Field Study of Tierney II Unit 34-80 Well

Daevin Dev PEGN 419 Final Project

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Field Study of Tierney II Unit 34-80 By Daevin Dev (CSM Logging Consortium)

5 December 2014

1.0 Overview This document provides an overview of the Tierney II Unit 34-80 Well. This well is based in Sweetwater

County, Wyoming along the Greater Green River Basin (Section 34, N19W94). It is primarily a gas

producing well with marginal oil production. The API number for this well is 49-037-27256. Drilling

operations on this well started in November of 2007 and was completed in February of 2008. The only

operator on the well was BP America Production Company. No operator change took place. The only

service company that assisted BP was Halliburton. They were primarily involved in the well log analysis

of the well. The total depth of this well is 10153ft and production comes from the Mesa Verde

formation.

2. 0 Single Well Petrophysics Give an overview of the petrophysics.

The petrophysics of this well is rather complicated because bulk of the formations in this well are shaly

sands which means that most of the well log data would have been skewed by the shale influence. At a

glance, there doesn’t seem to be many crossovers to indicate high presence of either gas or oil although

it is known that the formation contains a lot of gas in the production zone. This phenomenon could also

be attributed to the high shale content. The gamma ray curve seems to adhere to the expected lithology

type. There seem to be regions with extremely high gamma ray values and regions with mixed spikes of

high and low gamma ray values. The overlay applied to the gamma ray curve enables one to easily

distinguish between sandstone and shale. The resistivity curves seem to be consistent throughout the

logged interval. If there were sudden deflections, all resistivity curves would deflect alike. The Pef, Uma,

Rwa and Sw curves were added based on analytical computations. These curves were not provided in

the default LAS file. Water saturation seemed to be high in the non-pay zones and relatively low in the

pay zones.

3.0 Quality Assessment Depth, Tension, Caliper

Well depth extends to 10153ft. The top of the logged interval is 1448.8ft with the bottom logged

interval at 10138ft. The caliper readings do deviate from the bit size diameter. This is generally expected

from shaly sand. That being said, the deviation was roughly constant throughout the logged interval

which could also imply a tool calibration error However the deviation in this wellbore is marginal and

should not have affected the other measurements made. The tension curve is surprisingly smooth.

There don’t seem to be any abrupt changes throughout the logged interval.

Borehole conditions

The bit size used in the logged interval is 7.875in. The borehole seems to be in a reasonably good

condition. There was minimal deviation in the caliper measurement from the theoretical bit size. The


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tension curve is smooth which means there was nothing to abruptly stop the logging tool during its

travel throughout the wellbore logged interval.

Density, Pef

Bulk density data was available. A bulk density correction factor was also available. Both density porosity

and PEF data were readily available. However only sandstone based density porosity was present.

Limestone density porosity had to be calculated using the provided bulk density and the formation fluid

density.

Neutron

Thermal neutron equipment was used to log the neutron density of the formation. The neutron

lithology was calibrated to a sandstone lithology. No limestone neutron log was available and so it was

computed based on a built in Schlumberger correlation.

Resistivity

The resistivity curves have a minimum tool reading of 0.2 ohm.m with a maximum tool reading of 200

ohm.m. There do seem to be unwarranted resistivity curve jumps at specific points in the log but that is

probably a consequence of the shale.

Sonic

There wasn’t any sonic data provided and thus mechanical properties can’t be evaluated for this well.

4.0 Analysis How was the petrophysical analysis done?

Petrophysical analysis was done based on qualitative and quantitative analysis. Based on the quality of

the curves and how well in conformed to conventional notions, speculations on how reliant the log data

was could be deduced. Quantitative analysis was performed in more detail. Based on the measured data

several other properties were calculated such as Uma, Rhga and Tnph. Using these data and its general

behavior along the logged depth, potential pay zones could be identified. Two cross plots were made to

aid this process. These were the neutron-density cross plot and the Rhga-Uma cross plot.

