identifying risk zone along gail pipeline in 1

IDENTIFYING RISK ZONE ALONG GAIL PIPELINE

IN EAST GODAVARI DISTRICT A Project report in partial fulfilment of the requirement for the award of degree of

BACHELOR OF TECHNOLOGY

in

CIVIL ENGINEERING

by

K. P. VINEETH (11026A0132)

K. SAI KRISHNA (11026A0142)

K. SANDEEP (11026A0149)

S. SRAVAN (11026A0154)

A. VARUN VARMA (11026A0167)

Under the esteemed guidance of

Dr. V. SREENIVASULU

Professor of civil Engineering

HEAD OF DEPARTMENT

Department of Civil engineering

UNIVERSITY COLLEGE OF ENGINEERING KAKINADA JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY

KAKINADA-533003 ANDHRA PRADESH, INDIA

November 2014.

UNIVERSITY COLLEGE OF ENGINEERING KAKINADA JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY

KAKINADA ANDHRA PRADESH, INDIA

November 2014.

CERTIFICATE

This is to certify that the dissertation entitled Identifying Risk

Zone along GAIL Pipeline In East Godavari District is being

submitted for the partial fulfilment of the requirement for the award of

degree of Bachelor of Technology in Civil Engineering to University

College Engineering Kakinada, is a bonafied work done by K. P. Vineeth,

S. Sravan, K. Sai Krishna, A. Varun Varma, K. Sandeep under my

supervision during the Academic year 2014 and it has been suitable for

acceptance according to requirements of University.

Examiner Prof. Dr. Vemu Sreenivasulu

Head of the Department, Professor of Civil Engineer,

Department of Civil Engineering, University College of Engineering,

J.N.T.University, Kakinada-533003.

I

ACKNOWLEDGEMENT

We express our sincere thanks to GAIL AUTHORITY for their support,

suggestions and continuous encouragement which led to the successful completion of our

project work.

We express our indebtness and gratitude to our guide Prof. Dr. V. Sreenivasulu,

Head of Civil Engineering Department , Department of Civil Engineering, University

College of Engineering Kakinada, JNTUK, Kakinada, for his guidance and care taken

by him in helping us to complete the project work successfully.

We are extremely thankful to Prof. Dr. K. V. Rao, Programme Director

Petroleum Course, JNTUK, Kakinada, for giving his valuable suggestions for our

project work.

We also express our sincere gratitude to Prof. Dr. K. Padma Raju, Professor

and principal, UCEK, JNTUK Kakinada, for having made all facilities in the campus

for smooth carrying of the task.

We express our sincere gratitude to Prof. Dr. P. Subba Rao, Professor and vice

principal, UCEK, JNTUK Kakinada, for his encouragement during the course of

dissertation work.

Finally, we acknowledge all those who have helped us directly or indirectly for

the completion of this project.

K. P. Vineeth

S. Sravan

K. Sai Krishna

A. Varun Varma

K. Sandeep

II

ABSTRACT

Gas pipelines are environmentally sensitive because they cross varied fields, rivers,

forests, populated areas, desert and hills and. Underground gas pipelines have been very

economical and effective because of factors like low risk and low cost. Physical and

chemical properties of liquid gas, pipeline properties and environmental condition are

other important factors that determine the technical and environmental risk.

Gas pipeline blasts are major problem in India. These blasts are caused due to gas

leakages and they, in turn cause loss of human life and property. Loss of life and property

is more in India compared to other countries around the world due to trespassing and

neglecting instructions made by gas and oil companies. Government organizations and

gas companies are trying to prevent such mishaps by monitoring and controlling

situations wherever necessary.

Our intention behind this project to identify the risk zones i.e. zones which prone to

blasts. The intensity of the blast can be identified by knowing the diameter of the hole

formed and the heat intensity. The methodology and the results from this study project

could be useful to the gas companies. It would allow them to take measures against any

mishap.

III

Objective

Pipeline Problems

The leading cause of accidents in both transmission and distribution systems is damage

by digging near existing pipeline. Frequently, this damage results from someone

excavating without asking or without waiting the standard 48-hours for the gas company

to mark the location of its lines. Excavation damage accounted for almost 60 percent of

all reported distribution pipeline incidents between 1995 and 2004, according to statistics

kept by the U.S. Department of Transportation’s Office of Pipeline Safety. Other causes

include corrosion, a fire or explosion causing a pipeline incident, or even a vehicle

striking an aboveground meter or regulator. Corrosion sometimes results from excavation

damage, which, while not severe enough to trigger a puncture or failure of the pipeline,

could create weaknesses in the pipeline that later render it more susceptible to corrosion.

Why GIS

GIS is used to find the alignment of the pipeline and also risk zone identification by

creating a buffer to the pipeline. By this action we can get to know the areas which under

risk prone zones, in regards to any blast.

Mitigation Steps:

Mitigation at the design stage

Mitigation must start at ‘square one’ – namely, materials selection, which requires

careful review, testing and control such that they will be stipulated as ‘fit-for purpose’

for sour service. The materials selection process should reflect project-specific

requirements, intended design life, costings, failure evaluations as well as

environmental considerations, etc. As an absolute minimum, the following should be

taken into consideration:

• Design life and system availability;

• Pipeline system design – avoidance of deadlegs to mitigate stagnant conditions,

correct pipeline sizing to reduce water hold ups and solids deposition;

• Facilities and process systems design and layout – gas dehydration;

IV

• Full evaluation of operational and process conditions – H2S, CO2, O2 contents,

pressures, temperatures, flow velocities and regimes, entrained solids, biological

activity, etc.;

• Damage mechanism and failure modes with respect to health safety and

environmental consequences; and,

• Materials availability and cost implications

Mitigation at the manufacturing stage

Manufacturing of sour line pipe requires optimum steel chemistry and ‘steel

cleanliness’. The presence of free sulphur during steel manufacture causes a reduction

in overall steel mechanical properties, especially toughness; which dictates the

requirements for very low sulphur concentrations; typically 0.005–0.010 per cent.

Mitigation at the operational stage

So far examined have been a number of mitigation methods which provide certain

controls in terms of safeguarding sour service pipelines. In addition, can also be

introduced (additional measures) during the pipeline operational and maintenance

stage in the form of a robust pipeline management system.

Conclusion

Sour service pipelines carrying fluids or gases in addition to a wet internal environment

causes problems to the pipeline leading to corrosion and potential loss of containment

or complete breakdown of the pipeline. In addition, the presence of SRBs also play a

critical role in generating hydrogen sulphide gas and equally cause potential pipeline

corrosion problems.

V

CONTENTS

ACKNOWLEDGEMENT ................................................................................................... I

ABSTRACT ........................................................................................................................ II

Objective ............................................................................................................................ III

CONTENTS ........................................................................................................................ V

List of Figures ................................................................................................................. VIII

List of Tables ...................................................................................................................... X

List of Maps ....................................................................................................................... XI

Terminology ..................................................................................................................... XII

Chapter 1. Introduction ....................................................................................................... 1

1.1 About GAIL ............................................................................................................... 1

1.1.1 History................................................................................................................. 1

1.1.2 Infrastructure ....................................................................................................... 2

1.1.3 Natural Gas Transmission ................................................................................... 3

1.1.4. Gas Marketing .................................................................................................... 3

1.1.5 About GAIL Accident......................................................................................... 7

1.2 Remote Sensing ......................................................................................................... 8

1.2.1 Overview ............................................................................................................. 9

1.2.2 History............................................................................................................... 10

1.3 Geographic Information System(GIS) ..................................................................... 11

1.3.1 History Of Development ................................................................................... 12

Chapter 2. Study Area ........................................................................................................ 15

Demographics ................................................................................................................ 15

Chapter 3. Literature Review ............................................................................................. 17

3.1 Introduction .............................................................................................................. 17

VI

3.2 Assessment on Pipeline Alignment ......................................................................... 18

3.2.1. Background ...................................................................................................... 18

3.2.2. Development of Pipeline Alignment Technique .............................................. 19

3.2.3. Validation of Pipeline Alignment Technique .................................................. 20

3.2.4. Route Plan for Chennai-Bangalore Gas Pipeline ............................................. 20

3.2.5. Benefits: Summing up...................................................................................... 21

3.2.6. Concluding Remarks: Costs ............................................................................. 21

3.3 Why GIS Is Used ..................................................................................................... 22

Chapter 4. Data and Software Used ................................................................................... 24

4.1 Data Used ................................................................................................................. 24

4.1.1 Toposheet .......................................................................................................... 24

4.1.2. Toposheets used in our project: ........................................................................... 26

4.2. GAIL Map ............................................................................................................... 27

4.2 Software Used .......................................................................................................... 27

4.2.1 ERDAS ............................................................................................................. 27

4.2.2 ArcGIS .............................................................................................................. 32

Chapter 5. Methodology .................................................................................................... 36

5.1 Data Acquisition ...................................................................................................... 36

5.2 Pre-Processing.......................................................................................................... 37

5.2.2 Using ERDAS ................................................................................................... 38

5.2.3 ArcGIS .............................................................................................................. 43

Chapter 6. Results and discussion ...................................................................................... 57

Places that are affected are ............................................................................................. 57

6.1 For a hole of 100mm diameter ................................................................................. 57

6.1.1 At Radiation level of 37.5 kW/m2..................................................................... 57


VII

6.1.3 At Radiation level of 5 kW/m2.......................................................................... 57

6.2 For a hole of 50mm diameter ................................................................................... 58



6.2.3 At Radiation level of 5 kW/m2.......................................................................... 58

Chapter 7. Maps ................................................................................................................. 59

Chapter 8. Conclusion ........................................................................................................ 70

8.1.1 Construction ...................................................................................................... 70

8.1.2 Operations & Maintenance ............................................................................... 70

8.2 Ongoing monitoring, maintenance and safety measures for pipeline network

include ............................................................................................................................ 71

References .......................................................................................................................... 73

VIII

List of Figures

Figure 1.1. Gas Authority of India Limited Logo ................................................................ 1

Figure 1.2. Location map of Nagaram Accident.................................................................. 7

Figure 1.3. Illustration of the Remote Sensing process ....................................................... 9

Figure 1.4. ( a ) Airbourne sensor, ( b ) Spacebourne sensor ............................................ 10

Figure 2.1. Location Map of the Study Area ..................................................................... 16

Figure 4.1. Toposheet Indexing 4° latitude × 4° longitude ................................................ 24

Figure 4.2. Toposheet Indexing 1° latitude× 1° longitude. ................................................ 25

Figure 4.3. Toposheet Indexing 30′ latitude × 30′ longitude. ............................................ 25

