difficult to interpret because of unknown water salinities. There is little resistivity contrast between water-bearing reservoirs and oil-bearing reservoirs. The waterbearing reservoirs often contain fresh water, and thus show high resistivity readings. The resistivity in the oil-bearing reservoirs be variable because the reservoirs silty and contain water with low and variable salinity.

Fig.1 Resistivity ranges between in sand intervals of well 1, Assam.

WLA, India Regional Symposium, November 19-20, 2009

site, assisted in amic formation tester tool

The following sections first describe the low ; then the NMR

principles are the results from the

studied wells are presented, followed by the recommendations.

esponse and saturation in fresh water

(shaly and silty)

sandstones reservoirs are often difficult to interpret because of unknown

is little resistivity bearing reservoirs

bearing reservoirs. The water-contain fresh water,

and thus show high resistivity readings. The bearing reservoirs may

se the reservoirs are often with low and

Resistivity ranges between 5-7 Ohm.m in sand intervals of well 1, Assam.

Fig.2 Resistivity ranges between in sand intervals of well 1, Assam.

Often, due to high amount of silt, as shown in Fig. 3 and 4, by the GR, neutron and density log responses, the oilreservoirs show a high level of irreducible water saturation that may depressresistivity reading, thus making the pay identification from the resistivity log extremely difficult.

Fig.3 GR, Neutron and Density logs indicate mostly shaly and silty sand intervals of well 1, Assam.

20, 2009

2

Fig.2 Resistivity ranges between 5-7 Ohm.m in sand intervals of well 1, Assam.

e to high amount of silt, as shown by the GR, neutron and

the oil-bearing reservoirs show a high level of irreducible

may depress further the resistivity reading, thus making the pay

ion from the resistivity log

Fig.3 GR, Neutron and Density logs indicate mostly shaly and silty sand intervals of well

2nd SPWLA-India Symposium, November 19-20, 2009

WV

2nd SPWLA, India Regional Symposium, November 19-20, 2009

3

Fig.4 GR, Neutron and Density logs indicate mostly shaly and silty sand intervals of well 1, Assam.

Moreover, presence of clays and heavy minerals, as shown in Fig. 5 and 6, adds more difficulties in the interpretation. The formations often contain good amounts of micas (Muscovite and Biotite), feldspar, Montmorillonite and Kaolinite, and minor amount of Illite, as indicated by petrography and X-Ray diffraction studies. Mica complicates the log interpretation as most standard log indicators (GR, RHOB, NPHI) reflect that it is a high density shale, resulting in pessimistic reservoir evaluation.

Fig.5 SEM photo showing clays and heavy minerals.

Fig.6 SEM photo showing presence of montmorillonite clay minerals (arrow).

However, mica should be considered as structural part of the rock not affecting porosity and permeability. Furthermore, excessive surface conductance of the micas surfaces due to diagenesis alterations, can reduce the resistivity of the formation, and may cause low resistivity pay effects. Also, higher computed apparent clay volumes will lead to pessimistic reservoir evaluation. Therefore, if micas are not included in the petrophysical model the evaluation can be ambiguous.

Furthermore, the grain conductivity (due to the highly conductive clay and heavy mineral effects) is as important as the conductivity of the formation water; therefore, the conditions for the Archie

saturation equation relating resistivity solely to water saturation, no longer apply. This challenge is often compounded by the additional difficulty of precise determination of clay volume and clay types from conventional well logs. Small variations in the clay volume estimations can lead to larger changes in the derived ranges of water saturation. There are many available shaly-sand saturation equations in the literature. One of the most widely used model is the Waxman-Smits (1968), which relates the additional conductivity of shaly sands to the cation exchange molarity (or cation exchange capacity per unit pore-volume), QV. In water bearing rocks at high salinities this additional conductivity is proportional to QV:

2nd SPWLA-India Symposium, November 19-20, 2009

WV

2nd SPWLA, India Regional Symposium, November 19-20, 2009

4

σ0 = 1/F (σ w + BQV ) …(1)

where σ0 is the conductivity of the rock in S/m, σw (also in S/m) is the conductivity of the saturating brine, B is the equivalent conductance of counterions (S.cm2/meq) and F is the formation resistivity factor.

Fig.7 σo-σw plot for Qv determination.

