Figure 2-10: Seismic Well Ties for Correlation and Modelling. Table 2-2: Taglu Mapped Seismic Horizons

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GEOLOGY, GEOPHYSICS AND

PETROPHYSICS GEOPHYSICAL ANALYSIS

• Checkshot-corrected shale volume (V_sh) has excellent quadrature character tie to amplitude data

• Synthetic well ties were used for forward modelling and calibration

• V_sh well ties were used for general correlation and seismic stratigraphic geometry

• GR = gamma-ray R = 72% C10 A40 B00 C30 A00 P-03 Vsh Well Tie (checkshot corrected) A00 C10 A40 C30 B00

Velocity Density Impedance Vsh Synthetic Seismic

(m/s) (g/cm3₎ Time (s) HD /K B 2.4 2.3 2.2 2.1 2.0 1.9 Ti m e (s ) Ti me (s) 2.250 2.000 1.887 Trace Number 20 30 40 50

P-03 Synthetic Well Tie

V_shGR

Figure 2-10: Seismic Well Ties for Correlation and Modelling

For Taglu, five seismic horizons that correlated to key stratigraphic surfaces (A00, A40, B00, C10 and C30) within the reservoir interval were identified. They extend throughout most of the 3-D survey. All horizons were successfully correlated throughout the field fault block area. For the down thrown block north of the field, only the A00, A40 and B00 were correlated. Table 2-2 lists the horizon depths, seismic time and available velocity control data for each well.

Table 2-2: Taglu Mapped Seismic Horizons

Seismic Horizon A00 A40 B00 C10 C30 Available Velocity Data Well Name TWT

(msec) (TVD mSS)Depth (msec)TWT (TVD mSS)Depth (msec)TWT (TVD mSS)Depth (msec)TWT (TVD mSS)Depth (msec)TWT (TVD mSS) Sonic CheckshotDepth

C-42 2,171 2,849 2,211 2,920 2,265 3,027 2,374 3,207 2,422 3,329 Yes Yes

D-43 1,974 2,522 2,019 2,601 2,088 2,724 2,215 2,948 2,273 3,072 Yes Yes

D-55 2,336 3,155 2,399 3,299 2,464 3,049 N/A N/A N/A N/A Yes Yes

G-33 1,920 2,422 1,968 2,538 2,032 2,645 2,146 2,853 2,206 N/A Yes Yes

H-06 2,430 3,188 2,488 3,296 2,563 3,396 N/A N/A N/A N/A Yes Yes

H-54 1,940 2,446 1,986 2,531 N/A N/A 2,077 2,685 N/A N/A Yes Yes

P-03 1,995 2,577 2,053 2,661 2,115 2,773 2,248 3,013 2,309 3,139 Yes Yes

NOTE:

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2.2.3 SEISMIC MAPPING (cont’d)

Seismic time horizon maps were then constructed by tracking the reflection events between and away from the calibrated points at the well locations. Figure 2-11 shows an A Zone Taglu time structure map.

G-33 P-03

D-43

C-42 H-54

A40 Amplitude Anomaly Outline

Figure 2-11: Time Structure Map for the A40 Horizon 2.2.4 TIME TO DEPTH CONVERSION

2.2.4.1 Background

One of the major challenges of time-to-depth conversion at Taglu is the presence of a thick, variable permafrost layer down to a depth of about 500 m. The permafrost layer is generally a high-velocity zone, but significant melting has occurred near major waterbodies since the end of the last glacial period. The partial melting, or thermal degradation, of the permafrost has created pockets of near-surface low acoustic velocities, which can radically distort the shape of the seismic time surface relative to depth.

2.2.4.2 Amplitude Anomalies

At Taglu, additional information is available in the form of bright spots or amplitude anomalies (see Figure 2-12) that correlate to tested A pool gas. The gas–water contact of these reservoirs is tightly depth constrained by the C-42 well.

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G-33 C-42 D-43 P-03 Erosion Edge A40 Amplitude Anomaly Edge G-33

Figure 2-12: Horizon Amplitude Slice Showing A40 Amplitude Anomaly 2.2.4.3 Depth Conversion Method

A top-down, layer-cake, vertical scaling method was used to depth convert the Taglu reservoir time surfaces. This method involved creating a multilayer velocity model in which velocities vary spatially and as a function of seismic travel time.