Figure 4.4. Toposheet Indexing 15′ latitude × 15′ longitude ............................................. 26

Figure 4.5. Toposheet Indexing 7(1/2)′ latitude × 7(1/2)′ longitude.................................. 26

Figure 4.6. Pipeline network of GAIL K. G. Basin ........................................................... 27

Figure 4.7. Project Window ............................................................................................... 31

Figure 4.8. Main window ................................................................................................... 32

Figure 4.9. Geo service explorer ........................................................................................ 32

Figure 4.10. Multi scale 3D model in ArcGIS ................................................................... 34

Figure 5.1. Flow chart illustrating the methodology.......................................................... 36

Figure 5.2. Assigning Polynomial model properties ......................................................... 38

Figure 5.3. GCP tool reference setup ................................................................................. 39

Figure 5.4. AOI tool box .................................................................................................... 39

Figure 5.5. Tool to set geometric model ............................................................................ 41

Figure 5.6. GCP tool reference setup ................................................................................. 42

Figure 5.7. Geo correction tool box ................................................................................... 42

Figure 5.8. Add data tool ................................................................................................... 43

Figure 5.9. Adding Toposeet Layer ................................................................................... 43

Figure 5.10. Toposheet data in ArcGIS ............................................................................. 44

IX

Figure 5.11. Catalog in ArcGIS ......................................................................................... 44

Figure 5.12. Creating shapefile .......................................................................................... 45

Figure 5.13. Specifying name and feature type ................................................................. 45

Figure 5.14. Table of contents ........................................................................................... 46

Figure 5.15. Editor tool bar ................................................................................................ 46

Figure 5.16. Start editing window...................................................................................... 47

Figure 5.17. Digitized area................................................................................................. 47

Figure 5.18. Merge tool ..................................................................................................... 48

Figure 5.19. Excel sheet representing habitations ............................................................. 48

Figure 5.20. Conversion of csv file to shapefile ................................................................ 49

Figure 5.21. Adding GAIL map in ArcGIS ....................................................................... 50

Figure 5.22. Digitized pipeline .......................................................................................... 50

Figure 5.23. Attribute data ................................................................................................. 51

Figure 5.24. Adding field ................................................................................................... 51

Figure 5.25. Representation of shapefile ........................................................................... 52

Figure 5.26. Creating buffer............................................................................................... 52

Figure 5.27. Specifying input and output........................................................................... 53

Figure 5.28. Buffered area ................................................................................................. 53

Figure 5.29. Release rates for Natural gas ......................................................................... 54

X

List of Tables

Table 1. GAIL Details and Statistics ................................................................................... 1

Table 2. GAIL gas distribution network in A. P. Region (KG Basin) ................................. 4

Table 3. GAIL Consumer Details in A. P. Region .............................................................. 6

Table 4. Route Reconciliation details ................................................................................ 20

XI

List of Maps

Map 1. Base map of East Godavari ................................... Error! Bookmark not defined.

Map 2. East Godavari district ............................................ Error! Bookmark not defined.

Map 3. GAIL Base map ..................................................... Error! Bookmark not defined.

Map 4. Buffer Zone of 107m for 100mm Hole ................. Error! Bookmark not defined.

Map 5. Buffer Zone of 84m for 100mm Hole ................... Error! Bookmark not defined.



Map 8. Buffer Zone of 46m for 50mm Hole ..................... Error! Bookmark not defined.

Map 9. Buffer Zone of 36m for 50mm Hole ..................... Error! Bookmark not defined.



XII

Terminology

EPS Early Production Supply

SV Station Sectionalising Valve Station

Despatch Terminal Supplying gas into trunk line

Receiving Terminal Supply of gas from trunk line to sub station

Feeder Pipeline Supply of gas from wells to trunk line

LPG VSPL Pipeline Visakhapatnam to Secunderabad Pipeline

Junction Point In order to inspect pipeline

IP Intermittent Pigging Station

Main hub is Tatipaka and Oduru

1

Chapter 1. Introduction

1.1 About GAIL

GAIL (India) Limited is the largest state-owned natural gas processing and distribution

company in India, It is headquartered in NEW DELHI. It has following business

segments: Natural Gas, Liquid Hydrocarbon, Liquefied petroleum

gas Transmission, Petrochemical, City Gas Distribution, Exploration and Production,

GAILTEL and Electricity Generation.

Figure 1.1. Gas Authority of India Limited Logo

Table 1. GAIL Details and Statistics

1.1.1 History

GAIL (India) Limited was incorporated in August 1984 as a Central Public Sector

Undertaking (PSU) under the Ministry of Petroleum & Natural Gas (MoP&NG). The

company used to be known as Gas Authority of India Limited. It is India's principal gas

transmission and marketing company. The company was initially given the responsibility

of construction, operation & maintenance of the Hazira–Vijaypur–Jagdishpur (HVJ)

pipeline project. It was one of the largest cross-country natural gas pipeline projects in the

Type State-Owned Enterprise Public Company Industry Energy, Petrochemicals Founded 1984 Headquarters New Delhi, India Key people Shri B. C. Tripathi, Chairman & MD Products Natural Gas, Petrochemical, Liquid Hydrocarbons, Liquefied

Petroleum Gas Transmission, City Gas Distribution, E&P, Telecommunication, Electricity Generation.

Revenue 619 billion (US$10 billion) (FY2013–14) Net income 47 billion (US$760 million) (FY2013–14) Employees 3,994 (2013)

2

world. This 1800 kilometre long pipeline was built at a cost of 17 billion (US $280 m)

and it laid the foundation for development of market for natural gas in India. GAIL

commissioned the 2,800 kilometres (1,700 mi) Hazira-Vijaipur-Jagdishpur

(HVJ) pipeline in 1991. Between 1991 and 1993, three liquefied petroleum gas (LPG)

plants were constructed and some regional pipelines acquired, enabling GAIL to begin its

gas transportation in various parts of India. GAIL began its city gas distribution in New

Delhi in 1997 by setting up nine compressed natural gas (CNG) stations.

GAIL today has reached new milestones with its strategic diversification into

Petrochemicals, Telecom and Liquid Hydrocarbons besides gas infrastructure. The

company has also extended its presence in Power, Liquefied Natural Gas re-gasification,

City Gas Distribution and Exploration & Production through participation in equity and

joint ventures. Incorporating the new-found energy into its corporate identity, Gas

Authority of India was renamed GAIL (India) Limited on 22 November 2002.

GAIL (India) Limited has shown organic growth in gas transmission through the years by

building large network of trunk pipelines covering length of around 10,700 kilometres

(6,600 mi). Leveraging on the core competencies, GAIL played a key role as gas market

developer in India for decades catering to major industrial sectors like power, fertilizers,

and city gas distribution. GAIL transmits more than 160 mmscmd (million standard cubic

metres per day) of gas through its dedicated pipelines and have more than 70% market

share in both gas transmission and marketing.

1.1.2 Infrastructure

GAIL owns the country's largest pipeline network, the cross-country 2300 km Hazira-

Vijaipur-Jagdishpur pipeline with a capacity to handle 33.4 MMSCMD gas.

The Company supplies gas to power plants for generation of over 4,000 MW of power to

fertilizer plants for production of 10 million tonnes of urea and to several other industries.

The regional pipelines are in Mumbai, Gujarat, Rajasthan, Andhra Pradesh, Tamil Nadu,

Pondicherry, Assam, Tripura, Madhya Pradesh, Haryana, Uttar Pradesh and Delhi. The

Company has established six Gas Processing (LPG) Plants, four along the HVJ pipeline

two at Vijaipur, MP, one at Vaghodia, Gujarat and Auraiya, UP and one each in Lakwa,

Assam and Usar, Maharashtra. These plants have the capacity to produce nearly 1 million

tpa of LPG. GAIL has also set up several compressor stations for boosting the gas

pressure to desired levels for its customers and internal users.

3

1.1.3 Natural Gas Transmission

GAIL has built a network of trunk pipelines covering a length of around 11,000 km.

Leveraging on the core competencies, GAIL played a key role as gas market developer in

India for decades catering to major industrial sectors like power, fertilizers, and city gas

distribution. GAIL transmits more than 160 MMSCMD of gas through its dedicated

pipelines and has more than 70% market share in both gas transmission and marketing.

However, there are regional imbalances in gas supply across the country. To bridge this

gap in infrastructure, Ministry of Petroleum and Natural Gas, in the year 2007, authorised

five new pipelines to GAIL covering a length of over 5,500 km.

S. No. Pipeline Length km/ Capacity in MMSCMD Commissioning

1. Dadri Bawana Nangal 610 km/31 MMSCMD 2011–12

2 Chainsa Jhajjar Hissar 300 km/35 MMSCMD 2011–12

3. Jagdishpur Haldia 2000 km / 32 MMSCMD 2013–14

4. Dabhol Bangalore 1386 km/ 16 MMSCMD 2013–14

5. Kochi Kanjirikkod

Bangalore

860 km / 16 MMSCMD 2012–13

TOTAL 5156 km / 130 MMSCMD 2011-13

1.1.4. Gas Marketing

Since inception in 1984, GAIL has been the undisputed leader in the marketing,

transmission and distribution of Natural Gas in India. As India's leading Natural Gas

Major, it has been instrumental in the development of the Natural Gas market in the

country.

GAIL sells around 51% (excluding internal usage) of Natural Gas found in the country.

Of this, 37% is to the power sector and 26% to the fertiliser sector. GAIL is supplying

around 60 MMSCMD of Natural Gas from domestic sources to customers across India.

These customers range from the smallest of companies to mega power and fertiliser

plants. GAIL has adopted a Gas Management System to handle multiple sources of

supply and delivery of gas in a co-mingled form and provide a seamless interface between

shippers, customers, transporters and suppliers. GAIL is present in 11 states, i.e., Gujarat,

Rajasthan, Madhya Pradesh, Delhi, Haryana, Uttar Pradesh, Maharashtra, Tamil Nadu,

Andhra Pradesh, Assam, and Tripura. They are further extending their coverage to states

of Kerala, Karnataka, Punjab, Uttarakhand, West Bengal and Bihar through their

upcoming pipelines.