Fig. 7 shows a σ0 vs. σw plot. At high salinities, Eq. (1) predicts a linear relationship between σ0 and σw. The slope of the line is 1/F and the intercept on the x-axis is -B.QV. According to Waxman and Smits the curvature of the line at low salinities occurs because the equivalent conductivity, B, is a function of the water conductivity. An alternative explanation of the curvature at low salinities is given by Sen et al. (SGS model). They pointed out that the curvature is due to the fact that the surface and bulk tortuosities are different; also, as the formation water conductivity increases then the surface conductivity contribution to the bulk rock conductivity becomes less. When the grain surface conductivity is much higher than the formation water conductivity, then the current will prefer to flow through the grain surface paths; when the surface conductivity is small compared with the formation water conductivity the current “sees” the pore tortuosity. Similarly to the WS model, the SGS model predicts that as the water conductivity approaches zero grains may still conduct. This is very important at low Sw calculations, for the influence of bulk water

is low and the grain conductivity remains almost constant with increasing hydrocarbon saturation. Thus, assessing accurately the grain conductivity (Qv or CEC) is vital for the implementation of any appropriate shaly sand model, even more so, in fresh water reservoirs. In this environment, empirical relationships have been derived for calculation of CEC from clay volume and type. NMR Technology for Hydrocarbon identification in fresh water and low contrast resistivity pay zones of Assam reservoirs Since it’s early inception in the oil industry, NMR logging technology has been primarily used for determination of porosity independent of lithology, pore-size distribution and permeability (Freedman et al., 1997, Hussein, et. al., 1999), as shown in Fig.8. One of the most widely used NMR logging tools to date, has been the CMR-Plus* (Combinable Magnetic Resonance), shown in Fig 9. The CMR-Plus tool has been used in Assam reservoirs for various formation evaluation applications (porosity, permeability, pore-size distribution, Swir, etc), as shown in Fig.10.

Fig.8 NMR signals of two rocks with nearly identical porosity, but different permeability.

* Mark of Schlumberger

2nd SPWLA-India Symposium, November 19-20, 2009

WV

2nd SPWLA, India Regional Symposium, November 19-20, 2009

5

Fig. 9 The CMR-Plus tool.

Fig.10 NMR composite log display (well 1, zone A) showing T2 distribution (track4), NMR porosity, clay bound, capillary bound, free fluid (track 3), permeability and resistivity (track 2) and pore-size distribution (track 1). GR, Caliper and bit-size are shown on depth track. The late T2 arrivals (red circles) indicate possible presence of light hydrocarbons.

Moreover, the NMR wellsite information has been applied to optimize the dynamic formation tester tool acquisition operations, as shown in Fig.11.

Fig. 11 The percentage of unsuccessful tests with and without using NMR to help choose dynamic formation tester pre-test depths. There were a total of 164 pre-tests attempted with NMR and 169 without . Overall, NMR permeability has reduced the percentage of dry dynamic formation tester tests from 24% to 7%, and the number of seal failures from 13% to 5% (Moraes et al., 2000). Recent developments have demonstrated that NMR can also be used to address unsolved log interpretation problems with conventional Resistivity-Nuclear logs, i.e., fluid characterization in fresh water hydrocarbon reservoirs. One of the most widely used methods is the MRF (Magnetic Resonance Fluid characterization, Freedman, et al.) technique, which provides fluid typing (oil, gas, and brine), flushed-zone saturations, and oil viscosities in addition to the rock quality answers mentioned above. The main idea of the MRF acquisition technique is to record various CPMG sequences at each measurement depth, using different acquisition parameters; for example, changing the echo spacing helps resolve fluids by diffusivity (Fig. 12), or using multiple wait times assists in the determination of T1 (longitudinal relaxation time). In Fig. 10, the zones with late T2 arrivals (red circles) were selected for MRF station and formation tester measurements, which confirmed the presence of light HC.

2nd SPWLA-India Symposium, November 19-20, 2009

WV

2nd SPWLA, India Regional Symposium, November 19-20, 2009

6

Fig.12 MRF variable echo spacing assists in fluid characterization by diffusion contrast. One of the most advanced NMR fluid typing techniques, is known as Diffusion Editing (Hurlimann et al., 2002); it is designed to determine all three NMR properties simultaneously (T1, T2, D), and often has been applied successfully. The Diffusion Editing (DE) measurement is comprised of a series of CMPG echo trains, where the echo spacing TE of the first two echoes is chosen to be a multiple of the shorter TE used for the rest of the echoes. Such variation in echo spacing times allows the encoding of diffusion information into the echo amplitudes. Following MRF DE acquisition, all the acquired NMR data is then inverted and valuable information of T1 (longitudinal relaxation time) , T2 (transverse relaxation time) and D (Diffusion) is obtained. This information can be presented either as 2D maps of D vs T1, D vs T2, or fractional fluid volumes for bound and free water, oil and gas. NMR-MRF 2D maps of D vs T2 (Fig. 13), D vs T1, or T1 vs T2, can provide invaluable fluid characterization information, which can be used for resistivity independent formation evaluation of unconventional fresh water reservoirs.

Fig.13 2D Diffusion-T2 Fluid Typing Map.

