Project Report - Final Draft

Seismic Data Interpretation


An Internship report submitted in partial fulfillment of requirements for Masters of Business Administration (Oil and Gas Management) July, 2016

Under the guidance of

Internal Guide: External Guide:

Mr. Vibhav Prasad Mathur Mr. Debasis Nayak

Assistant Professor, UPES Senior technical consultant,

Greenojo Consulting Private

Limited

Submitted by

Honey Sharma

SAP id: 500044339

Enrollment Number: R020215109

Master of Business Administration (Oil and Gas Management)

2015-17

College of Management & Economic Studies, UPES

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Student Declaration

I hereby declare that this submission is my own work and that, to the best of my knowledge and belief, it contains no material previously published or written by another person nor material which has been accepted for the award of any other degree or diploma of the university or other institute of higher learning, except where due acknowledgment has been made in the text.

Honey Sharma

SAP id: 500044339

Enrollment Number: R020215109

Master of Business Administration (Oil and Gas Management)

2015-17

College of Management & Economic Studies, UPES

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Acknowledgement

In the process of carrying out any research, I was helped, motivated and guided by my mentor Mr. Debasis Nayak, my consultant Mr. Sabyasachee Panda, they guided my research and prototype development and educated me about the scope and work around in the process of prototype development. Special mention to Miss Sobhana Mohapatra, who provided her guidance and boosted my confidence and motivation in tougher times.

Honorary mention to our esteemed internal university mentor Mr. Vibhav Prasad Mathur who has been the source of inspiration and has provided the will power to never quit. And last but not the least Dr. Geo Jos Fernandez, without his assistance my project report would not have finished.

Thank you everyone for your input to complete my project research and develop this prototype that might perhaps develop into a solution and would contribute to quench the thirst for energy of a country like ours, India.

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Certificate

This is to certify that the summer internship report entitled Seismic Data Interpretation submitted by Honey Sharma to UPES for partial fulfillment of requirements for Masters of Business Administration (Oil and Gas Management) is a bonafide record of the internship work carried out by him under my supervision and guidance. The content of the report, in full or parts have not been submitted to any other Institute or University for the award of any other degree or diploma.

Mr. Debasis NayakSenior Technical ConsultantGreenojo Consulting Private LimitedCollege of Management & Economic Studies, UPES

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Certificate

This is to certify that the summer internship report entitled Seismic Data Interpretation submitted by Honey Sharma to UPES for partial fulfillment of requirements for Masters of Business Administration (Oil and Gas Management) is a bonafide record of the internship work carried out by him under my supervision and guidance. The content of the report, in full or parts have not been submitted to any other Institute or University for the award of any other degree or diploma.

Mr. Vibhav Prasad MathurAssistant Professor College of Management & Economic Studies, UPES

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Table of Content

Sr. No Content Description Page Number1 List of Tables VII2 List of Figures VIII3 List of Variables IX4 Executive summary X5 Introduction XI6 Literature review XIII7 Background of the study and objectives XIV8 Research methodology XVI9 Data Analysis XXII10 Conclusion XXIX11 Bibliography12 Appendices

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List of Tables

Sr. No Content Description Page Number

1 Literature review table containing details of the research papers and the .pdf referred. XIII

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List of Figures

Sr. No Content Description Page NumberFig-1 Process Flow-chart XIIFig-II Functional matrix flow chart-Part01 XVIIFig-III Functional matrix flow chart-Part02 XVIIIFig-IV Functional architecture XIXFig-V Technical Architecture XXFig-VI IBM Bluemix login page XXIIFig-VII IBM Bluemix user interface screenshot XXIIIFig-VIII IBM Bluemix catalog page XXIIIFig-IX Dash DB credentials creation page screenshot XXIVFig-X Dash DB homepage screenshot XXIVFig-XI Data loading homepage for dash DB XXVFig-XII Preview page for loaded data table XXVFig-XIII Target window screenshot for dash DB XXVIFig-XIV Final window for dash DB account creation XXVI

Fig-XV Screenshot of R coding console depicting integration of R with dash DB XXVII