4.1 Fluids Fluid endpoints were determined using the Rwa method. The SP curve deflection is

too high. This could be due to the presence of high shale. Plus, the Rw computed using

the SP method yielded erroneous Sw values which could only imply that the SP values

are being affected by shale significantly. The Pickett plot method relies a lot on the

ability to isolate water zones from oil and gas zones. However, because this formation

is a shaly sand formation, there won’t be much crossovers present and it is thus

difficult to isolate the water zones for use in the Pickett Plot. Hence, the only method

that would not succumb to the shale effect is the Rwa method. This is why the Rwa

method was selected over the other method


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Figure 1: Full Triple Combo For Tierney II Well


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4.2 Lithology Lithology was primarily determined using the Gamma Ray curve with some help of the neutron density

cross plot. If the gamma ray was too high it was speculated that the formation would most likely contain

a lot of shale while if the gamma ray was too low then it was speculated that the formation contains lots

of sandstone and limestone. This was further reinforced by the neutron-density cross plot. Based on the

screenshot below, it is apparent that there is some sandstone present, as shown by the blue points on

the cross plot (low gamma ray). The green points (medium to high gamma ray) are either most likely

shale or carbonates being pulled towards the southeast direction, away from the quartz line and

dolomite, due to the high clay content in shale.


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4.3 Porosity Porosity was determined using the neutron-density cross plot coupled with the average of sand density

porosity and thermal neutron sand porosity. The porosity averaged to about 0.225. Based on the cross

plot, a lot of the sandstone points are being pulled down because of the high presence of shale i.e. clay

which would be illite and kaolinite. The points towards the top right corner are probably gas filled

formations which is not uncommon in Wyoming.

4.4 Saturation The apparent water resistivity was calculated by assuming 100% water saturation in Archie’s equation.

This was then plotted on the triple combo. A clean sandstone formation within the pay zone was

identified and the lowest Rwa value was selected as the ideal Rw. Then Archies equation was used again

along with the true resistivity (was available in original LAS file) along with the ideal Rw to compute the

water saturation.

4.5 Elastic Moduli (Optional) What are the mechanical properties?

See next two pages for data


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Figure 2: Mechanical Properties Derived From Correlations

Daevin Dev PEGN 419 Final Project Final Project

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Mechanical Properties Interpretation

The mechanical properties for this well were calculated based on a correlation found in an extensive

study of clastic silicate rocks. This correlation relates the total porosity of the formation and the clay

volume to the primary and secondary wave velocities. These correlations were found in a geophysics

journal (Society of Exploration Geophysicists) from 1985 by Castagna et. al. These correlations were

compared to actual values measured using sonic tools and the average percentage error was about 1%

which is quite low. The correlations are given in the equations below:

𝑉𝑝 = 5.81 − 9.42∅ − 2.21𝑉𝑐𝑙

𝑉𝑠 = 3.89 − 7.07∅ − 2.04𝑉𝑐𝑙

The outputs are in km/s. Appropriate unit conversions were applied to convert this number to us/ft.

With these values, the corresponding compressional slowness and compressional shear rate values were

determined by taking the reciprocal of the velocity values. From there equations in the petrophysics

handbook were used to compute the bulk modulus (K), compressional modulus (M), shear modulus (G),

Young’s Modulus (E) and Poisson’s Ratio.

A screenshot of the final values are shown on the previous page. The values seem to comply with

conventional notions such as the fact that compressional slowness is usually of a lesser value than shear

slowness. Poisson’s ratio seems to be well within the acceptable range of 1 to 0.5

The following table gives the average values of the computed mechanical properties:

Intuitively the bulk and shear modulus values seem to drop in a when sandstone is encountered because

sandstone is a more porous rock compared to shale and is thus more susceptible to deformation when

placed under stress. This presumption is readily seen in the logs on the previous page. This would clearly

imply that the correlation used does give a somewhat good representation of the mechanical properties


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5.0 Petrophysical Model

5.1 Assumptions A = 0.62, M = 2.15, N = 2

These Archie exponents were given in one of the TIFF image logs by Haliburton.

