Percolation theory in research of oil-reservoir rocks ppt

Phan Ngoc Trung 41 Percolation theory in research of oil-reservoir rocks Distribution rule of lower Miocene sandstone in Cuu Long basin Determination of fractured basement permeability i

An Official Publication of The Vietnam National Oil and Gas Group Vol 10 - 2009

Eng Vu Thi Chon

Dr Hoang Ngoc Dang

Dr Nguyen Anh Duc

BSc Vu Xuan Lung

Dr Hoang Quy

Eng Hoang Van Thach

Dr Phan Ngoc Trung

41

Percolation theory in research of oil-reservoir rocks

Distribution rule of lower Miocene sandstone in Cuu Long basin

Determination of fractured basement permeability in White Tiger oil field from well log data by artificial neural network system using zone permeability as desired output

Prediction of aquatic organism impact on rig submerged structures of oil and gas field

At Cuu Long basin

66

In reality, the reservoir rock space is a very

complex metamerism; however when caculating

according to the common way, in many cases, we

consider the void structure in the rocks as similar

fractal, and use suitable statistical approximate

for-mula to demonstrate the space in form of effective

homogene When researching the layers, we take

the rock samples from one layer with different

col-lector parameter To get a parameter value (grain

density, porosity, permeability, saturation etc.) of a

researched object, we calculate the average value

of parameters measured from samples of the same

object Therefore, the real space is inhomogeneous

(metamerism) when we consider it in a small scale

(core sample), however we consider it

homogene-nous in large scale (formation, layer) with an

aver-age value according to a way of calculation

For example: In a volume V, with distribution of

parameter values Xi we can get the average value

−

Xiof the effective space according to: , or

if the values Xiare distributed standard

That way of calculation will not be suitable

when there is a strong inhomogene in the

research-ing space as in the case of fractured basement of

Cuu Long basin

Percolation Theory will help much in tion of permeability characteristic as an accidentalprocess in complex structures This theory wasintroduced more than half of centuries ago, and hasbeen applied widely and developed strongly sincemiddle 1970s in many fields: Matter formation,material technology, transport and forest-fire protec-tion, etc In this series of articles, the author onlywould like to introduce the application of percolationtheory in reasearching the percolation process (per-meation, disffusion) of fluid in void space with com-plex structure

calcula-Introduction to Percolation Theory

In this writing, the meaning of the term lation” is only limited within the permeation or thepenertration of the fluid into the solid matters withvoids When percolating into solid objects, the fluidpenetrates into sites which has capability of contain-ing fluid or it flows in bonds, capillary segments con-necting the sites in the space

“perco-Sites, bonds and types of percolation

Starting from simple cells, for example net ofsquares (Figures 2a) Cells with black round spotare called reservoir sites, white cells are calledempty sites (no reservoir) If we call p the probabili-

Ass Prof Dr Nguyen Van Phon

Hanoi University of Mining and Geology

Percolation theory in research

of oil-reservoir rocks

Abstract

Following the articles about fractal geometry in the research of oil-reservoir rocks [1, 2], in this article, the author will introduce the application of percolation theory in researching the permeability process of fluid

in void space in general, and fractured rock in particular.

Percolation theory is a mathematical method which has been introduced since the early 1950s, and it has been applied widely in social and human sciences, and technological sciences since 1970s Through this work, the author would like to suggest applying the percolation theory in researching the layers of oil- reservoir rock, based on the similarity between geometrical forms of percolation process and physical nature of permeability process of fluid in void space In the final part of this work, the author proposes the procedure of calculating the permeability in fractured rocks according to well-log datas, based on applica- tion of percolation theory.

ty of a reservoir site in the net, the probability of an

empty site will be (1 - p) Squares with shared

bor-der are called contiguous sites, squares with shared

angular vertex are called adjacent sites The fluid

can only penertrate from one cell in the net to the

contiguous cell (if that is a reservoir site), it can not

penertrate to a adjacent cell In a net with 2 or more

contiguous reservoir cells, these cells form a

clus-ter That way of fluid penertration is called site

per-colation (Figure 2a)

If all the squares cells are reservoir, a channel

allowing a connection between two contigous cells

is called a bond Bond is a conduit allowing the fluid

to penertrate into the space, it is also a conduit

between 2 contiguous reservoir sites If we call p the

probability allowing 2 contiguous sites to connect

with each other through a bond, (1 - p) is the

prob-ability ensuring no connection between them

(clogged, disconnected bond) When there are 2 or

more bonds connect contiguous sites continuously,

they form bond group (Figure 2b) That way of fluid

penertration is called bond percolation In space

with voids such as oil-reservoir rock, these groups

are sites connected with each other through a bond

Therefore the percolation in oil-reservoir has the

characteristisc of both site percolation and bond

percolation

Percolation threshold and unlimited group

For low value p, there are only groups with

dif-ferent sizes When p increases the number of

reservoir sites or the number of bonds also

increases, creating a chance for groups in the net

to increase their size If p continues to increase,

the groups also grow gradually, and they can

inte-grate to each other through a common bond to

form a bigger group Until reaching an ultimate

value p = pc, the big groups will become

unlimited-size group and the ultimate probability pcis called

percolation threshold The percolation threshold pc

is an ultimate probability enabling an unlimited

group to form in a large net With p > pcunlimited

group are enlarged more and more, extend form

margin to margin (in 2D) or from face to face (in

3D) of a large net With p < pcthere is no

unlimit-ed group in the net

Percolation threshold pc depend on type of

cell (square, triangle, hexagonal, etc.), number of

dimension and type of percolation Value of

perco-lation threshold pcstated in Table 1 is the

calcula-tion result of (2003) with different types of cell net

Table 1

The Figure 2a shows the layers of reservoir,the white cells are solid rocks, with no capability offluid containing, cells with round black spot are voidspace that can contain fluid, then the probability p isconsidered the common void ratio of the rock Ifcells (sites) are connected with each other through