Fig-XVI Screenshot of R console for object creation XXVIIFig-XVII Screenshot of R console for scatter plot function XXVIIFig-XVIII Scatter plot graph XXVIII

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List of Variables

Sr. No Stated Variable Description Page Number1

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Executive Summary

“Today with the launch of seismic data interpretation software solution, a new era of the oil and gas upstream industry has began”. With the software integrated with cloud cost of processing the raw seismic data has decreased by “X%” and has provided a level playing field to everyone in the business be it a giant or a start-up in this sphere of the industry – The New York times, Monday, August 12, 2017. This will be the headline of every paper in the world once this prototype ventures into the market. To be precise it is actually a revolution in the field of seismic data interpretation which will bring down the cost of processing the raw seismic data by, “X million US$”. Accessible to almost everyone in the industry for a fraction of a cost this software will enable the industry to explore the territory beneath the earth surface with more precision and accuracy and may add another decade or two to the, “hydrocarbon age” before migration to a new source of energy.

With the cost of seismic data processing ever increasing in the times of falling crude oil prices seismic data interpretation delivers what it claims to achieve, by the synchronization of technology with analytics. Raw, unstructured data is processed by an open source seismic processing and interpretation tool. After processing of the raw data and removal of unwanted information this processed file is then stored in the ASCII format. This is the point from where; “seismic data interpretation” carves out its own identity different from the conventional seismic data processing tools. These processed file parameters are extracted in the ASCII format and converted into the desired format (XLS, CSV, XLSX) so that can be fed as an input to an analytical tool. Cloud provides the infrastructure for the storage of a mammoth amount of data and also to speed up the analytical process. Analytical tool is then integrated with the cloud and analysis is done on the data on the real time basis. Parameters are analyzed against a common variable and the graph so plotted is then interpreted with the help of another presentation tool.

To summarize seismic data interpretation in a crisp one liner, it is “Business process as a service” (BPaaS). Complete seismic data processing process is tweaked and pushed online onto the cloud and the parameters so extracted are presented in the form of a graph. This process can be customized according to the requirement of the user, such as processing, storage, analysis and interpretation. Charges are applied on per GB of data processed or drawn right from the input of the data to the point of output as desired by the user. This solution can be further developed and many unexplored territory can be discovered, for example the quantum of the hydrocarbon reserve beneath the earth’s surface, location of the sweet spot that can provide maximum rate of extraction of the hydrocarbon present in the rock structure, interpretation of the deepwater horizon seismic data are some of the few spheres that can be explored in future.

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I. Introduction

“The Stone Age did not end due to lack of stone, and the Oil age will end long before the world runs out of oil.” So said Sheikh Ahmed Zaki Yamani, former Saudi Arabian oil minister in a statement delivered to media.

Sixteen years later Yamani’s words define the troubled state of oil and gas industry. It is evident that the industry is going through one of its phenomenal transformations which will change the industry as we know it. The dip in oil prices below US$40 (at the end of 2015) makes it very difficult for the producers to sustain and bear the cost of production. This also indicates the rampant supply amidst the weak global demand on account of slow economic growth.

Oil and gas exploration (the upstream) sector has taken the worst of the hit, with the enormous cost US$X of processing the raw segy data and low return on investment oil and gas exploration sector has come to a standstill. With Seismic Data Interpretation prototype, I propose the solution to this arduous problem. A solution that not only x% less expensive that the traditional method, but also equips the interpreters with more powerful angle to make more accurate predictions about the unidentified hydrocarbon reserves beneath the surface of the earth.

With the advent of the prevailing oil crisis, I was adamant to provide a solution to this problem. And when my consultant advised me to work in this direction during the interview session this idea began to take more solid form. With several months of preparation and two months of rigorous churning of thoughts of me, my mentor and of course my consultant we were able to carve out the plan of action into a work breakdown structure. In the beginning the base was build up understanding the structure of segy file, proceeded by the steps involved in the refining of the raw unstructured data, then identification of the vital parameters, extraction of these parameters, analysis with this parameters and finally the presentation.