5.2 Model 1: Shale Model


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Describe the model:

The first model is a shale model. This was probably one of the more important models to build since

bulk of the formations are either sand or shaly sand.

What logs were used in the model?

The logs used in this model include the Bulk Density, Sand Neutron Porosity, True Formation Resistivity

and the Gamma Ray.

Any zonation used in the model?

This workflow method was only applied beyond the Lewis formation which is roughly beyond 7100ft.

This was done because there were no Tnph and rhob data before this depth. It would have been

redundant to run the workflow at depths before 7100ft.

Input properties, uncertainties, weights, unflushed factors

The bulk density uncertainty was 0.027, for neutron porosity it was 0.015 and for the formation

resistivity the value came from the initialization workflow. The gamma ray input had a uncertainty of 6.

Bulk density and neutron porosity was weighted at 1 while the formation resistivity and gamma ray

were weighted at 0.3. As for the unflushed factors, the values were 0.25, 0.5, 1 and 0 for bulk density,

neutron porosity, formation resistivity and gamma ray, respectively. The tool type for neutron porosity

was changed from the default Nphi to TnPh.

Component specification, endpoints, wet clay porosity, CEC

6 components were to be evaluated in the shale model. They were illite, montmoriillonite, calcite

quartz, XI water and UI water. The bulk density of the lithologies were at their default values while the

bulk density of the XI water and UI water came from the initialization workflow. These came out to be

roughly 0.97821 g/cc. The wet clay porosity of illite turned out to be 0.1884 v/v while and 0.5911 v/v for

montmorillonite. The CEC for illite was 0.16meq/g and 1 meq/g for montmorilonite.

Special models, associated parameters

This shale model was constructed with deep resistivity input. The dual water method was used to

determine the true formation resistivity because shales have dual water saturation and this method

would represent the actual water distribution in the pay zone. From the initialization workflow, the

formation water resistivity was determined to be 0.4591 ohm.m

Additional constraints, Equivalent Hydrocarbon Ratio, Predefined constraints

No additional constraints were applied to this model because the initial model seemed to fit well.


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5.3 Model 2: Oil Sand Model

Describe your model.

This model is the oil sand model. It simulates the oil distribution in the Mesa Verde formation which is

where the pay zone is. The output gamma rays and output true formation resistivity’s seem to be a good

match to what was given as input. This is verified by the large orange shading covering bulk of the

gamma ray curves and resistivity curves above.



and the Gamma Ray.


Bulk of the quanti.elan workflow was simulated beyond the Lewis formation which is roughly beyond

7100ft. The screenshot shown above is within the Mesa Verde formation.


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Input properties, uncertainties, weights, Unflushed factors

The bulk density uncertainty was 0.027, for neutron porosity it was 0.015 and for the formation

resistivity the value came from the initialization workflow. The gamma ray input had a uncertainty of 6.

Bulk density and neutron porosity was weighted at 1 while the formation resistivity and gamma ray

were weighted at 0.3. As for the unflushed factors, the values were 0.25, 0.5, 1 and 0 for bulk density,

neutron porosity, formation resistivity and gamma ray, respectively. The tool type for neutron porosity

was changed from the default Nphi to TnPh.


7 components were analyzed in this model. They were Illite, Calcite, Quartz, XWater, XOil, UWater and

UOil. All component bulk densities were at their default values except for the water densities in the

flushed zone and the undisturbed zone. These had values of 0.9769 g/cc and 0.97821 g/cc. Only Illite

and Quartz had gamma ray end points which were 200 gAPI and 30 gAPI respectively. The wet clay

porosity of illite in this case was 0.188363 v/v, with a CEC of 0.16meq/g.


The only special model used was the dual water deep resistivity model. This method was selected to

accurately account for the clay bound water which is present within the wet clays.


The only constraint applied to this model was that water based mud was used.

5.4 Model 3: Gas Model


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Describe your model.

