Integrated Reservoir Studies - Luca Cosentino

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Editions TECHN 
FR0iZ-f THE §A;CIE PUB1 ISHER 
LiL"eF1 Prctcxtion Practical Hancibook 
H. CHC-1T 
Geophts.;~ of Reservoir and Civil Engrneering 
J.L. G. ,\REkS, D. CHAPELLIER, P. GALDIA\I 
*. \hiel{ Coa:pietion and Seri icing 
9. PERK \ 
iVel\ Tes2:ng: Interpretatior; Xtethods 
G. BOi ,XDXROT 
Rock h2erhanics. Vol. I . Theoretical Funclamentals. 
Vol. 2 . Pe:roieum Applications 
PH. C t iXdEZ 
Formati07 imaging by Acoustic Logging 
Edrted b\ I.L. ,UARI 
* 6asics oi Reservoir Engineering 
R. C O S C 
Drilling Xtud and Cement SIurry Rheology ktanual 
Bloavout Prevention and Lt'eII Control 
* Multiphaw Flow in Porous Media 
C.M. MiRLE 
The Resewoir Engineering Aspects of Fractured Formations 
i.H. REK.5 
Propertitu of Reservoir Rocks: Core Analysis 
R.P. ClO\iC\RD 
Enhanced Oil Recovery 
M. UTiL 
Comprel?ensive Dictionary of Petroleum Science and Techt~oiagy 
English-French / French-Engiirf? 
MP. MOLIZE,AU, G. BRACE 
Dictionary of Drilling and Boreholes 
English-French 1 French-English 
M. .UOC SEAU, G. BRACE 
INSTITUT FRANCAIS DU PETROLE PUBLICATIONS 
Luca COSENTINO 
Seniz 9eservoir Engineer 
Prole- Manager 
Bercrz-Eranlab 
Integrate 
eservoir 
foword by 
Jean-Claude Sabathier 
f Editions TECHN IP 7 , 71737 PARIS Cedex 15, FRANCE 
O 2001, Eclitions Technip. h r i s 
A Paofa e Michele 
The i~iltlzut. 3 p~uceedx oftlrtli~ book ~ Y l f be rise d by 
one of the ~r.orln"s Icirdi~lg nid and de~-trlopnttwt ngelzcies, 
to help t~nnsfo~.ni the 1i1.e~ oqf'children andfirnzilies ai70ur1d 
rhe ~t.orld irz their stnrggle crguinst y o~.ei.f)., klrrrgel- nnil 
ii!jicstice. 
Foreword 
Ejcr since the f 986 crisis, the price of crude oil has been fluctuating severely. Oil compa- 
nies h ~ v e had to comply with this situation by cutting their costs and putting more effort into 
estinating as accilratefy as possible the economics of projects and associated risks. 
Tz.. i .thnical advances in well design and drilling, now allow the drilling of horizontal wells 
selsnl kilometres long, as well as extended multilateral wells, wit11 new production-injec- 
tion architecture. Such wells permit the development of the fields using less wellheads and 
h ~ 1 1 i ~ more con\-enient surface infrastnlctures. Also, new types of structures have replaced 
tr-adjrional platforms, allow~ng a reduction in capital expenditure and hence increasing the 
possibility of deep offshore de\.elop~nent., 
l'ihile drilling less but longer wells allowed for a significant cost reduction, the technical 
nsL involved in such operations is higher. These con~plex wells are more expensive than 
n o r ~ ~ u l vertical wells and whenever there is a failure, the impact on the econo~nics of the 
prejsif is significant. Furthermore, such wells are prone to technical problems in the drilling 
phss . and also running logging tools is often not straightforward. 
!n addition, the types of completion commonly utilised for such conlples wells do not 
a l l ~ u for easy interventions, with the possible exception of horizontal wells completed with 
cemented liners. Early water or gas breakthrough may cause the well to shut-in prematurely, 
n-ith a significant decline in the total field production. 
Exen more than in the past, it therefore becomes essential to carefully plan the develop- 
ment strategy of the reservoir, both in terms of number and type of wells to drill and the 
recm ery process (depletion, inject~on.. .). These choices, together with a correct prediction 
of 31s field performance, will impact heavily on the surface structure design and hence the 
global economics of the project. To stay within fixed economic bounds and minimise risk. 
oil companies make use of reservoir studies. While such studies have always been per- 
fomlzd, in the present climate they have to be more accurate and less expensive. 
33s basic fluid flow equations have been used for more than 50 years and their most 
recent application is linked to the relatively recent development of reservoir simulators. Cur- 
renr models often work with 1 O5 gridblocks: but megacell simulations (lo6 gridblocks) are 
becoming increasingly common. Compositional simulation allows for a better modelling of 
L-anarions in fluid composition. while wellbore hydraulics and surface networks are being 
coupled to the reservoir model. 
Xsl ertheless, such models still represent a simplified approximation of a complex and 
u h o w n reality. The main problem is related to the knowledge of the reservoir parameters 
and their discretisatio~l on a large-scale support grid. In this respect, sophisticated upscalirlg 
tecltniques have bsen de-i zlo.~ed in thc Iast years, hotiever no definitive solutiorl is a~.aiIable 
yet and the infornjation loss which results from an) upscaling process has to be taken into 
account nhen defining the rc3servoir model. 
In any case, tk2 hno:iiedge of the reserxoir is the most critical factor. Ths p;vamcters 
gove~ning the dq-rlamic bshaviour of the field arz essentially: 
Stnlctural parameters (depth and thicknsss maps, faults.. .). 
Internal architecture (correlation schernei. 
Petsophysical properties (porosity, permeability, capillarq pressure, relatise pemea- 
bijiq). 
Fluid contacts. 
Thsr~nodynamical properties of fluids. 
These data are only partially accessible, gitzil the small number of sanzpling points 
(wells) and the difficul5 of in-situ measurements. Furthermore, these data are not directly 
measurable and irlstead must be inferred from other available measurements (e.g., resistiv- 
ity, radioactivity, pressure). Also, the drillimg of conlplex wells entails less salnpling points, 
while the interpretation of the available n-teasuren~ents becomes generally more difficult. In 
all cases, the estimatioil of the reservoir propsrriss rnust be psrfonned starting from just a 
few points. 
In recent years, data acquisition has been developing considerably, due to the improve- 
ment of existing techniques and the capacity to record new physical parameters that can be 
related to basic reservoir characteristics. One notable technique is 3D seismic, which com- 
pletely changed the structural rnodellirig of reservoirs and that, under favourable circum- 
stances, may help in assessing the distribution of some reservoir propel-ties; recent logging 
took, which discriminate mineralogy, fluids, porosity, faults, fraet~~res . . .; permanent 
gauges, which afiour for continuous reservoir monitoring. 
At the same time, interpretation tecl~niques haye become increasingly sophisticated and 
allow a better definition of reservoir characteristics. In this respect, the most spectacular 
progress conceras the spatial modelling of reservoir properties. Sequence stratigraphy repre- 
sents a rigorouaj framework for well to well correlstion, minimising the errors in difficult 
sedimentary environments. In addition, the probabilistic approach to the problem of estima- 
tion led to the development of Geostatistics. The parallet evolution of the theory, the numer- 
ical methods and the computer capabilities fom~cd the basis for the development of 
statistical methods that generate equiprobable images of the reservoir, starting from a sparse 
set of data. 
Such techniq~les require a high level of tectutical expertise, as well as powerful cornput- 
ing resources, but their successful application is still dependent upon the quantity and qual- 
ity of the available dftta. Mostly, as it has long been recognised, the cooperatiotl of the 
various specialists (synergy) and the concept of integrated study are the main issue, as far as 
the inlprovemet~t of results is concerned. 
Nonetheless, it is obvious that the realisation of such a concept is a difficult task. Compa- 
nies soon realised that putting a geophysicist, a geologist and a reservoir engineerin the 
same working roam was not enough, While these conditions are favourable to the generation 
of team work, they do not guarantee in themselves that the resulting study will be really inre- 
grcL:;.l'. The main problenis are In thc choice of the methods and the difficulty of managing 
difilrtnt tasks i n parallel, througli a continuous comniun~cation among the team members, 
E - i ~ l l phasc (tog interprttation, well test~ng. spatla1 artctIysis ...) may be carried out using 
\ar,,ws techniques, which can differ significantly in tenns of time and money involved. An 
old nilc of tliunlb says that 8 0 " ~ of the ivork caii be achit.\ ed in 20% of the time. It is ttierc- 
fcre necessary, from the planning phase. to choose the ir~teryretatlon techniques as a f~rnc- 
tio:i of the available data and the importance of that exm 20°h of results. 
