San Jorge Gulf Basin Complex Formation Evaluation with 2D ...shri/pub/spwla2009.pdf · The primary...

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SPWLA 50th Annual Logging Symposium, June 21-24, 2009

San Jorge Gulf Basin Complex Formation Evaluation with 2D NMR T1-T2 Data

Carolina Droeven and Carlos Acuña. Pan American Energy LLC

Emiliano Lopez, Shriram Sarvotham and Ron Balliet, Halliburton Energy Services

Copyright 2009, 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 50th Annual Logging Symposium held in The Woodlands, Texas, United States, June 21-24, 2009.

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ABSTRACT The evaluation of reservoirs with mixed mineralogy and formations containing primary and secondary porosity presents several challenges in the San Jorge Gulf Basin of Argentina. Throughout the formations of the San Jorge Gulf Basin, we observe striking differences in Rw values. We also encounter the presence of volcanic materials, a wide range of oil viscosities, and large variations in reservoir quality. These challenges can be effectively addressed using enhanced NMR measurements that are designed to provide important reservoir fluids information for economic recovery. The aforementioned evaluation challenges often occur together, rendering conventional interpretation techniques ineffective. Two dimensional fluid characterization (2DFC), using simultaneously acquired NMR T1 and T2 information, is proving remarkably useful in the identification of gas-bearing reservoirs and simultaneously providing valuable information about porosity and reservoir quality. Layers with lower porosity have also been tested with encouraging results using this method. Because NMR measurements are free from mineralogical effects, they provide a very good estimate of formation porosity. Moveable and bound fractional fluid porosities from NMR provide reservoir quality information, as well as data to be entered in a Coates equation to compute a permeability estimate. The 2DFC analysis provides a 2D graphic identification for the presence of methane, as well as volumetric estimates of flushed zone methane, NMR liquids distributions, and NMR methane distributions. The prognoses from this simultaneous NMR T1-T2 method are compared to production results from three San Jorge Gulf Basin wells. The correlation between the predictions and the actual production results

suggests that this method is valuable in the evaluations of other challenging, mixed mineralogy formations. INTRODUCTION The most prolific reservoirs in the San Jorge Gulf Basin are found in two fluvial stratigraphic units: Mina El Carmen formation (lower Cretaceous; Lesta, 1968) and Comodoro Rivadavia formation and its equivalent, Cañadón Seco formation (upper Cretaceous; Lesta, 1968) (Figure 1). The Mina el Carmen reservoirs are comprised of a large variety of rock types, with tuffs, tuffaceous sandstone with a large amount of litho-feldspathic components deposited as alluvial plains, with their origins from volcanic activity (Gonzalez et al., 2002). The clastic fraction includes vitreous tuffs, vitreous crystals, lithic volcanics and feldspathic clastics. The matrix type is clayey-tuffaceous, with clays of authigenic origin and an abundance of secondary growth of quartz and feldspar. The quality of the reservoirs varies from good to poor, depending on the fineness and matrix content. The Comodoro Rivadavia reservoirs contain lithic feldspathic to lithic quartz sandstones, with medium to large grain size, up to conglomerate, deposited in lobes and river channels (González et al., 2002; Salomone et al., 2002). The lithic fraction is predominantly tuffaceous and, to a lesser extent, plutonic and metamorphic. The matrix is commonly clayey, and the pyroclastic content is lower than that of the Mina El Carmen formation. The quality of the reservoirs is considered to be medium to good. The tuffaceous content, clayey matrix, complex mineralogy, wide range of hydrocarbon viscosities, and low water formation salinity present difficulties in the determination of the reservoir fluid content and the reservoir productivity in the San Jorge Gulf Basin.

