2 Regional Geological Setting

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1 Are We Underestimating Our Resources? Increased Original Gas-In-Place (OGIP) as a Result of Volumetric Analysis of RECON HDD TM Well Logs; Cardium Formation Alberta Volumetric estimation provides the basis for determining the Original Gas-In-Place (OGIP) of a prospect prior to flow testing and production. The OGIP determines the feasibility of a project and gives operators a starting point in order to characterize their prospects in terms of risk. Accurate calculations of OGIP give operators the confidence to move forward with development/investment and the degree of accuracy can have great implications for how a prospect is developed and exploited. The OGIP also has implications on the bookable reserves that a company can report and therefore their access to capital. Traditionally, the determination of OGIP is based on standard well log analysis, and the incorporation of core analysis and mapping to verify the properties and extent of the prospect. Based on previous core to log comparison work done by RECON, one can be confident that HDD TM well logs better represent the true porosity of the rock. Therefore, applying porosity cutoffs when doing volumetric interpretation to tight gas reservoirs, such as presented here, which have low effective porosities, is an ideal situation to use HDD TM log derived porosities in order to ensure better accuracy. RECON s HDD TM samples at 132 samples per meter, which is nearly four times greater than industry s standard (28-40 samples per meter) for high resolution logs, and sixteen times more than industry standard (8-10 samples per meter) main pass logs. RECON s standard non-hdd TM, main pass logging resolution is 33 samples per meter, equivalent to the industry standard for high resolution logs. Typical logging speed for industry standard main pass resolution logs is 9 meters per minute. RECON standard main pass logs, which are the equivalent of industry standard high resolution logs, are run at 9 meters per minute (industry high resolution logs are run at 4 to 5 meters per minute). In comparison, RECON HDD TM logs are run at ~7 meters per minute. These advantages mean more data for the same amount of rig time, equating to better reservoir understanding at no additional cost. RECON Petrotechnologies Ltd. developed the ability to sample at higher rates than previously seen in industry to allow for increased confidence in log derived porosity when compared to core, better thin bed resolution, and clearer interface boundary definition. This then results in an increase in volumetric OGIP using HDD TM logs compared to RECON standard main pass logs and other industry standard main pass logs. The following case study highlights the implications of higher sampling rates in order to more accurately characterize the rock and quantify the OGIP. Interpretation was done by hand as well as using RAW LAS Data files. This in turn leads to the question: Are we underestimating our resources? 1

2 Regional Geological Setting The Cardium Formation of Alberta is exposed along the Rocky Mountain Foothills and present beneath the Alberta Plains region. It is comprised of a terrigenous, muddy, sandy, and conglomeratic wedge deposited during the Late Cretaceous age. It lies along the western margin of the Alberta Foreland Basin. In the surface of the plains, the Cardium formation is encased in marine shales. The sediments were accumulated in muddy and sandy inner and outer shelves, beaches, lagoonal, barrier islands, tidal, estuarine and coastal plains. The deposits alternate between coarse- and fine-grained stages controlled by both autocyclic and allocyclic processes. The Cardium forms a large stratigraphic trap in its eastern shaleout, producing Canada s largest individual oil field Pembina. Accumulations of gas and liquids rich gas are consistently reported throughout the depositional area of the Formation. The thickness varies from 0 to over 134 meters throughout the extent. Volumetric Estimation Volumetric estimation is based on various geological inputs from core analysis, wireline log interpretation and geological mapping. It is termed the Geologists Method and is the fundamental starting point for any work with gas-in-place (GIP) and recoverable volumes. Volumetric estimation is the only available means to assess original hydrocarbons-in-place, prior to the acquisition of sufficient pressure and production information, in order to apply material balance and/or decline analysis. After pressure and production data have been collected for a reasonable period of history, volumetric estimation provides a valuable check on the estimates derived from material balance and decline analysis (Dean, L. 2007). Volumetric estimation is therefore the ideal solution in new field/pool and wildcat well situations. For the purpose of this case study we will be using the equation for OGIP for a typical gas reservoir: Gas-In-Place Calculation (Imperial): Gas Reservoirs OGIP (MMCF) = * A * H * Φ * Sg Bgi A = Area (Acres) H = Net Pay (ft) Φ = Porosity (fraction) Sg = Gas Saturation (fraction) Bgi = Initial Gas Volume Factor Bgi = (Psc * Ti * Zi) (Tsc * Pi) = 14.65*(Ti )*Zi (520*Pi) 2