4

Table 2. GAIL gas distribution network in A. P. Region (KG Basin)

Sl. No. Name Of The P/L Length (Km) Diameter (Inches) Godavari basin

1 Tatipaka - Kakinada Jn.Point 74.60 18 2 Tatipaka - K.Cheruvu 44.50 18 2a K.Cheruvu-Oduru-Kakinada Jn Point 30.10 18 3 Kakinada Jn.Point - Nfcl 19.40 12 4 Kakinada Jn.Point - Nfcl 19.40 18 5 Endamuru - Oduru 5.70 4 6 Endamuru - Oduru 5.10 8 7 Penumadam - Kavitam 5.80 4 8 K.Cheruvu - Rcl 15.20 4 9 Narsapur - Kovvur 72.00 8 10 Madduru - Apseb 0.80 8 11 Narsapur - Apseb 61.90 12 12 K.Cheruvu - Gvk 35.50 8 13 K.Cheruvu - Gvk 35.50 16 14 Timmapuram - Spgl 6.70 8 15 S.Yanam-Gudala 9.80 8 16 S.Yanam-Gudala 9.80 16 17 Adavipalem - Tatipaka 15.80 10/8 18 Tgl 1.40 4 19 Tatipaka - Narsapur 26.90 14 20 Tatipaka - Dindi (Old) 17.00 14 21 Y.V.Lanka - Narsapur (Old) 7.00 14 22 Akkamamba 0.90 4 23 Kesanapally(E) - Pasarlapudi 12.60 8 24 Mori – Dindi 9.00 12 25 Rolex Paper Mills 1.10 4 26 Pasarlapudi#8 - Bodasakurru 0.30 12 27 Pasarlapudi#8 - Bodasakurru 0.30 18 28 Lanco (Tatipakka - Kondapalli) 204.00 18 29 Gfcl 0.02 4 30 Bses 8.00 18 31 Vatsasa 2.00 4 32 Rapl 1.00 4 33 Rcl-Ii 3.80 4 34 Ponnamanda-Kadali 4.00 14 35 Kesanapally(W) - Ponnamanda 4.50 12 36 Ullamparru - Dpml 18.30 4 37 Peravali - Asl 6.70 4 38 Kovvur - Kapavaram Tap-Off 6.10 8 39 Vppl 0.50 2 40 Kapavaram Tap-Off To Tgl Tap-Off 3.00 4

( Table 2. Continued in next page )

5

Table 2. GAIL gas distribution network in A. P. Region (KG Basin)

Sl. No. Name Of The P/L Length (Km) Diameter (Inches)

Godavari basin 41 Gopavaram-Challapalli 1.60 4 42 Rel - Gowthami 1.80 12 43 Lanco - Bgl Pipeline 13.50 6 44 Gvk - Vemagiri 5.20 12 45 Muktheswaram - Konaseema 22.50 12 850.62

Mandapeta And Gopavaram Isolated Fields

1 Hitech 0.460 4 2 Siritech 1.360 4 3 Jaya Venkatarama 0.186 4 4 Ramakrishna Ice 0.067 4 5 Ganga Ice Factory 0.026 4 2.10

Krishna Basin / Lingala & Kaikuluru Isolated Fields

1 Vennar Ceramics 2.60 4 2.60 6 2 Global Steels 0.15 2 0.12 2 3 Afl 1.70 6 4 Srcl 0.58 4 5 Varalakshmi 0.04 4 6 Sentini Ceramics 1.10 4 1.10 4 7 Meena Jain 0.38 2 8 Shyamala Ice 0.04 2 9 Nagarjuna Cerachem 0.62 4 10 Vijayadurga 1.16 4 12.19 Total 864.91

6

Table 3. GAIL Consumer Details in A. P. Region

Sr. No. Consumer Name Sector Region 1 Andhra Fuels Pvt. Ltd Power AP

2 Andhra Sugars Ltd. Others AP

3 A.P.Gas Power Corp.Ltd-Stage-I Power AP

4 Bhagyanagar Gas Limited City AP

5 Coromandel Fertilisers Limited Fertiliser AP

6 Delta Paper Mills Ltd Others AP

7 Global Steels Limited Others AP

8 Gvk Industries Limited Power AP

9 Lanco Kondapally Power Pvt Ltd. POWER AP

10 Meena Enterprises Others AP

11 Nagarjuna Cerachem Pvt Ltd Others AP

12 Nagarjuna Fertilisers & Chem Ltd Fertiliser AP

13 Nagarjuna Fertilisers & Chem Ltd Fertiliser AP

14 Padmasree Steels Private Ltd. Others AP

15 Rama Krishna Ice Factory Others AP

16 Rcl Mummidivaram Others AP

17 Regency Ceramics Ltd Others AP

18 Reliance Infrastructure Limited Power AP

19 Rolex Paper Mills Ltd Others AP

20 Sentini Cermica(P) Ltd Others AP

21 Spectrum Power Generation Ltd. Power AP

22 Sree Akkamamba Textiles Ltd Others AP

23 Sri Ganga Ice Factory Others AP

24 Sri Rama Ceramics Pvt Ltd Others AP

25 Sri Syamala Ice Industries Others AP






31 Srivathsa Power Projects Ltd Others AP

32 Steel Exchange India Ltd. Others AP

33 Treveni Glass Ltd Others AP

34 Varalakshmi Ice & Cold Storage Others AP

35 Vennar Ceramics Limited Others AP

36 Vijaya Durga Industries Others AP

37 Vijaya Porcelain Products Ltd. Others AP

7

1.1.5 About GAIL Accident

Accidents don’t always happen in GAIL, but in spite of all the safety, June 27, 2014 a

massive fire broke out following a blast in Gas Authority of India Limited (GAIL)

Pipeline in East Godavari district of Andhra Pradesh, India. The accident took place near

Tatipaka refinery of Oil and Natural Gas Corporation (ONGC), about 550 km from

Hyderabad. GAIL operates on 11,000km of (6,840-mile) natural gas pipeline network and

seven gas processing units across India. The company is also involved in petrochemicals,

exploration, city gas distribution and wind and solar power. The accident on Friday is the

latest in a series of incidents in the region in the last two decades. But it is the first

incident where people of a residential area lost lives.

Figure 1.2. Location map of Nagaram Accident

Chain of Event

Gas Authority of India Ltd pipeline caught fire, engulfing the entire Nagaram village,

killing 15 people and leaving 25 severely injured in East Godavari district on Friday. The

18 inch pipeline supplies gas to a power plant operated by Lanco Infra. There is

speculation that the pipe was old and rusted, which caused a gas leak, people are angry

that GAIL authorities didn’t pay heed when they complained about the gas leaks.

Government has appointed high level probe into the incident.

8

ONGC, (the government controlled oil company) has shut down its gas field, just 50 kms

away, after the fire at the pipe line. The huge flames leaping out of the pipe line gutted

scores of houses and shops near the blast site villagers ran out of their houses in panic as

the fire accompanied by loud blasts engulfed a large area. The minister said: “The fire

caused massive losses. Coconut trees and other crops in over 10 acres were reduced to

ashes

The massive blaze started on early Friday morning in Nagaram village in the

coastal district, about 560 kms from Hyderabad. Apparently the leaked gas from the

pipeline ignited when a tea vendor lit his stove. Following the blast, the gigantic flames

scorched houses, coconut palms and everything else in the radius of half a kilometre.

Though there is a valve for every 40 kilometres of the pipeline that gets shut in case of a

leak, the gas within this area is sufficient for a catastrophe and hence people will not

permit the pipeline to pass through residential areas," said a member of the Victim's

Forum, which plans to revive the campaign against the backdrop of the latest mishap.

Causes

The fire broke out between 5.30am and 5.45am on the pipeline running through Nagaram.

The gas leak seems to have been taking place for the last four days. Since the gas is

odourless, its leakage was undetected until a tea stall owner, Vasu, lit a stove early

morning to prepare tea for a family, barely 200 metres from the place where the gas

leaked, leading to a blast followed by large balls of fire.

Andhra Pradesh Police said the lighting of a stove by a tea vendor might have sparked

today's fire in the GAIL pipeline in East Godavari district after leaked gas from the line

enveloped the area. As per initial information, there was a major gas leakage from the

pipeline around 4.30 AM at Nagaram village in Mamidikuduru mandal of the district

which spread to nearby areas and lighting of a stove at a tea shop triggered the fire and a

blast.

1.2 Remote Sensing

Remote sensing is the acquisition of information about an object or phenomenon without

making physical contact with the object and thus in contrast to in situ observation. In

modern usage, the term generally refers to the use of aerial sensor technologies to detect

and classify objects on Earth (both on the surface, and in the atmosphere and oceans) by

means of propagated signals (e.g. electromagnetic radiation). It may be split into active

9

remote sensing (when a signal is first emitted from aircraft or satellites) or passive (e.g.

sunlight) when information is merely recorded.

1.2.1 Overview

Passive sensors gather natural radiation that is emitted or reflected by the object or

surrounding areas. Reflected sunlight is the most common source of radiation measured

by passive sensors. Examples of passive remote sensors include film

photography, infrared, charge-coupled devices, and radiometers. Active collection, on the

other hand, emits energy in order to scan objects and areas whereupon a sensor then

detects and measures the radiation that is reflected or backscattered from the

target. RADAR and LiDAR are examples of active remote sensing where the time delay

between emission and return is measured, establishing the location, speed and direction of

an object.

Figure 1.3. Illustration of the Remote Sensing process

Remote sensing makes it possible to collect data on dangerous or inaccessible areas.

Remote sensing applications include monitoring deforestation in areas such as the

Amazon Basin, glacial features in Arctic and Antarctic regions, and depth sounding of

coastal and ocean depths. Military collection during the Cold War made use of stand-off

collection of data about dangerous border areas. Remote sensing also replaces costly and

slow data collection on the ground, ensuring in the process that areas or objects are not

disturbed.

Orbital platforms collect and transmit data from different parts of the electromagnetic

spectrum, which in conjunction with larger scale aerial or ground-based sensing and

10

analysis, provides researchers with enough information to monitor trends such as El

Niño and other natural long and short term phenomena. Other uses include different areas

of the earth sciences such as natural resource management, agricultural fields such as land

usage and conservation, and national security and overhead, ground-based and stand-off

collection on border areas.

1.2.2 History

Figure 1.4. ( a ) Airbourne sensor, ( b ) Spacebourne sensor

The modern discipline of remote sensing arose with the development of flight. The

balloonist G. Tournachon (alias Nadar) made photographs of Paris from his balloon in

1858. Messenger pigeons, kites, rockets and unmanned balloons were also used for early

images. With the exception of balloons, these first, individual images were not

particularly useful for map making or for scientific purposes.

Systematic aerial photography was developed for military surveillance and

reconnaissance purposes beginning in World War I and reaching a climax during the Cold

War with the use of modified combat aircraft such as the P-51, P-38, RB-66 and the F-

4C, or specifically designed collection platforms such as the U2/TR-1, SR-71, A-5 and

the OV-1 series both in overhead and stand-off collection. A more recent development is

that of increasingly smaller sensor pods such as those used by law enforcement and the

military, in both manned and unmanned platforms. The advantage of this approach is that

this requires minimal modification to a given airframe. Later imaging technologies would

include Infra-red, conventional, Doppler and synthetic aperture radar.