TL. , ,:L , in~portatice of ~nteyratioti is rc1ati.d to the s c a r c i ~ of the available data, that must be 
supienicnted through hypothesis, analogs and correlations, which in turn may have a sip- 
nifiz.int impact on the final results. These various eleinetits must be validated in tenns of 
co1:ilrency through all the phases of the study. For exan-tple, the reservoir engineer may sus- 
pezr rhe presence of a seallng fault on tlie basis of production data, but this must be consist- 
ent xi~th tlie geological scheme. The ditticulty lies in the fact that the study is divided in 
t a sk rhat are not independent. the results of each task representing tlie feedback for the 0th- 
ers. i f a t ~ o ~ ~ n . v t ~ c a n ? task is not consistent with another rcp.rtt-cam task, the latter should be 
rel3ed and this process may i~iiply a dela? In the project execution and a cost increase. It is 
the~zfore necessary that each specialist. before starting 3 new task, cross check the coher- 
enc! of the hypothesis with the other ciiscipl~nes, which in turn implies that all the tasks 
slioatd be perfomled, as much as possible. simultancousl~~. 
Ah can be appreciated. the planning and the rea1isatic;n of an integrated study is a consid- 
erable challenge. Usually, each specialist tends to propose and perform the best study and to 
atrz:;l the best results, eIren though this 1s not relevant t~ the global objective of the study. 
Fre<iiently. ~t is possible to see very sophisticated (and expensive) studies that are not uti- 
Iisei' if1 the subsequent study. An opposite attitude is also frequently encountered: specislists 
ma! iimit the degree of detail of their M ork. not being i~rsrested in pro\ iding more accurate 
rsscirs. For example. a geologist tinay generate a 3 layers 1 ertical discretisation (< since reser- 
iroir engineers hardly use more than 3 layers in their models )). Another geologist, on the 
cor?n.~q. might generate 15 layers, when really a 5 layers description would be a good coi~i- 
pro::iise from a static and a dynamic point of view. 
TIe concept of parallel planning, on the other hand, may pose some difficuIty to the spe- 
ciahr which does not intend to share intermediate results of his mark for feedback with 
other disciplines. Certainly. most professionals prefer to complete their work before deliver- 
ing rheir results. 
X?is quick review of the main hurdles of an integrated reservoir study highlights the 
importance of the role of the project manager. Together with the team members, he is 
responsible of the definition of the work plan, as &ell as of the modifications that inevitably 
a ill have to be considered during the realisation. He must check that the results of individual 
p h s t s be shared among the various disciplines at the right time, always taking into account 
possrble delays and, mostly, the overall objective of the study. A development study for a 
nem field, where few data are available, must be accurate fiom a global perspective. In a typ- 
ical Lqfill drilling project, on the other hand, more data are available and a more detailed 
stud) is required. Finally, a secondary or tertiaq recovery study requires an even more accu- 
rate ;;fiowledge of the reservoir, in terms for example of fluid distributions. 
Gtnerally, the project manager of an Integrated study belongs to one of the key disci- 
plines (geology, geophysics, reservoir engineering), hoa ever he must be aware of the funda- 
menials of the other domains. Of course, he cannot be specialist in every branch: siilce he 
individual discipf ines are Deconfng increasingly complex. 
Excellerlt textbooks exist in the market that deal ~ v i t t ~ individual I-esen-oir disciplines. 
Howevzr, thesz manuals are conczi~ ed for spcciatists and they are not suit;.d for the projscr 
manages, ~vho nlust und~rstand the added value of different and alterriatiye rechniques that 
he docs not necessarily 'wow in detail. A manila1 for the reservoir project manager should 
explain what could be done and at which price, without entering (as far as possible!) into 
nlttch technical detail. Smh book should be a guide for the project manager, by dealing with 
what can be done to me=t the objective of the study. optirnising at the same time the cost 
involved and the degree of technical detail. To my knowledge, such a n~ant~al bid not exist. 
Luca Cosentino has v;orked to fill this gap. '4 rescwoir engineer with a geological back- 
ground, be has managed seberal integrated studies on various type of reses~oirs, both for oil 
and consulting cornpanizs. Making use of his experience, he wrote this book. which presents 
a simple and complete summary of the differi.13~ methods that can be used in a resen-oir 
study and their utilisation. At the same time, he provides rts with an exterlsive bibliography 
to go into the techizical subjects in more depth. 
It is withotit hesitation that 1 recomme~ld to every professional to keep this precious man- 
ual at hand, as a reference for those decisions that are o~ttside their domain of competence. 
I only regret 1 had to wait till the year 2000 to encounter such a manual. Tile explanation lies 
perhaps in the large experience, the capacity to surnnzariss and.. . the amount of work that is 
necessaly to complete such a task. In any case, ths results are worth the effort! 
Jean-Claude Sabathier 
Preface 
Integration has probably been the most Fxshionable word in the Exploration and Production 
domain in the last decade. Papers, conferences arid technical meetings have focussed on this 
concept anti the adva~kages that it entails when successfully applied. 
Dtlring these last years professionals belonging to. the various resclvoir disciplines, i.e., 
Geology, Geophysics, Petrophysics and Reservoir Engineering, have been taught to work 
together, to search for sonle synergy and to integrate their individual pieces of work. Ven- 
dors have created integhted databases, shared earth models and interoperable applications. 
Managers, on the other hartd, have created asset teams, organised common working envi- 
ro~l~~len ts and encouraged cross-disciplina~y educational courses. They are searching for 
integration, believing that the extra value which could be gained is worth the effort. One 
cannot disagree, of course. Integration is one of those magic words that always has a posi- 
tive meaning, however it is applied. Integrated is always better than disintegrated, and it is 
right to look for some sort of integration. 
But when we come to the everyday working reality, what does integration really mean? 
What kind of change does it mean to our way of working? Is integration just a new solution 
to old problems or it is a completely different approach that also poses new and unexpected 
questions? And if this is the case, are we able to identify those problems and to propose ade- 
quate solutions? What new technical and professional challenges have to be faced? Who is 
responsible for the correct ill~ple~nentation of such integrationin the course of the study? 
The objective of this book is to try to give some answers to these questions and to high- 
light those aspects of a study that become relevant when we want to perfonn an I~~tegrated 
Reservoir- Sttrdy. 
Acknowledgements 
A book is never a one-man effort. In the case of liztegr-ated Re.~cr*~,oi~* Studies, a lot of people 
have been involved in the project and have helped me in soltle \+a? throughout the work, 
friends. colleagues, family. 
In the first place I want to thank Jean-Claude Sabathier, for believing in this work and 
supporting me in the finalisation phase, the most crucial part. His views are always ilfumi- 
nating. 
Giuseppe Spotti, ~ 7 1 1 0 shared so nlally years of professional life uith me in Agip first and 
Beicip-Franlab later, is the other friend I want to acknowledge especially. Many parts of this 
book arise from our long discussions on the most diverse aspects of reservoir studies. 
I am also grateful to a nunlber of friends who contributed to this work by reviewing all or 
parts of it and providing useful suggestions and corrections. Philippe Le Bars and Patrick 
Rou\.roy, for their help in the Database issues (Chapter 2), my old conlpanion Christian 
Ravenne, for his contribution to the geological parts, Pietro Consorini and Patrick Lemon- 
nier for reviewing the simulation part (Chapter 7), Pierre Lemouzy for providing his ideas. 
always original,-about the philosophy of upscaling and many other resenoir matters, Marco 
Thiele for his help in the streamline section. 
I also want to acknowledge other collegues: Jean-hdarc Chautru. Paul Van Lingen, Ber- 
nard Borbiaux, my venezuelian friends Roberto Muncsz and Jose Edmundo Gonzafez, the 
staff of the IFP school (Gilles Gabolde, Bernard Drlrand, Jean-Piesre Roy. Jean-Hector de 
Galard), the staff of Editions Technip (Sylvie Haxaire and Phiiippe Catinat). 