Case studies As discussed earlier, in fresh water bearing sands, the reduced resistivity contrast between hydrocarbon and water bearing intervals is not well described by the widely used resistivity based saturation equations. In two recent Assam wells drilled with WBM, conventional triple combo logs did not differentiate between oil and water bearing zones (salinity ~1-4ppk). Thus, NMR MRF technology was used for resistivity independent reservoir fluid characterization. NMR depth log and MRF DE (Magnetic Resonance Diffusion Editing Fluid Characterization) stations (Fig. 14 to 24) were acquired in order to assist the challenging log interpretation. The silty nature of these reservoirs is revealed by the high amount of capillary bound content, as shown in all the MRF 2D maps of Diffusion-T1, Diffusion-T2 and T1-T2. In well 1, zone A, depth 1, the 2D NMR maps shown in Fig. 14, 15 and 16 indicate the presence of light oil with an average Sxoil of 12%. Total NMR porosity was estimated to be equal to 24%. NMR Timur-Coates permeability was calculated as 50 mD, approximately. Oil viscosity was ~1cp. At depth 2, which is a streak in same zone, the 2D NMR maps are shown in Fig. 17, 18 and 19; they also indicate the presence of light oil with an average Sxoil of 10%.

Fig.14 2D Diffusion-T1 Fluid Identification Map (well 1, zone A, depth 1) indicating light oil (green arrow & circle), Sxoil=12%.

2nd SPWLA-India Symposium, November 19-20, 2009

WV

2nd SPWLA, India Regional Symposium, November 19-20, 2009

7

Fig.15 2D Diffusion-T2 Fluid Identification Map (well 1, zone A, depth 1) indicating light oil (green arrow & circle), Sxoil=12%.

Fig.16 2D T1-T2 Fluid Identification Map (well 1, zone A, depth 1) indicating the presence of light oil (green arrow).

Fig.17 2D Diffusion-T1 Fluid Identification Map (well 1, zone A, depth 2) indicating the presence of light oil (green arrow & circle), Sxoil=10%. Total NMR porosity was estimated to be equal to 22%. NMR Timur-Coates permeability was calculated as 5mD, approximately. Oil viscosity was estimated as ~1.1 cp.

Fig.18 2D Diffusion-T2 Fluid Identification Map (well 1, zone A, depth 2) indicating the presence of light oil (green arrow & circle), Sxoil=10%.

Fig.19 2D T1-T2 Fluid Identification Map (well 1, zone A, depth 2) indicating the presence of light oil (green arrow). Fig. 20 shows that the reservoir pressure gradient in zone A of well 1 is ~0.26psi/ft, confirming the presence of light oil.

Fig.20 Dynamic Formation Tester Pressure gradient (well 1, zone A) indicating presence of light oil. At depth 3, the 2D NMR maps are shown in Fig. 21 and 22; both MRF 2D maps indicate that this zone is water bearing. Total NMR

2nd SPWLA-India Symposium, November 19-20, 2009

WV

2nd SPWLA, India Regional Symposium, November 19-20, 2009

8

porosity was estimated to be equal to 18%. NMR Timur-Coates permeability was found to be 1 mD, approximately.

Fig.21 2D Diffusion-T1 Fluid Identification Map (well 1, depth 3) indicating that this zone is water bearing.

Fig.22 2D Diffusion-T2 Fluid Identification Map (well 1, depth 3) indicating that this zone is water bearing. In well 2, zone A, the 2D NMR maps shown in Fig. 23 and 24 indicate the presence of oil with an average Sxoil of 12%. Total NMR porosity was estimated to be equal to 29%. NMR Timur-Coates permeability was calculated as 100 mD, approximately. Oil viscosity was estimated as ~3 cp. In zone B, the 2D NMR maps shown in Fig. 25 and 26 indicate the presence of water only. Total NMR porosity was estimated to be equal to 33%. NMR Timur-Coates permeability was calculated as 1000 mD, approximately.

Fig.23 Diffusion-T1 Fluid Identification Map (well 2, zone A) indicating the presence of oil (green arrow), Sxoil=12%.

Fig.24 Diffusion-T2 Fluid Identification Map (well 2, zone A) indicating the presence of oil (green arrow), Sxoil=12%.

Fig.25 2D Diffusion-T1 Fluid Identification Map (well 2, zone B) indicating that this zone is water bearing.