Significant research work has been has been done in the proposed field of interpretation and analysis of the seismic data by Mr. Daniel Patel, Mr. Christopher Giertsen, Mr. John Thurmond, Mr. John Gjelberg, and Mr. M. Eduard Groller, ¨ Member, IEEE in their research paper, “The Seismic Analyzer: Interpreting and Illustrating 2D Seismic Data”. Similar efforts have been made by Mr. Kulbhushan and Miss Monalisa Mitra in their research paper, “Basics of Land Seismic Data Interpretation”. My attempts are not two cross roads with their individual researches or to extend their individual findings but to build my own idea with my own thoughts, findings, and comprehension. To provide a new sphere, a new outlook to seismic data interpretation and processing.

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Flowchart –

(Fig-I)

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Study of the structure of the seismic data from the field/ seismic surveys

List out the steps involved in the processing of the raw unstructured

data

Comprehending the application of these steps on the raw seismic data

Listing out the necessary steps and the workarounds if required

If the steps are necessary

Refining of raw seismic data

Extraction of these parameters

If found critical

Analysis of Parameters

Presentation of the achieved relation through graph

YESNO

YES

NO


II. Literature Review

To successfully complete the prototype building process the first and foremost requirement was to apprehend the seismic data processing and to understand the chronological steps to be followed so that these steps can be inculcated into the prototype functioning as well. To apprehend this I referred various research papers and text books (digital format) of the renowned authors such as Jon F. Claerbout’s, “Fundamental of Geophysical Data processing”, Ozdogan Yilmaz’, “Seismic data Processing”.

While exploring this reference materials for the knowledge of seismic data processing, I embarked upon the fact that amplitude and frequency are the two most important attributes that shape the information stored in the seismic data. As these are the characteristics features which change considerably mapping the under the earth topography. Hence the detail understanding of these two attributes was required to map back the changes brought about in the seismic waves received at the receiver geophones. For this purpose of the project I referred to the following research papers and text books. Hongliu Zeng’s, “Frequency-Dependent Seismic Stratigraphy for High-Resolution Interpretation of Depositional Sequences”, Steve Henry’s, "Understanding Seismic Amplitudes”.

The last but not the least was the decoding of the seismic interpretation process and understanding of the steps involved in the process. This is to bring to the notice of the readers of this report that interpreting the changes in the seismic data is a very general term as it is understood and practiced at will amongst the geologists and geophysicists. There are no standard procedures for this process. I referred some text books to provide my readers a general chronology of the steps that could be followed to interpret the changes in the seismic data. These steps are not standard and may be company specific or G&G team specific. I provide you these steps to have a general understanding of the process. To derive this chronology if referred Laurence R. Lines and Rachel T. Newrick’s, “Fundamental of Geophysical Interpretation”.

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III. Background of the study and Objectives

The oil industry, with its hikes and falls is suffering from the deepest downturn since the 1990’s if not earlier. Price of crude oil though has recovered from hitting the low of US$26.21 in February this year to US$47 for Brent and US$46 for WTI, but the prices are still low as to what is required of to drill a profitable well. Most of the consumer industries have enjoyed the dispensation period of the low crude oil price, but several petroleum industries have suffered huge losses. These losses have the rippling effects which further harm the economy of the country. Oil and gas giants are the ones who have taken up worst of the hits due to involvement of the sophisticated machinery in drilling and extraction of oil (at fixed price) and selling that same crude at lower prices (market determined) and thus incurring heavy losses. Share price of some of the integrated oil and gas giants have tumbled due to this recession with Exxon Mobil share falling 8.2%, Chevron falling 13.63%, British Petroleum falling 12.21%, Total 17.08%, Philips falling 8.57% to name a few.

And even with the crude oil market getting stable, price of oil and gas exploration would remain the same. This stands out as one of the biggest business problems in today’s oil and gas industry i.e. How to reduce the cost of seismic data processing in the times of falling crude oil prices and also reduce the risk in hydrocarbon exploration and production? With the introduction of seismic data interpretation prototype solution this problem can be answered. Seismic Data Interpretation does not follow the conventional protocol of processing the raw seismic data from the survey and then projecting the refined seismic image to the team of geologists and geophysicist. My objective is, “to provide a cheap and more reliable solution as compared to the conventional seismic data interpretation process”.