Sabrina Albornoz provided me with invaluable help for the bibliography, digging out the 
most obscure papers, while Geman Pana and mostly Gerard Dyot helped me in the long 
and tedious work of assembling the graphical part. 
I also owe a special acknowledgemnt to Ros Stallard, who patiently revised the English 
version. 
Finally, I an1 grateful lo Beicip-Franlab management and in particular to Jean Bums, 
Honore Le Leuch and Paul Bia for supporting me in bringing this book to fruition. 
Last but not least, thanks to my wife Janet for understanding. 
Contents 
Foi-ewor-rl . . . . . . . . . . . . . . . , . . . . . . . . . . . . . . . . . . . . . . . -. . , . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . , . . . . . . . . . . . VII 
Chapter 1 
INTEGRATION ISSUES 
1.1 What Is Integration? ........................................................................................... 
1.2 Systems Thinking ................................................................................................. 
1.3 A Change of Focus ................................................... .................................. -.- .......... 
- - 
. Ion ............ ......... ........ . ....................,.........................+... 1.4 Integrating the 1nf0rnr:~t' 
d.,5 Acctasacg vs. Precision ......................................................................................... 
1.6 Complexity vs. iiccuracy ........................................................-............a............... 
1.7 Other Integration Issues ........................................................... ...... ,.................. 
1.8 The Rofe of the Project Manager ........... . .. ..... , ,...... .. .. .... ........ .. ,.. .. ... ...~ .. . .. . ... .; ... 
Chapter 2 
THE INTEGRATED DAT %BASE 
2.1 Definitions ... ...... .... ....... . . . ... .. . .. .. .... . . . . . . . . . . . . . . . -..... , ...... . ........ 15 
2.2 The Problem of the Integrated Dabbase .............................................,......... 15 
.................................................................. 2.3 The Three Levels of E&P Databases 
........................................................................................... 2.4 The Project Database 
2.5 Project Database Xlanagcn~ent ........................................................................... 
2.6 Soft~vare Integratiorn .........................,................................................................. 
Chapter 3 
INTEGRATED GEOLOGICAL MODEL 
- 
.......................................................................................... 3.1 The Str~ttturaI AIodel 
3.5.1 Resen-oir Asct~itrcrure Definition ...................................................................... 
............................................................................................. 3.1.1 Faults hlodetiing 
3.1.2. f Accuracy of the Fnult hfodel ................................................................ - 
3.1.3 Structural Model Uncertainty ............................................................................ 
................................................................. 3 . I . 4 Building a 3D Stixctural Framework 
..................................................................................... 3.2 The Stratigraphic ?+lode1 
...................................................................................... 3.2. f Sequence Stratigraphy 
3.2.2 Other Techniques ......................... ,. ................................................................ 
............................................................................ 32.3 Building a Stratigraphic Grid 
3.3 The Lithological Model ........................................................................................ 
................................................................. 3.3 . 1 Concepmal Sedi~nentological Model 
........................................................................................ 3.3.2 Facies CJrtssification 
7 1 3.2.2.1 Facies Identification mc! Classification ................................................. 
............................. ...........*...........*..........-...*. 3.3.2.2 Facies Characterization .. 
......................................................................... 3.3.2.3 The Concept of Facies 
........................................................................................... 3.3.3 Facies Distribution 
3.3.3.1 The Srncharfic Approach ..................................................................... 
............................................... 3.3.3.2 Pixel-Based vs. Object-Based Modelling 
3.3.3.3 Geological Uncertainty Assecsmeut ................... ... .............................. 
......................................................... ...................... 3.3 Reservoir Heterogeneity .. 
..................... 3.4 . i Clmsificatiorl of Reservoir Heterogeneities ... ............................. 
3.4.1. I Sinall Scale Heterogeneities ................................................................. 
................................................................. 3.4.1.2 Large: Scale Heterogeneities 
3.4.1.3 Hetcrogerieity impact in Oil Rzcovery ................... .. ......a,........................ 
. Ian ...............................................*............... 3.4.2 Resesoir Heterogeneity Idcntififrclt' 
3.4.2.1 Geophysics ......................................................................................... 
3.4.2.2 Fluid Data .......................................................................................... 
3.4.2.3 Well Testing ..................................................................................... 75 
.................................................................................. 3.4.2.4 Production Data 81 
Chapter 4 
ROCK PROPERTIES 
...................................................................................4.1 Fetrophysical Evatrtation 
............................................................................ 4.1 . 1 IJ$icfoscopic Rcxk Properties 
................................................................. . 4.1 1.1 Pore Sl stein Characteristics 
4.1.1.2 Minemlogy ........................................................................................ 
..................................................................... 4.1.1.3 Investisatioil Techniques 
4.1.2 Grain Size and Sor-ting ..................................................................................... 
.......................................................................................................... 4.1.3 Porosity 
..................................................................................... 4.1.3.1 Core Porosity 
...................................................................................... 4.1.3.2 Log Porosity 
4.1.3.3 Integratin: Core and Log Porosity ........................................................ 
............................................................................................. 4.1.4 Water Saturation 
................................................................................. 4.1.4.1 Core Saturarions 
.................................................................................. 4.1.4.2 Log Saturations 
4.1.4.3 I~ltegrating Core and Log Measurements ............................................... 
................................................................................................... 4.1.5 Permeability 
........................................................................................ 4.1 5.1 Generdities 
4.1 5.2 Laboratory Measurein. en& on Core Samples .......................................... 
....................................................................... 4.1.5.3 Wireline Measuren~ents 
4.1.5.4 %'ell Testing ...................................................................................... 
............................................................................ 4.1 S.5 Flo\xmeter Logging 
........................................................................ 4.1 3.6 Elnpirical Correlations 
................................................................................ 4.1 5.7 Neural Setworks 
....................... ........*..*........................... 4.1 S.8 Integrating the Inforrnation ...-. 
.............................................................................................. 4.1.6 Net/'Gross Rafio 
4.1.6.1 The Cut-Off: a Dynamic Parameter ...................................................... 
4.1.6.2 Defining Cut-Off Criteria .................................................................... 
4 ..I. 6.3 Notes on Cut-Off Application .............................................................. 
.............................................................................. 4.2 Rock Properties Dktrihution 143 
.......................................................................................................... 4.2.1 I'orosity 145 
................................................................................. 4.2.1.1 2D Interpolation 145 
..................................................................... '1.2.1 -2 Seismic Data Integration I46 
..................................................................................... 4.2.1.3 3D Modelling 150 
........................................................................... 4.2.2 Kater Saturation Djrtributiori 
4.2.3.1 Direct Mapping of Water Saturation Values ........................................... 
4.2.2.2 Water SaturarionIPorosity RelationsIrip ................... ... ........................... 
3.2.2.3 Capillary Pressure Functions ................................................................ 
4.1.2.4 3D Water Saturation Distributions ...........................-......................... 
4.2.3 s e t Pay ........................................................................................................... 
4.3.3.1 3D Interpoiation .................................................................................. 
4.2.3.2 Seismic Data Integration .................................................................... 
4.2.3.3 3D Modelliilg ..................................................................................... 
................................................................................. 4.2.4 Fcmleabitity Distribution 
4.2.4.1 2D Interpotstion .................................................................................. , 
4.2.3.2 3D Penneabitity Distributions .............................................................. 
Chapter 5 
HYDROCARBON IN PLACE DETERMLYXT1OPI;h 
.......................................................................................... 5.1 Vr~iumetric Estimates 172 
5.1.1 hter~ninistic Evaluation ............................................................................. 173 
5.1.2 Pcobabilistic Evaluation ................................................................................... 174 
................................................................................. 5.2 MateGal Balance Estimates 
............................................................................................... 5.2.1 Gas Reservoirs 
................................................................................................. 5.2.2 Oil Reservoirs 
Chapter 6 
BASIC RESERVOIR ENGINEERING 
................................................................ 6.1 Reservoir Natural Drive Mechanisms 
6.1.1 Fhid Expansion ....................................s................. ..................................... 
.......................................................................................... 6.1.2 Solution Gas Drive 
.................................................................................................... 6.1.3 Water Drive 
................................................................. ........................... 6.1.4 Gas Cap Drive .,. 