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Figure 1 – San Jorge Gulf Basin, stratigraphic column (Sylwan 2001). RESERVOIR CHALLENGES IN THE SAN JORGE GULF BASIN The volcanic origins of the San Jorge Gulf Basin sediments pose several difficulties for reservoir evaluation with conventional sensors. The volcanic and feldspathic clastics often contain radioactive minerals, which reduce the gamma ray contrast across the basin’s reservoirs and shale sections (Fitzgerald et al., 1990). Porosity measurements assessed with density sensors are similarly challenged because the volcanic clastics contain minerals with a wide range of matrix densities, which result in erroneous porosity estimates from density measurements. A similar case may be made for neutron interpretation. NMR measurements are free from mineralogical effects, which enables NMR to provide an accurate estimate of formation porosity. Fluid filled porosity from NMR is fractionalized into moveable and bound fluid volumes, which provides reservoir quality information and inputs for a Coates equation permeability estimate (Coates et al., 1991; Miller et al., 1990; Coates and Denoo; Prammer et al., 1994). Variability in calculated irreducible water saturations often occur as a result of changes in grain and pore size

variations (Coates et al., 1991). In the San Jorge Gulf Basin, variable NMR T1 and T2 cutoffs are applied over specific reservoir sections, which are based on laboratory core NMR measurements. A spectral bound fluid volume method is also applied to NMR data, which is based on laboratory core NMR measurements (Coates et al., 1997). Over the past decade, NMR measurements have become the primary porosity measurements for in-field wells penetrating the Comodoro Rivadavia and Mina El Carmen formations by providing mineralogical independent porosity, moveable, and bound fluid volumes. In many in-field wells, the complete open hole logging suite may be limited to gamma ray, resistivity, four arm caliper, and NMR. NMR IN THE SAN JORGE GULF BASIN FOR MOVEABLE AND BOUND FLUID, TOTAL POROSITY, AND PERMEABILITY ESTIMATION Laboratory NMR core analysis performed on Comodoro Rivadavia and Mina El Carmen formation samples provide the basis for applying moveable-bound fluid cutoffs and spectral bound fluid coefficients to NMR field log data. Modified Coates permeability equations were also derived from laboratory NMR core analysis and are applied to well log NMR data.

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In a core study, more than 95 core plugs from the Comodoro Rivadavia and Mina El Carmen formations were analyzed for both traditional core and NMR properties. Laboratory NMR was performed on brine saturated core plugs from both formations and found good agreement with core and NMR porosities (Figure 2 and Figure 3).

Figure 2 – Core study, core porosity and NMR log porosity, Comodoro Rivadavia formation.

Figure 3 – Core study, core porosity, and NMR log porosity, Mina El Carmen formation.

Geometric mean T2 cutoffs were established for core samples from the Comodoro Rivadavia and Mina El Carmen formations (upper section) and applied to the NMR log data. Similarly, spectral bound fluid coefficients were derived for each formation and applied to the NMR log data. For the Comodoro Rivadavia formation, the geometric mean T2 cutoff used is 10.2 ms and a geometric mean T2 cutoff of 12.5 ms for the Mina El Carmen formation (upper section).

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Spectral bound fluid coefficients for the Comodoro Rivadavia and Mina El Carmen formations (upper section) were calculated to be 0.0687 and 0.0806, respectively. The geometric mean T2 cutoffs and spectral bound fluid coefficients were then used to calculate the bound fluid volume (BVI), using the

maximum of either T2 cutoff or spectral bound fluid volume. Permeability is computed from the NMR log data using NMR effective porosity (MPHI), BVI (bound fluid volume), and moveable fluid volume. Several variations on the Coates permeability equation were analyzed with three of the variations providing the best fit to laboratory core permeability. For the Comodoro Rivadavia formation, the following relationships were used to estimate permeability: Coates I K = (MPHI/9.088)4 * (FFI/BVI)2 Coates III K = (MPHI/13.27)3.988 * (FFI/BVI)2 For the Mina El Carmen formation (upper section), the following relationship was used to estimate permeability: Coates III K = (MPHI/12.57)8.548 * (FFI/BVI)2 Where K is permeability in units of millidarcies, MPHI is effective porosity from NMR, BVI is bulk volume irreducible (bound fluid volume), and FFI is free fluid index (moveable fluid volume). Figure 4, Figure 5, and Figure 6 show the laboratory correlation between Klinkenberg core permeability and MPERM (NMR log perm from Coates I and III).