3 Psc = Pressure at Surface (atmospheric = psi) Ti = Temperature Initial (Fahrenheit (Rankine)) Zi = Initial Compressibility (unitless) Tsc = Temperature at Surface (60 Fahrenheit (Rankine)) Pi = Pressure Initial (psi at reservoir) *All input values are in Imperial Area (A): Area or Drainage Area is the expected area of drainage for a particular well. In the case of original gas-in-place it may be a particular area for which volumes are being determined, based on a reasonable distance from the wellbore being evaluated. For the purpose of this case study, the area is one (1) section or 640 acres. This is based on the heterogeneous nature of the Cardium Sand in the study area. Net Pay (H): Net pay is the portion of the reservoir that can be produced at economical rates given a particular method of completion. Gross pay is the complete interval regardless of cutoffs whereas the net pay is the portion of the gross pay with a particular cutoff applied, determined by core analysis and comparative analysis of similar log signatures from productive reservoirs. These are determined based on the relationships between porosity and permeability, as well as water saturation and capillary pressure data from core. The cutoffs distinguish the portion of a reservoir deemed to be producible from those that are deemed non-productive. Due to the nature of the completion of this reservoir (perforate then frac or horizontal drilling and multi-stage frac ), the entire interval from top sand to base sand is recognized as the gross pay interval. Any portion of this zone exceeding 6% porosity cutoff is determined to be producible based on core analysis (to follow). Porosity (Φ): Porosity values are applied as an average over the entire Net Pay for the zone; in the case of this study they are applied as a weighted average over the Net Pay for the entire interval, top sand to base sand, for the portions exceeding the 6% porosity cutoff. Gas Saturation (Sg): Gas saturation is a function of the water saturation of the reservoir. The reservoir is being evaluated as a two phase system containing only water and gas for simplicity sake. Gas saturation is determined by the equation: Sg = (1-Sw) Historical data of production within the field suggest that water saturations between 25% and 35% are reasonable. For the OGIP calculations a mean value of 30% water saturation was used. 3

4 Initial Gas Volume Factor (Bgi): This factor is used to convert surface measured volumes to reservoir conditions and vice versa. The Bgi is a function of various inputs, many of which are considered constant for evaluation purposes. The three dependent factors are; Ti (Initial Temperature), which is determined from well logs and/or static gradients; Pi (Initial Pressure), which is virgin/initial reservoir pressure as determined by static gradients, build up tests and/or shut in tests; and Zi (Initial Compressibility), which is a function of pressure and temperature. All three variables were confirmed by the Operator for this case study. Porosity Cutoff Determination In order to determine porosity cutoffs for the Cardium Sand in the area of study two cores were evaluated; 00/ W6/0 and 00/ W6/0. Both cores were plotted porosity vs. permeability, one with all points included and one with only points with a Kmax above 0.1mD. Each was fitted with an exponential trend line to determine the effective permeability to gas porosity cutoff at a Kmax value of 0.1mD. Figures 1 and 2 are the data points for 7-14 and 4-13 respectively with no Kmax cutoff applied. The porosity cutoff values attained by extrapolating an exponential line of best fit for the data points and the corresponding porosity at 0.1mD are 6.8% (7-14) and 6.5% (4-13). The average porosity cutoff for the 2 wells is 6.65% W6 No Kmax Cutoff W6 No Kmax Cutoff Kmax (md) 0.1 Kmax (md) Kmax 0.1 Expon. (Kmax) Kmax Expon. (Kmax) Porosity Porosity Figure 1. Figure 2. Figures 1 and 2. Permeability vs. Porosity for the 7-14 and 4-13 wells with no Kmax cutoff applied. Figures 3 and 4 are the data points for 7-14 and 4-13 respectively with a Kmax cutoff of 0.1 md applied. The porosity cutoff values attained by extrapolating an exponential line of best fit for the data points and the corresponding porosity at 0.1mD are 4.6% (7-14) and 6.5% (4-13). The average porosity cutoff for the 2 wells is 5.55%. 4