( a ) ( b)

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The development of artificial satellites in the latter half of the 20th century allowed

remote sensing to progress to a global scale as of the end of the Cold War.

Instrumentation aboard various Earth observing and weather satellites such as Landsat,

the Nimbus and more recent missions such as RADARSAT and UARS provided global

measurements of various data for civil, research, and military purposes. Space probes to

other planets have also provided the opportunity to conduct remote sensing studies in

extraterrestrial environments, synthetic aperture radar aboard the Magellan spacecraft

provided detailed topographic maps of Venus, while instruments aboard SOHO allowed

studies to be performed on the Sun and the solar wind, just to name a few examples.

Recent developments include, beginning in the 1960s and 1970s with the development

of image processing of satellite imagery. Several research groups in Silicon

Valley including NASA Ames Research Centre, GTE, and ESL Inc. developed Fourier

transform techniques leading to the first notable enhancement of imagery data. In 1999

the first commercial satellite (IKONOS) collecting very high resolution imagery was

launched.

1.3 Geographic Information System(GIS)

A Geographic Information System(GIS) is a computer system designed to capture, store,

manipulate, analyze, manage, and present all types of spatial or geographical data.

The acronym GIS is sometimes used for geographical information science or geospatial

information studies to refer to the academic discipline or career of working with

geographic information systems and is a large domain within the broader academic

discipline of Geo informatics. What goes beyond a GIS is a spatial data infrastructure, a

concept that has no such restrictive boundaries.

In a general sense, the term describes any information system that integrates stores, edits,

analyzes, shares, and displays geographic information. GIS applications are tools that

allow users to create interactive queries (user-created searches), analyze spatial

information, edit data in maps, and present the results of all these operations. Geographic

information science is the science underlying geographic concepts, applications, and

systems.

GIS is a broad term that can refer to a number of different technologies, processes, and

methods. It is attached to many operations and has many applications related to

engineering, planning, management, transport/logistics, insurance, telecommunications,

12

and business. For that reason, GIS and location intelligence applications can be the

foundation for many location-enabled services that rely on analysis and visualization.

GIS can relate unrelated information by using location as the key index variable.

Locations or extents in the earth space–time may be recorded as dates/times of

occurrence, and x, y, and z coordinates representing, longitude, latitude, and elevation,

respectively. All Earth-based spatial–temporal location and extent references should,

ideally, be relatable to one another and ultimately to a "real" physical location or extent.

This key characteristic of GIS has begun to open new avenues of scientific inquiry.

1.3.1 History Of Development

The first known use of the term "geographic information system" was by Roger

Tomlinson in the year 1968 in his paper "A Geographic Information Systemfor Regional

Planning". Tomlinson is also acknowledged as the "father of GIS". E. W. Gilbert's

version (1958) of John Snow's 1855 map of the Soho cholera outbreak showing the

clusters of cholera cases in the London epidemic of 1854

Previously, one of the first applications of spatial analysis in epidemiology is the 1832

"Rapport sur la marche et les effets du cholera dans Paris et le department de la Seine".

The French geographer Charles Picquet represented the 48 districts of the city of Paris by

halftone colour gradient according to the percentage of deaths

by cholera per 1,000 inhabitants. In 1854 John Snow depicted a cholera outbreak

in London using points to represent the locations of some individual cases, an early

successful use of a geographic methodology in epidemiology. While the basic elements

of topography and theme existed previously in cartography, the John Snow map was

unique, using cartographic methods not only to depict but also to analyze clusters of

geographically dependent phenomena.

The early 20th century saw the development of photozincography, which allowed maps to

be split into layers, for example one layer for vegetation and another for water. This was

particularly used for printing contours – drawing these was a labour-intensive task but

having them on a separate layer meant they could be worked on without the other layers

to confuse the draughts man. This work was originally drawn on glass plates but

later plastic film was introduced, with the advantages of being lighter, using less storage

space and being less brittle, among others. When all the layers were finished, they were

combined into one image using a large process camera. Once colour printing came in, the

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layers idea was also used for creating separate printing plates for each colour. While the

use of layers much later became one of the main typical features of a contemporary GIS,

the photographic process just described is not considered to be a GIS in itself as the maps

were just images with no database to link them to.

Computer hardware development spurred by nuclear weapon research led to general-

purpose computer "mapping" applications by the early 1960s.

The year 1960 saw the development of the world's first true operational GIS in Ottawa,

Ontario, Canada by the federal Department of Forestry and Rural Development.

Developed by Dr. Roger Tomlinson, it was called the Canada Geographic Information

System(CGIS) and was used to store, analyze, and manipulate data collected for the

Canada Land Inventory – an effort to determine the land capability for rural Canada by

mapping information about soils, agriculture, recreation, wildlife, water fowl, forestry and

land use at a scale of 1:50,000. A rating classification factor was also added to permit

analysis.

CGIS was an improvement over "computer mapping" applications as it provided

capabilities for overlay, measurement, and digitizing/scanning. It supported a national

coordinate system that spanned the continent, coded lines as arcs having a true

embedded topology and it stored the attribute and locational information in separate files.

As a result of this, Tomlinson has become known as the "father of GIS", particularly for

his use of overlays in promoting the spatial analysis of convergent geographic data.

CGIS lasted into the 1990s and built a large digital land resource database in Canada. It

was developed as a mainframe-based system in support of federal and provincial resource

planning and management. Its strength was continent-wide analysis of complex datasets.

The CGIS was never available commercially.

In 1964 Howard T. Fisher formed the Laboratory for Computer Graphics and Spatial

Analysis at the Harvard Graduate School of Design (LCGSA 1965–1991), where a

number of important theoretical concepts in spatial data handling were developed, and

which by the 1970s had distributed seminal software code and systems, such as SYMAP,

GRID, and ODYSSEY – that served as sources for subsequent commercial

development—to universities, research centres and corporations worldwide.

By the early 1980s, M&S Computing (later Intergraph) along with Bentley Systems

Incorporated for the CAD platform, Environmental Systems Research Institute (ESRI),

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CARIS (Computer Aided Resource Information System), MapInfo Corporation and

ERDAS (Earth Resource Data Analysis System) emerged as commercial vendors of

GIS software, successfully incorporating many of the CGIS features, combining the first

generation approach to separation of spatial and attribute information with a second

generation approach to organizing attribute data into database structures. In parallel, the

development of two public domain systems (MOSS and GRASS GIS) began in the

late 1970s and early 1980s.

In 1986, Mapping Display and Analysis System (MIDAS), the first desktop GIS product

emerged for the DOS operating system. This was renamed in 1990 to MapInfo for

Windows when it was ported to the Microsoft Windows platform. This began the process

of moving GIS from the research department into the business environment.

By the end of the 20th century, the rapid growth in various systems had been consolidated

and standardized on relatively few platforms and users were beginning to explore viewing

GIS data over the Internet, requiring data format and transfer standards. More recently, a

growing number of free, open-source GIS packages run on a range of operating systems

and can be customized to perform specific tasks. Increasingly geospatial

data and mapping applications are being made available via the World Wide Web.

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Chapter 2. Study Area

East Godavari district is a district in Coastal Andhra region of Andhra Pradesh, India. Its

district headquarters is at Kakinada. As of Census 2011, it is the most populous district of

the state with a population of 5,151,549. Rajahmundry and Kakinada are the two large

cities in the Godavari districts. It is also known as the Rice Bowl of Andhra Pradesh with

lush paddy fields and coconut groves

The District is a residuary portion of the old Godavari District after West Godavari

District was separated in 1925. As the name of the district conveys, East Godavari

District is closely associated with the river Godavari, occupying a major portion of the

delta area.

The Headquarters of the District is located at Kakinada. East Godavari District lies North

- East Coast of Andhra Pradesh and bounded on the North by Visakhapatnam District and

the State of Orissa, on the East and the South by the Bay of Bengal and on the West by

Khammam and West Godavari Districts.

Area of the District is 10,807 Sq.Kms. The District is located between Northern latitudes

of 16o 30' and 18o 20' and between the Eastern longitudes of 81o 30' and 82o 30'. It has a

population of 48.73 lakhs as per 2001 Census. The District consisting of 5 Revenue

Divisions viz., Kakinada, Rajahmundry, Peddapuram, Rampachodavaram and

Amalapuram.

Demographics

According to the 2011 census East Godavari district has a population of

51,51,549, roughly equal to the United Arab Emirates or the US state of Colorado. This

gives it a ranking of 19th in India (out of a total of 640) and 2nd in its state. The district

has a population density of 477 inhabitants per square kilometre

(1,240 /sq mi).Its population growth rate over the decade 2001–2011 was 5.1%. East

Godavari has a sex ratio of 1005 females for every 1000 males, and a literacy rate of

71.35%.

East Godavari district has a total population of 51,51,549; 25,69,419 and 25,82,130 male

and female respectively. There was change of 5.10 percent in the population compared to

population as per 2001 census. The census data states a density of 477 in 2011 compared

to 454 in 2001.

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Average literacy rate of East Godavari in 2011 was 71.35% compared to 65.48% in 2001.

On a gender basis, male and female literacy was 74.91% and 67.82% respectively.

With regards to sex ratio in East Godavari, it stood at 1005 per 1000 males compared to

the 2001 census figure of 993. The average national sex ratio in India is 940 as per the

2011 census.

There were total 4,92,446 children under the age of 0-6 against 6,13,490 of 2001 census.

Of total 492,446 male and female were 2,50,086 and 2,42,360 respectively. The child sex

ratio as per census 2011 was 969 compared to 978 in 2001. In 2011, children under 0-6

formed 9.56% of East Godavari district compared to 12.52% in 2001.

Figure 2.1. Location Map of the Study Area

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Chapter 3. Literature Review

3.1 Introduction

Pipelines are the most efficient, cost effective and environmentally friendly means of

fluid and gas transport. Transmission or trunk pipelines are examples of engineering

marvels requiring high project cost and long gestation periods and operating life. Careful

planning of their route can save on cost, time and operating expenses and ensure longer

operational life and help prevent environmental fallouts.

Throughout the world, a large network of pipes transport oil, gas, water and different

products. Pipeline transport is most prevalent in USA where nearly two-thirds of oil is

transported annually through a network of more than two million kilometers of pipelines,

in some of the toughest terrains. Pipelines are by far the most economical, practical and

safe option of fluid transport. They save enormously due to their tenfold efficiency over

trucking / railroad operations and accure important environmental and safety benefits by

reducing the highway congestion, pollution and spill. This inexpensive, reliable and high

capacity transport is critical to national economy and security.

