literature reviews

write two literature reviews about:

– Environmentally Friendly Well Testing
– ANN Powered Virtual Well Testing.

summaries from the attached files.

Copyright 2002, Society of Petroleum Engineers Inc.

This paper was prepared for presentation at the SPE International Conference on Health,
Safety and Environment in Oil and Gas Exploration and Production held in Kuala Lumpur,
Malaysia, 20–22 March 2002.

This paper was selected for presentation by an SPE Program Committee following review of
information contained in an abstract submitted by the author(s). Contents of the paper, as
presented, have not been reviewed by the Society of Petroleum Engineers and are subject to
correction by the author(s). The material, as presented, does not necessarily reflect any
position of the Society of Petroleum Engineers, its officers, or members. Papers presented at
SPE meetings are subject to publication review by Editorial Committees of the Society of
Petroleum Engineers. Electronic reproduction, distribution, or storage of any part of this paper
for commercial purposes without the written consent of the Society of Petroleum Engineers is
prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300
words; illustrations may not be copied. The abstract must contain conspicuous
acknowledgment 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.

Abstract
Burning hydrocarbons during cleanup and well-testing
operations produces toxic gases, soot, acid rain, unburned
hydrocarbons and noise. Not only do these emissions have a
damaging impact on the environment, but they also impose an
economic impact: the cost of the oil and gas flared and the cost
of the equipment used during the flaring operations.

A joint task force between two offshore operating
companies in Abu Dhabi, United Arab Emirates (U.A.E.),
with the help of the contracted service provider, worked to
implement innovative solutions to achieve flaring-emission
elimination targets and eliminate environmental risk.

Innovative equipment modifications and design, coupled
with new operational procedures, were implemented to
neutralize the fluids used to prepare the well for production
(allowing well effluents to flow through the test separator
during the cleanup phase) and to use a boosting system to
pump the oil into the sealine (fully eliminating oil flaring).

The initial stages of the solution neutralized the acid
flowed back to surface and eliminated the oil flaring. A
multiphase flowmeter was then introduced to the system. Not
only did the multiphase flowmeter enhance operation
flexibility, confidence in the information acquired, and
accuracy of the results, but it also eliminated gas flaring after
the cleanup period. The minimal pressure drop across the
meter and the high-pressure rating of the meter allow well
effluents to flow naturally through the sealine without
separating the well-effluent phases. Deployment of the
multiphase flowmeter in the system reduced the gas flaring by
about 60% during the overall job.

Introduction
Conventionally, during cleanup and well-testing operations,
both oil and gas are burned away into the atmosphere. The
combustion efficiency of fully atmospheric oxidation is
insufficient. Oil- and gas-flaring operations create significant
amounts of emissions that contain unburned hydrocarbons,
carbon monoxide and nitrogen oxides, which produce acid
rain, smog, ozone at ground levels, and greenhouse gases in
the upper atmosphere.

Acid rain depletes soil, pollutes water, damages forests,
endangers animal habitats and food chains, and corrodes
human-made structures, such as buildings, statues,
automobiles, and other artifacts made of stone or metal. Smog
and ozone cause human respiratory ailments, such as asthma,
bronchitis and emphysema. Most scientists believe that
greenhouse gases are a major cause of global warming.
Increased concentrations of water vapor, carbon dioxide,
methane and other greenhouse gases trap heat energy in the
earth’s atmosphere. A gradual rise in the earth’s surface
temperature is expected to melt polar ice caps and glaciers,
expanding ocean volume and raising sea level, flooding some
coastal regions and even entire islands.

Middle Eastern countries, including the U.A.E. with its
low-lying coastal areas, are concerned about rising sea levels
and potential flooding caused by global warming. They are
concerned about increased radiation of heat and light from
global warming, leading to regional desertification. As major
producers of oil and gas, they are concerned about the
deterioration of air quality from inefficient combustion of a
sour gas supply.

Abu Dhabi is blessed with a charming environmental
heritage. Nevertheless, the environment in this part of the
world is no less fragile than anywhere else.

Recognizing mounting international concerns over
environmental issues, a joint task force was formed in June
1997. The Abu Dhabi Marine Operating Company and the
Zakum Development Company, with the assistance of
Schlumberger, the testing services provider, worked to
implement pioneering solutions for optimizing acid
neutralization, reducing flaring operations in the short term,
and ultimately achieving zero hydrocarbon flaring to eliminate
environmental risk during well-testing operations(1-2).

SPE 74106

Environmentally Friendly Well Testing
Y. El-Khazindar,SPE, Schlumberger, M. Ramzi Darwish, ADMA-OPCO, and A. Tengirsek, Schlumberger

2 Y. EL-KHAZINDAR, R. DARWISH AND A. TENGIRSEK SPE 74106

Environmentally Friendly Well Testing
This paper describes the equipment and processes used to
achieve zero hydrocarbon flaring.