This milestone is achieved by tweaking the existing seismic data processing and interpretation process in such a fashion that the resources needed to be deployed to interpret the processed image are drastically reduced, processing cost is plummeted, and huge volume of data is processed quickly and with more reliability to delineate a hydrocarbon reserve. Input was taken in the form of raw, unstructured data which was fed into the open source seismic interpretation software (OpendTect 6.0), thus saving the cost incurred from buying a licensed software. These processed files defined some critical parameters which were then pushed and stored into cloud and then integrated with analytical software (R 3.3.1) for analysis and the relation plotted into a graph and presented to the user, who can then take a decision to drill or not to drill. This process thus saves the cost paid to a geologist or geophysicist. Further the time required for analysis is reduced by storing the huge amount of

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data to cloud and the prediction of the co-ordinates to be drilled in order to find the hydrocarbon reserve is more reliable and involves less risk of failure.

In the designing and execution of this proposed solution, I embarked upon various hurdles which were either resolved or a work around was introduced to keep the progress of the solution building within the time limits. My research began with the study and understanding of the seismic data in the form of segy file. To understand what are the components of the segy file which can be exploited for stored geological information. Then I had to comprehend what possible noises or unwanted information might have crept into this presented geological information which might lead to the erroneous prediction of the co-ordinates of drilling. This study was accompanied by the study of seismic survey and study of the seismic exploration, seismic processing and seismic interpretation in parallel. Further to this objective I studies the steps involved in processing and cleansing of this data. And finally how is interpretation done of the processed seismic file to the geologists and geophysicist. Comprehending the commands and operation of tools was followed later on, after we began our operations on the input data as per the guidance of technical architecture.

Achieving this arduous objective of developing a prototype to cater to the defined business problem was not an easy task altogether. And it required the assistance of my mentor and my consultant for the concerned project. Path to this destination was full of research problems but, if I had to define the most problem amongst the lot that would be, “to define the hierarchy of steps to be followed in processing of the raw unstructured data”. Conception of the seismic processing laid the foundation on which the functional matrix was structured and then the functional architecture later on. Hence this was the most critical problem for my research and prototype building.

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IV. Research Methodology

Sr.No. Traditional Research Methodology 5-P Model1 Research Design Proof of concept2 Hypothesis Formulation Point of view3 Methods of data collection NA4 Analytical tools used Prototype5 Scope of study Point of view6 Testing of Hypothesis Pilot7 Limitations of the study NA8 -- Package

Applied research was adopted by me and the entire research was broken down into the 5P model. These five models are enumerated as follows:-

Point of View/Analysis – In the first stage of prototype development functionality of the subject was defined and the hierarchy of steps to be followed was outlined. To achieve the objective outlined in the prototype solution a functionality design was established.

Proof of concept/Design – This was the second stage of prototype building and once the foundation was laid by the functional matrix, a functional architecture was built on that foundation to define the activity flow.

Prototype/Develop – With the functional matrix and functional architecture in place a prototype was developed in this stage. Prototype software which could process a test seismic file and produce the claimed results. Also defining new avenues for the future development of the software solution.

Pilot/Testing – This stage was marked by the testing of the prototype and refining, to produce a more scalable software solution.

Package/Release – Release of the software to the market with a predefined business model and earning of the revenue from the same. This was the last but not the least stage of the prototype development.

A). Point of view/Analysis –

Rigorous study was done to understand the structure of the seismic data that is obtained from the field trace or observer’s log. Study comprised of the various application or operation to be done on the raw unstructured data to extract the valuable information. These steps include

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first the editing of the raw data, removal of noise, performing geometric correction, velocity analysis and enhancing of the seismic resolution by enhancing frequency.

Then these steps were jotted down in the form of flowchart and the process description was described in detail in the adjoining sheet similarly the steps involved in the interpretation of the seismic data was done and the flowchart was prepared for the same with the process description done in the next sheet.