Figure 4 – Klinkenberg core permeability to MPERM Coates I for the Comodoro Rivadavia formation.

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Figure 5 – Klinkenberg core permeability to MPERM Coates III for the Comodoro Rivadavia formation.

Figure 6 – Klinkenberg core permeability to MPERM Coates III for the Mina El Carmen formation. USE OF 2D NMR FOR METHANE PREDICTION A 2D fluid characterization is applied as a NMR-only fluids model designed for evaluating NMR well data with 2D inversion of simultaneous T1-T2 data. The primary application of this method is methane identification and quantification in either water-based or oil-based mud systems. The resulting outputs can be used in combination with conventional logs to enhance and improve log interpretation of gas (methane) bearing reservoirs. The primary output is a 2D map of T1-T2 distribution. Petrophysical information is extracted from the 2D map, including gas volume and liquid volume for water-based mud environments, and gas volume, oil volume, and water volume for oil-based mud environments. Individual T1 and T2 distributions for each fluid type are also computed from the 2D map. Fluids may be predicted within the 2D map, based on their physical properties at reservoir conditions and from characteristics of the NMR acquisition sequence, enabling fluid regions to be created within the 2D map. Individual fluid T1 and T2 distributions result from the

signal detected in the individual fluid regions. The fluid volumes are the sums of the signal in the individual fluid regions, which are also the sums of the individual fluid distribution. NMR acquisition sequences or activation-sets specifically designed for T1-T2 data acquisition are used for 2D analysis and computations. Six to eight wait times (Tw) are typical, with long wait times (Tw) of ≥ 8 seconds and several hundred echoes. Activation-sets are designed to maximize logging speed and provide an efficient method for acquiring T1-T2 NMR data that is appropriate for processing 2D NMR. COMPARISON OF NMR ANALYSIS TO PRODUCTION RESULTS Three wells will be considered in this paper from the San Jorge Gulf Basin of Argentina. The wells penetrate the Comodoro Rivadavia and Mina El Carmen formations and were drilled with water-based mud systems. Derived bound fluid cutoffs, spectral coefficients from NMR core data, and permeability correlations were applied. The primary formation evaluation objectives of NMR for these wells were to provide total fluid filled porosity, fractional porosities for bound and moveable fluid, a continuous permeability estimate, and near wellbore fluid saturations. The same NMR T1-T2 acquisition sequence was used on the three wells. The challenges include hole conditions (washouts), as well as methane identification and quantification with the potential of significant filtrate invasion. WELL #1: The logging suite in well #1 contained four arm caliper, resistivity, density, and magnetic resonance. Three reservoir units of the Mina El Carmen formation, shown in Figure 7, will be referred to as units one through three, with decreasing depth. From the 2D NMR analysis, all three reservoirs contain varying amounts of hydrocarbons. In Figure 7, Track 1 shows PE, SP, and the maximum hole size from the four arm caliper; Track 2 shows the resistivites and the permeability estimate from NMR using the Coats III relationship. Track 3 includes the NMR field porosity, as well as the fractional NMR porosities, micro, capillary bound, and moveable fluid (shaded green, gray and yellow respectively). Gas corrected NMR porosity, DMRP calculated from bulk density and NMR, shaded down to T1PTOT, which is NMR T1 field porosity are also presented. Track 4 displays the T2 distribution, and Track 5 contains the T1

