GEOL. CROAT. 49/2 289 - 293 3 Figs. ZAGREB 1996 

RevielV paper 

Economic Evaluation of Oil Exploration Projects 

Arso PUTNIKOVIC and Hrvoje LIPOV AC 

Key words: Economic evaluation, Reserve size, 
Monte-Carlo simulation. 

Abstract 
This paper has a goa l 10 describe, in brief, the mathematical cal­

culations in economic evaluation of oi l exploration projects. 
Described arc calculation of geological risks, reserve size, develop­
ment costs, production curves, production sharing and discoullting. 
Mathematical methods include cxpcc(Cd value theory, probability the­
ory and Montc Carlo simulation . All is put toget her in Ihe fonn of a 
compllicr data input sheet, as a single procedure with Ihe purpose of 
giving the ,1Ilswcr is expected profit from a potential pro spec I big 
enough to justify the risk and money invested ill exploration. 

I. INTRODUCTION 

The economics of oil exp loration is the topic of 
many articlcs and academic discussions. Therc are 
many computer programs available on the markct, 
somc of which are very complex and can compute prof­
itability in dctail. In exploration projects, which provc 
to be unprofitable to develop, the cause of failure is 
most frequcntly the incorrect evaluation of geological 
factors. However, it can be the ease that insuffie ienl 
and/or ovcrcomplex economic evaluation is the cause 
of such unproCitability, especially in marginal projects. 
Th e method described here is recommended for the 
eval uation of exploration projects because of its sim ­
plicity and versatility of use in diferent fiscal-tax 
regimes. 

2. DEFINITION OF PHASES 

The procedure of economic evaluation of oil explo­
ration projects can be divided into the following phases: 

Phase 1. Geological estimates of the probability of suc­
cess (factors for: the existence of a valid trap, reser­
voir quality, seal, source rock, favourable migration -
- coincidence of factors 10 produce a hydrocarbon 
accumulation); 

INA -Naftaplin, Seklor za iSlrazivanjc i razvoj, Subiceva 29, I-IR-
10000 Zagreb, Croatia. 

PROCEEDINGS 

Kljucnc rijcCi: ckonornska procjena, velicina rezervi, 
Monte-Carlo racunica. 

Sazetak 
Rad ima za eilj opisali matemalicki postupak u ekonomskoj 

ocjeni naftnih istrazllih projckala. Opisano jc racunanje s gcoloskim 
rizicima, racunanje kolicinc zaliha, razradnih lroskova, krivulja 
proi7.vodnje, raClll1anjc podjele proizvodnje i diskonliranjc. Ukljucene 
Sll postavke teorije ocek iv anc vrijednosti, teorijc vjcrojatnosti i 
Monle-Carlo sirnulacije. Svc jc objeclinjeno II jedinstveni poslupak, u 
obliku kompjulorskog programa, koji ima za cilj odgovoriti na pitanje 
jc Ii occkivana dobil projekt<l dovoljno velika cia opravda ulaganje 
kapilala II istra7.ivanja. 

Phase 2. Eva luation of exploration costs (bonuses, seis­
mic, drilling); 

Phase 3. Evaluation of reserve parametcrs (acreage, bed 
thickness, accumulations); 

Phase 4. Evaluation of field development parameters 
(price of oil, price of development well, required 
number of wells , cost of other dcvelopment, wcll li fc, 
perccntage of the oil sharing and the participation in 
the profit, taxes, choicc of the discount rate); 

Phase 5. Compilation and intcgration calculation 
(probability theory, expected values and Monte Carlo 
multiplication's) and conclusion . 

3. EXPLANATION OF THE CALCULATION 
PROCEDURE IN EACH PHASE 

Disregarding the problcm of [he evaluation of a 
parameters' sizes, the calculation procedure for each 
phase is as follows. 

PHASE I 

The probable factors are: 

• existence of a valid trap; 

reservoir quality; 

seal; 

• source rock; 

• favourable migration - co incidence or factor to pro­
ducc a hydrocarbon accuillulation. 



290 

Geological estimates of probabilities arc expressed 
as a percentage and, assuming that the evaluated geo­
logical probabilities arc independent, we can apply the 
multiplication theorem which will give the complex 
value - discovery probability. 

Each probability factor can be subdivided into morc 
components but in such a case the overall probability of 
success (discovery probability) will be Jower. 

As a rule each factor is expressed on a scale from 5-
90%. 80-90% probability indicates a good factor (vcry 
low risk), 60-80% is probable (low risk), 40-60% is 
possible (medium risk), 20-40% is likely (high risk) and 
5-20% represents the unlikely presence of a factor (very 
high risk). 

The comparison of diHercnt exploration projects has 
sense only if probabilities are expressed using the same 
number of factors. 