a bond, the propotion of void connected are calledopen void ratio or connection void ratio P Then P isthe probability ensuring that any site or bond

belonging to a largest group, P ≤ p In layers of

reservoir, the value P determines the permeability ofthe space

From above: When p < pc, there is only fluid inconnection group with small size In that situation, if

a well is designed to put at any site, it can easilypenertrate into a small group, the exploiting capaci-

ty of this well will decrease rapidly To get muchproduct, and long-term stable exploiting capacity,

the reservoir layer with p > pcshould be chosen toput the exploiting well, and the well has to pener-trate an unlimited group

A new problem is raised here: With the bility p in the square net, how we calculate the aver-age size (average number of sites and bonds) of thegroup and the proportion of sites belong to unlimit-

proba-ed group P?

The quantity of groups, average size and space

of group correlation

In net of squares, identify the probability so that

a random cell (site) is a group which has the mum size s = 1, which means that it is a reservoirsite and independently standing among nonreser-voir sites The reservoir site has its own probability,and around it is 4 adjacent nonreservoir sites with a

mini-probability of (1-p) for each site These five sites

cells (sites) are independent so they are

cooperat-ed in terms of probability by the product of ity: n1= p(1 - p)4

probabil-For the case of 2 reservoir sites standing

among 6 adjacent nonreservoir sites, these two

sites can be arranged in vertical or horizontal

direc-tion; therefore, n2= 2p2(1 - p)6 It is easy to conclude

that in a net of squares includes 3 aligned sites,

there will be n3 = 2p3(1 - p)8, for around 3 aligned

sites are 8 nonreservoir sites with the probability of

(1 - p) for each site.

We call n1, n2, n3,… the number of groups

which have 1, 2, 3,… aligned sites on the net of

squares More generally, the number of site

includ-ing S aligned sites, n is the probability so that these

groups can be formed in the net of squares We

write:

nS= 2pS(1 - p)2S+2 (1)

For p < 1, if S  ∞, nS  0 is the probability

for a group which has sites S  ∞ aligning in net of

works is very low, nearly reaching 0

In 3D, on a simple net of squares, each aligned

group including S will have (4S + 2) adjacent

non-reservoir blocks and sites which can be aligned in 3

perpendicular directions , the number of average of

groups (for a net of sites) is calculated as follows:

nS= 3pS(1 - p)2S+2 (2)For the case of hypercubic d-dimensions, each

site has 2d adjacent boxes; for internal sites of a S

group, sites creating lines will have (2d - 2)

non-reservoir sites If two ends are considered, Group of

S-sites in this case will have (2d - 2)S + 2 adjacent

nonreservoir sites In this case, the number of

groups are calculated as follows:

nS= dpS(1 - p)(2s-2)S+2 (3)The expression (3) is true for d = 1, 2, 3

The expression above is only accurate for

sim-ple case; however, natural world is so complicated!

They will not be true for cases in unaliged groups in

the net, for cases of 3 unaligned sites, the

alterna-tives of arrangement is abundant The Figure below

(Figure 3) shows that group S = 4 sites has 19

dif-ferent arrangements

If number of sites S of one group increases, the

number of arrangement (configuration) is increasing

rapidly For instance, if S = 5, there will be 63

alter-natives of arrangement; if S = 24, there will be 1023

different alternatives

Back to 2D case, for the probability p < pcon

the net of squares, there will be only groups of

aver-age size S The size S of the group is nearly equal

to the correlation length ξ, average distance

between two sites under a correlation group If p

pc, nearly equal to percolation, the scale (ratio level)

for typical average computation (volume in 3D, area

in 2D) is getting bigger to the scale “mini” around pc

Then, the ratios are equivalent to one another This

means that adjacent to level pcis a fractal which hasthe similar structure with scale D~2.5 in 3D [9] Thisexplains why at this level, the description of activespace becomes unsuitable for space which hasstrong homogene

Around percolation pc, correlation length ξ iscalculated as follows

ξ ~ |p - pc|-x, (4)

In which ultimate exponent does not depend onthe arrangement of net In 3D, x ≈ 0.88, 2D, x ≈ 1.33,[7, 11]

At the level pc small groups can connect toeach other, widen the size, increase correlation dis-tance In the net, there are sites under different cor-relation groups and formed unlimited groups

Point (crack) density in network of limitless group

Assume P (L) is billion parts of point in a work belonging to limitless group, and also averagedensity of points in limitless group In square nethaving area L2, this density is identified:

net-In which M (L) is number of center points in thesame group in area L2(L is positive integral odds 3,

5, 7, 9,…, because it is necessary to have odd ber in length of square to have a square in the mid-dle of net from which the others is symmantric)

num-It is clear that M (L) increase gradually inaccordance with area L2, P does not depend on L

but only depends to p; p is propositional to P Therefor M (L) is L’s function, at ~pc, it is proportion-

al to L2 P is the probability for any point (crack)

belonging to limitless group, when p is probability for any point (crack) to contain (connect) If p is con-

sidered as common porosity inaccordance with veying terms, P is connecting porosity or opening

sur-porosity (P ≤ p).