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distribution. Track 6 displays the methane T1 distribution computed from the methane partition of the 2D NMR map, and similarly the oil T1 distribution and water T1 distribution in Track 7 and Track 8, respectively. Track 9 displays volumetric computations from the two dimensional NMR analysis, where shading indicates methane volume as red, oil volume as green, and water volume as blue. Micro porosity is shaded green, starting at the axis, and capillary bound water volume is shaded gray. Figure 8 shows the results of 2D NMR analysis for reservoir unit 1. The 2D map is located in the upper-left corner with diagonal lines representing T1-T2apparent ratios from 1 to 100, ascending to the upper-left. Methane is identifiable in the 2D map with a high T1 value and a short T2apparent value. Bound fluids occupy the portion of the 2D map with short T1 and T2apparent values and moveable water is identifiable from its longer T2apparent values and low T1-T2 ratio. The remainder of the 2D map is considered to be liquid hydrocarbons-oil. A strong methane signal is observable in the upper-middle portion of the map, with bound fluid in the lower-left portion and a small oil signal in the upper-right corner. From the 2D map, one dimensional distribution of T1 and T2 are exhumed and displayed in the lower left and upper right portions of Figure 8. Volumetric computations from the 2D map are displayed in the lower right portion of the figure for water, oil, and gas (methane). In Well #1, three reservoir units were evaluated (Figure 8, Figure 9, and Figure 10), with unit 1 containing the highest near wellbore gas saturation measured by NMR. Unit 3 has the lowest predicted gas saturation from 2D NMR and may be more invaded by water-based mud filtrate than the other two units. The volumetric calculations in Figure 8, Figure 9, and Figure 10 represent NMR flushed zone analysis. As with all current NMR wireline technology, the depth of investigation is ≤ 4.5 inches. With light hydrocarbons, such as methane, the hydrogen index will affect the NMR moveable fluid volume estimate and, in turn, true

total porosity. Assuring an adequate polarization time should also be considered during job pre-planning when working with NMR in methane-bearing reservoirs (Bonnie et al., 2001). Job pre-planning work for this logging activation set took into consideration the longest wait time (Tw) for the activation set used (8 sec) and reservoir temperature and pressure, indicating a 90% polarization of any methane signal. The calculated hydrogen index for methane at reservoir conditions is 0.28. Although not presented, both hydrogen index and polarization corrections can be applied to the methane signal from the 2D processing to estimate true formation porosity. Log analysis NMR results for Well #1 are shown in Table 1 and are in good agreement with production results (Table 2). Production results were obtained during well swabbing operations before fracturing operations and after fracturing. Units 1, 2, and 3 were commingled during production tests with pre-fracturing treatment results of 600 liters/hour of water. Post-fracturing swab tests for production resulted in 265,000 m3/day of gas with a dynamic pressure of 1,090 psi. Samples were subject to laboratory gas chromatography, finding greater than 92% molar composition of methane with very small amounts of ethane and propane (Table 3).

Table 1: Well #1 NMR Prediction Results Depth

(m) ØNMR (pu)

KNMR (md)

Gas Sat (dec)

Oil Sat

(dec)

Water Sat

(dec) UNIT 1 X699.5 – X704.5

11.98 2.5 (ave)

0.197 0.117 0.686

UNIT 2 X691.5 – X697.5

15.15 2 (ave)

0.146 0.294 0.560

UNIT 3 X660 – X665

12.10 1.5 (ave)

0.135 0.204 0.661

Table 2: Well #1 Production Results (Swabbing)

Depth (m)

Press. (psi)

Pre-Frac Production

(l/hr)

Post-Frac Production

(l/hr) UNIT 1 X699.5 – X704.5

4,253

UNIT 2 X691.5 – X697.5

N/A

UNIT 3 X660 – X665

N/A

Water 600 l/hr

Gas: 265,000 m3/day

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Figure 7 –Well #1, three reservoir units of the Mina El Carmen formation; conventional data and 2D NMR analysis.

Figure 8 –Well #1, 2D NMR analysis for gas reservoir unit 1.

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Figure 9 –Well #1, 2D NMR analysis for gas reservoir unit 2.