The First Stages: Acid Neutralization and Oil Reinjection.
The separation and reinjection of oil was introduced in June
1998 (see Fig. 1). In this process, single-phase oil-reinjection
pumps, specially designed to overcome high sealine pressures
of up to 1300 psi, were used to reinject the oil back into the
production sealine. They covered the range from 12,000
BOPD at 410 psi to 3500 BOPD at 1300 psi. The use of
reinjection pumps, which eliminated the need for oil flaring
during postcleanup operations, reduced the total oil flared
by 65%.

To stimulate wells after drilling or workover operations,
10 to 15 gal/ft of 15% HCl are pumped downhole. The spent
acid flowing back to surface typically has a pH of 2 to 3. In
February 2000, an acid neutralization system was introduced
(see Fig. 2). In this process, neutralizing agents are used to
keep the pH of the effluents flowing back to surface after an
acid job between 5.5 and 6. Na2CO3 was chosen because it is
soluble in water, cost effective and generally available on
well sites.

The neutralization system keeps the pH of water dumped
overboard the rig between 5.5 and 6. Furthermore, it allows oil
separation from the start of flowback operations so that the
reinjection pumps can inject 100% of the oil back into the
production sealine.

The neutralization system involves pumping demulsifying
agents upstream of the choke manifold and pumping the
neutralizing agent, Na2CO3, downstream of the choke
manifold. The neutralizing agent then flows into a test
separator, a 100-bbl dual-compartment skimmer, where
chemical treatment reduces the oil-in-water content to 100
ppm before the water is dumped overboard.

Introducing the neutralization system and the reinjection
pumps eliminated the need for oil-flaring operations. Even
though initial stages considerably reduced emissions to
atmosphere, improving the system to eliminate gas flaring
during cleanup and well-testing operations remained
a challenge.

The PhaseTester multiphase flowmeter. In May 2001, the
PhaseTester* portable multiphase periodic well testing
equipment(3-5) was introduced to the setup (see Fig. 3). The
PhaseTester’s high working pressure of 5000 psi allows
placing the tool upstream of the choke manifold (see Fig. 4).
This tool was selected to

• reduce the amount of gas flared by
approximately 60%

• eliminate unnecessary shut-ins and flow disturbance
during testing operations (for example, during a
helicopter landing)

• provide a reliable, stand-alone flow rate
measurement.

* Mark of Schlumberger

The PhaseTester multiphase flowmeter simultaneously
measures the total mass flow rate of the stream passing
through, with a venturi meter, and the individual phase
fractions, with a dual-energy gamma fraction meter. Working
on the multiple-energy principle, the detector picks up the total
count of photons hitting the sensor, as well as the energy of
each hit. The high-speed detector produces a signature count
rate, over the two peak energy bands, which is a function of
the measured medium. Hits at two different energy levels are
filtered out and used for the fraction of oil, water and gas
calculations. The sampling frequency of 45 Hz is sufficient to
define the continuous variations in the process flow.

The measuring section includes the venturi meter and the
dual-energy gamma fraction meter. Both measurements—
performed at the same time and place—eliminate anomalies or
inaccuracies associated with meters that have a series of
sensors implemented along a pipe and that do not detect the
same flow instantaneously.

The PhaseTester multiphase flowmeter uses a blind tee in
the flowline upstream of the measuring section to impose a
predictable flow shape onto the flowstream. Effectively
removing flow anomalies imposed by downhole conditions
and surface piping, this tee eliminates high-frequency flow
instabilities in the measuring section.

With an American Petroleum Institute (API) 6A rating,
the PhaseTester multiphaseflowmeter does not require
additional shutdown and pressure-relief systems. The
PhaseTester Vx* multiphase well testing technology can be
used to accept fluids directly from the flowline and, after
measurement, flow them back naturally to the production
sealine without any boosting services. Pressure loss across the
system is typically 3 to 30 psi, much lower than for a
conventional test-separation system.

Testing. Between May 15 and October 1, 2001, 23 wells were
cleaned up and tested using the reinjection, neutralization
system and PhaseTester multiphase flowmeter combination.
The operation involved 36 postcleanup flow tests. Flowing
conditions during the flow periods were

• wellhead flowing pressure range: 90–1300 psi
• oil flow rate range: 400–5500 BOPD
• gas flow rate range: 0.3–4.0 MMscf/D
• gas volume fraction (GVF) range: 60–98%
• CO2 range: 1.5–8.0%
• H2S range: 0–4%
• basic sediment and water (BS&W) range: 0–34%

Figs. 5 through 11 plot the distribution of these parameters
against the number of flow periods.

The 36 flow tests were compared to a traditional test
separator(6-7). Various authors have mentioned the difficulty of
making comparisons with test separators (for example, Ting8
or Amdal et al.9). Separators are limited to performing a
reasonable liquid-to-liquid separation. In most tests the water
cut is calculated from in-line sampling from the choke
manifold. The PhaseTester tool obtains reliable water
measurements with the dual-energy spectral gamma-ray

SPE 74106 ENVIRONMENTALLY FRIENDLY WELL TESTING 3

composition meter because this nuclear determination is not
sensitive to the distribution of the phases.