When logM(L) and logL are represented in logacouple chart for net having large number of points,Staufer (2003) found that chart was a line havingangle factor D = 1.9 (Fingure 4) D ≈ 1.9 is fractalintegral number of limitless group in 2D presenta-tion space Fractal dimensional numbers of limitlessgroup do not depend on arranging form of network(triangle, square…) and only denpend on Euclidposition dimension In 3D scale D ≈ 2.5

In Figure 4, line chart shows that:

M (L) ~ L1.9, (6) Meams that M(L) grows with L1.9, average den-sity (5) is not a constant number but decrease L-0.1

times in rate grade The larger scope is, the larger

the difference is For example, average density P

(amount of workable oil) counted on an area of

reservoir with porosity of approximate pcand edge L

= 100km shall be smaller than area counted in

sam-ple with edge L = 10cm, in accordance with space

coefficient (106)-0.1~ 0,25 75% of remained amount

of oil in reservoir is not connected directly with

exploiting wells located in central point In 3D,

corre-sponding coefficient is much smaller: (106)-0.5~ 10-3

In fact, the counting result shall not always bad

because density P that is gradually constant to L

and p is larger than p c, at that time there is a

corre-lated length ξ(p), a limitation so that: M(L) L1.9to

L < ξ and M(L) L2to L > ξ

Limitation ξ is the limitation of the farest well in

the packing, it shall decrease similar to increasing p

than pc Therefore, explorer shall use a sample with

L that is larger than ξ to calculate amount of oil

which may be exploited more exactly

Of course, the amount of oil take from reservoir

layer depends on many other factors relating to fluid

flow in pore space and dynamtics characteristics in

osmotic packages such as diffission of fluid in mixed

space and force osmosis which shall be discussed

in another works

Bethe net

In order to have exact solution for complex

structures, problem above is studied in form of

branch separated tree – Bethe net Bethe net (or

Cayley tree) is tree shaped net with unlimited

dimensionals Approximated calculation Bethe is

used to give anwser for tree problems Therefore

complex structures with unlimited dimensional d are

Bethe net

In order to understand structures with unlimited

dimensional d, we will start with d = 2: Area of circle

with radius r is πr2, its circle is equal to 2πr Area S

of sphere (3D) radius r is 4πr2, and volume V is

propotional with r3 In d- volume dimensional of ball

shall be propotional r d , and surface area S is

propo-tional with r d-1 General calculation:

(7)(Symbol is used to count rate between val-

ues In several cases, this rate means approximate

limitation; d ∞)

Expression (7) shows that when dimension up

to unlimited point (d ∞), then area of the ball outer

will approach to the cubic content This remark is

true even with grid, cube, multi-cube etc.,

Construction of Bethe network

To construct a simple Bethe network (Figure 5)

to conduct as follow: To point of O origin site, ing four origin points (Z = 4) adjacent to A From Asite, four bonds are generated, one connecting to O,the other three ones connect to B site From B site,

pass-it connects to (Z - 1) = 3 of new spass-ite C, and morelengthened by this way Bethe Net is an unclosednet, which has no branch connecting to O originesite by any form Continuation with this process ofbranching, we will have an unlimited network, ofwhich the sites will increase in the distance from the

outside site to the O origin site with a structure

d-its dimension will be incresed: (distance)d

In example in Figure 5: Z = 4, original site iscovered by 4 sites A (the first system), second sys-tem (or layer) will have 12 B site, the third will be 36

C sites therefore, the point network consisting ofthe first system to the last system of 4 x 3r-1site is

the outer site Then, the network expand to r, the

last system consist of 4 x 3r-1/2 x 3r-1 = 2/3 of thetotal sites on Bethe net This is equal to and correct

to (Z-2)/(Z-1)any Bethe net with Z at random.

From this point of view, we can expand to the3D case, ratio of area of internal side and volume ofthe ball whose radius is of r will reach the approxi-mation ~1 when Z  ∞ This is fitted with the

expression (7) when 1/d 0 Now we can see thatBethe net is an abnormal model; thus, when men-tioning to the percolation of the Bethe net, one veryimportant thing is to imagine that it only occursinside the net but not effect the outerest surface The evaluations above imply that probability bywhich an infinite unit spreads all over the net is zero,

and the percolation threshold pc is given by:

(8)This calculus is suitable with the site percola-tion case and bond percolation case For further

understanding about the percolation threshold pc,take a look back Figure 5 There are four rays ateach site A (Z = 4) among which one connects with

O, (Z - 1), the three remained rays reach the site B,and the same for the case of site C etc Hence, (Z

- 1)-1will be ultimate probability for the creation ofinfinte unit, and called percolation threshold of theBethe net

Average size (S) of the unit when probability approximately reach the level p c

In order to calculate S, we assume T is theaverage size of each unit at four branches T is the

average number of sites connected with original site

to form each branch A Each of these separate

branch is continuously divided into three smaller

infinite branches, T will still be average size of the

unit in each branch

Next to site A, site B may be the containing with

probability p or non-containing with the probability

(1 - p) The non-containing sites will not be very

meaningful while the containing sites contribute (1 +

3T) point for this branch in which one is point B and

3T is three branches extended from this site

Therefore:

(9)

The size of group originating from the site O is

0 if this site is non-reservoir site or (1+4T) if it is

reservoir site Therefore:

(10)Referring to (8), so S can be adjusted

according to (pc - p)-1 for p < pc For p > pc, S will

branch off If p  pcwith the ultimate exponent x ≈ 1

S = (pc- p)-1 (11)