........................................................................................... 6 .I . 5 Compaction Drive 
6.2 Fltaid hoperties .................................................................................................... 194 i 
6.2.f Reservoir Hydrocarbon Fluids .......................................................................... 195 
6.2.2 M& Oil and Gas PVT Parameters .................................................................... 197 
.............................................................................. 6.2.3 Fluid S;tmpling Procedures 
6.2.3.1 Bottonl Hole Samples ......................................................................... 
..................................................... 6.2.3.2 Rccolnbi~lud Fluid Samples ...........- 
6.2.3.3 Reliability of the Fluid Sanlples ........................................................... 
6.2.3.4 Vertical and Lateral Fluid Property Variations ....................................... 
................................................................................ 6.2.4 PVT Labc>f-ntory Alnlysis 
6.2.4.1 Physical Meaning of rhc Laboraroq Experiments .................................. 
6.2.4.2 I, ahoratory Data Conversion for Reservoir Engineering Applications ...... 
6.2.5 Field PI-oduction Data ...................................... .. .............................................. 
.......................................................................... 6.2.6 Generalised PVT Correlations 
....................................................................... 6.2.7 Integrating the PVT Information 
.............................................................................. 6.2.8 Reservoir Water Properties 
........................................................................ 6.2.8.1 ChemicalCojnposition 
.................,..*............ ......*........,............. 6.2.8.2 PVT and Other Properties .... 
6.3 Rock-Fluid Properties ......................................................................................... 
6.3.1 Wettabilit~ ..................................................................................................... 
........................................................................................... 6.3.2 CapiI la~ Pressure 
...................................................................................... 6.3.3 Re1atij.e Permeability 
................................................................... 6.3.3.1 I, aboratory Measureinellts 
........................................................................ 6.3.3.2 E~llpirical Correlations 
6.3.3.3 Field Data .......................................................................................... 
...... 6.3.3.1 Relative Permeability from Nu~nerical Si~nulation (Pseudofunctioi~s) 
....................................................... 6.3.3.5 Three-Phase Relative Perrneabiliry 
6.3.3 Residual Oil Saturation .................................................................................... 
SIX 
................................................................................................. 
. 6.4 Pressure Analysis 
- 
223 
.......................................................................................... 6.4.1 Formati011 Pressure 224 
....................................................................... 6.4.2 Resen air Pressure Data Sources 224 
.............................................................. 6.4.2.1 Static Pressure Measurements 224 
.................... .......... 6.4.2.2 Reservoir Pressure from Well Test Interpretation .. 225 
............................................................................. 6.4.2.3 WFT Presstire Data 227 
6.4.3 Pressure hlodelling ............................ ..... ........................................................ 227 
................................................. 6.5 Reserroir Fluids Distribution and Monitoring 
............................................................... 6.5.1 Productjon aild Injection Realloication 
............................................................................... 6.5.2 Water Advance with Time 
................................................................................... 5 - 5 3 Gas Advance with 'l'irne 
6.5.4 4D Seismic Monitoring ..........................-........................................................ 
................................................................................................. 6.6 3laterial Balance 
......................................................................... 6.6.1 Why Run a Material Balance? 
6.6.2 Material Balance Application to Re ser~roir Studies .......................................;....... 
5.6.3 Material Balance vs . Numerical Si~nulation ....................................................... 
....................................................................................... 6.. 7 Streamlines Simulation 242 
XX Con tents 
Chapter 7 
hT3IERICAL RESERYOIR SL&I[ULATION i 
7.1 When To Run a Sixn~ulation Madel? ................................................................... 248 
7.2 Why Run a Simulation Model? ......................................................+.............-...-. - 250 \ 
7.3 Designing the Simulation hlodel ......................,.,........................... . . . . . 250 
7.3.1 Selecting the hlodel Geomsiry ................. .......... . .. ...... . . . . . . . . . . . ........... 25 1 
7.3.2 Selecting the Simulator Type ..................... .....-..-.-.. .. . ...... ....................... , ....-.... 253 
9.4 Building the Simulation Grid ....... ............. .. ... ..... ... .... . , ....................-....... - -....... -- 254 
7.4.1 GeologicalIssuer ...............,...............,...........,........................-..................... 254 
7.4.2 Dynamic Issues .......................,...,...........+...................-........................- * - ........ 256 
7.4.3 Xumerical Issue\ ..........~.......,.....,..a.~.....~.............................,...................... 2-56 
7.4.4 Choice of the Siilrulation Grid ... .. . .... . .... ..... ....-. . . . , . . . . .... . . . . . . . . . ....... . 258 
7.4.5 Building the Simulation Grid: Conclusions ................................................. . 260 
7.5 Assigning the Input Paranzeters ............... .... ....-*........ ........ ............ . ..=*.-.. ...-....... 
7.5.1 Reservoir Geometry ................... ..,............ ......*...... .. ...* ........... , ..... ......-....... -.-.. 
7.5.2 Rock Properties ................................................. +..,. ..*... .......... .. ...-.... , ......... -.--- 
7.5.2.1 The Upscaling Problem ....................... ..... . ..............................-...-..-- -.. 
7.5.3 nuid Properties ............................................... .... .. . . . ...... ..*........ ...-. ... .... -.- .... -* - 
7.5.4 Saturation Functions ... . .......... .-..... ...... .. .. . . . ......-. . . . . ,... . .. ,... ,. .... .... ... . . .- .......... -... 
7.5.4.1 Hysteresis .. .... .... ...... . . .... .... ..-.. .. .. . ........ ... . . . .. . .... ... ..... ... . ... +. . .. .. ......-... -- 
7.5.4.2 Assigoirlg Saturation Ftinctions to the Sirnulation Grid ......................-... - 
7.5.5 Production and Completion Data . . . . . , . .. .. . .. . . . . . ..-. . . . . . . . . . . . . . . . . . . . . . , . . . . . . . . . . . .... .. .- .. *.-. 
7'5.6 Model Initialisation ......,............,.,.............. .&...+...... .. ..*.................. . ........-.-.-. ---. 
7.6 Mistory Matchi~xg ,..,......,. .... . ..,,..,B.s......,...s ... . ........ ... . . ..... .. . . .=.. .... *.-... ... ........-...- -. 
7.6.1 Xmportant Aspects of the History Match Process ... .. . . . . . . . .... ... . . .. *.*...... . ... ..... . ...... - 
7.6.2 Matching Pwan~eters ....*,. . . .. . +. . . . . , . .. ... . *, . . . . ..... . .. , . . . . , . . . . . . . . . ..... . . ... . .. .. . . ...- ... .... --. 
7.6.2.1 Pressurc ..................,......,.,,...........~~.................,................ ..... .... -.... 
7.6.2.2 Water Production ....... ......,..,.......,,,.... .... ..'.. ... ...... ......*..-. -.* .... -..* ..... - 
7.6.2.3 Gas Production ...................,.~.....,.......... . .,... . ...- ... ........... , ....... ........ .. 
7.6.3 MatchingBrocedure ............................................. . ..,.. ......... ,..* ........- .... -..--..-.. 
7.6.4 Quality of the fvlatch ...,.....,....,..,.~,,,....~....,.............,.. ,.*.....-.... ..... + ............... 
7.7 Production Forrztas%s . . . . . . . .. .. . . .. . , , . . . , . . . ... + ,. . . . . .. ...... . . . . . . . . . , . . . . . . . . . .. . . *. . . . . . . . . . , , .+ ... . . . ..... 28 1 t 
7.7.1 Input Data for Predictions ....,..... .... ,,...... ............ . . ,... . . . ., . . . ..- -*.. ..... .,.. .. ..... . ......... 28 1 t 
282 7.7.2 Setting Guidelines and Constraints ....,.,......,........,................,........~.,..........~..~~.. Z 
7.7.3 Inflow and Outflow Well Performance .... . , .......,, .,. ,. . . . . ,, . ,... .* .,...v .,.. .. .*.*.+.., ...* .... 282 
7.7.4 Running the Prediction Cases ..,,..,......................,....,.,..........~..~.... ..... ....*..e....... 284 
286 7.8 Uncertainty Asseslsrnent ..,. . ...,..,,.....,..,.. ... .....*.*.,.. +.*...,....-,....-=.....* .... ... .... *.*...--- 
Chapter 8 
PLANNING A STUDY 
8.1 Plaxlniogvs.Ix~tegratio~l ................................,............................................. 290 
8.2 Estinlation of Individual Work Phases .... .. ........... .. .. .... . ....... .... .. .. . . .......... 291 
8.3 SequentialPlanning . . .. . . . . . . . . . . . . . . .. . . . . . . . . .. . . . . . . . . . . , , . . . . . . . . . . , . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 1 
8.4 Integrated Planning ...,...........................................,.....................................-....... 293 
8.5 Conclusions ............,....................................,.,......,~............,............*....~........-..... 295 
Integration Issues 
1.1 WHAT IS INTEGRATION? 
Following Webster's definition, integration is a combination and coordination of separate 
and diverse elements or units into a more complete 01- ham~onious whole [I]. Since it 
implies the creation of a more complete or hannolzious whole, integration can therefore be 
considered as a process whereby extra value is produced. Haas. this extra value can be Zener- 
ated is precisely the scope of this book. 