This model is the gas model which simulates the gas distribution within the Mesa Verde formation. The

gas model was important because almost 96% of the production from this well is gas.



and the Gamma Ray.


Similar to the two other previous models, this model also takes on formations beyond the Lewis

formation which is roughly beyond 7100ft. The screenshot shown above displays the Mesa Verde

formation which is where the pay zone is.

Input properties, uncertainties, weights, unflushed factors

Similar input properties used in the oil model were used.


7 components were analyzed in this model. They were illite, Calcite, Quartz, Xwater, UWater, XGas and

UGas. The gamma ray end points for illite and quartz were 200gApi and 30gApi respectively. The gamma

ray end points for water were 0 but the end points for the Xgas and Ugas were 1. The bulk densities of

the input fluids were calculated using the initialization workflow. This was also the case for the gas

neutron porosity values. Wet clay porosity stayed the same at 0.188363 v/v. The CEC also remained the

same at 0.16meq/g.


The dual water deep resistivity model was used to account for the clay bound water present in shale.

The inputs to this were a deep formation water resistivity and deep formation temperature which came

out to be 0.459 ohm.m and 181.4 deg F, respectively.


Initially this model didn’t exhibit enough gas distribution in the flushed zone. Thus an additional

constraint was added. Based on the current production data of water and gas, only 4% of water was

produced for every barrel of gas produced. Thus the Xwater value in the constraint was given a value of

1 while the Xgas value in the constraint was given a value of -0.04. These numbers were derived from

the equation Xwater=0.04Xgas. Rearranging this we get 1Xwater-0.04Xgas = 0. A base mud saturation

predefined constraint was added so that the workflow knew that the mud used during drilling was water

based mud.


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5.5 Combination Model Describe your combination logic


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Combination Logic

This combined model was computed using the three previous models. Three if statements were used to

determine whether or not the lithology was shale, gas or oil. For shale, if the gamma ray way greater

than 65 then it would interpret it as a shale layer. For the oil layer, if the sand density crossover was less

than 0.06 then it would interpret as oil. As for the gas, a different approach needed to be used since it is

relatively difficult to determine the presence of gas in shaly sand. The crossover method would not

indicate the presence of gas since the shale affect the Tnph and Dphi values. Given the limited

information provided, the gamma ray track and water saturation data were used to determine the gas

content. If the gamma ray was less than 80 (common in sandstone) and if the water saturation was less

than 0.6 then it would be gas. The gamma ray condition was to determine if it were

sandstone/limestone since it would be ideal to produce from sandstone/limestone instead of shale. The

water saturation condition assumes that if the water saturation is low, then the remaining saturation

must be of hydrocarbons and in particular gaseous hydrocarbons. By using water saturation though, we

only account for movable water and not the clay bound water which not have been accounted for in the

water saturation values. Thus when we combine both those conditions; it needing to be a sandstone

layer and having low water saturation- we can safely assume that the remaining saturation has to come

from gas. Oil isn’t really given much attention here because the oil production (based on production

data) is only about 4% of total production. About 96% of production is gas.

The screenshot above is from the pay zone/ production zone. It seems to conform to expectations.

There is minimal oil present there with some streaks of gas layers throughout the pay zone. There seems

to be minimal amounts of water and clay bound water. The probabilities of formations (in track 1) highly

suggest that there is a lot of shale present with some oil sands and gas sands.

5.6 Optional Analysis Permeabilities

Permeability ranged from 0.01mD to 1mD. These values were based on a correlation rather than a direct

calculation. With the provided core data, the maximum and minimum permeability values were

obtained. Using an excel if-statement along with the SP values, a correlation was derived. Based on the

dataset, it seemed that at SP values lower than 80mV the lithology was mostly sandstone/limestone.

Most other times the SP stayed high due to the highly impermeable shale. Significant SP deflection

occurred when sandstone was encountered. It is known that a SP deflection to the left (low SP values)

implies a permeable zone. Assuming a uniform permeability distribution given the SP constraint,

permeability values were simulated. This method isn’t accurate but is based off the Monte-Carlo

simulation which gives a rough estimate of what to expect given the conditions and constraints stated

above.