PHASE 2 

The explorat ion costs arc evaluated from experience 
and current market prices. The sum of these also reprc­
sent the risk that will have to be taken in case of failure 
as expressed financially. Many otherwise attractive pro­
jects with a high expected value can be dropped 
because of this parameter alone, as the investor simply 
feels the amount or risk moncy is to high for him to 
take. 

PHASE 3 

Profit eva lu ation starts with thc evaluation of the 
sizc of the reserves. Here it is possible to: 

a) eva luate the size or the reserves based on neigh­
bouring fields so in this case no further compl icated 
calculations are necessary. 

b) evaluate the parameters of reserve calculations, 
and by multiplication using the volumetric formula, 
reach the total size of the reserves. 

Reserves (m)) = acreage x bed thickness x porosity x 
oil saturation x perccntagc of recoverab ility of total 
reserve x volumetric factor. 

c) Many authors consider that the eva luation of 
paramcters such as a unique size does not represent the 
possible size of reserves, (as we can not be certain that 
the chosen size is realistic), but they recommend the 
parameters to be evaluated in the range from-to with or 
without stress on the most probable value, and that the 
usc or the special multipl ication procedure known as 
"Montc Carlo simu lation" lcads to the most probable 
reserves sIzes. 

The Monte Carlo cal cui at ion procedure can be 
explained in the following way: 

- imagine a graph with X and Y axes; 

- the first parameter is put on X axis and its smallest 
and biggest value should be marked; 

Y axis represents probability in the range from 0% to 
100%; 

Geologia Cronlica 49/2 

100 % r----,."":-r--~~--------c_---___, 

80% 

.~ 60% 

:g 
o 
o 
0: 40% 

20 % 

2% 
O%'--"_L 

~ 41 49 ~ U n ~ 87 M 102 
Recoverable reserves (MM nil) 

Fig. 1 Recoverable rese rves for Prospect "A", North Africa. 

- the cumulative probabil ity is set for all the parameter 

va lues on the X-axis slarting from the smallest one, 
by answering to the question "how sure are we lhat 

the parameter value is at least that grea t". 'vVe should 
get a declining line with thc 100% valucs on thc Y­

axis up to 0% for the largest value; 

- a random number in the rangc from 0 to 1 is chosen. 
II the chosen random number is i.e. 0.23, the value 

23% on the Y axis must be found; 

- from the drawn cumulative probability (declining 
lines) parameter values shou ld be read on the X-axis; 

- the procedure is repeated for all the parameters; 

- when the values are read off on the basis of the ran-

dom number for all the parameters enteri ng the for­
mula, the value is calculated and the result is written 

down. It is necessary to mention that it is not correct 
to multiply all the parameters with the same random 

number. When the same random number is used all 
the time, then the small value of one parameter would 
be multiplied with a small value of other parameters, 
and the aim that we want to accompl ish is the result 
of testing with all the possible combi nations of the in­

coming parameters (the biggest x the biggest, the 
biggest x the smallest, the smallest x the smallest); 

the procedure should be repeated until a satisfac tory 
number or written results is reached, usually 1000-
1500 times; 

- the written results are summed and the arithmetic 

average is calculated through the probability distribu­
tion in the following way: 

- the groups are establ ished (classes) in which the sin­

gle results are sorted and this provides us the infonna­
tion of how many single results each group contains. 
If the procedure is show n graphically throu gh the 
columns, the class with the highest column is so 
called modal class or the class where the most com ­

mon probability is found; 

- the class is calculated as a percentage of the total 
number of cases and this number is multiplied with 
the middle number (lower class boundary + (upper -
lower boundary) / 2) of each class; 



PUlllikovic & LiPOV:1C: Economic EV:llu:lIion of Oil EXl>lor~lion Projecl.~ 291 

"al;d "aD m;n max 
auaHIV ;1 90 120 

I , ; I 

" ; 

I " 1.3 
22 
50 

2:500 ~ 
3ToD ~Bt9 

; 'O~ 
; 

ooJ I ; 

I.", 

~ In" aav ; I 
~ [(j]jQjjjjfI lO027' .006577 m;n moan max = -, 1 ",,"'., fMMm' l 1.B B.7 1B. 
~ 3:5i< .. 6% numb" 01 w,II, 9 29 75 

= ....... 70ii 75% om" fMM$1 -. 52 26' 
i% 9% ~ 19% f'iA[UE OF LOSS(MM$f 3 

jQ 1.2. 1.3 , OF PROFIT IMM$1 9 
) VALUE OF 6 

~ 
1 vad,bla 

r;;;;t;;;W =fm'/d,vl 
m. ..- --;;;:n max Prospect: Albania onshore 

liS 120 
i% 110 
i% 95 
i% 90 

1i ii% Be • • 
, m"", i 

I of 36 dg,API oil 

Ig'~~:'~ feet*O.02832=Cubic metre 
metre*35.31 =Cubic Feet 

I IU ~Eati I" 

" roig.2 Economic cvaluat ion by 
Montc Carlo simu lati on; 
Prospect: A lbania onshore. IBTU ~ ;:~:; ;;;:;,:;;;'I;;';~':o~;~p:~,c;~ IKo"' ~, , i 0., c .. 