The basic depth conversion model was built in three stages:

1. Surface to base of permafrost – seismic velocity functions calibrated to well control.

2. Base of permafrost to top of reservoir – linear increase of velocity with depth (V0k method).

3. Within the reservoir – interval velocity method.

Following basic depth conversion and well calibration, final depth map adjustments were made to match the mapped outline of the amplitude anomaly (see Figure 2-13).

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G-33 P-03

D-43

C-42 H-54

A40 Amplitude Anomaly Outline

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GEOLOGY, GEOPHYSICS AND PETROPHYSICS

APPLICATION FOR APPROVAL OF THE DEVELOPMENT PLAN FOR TAGLU FIELD

PROJECT DESCRIPTION

PETROPHYSICAL ANALYSIS

2.3.1 SCOPE

Subsurface rock properties and other reservoir parameters are used in: • hydrocarbon volume-in-place calculations

• the geological model

• the reservoir simulation model These parameters include:

• net sand thickness – the net effective reservoir that contains hydrocarbons • porosity (phi or ɸ) – the percentage or fraction of free space, within the total

volume of rock, that is available to contain fluids

• fluid type and saturation – fluid type, such as gas, oil or water, proportions within porosity and their distribution

• permeability (k) – the degree of interconnection between pore spaces that allows fluids to move through rock. Permeability is usually measured in millidarcies (mD).

This section describes the data acquired and the analytical procedures used to determine these properties.

2.3.2 LOG DATA AND ANALYSIS

The rock properties listed previously cannot be determined directly in wellbores. Instead, they must be derived or interpreted from other physical measurements that can be made within wellbores. Within the petroleum industry, the most commonly used physical measurements include:

• electrical resistivity and potential • acoustic interval transit time • density

• natural radioactivity • hydrogen content

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PETROPHYSICS PETROPHYSICAL ANALYSIS

2.3.2 LOG DATA AND ANALYSIS (cont’d)

These measurements are collected by lowering various combinations of sensor equipment, i.e., logging tools, on cables to the bottom of a wellbore. Physical property measurements are made continuously as the logging tool is pulled back up the well at a controlled rate. Typically, these recorded measurements are displayed as a curve, called a log, which changes with depth. Interpreting these physical measurements to determine rock properties is called log or petrophysical analysis.

To derive the required rock properties, such as porosity or fluid saturation, from the measurable physical properties, log analysts use relationships established between the desired rock properties and measured physical properties. These property relationships have been obtained from extensive laboratory

measurements and studies of many different rock and fluid combinations. If no other information is available, these rock property relationships can be applied by making general comparisons to these standard relationships for different rock types, such as sandstone, limestone and others. However, more accurate results can be obtained if the measured log curves can be directly calibrated to actual property measurements of the rock being evaluated.

2.3.3 CORE DATA AND ANALYSIS

To obtain rock samples for measuring and calibrating, petroleum companies periodically retrieve lengths of core while drilling through reservoirs. Recovered cores are typically several metres long, and samples from them can be analyzed in laboratories to directly measure properties, such as porosity and permeability. However, because coring is more difficult, time consuming and considerably more expensive than drilling and logging, cores are not gathered continuously through a reservoir, or even in all wells. Instead, representative core samples are obtained across a field to calibrate log responses to measured core properties. These calibrations are then used to extrapolate rock properties over the entire reservoir, using log information.

There are two types of core analysis: • routine core analysis

• special core analysis (SCAL)

Routine core analysis consists of measuring porosity and permeability with air at standard conditions. Special core analysis includes measuring electrical

properties, capillary pressure and relative permeability, usually at net overburden conditions. Electrical property measurements were used at Taglu to correlate electric log data with measured porosity. Capillary pressure measurements were used to determine water saturation.

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2.3.4 TAGLU DATASET SUMMARY

Complete petrophysical evaluations have been conducted on all the wells at Taglu. Table 2-3 summarizes the available log data types for each well.