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Figure 10 –Well #1, 2D NMR analysis for gas reservoir unit 3

Table 3: Well #1 Gas Chromatography Composition Molar %

CO2 0.6880 N2 1.0840

C1 (CH4) 92.3512 C2 (C2H6) 3.4768 C3 (C3H4) 1.3474

iC4 (C4H10) 0.2111 nC4 (C4H10) 0.3971 iC5 (C5H12) 0.0862 nC5 (C5H12) 0.1354 nC6 (C6H14) 0.1013 nC7 (C7H16) 0.0712 nC8 (C8H18) 0.0503

Total 100.0000 Well #2: The logging suite for Well #2 contained gamma ray, four arm caliper, resistivity, and

magnetic resonance. Magnetic resonance was used as the primary and sole porosity measurement in Well #2. Figure 7 shows several reservoir units of the Mina El Carmen formation; these units will be referred to as units one through six, with decreasing depth. Figure 11 shows the 2D NMR maps of the reservoir units, along with NMR volumetric computations. The NMR porosity values average 11 to 13 pu in each of the six reservoir units, with average permeability estimates of approximately 6mD. NMR computed volumes of gas, oil, and water are easily recognized from the 2D analysis in Figure 11, far right track. Reservoir unit 6 demonstrates a wet reservoir unit, containing mostly water and a computed gas saturation of ~6% (Table 4). Water is easily identified in the 2D map (Figure 11, top map for unit 6) from its position in the map, with a low T1-T2 ratio. Reservoir unit #3 is primarily an oil-bearing zone, stratigraphically positioned between several gas bearing reservoirs with a computed oil saturation of 41%. Methane is also present in reservoir unit #3, which is reflected as the more producible fluid phase (Table 5). Computed 2D NMR gas saturations for reservoir unit 1 is 28% and significantly less in reservoir unit 2 at 16%, possibly reflecting greater mud filtrate invasion in unit 2.


(m) ØNMR (pu)

KNMR (md)

Gas Sat (dec)

Oil Sat (dec)

Water Sat (dec)

UNIT 1 X538.5 – X542.5

14.80 10 (ave)

0.278 0.385 0.338

UNIT 2 X512.5 – X516.0

11.82 5 (ave)

0.160 0.187 0.653

UNIT 3 X494.0 – X496.0

14.33 10 (ave)

0.174 0.409 0.417

UNIT 4 X485 – X486.3

7.98 2 (ave)

0.308 0.241 0.451

UNIT 5 X467-X470

14.63 8 (ave)

0.303 0.114 0.584

UNIT 6 X947-X952

18.00 10 (ave)

0.064 0.071 0.865

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Figure 11 –Well #2, 2D NMR analysis for reservoir units 1 through 6. Swabbing was performed on the individual reservoir zones shown in Table 5. The duration for swabbing varies and may be as high as 20 hours or more, with flow rates exceeding 1500 liters/hour.

Formation tester pressures were taken on the final log run in the well and are included in Table 5. Reservoir units 1 and 2, 3 and 4 were commingled during both pre-fracture treatment testing and post-fracture

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treatment testing. Units 5 and 6 were tested and produced separately. NMR analysis predictions and post-fracturing test results are in good agreement.

Table 5: Well #2 Production Results (Swabbing) Depth

(m) Pressure

(psi) Pre-Frac

Production (l/hr)

Pre-Frac Production

(l/hr) UNIT 1 X538.5 – X542.5

3,416

UNIT 2 X512.5 – X516.0

3,531 Water 400

l/hr

Injected Fracture Fluid

with Gas

UNIT 3 X494.0 – X496.0

3,295

UNIT 4 X485 – X486.3

Water 90

l/hr

Injected Fracture Fluid

with Gas

UNIT 5 X467-X470

3,067 480 l/hr Water with

Gas in abundance

n/a

UNIT 6 X947-X952

2,417 480 l/hr Water

n/a

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Well #3: Well #3 was drilled and logged under similar circumstances as Well #2. Four log intervals are considered for this paper, with reservoir unit 1 containing gas and unit 2 containing oil, unit 3 and 4 are tight formations containing gas (Figure 14). The 2D NMR maps of the reservoir units are displayed for the corresponding reservoir intervals (Figure 12, 13, 14, and 15).