- the sum o f the results gives the expected value or 
arithmetic average of a ll the results. 

The advantage of the MonIC Carlo method versus 
the eval uation of reserve size based on the most proba­
blc parameter va luc (dcscri bed in b) is tlwt it also takes 
into considcration all minimal and maximal parameter 
valucs. In thc dcsc ri bed cxample (rig. I) instead or a 
value of 5] x I 0" m ~ ror the most probable reserve size 
wc rcached the val ue of 64 x j ali m~ . Us ing the Montc­
Ca rlo s imu lation we proved that a highe r val ue o f 
reserves was probable. 

PI-lASE4 

Th e required number of well s and th e ex pecled 
profit is calculated simultaneou sly to the quantification 
of expected reserves. The numbe r of well s is dete r­
mined from the data o r initi a l and minimal da il y pro­
duc tion or cach well (N EWENDORP, 1975) . Using 
" Darcy 's Law " a co rrelati on is made between the 
ex pected value or the bed thickness and the initial daily 
product ion. The rormula for the exponent ial production 
curve is being used and it is corrected for the number of 
ex pected negative well s (N EWEN DORP, 1975). The 
result of the poss ibl e well number can be checked on 

ReMrv .. (NMm', 

th e basis of data on the usual density of the develop­
meIll well s grid applied to study area. Profit is calculat­
ed on the basis o f the contractual terms deta iling the 
product ion percentages of profit share and is d iscounted 
at the chosen rate. 

4. CONCLUSION (PHASE 5) 

The ultimate aim is a comparison of expected ga in 
versus risk. In order to do thi s the expected va lue o f 
prorit (ar ithmetic average of profit multiplied by the 
probabil ity of di scovery) was compared with the 
excepted value of loss (m ultiplication of the exploration 
cost w ith the probabilit y of a ncgative we ll ) . .If the 
ex pected profitability valuc is higher than the ex pected 
loss va lue a decision can be undertaken as 10 either 
undertaki ng the ri sk or funile r evaluation. 

T wo cases are described as ill ust rations: the Albania 
onshore (Fig. 2) and o rrshore Arrica (Fi g . 3). The rig­
ures al so represent computer data input sheets. Results 
arc gi ven in graphical and numcrical fo rm (minimum, 
maximum and most li kely size o f reserves). Expected 
val ue of loss and profi t are expressed in monetary unit s 
or uss. 



292 Geologia Croatica 49/2 

I I 
valid , 

I i i II 
sou,"a m,k , 

~ I , , II 

~ 
,oou, 

well ~ Ii 

olal '0"" 

mio 
~ 

, 
area ('q.km) 3' 

~ oel pay I 
oil ,.,ove>y 0.012802 0.02 
- da",'ty 1 (ra,a>ve, (MMm') 29.4 69.5 110.3 
- po>o,11y % lB.Oo/< 20% 22% loumbar otwall' 36 B2 287 
- (I·Sw) 55% 60% 65% Ipro," (MM$) 529 1151 
-Rt% 15% 20% 30% , VALUE OF LOSS (MMS) 9 

- Bo .16 1.2 1.25 , VALUE OF PROFIT (MMS) 76 
- 9a, ta,q,) , VALUE OF PROJEC (MM$) 67 

I IGraph, , I var'able, ,I vor'able 
oel pay I'oil.prod, "/dey) 

metre, m'n max Prospect: Africa offshore 
0% 100 500 600 , 
10% 95 450 490 
50% BO 35 450 
90% 65 200 300 

60 100 150 

'"~ 
,melre, 

Metr." 0'7., 
, 

Cub', , "'''. 
",'n 

BTU'; 'u . I 

of 36 dg.API oil , mei re 
i Feet "Eilt OO,'~ ", fig. 3 Economic evaluation by 

Montc Carlo simulation: BTU. per cu~!c foot ft 8.90196:=Kilocel per c.m. 
IK,al per cub', metre'O.I12335=BTU per of. 

One inadequacy of the mcthod is that the political 
and financial risks are not being evaluated. Also only 
the expected value of a single prospect is considercd so 
it docs not includc the total geological potential of the 
area. This can be ovcrcomc by applying separate evalu­
ations of political and financial risk (choosing an higher 
discount rate for example for those prospects which 
have such higher risks). For total evaluat ion of the geo­
logical potential of the area a series of calcu lations as 
described herein have to be taken for each potential 
prospcct and, by summing the expected values, a mea­
sure of the total geological potcntial of a certa in area 
can be achicvcd. 