A screenshot of the permeability curve is given below/next page. As it shows, the permeability values

spike when a shaly sandstone layer is encountered. It also conforms to the notion that low SP implies

high permeability.


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Figure: Triple Combo with Permeability Track placed adjacent to the Gamma Ray track for easy comparison.


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Differentiate Pay from non-pay. How many porosity-feet do you have? Quantify the pay.

The pay and non-pay zones were isolated in the well log. The non-pay zones (red) consists of only shale

formations with high water saturation content and low resistivity which implies that there is a lot of slay

bound water present there and probably minimal hydrocarbon content. The pay zone (green) consists of

shaly sand formations with a relatively low water saturation and a sudden increase in the resistivity from

the non-pay zone. Also note the sudden deflection of the SP curve in the pay zone/ production zone. A

high SP deflection to the left indicates high permeable zone.

The following is the estimated porosity-ft for all the entire pay zones.

There is a total of 116.72 Φ.ft

OGIP = (43560* 116.72*(1-0.4))

= 3MMscf/acre


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Core data, how was core data integrated into the interpretation?

Core data was used to determine the types of components to use when creating the petrophysical

models. Initially only illite and quartz were used but after having looked at the core data, it seemed

reasonable to include calcite as part of the model analysis. Additionally, the core data was also used to

determine the permeability of the formation. The permeability values from the core data was used as

the minimum and maximum permeability values so that a Monte Carlo simulation could be performed

to derive plausible permeability values.

Wellbore Images, show some interesting bedding, breakouts and fractures.

No wellbore images could be found for this well.


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Mud logs: Header of The Mud Log


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Mud Logs Continued

The mud log indicates lithology and gas content based on surface analysis. This data is then matched

with the ongoing weight on bit and the rate of penetration. At a glance of the whole mud log, it seems

that the gas content (extreme right track) seemed to spike whenever a sandstone formation was hit. It

stayed relatively low at shale formations. This makes sense as gas from sandstone could have easily

invaded into the wellbore while gas from shale wouldn’t have easily permeated out of the shale

formation. The screenshot above is the mud log from this well. It was done by BP back in 2007. The

interval shown above is the current producing interval. Note the sudden spikes in gas content whenever

a sandstone formation is encountered.


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6.0 Cased hole Analysis

6.1 Cement volume Compute the cement volume required for your casing run

𝑉𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑐𝑒𝑚𝑒𝑛𝑡 𝑓𝑜𝑟 𝑓𝑖𝑟𝑠𝑡 𝑐𝑎𝑠𝑖𝑛𝑔: 0.0009714 × (112 − 8.6252) × 1451

= 65.7bbl of cement

𝑉𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑐𝑒𝑚𝑒𝑛𝑡 𝑓𝑜𝑟 𝑐𝑎𝑠𝑖𝑛𝑔 2 ∶ 0.0009714 × (7.8752 − 4.52) × 10134

= 411bbl of cement

Total cement required for all casing

=411bbl + 65.7bbl = 477bbl of cement


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6.2 Bond Log Evaluate the bond log

The first screenshot shows the bond log for a top section of the wellbore while the second screenshot

shows a bond log of a lower wellbore section. The bond logs seem to indicate good cementing quality at

the bottom of the wellbore as compared to the top of the well bore.

7.0 References

Bratton, Tom “Petrophysics Handbook – Colorado School of Mines”, 2014

Castagna et. al “Relationships between compressional-wave and shear-wave velocities in clastic silicate

rocks” Society of Exploration Geophysicists Vol. 50, April 1985, p571-581.

http://www.ipt.ntnu.no/pyrex/stash/GPY00571.pdf

Wyoming Oil and Gas Conservation Commission, 12/5/2014. Web

PEGN 419 Well Log Analysis Laboratory Manual, Tom Bratton LLC, Colorado School of Mines

Documents

Field Study of Tierney II Unit 34-80 Well