Figure 12 – Well #3, 2D NMR analysis for gas bearing reservoir unit 1.

Reservoir units of the Mina El Carmen formation are often inter-layered with gas-bearing and oil-bearing units. Oil viscosities vary considerably from hundreds of centipose to single centipose and poses significant evaluation challenges (Fam et al., 2007).

Figure 13 – Well #3, 2D NMR analysis for oil-bearing reservoir unit 2.

An analysis of reservoir unit 2 indicates oil, a small amount of water, and very little/no gas. A bimodal distribution distinguishes the native oil and water in the 2D map (Figure 13), because a NMR response is sensitive to fluid viscosities (Coates et al., 1999). This particular 2D fluid characterization method is insensitive to subtle viscosity contrasts, which requires a more sophisticated acquisition and processing method. Production results from pre-fracture treatment swabbing tests resulted in 100,000 m3/day gas production with a dynamic pressure of 1,520 psi from reservoir unit 1. During the pre-fracture testing of reservoir unit 2, flow rates were 1200 l/hr producing oil with a 20% water cut. Units 3 and 4 are tight porosity formations, and identified solely by the NMR log. Post fracture testing results contained injected fracture fluid and gas. Because the production results recorded no water production, the NMR predicted water saturations reflect the amount of water-based mud invasion. Even with a significant mud filtrate invasion, 2D NMR analysis predictions are in good agreement with the types of fluids identified from production tests (Table 6 and Table 7).

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Figure 14 –Well #3, 2D NMR analysis for reservoir units 1,2, 3 and 4.


(m) ØNMR (pu)

KNMR (md)

Gas Sat (dec)

Oil Sat (dec)

Water Sat

(dec) UNIT 1 X565-X572

12.75 4 (ave)

0.276 0.113 0.611

UNIT 2 X320-X324

15.94 3 (ave)

0.062 0.269 0.669

UNIT 3 X660-X662

9 0.1 ave

0.110 0.229 0.661

UNIT 4 X650-X655

10 0.2 ave

0.114 0.174 0.172

Table 7: Well #3 Production Results Depth

(m) Press. (psi)

Pre-Frac Production (l/hr)

Post-Frac Production

(l/hr) UNIT 1 X565-X572

3,634 Gas: 100,000 m3/day

n/a

UNIT 2 X320-X324

3,431 Oil 1200 l/hr 20% water cut

n/a

UNIT 3 X660-X662

n/a

UNIT 4 X650-X655

n/a Dry

Injected Fracture

Fluid with Gas

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Figure 15 –Well #3, 2D NMR analysis for tight gas reservoir unit 3

Figure 16 –Well #3, 2D NMR analysis for tight gas reservoir unit 4

CONCLUSIONS NMR is now logged successfully for in-field well formation evaluation, often as a sole porosity and fluid identification source in the San Jorge Gulf Basin. With good agreement to well production results, 2D NMR analysis is now used routinely for identification and quantification of gas saturation and volume when evaluating and characterizing Mina El Carmen reservoirs with wide range of petrophysical characteristics.