Uncertainties are not limited to those of the individual oil
and gas meters installed on a test separator. Other factors that
can affect the separator uncertainties are

• meter factor drift caused by gas entrainment in the
liquid leg of the test separator

• slugging of the wells, which affects
test-separator performance

• an operator-dependent process
• poor liquid-to-liquid separation
• overall accuracy affected by the whole system (back

pressures, lines, controls, etc.)
• frequency of data gathering.
For a calibrated separator operated by a dedicated crew,

the uncertainties are expected to be
• gas uncertainty in the range of 5 to 8%
• total liquid uncertainty in the range of 5 to 8%.
Comprehensive preparation of the reference separator was

performed in advance, ensuring a proper conclusion. A
comparison of flow rate results between the PhaseTester tool
and the reference separator indicated that the PhaseTester tool
performed within its operating specifications, yielding the
PhaseTester multiphase flowmeter uncertainty figures +/- the
separator uncertainties.
The flow rate uncertainties(10) for the PhaseTester flowmeter
are as follows:

Liquid:
The larger of 2.5% of the reading, or 300 bbl/d, for
GVF between 0 and 98%

Gas:
The larger of 1% of the reading, or 140 scf/d, for
GVF between 0% and 30%
The larger of 3% of the reading, or 420 scf/d, for
GVF between 30% and 60%
The larger of 10% of the reading, or 1410 scf/d, for
GVF between 60% and 90%
The larger of 15% of the reading, or 2120 scf/d, for
GVF between 90% and 95%

Water-Liquid Ratio (WLR):
± 3% absolute for GVF between 0% and 70%
± 4% absolute for GVF between 70% and 80%
± 5% absolute for GVF between 80% and 90%
± 8% absolute for GVF between 90% and 95%.

Toward Zero Gas Flaring. The ultimate goal is to achieve a
flaring-free operation. As a step toward this goal, the
PhaseTester multiphase flowmeter was deployed to reduce gas
flaring to a minimum. The PhaseTester flowmeter was
expected to decrease gas flaring by approximately 60% during
the overall job, as well as eliminate unnecessary shut-ins and
flow disturbance during the testing operations.

After the flowback and acid neutralization, once the
wellhead pressure increased to overcome the production
sealine pressure, well effluents bypassed the conventional
separator system and flowed naturally through the PhaseTester

tool into the production sealine. Separating, boosting and
flaring operations were not required. The extremely low
pressure drop across the PhaseTester tool helped to achieve
gas-flaring reduction.

Confidence in the PhaseTester multiphase flowmeter’s
measurement reliability and flexibility was developed during
its evaluation over 36 flow tests. The PhaseTester multiphase
flowmeter was then deployed on three tests to reduce gas
flaring. Table 1 shows the reduction in gas flaring when the
PhaseTester multiphase flowmeter was integrated into the
system. (Gas is typically flared during the initial
cleanup phase.)

TABLE 1—[Summary of gas flaring reduction results]
Well no. 1 Well no. 2 Well no. 3
Total job duration, hours 75 80 41
Total gas produced, MMscf 13.6 8.3 3.6
Gas flared, MMscf 3.20 0.54 0.77
Gas flared, % 24 7 21
Gas injected, MMscf 10.4 7.7 2.9
Gas Injected, % 76 93 79

Future Plans. Various studies have been carried out to define
practical and economical means of fully eliminating the gas
flaring. A multiphase pump is currently being considered as a
solution. The challenges to designing a multiphase pump for
this application include the pump capability to cover a wide
range of flow conditions, as well as size and power
requirements. The joint task force is currently studying
technical details and performance of a multiphase pump
specifically designed to suit a wide range of flow conditions.

The current water treatment skimmer system is capable of
achieving an oil-in-water level of approximately 100 ppm.
Different options were considered to achieve an oil-in-water
level of less than 15 ppm, the environmental emissions
guidelines limit specified by Abu Dhabi National Oil
Company’s Health, Safety and Environment policy. The joint
task force proposed an integrated water deoiling package. Full
feasibility and Hazardous and Operability studies were
performed. The water deoiling unit, currently being
manufactured, is planned for utilization in January 2002.

The water deoiling unit was designed to meet the
following criteria:

• a compact integrated solution including a degasser,
hydrocyclone, reject oil tank, pumping units and
control panel, all on one skid that can be
accommodated easily on the rig/barge without
inflicting space limitations

• the ability to recycle the liquids within the system to
ensure an optimum oil-in-water level

• the ability to cover a wide range of flow rates and
cope with changing flowing conditions. The system is
capable of handling up to 6,000 B/D of water with
oil-in-water levels of up to 1% at the inlet of
the system.

4 Y. EL-KHAZINDAR, R. DARWISH AND A. TENGIRSEK SPE 74106

Figure 12 is a schematic for the proposed future setup.

Conclusions
During the initial stages of the project, utilizing acid
neutralization and oil-reinjection systems achieved zero oil
flaring. After integrating the PhaseTester multiphase
flowmeter into these systems, the following benefits
were observed:

• When the wellhead pressure is high enough to
overcome the production sealine pressure, the well
effluents flow naturally through the PhaseTester
multiphase flowmeter (bypassing the separator) into
the production sealine. Gas flaring is eliminated
(because of the minimal pressure drop across the
PhaseTester flowmeter), reducing the amount of gas
flared during the whole job by more than the
targeted 60%.