5 W6 0.1 Kmax Cutoff W6 0.1 Kmax Cutoff Kmax (md) 0.1 Kmax (md) Kmax 0.1 Expon. (Kmax) Kmax Expon. (Kmax) Porosity Porosity Figure 3. Figure 4. Figures 3 and 4. Permeability vs. Porosity for the 7-14 and 4-13 wells with a Kmax cutoff of 0.1 md applied. In order to remain conservative when doing the volumetric estimation for the four wells in the case study a porosity cutoff of 6% was used based on the preceding data. Volumetrics by Interpretation Due to the nature of the completion strategy for wells such as those in the examples, the volumetric estimates are based on the entire interval from top sand to base sand (within reasonable limits). Given this, all reservoir rock that exceeded the 6% porosity cutoff is considered pay. The intervals were defined with a top and base. Net pay was measured along with the average porosity for each interval. The values were then converted to a weighted average for the well. Each well was evaluated by hand using both the standard main pass (33 samples per meter) data (1:240 scale stretched over a 1:60 scale) and the HDD TM pass (132 samples per meter) data (1:60 scale). Due to the density of the samples in the main pass data (33 samples per meter) the data needs to be filtered in order to display it at a comparable scale to that of industry standard 1:240 logs. This filtering essentially converts the data to a 15 samples per meter data set, still more detailed than industry standard 8-10 samples per meter data. The purpose of this is to illustrate, to the best of our ability, what the comparison would be like if industry standard main pass data was used. Each example shows the intervals chosen as pay (highlighted boxes) and summarizes the volumetric parameters, as well as the top pay zone and base pay zone. The following are the results of by hand log evaluation: 5

6 Gamma Ray 0 API SP MV 9 Density Porosity (SS) Shallow Resistivity 0.45 V/V Neutron Porosity (SS) 0.2 OHMM Medium Resistiivity OHMM 0.45 V/V Deep Resistivity PE 0.2 OHMM 0.0 BARNS/E :48.0 MD in M Non Reservoir Top m Net Pay = 12.6m (41ft) Avg Φ = 9.36 Sw = 30% Base m Figure 5. 00/ W6/0, RECON Main Pass (33 samples/meter) 1:240 scale stretched over 1:60 scale for comparison purposes, objective volumetric interpretation. 6

7 Gamma Ray 0 api SP mv 22 Density Porosity (SS) Shallow Resistivity Neutron Porosity (SS) Medium Resistiivity Deep Resistivity PE 0.0 b/e :48.0 MD in M Non Reservoir 1120 Top m Net Pay = 11.8m (39ft) Avg Φ = 9.80% Sw = 30% Base m Figure 6. 00/ W6/0, RECON HDD TM purposes, objective volumetric interpretation. Pass (132 samples/meter) 1:60 scale for comparison 7

8 Gamma Ray 0 API SP MV 30 Density Porosity (SS) Shallow Resistivity 0.45 V/V Neutron Porosity (SS) 0.2 OHMM Medium Resistiivity 0.45 V/V PE 0.2 OHMM Deep Resistivity 0.0 BARNS/E OHMM :48.0 MD in M Non Reservoir Top m Net Pay = 7.6m (25ft) Avg Φ = 8.42% Sw = 30% Base m Figure 7. 00/ W6/0, RECON Main Pass (33 samples/meter) 1:240 scale stretched over 1:60 scale for comparison purposes, objective volumetric interpretation. 8