Table 2-3: Taglu Well Log Data

Well Number

Data Type G-33 C-42 P-03 D-43 H-54

Year drilled 1971 1972 1972 1973 1976

Dual induction Yes Yes Yes Yes Yes

Borehole compensated sonic Yes Yes Yes Yes Yes

Bulk density Yes No Yes Yes Yes

Compensated neutron No No No No Yes

Sidewall neutron porosity Yes Yes Yes No No

Gamma-ray Yes Yes Yes Yes Yes

Service company Schlumberger Baker-Atlas Schlumberger Schlumberger Schlumberger

Petrophysical analysis at Taglu involved integrating all available log and core data, to:

• calculate rock properties, including shale and clay volume, porosity and water saturation

• identify relationships between porosity, permeability and water saturation • determine appropriate overburden corrections to adjust porosity and

permeability to reservoir conditions

Core samples collected at Taglu were analyzed using conventional and SCAL techniques. Table 2-4 summarizes the available core data and the analyses performed. To supplement the Taglu field core data, one well, D-55, from outside the pool was used.

Table 2-4: Taglu Core Data

Well Number

Core Data and Analysis G-33 C-42 P-03 D-43 H-54 D-55

Length (m) 27 144 9 0 0 26.5

Number of plugs cut for SCAL 18 26 0 0 0 15 Capillary pressure measurements 1 _Yes _Yes _No _No _No _No Electrical property measurements 2 Yes Yes No No No Yes Note:

1. Air–brine capillary pressure tests. 2. Formation factor and resistivity index.

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2.3.5 NET SAND DETERMINATION

Taglu reservoir intervals comprise interbedded successions of sandstone, siltstone and shale. These rock types contain variable amounts of siliclastic grains, which are composed of minerals or rock fragments, and clay. In sandstone, the grain component dominates, and clay content is minor. In shale, the grain component is small and the clay component dominates. However, between sandstone and shale, a compositional spectrum, which includes siltstone, exists. The transition between these rock types is gradational, particularly

between fine-grained sandstone and siltstone. This distinction is important, as siltstone is an ineffective reservoir rock that does not contribute to production because of low permeability, even though it contains some gas in pore spaces. Consequently, siltstone pore volumes need to be excluded from volume estimates.

The method used to distinguish siltstone from fine-grained sandstone was based on the amount of clay or shale contained within the rock. The shale volume (Vsh) cutoff was determined using the gamma-ray logs calibrated to core measurements and well test results. Porosity values were not determined for rocks above the Vsh cutoff.

2.3.6 POROSITY

After using the Vsh method to exclude nonreservoir intervals, total porosity in the Taglu wells was determined using density and sonic log data calibrated to ambient core porosity measurements (see Figure 2-14). These analyses show that calibrated log porosity values in the Taglu sandstones range between 5 and 25%.

2.3.6.1 Porosity Overburden Correction

Most Taglu core porosity measurements were taken at ambient surface

conditions. However, porous rocks shrink slightly when buried, because of the compression from the weight of the overlying rocks, i.e., the overburden pressure. Therefore, porosity values in the Taglu reservoir need to be corrected for overburden conditions. The correction factor is obtained by taking samples from the core and measuring porosity at both the ambient and the overburden pressure conditions in the reservoir. The ratio of these measurements is the amount of reduction in porosity required to match reservoir conditions. At Taglu, linear regression analysis resulted in a correction multiplier of 0.957 (see

Figure 2-15), which was used to reduce the calibrated porosity values. After corrections, the average porosity of the field on a hydrocarbon pore volume weighted basis is 15.6%.

2.3.7 PERMEABILITY

Permeability models for the Taglu reservoirs were developed from porosity and permeability measurements taken from core samples. The core data points were sorted based on the interpreted environment of deposition, or facies, as outlined in Section 2.1, Geological Description. Statistical analysis of the data revealed four logical groups, based on the original interpretation of the environment of deposition (see Table 2-5).

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0 0 0 O cc u rr en ce s O cc u rr en ce s Bulk Density Core Porosity Bulk Density 510 255 0 2 2.5 3 540 270 0.25 0.5 0.25 C o re P o ro si ty 0.5 0 2.5 2 Point Plot C-42 Core (2000.00, 3700.00) G-33 Core (2000.00, 3700.00) P-03 Core (2000.00, 3700.00) Regression Equivalents RHO (ρ) matrix = 2.72 gm/cm3 RHO (ρ) fluid = 0.816 gm/cm3

Figure 2-14: Calibration of Bulk Density to Core Porosity

0.3 0.25 0.2 0.15 0.1 0.05 0.3 0.25 0.2 0.15 0.1 0.05 0 0 Ov er bur de n Por o si ty Ambient Porosity C-42 φob

D-55 φob Linear (D-55 φob) Linear (C-42 φob) 1:1 Reference line y = 0.9582x

R2 _{= 0.9882}

y = 0.957x R2 _{= 0.991}

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2.3.7 PERMEABILITY (cont’d)

Using regression analysis, porosity to permeability transforms were developed for each data group (see Figure 2-16 for an example of one of the groups).

