new approach to obtain in-situ live fluid compressibilty in formation testing

SPWLA 55th

Annual Logging Symposium, May 18-22, 2014

1

A NEW APPROACH TO OBTAIN IN-SITU LIVE FLUID

COMPRESSIBILITY IN FORMATION TESTING

Li Chen, Adriaan Gisolf, Beatriz E. Barbosa, Julian Youxiang Zuo, Vinay K. Mishra, Hadrien Dumont,

Thomas Pfeiffer, Vladislav Achourov

Copyright 2014, held jointly by the Society of Petrophysicists and Well Log

Analysts (SPWLA) and the submitting authors.

This paper was prepared for presentation at the SPWLA 55th Annual Logging

Symposium held in Abu Dhabi, United Arab Emirates, May 18-22, 2014.

ABSTRACT

Compressibility and density are important fluid

properties that are used in dynamic reserve

calculations such as the material balance equation

and reservoir simulation. Compressibility is

normally obtained from pressure-volume-

temperature (PVT) measurements performed in the

laboratory. However, for samples obtained at

reservoir pressures exceeding 15,000 psi,

extrapolation techniques are sometimes used,

introducing uncertainty in the calculated results.

A new approach has been developed to obtain

compressibility from downhole fluid analysis

measurements up to 25,000 psi. A formation

testing tool pumps formation fluid from the

reservoir. Pressure and density of the pumped fluid

are measured in the flowline of the tool. A change

in pressure may be induced by a change in pump

rate and/or by closing valves in the tool. These

dynamic pressure and density data are used to

calculate compressibility.

When the density sensor is placed between the

formation interface module and the pump, density

is measured at and below formation pressure. In

this scenario, no extrapolation is required to derive

compressibility in situ at reservoir conditions. It is

possible to place the density sensor downstream of

the pump. Density is then measured at and above

mud pressure. The obtained pressure-density cross

plot can be used, not only to derive the fluid

compressibility, but also to extrapolate the density

and compressibility to reservoir pressure or, if

desired, to saturation pressure. The measurement

of pressure and density, the compressibility

calculation, and the density extrapolation are all

performed in real time during data acquisition with

the tool in the well.

This method has been successfully applied in Gulf

of Mexico and other deep-water wells for various

fluid types. The presented data examples cover

high pressure (>20,000 psi) environments. The

calculated compressibility and measured or

extrapolated density values are validated by

laboratory measurements for the lower pressure

examples. Additionally, a best practice has been

developed for various formation testing tool

configurations to maximize the quality of the

obtained compressibility data.

INTRODUCTION

Availability of accurate fluid properties (McCain,

1990) is critical to the success of reservoir

engineering processes such as reserve estimation,

production potential and design, field development

planning, and flow assurance. Fluid properties are

routinely determined from laboratory

measurements performed on fluid samples. Such

samples can be obtained with, for example,

wireline formation testers, formation sampling-

while-drilling tools, bottom hole drill stem test

(DST) sampling tools, or separator samples.

Today, many fluid properties, such as (limited

range) composition, gas-oil ratio (GOR), density,

and viscosity can also be obtained downhole in

real time with formation tester fluid analyzers

(Mullins, 2008; Achourov et al., 2011). There are

many reasons that real-time availability of fluid

properties is important: fluid DFA and sampling

programs can be optimized, Fluid grading studies

can be performed and downhole fluid analysis

(DFA) can be a critical input to reservoir

compartmentalization studies. Furthermore,

decisions about the reservoir, the well, completion,

and production can be made based on DFA before

laboratory analysis is available. DFA

measurements can also be used in the absence of

the laboratory measurements, when fluid samples

are not available or, in some cases, when reservoir

condition exceeds the laboratory pressure or

temperature limits. This paper will discuss a new

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method to determine isothermal fluid

compressibility downhole.

Fluid compressibility is used in dynamic reserve

estimation and in assessment of production

potential through different drive mechanisms.

Compressibility is a function of pressure. For

compressible fluids such as hydrocarbons above

saturation pressure, compressibility changes

continuously with pressure in a nonlinear fashion.

Therefore, compressibility measurements are

performed over a range of pressures at constant

temperature. Typically the reservoir temperature is

chosen.

COMPRESSIBILITY FROM LABORATORY

MEASUREMENT

Isothermal compressibility is historically

determined from constant composition expansion

(CCE) experiments conducted in the laboratory.