9 Gamma Ray 0 api SP mv 32 Density Porosity (SS) Shallow Resistivity Neutron Porosity (SS) Medium Resistiivity Deep Resistivity PE 0.0 b/e :48.0 MD in M Non Reservoir Top m Net Pay =7.3m (24ft) Avg Φ = 9.12% Sw = 30% Base m Figure 8. 00/ W6/0, RECON HDD TM purposes, objective volumetric interpretation. Pass (132 samples/meter) 1:60 scale for comparison 9

10 Gamma Ray 0 api SP -72 mv 28 Density Porosity (SS) Shallow Resistivity Neutron Porosity (SS) Medium Resistiivity Deep Resistivity PE 0.0 b/e :48.0 MD in M Non Reservoir 1136 Top m Net Pay = 10.9m (36ft) Avg Φ = 8.10% Sw = 30% Base m Figure 9. 00/ W6/0, RECON Main Pass (33 samples/meter) 1:240 scale stretched over 1:60 scale for comparison purposes, objective volumetric interpretation. 10

11 Gamma Ray 0 api SP -76 mv 24 Density Porosity (SS) Shallow Resistivity Neutron Porosity (SS) Medium Resistiivity Deep Resistivity PE 0.0 b/e :48.0 MD in M Non Reservoir Top m Net Pay = 10.7m (35ft) Avg Φ = 9.03% Sw = 30% Base m Figure / W6/0, RECON HDD TM purposes, objective volumetric interpretation. Pass (132 samples/meter) 1:60 scale for comparison 11

12 Gamma Ray 0 api SP -78 mv 22 Density Porosity (SS) Shallow Resistivity Neutron Porosity (SS) Medium Resistiivity Deep Resistivity PE 0.0 b/e :48.0 MD in M Non Reservoir Top m Net Pay = 16.1m (53 Avg Φ = 8.30% Sw = 30% Base m Figure / W6/0, RECON Main Pass (33 samples/meter) 1:240 scale stretched over 1:60 scale for comparison purposes, objective volumetric interpretation. 12

13 Gamma Ray 0 api SP -74 mv 26 Density Porosity (SS) Shallow Resistivity Neutron Porosity (SS) Medium Resistiivity Deep Resistivity PE 0.0 b/e :48.0 MD in M Non Reservoir Top m Net Pay = 15.6m (51ft) Avg Φ = 9.47% Sw = 30% Base m Figure / W6/0, RECON HDD TM purposes, objective volumetric interpretation. Pass (132 samples/meter) 1:60 scale for comparison 13

14 Volumetrics from LAS In order to eliminate the bias that may be perceived by doing volumetric estimates by hand/interpretation the LAS File Data was used as a check in order to show that in fact the increased step rate/sampling rate does have an effect on the overall OGIP calculations. Due to the principles of area under a curve one can show that by decreasing the step rate (increasing sample rate) (Figure 13), which in turn creates more information under the curve, you increase the mathematical certainty of the calculated porosity values, making reserve estimates more accurate. RECON Main Pass 33 samples per meter RECON HDD TM 132 samples per meter Figure 13. Decreased step rate (increased sampling rate) translates into more accurate averaging of the area under a curve. The LAS files are the numerical data outputs, tied to depth, for each log curve that was run in the well logging program. In the case of RECON s main pass logs (33 samples per meter) there is an output of data points every meters (Step Rate). This means there is a reading generated with values for density porosity, neutron porosity, GR, shallow/medium/deep resistivity and so on, every 3.03 cm throughout the run. RECON HDD TM logs (132 samples per meter) generate a data set every meters (Step Rate) or less than every 1 cm throughout the run. Using raw data rather than log interpretation is a feasible means of evaluating reservoirs where cutoffs such as porosity and resistivity are normally applied, generally in both tight oil and gas reservoirs. Tops and bases of the intervals were determined from logs and then verified with the LAS data. In the case of the tops, the point at which you begin seeing the data values for the Neutron porosity (NPSS) and the Density porosity (DPSS) approaching, where the DPSS exceeded the 6% porosity cutoff. In some case the point at which crossover occurred was determined to be the top. The base was the point within the zone of interest where the DPSS fell below the 6% porosity cutoff. The data was then transferred into an excel spreadsheet where the Net Pay was determined for all intervals exceeding the 6% porosity cutoff for both the main pass and HDD TM LAS data, as well the average porosity for the entire Net Pay interval. 14