Table 2-5: Facies Type Combinations

Group Facies Types

1 Fluvial and nonmarine to tidal

2 Distributary channel, inner stream mouth bar 3 Outer stream mouth bar, proximal delta front 4 Prodelta, all distributary bay, overbank

Overburden-Corrected Core Porosity 10,000 1,000 100 10 1 0.1 0.01 0.001 0 0.05 0.10 0.15 0.20 0.25 0.30 Exponential (kmax) Outer Stream Mouth Bar

Amb ie n t Co re kma x (mD)

Facies Group 3: Outer Stream Mouth Bar and Proximal Delta Front

Proximal Delta Front

Figure 2-16: Log Permeability versus Overburden-Corrected Porosity for Facies Group 3

Where there was no direct core information, these transforms were applied to the previously discussed log-calculated porosity values, based on the interpreted environment of deposition model developed for each well. This allowed corresponding permeability values to be generated.

2.3.7.1 Permeability Overburden Correction

As with porosity measurements, most permeability measurements from core were taken at ambient surface conditions and corrected for overburden pressure. The correction factor was obtained by taking samples from the core and measuring permeability at both the ambient and the overburden pressure conditions in the reservoir. Correcting permeability was more complex than correcting porosity, because it required two relationships, one linear and one nonlinear, depending on the initial ambient permeability value. These relationships are outlined as

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If permeability is _{≥ 2 mD:}

kob = kamb • 0.66

If permeability is < 2 mD:

kob = kamb • (kamb • 0.173 + 0.328)

where:

kob = overburden permeability at 34 MPa (5,000 psi)

kamb = ambient permeability

At Taglu, these relationships were used to reduce the calibrated permeability values to reservoir conditions (see Figure 2-17).

1,000 100 10 1 0.1 0.01 0.01 0.1 1 10 100 1,000 Ov er bur den Per m ea bil it y Ambient Permeability kob =kamb •(kamb •0.173 + 0.328) kob = 0.66 • kamb

Figure 2-17: Taglu Overburden Permeability versus Ambient Permeability 2.3.8 FLUID SATURATION ANALYSIS

2.3.8.1 SCAL Capillary Pressures

An important type of SCAL data obtained at Taglu was capillary pressure data. Capillary pressure is the pressure difference across an interface between immiscible fluids, such as water and gas. It is a function of interfacial fluid tension, pore surface wettability and effective pore geometry.

The pore space of reservoir rocks within a petroleum reservoir commonly contains two fluid types:

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2.3.8.1 SCAL Capillary Pressures (cont’d)

• water, which is always present • trapped hydrocarbons

The equilibrium relationship between capillary pressure and buoyancy controls the relative proportions, or saturation, of the water and hydrocarbon within the rock pore space. With increasing height above the free water level, the

hydrocarbon saturation generally increases and the water saturation decreases until a minimum level of background water saturation (Sw(irr)) is reached. Different rock types, with different pore geometry, will have different capillary pressure curves and thus, different saturation levels at the same elevation. SCAL measurements of capillary pressure from core samples allow these different saturation versus height functions to be defined for the various rock types within a reservoir. This information, combined with other reservoir parameters, can be used to calculate the total hydrocarbon resource contained within a reservoir.

2.3.8.2 Taglu Fluid Saturation Determination

Fluid type and saturation values at Taglu were determined using induction logs and capillary pressure measurements that were calibrated to recovered reservoir fluids from tests. Water saturation (Sw) was determined using the resistivity data (dual water method) and capillary pressure data.

At Taglu, many individual gas reservoir sands range from 1 to 3 m thick. This presents a problem for induction log data, as it underestimates resistivities from beds less than several metres thick. This problem leads to overprediction of water saturation values in these sands.

Taglu has many high-quality capillary pressure measurements obtained from core samples across the full range of reservoir permeability values. Analyses of these measurements allowed the development of a single relationship to determine Sw as a function of porosity, permeability and the height above the reservoir free water level. Figure 2-18 shows the relationship for various permeability values. Comparisons of the relationship with water saturation values calculated from induction log analyses from reliable bed thickness measurements indicated good agreement. Consequently, a capillary-pressure-based water saturation model was adopted for Taglu.