• The PhaseTester multiphase flowmeter results,
compared to a reference test separator, showed that it
performed within its operating specifications and
provided reliable, stand-alone flow rate
measurements.

• Shut-ins to accommodate helicopter landings were
eliminated because fluids flow directly through the
PhaseTester multiphase flowmeter into the
production sealine. Furthermore, helicopter flights are
no longer dependent of the flow programs.

Eliminating the risk to the environment posed by flaring
hydrocarbons during well testing operations is a challenge.
Keys to achieve environmentally friendly well
testing operations:

– understanding the flow conditions and
operational constraints.

– setting reasonable targets to achieve flaring
free operations.

– examining and revising the existing
operational procedures.

– looking for areas in which a significant value could
be achieved by implementing innovative ideas
and solutions.

The deployment of an acid neutralization system and oil
reinjection pumps to completely eliminate oil flaring as well
as the utilization of the PhaseTester multiphase flow meter to
reduce the gas flaring by more than 60% are examples of
solutions that can be implemented to achieve environmentally
friendly well testing operations.

Acknowledgments
The authors would like to thank the management of ADMA-
OPCO and Schlumberger for permission to publish this paper.

References

1. Messiri A., Al Attas, M.O., and Mohamed, N.: “Towards
Zero Flaring Emission,” paper ADIPEC 0964, presented at
the 2000 Abu Dhabi International Petroleum Exhibition and
Conference, Abu Dhabi, UAE, October 15–18.

2. Hassan, M.M., Fadaq, A.S. and Beadie, G.: “Reduction of
Well Emission During Clean Up and Testing Operations by
Rig,” paper SPE 68151, presented at the 2001 SPE Middle
East Oil Show and Conference, Bahrain, March 17–20.

3. Kontha, I.N.H, Weimer, B, Retnanto, A., Azim A and
Martinon, D.: “Monitoring Well Performance Using
Multiphase Flow Meters,” paper SPE 68718, presented at
the 2001 SPE Asia Pacific Oil and Gas Conference and
Exhibition, Jakarta, Java, Indonesia, April 17–19.

4. “Multiphase Flow Meters: A New Way to test Wells in
Production,” Hart First Look (September 1999).

5. Mus, E. A, Toskey, E.D and Bascoul, S.J.F: “Added Value
of a Multiphase Flow Meter in Exploration Well Testing,”
paper OTC 13146, presented at the 2001 Offshore
Technology Conference, Houston, Texas, USA, April 30–
May 3.

6. Atkinson, D.I, Berard, M and Segeral, G.: “Qualification of
a Nonintrusive Multiphase Flow Meter in Viscous Flows,”
paper SPE 63118, presented at the 2000 SPE Annual
Technical Conference and Exhibition, Dallas, Texas, USA,
October 1–4.

7. Theuveny, B.C., Segeral, G. and Pinguet, B.: “Multiphase
Flowmeters in Well Testing Applications,” paper SPE
71475, presented at the 2001 SPE Annual Technical
Conference and Exhibition, New Orleans, Louisiana, USA,
September 30–October 3.

8. Ting, V.C.: “Effects of Non-Standard Operating Conditions
on the Accuracy of Orifice Meters,” SPE Production &
Facilities (February 1993) 58.

9. Amdal, J., Danielsen, Dykesteen, E, Flølo, D., Grendstad,
J., Hide, H.O., Moestue, H., and Torkildsen, B.H.:
Handbook of Multiphase Metering, Norwegian Society for
Oil and Gas Measurement, (1997).

10. Retnanto, A.: “Production Optimization Using Multiphase
Well Testing: A Case Study from East Kalimantan,
Indonesia,” paper SPE 71556, presented at the 2001 SPE
Annual Technical Conference and Exhibition, New
Orleans, Louisiana, USA, September 30–October 3.

Metric Conversion Factors
cp x 1.0* E–03 = Pa·s
bar x 1.013 25* E+05 = Pa
psi x 6.894 757 E+00 = kPa
bbl x 1.589 873 E–01 = m3
B/D x 6.624 471 E–03 = m3/h
ft3 x 2.831 685 E–02 = m3
ft3/D x 1.179 869 E–03 = m3/h
lb/ft3 x 1.601 846 E+01 = kg/m3

*Conversion factor is exact.