In the petroleuni industry and in particular in the Exploration and Productioll (E&P) 
domain, integration is primarily concerned with the manner in which different disciplines 
are combined to improve an established (or create a new) analytical process. 
Actually, however, integration is a difficult concept to define. E&P processes - are highly 
complex activities, which i~lvolve a nuniber of disciplines. each one carrjing its awn inte- 
gration pr-oblenls. Therefore, integration of disciplines actually lllerns integration of all the 
aspects which constitute those disciplines. There is the aspect of irrtzgrating different picces 
of work, produced by different psofessionals, but also the aspect of integrating different geo- 
scientists belonging to different professional cultures. Moreover, there is the need to inte- 
grate different lluinan beings, coming Ron1 different places and talking different languages. 
Finally, there is the problem of the integration of the sohrare and hardware platfonns, a 
necessary condition of any integrated study, 
To hrther complicate the issue, the degree of il~tegration within a team or a work process, 
by definition, is always changing. Consequently, while the study progresses, the in egration 
problem changes and needs to be continuously redefined. 
Despite the inhesent comnplexity of defining what integration is, it is possible tc) t ~ - and 
categorise some basic issues, which relate in par*icular to an Integrated Reservoir Siudy: 
* Vertical tfs. horizo~drnl integrafhrj. A basic distinction that could be made refers to 
ser-tical integration within an E&P discipline and horkontul i~tegratiun across the dis- 
ciplines. h example of vertical integration is the worldlow along the seismic interpre- 
tation of a particular reservoir, when different geophysicists work together on the same 
platfol-ni on the different aspects of the geophysical interpretation, e.g. the ~itmchtral 
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interpretation on the originat volume and the attribuie analysis on a processed volume 
of data, The need for vertical integration within each discipline is 0b.i-ious and has 
already been implemented in many computing systems. It is also normally easy to 
achieve, since rhe professio~laIs belong to the same branch, geophysics in this exam- 
ple, and can eaqily communicate. In contrast, horizonfcrl integration means integration 
across the different disciplines of an integrated study and it is a far more complex 
problem. The main hurdles are the relatively reduced interoperability of the software 
platforms normally available and mo: tly the tendency of the different professionals to 
concentrate on their particular study and to have a reduced f e td of cornrnunication 
\vith other professionals of the team. 
Loose vs. tight infegration. Another useful distinction that could be made refers to 
loose and tigh integl-ation. As will be discussed in the next chapter, this mainly refers 
to the degree of interoperability of the different software applications, but loose and 
tight integratiu11 can easily be defined in a general sense aiso for the work process. 
When a geologist discusses with a reservoir engineer about the position of the faults 
that he has identified in his study, we cotrld talk about loose integration. When the 
same geologist and engineer work together to define the position of the faults, using 
their respective tools, we can probably talk about tight integration. 
In the next sectians, it will be shown how the integration problem has been defined and 
tackled in the comp!etely different field of business administration and how those concepts 
can be applied to E&P projects, Later, some of the relevant integration issues within an res- 
ervoir study will be discussed, with particular ernpliasis on those aspects that mark the dif- 
ference between a integrated and a traditional reservoir study. 
1.2 SYSTEMS THINKING 
Some years ago, during the crisis of the American cars market, same engineers of a Detroit 
Company decided to deconstnrct a Japa~lcse car, to try to understand some a f the secrets that 
made those cars so cheap and efficient. Looking into the engine they found, arnong other 
thii~gs, that a particular type of past was used three tinles to join different components in 
three different parts of the cylinder bIack. Tn the American cars, the same assembling 
required 3 different types of parts, which needed 3 different keys and 3 inventories of parts, 
so that the assembling procedure was mtrch slower and more eexpeusive. 
Analysing the design organisation of the 2 companies, it was realised that in the Detroit 
Company there were different depai-tments in charge of the desiga and production of the 
3 parts. The engineers responsible for their product considered that their otvn components 
were good and that they fitted perfectIy in the assembling procedure, By conpast, in the Jap- 
anese Company, one- single engineer was supervising the \n.hole assembling procedure of the 
engine, 
This simple example is taken from a best seller book on Organisations Management [21 
and it shows how* in a complex activity like engine buildi~g, joinillg components of good 
quality is not enough, when the global efficiency of the project is being considered. The Japa- 
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nese engineer. IvhiIe supcl~ising the whole des ip proccdurc, was able to focus on tllc links 
betweet? thc dirf'ercnt parts of the assembling process arzd to optirnise the process itself, 'There 
is no doubt that the sil~gle parts produced by the 3 departrllcllts \yere good products 1:: them- 
selves; however, when the general objective of the project is considered, their intrinsic cll~ality 
becomes 'Icss important. and the simpler and ~r-rui-e flexible Japanese part proves to be better. 
The idea of concentrati~lg or1 the patterns of a particular process, rather then 011 its tonsti- 
tuting parts, is the key collcept of Systems Thinking. 
Systems Thinking is a discipline whose pi-inciples and tools have been developed in the 
last 50 years in the different fields of Physics. Erlgineering, Biology and IS4attrematies and 
that in recent years has been largely applied in Organisations Management. 
Simplifying the issue, Systems Thinking is a way to analyse a process (physical, natural, 
economic, political and it1 general any kind of process), whicll is based on the study of all 
the factors, internal or external, that have an influence on tlte devclopmerlt and o~rtcarnes of 
such a process. Leaving the interested reader to the huge existing bibliography on this 
theme, m7e can try to sumtnarise here some of the bask prirlciples of Systems Thinking: 
Understand the process of change, instead of focussing on thc individual constituent 
parts of the process itself. 
Uz~dersta~ld the intet-relationships among all the constituentparts, rather than the linear 
cause-effect concats~lations. 
Concentrate on the clryna?nic complexity of the process, tather than on the static com- 
plexity of its details. 
Any srpe of process can be considered this way, from the study of the orbit of an electron 
around its nucleus-to the analysis of the global heating of the earth. in Systems TIzinking, 
there is no one sitrryfe cause that generates one effect. Rather: tItere is an interreIation of dif- 
ferent elements (the system) that determined the effect Ge are considering. 
As far as reservoir studies are concerned, Systems TI-tirdcing provides a usefit1 theoretical 
framework to describe the different approach required by integration. 
An integrated reservoir study is by definition a co~ltptex process, which results from the 
integration of different disciplines and which has a definite objective. Like the ertgi~leers of 
the Detroit Factory, 7ive are faced with the problern of integrating elements that ha-r-e been 
produced by different professionals (departments). We also have the samc objective, which 
is producing a study (an engine) that is reliable and accurate. And in the same way, we 
would not ~var-tt to fall into the same trap, which is produci~lg good pieces of the sttidy (parts) 
that are not linked to the overall objective of the project in an ef&:ctive way. 
When perfomling a study, we need to understand the patterns of interconnections of the 
different activities and, like the Japanese engineer. we need to plan the work flow by think- 
ing about the optimisation of the process. Systems Thinking may help us to see throilgh the 
static (intrinsic) cortlpletiity of the geological or engineering \vork and to idcntift- those 
parameters that have an impact over the global objectives of the reservoir study. 
In the course of this chapter, some of those aspects of a Reservoir Study which are critical 
to the integration process will be discussed. Most of these aspects require a change of focus 
with respect to the traditional way of working. This change can be often be related to Sys- 
tems 'f ]:inking principles. 