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0 100 200 300 400 500 600 700 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Water Saturation H e ig ht A b ov e Fr ee W ate r ( m ) k₁₀ k₁₀₀ k1,000 k10,000 k1 k0.1 k0.01

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GEOLOGY, GEOPHYSICS AND PETROPHYSICS

APPLICATION FOR APPROVAL OF THE DEVELOPMENT PLAN FOR TAGLU FIELD

PROJECT DESCRIPTION

RESERVOIR PARAMETERS AND VOLUMETRICS

2.4.1 INTEGRATED GEOLOGICAL MODEL

Structural and petrophysical interpretations of the Taglu field were integrated into a geological model built using PETREL modelling software. The model is a synthesis of all the interpreted field information.

The area of the model is 97 km2_{, encompassing the main fault block between the} north and south-bounding faults, as mapped on the Taglu 3-D survey. As the field closure area is about 30 km2_{, this model extends well into the reservoir} aquifer regions. The model consists of 6 million active or populated cells. Each cell is about 100 by 100 m in area by 1 m thick.

The gross rock volume framework of the model was constructed using depth converted maps of the key seismic horizons discussed in Section 2.2,

Geophysical Analysis. Within this gross rock volume framework, model cells were populated with an appropriate geological facies type based on the stratigraphic interpretations outlined in Section 2.1, Geological Description. Reservoir parameters were assigned to each cell based on the facies keyed relationships outlined in Section 2.3, Petrophysical Analysis. Using the

saturation-height method outlined in Section 2.3, a unique water saturation value was calculated for each cell based on its porosity, permeability and height above the most likely free-water level.

2.4.2 AVERAGE RESERVOIR PARAMETERS

Table 2-6 summarizes the average in-situ field parameters extracted from the geological model by reservoir system. The somewhat coarser grained and shallower A sands have average porosities of about 17% and permeabilities of about 150 mD. The finer grained and deeper B and C sands have average porosities of about 14 % and permeabilities of about 25 mD.

2.4.3 RESERVOIR VOLUMETRICS

The most likely original raw gas-in-place volumes for the Taglu field have been extracted from the completed geological model and are summarized by reservoir system in Table 2-7. Because the average reservoir property values shown in Table 2-6 were rounded, calculated hydrocarbon pore volume or original

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gas-in-GEOLOGY, GEOPHYSICS AND

PETROPHYSICS RESERVOIR PARAMETERS AND VOLUMETRICS

2.4.3 RESERVOIR VOLUMETRICS (cont’d)

place (OGIP) using these parameters will have about a 3% variation compared to the values reported in Table 2-7.

Table 2-6: Taglu Reservoir Properties by System

Reservoir Interval

Reservoir Parameters A B2 UC LC LC2

Gross pay1 avg. (m) 79.4 14.4 61.5 52.5 8.3 NTG (fraction)2 0.78 0.85 0.86 0.81 0.82 Porosity3 avg. (fraction) 0.17 0.14 0.14 0.14 0.17 Permeability3 avg. (mD) 153 8 24 21 93 Sg3 (fraction) 0.68 0.60 0.60 0.60 0.67 Note:

1. Represents all rock > 0.01 mD.

2. Net cutoff varied to match hydrocarbon pore volume from model (about 0.1 to 0.05 mD). 3. Average of reservoir > 0.25 mD.

Table 2-7: Taglu Most Likely Raw Gas-In-Place Volumes

Reservoir Interval

Volumetric Parameters A B2 UC LC LC2 Total

Free water level (mSS) 2,888 2,937 3,092 3,134 2,985

Area (km2) 33.7 18.5 25.3 16.3 4.4

Gross rock volume (Mm3) 2,672.3 267.0 1,556.3 857.8 36.5 Hydrocarbon pore volume1 (Mm3) 234.3 19.2 113.7 59.3 3.3 Gas expansion factor from in situ

to surface conditions (scm/rcm)

253.8 255.1 257.5 258.9 267.9

Original gas-in-place (Mm3) 59,458.2 4,905.2 29,279.2 15,346.9 893.3 109,882.7 Original gas-in-place (Bcf) 2,099.7 173.2 1,034.0 542 31.5 3,880.4 Note: