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Copyright 2006, Society of Petroleum Engineers

This paper was prepared for presentation at the 2006 SPE Gas Technology Symposium heldin Calgary, Alberta, Canada, 15–17 May 2006.

This paper was selected for presentation by an SPE Program Committee following review ofinformation contained in an abstract submitted by the author(s). Contents of the paper, aspresented, have not been reviewed by the Society of Petroleum Engineers and are subject tocorrection by the author(s). The material, as presented, does not necessarily reflect anyposition of the Society of Petroleum Engineers, its officers, or members. Papers presented atSPE meetings are subject to publication review by Editorial Committees of the Society ofPetroleum Engineers. Electronic reproduction, distribution, or storage of any part of this paperfor commercial purposes without the written consent of the Society of Petroleum Engineers isprohibited. Permission to reproduce in print is restricted to an abstract of not more than300 words; illustrations may not be copied. The abstract must contain conspicuousacknowledgment of where and by whom the paper was presented. Write Librarian, SPE, P.O.Box 833836, Richardson, TX 75083-3836, U.S.A., fax 01-972-952-9435.

AbstractThe Oligocene Vicksburg formation in South Texas has been a

prolific play for many years with targets of thick and stacked

sand bodies. These thick sections have been primarily

exploited and produced. Still existing are many previously

considered uneconomical sequences. These marginal sections

consist of highly laminated sand shale sequences along with

disbursed clay in sand. Standard cutoffs from basic log

evaluation work correctly for the disbursed clay sections. But

the cutoffs are inadequate for the highly laminated sequences;many thin, high-quality sands have been overlooked. These

sections can now be discerned using microresistivity

measurements in oil-based mud systems and new high-

resolution cutoffs can be employed.

A production prediction model is critical to enhance the

chance of success. The model used here employs a

petrophysically consistent high-resolution permeability

estimate, fracture geometry prediction, and formation

pressure. The methodology identified several sands as

commercial that have been bypassed in offsets with the old

cutoffs.

Over a two-year drilling program, data gathered from several

field example wells were analyzed. These are presented here

to illustrate how production data was utilized to continuously

adjust and calibrate the high-resolution petrophysical model.

The incremental revenue from the added pay exceeded the

cost of this new methodology and enhanced the economic

viability of the field.

This integrated process of measurement, analysis, prediction,

evaluation, and model adjustment enables the operator in

South Texas to make timely completion decisions as well as

set-pipe decisions. This process is becoming a useful tool for

further exploitation of the mature Oligocene Vicksburg

formation of South Texas.

IntroductionThe Vicksburg formation in South Texas has been exploited

since the 1920s and is still a prolific producer with over 20

Bcf per year average rate (Fig. 1). The play has seen both

productivity increases and declines depending on gas pricesand technology drivers. Since the mid-1990s, however, the

trend has been ever-decreasing productivity and faster rate

declines. At the same time, only 12% of the estimated 3,860

Bcf ultimate recoverable designated tight gas in Vicksburg has

been produced1, leaving much to be recovered. Some of this

recovery can be enhanced with recently developed high-

resolution technology.

The decision on whether to set pipe or complete a particular

zone usually is made once the logging run is complete

During the standard logging run, the analyst will view the

density porosity output and question the economics. “What is

the porosity cutoff to make a well here?” The answer is found

over years of experience and the school of hard knocks

Typically a “Rule of Thumb” is used and a line is drawn (Fig

2). Many South Texas partners make their decisions based on

these cutoffs and individual experience. Worthington gives a

comprehensive perspective on the use of these cutoffs2. The

cutoff number most often used in the Oligocene Vicksburg

trend of South Texas is 15-16% porosity (Fig. 2). More

recently there has been success at much lower porosity in the

range of 8-10%3. Obviously, if a 16% porosity cutoff was

applied routinely, then somewhere in the thousands of wells

drilled, some pay has been bypassed.

One solution that has been used primarily in water-based

systems has been laminated sand analysis. This type ofanalysis has been applied since the early 1990s primarily in

turbidite plays4 and not verified with production. The analysis

used here verified with production data, provides a better

answer for the less obvious and often bypassed pay sands.

Thin Bed Production Optimization TechniqueOverview:

The standard cutoff technique that has been described above

can be applied to high-resolution petrophysical analysis

utilizing enhanced stratigraphic imaging. The method used

here was to predict the well’s initial production via high

SPE 99720

Oligocene Vicksburg Thin-Bed Production Optimization Derived From Oil-Based MudImaging: A Case StudyD.L. Fairhurst, B.W. Reynolds, S. Indriati, and M.D. Morris, SPE, Schlumberger, and E.G. Hanson, Abaco Operating LLC

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resolution petrophysical analysis, measured or estimated initial

reservoir pressure and assumption of the fracture height,

length and conductivity. Production data was compared with

the prediction and the high resolution cutoffs were corrected to

make a match. This iterative process was applied over a series

of wells. The result is a working predictive model that can

ensure the operator the best possible economic decision for

completion. The main difference from the standard resolutioncutoffs is net height and to a lesser extent, the relative

permeability to gas value. With the high-resolution analysis, it

can be shown that the net height of producible pay can go

from 10 feet to 50 feet, or stay at 10 feet in the instance of

disbursed clay.

Petrophysical Analysis:

Enhancement of the petrophysical analysis using high-

resolution stratigraphic imaging has been applied since the

early 1990’s in water-based muds. In the oil-based mud

system, a similar analysis has only been available since 1999.

The importance of this is that most of the current drilling for

South Texas Vicksburg wells is now done with oil-based mud

for cost and better wellbore integrity. Cheung5 and Tabanou6

defined a convolution process for oil-based mud imaging. The

original intention of this device was to provide the geologist

with a stratigraphic image. For thin bed analysis however, the

same data can be used to compute a resolution enhanced

petrophysical analysis. Standard resistivity resolution is about

18 inches for 100% of the signal (fig. 3). The enhanced image

from oil-based mud imaging has an effective resolution of 1.2

inches6. This ultra-fine resolution gives a much clearer picture

of the highly laminated sand sequences of the Vicksburg

formation. Many sands are on the order of several feet down

to a few inches in thickness. Imaging tools also differentiate

between disbursed clay and laminated sand shale sequences

(fig 4). Thinly laminated sands are masked by the resolution ofstandard triple combo logging tools and appreciable pay can

be bypassed.

The use of microimaging tools along with triple combo data

opens the door to resolution enhanced interpretations. The first

step to this “sharpened” analysis is to use the image data to

determine a lithofacies model of sand shale sequences. Next,

this lithofacies model is used with a mathematical method to

correct standard resolution logs to the best representation of

what they would read if they had the vertical resolution of

microimaging tools. The mathematical model is part of an

optimization process that calculates a squared output that is in

turn convolved and compared to the original data. Multipleiterations refine the sharpened log by minimizing the

difference between the convolved output and the original logs.

The convolution process is used to correct the resistivity,

density, neutron, and gamma ray (Fig 5). These sharpened

outputs can then be used to compute an enhanced

petrophysical analysis that yields corrected effective

porosities, water saturations and estimates of relative

permeability for water and gas. Ahmed, Crary and Coates

described the techniques used for log derived permeability

estimates7. Here, a similar process is applied with magnetic

resonance permeability when available. The water saturation

correction is reliable wherever the mud invasion is low. It is

typical that there is very little mud invasion in the deep

Oligocene Vicksburg and it is easily verified with the

induction curves.

Stimulation model:

Stimulation practices have been described over time for the

Vicksburg. Tucker describes “steep declines” observed and

attributed to proppant embedment8

. Abrams and Vinegarfound that the water block additives when laboratory tested on

Vicksburg core samples were not effective9. Brin found tha

high strength proppants led to 15% increase in NPV even

though the cost of the proppant was 6 times higher than

sand10. Similar practices were applied in all the wells in this

study. A pseudo 3D fracture design model was used and from

25 to 50 different layers were characterized, utilizing the

calibrated sharpened analysis described in the previous section

(Fig. 6). Fracture direction and height were then synthesized

via log data and the fracture modeling. The fracture design

lengths and conductivities were optimized for each of the

Vicksburg multi-strings based on the net height, permeability

values and layers obtained from the sharpened process. The

resultant stimulations varied in size from 140,000 lbs. to

400,000 lbs. of bauxite (20/40). The number of stages pe

well also varied from 2 stages to 6 stages.

Production Data Analysis for Petrophysical Mode

Calibration:

The first step to calibrate the cutoff and the permeability to gas

estimate of the sharpened petrophysical model was to perform

production history matching. Next was to characterize the

reservoir and fracture properties layer by layer in the wellbore

The methodology to calculate the production from individua

layer and to characterize the reservoir and fracture properties

layer by layer in a wellbore has been well documented in

several papers.11,12 Daily production and wellhead pressurewas collected for three to six months from several wells in the

field. These wells have commingled production from severa

stimulated layers. The goal of the production history matching

was to characterize the reservoir properties layer by layer

rather than the average value. A computational model was

used to generate individual production and flowing bottom-

hole pressure history from the total commingled production

with the aid of production logging13. Once the individual laye

production contribution and pressure traverse components

were determined, a unique rate transient analysis was

performed on each layer as if it was the only zone being

produced from the well14. The analysis was performed

graphically by matching the production data slope with thetheoretical slope of each of the transient flow regime in a

fractured well. The slope from each of the flow regime yield

specific reservoir and fracture properties layer by layer in the

wellbore. The result from this graphical rate transient analysis

was then compared and refined with the result from a

comprehensive analytical production simulator 15.

The second step was to compare the results obtained from

production data analysis to the initial estimate from the

petrophysical model and fracture modeling. When the initia

estimate of the reservoir properties from the petrophysica

model did not support the results obtained from the production

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SPE 99720 3

data analysis, the petrophysical model was adjusted. Primary

adjustments were made to saturation and porosity cutoffs to

determine net pay as well as the permeability model to

estimate permeability to gas.

The third step in the process and probably the most critical

was to perform production forecasting utilizing the adjusted

petrophysical model in order to evaluate the economics ofeach potential zones and frac stage. The analytical production

simulator described above was utilized to forecast the

production layer by layer along with the commingled

production of the entire wellbore. The newly adjusted net pay

calculation, porosity, saturation, permeability to gas and water,

initial reservoir pressure, drainage area and modeled fracture

properties are the primary inputs in the production forecasting

step. For comparison purposes the decline analysis was

performed using petrophysical outputs based on standard

resolution analysis as well as the calibrated sharpened

petrophysical analysis.

Surface daily production and wellhead pressure were then

monitored and compared with the production forecast using

the calibrated model. This iterative method was conducted

until the production forecast using the calibrated petrophysical

model and the actual production data matched, resulting in an

optimized calibration for the petrophysical model.

Case StudyWell A is representative of the initiation of the sharpened

analysis study. Production forecasting was not available for

this well at the time of completion. In this well, with standard

analysis, density, neutron with a field cutoff of 15% gave a net

pay of 20 feet in the lower zone. Conventionally, this lower

zone would have been viewed as non-economic and bypassed.

The induction log was sharpened with microimaging, a fieldcutoff of 15% was still utilized and net sand count improved to

55 feet of pay in this process. Stimulation was performed for

this lower zone. Production testing showed initial production

of 2MMcf/D coming from this zone that conventionally would

have been bypassed. There were two fracture treatments

performed on this wellbore. After the production was

commingled, a production log was run to further confirm the

contribution of the lower sand. The production log verified

that this zone was still contributing as the lowermost of three

commingled stimulation treatments (Fig 7). Four months of

production and wellhead pressure data were collected. The

methodology to characterize the reservoir and fracture

properties described above was applied (Fig. 8). The production data analysis resulted in a change of the porosity

cutoffs from 15% to 12 %.

Well B is a representative of the second group of wells that

utilized the first attempt of the calibrated high-resolution

analysis. The production prediction yielded an estimate that

initial production would be 12 MMcf/D. This is a combined

two stage completion. Each stage was predicted using the

production optimization technique described above. The result

was under-calling the production. Actual field well production

came in higher than the prediction by 2mmcfd (Fig. 9).

Production logging was not available for this well, thus the

analysis was done on commingled basis. The production

analysis indicated that further adjustment of the porosity and

water saturation cutoffs was needed to predict more net pay

Porosity cutoff was changed from 12% to 9% and water

saturation cutoff changed from 65% to 70%. The result was a

close match to the actual field production.

Well C and Well D utilized the calibrated results from Well Bfor production prediction. Well C is a combination of three

stages of completion. Production logging confirmed the

contribution from all three stages and the actual production

followed closely to the production prediction, within 10-15%

range (Fig. 10). Well D only has one stage completed. The

actual production is in the ± 10-15% range of the production

prediction (Fig. 11). The optimization process now appears to

be a working predictive model for this field. The result is an

aid for the operator to make timely and effective completion

decisions.

Some of the wells declined faster than predicted after 50-60

days. It is believed that the reason is due to proppanembedment from the high closure stress and soft shales

Furthermore, geopressured reservoirs tend to have some

permeability compaction through time and faster fracture

degradation.

ConclusionsThin bed production optimization can greatly improve the

operator’s economical success in completing thin bed sands

Intervals that could be bypassed using standard log resolution

are revealed with the new cutoffs applied (Fig. 12). The

application tested here can be applied in many other laminated

sand reservoirs. The process will work for both oil-based and

water-based drilling systems.

The enhanced thin bed cutoff process used in this study relied

on already proven techniques. The process however is a

summation utilizing these techniques along with comparison

over time and adjustments to the model. The adjustments were

made through actual production analysis. The same process

may result in different cutoffs for other geological plays and

add again to the bank of bypassed pay discoveries.

After several iterations and changes, the actual production

matched the prediction more closely (within 10-20%). A

working predictive model that can ensure the operator the best

possible economic decision for completion in this field has

been established. The model can be easily re-applied toevaluate different completion scenarios.

Although some wells declined faster than predicted after 50-

60 days, it is assumed that this is approximately the point at

which proppant embedment occurs. However, this

phenomenon is beyond the scope of this study and further

effort is necessary to prove this.

AcknowledgementsWe thank Schlumberger for the time to complete this study

Also B.J. Drehr and Kathy Huddleston of Abaco for their help

in providing data.

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Nomenclaturek = permeability, L2 , md

h = height

References1. Gas Technology Instiute, “Tight Gas Resource Map of the

United States,” GTI-01/0114 (2000)

2. Worthington, P.F., “The Role of Cut-offs in IntegratedReservoir Studies,” SPE Paper 84387 (2003) 16 p.

3. Erskine, R.D., “Out Front In Deep Gas” The American Gas

Reporter, (Dec. 2001) p. 44-54.

4. Hansen S M, T Fett.2000.Identification and evaluation of

turbidite and other deepwater sands using open hole logs and

borehole images. In A H Bouma and C G Stone. Fine-grained

turbidite systems, AAPG Memoir 72/SEPM Special

Publication 68. U.K.: Geological Society Publishing House,

317～338.

5. Cheung, P. et al.: “Field Test Results of a New Oil-Base

Mud Formation Imager Tool,” (2001), SPWLA 42nd Annual

Logging Symposium.

6. Tabanou, J.R. et al.: “Thinly Laminated ReservoirEvaluation In Oil-Base Mud: High Resolution Versus Bulk

Anisotropy Measurement-A Comprehensive Evaluation,”

(2002), SPWLA 43th Annual Logging Symposium.

7. Ahmed, U., Crary, S.F., Coates, G.R., “Permeability

Estimation: The Various Sources and Their

Interrelationships,” SPE Paper 19604 (1991)

8. Tucker, R.L., “Practical Pressure Analysis in Evaluation of

Proppant Selection For The Low Permeability, Highly

Geopressured Reservoirs of the McAllen Ranch (Vicksburg)

Field,” SPE Paper 7925 (1979) 9 p.

9. Abrams, A. and Vinegar, H.J., “Impairment Mechanisms in

Vicksburg Tight Gas Sands,” SPE Paper 13883 (1985) 12 p.

10. Brin, H.P., “A Post-Audit of Fracture Stimulations in theVicksburg Formation of South Texas,” SPE 15508 (1986) p 8.

11. England, K.W, Poe, B.D.Jr., Conger, J.G.,

“Comprehensive Evaluation of Fractured Gas Wells Utilizing

Production Data,” SPE Paper 60285 (2000).

12. Larkin, S.D, Pickrel, H.M, et.al,”Analysis of Completion

and Stimulation Techniques in a South Texas Field Utilizing

Comprehensive Reservoir Evaluation”, SPE Paper 93996

(2005) 10 p.

13. Poe B.D Jr., Villarreal, R., et.al. Production Optimization

Methodology for Multilayer Commingled Reservoirs Using

Commingled Reservoir Production Performance Data and

Production Logging Information,” US Patent Pending Sept

(2000).

14. Poe, B.D. Jr., Conger, J.G., et al.: “Advanced Fractured

Well Diagnostics For Production Data Analysis,” SPE Paper

56750 (1999) 22 p.

15. Poe, B.D. Jr., Zheng-Poe, A., Boney, C.L.: “Production

Data Analysis and Forecasting Using a Comprehensive

Analysis System,” SPE Paper 52178 (1999) 8 p.

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0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Time

G a s R a t e ( M M C

0

250

500

750

1000

1250

1500

1750

2000

2250

2500

N u m b e r o f W e l l

Gas Rate (MMSCF) Number Of Wells

Net Pay Using 15 PU

Cut-off = 20ftConsidered non-

economical

Net Pay Using 15 PU

Cut-off = 20ftConsidered non-

economical

Fig 1. Vicksburg production data, 1965 to 2005, gas rate compared to number of wells drilled.

Fig 2. “Rule of Thumb” cut-off technique applied in the Vicksburg.

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Rt

AIT Correct

RtTCOM misses 100%

Rt

AIT “Misses” 50%

RtRt

AIT CorrectAIT Correct

RtRtTCOM misses 100%TCOM misses 100%

RtRt

AIT “Misses” 50%AIT “Misses” 50%

Induction Neutron/DensityLaminated Pay

Disbursed Clay

Induction Neutron/DensityLaminated Pay

Disbursed Clay

Fig 3. OBMI Resistivity compared with Array Induction, Density and Neutron.

Fig 4. Disbursed clay compared with laminated sand/shale sequence.

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SPE 99720 7

GR RT

n

ρ

Sharp outputs

BFV

GR RT

n

ρ

Sharp outputs

BFV

Fig 5. Sharpened outputs after 5 iterations.

Fig 6. Geometrical pseudo 3d stimulation model used per layer.

FracCADE*

*Mark of Schlumberger

ACL Fracture Profile and Proppant Co ncentration

ABACO OPERATING6Vicksburg 512-07-2004

0 500 1000 1500

Fracture Half-Length -ft

< 0.0 lb/ft2

0.0 - 0.3 lb/ft2

0.3 - 0.5 lb/ft2

0.5 - 0.8 lb/ft2

0.8 - 1.0 lb/ft2

1.0 - 1.3 lb/ft2

1.3 - 1.6 lb/ft2

1.6 - 1.8 lb/ft2

1.8 - 2.1 lb/ft2

> 2.1 lb/ft2

-0.2 -0.1 0 0.1 0.2

ACL Width at Well bore -in

13200 14000 14800

Stress - psi

14100

14150

14200

14250

14300

14350

W e l l D e p t h - f t

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0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

0 20 40 60 80 100 120 140 160

Time (Days)

G a s R a t e ( M S C F / D

Actual Gas Rate Sharpened Analysis History Match Forecast with Standard Analysis

Fig 7. Well A production log confirmation of the contribution from the added zone.

Fig 8. Well A actual production rate, production history match with sharpened analysis, production forecast with standard

analysis for comparison purposes.

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0.00

2,000.00

4,000.00

6,000.00

8,000.00

10,000.00

12,000.00

14,000.00

16,000.00

0 10 20 30 40 50 60 70

Time (Days)

G a s R a t e ( m s c f / d

Forecast with adjusted cutoffs Actual Gas Rate Forecast with conventional cutoffs

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

0 10 20 30 40 50 60 70 80

Time (Days)


Actual Gas Rate Forecast with Sharpen Analysis

Fig 9. Well B Field production is 2 MMcf/D higher than predicted.

Fig 10. Well C Production matches prediction up to 30 days.

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0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

0 10 20 30 40 50 60 70 80 90 100

Time (Days)


Forecast with Sharpened Analysis Actual Gas Rate

Fig 11. Well D Production matches prediction up to 50 days.

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Fig 12. Standard resolution rate predictions compared with high resolution production predictions.

Clark Sain #12

Flow Prediction From Standard Log Analysis.

Flow Prediction From Sharpened Log.

Both Standard and High Resolution

See The Thick Sand, Same Rates.

The Sharpened High Resolution

Predictions Show Pay That Would

Have Been Bypassed.

The Actual Well Production Matches

The High Resolution Prediction.

Clark Sain #12Clark Sain #12







Have Been Bypassed.









Have Been Bypassed.



Clark Sain #12







Have Been Bypassed.



Clark Sain #12Clark Sain #12







Have Been Bypassed.









Have Been Bypassed.



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SPE-99720-MS.pdf