In India the trend towards pipeline transport is increasing – and is likely to accentuate

with privatization of petroleum sector and growth of cities. The pipeline network will see

an exponential growth from the current installed network of nearly one lakh kilometer.

Petroleum and petroleum products being basic raw material for many industries, the use

of pipelines shall increase for sectors like fertilizers, power, petrochemicals,

pharmaceuticals, plastics, industrial chemicals, transport etc., Growth of cities will

likewise increase the demand for water pipelines. The method as such is general enough

to apply to all linear infrastructure like transport network etc and can be applied to road,

rail and conveyor transport and to transmission and distribution of power and data.

The pipelines are used for transmission, distribution and gathering. The most

sophisticated and large pipelines fall in the transmission category. These are often large

diameter (>100 cm dia.) pipelines incorporating automated monitoring and control of

flow, pressure and fluctuations. These pipelines are capital-intensive installations with

long lead and long life. Typical installation costs range from Rs. 6 million / km for water

pipeline to Rs. 20 millions/km for gas pipelines with oil pipelines at intermediate range of

10 million. Material and laying components account for 70-90 per cent of cost. The

18

construction of pipeline is facilitated by proper analysis of route location for access to

right of way, terrain for obstructions and weather for movement of equipment.

3.2 Assessment on Pipeline Alignment

Pipeline operation entails a comprehensive strategy for routine operations and

maintenance, damage prevention, safety, security, environmental protection and

emergency response. Many of these factors fall within the regulatory framework and

require compliance. Routes passing through unusually sensitive areas like water supply

reservoirs, populated areas and ecologically sensitive areas need extra precautions against

accidental spillage. Mapping of pipelines for administering a sound operations program is

now considered essential.

Scientific planning of pipeline route can reduce cost and time of project execution and

hence the operating expenses. Pipeline alignment is basically an optimization between

costs of the material and the construction. Natural and man-made terrain obstructions

cause spatial variation in construction cost due to changing thematic features like types of

soils, intervals of slope, etc. Manual pipeline route planning uses available maps, surveys

and experience and is seriously constrained due to lack of updated data and quantitative

approach. This is accentuated for complex terrains and long routes. Remote sensing (RS)

and GIS method on the contrary uses updated maps from latest RS data, integrates

thematic cost layers in GIS environment and computes all possible routes with associated

costs. Apart from saving 5-15 % route length, the method has potential benefits like

cadastral overlays on route for gadget notification, precise location data on installations

and organization of O&M (Operations and Maintenance data).

3.2.1. Background

In response to industry demand, SAC has developed a methodology for pipeline

alignment using remote sensing and GIS techniques. A small study on pipeline routing

using remote sensing derived land use formed the basis for this development. This study,

completed in 1999 for a survey company under training project, saved 1 km pipeline

length over existing 35-km alignment. Realizing the potential, SAC initiated in-depth

evaluation of potential of RS/GIS techniques for pipeline alignment in 2000.

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3.2.2. Development of Pipeline Alignment Technique

A methodology for semi-automated pipeline alignment was developed using sample data

of 15x12 km area. Objective of the study was to develop a comprehensive package for

semi-automated pipeline alignment on an image processing and GIS software backbone.

The available algorithms of GIS software like path analysis and drainage analysis on

accumulated cost surface, conducted by NASA under Commercial Remote Sensing

Program in 1997, have limitations of local optimization, boundary constraints and high

computational load, and lack flexibility in assigning start, intermediate and end points for

route optimization.

The cornerstones of the SAC methodology are the cost surface and the route analysis on

this surface. The cost surface is generated by combining all the thematic costs of laying

the pipeline on a given terrain by a system of ranks and weights. This is consistent with

the basic problem as the pipeline routing is a compromise between the minimum (straight

line) distance from source to destination and the physical conditions existing above and

below ground. The themes relevant for cost surface represent the physical conditions of

terrain and their choice may vary by locale and project requirements. In development

phase a fairly general set of themes like slope, soil, land use, geology, road/rail networks

and streams are considered for generating cost surface. The cost ranking for features

within the themes and weights for each theme are assigned by general recommendations,

subject knowledge and expert opinion.

The route of least cost between source and destination points is searched iteratively over

corridors of narrowing width using network analysis approach. The cost is computed as

weighted sum of material cost of pipeline, the construction cost of laying the pipeline and

the access cost of approaching the route. Thus the first rough route is obtained over entire

rectangular area encompassing start and end points. The subsequent route search is

limited to a broad buffer zone around the previous route. Generally third iteration with

narrow corridor of buffer zone ends this global search option. Path analysis is then used to

locally optimize the route, which yields final alignment.

Dry run showed clearly that routing between start and end points passed through

minimum cost areas. The final route was 51 % longer than the straight-line path and has

cost implications of just a fraction of percent of the straight-line cost, because the straight

line passed over a hilly terrain.

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3.2.3. Validation of Pipeline Alignment Technique

A 42-km water pipeline in south of Udaipur (India) was manually aligned by a private

company ( M/s MultiMantech, Ahmedabad) for carrying water from a reservoir at 800

MSL to Hindustan Zinc Ltd plant at 500 MSL under gravity flow ( i.e. without pumping).

The terrain is hilly and the manual alignment mostly followed highways and roads. This

problem was repeated using SAC technique as validation exercise, which was completed

in two-month time.

Twelve cost layers (topography, slope, geology, soil, land use, road, distance from road,

rail, forest, water bodies and streams) are selected and created using satellite data and

other maps and ranked for cost contributions by features distribution. Variable weights

are assigned to each of the layers to reflect the project requirements and general routing

criteria. The Combined Weighted Cost Surface (CWCS) is generated and semi-automated

route search with three narrowing corridors is executed with cost ratio of 60:40 for

material and construction costs (access cost were not considered).

The route obtained by RS/GIS method shows 5.7 km saving (13.4%) over the original 42

km alignment obtained by existing survey method. This route after reconciliation has now

been accepted as final alignment after ground visits confirmed the feasibility.

Table 4. Route Reconciliation details

Route Source Actual Length

(m)

Mean cost / unit

(Relative)

Total cost (Relative)

Difference (%)

(Route length)

Difference (%)

(relative cost) Reference route (Survey based by MMIL, Ahd)

42572 1339.6 (1.00)

57046480 0.00 0.00

SAC Route 36868 1334.65 (0.996)

49205876 -13.40 -13.50

3.2.4. Route Plan for Chennai-Bangalore Gas Pipeline

Projects and Development India Ltd (PDIL) Noida had expressed interest in optimum

routing of Chennai-Bangalore pipeline, which was entrusted to them on behalf of Gas

Authority of India Ltd (GAIL), New Delhi.

The cost surface search methodology was applied on this important section to further

demonstrate the utility of the technique. Twelve cost layers were derived from thematic

maps on soil, geology, water bodies, drainage, transport network, elevation model, slope,

21

road distance, land use, forest maps etc on 1:250 000 scale. CWCS was generated using

suitable ranks and weigh tags and route analysis was performed for two points west and

east of Chennai and Bangalore respectively using varying material, construction and

access cost ratios. Costs were optimized with mean values having relative significant

only. Six different routes based on combination of material, construction and access

criteria and having up to 12 per cent saving as compared to a straight-line route have been

generated.

3.2.5. Benefits: Summing up

The method for semi-automated alignment of pipelines using RS and GIS tools has

unique advantages like

• Updated and integrated information on terrain,

• Shortest route by automated and computation based search techniques,

• Spatial and numerical data organization of layout,

• Cadastral overlays for route ROU/ROW measures,

• Cost well compensated by high benefits and speedy implementation and

• Downstream options for O&M support.

The method is general enough to be applicable for other sectors related to linear

infrastructure planning like alignment of electric transmission lines, network plans for

roads and rail etc.

3.2.6. Concluding Remarks: Costs

Two different methods of semi-automated alignment of pipelines using RS and GIS tools

have been developed and tested. The cost in terms of budget and time for implementing

RS/GIS method seems unnecessary at first glance, but the experiments carries out so far

indicate its high benefits compared to cost. In fact various studies point towards almost

guaranteed saving of 5-15 per cent. As the cost of implementing the method is merely

one-thousandth part of the project cost, cost benefit ratio of over 50 is expected in worst

case scenario.

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3.3 Why GIS Is Used

Utility organizations are beginning to look at GIS as a way to manage all their assets and

infrastructure. A GIS can reveal important information that leads to better decision

making. The implementation strategies of a utility GIS are what organizations use to help

achieve their overall goals of the system. These strategies can be somewhat different

between implementation. A number of factors go into the implementation of a utility GIS,

and depending on the size of the system, these factors can be overwhelming at times.

Many organizations adopt GIS with the assumption that it will make their work easier to

complete, lower the cost to do their work, and provide the customers with better service

.The assumption will be true if the GIS implementation is carried out correctly, and

within a timely manner. Obstacles related to training, education, and general

understanding of the technology seems to inhibit the successful implementation of the

overall system.

Geo-databases are used to store geographic/ spatial and non-spatial data. These databases

brought incredible change in mapping of network based information system as well as

geo-spatial analysis. Geo-spatial data mapping is now a powerful tool for geo-analysis. In

gas network management it is used to map, manipulate, analyze, and display the metrics

of pipelines in an appropriate form. GIS in oil and gas exploration scenario is used to

characterize and analyze reservoirs, characterize isotopic data, seismic and geological

data, and Lineament data. GIS is also used to enhance tracer analysis which is done by

incorporating GIS functions, such as statistical analysis of networks and cartographic

mapping, in different software interfaces like conventional information system interface.

GIS is important not only in exploration but also in generating self-revenue by utilizing

the services of petroleum exploration data management.

Applying Indexing Method to Gas Pipeline Risk Assessment by Using GIS: A Case

Study in Savadkooh, North of Iran

1. Aims at finding out the potential accidents.

2. Analysis on the causes as well as improvements to reduce the risks

3. Indexing method is used which is more practical than other methods

4. Entire pipeline was divided into 500m intervals and risk was calculated at each

section

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5. Existing faults, corrosion along the pipeline, pipeline along landslide zone, high

voltage transmission lines, residential areas, permanent and temporary river flow,

and presence of roads.

Indexing Method

Indexing tries to handle two things: A fast routine that gives you a set buckets in which

you collect objects that you can spatially distinguish (the buckets!). And Boxes are easy

to calculate and to handle. A set of relations (overlap, touch) to distinguish or relate the

spatial stuff (the objects). Index Overlay has the least running time with comparing to

other models. The reason for this can be originated from its operator linear operation.