Relating to the expression (4) we find that if p

 pc, average size S of group and the correlation

length ξ are equal and reach infinity

By similar inference, it is possible to identify

ratio P of sites under unlimited group in the space

which has p greater than permeability pc P, as

ana-lyzed above, is the probability for the original site O

under unlimited group It can arrange different

abili-ties so that original site O is connected to 4

neigh-boring sites (Figure 6) On the drawing, each arrow

is an unlimited daisy chain connected from original

site O Figure 6

We consider Q as the probability connecting

from O to adjacent site A which is discontinuous

(congestion) According to Figure 6, we find that

probability P is identified referring to the

probabili-ties of three final alternatives c, d or e If each arrow

is an unlimited daisy chain, O must be of two

unlim-ited chains which consider them as the connection

part of a permeability group

The probability to exist the arrow between O

and site A is (1 - Q) For case (c), we have

probabil-ity 6Q2(1 - Q)2(including 6 probabilities of

arrange-ment so that from O there are two arrows and 2

nonarrows which rotate indifferent directions) For

case (d) it will be 4Q(1 - Q)3; and for the case (e), it

will be (1 - Q)4 Total probability will be:

(12)

In fact, probability has function relation with

probability p In fact, a chain line connecting from O

to site A is discontinuous if O and A are not

connect-ed (probability1 - p), or if O and A are connectconnect-ed but fragmented on connecting to A (probability pQ3), itcan be computed as follows:

Q = (1-p) + pQ 3 (13)Equation (12) has the simplest result Q = 1, P

= 0, which means that at that time the system isunder the percolation threshold Furthermore, thisequation also has two different results, but theseresult mentioned below have physical meaning:

(14)

Q reduces from 1 to 0 if p increases from to 1.

In the range p < pc, Q = 1, P ≡ O From two

depend-ent relations between P(Q) and Q(p) it is possible to find out P(p) Around the threshold pcwe find that P

change referring to the form (p - pc)2:

=0 This means the reservoir rock space has thecritical void ratio (pc), the space will have the perme-ation or the permeability will occurs at that time.The finner the grain of the clastic rocks is, thehigher the critical void ratio value (pc) is; the voidrate of the fractured rock with the kinetic penetration

is usually lower than that of the crumb rock Thisrelates to the specific surfaces and the channelbend of the two mentioned above rocks

In the basement of Bach Ho oil field and otherfields in Cuu Long basin, the hydrodynamic penetra-tion occurs at fractures spaces (Ff) and macrofrac-tures while the capillary penetration occurs inmicrofractures The result of 270 granitoit fracturedsamples analysis (2001) in basement of Bach Ho oilfield by Mr P A Tuan showed that their average gen-eral void ratio is 3.1% while the average open voidratio is only 1.88%, it means that the close non-con-nected void ratio makes up nearly 40% of the gener-

al void ratio The equivalent numbers in the analysis

of the well-logs in a well of Rang Dong field are5.455%, 0.617% and 89%

Percolation through fractured rock space

Percolation theory gives us a decriptionmethod of strongly heterogeneous space in reser-voir rocks Here meaning of threshold is often

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research carefully Threshold effect can be seen

clearly in many phenomena occuring in the nature

and human society The permeability of fractured

rocks space are emphasized here

Fractured rock space

The process of development and mature of the

rocks result in the appearance of interrupted

frac-tures within rocks due to various reasons: volume

shrinkage (the freezeing process of igneous rock ),

load reduction (weathering and erosion),

mechani-cal stress (tectonic activities), corrosion and

elutria-tion (thermalizaelutria-tion activities), etc Fractures are the

results of the disruption of the initial uninterrupted

structure To simulate the fractured rock space, we

will look through dished fractures and evaluate them

in terms of dip anglar, strike, aperture, radius, filling

– up level of secondary minerals and density in

rocks

We consider space as rocks with different

frac-tures of random distribution If there are not many

fractures in the space, it is unlikely that such

frac-tures cut each others, low connectivity, zero

perme-ability.The higher the density of fractures is, the

greater the probability for such fractures cut each

others If the critical density is to be outnumbered,

there will be a ratio f representing intersecting

frac-tures in the space, which forms the “unlimited

group” (Figure 8) and enables the fractured space

to let the fluid through – that means the non-zero

permeability

Using the model Bethe net (Figure 5) with Z =

4 to demonstrate the fracture net , we have: f is

den-sity P, and pc= 1/3, and p is the probability so that

two random intersecting fractures cut At different

value of p, which is greater than the critical

proba-bility pc, then p would be directly proportional to P,

which is the ratio of intersecting fractures and

belong to unlimited group within the scope of

stud-ied volume As P increases, the probability of leting

fluid through the space also increases This

princi-ple is also applicable for the conductance of fracture

net if the carrying fluid follows the saturated fluid

(water) in the empty space of fractures In this case

the Ohm Law and Darcy Law is compatible

For the purpose of calculation, we demonstrate

the fractured rock space as in Figure 9 and assume

that all fractures have the dished form, radius c,

aperture 2w and density N ≈ 1/ℓ3, in which ℓ is the

average distance between fractures (Figure 9)

Estimating pressure p according to c, w and

density N

Assume that p is the pressure to have two dom intersecting fractures As the density N andradius c increase, p also increases The number offractures over a partial volume, the greater the aver-age dimension c of fractures is, the higher the prob-ability that these fractures cut each others Theproduct Nc3is the non-dimensional quantity, so p=0

ran-as one of the two parameters (N or c) is equal to 0,

so it can be assumed that p changes accordingly

with Nc3:

(16)

To calculate the pressure p and determine the

percolation zone and permeability, we introduce a

new concept: Peripheral volume V exis the maximalvolume containing a random fracture with center O

to have a second fracture O’ ( with the same radiusc), which is arranged randomly in the volume Theresult is that the two fractures will cut each others

In the fluid crystal physics (De Gennes, 1976), thisvolume is defined as:

Vex= π2c3 (17)

At a density of fracture N, the average number

of intersecting instances of each fracture is v = NVex.The Bethe net in Figure 5 shows that the probabilityfor an isolated fracture (does not have any fracture)

is po = (1 - p)4 This probability can be presentedaccording to v as follows Assume Vois wide volume,

in which there are disorder distribution of centre O offractures with the density N Probability for a randompoint in Vofalls into a volume V which is smaller (V⊂

Vo) will be V/V o Assume that n is a random in Vothe

probability pmfor m points (m<n) falls into V will becalculated as follows:

(18)

In which:

If we calculate the limit of the expression (18)

as n and Vowill reach the unlimited pole n/V o =N we

will have:

(19)

If there is no point falling into V, m=0 then the

limit (19) po= e-nwill be the probability for a fracture

to be isolated (does not cut any fracture) But then

po= (1 - p)4 So we have:

(20)According to (20) we can see if the density N

 ∞, that means v  ∞ then the probability for p so

that two random intersecting fractures will be nearly

equal to an unit If N is so small, n << 1 that p ~

v 4 Because so (20) express

the connection between p, c, ℓ and N.

Take and , the conditions for

percolation threshold pc= 1/3 will divide (mặt) (ℓ, c)

into two domains (Figure 10), the percolation

domain in the right side of the dividend line,

corre-sponding to values and

There will not be percolation if ℓ is too great,

density N is too small or c is too small (small

frac-tures)

This is suitable for (15) as p moves upwards to

pcfrom the greater value (p > pc), the

pemermabil-ity will be proportional to (p - pc)2 This is also

cor-rect for the conductance if the electric current is the

ion current flowing through the saturated fluid in the

fractured voids

Percolation effect

In fact the intersections between fractures in

the fractured rock space is at random The fractured

space has the permeability as the intersections

between fractures form an unlimited group and the

number of groups or average dimension of the

group stretchs out, number of springs P belonging

to the unlimited group is greater To calculate the

permeability of the fractured rock space, let’s get

back to the model (Figure 9), and apply Darcy Law

In mechanics, call q as the Darcy speed of the fluid,

which is equal to the volume of the fluid through the

cross-section S perpendicular to the speed direction

over an area unit in a time unit: The volume of fluid

is equal to q If the fluid has the viscosity h and

gra-dien with pressure , then the Darcy Law will be

presented as:

(21)

In which k is the permeability with the

perme-ability factor [m2]

Darcy speed is the volume flux (not the actual

speed of the fluid) and can present the connection

between it with the average speed of the fluid in the

porous hole F according to Dupuit-Forcheimer Law

q = vΦ (22)

In the fractured space, the average speed ―v of

the fluid between the two parallel sides will apply

Landau-Lifshitz Law (1971):

(23)From (23) and (21) we can easily conclude that:

(24)Take the approximate porosity F of the frac-tured rocks as an replace (24) we can calcu-late that:

(25)

Here, once again it is proved that in the tured rock space, permeability k depends on threemicro-structure factors: c, w and ℓ

frac-Expression (24) and (25) are true for the meability which all fractures will connect with each

per-others completely, that’ means p ≡ P as the model

of Warren – Root (see instruction documents ofchapter [6], chapter7) But in reality, such casesoccur rarely, because the intersections between thefractures in the fractured rock space are at randomand we have to apply the permeability theory toevaluate the intersecting level between the frac-tures in the net

From part 3.2 result, peripheral volume Vex =

π2c3and estimated , we can see: If, thepossibility of intersections between any fractures islow, the space does not have permeability, k = 0; in

contrast, if p > pc, unlimited group is created and the

space is capable of permeability, and increase in

multiplication factor f = (p - pc)2 (see (15) and Figure

10.) This factor is permeability probability P(p)

shown in Figure 7

In calculation, permeability factor calculated as(24) and (25) need to be multiplied with f factorbecause of permeability effect:

(26)

In which, W is aperture of fracture, p is mon porosity, pc is porosity limen for fuildl passingpermeability space, and Φ is leaky porosity or openporosity, including carven and fracture porositywhich are called secondary one in some docu-ments

com-Define permeability basing on well log datas

According to the porosity result in bore well,

common porosity (p) in fractured rocks is calculate by

average of porosity ΦDand ΦNat the same depth;secondary porosity Φ is calculated as following:

(27)

In which ΦS is calculated basing on sonic

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method at matrix rock without fracture

Dimensions w, c and ℓ or fracture density in the

space is identified from analysis results of FMI and

FMS datas For fractured basement at White Tiger

and Dragon oilfield, Institute of Marine Research,

Vietsovpetro considered porosity threshold

perme-able in fractured rocks pc = 0,01 in calculation of

hydrocarbon in inplace and oil recovery factor;

aver-age aperture of fractures From that statistics, we

could calculate permeability of fractured base

based on:

(26)For example, result of open porosity Φ = 0,018,

common porosity p = 0,031 to be replaced in (26),

we have:

Permeability of fractured rocks depends on

aperture of fractures Aperture changes twice,

meability will changes four times In addition,

per-meability of fluid in fractured rocks depend on

draught of fractures The draught of fractures

increase, the permeability decrease Up to now,

many authors research more this projects

Suggestion and Conclusion

Reservoir is pore space which its

microstruc-ture is complex and changes along with rock

devel-opment process Each kind of rock contains pore

microstructure with typical characteristics but

asyn-chronous Strong asynchronous state is

characteris-tic of fractured stocks: They have two porosities,

two permeability abilities They are fractured and

internuclear porosity (or block porosity); kinematical

permeability in large fractures and caves, capillary

in internuclear porosity and fractures Large

frac-tures have small ratio in common porosity but play

an important and decisive role in effective

perme-ability Minimum fractures and porosity of particles

play an important role in determining the ability of

product area The penetration of fluid into an space

with 2 porosity is a complex process In the

porosi-ty space of fractures, they are up to gradient

pres-sure of fluid, the minimum fractures and porosity

among particles are determined by wettability and

capillary force The physical nature of permeability

in multi-fracture space are suitable with shape of

permeability The analogy is the base to apply the

theory of permeability in an unsuitable space such

as fracture stones in Bach Ho oil field and other

fields in Cuu Long basin

The evaluation and use of the permeabilitydensity P(p) as the permeability effect factor is thespecific result of this construction to overcome thedisadvantage of Warren- Root model in order todetermine the k permeability in the fracture rockspace with two void and two permeabilities objects.The author would like to thank fellows for help-ing and exchanging experiences and ideas in thework implementation…

This work is the result of the research project KHCB 7.1.5206 sponsored by Ministry of Science and Technology

References

[1] Nguyen Van Phon (2007) Fractal

geome-try for researching reservoir (I) Petrovietnam Rev.

Vol 2 - 2007, pp 23-26

[2] Nguyen Van Phon (2007) Fractal

geome-try for researching reservoir (II) Petrovietnam Rev.

Vol 3 - 2007, pp 14-21

[3] Hoang Van Quy, Phung Dac Hai and

Borixov A.V (1997) Collecting data of geologic

structure, determining oil & gas an condensat tent of Dragon oilfield Report of Institute of

con-Scientific Research and Statistics – Vietsovpetro.Vung Tau 1997

[4] Pham Anh Tuan (2001) Physical features,

heterotrophic features and hydrodynamics of reservoir rocks of complicated structures in the con- ditions of modeling pressure and temperature of the formation Ph.D thesis – University of Mining and

oil-Geology

[5] De Gennos P.G (1976) The physics of

fluid crystals Oxford University Press.

[6] Golf-Racht Van T.D (1982) Fundamentals

of Fractured Reservoir Engineering Elsevier

Scientific Publishing Co

[7] Guéguen V., and Palciauskas V., (1994)

Introduction to the Physics of Rocks Princeton

University Press

[8] Landau L., and Lifshitz (1971) FluidMechanics Moscow Edit “Mir”

[9] Mandelbrot B.B (1982) The Fractal

Geometry of Nature San Francisco Freeman.

[10] Snarskii A.A (2007) Did Maxwell know

about the percolation threshold? Uspekhi

Fizicheskikh Nauk 177(12) 1341 – 1344

[11] Stauffer D., and Akarony A., (2003)

Introduction to Percolation Theory Taylor and

Francis (2003)

Fig 1 Inhomogeneous real space and effective homogeneous space

Fig 2 Site percolation and bond percolation

Fig 3 Different configuration of the group

of 4 sites in net of squares Fig 4 M(L) is L’s funtion, for network

having area L 2 =10 10 arrange in rectangle net p c = 1/2

(according to Staufer 2003)

petroleum EXPLORATION & PRODUCTION

Fig 5 Construct of Bethe network

Fig 6 Five difference probabilities in which original site O

can connect to 4 adjacent sites

Fig 7 The permeation probability P

depends on the probability p

of the site (the bond)

Fig 8 The development of fractured space as

the density of fractures increases

Fig 9 Dished fractures, radius c, aperture (w << c)

Fig 10 Surface percolation zone (ℓ, c)

Lower Miocene sandstone is the first

oil-bear-ing reservoir discovered as the well BH-1 was

drilled and tested in 1975 Over 30 years, oil

com-panies have drilled more than 80 exploration and

appraisal wells with chance of success of about

52% However, if only exploration wells are

consid-ered, the chance of success remains just about

30%, which is not high while drilling just

concen-trates in the centre of Cuu Long basin For this fact,

a need of studies to reevaluate the actual potential

of this reservoir is raised in order to increase the

performance of exploration as well as of production

With this reason, the study of characteristics, origin

and distribution rule of Lower Miocene sandstone

was deployed

Method

The study was based on collecting and

classi-fying the analyses of BI.1 and BI.2 sandstones:

petrology and sedimentology (grain size, cement

and matrix compositions), reservoir properties

(porosity, water saturation, NTG ratio); and

deposi-tional environments and constructing cross sections

and maps:

- Constructed 6 geophysic-geological cross

sections (3 strike sections through the basin and 3

sections across the basin):

Section 1: NW-SE, at the Northern part of thebasin, through blocks 15-1 and 01-02 (Figure 1) Section 2 : NW-SE, at the central part of thebasin, through blocks 16-1, 09-1 and 09-3 (Figure 2).Section 3: E-W, at the Southern part of thebasin, through blocks 16-2, 09-1 and 09-3 (Figure 3) Section 4: NE-SW, at the Western margin ofthe basin, through blocks 15-1, 15-2, 16-1, 16-2 and

17 (Figure 4)

