APPLICATIONS OF NMR MEASUREMENTS FOR PETROPHYSICAL EVALUATION OF LOW-RESISTIVITY PAY ZONES

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1 APPLICATIONS OF NMR MEASUREMENTS FOR PETROPHYSICAL EVALUATION OF LOW-RESISTIVITY PAY ZONES Gary M. Ostroff* and David S. Shorey, Baker Atlas, Division of Baker Hughes, Houston, Texas USA Introduction The identification and petrophysical quantification of low-resistivity pay has historically been one of our most serious formation evaluation challenges. The causes of low-resistivity pay include: Thinly bedded sand-shale sequences with reservoir bed thickness less than the vertical resolution of the resistivity tool. The tool measurement volumetrically averages sand and shale conductivities, resulting in resistivity bias to the high conductivity shale beds. Thus the computed apparent high bulk water saturations are not representative of the reservoir layers. Low resistivity reflects high capillary-bound (or irreducible) water resulting from small pores, typically associated with fine grained rock fabrics. The computed apparent high water saturations may be accurate, but the water is entirely immobile and the reservoir will produce hydrocarbons without any water-cut. Dispersed clay provides an additional conductive pathway through the pore system, adding conductivity (reducing resistivity) via cationic exchange mechanism, and increasing the pore surface area and irreducible film water. Electrical properties of reservoir can be severely affected, and if not properly accounted for, the computed apparent water saturation values will be too high. Apparent high water saturation values, typically computed for low-resistivity pay zones, can obscure the true hydrocarbon potential and result in commercially viable productive intervals being bypassed. Pore volumetrics, rock volumetric, permeability, and electrical properties data derived from nuclear magnetic resonance (NMR) logs can significantly improve our ability to identify and evaluate these low-resistivity reservoirs. These data, combined with conventional porosity and resistivity data provide more robust water saturations and more complete reservoir descriptions for improved accuracy of reserve estimates and productivity predictions, while minimizing the possibility of bypassed pay. Pore volumetrics Unlike conventional porosity measurements (neutron, density and acoustic), NMR porosity is lithology independent. Additionally, analysis of the T 2 distribution, derived from inversion of NMR echo measurements provides a subdivision of the NMR total porosity into volumetric components of clay bound water (CBW), capillary-bound water (BVI) and free-fluid or bulk volume moveable (BVM). The free-fluid volume (BVM) is equal to the hydrocarbon storage capacity of the reservoir and stems exclusively from the reservoir layers as illustrated at right in figure 1. When hydrocarbon-bearing reservoirs are at Vertical irreducible water saturation, the measured BVM Tool is equal to the hydrocarbon pore volume of the Resolution Sand Shale T 2 Cutoff reservoirs. In thinly bedded, laminated BVI BVM sequences, where resistivity-based water saturation analysis is problematic, the NMR T 2 [ms] free-fluid volume (BVM) can be integrated over the sequence to quantify the total hydrocarbon pore volume independent of resistivity. Figure 1. NMR tool response in laminated sequence Incremental

2 High capillary bound (irreducible) water is a frequent cause of low resistivity in many hydrocarbon-bearing shaly-sand reservoirs that exhibit water free production. The integration of NMR-derived capillary bound water data with resistivity-derived water data provides a means to identify low resistivity pay. Moveable water in the formation can be quantified from comparison of resistivity-derived water saturation (Sw) with NMR-derived capillary-bound water saturation (Swir). Water-free production is predicted when Sw equals Swir. Conversely, when Sw is greater than Swir, water-cut may be expected. Rock Resistivity & Fluid Saturation Pore T2 Spectra GR, SP & Caliper Resistivity Neu. & Den. Porosity Fluid Saturations Pore G/W Contact? O/W Contact? Transition Zones? O/W Contact? Figure 2. Conventional Log Analysis In the log example of Figure 2, dramatic variations in resistivity and computed Sw suggest segregated reservoir units, multiple hydrocarbonwater contacts and transition zones based strictly on conventional log interpretation. After integration of the NMR log data (Figure 3), only one hydrocarbon-water contact is interpreted. Above a short transition zone, the reservoir is at an irreducible water saturation (Swir) condition. Variations in the magnitude of Swir (as a function of pore size and grain size) explain the observed anomalies in the resistivity response. In the log example of Figure 4, the bottom interval showing very low resistivities was originally not considered to meet net pay thresholds based on a conventional log analysis. An integrated NMR-resistivity interpretation subsequently confirmed that the very low resistivities observed in the bottom interval (which tested water-free at ) were simply due to the presence of high capillarybound (immobile) water, reflecting the very finegrained fabric of this prolific sandstone reservoir. Figure 3. Fully Integrated Log Analysis with NMR 0 GR (api) Rxo SP (mv) Medium Resistivity Deep Resistivity 20 2 (md) Density Porosity 0 60 NMR Effective Porosity 0 60 Neutron Porosity 0 Free Water Water-Free Hydrocarbons Immobile Water Figure 4. Low-Resistivity Water-Free Production