This analysis was done to comprehend what operations are needed to be done before the data is actually presented to geologists and geophysicists for interpretation. So that same functions could be incorporated in the prototype and the software could be able to deliver the same with precision.

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(Fig-II)

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(Fig-III)

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B). Proof of concept/Design –

(Fig-IV)

Based on the functional matrix the hierarchy of steps was decided and the how the steps should be delivered, the plan of action was framed. Design was divided into four layers the infrastructure layer, the database layer, the application layer, and the presentation layer. Seismic data in the form of raw data will be present in the storage which is then processed by the tool in application layer further processing will be done on this raw data then converted into required format and exported into an analysis tool and further presented in the presentation layer.

Technology associated with the design is presented in the technical architecture below. It specifies the technology associated with the particular technology with each layer that we followed in our prototype development.

But this is to bring to the notice of my readers that it is the functionality which remains permanent and technology can be modified with the affordability and the availability.

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(Fig-V)

C). Prototype/ Development –

Development of the full fledged prototype began with an idea, to decrease the cost of processing the seismic data so that overall profitability could be enhanced. As in the Economics we study profit earned by reducing the cost of production becomes the soul of your business architecture which no rival can copy.

This business idea than took the same of, “Scope of work” and the skeleton of a definite solution to a definite business problem was fabricated. This outlined the tools to be used at different stages and milestones to be achieved as the internship program progresses. Objective to be achieved was clearly defined.

With the scope of work outlined entire work to be done in the span of two months was broken down into a work breakdown structure. With the time span allotted to different deliverables and the time limit set a pace was set for the prototype development. Time for review meetings was also set so as to analyze the synchronization of the progress with that of outlined objective. Further to the approval of the work breakdown structure functional matrix defining the functionality of the prototype was designed and approved. With each footstep explained in detail and agreed upon to be included and acted upon by mutual discussion of the mentee, mentor, and the consultant.

Based on the foundation of functional matrix, a functional architecture was designed to project how the functionality defined in the functional matrix is to be achieved. Technical architecture defined the technology to be used at every step in conformance to the functional architecture. Workarounds were decided accordingly in case a particular technology did not seem viable over the definite time span of two months. Operations were performed as defined by functional

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matrix and guided by functional architecture with the assistance of tool mentioned in technical architecture. A lot of time, brainstorming, intellect and sweat were put into the project and with the dedicated hard work a prototype was developed over the span of two months. And it took great courage and determination for a three member team, the mentee, the mentor and the consultant to achieve this difficult task.

D). Pilot/Testing –

Pilot phase is the presentations of the software to the customer and conditioning its functions according to the requirement of the user. There can be customer who might possess their own seismic interpretation and processing tool and would like to integrate the process with their own tool. Similarly different user may not like to see the graph instead would like to see a processed seismic image to be presented to their geologists and geophysicist team.

These customizations can be incorporated within the value network and modifications can be done to the fundamental design architecture of the prototype. Charges applied can be breakdown into processing fee, customization fee, installation fee, and utilities such as storage and presentation. Charges will be applicable on per GB of data input and per unit of processing time consumed for a user. Also charges could be applicable for every upload and download of the data.

E). Packaging –

This prototype could be sold as a complete business process or business process as a solution (BPaaS). With and integrated seismic data processing and analytical tool, along with added services of storage and presentation to different customers.

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V. Data Analysis

The essence of my prototype solution lies in the analysis of the parameters extracted from the processed seismic files and then predicting the most fruitful co-ordinates to drill from that analysis. Until the transcend of this boundary, seismic data interpretation does not deliver anything different from what others software’s have to offer in the market. It is this special ability of this prototype which makes it stand out from the crowd.

Data analysis begins with the data acquisition and storage for further analysis. Processed seismic files from OpendTect6.0 are exported in the ASCII format and then manually converted into excel files in XLS format. These files are then loaded into a dash DB account on IBM Bluemix (cloud). Steps for creating an online dash DB account on cloud are as follows:-

First login into your IBM Bluemix account with your username and password credentials

(Fig-VI)

You will be guided to an interface window as shown below. This basically enlightens a user about the services offered by IBM Bluemix as a cloud.

