PPETROLEUM PRODUCTION Engineering; Term Paper Project

8/17/2019 PPETROLEUM PRODUCTION Engineering; Term Paper Project

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REPUBLIQUE DU CAMEROUN REPUBLIC OF

CAMEROON

Paix-Travail-Patrie Peace-Work-Fatherla!

***************

MINI"TERE D#ETUDE" "UPERIEURE"

********

MINI"TERE DE L#EMPLOI ET DE LA

FORMATION PROFE""IONEL

********

MINI"TR$ OF %I&%ER EDUCATION ********

&ULF FIELD NATIONAL AD'ANCED

"C%OOL OF PETROLEUM

MINI"TR$ OF EMPLO$MENT AND

PROFE""IONAL TRAININ&

********

%ND( PETROLEUM EN&INEERIN&

TERM PAPER ON PRODUCTION ENGINEERING

TOPIC; THE SEQUENCIAL BASIC STEPS OFSIMULATION

Course professor; Eng. Tambe Ayuk

PETROLEUM PRODUCTION ENGINEER

Faculté d'ingénierie du pétroleFac)lt* o+ Petrole), Eieeri level .


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AC/NOWLED&EMENT

I like to extend my gratitude to the persons who helped me for the accomplishment of this

book and a sucesss to the entire work carried out

O!I "##"$"%CI& #um()for all the finacial* social and moral support including the

one+s i cant mention)#y friend,brother "-&%." !&-I/O /IF&%." for his encouragement and prayers)"ng) %.&/" !$0%O %.O#!& for the basic words of enpowerment to be what I seek to

achie1e)

GFNASP-LIMBE

TERM PAPER PROJECT

It is possible

2one and presented by CASSIE O!I"A#O IA$!A

GFNASP


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DEDICATION

I dedicate this book is dedicated to the &lmighty .od for 3is infinite lo1e for me and to

bestow his wisdom and inspirations which lead me through to the success of this book) &lso

to my #um O!I "##"$"%CI& and a friend engr) %gale !runo for their moral* social* and

financial support)


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PREFACE

This book comprises the basic steps of simulation carried out in the petroleum industry andthe importance of the 1arious procedures implemented)


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TABLE OF CONTENTS

&cknowledgement((((((((((((((((((((((((((((((

2edication((((((((((((((((((((((((((((

Preface(((((((((((((((((((((((((((((4)Introduction of simulation,definition 4

4)4)!ackground,history 5

5)-e6uential basic steps of simulation 7

5)4) Purpose of simulation((((((((((((((((((((((((

!! Uses "#$ %'tio# o%si(&l"tio#)))))))))))))))))))))))))))))))))))

7)Classification and characteristics of simulation(((((((((((((((()8 )Importance of simulation 9

:) Conclusion(((((((((((((((

;) $eference((((((((((


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.0Itro!)ctio a! !e+iitio o+ 1i,)latio

& simulation is the execution of a model* represented by a computer program that gi1es

information about the system being in1estigated) The simulation approach of analy


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4)4) !ackground and history of simulation

The Information &ge is synonymous with knowledge) If* howe1er proper science is not used*

information alone cannot guarantee transparency* which is the pre=condition to nowledge)

Proper science re6uires thinking or imagination with conscience* the 1ery essence of

humanity Imagination is necessary for anyone wishing to make decisions based on science

and always begins with 1isuali


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20"EQUENTIAL "TEP" OF "IMULATION

The !asic -teps of a -imulation -tudy

The application of simulation in1ol1es specific steps in order for the simulation study to be

successful) $egardless of the type of problem and the obDecti1e of the study* the process by

which the simulation is performed remains constant) The following briefly describes the basic

steps in the simulation processE

20.0 Pro3le, De+iitio

The initial step in1ol1es defining the goals of the study and determine what needs to be

sol1ed) The problem is further defined through obDecti1e obser1ations of the process to be

studied) Care should be taken to determine if simulation is the appropriate tool for the

problem under in1estigation)