3 NMR APPLICATIONS FOR PETROPHYSICAL EVALUATION OF LOW-RESISTIVITY PAY MPHE (%) MPHE (%) Shale & sand volumetric distribution Dispersed Laminated NMR-derived clay bound water volume (CBW) Pore Filling can be used to compute bulk shale volume (Vsh) from Vsh = CBW CBWsh. Vsh can be further subdivided in dispersed, structural and laminar Grain Replacing components based on the rate of NMR porosity reduction as a function of bulk shale volume (Vsh). The NMR effective porosity (MPHE) Vsh (frac.) Vsh (frac.) versus Vsh cross plots (Figure 5) display two distinctly different shaly sand systems, one Figure 5. Shale Distribution Plots where the bulk shale volume is dominated by dispersed clay in the pore system and the other by true laminar shales. If grain size is the dominant control on pore size, the bulk sand grain volume can also be subdivided into a fine and coarse component based on the BVI:BVM ratio. The rock volumetrics shown in Figures 3 and 6 combine the matrix and pore volumetrics from NMR-based models. Coates -Timur Model : The Coates-Timur model is used to compute permeability from NMR pore volumetric data in hydrocarbon-bearing NMR a b reservoirs. This model incorporates the NMR-derived BVM = BVI C effective porosity and the BVM:BVI ratio to compute permeability. In the absence of core or formation test-derived permeability calibration data, the model parameters (C = 10, a = 4, and b = 2) are applied to produce a permeability index. In thin-bedded, laminated sand-shale sequences, the apparent NMR-derived bulk permeability computed from bulk NMR-porosity data can be orders of magnitude too low with respect to the true permeability of the actual sand laminations. If the re-constructed sand lamination pore volumes are substituted in the CoatesTimur model, a re-construction of the sand lamination permeability can also be made (figure 6). k φ In a thinly bedded sand-shale T Formation Resistivity & Sand Lam. Effective Bulk Pore Perf s trics Spectra sequence, sand lamination pore Pore trics trics volumetrics and permeability can be Original Perfs re-constructed at the tool s vertical resolution if the laminar shale volume and NMR properties are known. The log example of Figure 6 shows the re800 constructed sand lamination reservoir properties for a highly laminated progradational sand-shale sequence. Added Perfs Note that the computed bulk reservoir properties appear to degrade with depth in response to decreasing net to gross ratio, whereas the true reservoir porosity (track 4) and permeability 733 (track 3 red shading) exhibit no such degradation. The combined NMRFigure 6. Highly Laminated Example resistivity analysis indicates the entire sequence to be at an irreducible water saturation. The original completion of the upper interval produced water free at a rate of 800. The subsequent completion of the lower laminated interval (which based on conventional logs, was not considered to meet net pay thresholds) added 733 of water-free production, confirming productivity as predicted from the combined NMR and resistivity-based interpretation. 2

4 Electrical properties and water saturation The clay electrical conductance parameter Qv (cationic exchange capacity per unit pore volume), used in the Waxman-Smits water saturation model (figure 8), can be determined from NMR-derived clay bound water saturation (Swb) using the Hill, Shirley and Klein model (1979). This model (shown in figure 7) relates Qv to Swb (CBW total ) and the flushed zone water salinity (C o ) in equivalents/liter. Through the integration of NMR-derived total porosity ( total ), Qv, bulk shale volume (Vsh), and resistivity data, continuous variable intrinsic cementation exponent (m*) values can be computed. Apparent intrinsic cementation exponent (m* a ) is computed through substitution of log-measured Rt for Ro in Waxman-Smits formation resistivity factor model and cross plotted versus Vsh. A regression through the water-bearing trend establishes a model for a Vshdependent variable m*, used to NMR total Swb Q V = NMR Swir ( C compute a variable in-situ value NMR CBW o ) for the intrinsic formation resistivity factor (F*). Cementation Exponent Saturation Exponent Intrinsic saturation exponent (n*), used in the Waxman- Smits water saturation model (Figure 8) can be determined from a cross plot of apparent intrinsic resistivity index (RI* a ) vs. NMR-derived irreducible water saturation (Swir). Apparent RI* is derived from the Waxman-Smits water saturation model using the above computed F* with substitution of Swir for Sw. The log 10 of apparent RI* values are cross-plotted versus the log 10 of Swir, and the slope of the regression fit to the trend representing zones at an irreducible water saturation is equal to the average in-situ value for n*. m* a Figure 7. Electrical Properties Models Using NMR Data W axm an-sm its M odel: C t Apparent m* vs. Vsh Hydrocarbon - Bearing Zones 100% Water Trend Vsh C = w F* S w n* Apparent RI* vs. Swir Saturation Exponent (n*) = slope = B Q v + Figure 8. NMR Enhancement of Waxman-Smits Model Conclusions We have summarized an interpretation package for identification and evaluation of low-resistivity pay using NMR log data which permits one to capitalize on the pore volumetrics, rock volumetrics, permeability and electrical properties data derived from NMR measurements. These data, combined with conventional porosity and resistivity data provide more robust water saturations and more complete reservoir descriptions and improve the accuracy of reserve estimates and productivity predictions while minimizing the possibility of bypassed pay. References Ostroff,G.M., Shorey,D.S., and Georgi,D.T., Integration of NMR and Conventional Log Data for Improved Petrophysical Evaluation of Shaly Sands, 40 th SPWLA Transactions, Paper OOO, May RI* a F* Swir Trend Water -Bearing Zones Swir Elem ents of the m odel enhanced from NM R data S w

5 ABOUT THE PRESENTER Gary M. Ostroff is Petrophysical Advisor for Baker Atlas in their Houston-based GeoScience Division. He is currently working in coordination with Baker Atlas Technology on NMR log Interpretation Development. Gary has a B.Sc. in Geology, and has authored/co-authored several papers on NMR technology and core-log integration. His broad international experience spans 19 years as a formation evaluation geologist for Esso Exploration and Conoco International Exploration, as Director of Reservoir Studies for Core Laboratories, and as an independent Petrophysical Consultant prior to joining Baker Atlas in 1997.

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