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(Fig-VII)

From there go to the catalogs and select the dash DB icon from the available options.

(Fig-VIII)

Next window will prompt you to enter the credentials like space, app, service name, credentials name, and the entry plan. Choose as you like and click, “create”.

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(Fig-IX)

dash DB would be created in your account where you can perform various operations such as storage, analysis etc. You need to push data into cloud because seismic data from a particular survey is in peta bytes. And if you have to process data for multiple clients the data volume is unimaginable hence cloud is the only option for fast results. For loading data into the cloud you need to launch the dash DB and load your data into the dash DB. Data to be loaded must be in XLS, XLSX or CSV format.

(Fig-X)

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To load your data various options are available which are highlighted in the screenshot above. In my research I loaded the data from the desktop, but as the scalability of the solution is enhanced data can be loaded from one cloud to another.

After you specify the location of the data to be loaded next window will prompt you to select the file form your local storage location, characteristics of the row and select preview.

(Fig-XI)

Next window will show you the preview of the table loaded, preview will be of 10 rows. Carefully examine the data any discrepancy in the data and click next.

(Fig-XII)

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Next window will prompt you to select the target where the data is to be loaded, i.e. you want your data to be stored into an existing table or you want to create a new table. Choose the option according to your requirement in my research I had to create a new table for data.

(Fig-XIII)

As soon as you hit next, the forthcoming window will show the demo of the column and the name of the table that will be created this is the cue to change the column name or the name of the table as you like or is required for further analysis.

(Fig-XIV)

Once table name, column name and the serial number is fixed click finish to complete the process. In the similar fashion you can load more data tables for analysis. This finishes one phase of the analysis the second phase is the connecting the dash DB with R and carrying out further analysis.

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To set a connection in R the steps are as follows:-

Open R studio and install the iamdbr plug-in from the packages console so that IBM Bluemix can be integrated with R and analysis can be done with data in real time.

(Fig-XV) You need to form objects in R where data is called into these objects from cloud on

real time basis and analysis is carried out the code for the same is as follows. For my analysis I have merged two data sets into one common data so that graph can be plotted and the change of parameters (power1, power2) could be examined against a common parameter (Frequency) and a comparative analysis could be done.

(Fig-XVI)

Finally a scatter plot graph was generated but first you have to install the scatter plot package from the installation package.

(Fig-XVII)

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This will present a graph in viewer which can be interpreted by a geologist or a geophysicist as a comparative study of two parameters for two different seismic files against a common parameter. This is to bring to your notice that a petroleum engineer who is familiar with the seismic waves and their behavior below the surface of the earth can interpret the graph so formed. A geologist or a geophysicist is not a necessity.

(Fig-XVIII)

(Fig-XIX)

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VI. Conclusion

Don’t pray for tasks equal to your powers, pray for powers equal to your task – Phillips brooks. This was the quote I could think off, when I concluded my research for seismic data interpretation. Future of seismic data interpretation looked dull and hazy in the light of falling crude oil prices. Crude oil prices have taken a dip from a peak of $115USD to $35USD. This collapse in the oil prices has lead to a drop in the investment in the oil and gas industry. With the investment falling from $700 billion USD in 2014 to $550 billion USD in 2015, sharp decline in other sectors has also contributed to overall slow economic growth. Also price of the seismic data processing has been on the rise for past three years to 2015 with 1.5% increment annually. With the demand for energy on the rise, especially in the undeveloped economies oil and gas exploration activity is a necessary burden needed to be carried on the shoulders of the industry.

With the study of the scholarly articles conclusion was raised that oil and gas exploration industry is in a desperate need of a solution that can provide fast and accurate processing of the seismic data at comparatively low cost. Therefore my study should be guided in the direction of enhancing the seismic data processing method and for which one must have thorough understanding of the, “seismic data processing” process. The scholarly articles followed by me for my research educated me about the necessary operations needed to be done on the raw data to improve its quality. Further my study enlightened me about the integration of conventional data processing activities with the analytical tools so as to enhance the probability of discovering the probability of hitting a hydrocarbon rich rock structure.