SPE 74106 ENVIRONMENTALLY FRIENDLY WELL TESTING 5

Fig. 1—Schematic of the surface well testing setup and
reinjection pump

Fig. 2—Schematic of the surface well testing setup,
reinjection pump and neutralization package

Fig. 3—Schematic of the surface well testing setup,
reinjection pump, neutralization package and PhaseTester
multiphase flowmeter

Fig. 4—The PhaseTester multiphase flowmeter

0

2

4

6

8

10

12

90-200 200-500 500-750 750-1000 1000-1500 1500-1800

Wellhead Flowing Pressure (Psi)

N
um

be
r

of
te

st
s

pe
rf

or
m

ed

Fig. 5—Wellhead flowing pressure distribution versus the
number of tests

0
2
4
6
8
10
12

14

>400 400-1000 1000-
2000

2000-
3000

3000-
5000

4000-
5500

Oil Flow rate (bbl/d)

N
um
be
r
of
te
st
s
pe
rf
or
m
ed

Fig. 6—Oil flow rate distribution versus the number
of tests

6 Y. EL-KHAZINDAR, R. DARWISH AND A. TENGIRSEK SPE 74106

0.0
1.0
2.0

3.0
4.0
5.0
6.0
7.0

8.0
9.0

10.0

>0.3 0.5-0.75 0.75-1 1-1.5 1.5-2 2-3 3-4

Gas Flow Rate (MMscf/D)

N
um
be
r
of
te
st
s
pe
rf
or
m
ed

Fig. 7—Gas flow rate distribution versus the number
of tests

0
2
4
6
8
10
12
14

16

60-70 70-80 80-90 90-95 95-98

GVF (%)

N
um
be
r
of
te
st
s
pe
rf
or
m
ed

Fig. 8—Gas volume fraction distribution versus the
number of tests

0
2
4
6
8
10
12
14
16

18

1.5-2 2-3 3-5 6-8

CO2 (%)

N
um
be
r
of
te
st
s
pe
rf
or
m
ed

Fig. 9—Carbon dioxide distribution versus the number
of tests

0
2
4
6
8
10
12
14
16
18

0 0.2-1 1-1.5 1.5-2 2-4

H2S (%)

N
um
be
r
of
te
st
s
pe
rf
or
m
ed

Fig. 10—Hydrogen sulphide distribution versus the
number of tests

0
2
4
6
8
10
12
14

0 1 2 3 4 5 6 7 8 9 10 24 34

BS&W

N
um
be
r

of
T

es
ts

p
er

fo
rm

ed

Fig. 11—Basic sediment and water distribution versus the
number of tests

Fig. 12—Schematic of the reinjection pump, neutralization
package, PhaseTester multiphase flowmeter, water
deoiling unit and multiphase pump

OTC 24981

ANN Powered Virtual Well Testing
A. Aggarwal, S. Agarwal, Indian School of Mines

Copyright 2014, Offshore Technology Conference

This paper was prepared for presentation at the Offshore Technology Conference Asia
held in Kuala Lumpur, Malaysia, 25–28 March 2014.

This paper was selected for presentation by an OTC program committee following review
of information contained in an abstract submitted by the author(s). Contents of the paper
have not been reviewed by the Offshore Technology Conference and are subject to
correction by the author(s). The material does not necessarily reflect any position of the
Offshore Technology Conference, its officers, or members. Electronic reproduction,
distribution, or storage of any part of this paper without the written consent of the Offshore
Technology Conference is prohibited. Permission to reproduce in print is restricted to an
abstract of not more than 300 words; illustrations may not be copied. The abstract must
contain conspicuous acknowledgment of OTC copyright.

Abstract

Due to the drying up of old oil fields throughout the

globe, the age of easy oil is over and the newly discovered

fields have reservoirs with complex heterogeneous media.

The reservoir parameters are identified indirectly by

correctly interpreting well test model which is recognized

by the feature of pressure derivative curves. Well testing

involves creation of disturbance in fluid flow by injecting

liquids and simultaneously recording the pressure

transient data. Lost production, equipment and personnel

costs turn well testing as highly cost intensive job making

it difficult to cover all the important wells in a particular

field. But with the advent of artificial neural networks

(ANN) it is now possible to generate synthetic pressure

transient data. This technique provides a basis to leach out

detailed information from the available pressure transient

data and it doesn’t eradicate the need for actual well tests.

This technique can also prove to be very vital in cases

where equipment breakdown may have taken place and

full set of data couldn’t be availed. This simulated well

testing involves training of a neural network from

pressure transient data obtained from designated wells in

the field, which has the potential to generate pressure

transient responses at other well sites where no well test

has been conducted.

In this paper a 3 layer multi-layer perceptron (MLP) Time

Delay Neural Network – NARX model has been designed

working on resilient backpopagation algorithm for

training. Cubic Spline Interpolation has been used from

enriching the data before feeding it to NARX model. A

simulated example which highlights the efficiency of

NARX model in attaining accurate synthetic pressure

transient data has been discussed. The neural network is

successful in predicting well test interpretation model.

The ANN thus produces expeditious and reliable synthetic

data which has the potential to revamp the industry.

Introduction

Due to the complex structures and heterogeneous media,

of oil and gas reservoirs, characterizing reservoirs

precisely is a herculean task. Petroleum Engineers solve

this challenge by acquiring and analyzing in depth

reservoir information, which is crucial for the study of the

reservoir performance (Vaferi et al., 2011). Well Testing

or Pressure Transient Testing has proved to be a powerful

reservoir characterization tool to study such complex

media (Muskat, 1937). Pressure testing is conducted by

recording the well bore bottom hole pressure responses

which are created as result of induced flow disturbances.