CCE experiments are performed by placing a fluid

sample in a visual pressure-volume-temperature

(PVT) cell at constant temperature. Incremental

pressure changes are induced, and the change in

fluid volume is measured at each pressure step. As

long as the pressure remains above the fluid

saturation pressure, the isothermal compressibility

can be derived from the recorded pressure and

volume data. For a crude oil system, the

isothermal compressibility coefficient of the oil

phase is defined for pressures above the saturation

pressure by one of the following expressions

(Tarek, 2006):

TP

V

VC

1 (1)

TPC

1 (2)

V can be replaced by relative volume Vr.

bp

Tr

V

VV (3)

Many methodologies exist to derive

compressibility from Eq. 1 using CCE data. The

measured volume, change in volume, and change

in pressure resulting from each CCE step can be

entered into the equation. This is the simplest

method, but for black oil, for which the change in

measured volume can be very small, the measured

volume change will be sensitive to measurement

error. This measurement error may result in large

variability in the obtained compressibility. A

method that is less sensitive to noise involves

plotting the measured volume versus pressure on a

linear scale. A function is then fitted to the data;

this can be an exponential function (Eq. 4), a

natural logarithmic function (Eq. 5), or other

function that fits the measured data. Many fitting

functions exist in the industry, but only Eqs. 4 and

5 are discussed here:

cP

ebay . (4)

bPay )ln(. (5)

The variable y in Eqs. 4 and 5 represents volume

when used in combination with Eq. 1, and density

when used in combination with Eq. 2. The

constants a, b, and c represent fitting parameters.

Standard derivative solutions exist for Eqs. 4 and

5. The derivative of volume with respect to

pressure from Eq. 4 can be substituted into Eq. 1.

When the pressure term is eliminated using Eq. 4,

the following expression for compressibility is

obtained:

cV

aV

P

V

VC

T .

1

(6)

Combining Eq. 1 with the derivative of volume

with respect to pressure from Eq. 5 yields

P

a

VP

V

VC

T

.11

(7)

Using Eq. 6 or Eq. 7, isothermal compressibility

can be determined for each recorded pressure and

volume value. Which fitting function is used

depends on the fluid encountered, preference of

the laboratory performing the measurements, and

the accuracy of the obtained function fit.

COMPRESSIBILITY FROM DFA

Formation tester DFA measurements that are

available today include accurate fluid density and

pressure. When Eq. 2 is used instead of Eq. 1, we

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can derive compressibility from pressure-density

data.

Using the downhole pump and a formation

interface module (Schlumberger, 2006; Dong et al.,

2008), fluid is pumped through the tool and into

the borehole. A downhole fluid density sensor

provides the real-time in-situ density and pressure.

Fluid contamination is measured using a dedicated

contamination monitoring system. When

contamination is below a certain threshold, a fixed

volume of fluid in the tool is exposed to either

increasing or decreasing pressure. This can be

achieved through different methods, discussed in a

subsequent section of this paper. The methodology

to extract compressibility from density-pressure

data is similar to extracting compressibility from

volume-pressure data. Measured density, the

change in density, and the change in measured

pressure can be entered into Eq. 2. Alternatively,

an exponential function (Eq. 4), a natural

logarithmic function (Eq. 5), or other function can

be fitted to the densitypressure data. Following

similar mathematical manipulations used earlier,

Eqs. 8 and 9 can be derived for compressibility:

c

a

PC

T .

1

(8)

P

a

PC

T

.11

(9)

Using Eq. 8 or Eq. 9, isothermal compressibility

can be determined for each recorded density and

pressure value.

IN-SITU DENSITY MEASUREMENT

The fluid density sensor is a rod sensor that

measures the thermophysical properties of the

fluid by the vibration of a mechanical resonator

submersed in the flowline fluid and which provide

density and viscosity measurements (OKeefe,

Erikson, et al., 2007; OKeefe, Godefroy, et al.

2007). To make a measurement, the rod is excited

by an electromechanical actuator. The interaction

of this excitation with the fluid creates the

resonance. The geometrical arrangement is well

designed, which can minimize the temperature and

pressure effects. From the resonance, the two

parameters analyzed are frequency and damping.

The frequency will relate to the density and the

damping to the viscosity. The basic structure of the

density and viscosity sensor is shown in Fig 1. The

sensor has integrated electronics, simplifying the

characterization and deployment. The sensor

measures fluid under flowing or static

condition with a zero dead volume providing an

accurate measurement.

Fig.1 Density and viscosity sensor sketch.

The density sensor is designed so the dual

resonance modes operate to directly compute

density from the resonator-fluid interaction at a 1-s

frequency (Fig.2).

The key benefit of the dual resonances is to reduce

the common mode effects. Those effects include

the Young modulus changing with temperature

and pressure, instabilities and drifts in the

mechanical resonator, electronics frequency

stability at high temperature, etc. Using

characterization parameters based on the response

of standard fluids also enables assessing

measurement quality in real time to ensure that the

sensor response spectrum is within specification.

Fig.2 Density rod dual resonance mode design,

displacement mode (left) and sharing mode (right).

Table 1 shows the specification of the density

sensor. The measurement range covers the wide

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range from 0.05 to 1.2 g/cm3, with an accuracy of

0.012 g/cm3. The pressure rating is 25,000 psi for

the extra high pressure version of the sensor. The

sensor is able to measure the fluid density at the

extra high pressure condition which can fill the

gap in fluid laboratory PVT testing, which is

normally capped by the pressure limit of 15,000 to

20,000 psi capacity. When the reservoir pressure is

high with low GOR oil, the density change with

pressure change will be small. However, the

resolution of the density measurement is as high as

0.001 g/cm3, which provides enough measurement

variation to estimate the in-situ fluid

compressibility.

Table 1. Density Sensor Specifications

Measurement Density

Sensor

Range, g/cm3 0.05 to 1.2

Accuracy, g/cm3 0.012

Resolution, g/cm3 0.001

Mechanical

Conventional Version

Temperature rating, C 150

Pressure rating, psi 15,000

HPHT Version



Extra HT Version



DISCUSSION ABOUT ACCURACY

A detailed discussion about the accuracy of

compressibility is beyond the scope of this paper.

However, since a new method of obtaining

compressibility downhole is introduced, a

comparison with existing methods is warranted.

Comparing Eq. 1 with Eq. 2 shows a strong

similarity in fundamentals. We therefore simplify

the comparison of accuracy to a comparison of the

input measurements and methods. Both the

downhole and CCE methods require pressure, a

fitting parameter, and either volume or density as

inputs.

Pressure gauges used in both the laboratory and

DFA measurements are highly accurate and

contribute the least amount to the compressibility

error. The pressure gauge used in the case studies

has a typical accuracy of 10-4

of the full scale

reading and maximum error of 2.5 10-4

of full

scale reading. In the case studies for this paper, a

25,000-psi gauge was used which translates to a

2.5 psi and 6.25 psi typical and maximum error,

respectively. All case studies were over 6,000 psi

pressure, which translates to 0.1% error when

pressure is used in the compressibility calculation.

When a pressure change is used in the calculation

instead of absolute pressure, the error is typically

reduced even further. The accuracy of pressure

gauges used in CCE experiments might deviate

slightly from the DFA gauges, but the impact of

the accuracy difference on obtained

compressibility results will be insignificant.

Relative volume is used in the laboratory

computation, and density is used in the downhole

method. Density accuracy and resolution is

defined in Table 2. For oil, which could be argued

to range between 0.5 and 0.9 g/cm3, the error

introduced through the density measurement

would not exceed 2.5%. This error will increase

for gas. When a total pressure change of several

thousand psi is exerted onto a black oil, a density

change of 0.02 g/cm3 or larger will typically be

recorded. With a sensor resolution of 0.001 g/cm3,

this represents a 5% error over the recorded range.

This error will decrease with increasing

compressibility.

The accuracy of CCE relative volume is not

widely quoted in open literature. In a typical CCE

experiment, the PVT cell is charged with 30 to 40

cm3of fluid. The change in volume resulting from

a pressure change is then determined from the cell

piston position. In black oil examples examined in

this paper, the observed typical single-phase

volume change was less than 2 cm3. From the

variability in the recorded volume readings, the

error in volume change appears to be

approximately 1%.

There will be a function-fitting error. The same

function-fitting techniques can and must be used

on DFA data and CCE experiment data when

comparing the two. A wide range of function-

fitting quality quantification techniques and

quality control techniques are available in the

industry. When the quality of the input data is

good, then fitting errors are minimal. Indeed, data

quality is the key to obtaining reliable

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compressibility from DFA. As with any

measurement, data quality control and processing

is required. DFA measurements are made at in-situ

conditions on fluid that has not been exposed to

surface conditions or bottle transfers. However,

the pressure cycles normally contain less

stabilization time, and temperature cannot be

actively controlled. When formation testing

operations are planned, executed, and quality

controlled appropriately, the compressibility

obtained with the new DFA method can have an

accuracy approaching the laboratory CCE method,

obtained at in-situ conditions in real time.

BEST PRACTICES AND DFA PLACEMENT

SCENARIOS

Formation testers are modular, and sensor

placement within the toolstring is very flexible

(Weinheber 2008) Pressure and density sensors

can be run upstream or downstream of the pump

(Mullins, 2008; Schlumberger, 2006). The location

chosen depends on the specific objectives of the

formation testing job. This sensor placement

determines the method by which a fluid pressure

change can be induced and whether the sensor is

exposed to flowing pressure or mud column

pressure. Advantages and disadvantages of each

scenario will be discussed. Note that only the

relevant sensors and generic modules are

mentioned.

The following configuration, referred to here as

setup 1, is very commonly encountered: formation

interface, pump, density and pressure sensor,

sample receptacle, exit. Fluid is drawn from

formation through the formation interface into the

pump. Fluid is subsequently expelled from the

pump at mud column pressure and passes through

the sensors into the borehole. When sample

capture is desired, the sampling receptacle is

opened and the exit is closed. Fluid is now

directed into the sample receptacle. When the

sample receptacle is filled, the pump pressurizes

the sample and the sample receptacle is closed.

Compressibility can be derived from the recorded

pressuredensity data. Additional pressure changes

can be induced at any time by closing the exit,

even when a sample receptacle is not filled. The

fluid in the flowline will be pressurized and the

pressuredensity data recorded. The following

points are worth highlighting:

Receptacle filling duration (minutes) is short

enough to assume constant ambient temperature

and long enough to ignore pressure-induced

temperature fluctuations.

Sample receptacle fluid is exposed to a 4,000 to

10,000 psi pressure increase.

Pressure and density DFA data are recorded.

Compressibility is calculated from the DFA

data.

CCE experiments and DFA data are obtained

from the same fluid sample.

DFA and CCE derived compressibility can be

compared.

The next configuration will be referred to as setup

2: formation interface, density and pressure sensor,

pump, exit. Sample receptacles and additional

DFA modules can be placed anywhere in this

configuration, but are they are not required to

obtain compressibility. Fluid is drawn into the tool

through the formation interface and flows past the

density and pressure sensors. Two different

methods can be used to create a pressure change.

Method 1 is typically applied during the late time

clean up. The drawdown, defined as the difference

between formation pressure and the flowing

pressure, is manipulated by changing the pumping

rate. As long as the flowing fluid is in single phase

and at constant contamination, the density value

obtained at different flowing pressures can be

plotted, and compressibility can be derived. The

following conditions apply to this method:

The pressure range obtained may be limited if

permeability is high.

Temperature and contamination must be

monitored and constant.

Pressure ranges that can be applied are limited

by mobility and rate.

Typically, the density and pressure data cover

formation pressure and may cover saturation

pressure.

There must be constant contamination for the

interval used in this method. Focused sampling

can help to clean up the flowline to reach an

undetectable contamination level in relatively

short time.

Method 2 can be called trapped fluid analysis,

and it differs slightly from method 1, but may

improve the compressibility results significantly.

Fluid is again pumped from formation, but when a

compressibility measurement is desired, a valve at

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the formation inlet is closed. Fluid in the flowline

between the closed inlet and the pump is

depressurized and a rate determined by the pump.

Pressure versus density data is recorded over a

large range of pressure, and compressibility is

calculated. . With this method

Depressurization can be applied step-by-step or

continuously

Depressurization is short enough to assume

constant ambient temperature and long enough

to ignore pressure-induced temperature

fluctuations.

The pressure range that can be applied will be

larger and will include saturation pressure.

To obtain sufficient data to perform the

compressibility estimation, there are other general

practices to improve the data quality for all

scenarios:

Reliable density measurement is critical.

Sanding of the formation must be avoided to

eliminate any effect on the density

measurement and the flowline fluid must be

kept single phase.

The isothermal condition should be met, so it is

necessary to have a temperature sensor to check

the measurement interval. With longer cleanup,

the temperature normally can achieve

stabilization in the flowline.

A larger pressure interval will yield more

accurate compressibility estimation. Having

flexible control over pressurizing or

depressurizing trapped fluid in flowline gives

the ability to increase the pressure coverage.

And for each pressure step, a clear stabilized

reading can improve the quality of the data.

DENSITY AND COMPRESSIBILITY

EXTRAPOLATION

When a density sensor is placed downstream of a

formation tester pump (setup 1), the density data is

recorded at or above mud column pressure.

However, for reservoir studies (Vinay, 2012) and

pressure gradient validation, fluid density at

formation pressure is required. A pressure

correction can be applied through equation of state

modeling based on DFA composition. However, a

more accurate result can be obtained by fitting a

function to the DFA pressure- density data and

extrapolating this function from mud column

pressure down to reservoir pressure. This method

can be highly accurate, particularly when the

difference between mud column pressure and

formation pressure is small (e.g., less than 1,000

psi) and the range of pressure over which

pressuredensity data was recorded is large.

Additionally, the compressibility value can be

extrapolated from compressibility-pressure plots.

CASES

There are six cases of compressibility estimation

in Gulf of Mexico and other deep water fields in

the following section, covering different toolstring

setups, various pressure ranges, black oil,

condensate gas, and water.

Case 1: Black Oil.

This case is from the Gulf of Mexico, with one

fluid sampling station with approximately 3 hours

of cleanup using a non-focused probe. Setup 2 is

used, indicating the fluid analyzer is located

upstream from the pump module. Fluid properties,

measured in real-time, include GOR, composition,

viscosity, density, pressure, and temperature

(Fig.3).

Fig.3 Fluid scanning and sampling, case 1

1000

1100

1200

0.0

0.2

0.4

0.6

0.8

1.0

1.0

1.5

2.0

2.5

3.0

0.725

0.730

0.735

0.740

0.745

175

176

177

178

179

180

6800

7000

7200

7400

7600

0 50 100 150

IFA_1

ft3/

bbl

unitl

ess

cPg/

cm3

degF

psi

ETIM (min)

GOR_IFA1 Low Quality Medium QualityHigh Quality

CHCR_IFA1[4] CHCR_IFA1[0] CHCR_IFA1[1]CHCR_IFA1[2] CHCR_IFA1[3]

RODVIS_IFA1

RODRHO_IFA1

RODTEMP_IFA1 SOIPRES_IFA1

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The contamination for the samples taken is

confirmed by the PVT laboratory to be 2.4%.

Method 1, which covers the flow period, is applied

in this case. To eliminate contamination effects,

only the later part of the data with constant

contamination is used.

Fig.4 Density-pressure function fitting.

The temperature in the flowline is observed to be

constant during the test ensuring the isothermal

condition is met. As shown on Fig. 4, the points

are carefully selected to cover the maximum

pressure interval, increasing the reliability of the

approach. In Fig.4, the density and pressure curve

is fitted by the modified exponential function,

which is shown on the plot. Then the

compressibility is estimated based on the fitted

parameters.

Fig.5 PVT density and compressibility comparison.

The PVT density is again shown together with rod

density in

Fig.5 (left). There is a 0.005 g/cm3 difference

between rod density and laboratory (PVT) density

curve. On the right in Fig.5, compressibility results

from the laboratory and the rod sensor show good

agreement. The results are summarized in Table.2.

Table 2 Density and compressibility at reservoir

condition

Density

g/cm3

Compressibility

psi-1

Rod sensor 0.727 9.49 E-06

PVT laboratory 0.722 9.53 E-06

Case 2: Black Oil.

This example, from the Gulf of Mexico, illustrates

black oil fluid scanning and sampling. As shown

in Fig.6, black oil is pumping at a GOR of

approximately 1200 ft3/bbl. The sampling was

done using a non-focused sampling tool with

toolstring setup 2. Contamination of the fluid

sample is 2.5%. Fluid cleanup has stabilized

towards the end of the station, which can be seen

from the GOR/ compositions/ viscosity/ density in

Fig.6.

Fig.6 Fluid scanning and sampling, case 2.

0

500

1000

1500

0.0

0.2

0.4

0.6

0.8

1.0

1.0

1.5

2.0

2.5

0.70

0.72

0.74

0.76

139.2

139.4

139.6

139.8

7500

8000

8500

0

1

2

3

4

0 10 20 30 40 50 60 70 80

IFA_1ft

3/b

bl

unitle

sscP

g/c

m3

degF psi

cm3/s

ETIM (min)

GOR_IFA1 Low Quality Medium Quality High Quality


RODVIS_IFA1

RODRHO_IFA1


POFR

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Fig.7 Density-pressure fitting function.

The temperature in the flowline is unchanged after

70 min, which satisfies the isothermal condition.


in this case. The density and pressure crossplot

which covers the data between 76 and 86 min is

shown in Fig.7.

The selected interval includes the very short period

of the final cleanup of the flowline and bottle

filling period where the density sensor pressure is

increasing to the formation pressure after finishing

filling the bottle. While the pressure was

increasing, the density was increasing accordingly.

Exponential fitting with Eq. 4 is applied on the rod

density, and the fitting function is shown on Fig.7.

Fig.8 PVT density and compressibility

comparison.

The measured density and calculated

compressibility is shown on Fig.8 together with

the laboratory CCE results. There is only a 0.003

g/cm3 difference in the density measurements, and

the compressibility also has a good match. The

results are summarized in Table.3.

Table 3 Density, compressibility at reservoir

condition

Density

g/cm3

Compressibility

psi-1

Rod sensor 0.745 7.80 E-6

PVT laboratory 0.748 7.20 E-6

Case 3: Black Oil.

This case, from the Gulf of Mexico, uses tool

string setup 2 at a pressure environment in the

approximate range of 22,000 to 25,000 psi. Fig.9

shows the fluid scanning and sampling log. The

focused sampling technique was used successfully.

Shortly after the flow was split at 56 min, the

sample line fluid was clean, which was later

confirmed by the PVT laboratory results.


After 80 min, the flowline temperature is

stabilized at 209 F, which indicates the isothermal

condition is met for the rest of the testing interval.

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in this case. The density-pressure points are

carefully selected at each pressure step in the

interval of interest and are shown on the left in

Fig.10. The exponential function fit to the

pressure-density curve can be used to calculate the

compressibility at each pressure, which is shown

on the right.

Fig. 10 Compressibility estimation from density

sensor

Case 4: Condensate Gas.

This case shows the compressibility measurement

using method 2 with trapped fluid in the flowline.

After sample capture has been completed, there is

clean fluid still inside the flowline. By closing the

formation interface seal valve, this fluid is isolated

and can be pressurized or depressurized as needed.

At the same time, the pressure, temperature, fluid

density, viscosity, GOR, compositions,

fluorescence, and other parameters can be

measured with the change of the pressure, as

shown in Figure 11 for this case. The temperature

is observed to be constant during the whole testing

period, meeting the isothermal condition.

A step by step reduction in flowline pressure is

induced by the pumpout module. Dew

precipitation is detected at approximately 12 min

by the fluid analyzer through the presence of

liquid hydrocarbon and dropping of GOR. Only

single phase gas must be used for the

compressibility analysis. In this case only the

pressure-density data obtained at pressures higher

than the pressure of the phase change will be used.

For each step change of the flowline pressure, the

stabilized density values are selected and shown

together with corresponding pressure in Fig.12.

The density shows the expected curvature; the

fitting with Eq. 4 matches the rod density, which is

shown in the plot. The compressibility curve is

estimated based on the density function, which is

shown in Fig.13.


Fig.12 Fluid density of condensate gas case

This trapped volume decompression method

covers a large pressure range, in this case more

than 4,000 psi. This improves the reliability of the

compressibility estimation, especially for this

high-compressibility fluid.

10000

20000

30000

0.0

0.2

0.4

0.6

0.8

1.0

0.35

0.40

0.45

0.50

0.55

126

128

130

132

134

6000

8000

10000

0

1

2

3

0 2 4 6 8 10 12 14 16 18

IFA_1

ft3

/bb

l

un

itle

ss

g/c

m3

de

gF p

si

cm

3/s

ETIM (min)

GOR_IFA1 Low Quality Medium Quality High Quality


RODRHO_IFA1


POTFR

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Fig. 13 Compressibility for condensate gas case

Case 5: Water.

This case is a water sampling case with toolstring

setup 1, in which the rod density sensor is located

downstream of the pumpout module. This shows

the benefit of the toolstring setup that can

overpressure the sample bottle thousands of psi

above hydrostatic pressure; the large pressure

interval makes it possible to have good estimation

of compressibility, even if the fluid is nearly

incompressible.

Fig.14 Fluid scanning and sampling for water.

Water contamination is monitored by the

resistivity cell in the fluid analyzer, and Fig. 14

shows good cleanup, achieving clean fluid. As

shown in the figure, after 150 min, four bottles are

filled. After filling the bottles, the pumpout

module continues to pressurize the bottles, results

in the steep pressure and density increases

observed in the top two tracks. This increase in

pressure of more than 5,000 psi provides a large

pressure change that is used apply for the

compressibility estimation. The temperature is

stable at approximately 164F and with less than 1

F fluctuation, thus constituting an the isothermal

process.

Fig.15 Water density measurement for four bottles.

For all the four samples, as shown on Figure 15,

the density measurement indicates good

repeatability and overall shows a clear trend.

Fig.16 Water pressure-density curves for four

consecutive bottles.

Fig.17 Water compressibility from different

bottles.

1.01

1.02

1.03

1.04

1.05

160

162

164

166

168

170

2000

4000

6000

8000

0.0

0.5

1.0

0.050

0.055

0 50 100 150 200 250

IFA_1

g/c

m3

de

gF p

si

un

itle

ss

oh

m.m

ETIM (min)

RODRHO_IFA1


LEGS_IFA1 WATF_IFA1 HAFF_IFA1

FFRES_IFA1

SPWLA 55th


11

All each bottle, the compressibility can be

estimated separately from the pressure-density

curves plotted in Figure 16. The compressibility

curves estimated from different bottles (shown on

Fig.17) overlay, showing the consistency of the

results.

Fig. 18 Density and compressibility extrapolated

to reservoir pressure.

Since the rod density sensor is located at

downstream of the pump, it only measures the

properties at and higher than the hydrostatic

pressure. With the curve fitting on Fig.16 and

Fig.17, the density and compressibility can be

extrapolated to reservoir pressure. As shown in

Fig.18, the density and compressibility

extrapolation results from four bottles have a very

good agreement.

Case 6: Black Oil.

This is a light oil sampling station with toolstring

setup 1 in which the rod density sensor is located

downstream of the pumpout module. The pump

pressurized the bottles 5,000 psi over hydrostatic

pressure after filling the bottles, which provides a

high quality data with significant pressure-density

variation.

Fluid cleanup is achieved using focused sampling.

After 1.6 hr the flowing fluid is free of

contamination. The temperature variation is less

than 1 F during the sample over pressuring,

which can be treated as isothermal condition.

Three consecutive samples are taken at the end of

the station. The overpressure for each bottle can be

seen as pressure and density spikes on Fig.19.

Fig.19 Fluid scanning and sampling for light oil.

Fig.20 Light oil pressure-density fitting for three

bottles.

For each bottle, the pressure-density curves are

fitted by Eq. 4. The fitting functions are showing

on Fig.20. All the curves have a good fit. The

compressibility can be estimated by Eq. 8 based on

the density curve fitting for each bottle. The

compressibility estimation curves for the three

bottles in Fig.20 show good agreement for all the

bottles.

Fig.21 Compressibility from three bottles.

2500

2600

2700

2800

2900

3000

0.0

0.2

0.4

0.6

0.8

1.0

0.58

0.60

0.62

0.64

270

271

272

273

274

275

10000

12000

14000

16000

18000

0 2000 4000 6000 8000 10000 12000

IFA_1

ft3

/bb

lu

nit

less

g/c

m3

de

gF p

si

ETIM (s)

GOR_IFA1

CHCR_IFA1[4] CHCR_IFA1[0] CHCR_IFA1[1] CHCR_IFA1[2]CHCR_IFA1[3]

RODRHO_IFA1


SPWLA 55th


12

Since the density is measured at hydrostatic

pressure, fluid density at reservoir pressure is

extrapolated. Fig.22 shows the density and

compressibility extrapolated to reservoir pressure.

The laboratory measured density was 0.588 g/cc.

In this case study, compressibility and pressure

corrected densities gave very consistent results.

Fig.22 Density / compressibility extrapolation.

CONCLUSION

With in-situ density measurement in the formation

testing tool, fluid compressibility can be

determined under reservoir conditions, downhole

in real time. This is a new approach that is based

on in-situ fluid density instead of using the relative

volume method. The approach is applicable for the

reservoir fluids from nearly incompressible fluid

to highly compressible fluid. The accuracy of the

compressibility measurements from this approach

can be similar to that from a laboratory, as

confirmed by the case studies. The method

provides the real-time answers which enables field

study at early stage.

REFERENCES

Achourov, V., Gisolf, A., Kansy, A., Eriksen, K.O.,

O'Keefe, M., and Pfeiffer, T., 2011, Applications

of accurate in-situ fluid analysis in the North Sea:

Paper SPE 145643-MS presented at Offshore

Europe, Aberdeen, UK, 68 September.

Dong, C., O'Keefe, M.D., Elshahawi, H., Hashem,

M., Williams, S., Stensland, D., Hegeman, P,

Vasques, R., Terabayashi, T., Mullins, O.C., and

Donzier, E., 2008, New downhole-fluid-analysis

tool for improved reservoir characterization: SPE

Reservoir Evaluation & Engineering 11 (6): 1107

1116. SPE 108566-PA.

McCain, W.D., 1990, The Properties of Petroleum

Fluids: Tulsa, Oklahoma, PennWell Publishing.

ISBN 0-87814-335-1.

Mishra, V. K., Skinner, C., MacDonald, D. et al., 2012,

Downhole fluid analysis and asphaltene nanoscience

coupled with vertical interference testing for risk

reduction in black oil production: SPE Annual

Technical Conference and Exhibition, paper SPE

159857

Mullins, O.C., 2008, The Physics of Reservoir

Fluids; Discovery through downhole fluid analysis,

Houston, Texas, Schlumberger. ISBN-10: 0-

97885-302-4.

OKeefe M., Eriksen K. O., Williams S., Stensland

D., and Vasques R., 2007, Focused sampling of

reservoir fluids achieves undetectable levels of

contamination: Paper SPE 101084 presented at

SPE Asia Pacific Oil & Gas Conference, Jakarta,

Indonesia, 30 October1 November.

OKeefe, M., Godefroy, S., Vasques, R., Agenes,

A., Weinheber, P., Jackson, R., Ardila, M.,

Wichers, W., Daungkaen, S., De Santo, I., 2007,

In-situ density and viscosity measured by wireline

formation testers: Paper SPE 110364 presented at

SPE Asia Pacific Oil & Gas Conference and

Exhibition, Jakarta, Indonesia, 30 October1

November.

Schlumberger, 2006, Fundamentals of formation

testing: Houston, Texas, Schlumberger.

Tarek A., 2006, Reservoir Engineering Handbook

Third Edition: Gulf Professional Publishing.

ISBN-10: 0-7506-7972-7.

Weinheber P,, Gisolf AG., Jackson RR., De Santo

I., 2008, Optimizing Hardware Options for

Maximum Flexibility and Improved Success in

Wireline Formation Testing, Sampling and

Downhole Fluid Analysis Operations: Paper SPE

119713 presented at Nigeria Annual International

Conference and Exhibition held in Abuja, Nigeria,

4-6 August 2008.

SPWLA 55th


13

ABOUT THE AUTHOR

Li Chen is a Senior Reservoir

Engineer and Associate Reservoir

Domain Champion with

Schlumberger, Houston, Texas. He

has received the Masters Degree in

Reservoir Engineering from China

Petroleum University. His previous positions include

senior reservoir engineer, associate reservoir domain

champion, answer product analyst for formation testing

in China.

Adriaan Gisolf is a Reservoir Domain

Champion with Schlumberger, based

in Sugar Land. Previous positions

held in Schlumberger include Field

Engineer in Indonesia and Nigeria,

Service Quality Coach in Colombia

and Reservoir Domain Champion in Angola and

Norway. He holds a master's degree in mechanical

engineering from Delft University of technology.

Beatriz E. Barbosa is a Reservoir

Pressure & Sampling Product

Champion with Schlumberger,

Wireline HQ. Under her

responsibilities are the alignment of

the domain road map with the industry

needs and the development of the

required technologies. Her previous positions include

Wireline Geomarket manager (Peru, Colombia and

Ecuador), Middle East & Asia Wireline Training Center

Manager and Country Wireline operations manager,

and Field Engineer and Technical Sales representative

in Angola, Colombia and Ecuador. Beatriz holds a

degree in Bsc. Civil Engineering from Los Andes

University in Bogota, Colombia.

Dr. Julian Youxiang Zuo is currently a

Scientific Advisor and FLCN

Interpretation Architect at

Schlumberger Houston Pressure &

Sampling Center leading the effort to

develop new answer products for new

formation testing tools. He has been working in the oil

and gas industry since 1989 and coauthored more than

160 technical papers in peer-reviewed journals,

conferences and workshops. Zuo holds a Ph.D. degree

in chemical engineering from the China University of

Petroleum in Beijing.

Vinay K. Mishra is Principal

Reservoir Engineer and Domain

Champion with Schlumberger,

Houston, TX. Previously he has

worked in different roles of

petroleum engineering based in

Canada, Libya, Egypt and India. He has co-authored

over 25 publications in international conferences

including SPWLA and SPE. He has done B.S. in

Petroleum Engineering from Indian School of Mines,

Dhanbad, India. Vinay has been committee member

and session chairs in several of SPE events. He is also

registered with Association of Professional Engineers

and Geoscientists of Alberta (APEGA).

Hadrien Dumont is a Reservoir Domain

Champion with Schlumberger, based in

Houston. Previous positions held in

Schlumberger include Field Engineer in

Norway, Kazakhstan, and Malaysia and

Reservoir Domain Champion in Egypt,

Sudan, Syria, Indonesia, and the United States. He

holds a MSc in Mining Engineering from University

Libre de Bruxelles, Belgium and a MSc in Petroleum

Engineering from Institut Francais du Petrole, France.

Thomas Pfeiffer is a Reservoir Domain

Champion in Schlumberger, Stavanger,

Norway. He has received Masters

Degree in Petroleum Engineering in

Texas A&M University, Masters

Degree in Electrical Engineering from

Technical University of Munich, Germany. His

previous positions held in Schlumberger include Field

Engineer in North Sea, Egypt, Netherlands, Austria,

Gulf of Mexico, and Location Manager in Austria and

Hungary.

Vladislav Achourov is a Reservoir

Domain Champion with

Schlumberger, based in Norway. He

has received Master's Degree in

Physics from Russian University of

Oil & Gas. He joint Schlumberger in

Russia and worked with reservoir simulations and

production engineering. In his current role he provides

technical support for wireline formation testing.