2020 Pro4ect Plai

The tasks for completing the proDect are broken down into work packages with a

responsible party assigned to each package) #ilestones are indicated for tracking progress)

This schedule is necessary to determine if sufficient time and resources are a1ailable for

completion)

2050 "*1te, De+iitio

This step in1ol1es identifying the system components to be modeled and the performance

measures to be analy


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arri1al rate of a specific part to the manufacturing plant may follow a normal distribution

cur1e)

20:0 Mo!el Tra1latio

The model is translated into programming language) Choices range from general

purpose languages such as fortran or simulation programs such as &rena)

200;0 'eri+icatio 9 'ali!atio

erification is the process of ensuring that the model beha1es as intended* usually by

debugging or through animation) erification is necessary but not sufficient for 1alidation*

that is a model may be 1erified but not 1alid) alidation ensures that no significantdifference exists between the model and the real system and that the model reflects reality)

alidation can be achie1ed through statistical analysis) &dditionally* face 1alidity may be

obtained by ha1ing the model re1iewed and supported by an expert)

20


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>hen simulation is applied inappropriately* the study will not produce meaningful results)

The failure

to achie1e the desired goals of the simulation study may induce blaming the simulation

approach itself when in fact the cause of the failure lies in the inappropriate application of

simulation)

To recogni


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has the ability and experience to determine both the model's appropriate le1el of detail and

how to 1erify and 1alidate the model) >ithout a trained simulator* the wrong model may be

de1eloped which produces unreliable results) &dditionally* the allocation of time should not

be so limited so as to force the simulator to take shortcuts in designing the model) The

schedule

should allow enough time for the implementation of any necessary changes and for

1erification and 1alidation to take place if the results are to be meaningful)

Co1t1( Cost considerations should be gi1en for each step in the simulation process*

purchasing simulation software if not already a1ailable* and computer resources) Ob1iously if

these costs exceed the potential sa1ings in altering the current system* then simulation should

not be pursued)

Availa3ilit* o+ Data( The necessary data should be identified and located* and if the data does

not exist* then the data should be collectible) If the data does not exist and cannot be collected*

then continuing with the simulation study will e1entually yield unreliable and useless results)

The simulation output cannot be compared to the real system's performance* which is 1ital for 1erifying and 1alidating the model)

The basic steps and decisions for a simulation study are incorporated into a flowchart as

shown belowE

"te81 a! Deci1io1 +or Co!)cti a "i,)latio "t)!*


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Once simulation has been identified as the preferred approach to sol1ing a particular problem*

the decision to implement the course of action suggested by the simulation study's results does

not necessarily signify the end of the study* as indicated in the flowchart abo1e) The model

may be maintained to check the system's response to 1ariabilities experienced by the real

system) 3owe1er* the extent to which the model may be maintained largely depends on the

model's flexibility and what 6uestions the model was originally designed to address)

20.0 P)r8o1e o+ 1i,)latio

The purpose of simulation is estimation of field performance e)g)* oil reco1eryA under one or more producing schemes) >hereas the field can be produced only once* at considerable

expense* a model can be produced or run many times at low expense o1er a short period of

time) Obser1ation of model results that represent different producing conditions aids selection

of an optimal set of producing conditions for the reser1oir)

2020U1e a! +)ctio o+ 1i,)latio

2020.0 U1e1 o+ 1i,)latio


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$eser1oir simulation models are used by oil and gas companies in the de1elopment of new

fields) &lso* models are used in de1eloped fields where production forecasts are needed to

help make in1estment decisions) &s building and maintaining a robust* reliable model of a

field is often time=consuming and expensi1e* models are typically only constructed where

large in1estment decisions are at stake) Impro1ements in simulation software ha1e lowered

the time to de1elop a model) &lso* models can be run on personal computers rather than moreexpensi1e workstations) For new fields* models may help de1elopment by identifying the

number of wells re6uired* the optimal completion of wells* the present and future needs for

artificial lift* and the expected production of oil* water and gas)