Section 5: NE-SW, at the central part of thebasin, through blocks 01, 15-1, 15-2, 16-1, 09-1 and09-3 (Figure 5)

Section 6: NE-SW, at the Eastern part of thebasin, through blocks 01, 15-2, 09-2, 09-1 và 09-3(Figure 6)

- Constructed 12 distribution maps of ness, grain size, matrix and cement content, poros-ity, water saturation and net to gross ratio of BI.1and BI.2 subsequences

thick-Thickness

The distribution maps of thickness are shown

in Figures 7 and 8 In Figure 7, BI.2 subsequence isthinnest (80-100m) to the Eastern and Westernbasin margins Along the central NE-SW axis of thebasin, the subsequence thickness varies in range of100-500m The thickness tends to increase towardsthe centre of the basin Maximum value reaches

Dr Ngo Thuong San, Dr Cu Minh Hoang

Petrovietnam Exploration and Production Corporation

MSc Pham Vu Chuong

Salamander Energy Limited

Distribution rule of lower Miocene sandstone in Cuu Long basin

Abstract

This article presents the study results of distribution rule of thickness, lithology and sedimentology, reservoir characteristics and depositional environments of Lower Miocene sandstones (subsequences BI.1 and BI.2) in Cuu Long basin The study was performed by constructing cross sections and maps for the entire basin The results show that good reservoirs can be found in the Northern part at BI.2, while they can

be found in the Southern part at BI.1

petroleum EXPLORATION & PRODUCTION

nearly 800m in block 09-1, a part of blocks 16 and

09-2

Figure 8 shows that the thickness of BI.1 tends

to increase towards basin centre However, the

thickest (approximately 900m) locates on block 16,

a part of block 09-1 and 09-3 Generally, BI.1 is

thin-ner than BI.2, except in block 16 and 09-3

Grain size

Maps showing grain size distribution of BI.2

and BI.1 are presented in Figures 9 and 10 Figure

9 show that the grain size of BI.2 sands varies from

very fine-fine (<0.25mm) in block 16, the Southern

of block 09-1, the Eastern of block 09-2 and the

Northwestern of block 15-1, to medium

(0.25-0.5mm) in the remaining area, except in the centre

of block 01-02 where grain size becomes very

coarse (0.5-1mm)

Figure 10 shows that the grain size of BI.1

sands varies from very fine-fine (<0.25mm) in the

Nothern of block 16, 1, the Southern of block

09-2, to medium (0.25-0.5mm) in the Southern of block

16, the Northern of block 15-1 and 01, the Eastern of

block 15-2, to coarse (0.5-1mm) in the Southern of

block 01, 15-1 and the Northeastern of block 15-2

Commonly, the grain size tends to larger in

the Northern part of the basin than in the Southern

Especially in BI.1, the boundary between the two

area can be seen clearly (black bold line in Figure

10)

Matrix and Cement

The average contents of matrix and cement

were collected and mapped over the whole basin for

BI.2 and BI.1 subsequences individually (Figures 11

and 12) Figure 11 shows that the matrix and

cement content of BI.2 varies from 4 to over 30%

The contents less than 15% locate in the Northern,

a part of block 16 and 09-1

Figure 12 shows that the matrix and cement

content of BI.1 also varies from 4 to over 30%

However, the contents less than 15% gather into

bands from R, BH, TGT, HST, HSD to TL, DD, P to

SD

Porosity

Porosity distributions of BI.2 and BI.1 are

pre-sented in Figures 13 and 14 Figure 13 shows that

porosity of BI.2 sands varies from 12% (TGC) to

27% (SD) Very good porosity locates in area from

SD to the Eastern of block 15-2 (22-27%) In block

01-02 and the Northeastern margin of block 16

(TGT), porosity is slightly lower with value of

18-20% There possibly is a boundary separating themap into two areas (the black bold line in Figure 13).Figure 14 shows that porosity of BI.1 variesfrom 13% (in block 09-2: COD) to 21% (block 15-1:SD) Good porosity locates in a trend from BH toTGT, HST, HSN, RD to SD (19-21%), and theNortheastern (block 01-02 and Eastern of block 15-2) and the Southwestern basin margin (R-DM andthe Southern of block 16) with values of 18-20%

It is clear that good porosity mostly gathers inblock 01-02, 15-1, centre and the Eastern of block15-2, the Eastern of block 16, 09-1 and the Western

of block 09-3 Porosity tends to decrease wards from BI.2 to BI.1 in block 01-02, 15-1 and theEastern of block 15-2 In block 01-02, porosity ofBI.1 is lower than those of BI.2 probably due tostrong extrusive activities in this period Meanwhile,

down-in the centre of block 15-2, the Eastern of block 16and block 09-1, porosity of BI.1 is higher than those

of BI.2 The reason probably is BI.2 sediments weredeposited far from sedimentary source in deltaicenvironment to shallow marine with more abun-dance of clays

Water saturation

Water saturation distributions of BI.1 and BI.2are presented in Figures 15 and 16 Figure 15shows that in BI.2 low water saturation areas are inthe Northern to the Eastern and the centre of block15-2 In blocks in the Southern, water saturation ishigh (over 80%), except block 09-3, a part of block09-1 and 16 The boundary between the two areas

is presented by the black bold line in the map

In reverse to BI.2, water saturation in BI.1varies in range of 40% in a trend from block 09-1 tothe Eastern of block 16 and the centre of block 15-

2, to more than 80% in block 15-1, the Eastern ofblock 09-2, and a part of block 16 In Northern area,sands are mostly water filled (Figure 16)