ACKNOWLEDGEMENTS The authors wish to thank the management of Pan American Energy for the release of this data. Thanks are also due to the many contributors to the global NMR knowledge base who have improved reservoir understanding through NMR. REFERENCES Acuña, C., Gonzalez, G., Bonzani, H. & Lopez, E. 2003. La utilización de la resonancia magnética en la detección de petróleo en la cuenca del Golfo San Jorge. Paper presented at II Congreso de Hidrocarburos 2003 held in Buenos Aires, Argentina, 29 June- 2 July. Bonnie, R.J.M., Marschall, D.M., Fam, M.Y. and Siess, C.P. 2001. Advanced forward modeling helps planning and interpretation NMR logs. Paper SPE 71735 presented at the 2001 SPE Annual Technical Conference and Exhibition in New Orleans, Louisiana, 30 September-3 October 2001. Coates, G.R., Miller, G.R., Gillen, M., Henderson, G. 1991. The MRIL in Conoco 33-1 An investigation of a new magnetic resonance imaging log. Paper presented at the 32nd Annual Logging Symposium, SPWLA, Midland, Texas, 16-19 June. Coates, G.R. and Denoo, S. 1981. The producibilty answer product. Schlumberger Technical Review, 29, No.2, 55. Coates G.R., Xiao, L., and Prammer, M. 1999. NMR logging principles & applications, Halliburton Energy Services. Coates, G.R., Marschall, D.M., Mardon, M., and Galford, J. 1997. A new characterization of bulk-volume irreducible using magnetic resonance, paper QQ, 38th Annual SPWLA Logging Symposium Transactions, 14 p. Also published in 1997 in DiaLog, London Petrophysical Society, v. 5, no. 6: 9–16. Fam, M.Y. and Vieiro, J.A. 2007. Characterizing the complex shaly sand oil reservoirs in San Jorge Basin, Argentina, using advanced magnetic resonance technique. Paper SPE 107447 presented at the 2007 SPE Latin American and Caribbean Petroleum Engineering Conference in Buenos Aires, Argentina, 15-18 April, 2007.

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Fitzgerald, M., et al. 1990. Evolution of the San Jorge Basin, Argentina. AAPG Bulletin, V74, 879-920 p. González, M., Taboada, R. and Stinco, L. 2002. Los reservorios del Flanco Norte. V Congreso de Exploración y Desarrollo de Hidrocarburos, Mar del Plata, Argentina. Lesta, P.J. 1968. Estratigrafía de la cuenca del Golfo San Jorge. Actas III Jornadas Geológicas Argentinas, Buenos Aires, I: 251-289. Miller, M.N., Paltiel, Z., Gillen, M.E., Granot, J., and Bouton, J.C. 1990. Spin echo magnetic resonance logging: porosity and free fluid index determination. Paper SPE 20561 presented at the 65th Annual Technical Conference and Exhibition, Society of Petroleum Engineers, New Orleans, Louisiana, USA. 23-26 September, 1990. Prammer, M.G. 1994. NMR pore size distributions and permeability at the wellsite. Paper SPE 28368 presented at the 1994 SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, USA, 28-28 September. Salomone G., Biocca M., Amoroso A., Arocena J., Ronanduano., Guerberoff D., and Palacio L. 2002. Los reservorios del Flanco Sur. V Congreso de Exploración y desarrollo de Hidrocarburos, Mar del Plata. Sylwan C. 2001. Geology of the Golfo San Jorge Basin, Argentina. Journal of Iberian Geology, 27, 123-157. ABOUT THE AUTHORS

Carolina Droeven holds a geology degree from University of Argentina. Carolina has more than 13 years of experience in the oil industry and is currently working for Pan American Energy LLC as a sr. geologist within the Reserves Development department for San Jorge Basin Operations in Argentina.

Carlos Acuna holds a geology degree from University of Argentina. Carlos has more than 20 years of experience in the oil industry and is currently working for Pan American Energy LLC as a geologist leader within the Reserves Development department for San Jorge Basin Operations in Argentina.

Emiliano Lopez is the technical advisor for Halliburton Argentina, Wireline and Perforating product service line. With more of 12 years of experience in the oil industry, he has held several technical positions within Halliburton Energy Services. Emiliano is member of the SPWLA and SPE. Shriram Sarvotham is a senior research scientist for Halliburton. Shriram joined Halliburton in November, 2007 and his primary research interests lie in NMR data acqusition and processing. Shriram holds PhD and MS degrees in electrical engineering from Rice University (Houston, Texas) and B.Tech degree in electrical engineering from Indian Institute of Technology, Madras, India. Ron Balliet is the global NMR product champion for Halliburton. Ron 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. Ron holds BS degrees in geology and geophysics from the University of Minnesota (1984). He is a member of the Society of Professional Well Log Analysts and the Society of Petroleum Engineers.

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