Index Overlay execution: This model was executed in two stages. First, in each class,

factor maps were integrated with respect to Table 1 and were resulted in four class factor

maps. Then, output factor maps were integrated using designed interface. Although it is

possible to model vector data in a relational form, the required level of normalization and

a lack of suitable multidimensional indexing methods place severe limitations on

performance. Effective integration of spatial and non spatial data management has

become possible only with the development of suitable abstract data types and indexing

mechanisms as integral components of modern database systems

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Chapter 4. Data and Software Used

4.1 Data Used

4.1.1 Toposheet

A toposheet is a shortened name for 'Topographic sheet'. They essentially contain

information about an area like roads, railways, settlements, canals, rivers, electric poles,

post offices etc.

In modern mapping, a topographic map is a type of map characterized by large-scale

detail and quantitative representation of relief, using contour lines but, historically, using

a variety of methods. Traditional definitions require a topographic map to show both

natural and man-made features. A topographic map is typically published as a map series,

made up of two or more map sheets that combine to form the whole map. A contour line

is a combination of two line segments that connect but do not intersect; these represent

elevation on a topographic map.

Toposheet Indexing Survey of India produces the topographic maps of India. These maps are produced at

different scales. In order to identify a map of a particular area, a numbering system has

been adopted by the Survey of India.

For the purpose of an international series (within 4° N to 40° N Latitude and 44° E to

124° E Longitude) at the scale of 1: 1,000,000 is considered as a base map. This map is

divided into sections of 4° latitude × 4° longitude and designated from 1 to 136 consisting

of the segments that cover only land area.

Figure 4.1. Toposheet Indexing 4° latitude × 4° longitude

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Each section is further divided into 16 sections (4 rows and 4 columns) each of 1°

latitude× 1° longitude. The sections start from Northwest direction, run column wise and

end in Southeast direction.

Figure 4.2. Toposheet Indexing 1° latitude× 1° longitude.

The 1°×1° sheets are further subdivided into four parts, each of 30′ latitude × 30′

longitude. These are identified by the cardinal directions NE, NW, SE and SW.

Figure 4.3. Toposheet Indexing 30′ latitude × 30′ longitude.

26

The 1°×1° sheets can also be divided into 16 sections each of 15′ latitude × 15′ longitude

and are numbered from 1 to 16 in a columned manner.

Figure 4.4. Toposheet Indexing 15′ latitude × 15′ longitude

A 15′×15′ sheet can be divided into 4 sheets, each of 7(1/2)′ and are numbered as NW,

NE, SW and SE.

Figure 4.5. Toposheet Indexing 7(1/2)′ latitude × 7(1/2)′ longitude

4.1.2. Toposheets used in our project:

65.F16,G9,G10,G11,G12,G13,G14,G15,G16,H9,H10,H11,H12,H13,H14,H15,K1,K2,K3,

K4,K5,K6,K7,K8&K12,K10,K11,L1,L2,L3,L4 and L5.

27

4.2. GAIL Map

GAIL image which was used for digitalization of pipelines was collect from GAIL office

which was located in NFCL, Kakinada.

Figure 4.6. Pipeline network of GAIL K. G. Basin

4.2 Software Used

4.2.1 ERDAS

ERDAS IMAGINE is a remote sensing application with raster graphics editor abilities

designed by ERDAS for geospatial applications. The latest version is 2013, version

13.0.2. ERDAS IMAGINE is aimed primarily at geospatial raster data processing and

allows the user to prepare, display and enhance digital images for mapping use in

Geographic Information System (GIS) or in Computer-Aided Design (CAD) software. It

is a toolbox allowing the user to perform numerous operations on an image and generate

an answer to specific geographical questions.

By manipulating imagery data values and positions, it is possible to see features that

would not normally be visible and to locate geo-positions of features that would

otherwise be graphical. The level of brightness or reflectance of light from the surfaces in

the image can be helpful with vegetation analysis, prospecting for minerals etc. Other

28

usage examples include linear feature extraction, generation of processing work flows

("spatial models" in ERDAS IMAGINE), import/export of data for a wide variety of

format, stereo and automatic feature extraction of map data from imagery.

Product History

Before the ERDAS IMAGINE Suite, ERDAS, Inc. developed various products to process

satellite imagery from AVHRR, Landsat MSS and TM, and Spot Image into land cover,

land use maps, map deforestation, and assist in locating oil reserves under the product

name ERDAS. These older ERDAS applications were rewritten from FORTRAN to C

and C++ and exist today within the ERDAS IMAGINE Suite which has grown to support

most optical and radar mapping satellites, airborne mapping cameras and digital sensors

used for mapping.

ERDAS Imagine Image Catalogue

The ERDAS IMAGINE Image Catalogue database is designed to serve as a library and

information management system for image files (.img) that are imported and created in

ERDAS IMAGINE. The information for the image files is displayed in the Image Catalog

Cell Array. This Cell Array enables you to view all of the ancillary data for the image

files in the database. When records are queried based on specific criteria, the image files

that match the criteria are highlighted in the Cell Array. It is also possible to graphically

view the coverage of the selected image files on a map in a canvas window. When it is

necessary to store some data on a tape, the ERDAS IMAGINE Image Catalog database

enables you to archive image files to external devices. The Image Catalog Cell Array

shows which tape the image file is stored on, and the file can be easily retrieved from the

tape device to a designated disk directory. The archived image files are copies of the files

on disk—nothing is removed from the disk. Once the file is archived, it can be removed

from the disk, if you like.

Editing Raster Data

ERDAS IMAGINE provides raster editing tools for editing the data values of thematic

and continuous raster data. This is primarily a correction mechanism that enables you to

correct bad data values which produce noise, such as spikes and holes in imagery. The

raster editing functions can be applied to the entire image or a user-selected area of

interest (AOI). With raster editing, data values in thematic data can also be recoded

29

according to class. Recoding is a function that reassigns data values to a region or to an

entire class of pixels.

Digitizing

In the broadest sense, digitizing refers to any process that converts non digital data into

numbers. However, in ERDAS IMAGINE, the digitizing of vectors refers to the creation

of vector data from hardcopy materials or raster images that are traced using a digitizer

keypad on a digitizing tablet or a mouse on a displayed image. Any image not already in

digital format must be digitized before it can be read by the computer and incorporated

into the database. Most Landsat, SPOT, or other satellite data are already in digital format

upon receipt, so it is not necessary to digitize them. However, you may also have maps,

photographs, or other non digital data that contain information you want to incorporate

into the study. Or, you may want to extract certain features from a digital image to

include in a vector layer.

Georeferencing

Georeferencing is the process of linking the raster space of an image to a model

space(i.e., a map system). Raster space defines how the coordinate system grid lines are

placed relative to the centres of the pixels of the image. In ERDAS IMAGINE, the grid

lines of the coordinate system always intersect at the centre of a pixel. GeoTIFF allows

the raster space to be defined either as having grid lines intersecting at the centres of the

pixels (PixelIsPoint) or as having grid lines intersecting at the upper left corner of the

pixels (PixelIsArea). ERDAS IMAGINE converts the georeferencing values for

PixelIsArea images so that they conform to its raster space definition.

Geocoding

Geocoding is the process of linking coordinates in model space to the Earth’s surface

Geocoding allows for the specification of projection, datum, ellipsoid, etc. ERDAS

IMAGINE can interpret GeoTIFF geocoding so that latitude and longitude of the images

map coordinates can be determined.

Vector Data from Other Software Vendors

It is possible to directly import several common vector formats into ERDAS IMAGINE.

These files become vector layers when imported. These data can then be used for the

analyses and, in most cases, exported back to their original format (if desired). Although

30

data can be converted from one type to another by importing a file into ERDAS

IMAGINE and then exporting the ERDAS IMAGINE file into another format, the import

and export routines were designed to work together. For example, if you have information

in AutoCAD that you would like to use in the GIS, you can import a Drawing Interchange

File (DXF) into ERDAS IMAGINE, do the analysis, and then export the data back to

DXF format.

Enhancement

Image enhancement is the process of making an image more interpretable for a particular

application.

The following enhancement techniques are available with ERDAS IMAGINE:

• Data correction—radiometric and geometric correction

• Radiometric enhancement—enhancing images based on the values of individual

pixels

• Spatial enhancement—enhancing images based on the values of individual and

neighbouring pixels

• Spectral enhancement—enhancing images by transforming the values of each

pixel on a multiband basis

• Hyperspectral image processing—an extension of the techniques used for

multispectral data sets

• Fourier analysis—techniques for eliminating periodic noise in imagery

• Radar imagery enhancement—techniques specifically designed for enhancing

radar imagery

Multispectral Classification in ERDAS:

Multispectral classification is the process of sorting pixels into a finite number of

individual classes, or categories of data, based on their data file values. If a pixel satisfies

a certain set of criteria, the pixel is assigned to the class that corresponds to that criteria.

This process is also referred to as image segmentation. Depending on the type of

information you want to extract from the original data, classes may be associated with

known features on the ground or may simply represent areas that look different to the

31

computer. An example of a classified image is a land cover map, showing vegetation,

bare land, pasture, urban, etc.

Radar Concepts

Radar images are quite different from other remotely sensed imagery you might use with

ERDAS IMAGINE software. Radar images, do, however, contain a great deal of

information. ERDAS IMAGINE has many radar packages, including IMAGINE Radar

Interpreter, IMAGINE OrthoRadar,

IMAGINE StereoSAR DEM, IMAGINE IFSAR DEM, and the Generic SAR Node with

which you can analyze your radar imagery.

A Few Working Images of ERDAS

Figure 4.7. Project Window

32

Figure 4.8. Main window

Figure 4.9. Geo service explorer

4.2.2 ArcGIS

ESRI's ArcGIS is a Geographic Information System(GIS) for working with maps and

geographic information. It is used for: creating and using maps; compiling geographic

data; analyzing mapped information; sharing and discovering geographic information;

33

using maps and geographic information in a range of applications; and managing

geographic information in a database.

The system provides an infrastructure for making maps and geographic information

available throughout an organization, across a community, and openly on the Web.

Product History

Prior to the ARCGIS suite, ESRI had focused its software development on the command

line ARC/INFO workstation program and several Graphical User Interface-based

products such as the ARC View GIS 3.x desktop program. Other ESRI products included

Map Objects, a programming library for developers, and ARC SDE as a relation database

management system. The various products had branched out into multiple source

trees and did not integrate well with one another. In January 1997, ESRI decided to

revamp its GIS software platform, creating a single integrated software architecture

USES : Planning and Analysis

Improve your ability to anticipate and manage change by using spatial analysis. ARCGIS

gives you

• A set of comprehensive spatial analysis tools

• A platform for viewing and disseminating results

Asset/Data Management

Enable better use of resources by making data available to those who need it. ARCGIS

empowers you with

• Online data and maps you can use in your projects

• Tools and services for maintaining your data integrity

• Industry-standard templates that help you organize information

Operational Awareness

Get a comprehensive understanding of the activities affecting your organization. ARCGIS

offers

• Web-based applications that can be configured to meet the needs of the people

using them, ranging from executives, to technical staff, to field workers.