5, FORMULAS AND DEFINITIONS 

I) Definition of probability: 

P(D):= min 

The probability of the event 0 is the ratio of all the 
favorable cases (m) to the number of all possible cas­
es (n), where P = probability. 

Prospect: Arrica orrshore. 

2) Complex probability equals the probability product 
or each event 

P(D I &D2& ... &Dn) = P(D I )xP(D2)x ... xP(DIl) 

Pro bability "j~i", multiplication theorem. If the 
events D I, D2, ... Dn are independent, but they arc 
110t mutually exclusive, in other words all of these or 
several of these, can appear sequentially, then this 
probability is called complex probability. 

3) Formulas ror estab li shing the parameter values 
(NEWENDORP, 1975) based on the random nllmber 
(cf) if the known is: 
a) minimal and maximal value of parameter x: 

x = xmin+cf*(xmax-xmin); 

b) minimal, maximal andlhe most probable value of 
parameter x: 

- for the x values s:; xmode 

x=xm in+(xmaX-X111in)*sqrt(cf*(xlllodc-xrnin)/(xmax-xmin» 

- for the x values :?: xmode 

x=xmin+(xmax-xmin)*( I-sqrt« I-ct)*( 1-(xmodc-m in)1 

(xmax -xmin»)) 



Putnikovic & LipoV;lC: Economic Evaluat ion or Oil Exploration Projects 

considering that if c1' .:s; (xmode-xmi n)/(x max ­
xmin) the first equat ion is used, a nd if e1' 2: 
(xmode-xmin)/(xmax -xmi n) the second one is 
app lied. 

e) if the known cu mul ative parameter frequ e ncy is 
wri!len in the shape of: 
value 
of variable: 146 150 154 163 174 

cUl1\u lative 
frequency : 0 0.4002 0.7959 0.9006 0.9674 

the formula is: 

x=x(n)+( cf-cf(n» *(x(n+ I )-x(n»/( ef(n+ I )-ef( n» 

the calculation procedure is as fo llows: 

the random number is chosen cf (from 0 to I) 

the test is made if cf.:s; cf (2) 

ifso than n:::ol and solve the equat ion for x 
if not question whether cr.s; cr (3) 

if so than n:::o2 and solve the equation ror x 
if not question whether cr.s; c f (4) 

if so than n:::o3 and solve the equation for x 
if not quest ion if of <; cf (5) 

if so than n:::o4 and solve the equation for x 
if not than n:::o5 and solve the equation for x 

lS I 

4) Formula fo r the exponential declining production 
curve (used wh ile calculating the reserves per we ll ): 

a= ln(q/q :,)/t 

where a:::o dec linin g coeffi c ient; In :::0 natural loga ­
rithm ; qt :::0 in it ial well production (tons/year) ; (h :::0 

fina l wcl! production (tons/year); t :::o production time 
(year). 

5) Rccovcrable reserves per well 

dcln = ( l/a)*(ql -q2) 

6) Number of wells per fie ld: 

number of wells == total reserves / deln 

7) Number of negative developmen t wells 
if the number of wells> 20 

!leg = (2/15)*number of wells+ 3.33 

if the number of wells .:s; 20 

neg == (4/ 15)*nulllber of wells +0.67 

(the numbers arc empirical) 

293 

8) time needed ro r the compl etion of the deve lopm ent 
wells 

ti me = number of wel ls (posi ti ve and negative)/16 

(time in years) assuming that 16 we ll s per year can 
be completed 

9) d iscounted value of the development costs: 

npvdeosl = development cost*e·O.I(,.,ime/2 

10) discounted production value 

income = reserves*priee (a/(a+.DV« l _e-t .("'j))!( I-c""') 

a :::0 exponentia l c urve coeffi cien t; j :::0 discount rate; t 
:::0 time in years (field duration) 

II ) discounted income 

npvincome = ineome*e·O.]( .. ti m r/2 

12) Defin it ion or the expected value 4) : 

The expected value of some evcnt is the multiplica­
tion or the events probability and the profit value of 
the same event. 

e.g. emv = 20%*3 MM$ = 0.6 MM$ 

The expected value of the decision whether or not to 
accept the ri sk (expected value of a decis ion altcrna­
tive) is the sum of the expected values o f all the pos­
s ible events that are the subject of the decision. 

e.g . SQOlo*$2MM = $1 MM Expected profit value 

50%*-$1 MM = -$ O.SMM Expected value 01" loss 

$1 MM~$O .5MM = $O.5MM 

As the e xpected profit value is h ighcr than the 

expected loss value the decision oj" accepting the risk 
should be made. 

6. REFERENCE 

NEWENDROP. P.D. (1975): D ecis ion analysis fo r 
pe tro leum exploration.- Penwell Boo ks, Tul sa, 
5611'. 



294 Geologia Croatiea 49(2