Limitations in the study due to non-availability of time and intellect were to extract and analyze some of the many parameters available which affect the seismic data processing. More the parameters extracted and analyzed higher is the accuracy and reliability of analysis in the analytical tool. Also guidelines to explain the graph so generated of the parameters so analyzed was missing, it could have facilitated and familiarize the geologists and the geophysicists and facilitated in the interpretation.

In future this solution might see the light as an integration of open source seismic data processing tool with analytical tool. Also it can enhance the scope of work by predicting the quantum of hydrocarbon reservoir beneath the surface and also predicting the sweet spot to drill to have maximum hydrocarbon recovery.

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VII. Bibliography

Reference Author Name

Description Key Leanings Remark

AAPG Data pages

Hongliu Zeng

Frequency-Dependent Seismic Stratigraphy for High-

Resolution Interpretation of Depositional Sequences

Effect of frequency on

seismic resolution

Changes in the frequency of wave when it encounters

hydrocarbon reserve

Fundamental of Geophysical

Data Processing - pdf

Jon F. Claerbout

Fundamental of Geophysical Data Processing

Comprehension of various data

processing steps

Application of various

processes such as

deconvolution, NMO

correction, DMO

correction etc.

www.academia.edu

Laurence R. Lines

and Rachel T. Newrick

Fundamental of Geophysical Interpretation

Comprehension of steps involved

in data interpretation

Understanding discontinuity

and interpretation

steps

Google drive Ozdogan Yilmaz Seismic Data Processing

Flowchart of steps involved in

seismic data processing

Study of hierarchy of

steps

AAPG Data pages

Steve Henry

Understanding Seismic Amplitudes

Factors affecting amplitude of

seismic waves

Understanding of below the

earth phenomenon that change

seismic amplitude

www.academia.edu

Friedrich Nietzche

Common techniques for quantitative seismic

interpretation

Processes involved in seismic data processing

Process and common pitfalls in conventional interpretation

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http://www.academia.edu/9688828/QSI_Chapter_4

http://www.academia.edu/9688828/QSI_Chapter_4

http://www.searchanddiscovery.com/documents/2004/henry/index.htm

http://www.searchanddiscovery.com/documents/2004/henry/index.htm

https://drive.google.com/file/d/0ByXwJpHRZ-UvcnNKNDItLTVBTUk/view

http://www.academia.edu/12097166/Fundamentals_of_Geophysical_Interpretation

http://www.academia.edu/12097166/Fundamentals_of_Geophysical_Interpretation

http://sepwww.stanford.edu/sep/prof/fgdp5.pdf



http://www.searchanddiscovery.com/documents/2010/40501zeng/ndx_zeng.pdf

http://www.searchanddiscovery.com/documents/2010/40501zeng/ndx_zeng.pdf


VIII. Appendices

In seismology, waveform cross correlation has been used for years to produce high-precision hypocenter locations and for sensitive detectors. Because correlated seismograms generally are found only at small hypocenter separation distances, correlation detectors have historically been reserved for spotlight purposes. However, many regions have been found to produce large numbers of correlated seismograms, and there is growing interest in building next-generation pipelines that employ correlation as a core part of their operation. In an effort to better understand the distribution and behavior of correlated seismic events, we have cross correlated a global dataset consisting of over 300 million seismograms. This was done using a conventional distributed cluster, and required 42 days. In anticipation of processing much larger datasets, we have re-architected the system to run as a series of MapReduce jobs on a Hadoop cluster. In doing so we achieved a factor of 19 performance increase on a test dataset. We found that fundamental algorithmic transformations were required to achieve the maximum performance increase. Whereas in the original IO-bound implementation, we went to great lengths to minimize IO, in the Hadoop implementation where IO is cheap, we were able to greatly increase the parallelism of our algorithms by performing a tiered series of very fine-grained (highly parallelizable) transformations on the data. Each of these MapReduce jobs required reading and writing large amounts of data. But, because IO is very fast, and because the fine-grained computations could be handled extremely quickly by the mappers, the net was a large performance gain.

Page | XXXII of Seismic Data Interpretation, Submitted by Honey Sharma of UPES

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Project Report - Final Draft