2nd SPWLA-India Symposium, November 19-20, 2009

WV

2nd SPWLA, India Regional Symposium, November 19-20, 2009

9

Fig.26 2D Diffusion-T2 Fluid Identification Map (well 2, zone B) indicating that this zone is water bearing. Summary-Conclusions and Recommendations In this paper we presented a variety of NMR fluid characterization examples for evaluation of unconventional hydrocarbon bearing reservoirs with fresh formation water, in Assam, India. We applied advanced NMR MRF logging techniques to differentiate hydrocarbon from fresh water bearing formations. Processed NMR MRF data demonstrated the application of the MRF method in such environments for the prediction of oil and water saturations, as well as, porosity, permeability and oil viscosity. Oils were distinguished from water by NMR 2D maps, thereby, helping decide which sands to complete in difficult cases. Determination of free fluid and permeability logs also can help decide which sands and which intervals in the sands to perforate, thus, reducing perforating intervals and costs. Our main recommendations would be that NMR has a unique role to play in the formation evaluation of fresh water unconventional reservoirs, and can be used effectively to improve the efficiency of the interpretation workflow. For further work, it is recommended to integrate the advanced

NMR logging techniques in these reservoirs with NMR core analysis and SCAL. Moreover, NMR radial saturation profiling technology may provide additional information regarding the mobility of the detected hydrocarbons, by comparing the saturations at different depths of investigation. Acknowledgements The authors would like to thank ONGC and Schlumberger for their permission to publish this paper.

References Archie, G.E., 1942, “The Electrical Resistivity Log as an Aid in Determining Some Reservoir Characteristics, ”Transactions AIME, 146 (1942), p. 54-62. Clavier, C., Coates, G.R., and Dumanoir, J., 1984, “Theoretical and Experimental Bases for the Dual-Water Model for Interpretation of Shaly Sands”, J. Pet. Tech., April 1984. Freedman, R., Boyd, A., Gubelin, G., McKeon, D., Morriss, C.E., and Flaum, C., 1997, “Measurement of Total NMR Porosity Adds New Value to NMR Logging,” presented at the SPWLA 38th Annual Logging Symposium, 1997, paper OO. Freedman, R., Lo, S., Flaum, M., Hirasaki, G.J., Matteson, and Sezginer, A., 2001, A new NMR method of fluid characterization in reservoir rocks: Experimental confirmation and simulation results: SPE Journal, vol. 6, no. 4, p. 452–464. Freedman, R., Heaton, N., and Flaum, M., 2002, Field applications of a new nuclear magnetic resonance fluid characterization method: SPE Reservoir Evaluation & Engineering Journal, vol. 5, no. 6, p. 455–464.

2nd SPWLA-India Symposium, November 19-20, 2009

WV

2nd SPWLA, India Regional Symposium, November 19-20, 2009

10

Hurlimann, M. D., Venkataramanan, L., Flaum, C., Speier, P., Karmonik, C., Freedman, R., and Heaton, N., 2002, Diffusion Editing: New NMR Measurement of Saturation and Pore Geometry, paper FFF, presented at the SPWLA 43rd Annual Logging Symposium, Japan. Hussein, S., Hassan, S., Klimentos T., and Zeid A., 1999, "Using NMR and Electrical Logs for Enhanced Evaluation of Producibility and Hydrocarbon Reserves in Gas Reservoirs with High Irreducible Water Saturation", presented at the 40th SPWLA Annual Logging Symposium, Oslo, 1999. Kenyon, W.E., 1997, ”Petrophysical Principles of Applications of NMR Logging”, The Log Analyst (March-April 1997), 38 No.2, 21. Minh, C.C., Freedman, R., Crary, S., and Cannon, D.E., 1998, “Integration of NMR with Other Openhole Logs for Improved Formation Evaluation”, paper SPE 49012, presented at the SPE Annual Technical Conference and Exhibition, September 1998. Moraes J., Brandao, R., Tellez, R., Vallejo, J., Garcia, G., Singer, J., “NMR Logging Improves Wellsite Efficiency, Completion Decisions, and Formation Evaluation in a Freshwater, Shaly Reservoir”, paper SPE 63213, presented at the SPE Annual Technical Conference and Exhibition, September 2000. Sen, P.N., Goode, P.A., 1988, “Shaly sand conductivity at low and high salinities,” presented at the 29th Annual Logging Symposium of the SPWLA (June 1988) paper F. Sen, P.N., 1987, “Electrochemical Origin of Conduction in Shaly Formations,” presented at the Annual Technical Conference and Exhibition of the SPE (1987), SPE 16787. Sen, P.N., Goode, P.A., Sibbit, A.M., 1988, “Electrical conduction in clay bearing

sandstones at low and high salinities,” J. Appl. Physics 63(10), (May 1988), p. 4832-4840. Waxman, M.H., Smits, L.J.M., 1968, “Electrical Conductivities in Oil-Bearing Shaly Sands,” Transactions AIME 243 (1968) p. 107-122.

2nd SPWLA-India Symposium, November 19-20, 2009

WV