For ongoing reser1oir management* models may help in impro1ed oil reco1ery by hydraulic

fracturing) 3ighly de1iated or hori


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coefficient* and parameters to estimate the changes in absolute permeability as a function of

pore=pressure depletion and gas desorption)

202020 F)ctio o+ 1i,)latio

https://en.wikipedia.org/wiki/Diffusion_coefficienthttps://en.wikipedia.org/wiki/Permeability_(earth_sciences)#Intrinsic_and_absolute_permeabilityhttps://en.wikipedia.org/wiki/Permeability_(earth_sciences)#Intrinsic_and_absolute_permeabilityhttps://en.wikipedia.org/wiki/Diffusion_coefficienthttps://en.wikipedia.org/wiki/Permeability_(earth_sciences)#Intrinsic_and_absolute_permeability


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G) Classification and characteristics of simulation

CLASSIFICATION OF SIMULATION MODELS

• MONTE CARLO SIMULATION

o $es'+ibe s,ste(s .i'. "+e bot. sto'."sti' "#$ st"ti'

• CONTINUOUS SIMULATION

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o t.e s,ste(s (o$ele$ "+e $,#"(i' b&t (", be eit.e+ $ete+(i#isti'o+ sto'."sti'

• DISCRETE0-E1ENT2 SIMULATION

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o t.e s,ste(s (o$ele$ "+e $,#"(i' "#$ "l(ost i#/"+i"bl,4 sto'."sti'

• COMBINED DISCRETE5CONTINUOUS SIMULATION 0H6BRID2

o

'o(bi#"tio# o% $is'+ete "#$ 'o#ti#&o&s /"+i"bles

CHOICE OF SIMULATION MODEL IS A FUNCTION OF THECHARACTERISTICS OF THE S6STEM AND THE OBJECTI1ES OF

THE STUD6!

The term simulation* or more specifically computer simulation* refers to a method for

implementing a model o1er time 2o2 4HH;A) The computer simulation includes the

analytical model which is represented in executable code* the input conditions and other input

data* and the computing infrastructure) The computing infrastructure includes thecomputational engine needed to execute the model* as well as input and output de1ices) The

great 1ariety of approaches to computer simulation is apparent from the choices that the

designer of computer simulation must make* which include

* stochastic or deterministic* steady=state or dynamic* continuous or discrete and

* local or distributed)

http://sebokwiki.org/wiki/Simulation_(glossary)http://sebokwiki.org/wiki/Simulation_(glossary)http://sebokwiki.org/wiki/Simulation_(glossary)http://sebokwiki.org/wiki/Computer_Simulation_(glossary)http://sebokwiki.org/wiki/Input_(glossary)http://sebokwiki.org/wiki/Output_(glossary)http://sebokwiki.org/wiki/Output_(glossary)http://sebokwiki.org/wiki/Simulation_(glossary)http://sebokwiki.org/wiki/Computer_Simulation_(glossary)http://sebokwiki.org/wiki/Input_(glossary)http://sebokwiki.org/wiki/Output_(glossary)


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Other classifications of a simulation may depend on the type of model that is being simulated)

One example is an agent=based simulation that simulates the interaction among autonomous

agents to predict complex emergent beha1ior !arry 5HA) They are many other types of

models that could be used to further classify simulations) In general* simulations pro1ide a

means for analyithin the 0nited -tates defense community* it is common to refer

to simulations as li1e* 1irtual* or constructi1e* where li1e simulation refers to li1e operators

operating real systems* 1irtual simulation refers to li1e operators operating simulated systems*

and constructi1e simulations refers to simulated operators operating with simulated systems)The 1irtual and constructi1e simulations may also include actual system hardware and

software in the loop as well as stimulus from a real systems en1ironment) In addition to

representing the system and its en1ironment* the simulation must pro1ide efficient

computational methods for sol1ing the e6uations) -imulations may be re6uired to operate in

real time* particularly if there is an operator in the loop) Other simulations may be re6uired to

operate much faster than real time and perform thousands of simulation runs to pro1ide

statistically 1alid simulation results) -e1eral computational and other simulation methods aredescribed in -imulation #odeling and &nalysis /aw 5:A)

This paper discusses the internal characteristics of simulations) The maDor part of it is

concerned with models and their relation with the domain) -ome central concepts regarding

modelling and simulation are defined) These include concepts regardingE

* the structure and characteristics of the model

* the relationship to the system that is being modelled* the interaction of the learner or other agents with the model)

& classification of model types is presented* accompanied by a first idea on the representation

of the se1eral types of models) The classification includes the distinction between 6ualitati1e

and 6uantitati1e models) #odels can further be classified into dynamic and static models*

determined by the time dependency of the model) The basic elements of any simulation model

are the state of the model* describing the properties of the system that is modelled* and a set of

rules determining the possible de1elopment of the model state) -tate space is the collection of all possible states) In 6uantitati1e models the basic elements of the state are 1ariables* which

http://sebokwiki.org/wiki/Complex_(glossary)http://sebokwiki.org/wiki/Complex_(glossary)http://sebokwiki.org/wiki/Emergence_(glossary)http://sebokwiki.org/wiki/Environment_(glossary)http://sebokwiki.org/wiki/Simulation_Modeling_and_Analysishttp://sebokwiki.org/wiki/Simulation_Modeling_and_Analysishttp://sebokwiki.org/wiki/Complex_(glossary)http://sebokwiki.org/wiki/Emergence_(glossary)http://sebokwiki.org/wiki/Environment_(glossary)http://sebokwiki.org/wiki/Simulation_Modeling_and_Analysis


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can be dependent or independent) 2ependent 1ariables are 1ariables of which the 1alue is

determined by the independent 1ariables) The model rules are e6uations* determining the

de1elopment of the 1alues of the 1ariables) Juantitati1e models are classified into discrete

and continuous models* depending on the structure of the state space) Jualitati1e models ha1e

a state space consisting of propositions about the modelled system) In this case* the model

rules ha1e a more descripti1e character)

& brief discussion of the relationship between the model and the corresponding real system is

gi1en) Three types of real systems are distinguishedE physical* artificial and abstract) The main

criterion for a distinction between these types of systems is the possibility of constructing a

model that describes the system completely a base modelA)

The interaction of the learner with models and simulations is described by introducing the

concepts of interaction and scenario) The interaction describes the se6uence of operations thatare performed upon the model* the scenario includes the interaction and the agents who take

part in the interaction)

Classifications of instructional simulation en1ironments often Dust calledE instructional or

educationalA simulationsA are discussed) The usefulness and features of these classifications

are in1estigated) #any of the existing classifications do not distinguish 1ery well between

rele1ant aspects of simulation learning en1ironment)

Three sections describe the relationship between the internal characteristics of simulations and

the four themes introduced in de Kong this 1olumeAE domain models* learning goals* learning

processes and learner acti1ity) !ecause simulation models are discussed extensi1ely in the

first section of this paper* the section on domain and simulation models gi1es an o1er1iew of

domain aspects that are not explicitly referred to in the model) 3ere* an additional knowledge

base* called the cogniti1e model will be introduced) For each type of learning goal the relation

with the domain model or scenario is elaborated) The relationship between learning processesand learner acti1ity and domain models is discussed by relating the possible types of learner

acti1ity with the model and scenario elements* resulting in demands for the structure of the

model or scenario)


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Importance of simulation

ImportanceE It is not uncommon for a roadway corridor to experience increasing traffic

congestion and operational problems* due to rapid growth in traffic 1olumes associated with

maDor land use de1elopment)

This increase in traffic 1olumes* coupled with often short distances between

intersections,interchanges* hea1y turning mo1ements* numerous on,off ramps or dri1eway

locations* and increased cross street traffic demand* re6uires the transportation professional to

adopt a Lsystems analysisM approach to properly address traffic congestion) In doing so* the

impacts of potential design and traffic control impro1ements along the roadway corridor can

be better e1aluated)