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4 Chccp~er I . In iep-ation Issties 
1.3 A CE-'LANGE OF FOCUS 
Until a few years ago, the tvol-kflow of a resenoir study was a far different process than it is 
today. The approach was a seyuential-type one, where the geophysicist, the reservoir geolo- 
gist, the pstropkq~sicist and the reservoir engineer worked almost indeyetiderz:ly, while the 
results of each one wers passed to the other without significai~t feedback. 
One of the main consequences of this kind of approach is that each discipline defines its 
own ob-jectives, uihich are in general different from the otllers and possibly only IooseIy 
related ro the genera1 objectives of the reservoir study. Within each single discipline, profes- 
sionals tend to thirlk tiiat the more detail is used in the anatysis, the better the q~lafity of the 
restilts. Therefore, the t-arious professionals deliver a product that is the best they can 
achieve with the available technology, con~patibly with their professional experiznce and the 
time available. In this approach, the besr annlysis implicitly brings the best resrtlts. 
The process of integrating different E&P disciplines ta pcrforil~ an integrated reservoir 
str~dy reqrtires a change of focus, According to Systems Thinking, the approach becomes 
multidisciplinary afid tlae professionals work in an interrelated way, where the feedback 
from other disciplil~cs is fundamental to the validation of the work that is being performed 
(Fig. 1.1). 
Geophysics Petrophysics 
Geology Petrophysics 
-0 
Reservoir engineering Reservoir 
a engineering Q 
Response Response 
Figure 1.1 Professional links in a reservoir s a y : traditional (left) 
and integrated (tight) appmach. 
In this case, the key point becomes the rxnderstanbing of d ~ e global objectives of a partic- 
ular study, while ail the disciplines involved in the project must define sub-objectives which 
are strongly dependent upoil the global ones. This also impties that, in the frrunework of an 
integrated reservoir study, the technology available to the different disciplines needs to be 
cumpared to the common goal, in ordcr to select the appropriate tools. In fact, as it will be 
discussed later, a sirrtpler and more traditional approach can sometimes be prefkrred to lead- 
ing edge technology. 
This change of focus is a difficuft task to achieve, because it poses to the professionals 
and to the project rnmager unusual qijestions and probkms, which need to be tackled 
and solved from a new point of view. In the next sections, some of the key points that 
become important and nlust be considered when performing an integrated study wit1 be 
malyscd, 
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An integrated study is a c11;tllenging task to perfor111. The rcser\loir is actually a very corn- 
plex object in itself, \vhich must he characterised from 3 \ ariety of ~iewpoints, u.ith a large 
number of paramctcrs and n it11 a remarkable degree of aocu~~icy. Ftirtheruzore, in additio~l to 
the inherent, rzu~ural complexity of reservoirs, we may also have the man-induced coratribu- 
tio~ls to reseilioir unlcnojvns, i.e. fractures, formation dan-tagrl, cententation problems. etc. 
As a consequence of these factors. studies always c a q a degree of uncertainty, tvtlich in 
turn can be considered as an (unk~lowrt) measure of our inlperfect knon Icdge of the tesorvoir. 
As a tnattcr of fact, geoscientists, unlike other scientists, have a vel?; lin?ited access to the 
object of their investigatio~ls, i.e. the reservoir. The itlformatioi~ that is rloilnatly a~ailable to 
them is peculiar for at least 3 reasons: 
If is mostly irrdirrzct. The only direct access to the reservoir is through coring. ilsing 
samples of core \Ire ciln perform some direct rneart~rements of the resenioir properties, 
u.g. rock porosity. In all other cases we can derive information on those properties 
indirectly, via some other type of measurement. Then, we col~eiate those mcnsure- 
ments to the reservoir parameters of interest tflro~lgh some type of transfer function. 
For example, in a geopllysical survey we measure travel times and we convel-t them 
into depths through a finddepth relationship. Like\\-ise, during a \yell test \\-e measure 
pressures and then we co~lvert them illto a number of parameters of interest f ike penne- 
ability or skin factors. The transfer function is in this case some type of solution of the 
diffusivity equation. 
If is bused oft a srnafl support ~wlrmze. Wit11 the notablc exception of seismic and, to a 
lesser extent, well testing, afll the information that we inte~pret is computed on a smnall 
or very small supp~r-t volrrine, that we implicitly assume ro be representative of the 
wlzole resenioir. For example, we measure rock wettability on a few 1 or 1-112 irlcl~ 
plugs and the11 we ehtend the results to the whole resen-oir or to a pal? of it. Likewise, 
tve observe diagenetic phenomena through the nlicroscope on a thin sectiolt of tlie res- 
esvoir rock and we ttssuIile that the sanze phe~~or-rzena will ha\-e acted tfiroughout the 
- producing unit. Fig. 1.2 coi-~lpares the usual sources of measurerlient of reservoir data 
in a dimensional scals, nonnalised to a reservoir ~ o l u m e imit. It can be appreciated as 
most of the data refer to what can be defined as macroscale, u-fiile typical modelling 
work xvill be performed at the megascale. The figure highlights the problem of scale 
ir~zplicit in rescix+.oir studie:; and tile consequent rclated upscaling issues. Ref. [J] pm- 
 ides a cornprehensit.e discussion about these points. 
I f is vnried. Info,~?ation is gathered in a number of different lxays, in the core, in the 
bclretlole or fro13 the surface. The arnountof mcthodoiogies used to infer reservoir 
propet-ties is surpr.jcin~ly hizh. from analog geological oritcrops studies to axial t-o~nog- 
rapizy on a smalI pIirg in the laboratoiy. To firrthsr connplicate the issue, thc sanz? res- 
enloir property can be conlputed by means of different methodologies. 11'hich pr2vide 
independent estinlatcs at different scale. 
From this point of view, it is dear that one of the most relevant problems of a reservoir 
study is to properly integrate all this information into a consisterlt model. The cllallenge is 
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Microscale I Macroscale I Megascale Gigascale I 
8 Note: the dimension scale has been normalized for a 600 metres spacing reservoir voiume. 
-- - 
Figure 1.2 Comparison of sampling teckrliques as a function of 
reservoir scale. 
noteworthy, since integrating the ir~fom~ation means to coordinate and combine different 
kinds of data, coming from different sources, acquired by means of tools which access dif- 
ferent portions of the reservoir and with a different resolution. 
A typical example is porosity. Normally, we can get infomation on reservoir porosity by 
means of 3 types of data: cores, logs and seismic. Porosity coming from cores is a direcf 
meastirements, which is computed on a very small portion of the reservoir, the plug, which 
represents normally a fraction of about 1W9 of the total reservoir volume (Fig. 1.2). 
On the other hand, porosity from logs is an indirect measurement. What we a c h ~ d y 
measure is attenuation of gamma rays, hydrogen content or formation travel times and then 
we convert these quantities into porosity using different eqtiations (transfer functions). In 
this case the vuIume of rock upon which the computation is made is approximately 3 QOO 
times greater with respect to the core plug, but still it is absolutely negligible wit11 respect to 
the reservoir volume. 
Porosity c m alsa be derived from seismic data, again as indirect measurealent. We meas- 
ure travel times, we process it to derive amplitude a-t#crr impedance and then we look for a 
correlation with porosity. In this case the whole volume of the reservoir is sampled, but the 
resolution is normally very poor. 
Eventually, when we need to compute porosity for the final reservoir model, we are faced 
with the problem ef integrating all this different data, which we relevant to the same prop 
erty but carry different pieces of information as fa as accuracy, resotutio~~ and areal distri- 
bution are concerned, 
Another typical md possibly more interesting example is permeability. Like porosity, 
pem~eability can be measured directly or indirectly, using in this latter case some type of 
transfer function. Additionally, its value depends on the satrimtion condiems of the ressr- 
voir rock, so that permeability values can be compared only when those snhration condi- 
tions arc: identical. 
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Frorn a general viewpoint, we can con~pute permcsbility on cores, in a borehole or by 
ineans of well testing, 
In the laboratory, we can measure gas absolute permeability in a core by rneans of a min- 
ipermeameter or flowing core plugs with gas. In this case, the transfer function is Darcy's 
law and the fi-action of reservoir that we sample, as in the case of porosity, is infini~esimal 
We can also colnpute absolute permeability in the borehole from NMR togs, sampling in 
this case a xnuch bigger volurne of rock, at resefvoir (pressure and temperature) conditions 
and using a different transfer function. Still in the borehole envirutlme~lt. through WFT 
~~~easurements, we can derive effective permeability to rtit at a scale possibly sitnilnr to that 
of core plugs. 