The most general test methods are 1) By creating a

pressure drawdown in the wellbore by producing the well

at a constant rate, after keeping it in shut-in condition for

a set period (Figure 1); 2) By developing a pressure

build-up in the wellbore by shutting-in at the bottom hole,

due to which formation fluids c

ann

ot flow into the

wellbore. Thus, measured flow rates and pressures during

these tests can provide sufficient information for the

characterization of the tested well (Matthews et al., 1967;

Earlougher et al., 1977).

Well testing enables accurate determination of cash flow

of the well by obtaining comprehensive reservoir

description. The core purpose of well testing lies in the

determination of the fluid production capability of a

formation and incurring the inherent reason for

productivity of well. A meticulously designed and

performed well test operation can provide accurate facts

and figures about formation permeability, average

conductivities, extent of wellbore damage or stimulation,

reservoir pressure, and extended testing may deliver

information regarding underlying geological barriers

(faults, pinch-outs, etc.), reservoir boundaries and

heterogeneities (Matthews et al., 1967; Earlougher et al.,

1977). On the same note Da Prat et al. in 1992 have

classified well test objectives into short term and long

term objectives. Obtaining the reservoir description in the

vicinity of the well bore, from analysis of gathered data,

are categorized as short term objective. While, long term

objectives of well test are to analyze gathered data for

obtaining complete description of the whole reservoir.

However, huge production time losses, manpower and

equipment costs turn it into a cost intensive affair

(Dakshindas, 1999). Since its introduction to the

2 OTC-24981-MS

petroleum engineering industry around 1937 by ground

water hydrology scientists (Gringarten, 2008), plethora of

novel technologies have been introduced both for data

acquisition and analysis. The introduction of electronic

pressure gauges was a great step ahead in enhancing

obtaining of reservoir description, and continuously

upgraded versions of these are being developed to meet

current challenges. Moreover, for expeditious data

acquisition and interpretation, and in depth rigorous

insight into the reservoir, industry anticipates close

integration of advanced microprocessors and innovative

computational techniques.

Theory

This paper features a unique approach for synthetic

pressure transient data generation by application of Cubic

Spline Interpolation technique and Artificial Neural

Networks (ANN). The main objective of the work

presented is to formulate an artificial neural network

which has the ability to forecast transient pressure

responses without any need for an actual well test. The

data thus generated can be analyzed using traditional well

test analysis methods for reservoir characterization. In a

nutshell, this is made possible by training the neural

network using pressure transient data available from

proximate wells, flow parameters values and reservoir

characteristics, and then the error is reduced significantly

by retraining the network and testing the network

efficiency by comparing the network results to the data

available from actually conducted well tests.

Cubic Spline Interpolation

Data points are interpolated by using cubic spline curve

fitting (CSCF) technique. This can be realized by

arranging the available data into a table [pi,qi], i ϵ [0,n],

thus providing with n intervals for n+1 control points. The

cubic spline curve is a continuous piecewise third order

polynomial, satisfying all the input values (Figure 2).

Each polynomial’s second derivative is usually set to zero

at the endpoints, as this offers a boundary condition which

makes the system of n-1 equations complete. Also this is

not the only possible option, as other boundary conditions

can also be used. Thus a “natural” cubic spline is

produced that results to an elementary tridiagonal system,

which is computed to deliver coefficients of the

polynomials.

The main purpose of using CSCF technique is to increase

data points between minimum and maximum, as with

higher amount of input data points better results through

ANN can be obtained. Moreover its high accuracy of

estimation and capacity to produce seamless curves makes

it a popular interpolation technique.

Neural Networks

Neural Networks are a form of massive parallel

distribution of biologically inspired processing units

comprising of smaller units termed as neuron, as these

seek to imitate the brain microstructure (Noriega, 2005).

The multilayer perceptron, a widely applied example of

artificial neural networks, is a programming epitome that

was developed around half a century back by W.S.

McCulloch and W. Pitts in 1943. The neural network is an

intensely parallel, distributive, adaptive, non-arithmetic

and non-digital system, which can solve problems ranging

from pattern recognition, to innovative symbolic

manipulation

Biological Basis

The neural network attempts to model functioning of

biological nervous system or human brain. The brain is

made up of huge parallel interconnected processing units.

These processing units or neurons are electrically

excitable, which through electrical and chemical signals

transmits and process information. Majorly dendrites,

soma and axon form the neuron body. A neuron receives

the input information from other neurons in the network

connected to it at synapses through dendrites. The

received information is then processed at the soma, which

integrates it over time and space, the generated output is

activated depending on the input. The synapses located at

the end of axon transmit the output signal to connected

dendrites. This forms an intensely complex parallel

computer.

Similarly, an artificial neural network is a composite

architecture of soft computing based neurons integrated

into numerous parallel layers which are connected to all

the neurons of preceding and succeeding layers by the

means of weights. A variety of developed neurons and

network specifications can be programmed to interpret,

recognize and retrieve patterns to solve optimization

problems and clear noise from input data (Kumar, 2012).

Network Mechanics

The neural network has two types of learning processes,

supervised and un-supervised. Supervised learning or

training process is the crux of ANN mechanics, initially

random weight values (between -1 to 1) are assigned to

network connections, furthermore the network analyses

input data to estimate weights of the connections between

successive neuron layers (Figure 3). The data is input to

the first layer in which each neuron multiplies the data

with the associated weight and is summed with a bias.