15 The following tables summarize the data obtained for each well via this method: 00/ W6/0 00/ W6/0 RECON Standard Log (33 sample/meter) RECON HDD TM Log (132 Samples/meter) RECON Standard Log (33 sample/meter) RECON HDD TM Log (132 Samples/meter) 394 Data Points 1575 Data Points 334 Data Points 1246 Data Points Net Pay (ft) Net Pay (ft) Por Avg Por Avg 9.48% 9.88% 8.00% 9.00% Figure 14. Reservoir parameters for the 9-30 well. Figure 15. Reservoir parameters for the 5-17 well. 00/ W6/0 00/ W6/0 RECON Standard Log (33 sample/meter) RECON HDD TM Log (132 Samples/meter) RECON Standard Log (33 sample/meter) RECON HDD TM Log (132 Samples/meter) 244 Data Points 928 Data Points 511 Data Points 2027 Data Points Net Pay (ft) Net Pay (ft) Por Avg Por Avg 8.69% 9.16% 8.74% 9.45% Figure 16. Reservoir parameters for the well. Figure 17. Reservoir parameters for the 6-23 well. OGIP Calculation In order to complete the gas-in-place calculation there are multiple parameters that need to be determined that cannot be acquired from the log data. More specifically these factors are the reservoir pressure, temperature, compressibility factor (Zi), and acreage. For simplicity of comparison purposes acreage of 640 acres was assumed in all the calculations. Parameters such as temperature and pressure were assigned to the wells in two groups: Group 1: Wells: 00/ W6/00, 00/ W6/00, 00/ W6/00 Reservoir Pressure: 1217 psi (8390 kpa) Reservoir Temperature: 99 o F (37 o C) Reservoir Acreage: 640 Acres Initial Compressibility (Zi): 0.81 Group 2: Well: 00/ W6/00 Reservoir Pressure: 1372 psi (9460 kpa) Reservoir Temperature: 102 o F (39 o C) Reservoir Acreage: 640 Acres Initial Compressibility (Zi):

16 Each of these parameters was confirmed by the Operator of the wells along with an independent analysis of available public data. The following table summarizes the results of the OGIP calculations for each well. Data is stated for the by hand log interpretations both main and HDD TM, as well as the data obtained by LAS file analysis. Volumetric Analysis RECON Standard Logs Vs. RECON HDD TM Logs RECON Standard Log (33 sample/meter) (Filtered) Log Interpreted RECON HDD TM Log (132 Samples/meter) RECON Standard Log (33 sample/meter) Raw LAS Data File RECON HDD TM Log (132 Samples/meter) Well Location 00/ W6/0 Net Pay (ft) Porosity (%) Sw (%) OGIP (MMCF/Sec) 7,149 7,120 6,887 7,178 Well Location 00/ W6/0 Net Pay (ft) Porosity (%) Sw (%) OGIP (MMCF/Sec) 3,921 4,077 3,885 3,925 Well Location 00/ W6/0 Net Pay (ft) Porosity (%) Sw (%) OGIP (MMCF/Sec) 5,432 5,888 4,918 5,197 Well Location 00/ W6/0 Net Pay (ft) Porosity (%) Sw (%) OGIP (MMCF/Sec) 9,422 10,344 9,547 10,120 * 6% Porosity Cutoff *Based on a single well analysis and does not account for lithology changes that may occur across the section of interest. Mapping may alter volumetric assignment to the entire section. Table 1. Resulting volumetric analysis parameters and calculated OGIP from by hand log interpretation and LAS data file analysis 16