Finally, in a well test, by means of another different transfer hnctioll (the diffusivity 
equation), we can again derive effective peraneabilib to oi l at reservoir satur-ation, sam- 
pling a portion of reservoir which is normally millions tirnes bigger than the gas absolute 
pern~eabifity that we have measured on core plugs. 
This sl~ort digl-essio~i I~ighliglxts the co~nplexity of the problem. However, this is not lirn- 
ited to porosity and permeability. If we listed every variable of a reservoir study, we would 
realise that almost all of the111 can be san~pled at different scale levels and aften in an ilzdi- 
rect way. 
Sure enot~gl~, liiethodologies have been developed to deal with these kind of problems. 
Petrophysicists for example, know very well how to reconcile Density and Sonic porosity 
with core data. However, when the same propesty has been computed in differerrt profes- 
sional domains, then the cossect integration of data is no longer straightforward. 
In its broadest sense, integrating the infor~~lation means to highlight the differences, to 
tlnderstand the relative colxtribution of each piece of infosnlation and to ink-estigate all the 
possible ways of reconcili~lg the data. This will allow the choice of the best way of repre- 
senting the reservoir, in I-elation to the objectives of the study. 
Integrating the infomation is probably the most cl~allenging &k in an Integrated reser- 
voir study, because there is no one single way to tackle this problern. As we will see, it is the 
Project Manager's ~*esponsibility to guarantee its ii~~plement;ition, 
1.5 ACCUUCY VS. PRECISION 
The terms accuracy and precision, applied to reseivoir measurements, are often used inter- 
changeably in the geoscience practice. In fact, the two words are related to different concepts: 
accuracy refers to the exteat to which a measurement approaches the true vahe of the 
measured quantity, while precision refers to the degree of refinenlent of a rq.-asm. In other 
words, precision is not related to the true value of the quantity being measured, brtf rather to 
the repeata"ili9 of the measurement process. Fig. 1.3 grap'hically ill~stsate:~ the concept. 
1. It could be argued that, being based on the appIication of a transfer kncrion, even laborztoi-y meas- 
urement on core samples are indirect estimations. In this context however, they wi!! be ccmsidered as 
direct measurements. 
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Precise 
Accurate lnaccu rate 
Imprecise 
E 
Figure 1.3 Accuracy vs. precision. 
Typical measurements avaitable in a reservoir study span all possible conhinations of 
accuracy and precision and it is important to understand, case by case, what kinds of data are 
being handled and whicl~ nre t k i r characteristics. 
In many instances, reservoir parameters of interest can be estimated by mems of different 
techniques, which providc coniplen~entary infornlation about the parameten the~nselves. 
Saturation pressure, for example, can be measured with a good degree of precision in the 
laboratory on fluid samples, but these measurements are often inaccurate, because of the 
poor representativeness of the fluid sainples. By contrast, saturation pressure can in some 
cases be accurately (but x-iot precisely) estimated by means of field productioo data. 
Understanding the characteri~ics of the inforn~ation that is being handled is a critical fac- 
tor in any estimation problem. In general, the usd ofa single type of data will not provide the 
best resziits. On the other hand, rhe possibility of integrating different sources dindepedent 
data, each one related to a certain degree of accuracy and precision, may help In providing 
unbiased estimates of the reservoir parameters, 
1.6 COMPLEXITY V S ACCUMCY 
A model is by defii~ition a simplificatioi~. The degree of such a simplificatio~~, or conversely, 
the degree of cornplsxiv of a given study, depends on the information available and the 
h~unan and technologicaf resources allocated to the project. 
An important problem that makes integration diftlcult is the increasing e.cbrrlplex~$cafiorr 
which the individual EWP disciplines are cmrentfy undergoing.Complmification is the 
process of adding incremental levels of detail to a study to represent its complexity more 
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rigorously [4]. To a greater or lesser degree, co~nplexification is a process that affects every 
E&P disciplitte. A typical cycle of complexification is shown in Fig. 1.4. 
P technology 
More 
% 
More 
questions 1 complex models 
Figure 1.4 Cycle of complexification [4]. 
New tecl~~nologies offer today to the geoscientist powerful tools to investigate the details 
of a particular problem. However such detail can be nlore difficult (and sometimes less rele- 
vant) to integrate in the study work flow, In other words, new technatogies pose new prob- 
lems of integration, unknown until a few years ago. 
The analysis of a cycle of complexification raises 2 iniportant points related to the inte- 
gration process: 
Increasing the level of coinplexity of a particular work does not necessarily ensure 
in~pro.red accuracy in overall results. 
* Improved accuracy does not auton~atically guarantee compliance with the objectives 
of the study. 
When perfo~ming an integrated study, we need to make sure that we are not allocating 
l~urnan and technological resources in searching for some false detail or for an accuracy 
which does not add anything but complexity to the study itself, The degree of accuracy ]nust 
always be measui-ed against the overall objective of the study. This is why, where an inte- 
grated study is concerned, the concept of good or best work changes. 
An interesting example of complexification is presented in Fig. 1.5, which shows the 
interpreted fault pattern for a North African oil field. The original 3D seismic interpretation 
(left figure) has been revised recently through the analysis of seismic attributes, like dip azi- 
muth, dip magnitude, coherency cube and amplitude, providing a much more detailed iinage 
of the fault pattern (rigllt figure). The revised interpretation could delineate faults with 
t.hrows less then 25 ft or tight flexure zones likely to be heavily hctured. A total of more 
than 4 000 faults have been picked in this interpretation. 
The level of detail attained by this study is certainly relnarkable and the right image is 
possibly a bcrter rep~esentation of the actual fault pattern of the resen oir, conlpared with the 
original. However, this level of detail could hardly be maintained in a simulation study, xvith 
?:he tecllnology available today. It is impossible to reproduce all these sinall scale faults in a 
normal sinlulation model, where the practical cell size is bigger than most of such features. 
'The most likely reaction of the resenioir engineer would be a si~nplification of the proposed 
fault pattenl, ~vhereby only the rllost important faults vrould be retaiued. The effort of detail- 
ing a fault partern of the reser~oir virouId be followed by the effort of siinplifying the same 
pattern, the net result being probably a 10;s of time. 
Another obxious example of complexification is illustrated in Fig. 1.6, taken from a 
recently published paper [4]. The figure shows a set of 263 imbibition relative permeability 
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Figure 1.5 F a ~ ~ l t pattern for a North African field, original (left) and 
revised interpretation. 
curves used to describe a given reservoir. When considering the particular saturatiorl func- 
tions study &at led to the generation of those cu-ves, it could be said that the degree of detail 
that has been attained certainly captures the rock property variability within the reservoir 
and, from this viewpoint, the study is certainly accurate. However, from another point of 
view, some questions should be considered: what does that degree of accuracy add to the 
study? And what is the impact in the following phases of the project? If the results of such a 
study have to be used in a reservoir simulation study, this comptexity becomes ovenvhelm- 
ing for the engineer to manage. Trr addition to that, such complexity may also prave to be 
totally unpracticat (ironically perhaps) in exploring the impact of relative permeability vari- 
ations in xnodel results. Is looking for such detail really justified? 
The same paper also quotes that the simulation model results obtained with a single aver- 
age relative permeability curve were comparable with those obtained with the complete set 
of 263 curves. This raises another key point: what is the degree of co~nplexity that we can 
cope with in a reservoir study? The problem of course is stibtle, and while in tbis particular 
relative permeability example the answer is clear, in the majority of cases it wotdd not be so 
obvisus, 
Another aspect of the problem of comptexification is related to the misallocation of tech- 
nology between the vwious phases of the study. As a matter of fact, leriding edge technolo- 
gies are kequently applied in domains which have a second-order effect on reservoir 
performance, while most reicvant resewoir characteristics are given less or little attention. 