The result is then fed to an activation function, or transfer

function, of the neuron which determines if the result is

above the threshold or not, to instruct the neuron for

transmitting data. This output to activation function is

calculated by

The output values and the input values are compared for

error and then accordingly weights are updated for

OTC-24981-MS 3

minimising error. This process is repeated a number of

times (epochs) to achieve precise weight values. Now, the

network output is tested against the input data sets for

measuring the prediction quality, which if acceptable can

be applied to generate data, for environments whose

outputs are unknown.

Background

These are being applied to counter a large variety of

challenges from simple pattern-recognition task, to

advance symbolic manipulation (Noriega, 2005). Neural

networks have gained ground in geophysical application

and well test interpretation in last couple of decades.

Their effective application is to provide remarkably

precise solution to solve a range of problems like well-log

analysis (Huang et al., 1991), seismic deconvolution

(Wang et al., 1992; Calder´on–Mac´ias et al., 1997),

waveform recognition and first-break picking (Murat et

al., 1992; McCormack et al., 1993); for electromagnetic

(Poulton et al., 1992), magnetotelluric (Zhang et al.,

1997), and seismic inversion purposes (R¨oth et al., 1994;

Langer et al., 1996; Calder´on–Mac´ias et al., 1998);

event classification (Dowla et al. 1990; Romeo, 1994),

zone identification (White et al., 1995), trace editing

(McCormack et al., 1993) and for shear-wave splitting

(Dai et al., 1994), in geophysics domain (Van der Bann et

al., 2000), while making significant contribution to the

well test interpretation domain by enabling permeability

prediction (Singh et al., 2005), reservoir model

identification (Vaferi et al., 2011), well test model

recognition (Sung et al., 1996) and many more.

However, not much application of this Artificial

Intelligence (AI) technology has been observed in the

generation of synthetic well test Pressure Transient Data

(PTD) and rigorous utilization of advanced neural

network functions, training algorithms and advanced

computational techniques in this specific field have not

been witnessed.

Time Delay Neural Networks (TDNN

)

The designing of neural networks can be understood by

classifying them into two categories dynamic and static.

Static networks are comparatively simple with neither

feedback elements nor delays. On the other hand, in case

of dynamic networks, the output generated is governed by

current inputs, previous inputs, outputs and network

states. However, dynamic networks can be trained by the

same algorithms used by static networks but due to

complex nature of error surfaces computing gradients is

more intensive. Moreover in this study NARX model has

been implemented which is a type of dynamic network or

more specifically Time Delay Neural Network (TDNN)

(Figure 4).

NARX Neural Network Model

Instead of conventional dynamic networks that are either

feedforward networks or focused networks, with only

input layer dynamics. In this, fully connected feedback

connections are enclosed in numerous layers of a

recurrent dynamic network which is Nonlinear

Autoregressive with eXogenous inputs (NARX recurrent

models). Contrary to other recurrent networks, feedback

comes only from output neuron instead from hidden

states, it is modeled with a tapped delay line (embedded

memory) which is clubbed to a delayed feeding line from

the output, second tapped delay line. This type of limited

viewed on part of the input series is referred to as time

window (Diaconescu, 2008). A NARX model is

formulated by the following equation

y(t) = f(y(t-1), y(t-2),….., y(t-ny), u(t-1), u(t-2),….., u(t-nu))

where, y(t) and u(t) denotes input and output signal of the

network at time t, ny and nu signifies input and output

order, while f represent the mapping performed by

Multilayer Perceptron (Siegelmann et al., 1997). The

value of dependent output y(t) is reverted to earlier output

values and values of eXogenous input (Figure 5). This

real-time feeding of output to the network is called as

parallel architecture, which results in more accurate

training and due its purely feedforward architecture, static

backpropagation can be used.

Application Methodology

The steps incurred in throughout this process are

explained below:

1. Candidate Identification: A set of wells are chosen,

which are producing from a particular formation

(zone). Segregate the wells where prediction is to be

done.

2. Information Gathering: Pressure Testing is

performed on the selected zone at all picked well

locations and PTD is recorded. Other vital

information about the test and reservoir is also

acquired.

3. Data Enrichment: NARX works best when highly

dense data sets are fed to it. Thus according to the

type of cures produced by the specific pressure

testing procedure followed, data interpolation

technique is chosen and applied.

4. Normalization: The input data for the NARX is

scaled down to the range of 0 to 1.

5. Network Training: The network is configured with

appropriate parameters and then input data is fed to

the network for training it.

6. Network Testing: Performance of the network is

validated against known data sets, if the results are

4 OTC-24981-MS

unsatisfactory then the network parameters are

tweaked and the NARX is retrained.

7. Prediction: Utilize the trained NARX for simulating

data for unknown outputs.

To minimize error and produce the best pressure transient

predictions, the whole process was tested numerous times

with varied network models and interpolation techniques.