• Ability to use live feeds and automated analysis and alert tools

34

• Capability to present large volumes of disparate data in an intuitive map-based

format

Field Workforce

Experience better and more coordinated decision making as well as faster and more

efficient field operations. ARCGIS provides

• Ability to get up-to-date information to field operations

• Tools that are easy for field staff to use and that support a variety of field device

types.

Multi scale 3D models

Figure 4.10. Multi scale 3D model in ArcGIS

3D is an integral part of ARCGIS, allowing you to work with your 3D models across the

ARCGIS platform.

Add 3D to your design process. Sketch in 3D or use the power of procedural rules to

quickly generate 3D master plans.

Use our 3D Cities solution to organize your urban data and simplify the creation,

management, and analysis of your 3D city or facility.

Geodatabase

The geodatabase is the common data storage and management framework for ARCGIS. It

combines "geo" (spatial data) with "database" (data repository) to create a central data

35

repository for spatial data storage and management. It can be leveraged in desktop, server,

or mobile environments and allows you to store GIS data in a central location for easy

access and management.

The geodatabase offers you the ability to

• Store a rich collection of spatial data in a centralized location.

• Apply sophisticated rules and relationships to the data.

• Define advanced geospatial relational models (e.g., topologies, networks).

• Maintain integrity of spatial data with a consistent, accurate database.

• Work within a multiuser access and editing environment.

• Integrate spatial data with other IT databases.

• Easily scale your storage solution.

• Support custom features and behaviour.

• Leverage your spatial data to its full potential.

36

Chapter 5. Methodology

Figure 5.1. Flow chart illustrating the methodology

5.1 Data Acquisition

This generally deals with the collection of required information. With

reference to RS & GIS, Data Acquisition means the collection of toposheets, Thematic

Maps, Satellite Images etc.

Toposheet

Data acquisition

GAIL map

Pre-processing

ERDAS ArcGIS

Rectification of Toposheet

Subset and Mosaic

Rectification of GAIL Map

Layer Creation

Digitization

Buffering

Analysis

Output

Data export

Base map

37

Toposheet

A toposheet is a shortened name for 'Topographic sheet'. They essentially contain

information about an area like roads, railways, settlements, canals, rivers, electric poles,

post offices etc. According to their usage, they may be available at different scales (e.g.

1:25000, 1:50000 etc, where the former is a larger scale as compared to the latter). They

are made on a suitable projection for that area and contain lat-long information at the

corners. Thus any point on it can be identified with its corresponding lat-long, depending

upon the scale (i.e. if the scale is large, more accurate lat-long). Survey of India produces

the topographic maps of India. These maps are produced at different scales. In order to

identify a map of a particular area, a numbering system has been adopted by the survey of

India.

Toposheets used in our project:

65.F16,G9,G10,G11,G12,G13,G14,G15,G16,H9,H10,H11,H12,H13,H14,H15,K1,K2,K3,

K4,K5,K6,K7,K8&K12,K10,K11,L1,L2,L3,L4 and L5.

GAIL Map

GAIL image which was used for digitalization of pipelines was collect from GAIL office

which was located in NFCL, Kakinada.

5.2 Pre-Processing

Data pre-processing describes any type of processing performed on raw data to prepare it

for another processing procedure. Commonly used as a preliminary data mining practice,

data pre-processing transforms the data into a format that will be more easily and

effectively processed for the purpose of the user -- for example, in a neural network.

There are a number of different tools and methods used for pre-processing,

including: sampling, which selects a representative subset from a large population of data;

transformation, which manipulates raw data to produce a single input; denoising, which

removes noise from data; normalization, which organizes data for more efficient access;

and feature extraction, which pulls out specified data that is significant in some particular

context.

38

5.2.2 Using ERDAS

Rectification of Toposheet

1. Select viewer.

2. Add toposheet to the viewer.

3. Select Raster, then Geometric correction, a dialog box appears in that select

Polynomial. Click OK.

Figure 5.2. Assigning Polynomial model properties

4. In the above dialog box select PROJECTION. And then select Add/Change

Projection.

5. Then a dialog box appears, select CUSTOM. In that specify

PROJECTION TYPE: Geographic (Lat/Lon)

SPHEROID NAME : WGS 84

DATUM NAME : WGS 84

And then click OK.

6. Now in Polynomial Model Properties, select Set Projection from GCP Tool. Then

a dialog box appears, then select KEYBOARD ONLY and then click OK.

39

Figure 5.3. GCP tool reference setup

7. Select GCP tool zoom in the coordinates and place the GCP tool on the

coordinates and also specify

X Ref:

Y Ref:

8. Click on RESAMPLE icon. And mention output file name and click OK.

Subset and Mosaic of Toposheets

Subset

1. Click on viewer.

2. Open rectified toposheet in the viewer.

3. Select AOI, in that select Tools, select polygon tool.

Figure 5.4. AOI tool box

40

4. Mark rough area around toposheet and the double click.

5. Again select AOI, select Reshape, reshape boundaries.

6. Once reshaped click outside and inside boundary.

7. Then go to FILE, select SAVE, and then select AOI LAYER AS, and then specify

name of the file. Click SAVE.

8. Select DATA PREPARATION, select SUBSET IMAGE, specify input and output

file.

9. Select AOI and click on AOI file and specify AOI file name specified before.

Mosaic

1. Go to RASTER, select MOSAIC IMAGES.

2. Select Process, and then select Run mosaic. Rectification of Image Using GCS File Rectification is the process of projecting the data onto a plane and making it conform to a

map projection system. Assigning map coordinates to the image data is called geo-

referencing. Since all map projection systems are associated with map coordinates,

rectification involves geo-referencing.

Perform Image Rectification using GCS file

In this, we rectify a image of GAIL, using GCS of the same area. The image is rectified to

the State Plane map projection.

In rectifying the image, you use these basic steps:

• Display file

• Start Geometric Correction Tool

• Polynomial Rectification

• Record GCPs

• Resample the image

• Verify the rectification process

41

Display Files

1. Click the Viewer icon on the ERDAS IMAGINE icon panel to open a second

Viewer. The second Viewer displays on top of the first Viewer

2. In one viewer display image of GAIL PIPE LINE and in second viewer display

Mosaic image of toposheet.

Start GCP Tool

You start the Geometric Correction Tool from the first Viewer—the Viewer displaying

the file to be rectified (GAIL).

1. Select RASTER | GEOMETRIC CORRECTION from the first Viewer’s

menu bar. The Set Geometric Model dialog opens.

Figure 5.5. Tool to set geometric model

2. In the Set Geometric Model dialog, select POLYNOMIAL and then click OK.

The Geo Correction Tools open, along with the Polynomial Model Properties

dialog.

Polynomial Rectification

1. In this dialog box select PROJECTION.

2. In this select set Projection from GCP Tool.

42

Figure 5.6. GCP tool reference setup

3. Accept the default of EXISTING VIEWER in the GCP Tool Reference Setup

dialog by clicking OK. The GCP Tool Reference Setup dialog closes and a

Viewer Selection Instructions box opens, directing you to click in a Viewer to

select for reference coordinates.

4. Click in the second Viewer, which displays GAIL.img. The Reference Map

Information dialog opens showing the map information for the georeferenced

image. The information in this dialog is not editable.

Record GCPs

1. Select GCP tool and record common junctions in Mosaic image and GAIL.img.

Resample the image

Figure 5.7. Geo correction tool box

1. Select the icon RESAMPLE.

2. Then click OK.

43

Verify the rectification process

1. Open both MOSAIC image and GAIL image in one viewer. 5.2.3 ArcGIS Adding Data

1. Click the Arc Map 10.1 icon and the page opens.

2. Click on the Add Data icon and the list of drivers opens.

Figure 5.8. Add data tool

3. Choose the mosaic file of toposheets which is done in the ERDAS.

Figure 5.9. Adding Toposeet Layer

4. Mosaic file of study area will be displayed.

44

Figure 5.10. Toposheet data in ArcGIS

Adding Layers

1. First go to the driver where you want to save the layers and create a new folder

named Layers.

2. Go back to the Arc Map and click catalog icon and you can get a new screen,

where you need to go to the Layer which is created.

Figure 5.11. Catalog in ArcGIS

3. Right click on the Layer folder and choose option New and choose the sub file

Shape file.

45

Figure 5.12. Creating shapefile

4. Pop up appear where Name and Feature Type should be given.

Figure 5.13. Specifying name and feature type

5. Generally Feature Types used are Line, Polyline and Point.

6. Shape files created are

7. Roads, GAIL Pipe Lines and Railways in Line Feature.

8. Water bodies, River and Forests in Polyline Feature.

9. Habitations and Pipe terminals in Point Features.

46

Digitization

1. Layers are added to the Table of contents.

Figure 5.14. Table of contents

2. Click on Editor and Start Editing, then click the Create Feature icon and all the

Shape files will be shown.

Figure 5.15. Editor tool bar

3. Select the required Feature and start digitalization.

47

Figure 5.16. Start editing window

4. After digitalization for any type of corrections in any Feature tools like Split tools,

Snapping tools, Trim tools and merging operations are available.

Figure 5.17. Digitized area

48

Figure 5.18. Merge tool

5. Habitations can be created in Excel Sheet by giving name and location.

Figure 5.19. Excel sheet representing habitations

6. Convert it to csv file and can be attached to the mosaic File by clicking on File,

then Add Data and Add XY Data from where the habitations sheet is attached.

49

Figure 5.20. Conversion of csv file to shapefile

7. For GAIL related pipe lines and terrains we need to add image of Pipe Network of

GAIL in K.G. Basin for which coordinates are created in ERDAS and start

digitalization.

50

Figure 5.21. Adding GAIL map in ArcGIS

Figure 5.22. Digitized pipeline

Creating Attributes

1. Right click on the Feature and select Attribute table.

51

Figure 5.23. Attribute data

2. Then Table option and Add Field

Figure 5.24. Adding field

3. Then create Feature name, type etc.

4. To change the properties of any Feature just click on it change it in which way

you like it.

52

Figure 5.25. Representation of shapefile

Buffering

A buffer in GIS is a zone around a map feature measured in units of distance or time. A

buffer is useful for proximity analysis.

A buffer is an area defined by the bounding region determined by a set of points at a

specified maximum distance from all nodes along segments of an object.