"xcessi1e traffic demand along a corridor more often than not results in traffic congestion due

to effects of o1erlapping bottleneck locations) The spillo1er effect of traffic congestion from

one location to another negates the use of con1entional traffic engineering measures of

effecti1eness such as roadway capacity or le1els of ser1ice analysis techni6ues)

Traditional capacity analysis methods can pro1ide le1els of ser1ice estimates at a gi1en point

in space and time* but these methods do not pro1ide an assessment of the impacts on the rest

of the roadway corridor) &s a result* the effects of one bottleneck location on another locationare not recogni


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CO%C/0-IO%

In each of the scenarios abo1e* we ha1e demonstrated the functionality and utility of using

simulation as a tool for traffic management) In the two cases presented abo1e* the applicationof traffic simulation sa1ed se1eral million dollars of tax re1enuesA in right of way and

construction costs) That does not e1en begin to account for the intangible sa1ings of lost time

to tra1elers due to the congestion created during the construction period)

The simulation tools are also 1ital in exploring new traffic control techni6ues* systems and the

ad1anced traffic management centers of the future as seen in the research problem) %ot

mentioned here is another effort that is now in the demonstration phase where a li1e

intersection controller has been integrated with T-I- and CO$-I#* and the simulated

intersection is actually under the control of the actual intersection control hardware) This

demonstration can be used to assess the effects of different manufacturer+s intersection

controllers in a controlled en1ironment* or can be an effecti1e teaching aid)

Though the simulation model CO$-I# has existed in 1arious forms for many years* the

recent ad1ances by the insertion of ad1anced computer techni6ues and software de1elopment

technology has allowed CO$-I# to be taken to new le1els of performance* new le1els of

integration and ease of use) Practitioners and researchers around the world are using the

CO$-I# micro=simulation model to problems* and new doors are being opened each day as

the computer technology mo1es forward)


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H) $"F"$"%C"

The #erriam=>ebster 2ictionary* %ew $e1ised "dition) 57) %ew @ork CityE #erriam=>ebster)

Coats* )3) 4H;:) $eser1oir -imulation) Petroleum "ngineering 3andbook* 3)!) !radley ed)* Chap) 7;) $ichardson* TexasE -P")

Odeh* &)-) 4H;4) Comparison of -olutions to a Three=2imensional !lack=Oil $eser1oir -imulation Problem) K Pet Technol GG 4AE 4GN58) -P"=H:5G=P&)httpE,,dx)doi)org,4)544;,H:5G=P&

>einstein* 3).)* Chappelear* K)")* and %olen* K)-) 4H;9) -econd Comparati1e -olution ProDectE & Three=Phase Coning -tudy) K Pet Technol G; GAE G78=G8G) -P"=47;H=P&)httpE,,dx)doi)org,4)544;,47;H=P&

enyon* 2) 4H;:) Third -P" Comparati1e -olution ProDectE .as Cycling of $etrograde Condensate $eser1oirs) K Pet Technol GH ;AE H;4=HH:) -P"=455:;=P&)httpE,,dx)doi)org,4)544;,455:;=P&

&atts* K)>) 4H;9) & Compositional Formulation of the Pressure and -aturation "6uations) -P" $es "ng 4 GAE 57GN585) -P"=45577=P&) httpE,,dx)doi)org,4)544;,45577=P&

!lair* P)#) and >einaug* C)F) 4H9H) -olution of Two=Phase Flow Problems 0sing Implicit 2ifference "6uations) -P" K) H 7AE 74:=757) -P"=54;8=P&)

httpE,,dx)doi)org,4)544;,54;8=P& -tone* 3) and .arder* &)O)K) 4H94) &nalysis of .as=Cap or 2issol1ed=.as 2ri1e

$eser1oirs) -P" K) 4 5AE H5N47) -P"=484;=.) httpE,,dx)doi)org,4)544;,484;=.
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PPETROLEUM PRODUCTION Engineering; Term Paper Project