Due to the monotonically increasing nature of pressure

curve generated using PTD the best suited technologies

are identified was Cubic Spline Curve Fitting (CSCF) and

NARX neural model (Figure 6).

Case Study

This case deals with simulator generated pressure build-

up test data considering of five active producing wells for

an infinitely large homogenous field (Figure 7). The data

was generated using analytical simulator, which is based

on principle of superposition and infinite acting line

source solution. All the five wells considered had

identical shut-in and production time, and different flow

rates. The wells are shut-in at 215 hours and the data is

recorded for 67 hours.

The ANN is trained from pressure responses of four wells

and the data for well 5 is predicted. Complete data set for

each well is prepared for training the NARX network.

Each well’s pressure transient data, (tp+∆t)/∆t, modified

inter-well distances, and flow rates along with their

functional links are used as inputs for the network

(Figure). The output from the network is pressure which

is generated by also taking into account the interference

effects from the proximate wells. Modified distance is

formulated to be:

Distmod = QW1 * Dist(W1 – W2)/QW2

After taking into account modified distance and functional

links better prediction results were observed but with

increased CPU usage and network training time.

Similarly, initially 60 data values were available, which

by using Cubic Spline Interpolation were increased to 89.

Resilient backpropagation algorithm was used to update

bias values and connection weights during training

because it enables optimizing the magnitude of weight

change by reducing it in case weights are oscillating and

increasing it when for several iterations weight changes

continuously in the same direction. Sigmoid or

‘squashing’ functions were used as transfer functions for

all the neurons at every layer, as the derivative of sigmoid

function can be swiftly calculated which is needed to be

backpropagated to calculate error. It is defined by:

F(i) = (1+e
-i
)

-1

Where, F(i) is the function output and ‘i’ is the input. The

performance of the network was judged on the basis of

mean square error (MSE). The input set was divided using

interleaved indices. The applied NARX model is a three

layer network with 18 input layer neurons, 25 neurons in

hidden layer and 1 output layer neuron. The time delay

configured for this particular problem is 1:2, thus

producing 87 outputs for 89 inputs.

A good match is observed is observed between the

network and the simulator output (Figure 8-11),

confirming that NARX has the potential to predict well

test pressure responses accurately. The prediction

performed by the network for well 5 is also remarkable

considering the complexity of the problem (Figure 12).

With increased available data from more number of wells

the network will deliver more precise predictions but the

wells must be chosen meticulously to ensure inclusion of

interference effects, flow rate effects, shut-in and

production effects, boundaries and heterogeneity effects.

But the risk that network might get over-trained also

escalates with increased number of training wells.

Conclusions

In this paper a unique synthetic pressure transient data

generation has been introduced. The discussed simulated

field case study has justified the approach and delivered

recommendations on how to increase the accuracy of the

prediction. The precision of the network remarkably

improves when neural network is exposed to variety of

data from more number of strategically selected wells.

Incorporation of more data functional links, multiple

inputs from the field after rigorous iterative testing can

increase reliability of the network.

The NARX model has been able to analyze and interpret

the response almost perfectly which is proved by the

fashion in which the output curve closely traces the

simulator curve. Moreover, for cases where due to some

downhole equipment failure test couldn’t be completed or

some data is lost, by using this we can complete the data.

This doesn’t eradicate the need for actual well tests, but it

remarkably curbs the frequency of actual tests when

clubbed with tactical well test pattern planning. Using

NARX technology more informed well tests can be

designed by extracting more information from the

available data, thus reflecting enormous potential to

revamp the petroleum industry by delivering expeditious

and reliable solutions.

Acknowledgements

The authors are indebted to officials from Schlumberger,

ONGC, Reservoil and Dept. of Petroleum Engineering,

Indian School of Mines Dhanbad for their constant

support during this research. The authors are also obliged

to Mr. Mohit Punjabi, Mr. Swapnil Gupta, Mr. Ajay

Singh, Mr. Paras Goel and Mr. Praveen Pushkar, all from

Indian School of Mines Dhanbad, for always stimulating

and rejuvenating us.

OTC-24981-MS 5

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6 OTC-24981-MS

Figure 1: A Schematic diagram of drawdown test of a homogeneous reservoir with infinite acting boundary (Vaferi et al. 2011)

(a) (b)

Figure 2: (a) Three polynomials making up a cubic spline; (b) Input curve for cubic spline interpolation

OTC-24981-MS 7

Figure 3: Mathematical neuron (Baan et al., 2000)

Figure 4: A fully connected recurrent Time Delay neural network (Siegelmann et al., 1997)

Figure 5: A NARX neural network with ny = 2, nu = 3 and H= 4 (Lin et al., 1998)

Figure 6: Overall flowchart for synthetic pressure transient data generation system

8 OTC-24981-MS

Figure 7: Case evaluating infinitely large field with 5 wells

Figure 8: Comparison of simulator and ANN data for Training Well 1

Figure 9: Comparison of simulator and ANN data for Training Well 2

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OTC-24981-MS 9

Figure 10: Comparison of simulator and ANN data for Training Well 3

Figure 11: Comparison of simulator and ANN data for Training Well 4

Figure 12: NARX model prediction result for Well 5

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