1. Open the layers to be buffered and click on start editing.

2. Click on Geoprocessing and select Buffer option.

Figure 5.26. Creating buffer

53

3. Buffer table opens and give input, output locations and distance value.

Figure 5.27. Specifying input and output

4. Buffered layer will be created.

Figure 5.28. Buffered area

54

5.5 IDENTIFYING RISK AREAS BY USING CALCULATIONS

Figure 5.29. Release rates for Natural gas

IN CASE OF JET FIRE

Case 1:If a hole of 100mm diameter is formed at a pressure of 50 barg then, Release rate

of gas is 60 kg/s which is obtained from above graph

A simple correlation for the length L (m) of a jet flame due to

Wertenbach:

L = 18.5 Q0.41 [Q = mass release rate (kg/s)]

Based on calculations using the Chamberlain model, the following rough relationships for

distance along the flame axis to various thermal radiation levels have been calculated:

• 37.5 kW/m2 : 13.37 Q0.447

•12.5 kW/m2 : 16.15 Q0.447

•5.0 kW/m2 : 19.50 Q0.447

55

From above equation length can be calculated as

L = 18.5 (60)0.41

= 107 m

Distance along the flame axis to various thermal radiation levels

Radiation level at 37.5 kW/m2

L = 13.37 (60)0.447

= 84 m


L = 16.15 (60) 0.447

= 101 m


L = 19.50 (60)0.447

= 121 m

Case 2:If a hole of 50mm diameter is formed at a pressure of 50 barg then, Release rate of

gas is 9 kg/s which is obtained from above graph

A simple correlation for the length L (m) of a jet flame due to Wertenbach:

L = 18.5 Q0.41 [Q = mass release rate (kg/s)]

Based on calculations using the Chamberlain model, the following rough relationships for

distance along the flame axis to various thermal radiation levels have been calculated:

• 37.5 kW/m2 : 13.37 Q0.447

•12.5 kW/m2 : 16.15 Q0.447

•5.0 kW/m2 : 19.50 Q0.447

From above equation length can be calculated as

L = 18.5 (9)0.41

= 46 m

56

Distance along the flame axis to various thermal radiation levels


L = 13.37 (9)0.447

= 36 m


L = 16.15 (9) 0.447

= 43 m


L = 19.50 (9)0.447

= 52 m

57

Chapter 6. Results and discussion

Places that are affected are

6.1 For a hole of 100mm diameter

For a hole of 100mm diameter and considering buffer zone of 107m, following risk zones

are identified

Kutukudumalli; Timmapuram; Penumarti; Surya Rao Peta; Goddetipalem;

Pepakayalapalem; Chipallilanka; Mandapeta; Matukamilli; Kottapeta; Batlapalem;

Tatipaka; Manepalle; Vadrevapallem; Narsapuram; Ramarajulanka.

6.1.1 At Radiation level of 37.5 kW/m2

For a hole of 100mm diameter and considering buffer zone of 84m with respect to

radiation level at 37.5 kW/m2, following risk zones are identified

Surya Rao Peta; Goddetipalem; Mandapeta; Kottapeta; Batlapalem;

Tatipaka; Vadrevapallem; Manepalle; Narsapuram; Ramarajulanka




Kutukudumilli; Timmapuram; Surya Rao Peta; Goddetipalem; Mandapeta; Matukamilli;

Kottapeta; Batlapalem; Tatipaka; Manepalle; Narsapuram; Vadrevapallem;

Ramarajulanka

6.1.3 At Radiation level of 5 kW/m2


radiation level at 5 kW/m2, following risk zones are identified

Kutukudumilli; Timmapuram; Penumarti; Surya Rao Peta;

Goddetipalem; Pepakayalapalem; Mandapeta; Chipallilanka; Matukamilli; Kottapeta;

Batlapalem; Tatipaka; Manepalle; Vadrevapallem; Narsapuram; Ramarajulanka

58

6.2 For a hole of 50mm diameter

For a hole of 50mm diameter and considering buffer zone of 46m, following risk zones

are identified

Surya Rao Peta; Goddetipalem; Mandapeta; Kottapeta; Batlapalem; Tatipaka;

Manepalle; Vadrevapallem; Narsapuram; Ramarajulanka




Goddetipalem; Mandapeta; Kottapeta; Batlapalem; Vadrevapallem; Ramarajulanka




Surya Rao Peta; Goddetipalem; Mandapeta; Kottapeta; Batlapalem; Tatipaka;

Narsapuram; Vadrevapallem; Ramarajulanka

6.2.3 At Radiation level of 5 kW/m2


radiation level at 5 kW/m2, following risk zones are identified

Surya Rao Peta; Goddetipalem; Mandapeta; Kottapeta; Batlapalem; Vadrevapallem;

Manepalle; Ramarajulanka; Tatipaka; Narsapuram

The portion of areas which are covered by Pipeline must be evacuated inorder to prevent

further major accidents

59

Chapter 7. Maps

Map 1. Base map of East Godavari District

60

Map 2. East Godavari district

61

Map 3. GAIL Base map

62

Map 4. Buffer Zone of 107m for 100mm Hole

63


64


65


66


67


68


69


70

Chapter 8. Conclusion

8.1 Preventive measures

8.1.1 Construction

• Inspecting all welds both visually and with ultrasonic or radiographic equipment

to check to test their integrity

• Installing cathodic protection equipment that further protect from corrosion by

applying a low voltage current to the pipe

• Once the pipeline is in the ground and before it is placed into service, it is

pressure-tested with water in excess of its operating pressure to verify that it can

withstand high pressure.

• Hydro test the pipelines before putting them into service by pressurising them to

higher than the maximum operating pressure

• Inspecting the pipelines visually or by other means to ensure no harmful damage

occurred during installation

• In accordance with the Regulations, aboveground pipeline markers are used to

alert the public of the presence of pipeline. These markers, which contain the

name of the pipeline operator and emergency contact information, are usually

located near road, rail and water crossings.

8.1.2 Operations & Maintenance

• Once the pipeline begins moving natural gas, we focus on safety through:

• Pipeline operation is continuously monitored and Telecommunication system

wherein any deviation from normal operation can be immediately detected and

addressed. Leak detection system is provided to detect accurately location of leak

within reasonable time and take suitable action.

• Constantly monitoring, analyzing and controlling natural gas flows, pressures,

temperatures and quality to ensure that all parameters stay within engineering

safety limits

• Using compressors, block valves located strategically along systems to safely

satisfy customer needs and to control gas flows.

71

• Monitoring and responding to system alarms and calls from the public and

emergency responders that indicate possible problems

• Responding to reports of digging near pipelines to be sure that excavation around

pipelines is conducted in a safe manner

• Safety information regarding our operations will be distributed annually to

landowners, residents and businesses located near our facilities.

8.2 Ongoing monitoring, maintenance and safety measures for pipeline

network include

• Real time pressure monitoring from our 24/7 control room which maintains the

flowing pressure in our system within safe operating guidelines. Pressure

regulator stations and overpressure protection devices are maintained throughout

the system.

• Leak surveying of transmission and distribution pipelines through:

• Aerial inspections of transmission pipeline corridors monitor for obvious signs of

leaks.

Ground patrols using vehicle mounted and handheld devices measure for natural

gas levels in the air in the vicinity of pipelines.

Corrosion Control teams measure and test cathodic protection on steel

pipelines. Cathodic protection involves enabling steel pipelines to resist corrosive

effects of surrounding soil.

• External Corrosion Direct Assessment excavations are conducted. BGE

analyzes collected data and periodically excavates sections of pipeline to directly

assess the pipeline integrity and conduct any maintenance or repairs.

• Adding mercaptan to make gas detectable by scent, which enables leaks to be

detected fast.

• Participation in the Maryland one-call system to promote damage prevention

awareness.

• Dig Alert process which requires a damage prevention inspector to monitor work

near pipelines and remain at the sites where work is within 10 ft of the pipeline.

72

• Pipeline markers are placed where necessary to indicate pipeline locations.

However, never rely on the presence or lack of markers to determine exact

locations of underground utilities.

• Vegetation management is conducted on transmission pipeline corridors to make

the pipelines visible from the air and open for routine and emergency access.

• Hydrostatic pressure testing tests new pipelines during construction. Before a

pipeline goes into service, it's filled with water and pressurized to levels exceeding

the operational pressure for the pipe.

73

References

1. ERDAS IMAGINE Tour Guides ERDAS IMAGINE V8.4

2. Hamid Reza Jafari, Saeed Karimi, Gholamreza Nabi Bidhendi, Mousa Jabari

and Nasim Kheirkhah Ghahi (2011) “Applying Indexing Method to Gas

Pipeline Risk Assessment by Using GIS: A Case Study in Savadkooh, North

of Iran”, Journal of Environmental Protection

3. http://eastgodavari.nic.in/

4. http://en.wikipedia.org/wiki/Erdas_Imagine

5. http://en.wikipedia.org/wiki/GAIL

6. http://en.wikipedia.org/wiki/Pipeline_transport

7. http://GAILgas.com/GAIL_Network.html

8. http://nptel.ac.in/courses/105102015/16

9. http://oceanservice.noaa.gov/facts/remotesensing.html

10. http://resources.arcgis.com/en/help/getting-

started/articles/026n00000014000000.htm

11. http://www.aidic.it/cet/14/36/049.pdf

12. http://www.answers.com/Q/What_is_toposheet

13. http://www.epa.gov/reg3esd1/data/gis.htm

14. http://www.esri.com/software/arcgis/arcgis-for-desktop

15. http://www.GAILonline.com/final_site/naturalgas_transmission.html

16. http://www.gislounge.com/what-is-gis/

17. http://www.hexagongeospatial.com/products/remote-sensing/erdas-

imagine/overview

18. http://www.hindustantimes.com/india-news/14-killed-in-blast-at-GAIL-

pipeline-in-andhra-pradesh/article1-1234108.aspx

19. http://www.nrcan.gc.ca/earth-sciences/geomatics/satellite-imagery-air-

photos/satellite-imagery-products/educational-resources/9363

74

20. http://www.nrsc.gov.in/Learning_Centre_Training.html

21. http://www.pngrb.gov.in/newsite/OurRegulation/pdf/Reference-

Regulation/GSR808(E).pdf

22. http://www.propublica.org/article/pipelines-explained-how-safe-are-americas-

2.5-million-miles-of-pipelines

23. OGP Risk Assessment Data Directory Report No. 434 – 7 March 2010

24. Spyros Sklavounos, Fotis Rigas (2005) “Estimation of safety distances in the

vicinity of fuel gas pipelines”, Journal of Loss Prevention in the Process

Industries

identifying risk zone along gail pipeline in 1

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