While digging into the existing reservoir studies and reports, it is not uncommon, for 
example, to find exhaustive sedinrentological studies and facies stochastic modelling 
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: Fignre 1.6 Relative permeability curves for resen-nir 
E characterization 1141. 
applied to fields where resenioir compartlllentalisation is the main driving parameter in res- 
ervoir perfor~l~ance, as could be the case for many pre-cretaceous reservoirs in tlle Nortli 
Sea. I11 such cases, when the objective of the Reservoir Study is to define the (re)develop- 
~nent plan of the field, it is obviorls that the structural model of the reservoir is far more 
important than the lithological mdfor petrophysical descriptions, since the number of wells 
to be drilled (and consequently3tte capital cost of the project) basically depends upon the 
connectivity of the reservoir. Sirnufation models x-un using different stochastic realisatio~is of 
the sedimentological model s i l t not capture the critical features linked to the structural 
model of the reservoir and will provide a biased image of the resmoir performance. This, in 
tunz, could spen the way for unwise mai-tage~nent decisions. 
This sin-tple example sho~vs that, when planning a Resen-oir Study, we must be able to 
identify the critical factors with respect to the general objective of the study, and concentrate 
our efforts accordingly. In the above examnple, it would have been much better to dedicate 
more time and more technology to the definition of the structural settii~g of the rsssnoir, while 
modelling lithology and petropl~ysics thro jgh fdster and more conventional metl~odotogies. 
These concepts can be generalised and the following points should be considered, when 
starting an integated study: 
* Identify the critical featares of the field, with respect to the overall objectives of the 
study. This in tuzn allows ibr the identification of the main requirements of the project, 
in terms of human and technical resources. 
Rank the critical paran-teter:;. This process aims at identi@ing those parts of the study 
that do not have a considerable impact in the final results, i.e. the parts that do not 
make a difference. 
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* Define the degree of coinplsxity of the critical phases of the study, compatibly tr-irh the 
project constraints lavai lability in terms of budget, time, professionals and techno!ogy). 
Managing thi: degree of complexity of an integrated study and its constitusilt paas is a 
kery important issue and should be faced early in the project life. It represents one of the 
main aspects of the planningof the study, since it defiilss how and where the bulk of the 
resources will be allocated. These issues wilt be discussed in more detiiil in Chapter 8. 
\ 
\ 
1.7 OTHER IEXTEGMTfON f SSUES 
Properly integ-rating the m~ailable infornlation is certainty the most difiicult stage of an inte- 
gated study. However a :lumber of other integration i ss~~es can frequently be faced in a res- 
ervoir study project. 
One aspect that is often encountered is the difficulty of integrating professionafs belong- 
ing to different disciplines. Different backgrounds, differsnt cultures and possibly different 
Imguages are the main factors that make comrntrnication difficult within a workteam. In 
most cases, the global objectives of the integrated shtdy are hardly understood by the variotls 
geoscientists, who tend to apply methodologies that proved to work in the past-and that are 
more familiar to them. Additionally, the lack of knowledge of other professionals' work is a 
factor that can induce diffidence in the relatior~ships anlong the team members. Behind this 
attitude, which unfortunately is very common among E&P professionals, there is probably 
the fact that working independently is somehow easier than working with other people, who 
often discuss our ~nethodology or question our conclusions. It is the Project Manager's task 
to smooth over any existing tensions anlong the team members and to find a compromise 
among different technical positions. The key factor in this case is always the comprehension 
of the workflow and the objectives of the project by thc various team n~embers. 
Another aspect which is frequently enco~tntered within oil or consulting conlpanies is the 
physical distance among the various constihtents of the team. This distance can be measured 
in meters or thousand of kilometres, but often the impact over the study is the same: lack OF 
commrtnication and hence of integration. There is no doubt that today's commu~~ication 
technologies are powerfill and allow the different team members to share inhrmation of any 
type in a fast and effective way. World wide csrnputer networks, video conferences and vir- 
tual teams W E only a few examples of the available means to minimise the effect of the 
physical distance, A recently published paper fS] illustmtes the example of a study success- 
fidly performed through the co-operation of State and Federal Agencies, an industry partner, 
a national laboratory, one university and 2 independent consulting companies. A virtual 
enterprise was established among these participants, where the professionals interacted in a 
common workspace everyday, using the worldwide web. 
However> despite e ~ ~ m p f e s of this type, the physical distance among team members does 
not facilitate in itself the integration, More often than not, this can be taken as a good justifi- 
cation for the most reticent professionals to perfom the work in an independent way. When 
no alternatives existfor the physical distance among the team members, the Project Manager 
has to watch over this problem, which always constitutes a danger to the successful develop- 
ment of the stttdy. 
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Chapter I , Integration Isnm 13 
Last but not least, problems of integration can derive also from the differences that exist 
in the databases rrsed by the karious groups of professionals and in the lirt~ited interoperabil- 
ity of d-re applications software. More often than not, Geophysicists, Geologists, Petrc'physi- 
cists and Resen-oir Engineers have their own jnte~yretation packages and do not share a 
common database. This issue will be the object of' Chapter 2. 
1.8 THE ROLE OF THE PROJECT RIANAGER 
rated reservoir study al~ust have a Project Manager. Simply stated, the respo~rsibility 
ect hlanager is to successfully achieve the objectives s f the study, within the alIo- 
cated budget and tirnefrasne. More specifically, a number of points can be identified, where 
the Project Manager is directly responsible: working environment, teain effectiveness, budg- 
eting and reporting, project commissioning and decommissioning, links with higher level 
management and so on. However, as far as integration is concerned, there are some specific 
points that can be identified, where the responsibility of the Project Manaser is direct and 
where the role proves to be very iniportant: 
Define the general objectives of the project, identifying those phases of the work that 
are criticai to the final results. 
Allocate the human and tecl~t~ological resources to the PI-oject, according to the priori- 
tisation of work phases mentioned above. 
Guarantee the cor-rect integration of the different sources of infon~lation. 
* Mzke swe that the correct level of tecllnology is applied xithin each discipline, avoid- 
ing the use of expensive and time-consuming nlettlodologies in non-criticaI tasks. 
Erlsure that factors like lack of cornrnunication among tear11 members, physical dis- 
tance and loti. interoperability of the different sofiivare application, have a minirnai 
i~npact eyer the development of the project. 
The importmce of the role of the Project Manager cannot be overstated. Projects often 
fail because of bad management, and in many cases the problem can be identified to be a 
lack of undersfanding of all the different aspects of an integrated study. Typical examples 
refer to studies ivhere the Project Leader has a strong background in one of the disciplines 
and knows little about the others. Sarnetinles for example, hen the project rnanager is a 
Reservoir Engineer, the integrity of a sound geclogical n-iu-rdel can be sacrificed to meet his- 
tory matching in the resen-oir simulation phas-.. This attitude not only can be dangerous 
from a technirai ~ i e \ \ ~ o i ~ l t , but can also generati? fmstratior~ in the geologists who worked 
hard to produce the best image of the subsurface. Conversely, when the Project Leader is a 
geoscientist, tlzs bulk of the work can be ~llocated to the sratic part of the study and very 
often too little artenti011 is paid to the production performance of the field and to the poten- 
tially huge amount of i~farmation that can be derived front dyaamc data. 
For these reasons, the Project Manager must have a good tmderstanding of all the phases 
of the project and must be able to balance the activities accordingly to the type o f study and 
its objectives. 
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11 Cl~apler I . Ir7rega tion Issues 
He has to distinguish between sound application OF high level technology and n~jsuse of 
technology in evely discipline of the project, even though he does not need to be (and indeed 
he cannot be) aware ofaii tbs details. 
Most importantly, he must look for integration within his team. He has to understand all 
the benefits that could bz gained when integration i:: achie\.ed, but also he has to be aware of 
all the problems that ma>- be encountered dtulng the study, trying to integrate people and 
tools. 
111 any case. if we accept that integration is ;he key to the realisatioxl of high q~lality reser- 
voir studies, then the Project ?vfanagsr is directly responsible for its application. 
There is no single solution to integration m;lnagement. However, talking Systems Think- 
ing, a holistic attitude by the Project Leader is the best guarantee for the integration to be a 
success. 
References 
i Webster's Third New Imtemtional Dictionary (1981). Merrim Websrer Inc. 
2 Senge PM (1990) The Fifrh Disciplinz. Doubleday. 
3 Haldorsen I I W ( 1986) Sirnulator parameter assignment and the problem of scale in reservoir engi- 
neering. In: Reservoir Cbmcterization. Academic Press. 
4 Saleri NG (1998) Re-Engineering Simulation: Managing complexity and