17 For convenience the OGIP numbers have been combined into a single table highlighting the increase in OGIP calculated via both methods for all four wells used in this case study. Summary Table RECON Standard Logs Vs. RECON HDD TM Logs Log Interpreted Raw LAS Data File Well Location RECON Standard Log (33 Samples/meter) (Filtered) RECON HDDTM Log (132 Samples/meter) Percent Increase RECON Standard Log (33 Samples/meter) RECON HDDTM Log (132 Samples/meter) Percent Increase Original Gas-In-Place (OGIP) (MMCF/Sec) Original Gas-In-Place (OGIP) (MMCF/Sec) 00/ W6/0 7,149 7,120 0% 6,887 7,178 4% 00/ W6/0 3,921 4,077 4% 3,885 3,925 1% 00/ W6/0 5,432 5,888 8% 4,918 5,197 5% 00/ W6/0 9,422 10,344 9% 9,547 10,120 6% * 6% Porosity Cutoff Average Increase 5% Average Increase 4% *Based on a single well analysis and does not account for lithology changes that may occur across the section of interest. Mapping may alter volumetric assignment to the entire section. Figure 2. Summary Table of OGIP calculated by hand and with LAS Raw Data. Both methods show an increase in OGIP from RECON main pass logs to RECON HDD TM logs. Conclusions The OGIP for these four wells clearly identifies that there is a benefit/relationship to increased sampling rate, in order to determine more accurate reservoir parameters to incorporate into the OGIP calculation when doing standard well log volumetric analysis. The LAS data results are based on what the industry considers to be High Resolution logs (33 samples per meter) and what is now seen as Ultra High Resolution or HDD TM logs (132 samples per meter). A significant average increase over the four wells confirms the importance of more accurate log data. Although an attempt has been made to represent industry standard data in the by hand evaluation (33 samples per meter, filtered on to a 1:240 scale), it can be inferred that if log analysis were carried out on what industry is deeming to be true main pass log data (i.e samples per meter) there would be a greater average increase in OGIP than the 5% shown here. It can be suggested that an increase in OGIP of ~10% would not be improbable. However, given the relative inaccuracy of 8-10 samples per meter data, when compared to 17

18 33 and 132 samples per meter data, can one really trust the accuracy of industry main pass evaluations? Work continues in conjunction with this paper to better define the ability of HDD TM log data to correlate with porosity values and saturations obtained by standard core testing. Preliminary results continue to suggest a better correlation between HDD TM (132 samples per meter) log data and core than main pass (33 samples per meter) and core. HDD TM well logging services more accurately represent actual reservoir parameters with increased confidence. So the question is: Are You Underestimating Your Resources? Written By: Jarett Gough, P.Geol. Senior Technical Advisor, Recon Petrotechnologies Ltd. Acknowedgements: Shawn Lafleur Caltex Energy Inc. and the Caltex staff and Management for allowing RECON to use and publish the data herein and collaborating on the OGIP calculation parameters. James Ablett Technical Training Manager, Recon Petrotechnologies Ltd. for coordinating the collaboration with Caltex Energy Inc. Ron Krawchuck Reservoir and Production Services, Recon Petrotechnologies Ltd. for generating the figures used. For more information regarding RECON and their services, as well as case studies highlighting core vs log porosity comparison between main pass and HDD TM logs please visit: References: Lisa Dean, Fekete Associates Inc. Reservoir Engineering For Geologists, Part 3 Volumetric Estimation, Reservoir Issue 11, December 2007, Canadian Society of Petroleum Geologists 18