Similar to the distribution of porosity, low watersaturations gather in block 01-02, 15-1, the centreand Eastern of block 15-2, the Eastern of block 16,09-1 and the Western of block 09-3 However,water saturation increases from BI.2 to BI.1 in block01-02, 15-1 and the Eastern of block 15-2.Reversely, in the centre of block 15-2, the Eastern

of block 16 and block 09-1 water saturationdecrease from BI.2 to BI.1 The reason is the rela-tionship between water saturation and porosity Inthe Northern area (SD), in spite of high porosity inBI.1, water saturation is still high The reason prob-ably is that there is no top seal to against oil tomove upwards to BI.2

Net to Gross ratio

The maps showing the distribution of this

parameter of BI.1 and BI.2 are presented in Figures

17 and 18 Figure 17 shows that the Net to Gross

ratio of BI.2 varies from 0% to more than 70% The

area with high Net to Gross ratio (>30%) is the

Northern of block 01-02, 15-1, the centre and

east-ern of block 15-2, except SV area

Figure 18 shows that in BI.1 the area with high

Net to Gross ration is just R, BH, TGT, HST and PD,

TL, DD to P In the Northern area, in spite of high

porosity, Net to Gross ratio is still very low due to

water-filled sands

Generally, there exists a boundary with high

Net to Gross of BI.2 in the Northern of the basin and

high Net to Gross of BI.1 in the Southern of the

basin as shown in Figure 17

Conclusion

From the analysis of reservoir distribution rule,

it can be concluded that good reservoirs can be

found in the Northern part at BI.2, while they can be

found in the Southern part at BI.1 The possible

rea-sons are:

- Different sedimentary sources of the two

areas Sediments of the Northern area might be

supplied by the paleo Dong Nai river or other

paleo-rivers in the central Vietnam with short

transporta-tion pathway Sediments of the Southern area might

be supplied by paleo-rivers in the Southern Vietnam

with rather far transportation pathway

- Either structures of BI.1 in the Northern area

were destroyed by extrusive activities or there is no

top seal due to lack of claystone

- In BI.2, although structures exist, reservoir

capacity is still low due to abundant claystone

Reference

1 La Thi Chich (2001), Petrology, Publishing

house of Ho Chi Minh City National University, p

265-322

2 Nguyen Ngoc Cu et al (1998), “Oil-bearing

reservoir formations in Vietnam”, Vietnam

Petroleum Institute science conference, Hà Nội.

3 Pham Tuan Dung and Pham Van Hung

(2001), “Geological structure of Lower Miocene No

23 reservoir, Bach Ho field”, Petroleum conference,

Hà Nội

4 Nguyen Van Dung (2004), Petrological

char-acteristics, postdepositional deformations and their

impacts on porosity and permeability of

Oligocene-Early Miocene sandstone reservoirs in Su Tu Den field, block 15-1, Cuu Long basin, Master thesis,

University of Natural Sciences of Ho Chi Minh City

5 Pham Xuan Kim (1988), Characteristics of

petrology, petrofacies, formation environment and distribution of Early Oligocene-Early Miocene reser- voirs in Cuu Long basin, Vietnam Petroleum

Institute

6 Chu Duc Quang (2004), Depositional

envi-ronment and organic facies of Oligocene-Early Miocene block 15-1 Cuu Long basin, Master thesis,

University of Natural Sciences of Ho Chi Minh City

7 Pham Tuan Dung, Phung Dac Hai, TranXuan Nhuan (2006), “Formation Characteristics OfEarly Miocene Deposits In The Bach Ho and Rong

Fields”, Technical Forum Clastic_Carbonate

Reservoir, Ho Chi Minh City.

8 F.K.North (1985), Petroleum Geology, Unwin

Hyman, USA

9 G.M.Friedman & J,E.Sanders (1978),

Principle of Sedimentology, John Wiley & Sons,

NewYork, Chichester, Brisbane Toronto

10 Tran Van Hoi, Phung Dac Hai, Tran XuanNhuan, Pham Tuan Dung, Bui Nu Diem Loan(2003), “The Geological Characteristics Of ClasticReservoir In The White Tiger And Dragon Fields”,

Technical Forum: Cuu Long basin Chalenges and Opportunities, Ho Chi Minh City.

Production-11 Howel Williams, Francis J Turner, Charles

M Gilbert (1982), Petrography: An Introduction To

The Study Of Rocks In Thin Sections, W H.

Freeman and Company, San francisco, USA

12 J.H.Barwis, J.G.McPherson & J.R.J

Studlick (1990) Sandstone Petroleum Reservoirs,

Springer-Verlag

13 Maurice Tucker (1989), Techniques In

Sedimentology, Blackwell Scientific Pub

16 Ngo Thuong San và nnc (1993),

“Stratigraphy and Lithology of The Mekong Basin”,

Proceedings of the international seminar on the stratigraphy of Southern shelf of Vietnam Ha Noi.

17 Supakorn Krisadasima, Nguyen Tien Long,Hoang Thanh Bang, Ngo Quang Hien, ChanwichaiSuksawat and Nguyen Thanh Long (2006),

“Overview of Clastic Reservoir Potential in Block

9-2 Cuu Long Basin, Vietnam”, Technical Forum

Clastic_Carbonate Reservoir, Ho Chi Minh City.

petroleum EXPLORATION & PRODUCTION

Fig 1 Geophysic-geological cross section along NW-SE direction in the Northern area

Fig 2 Geophysic-geological cross section along NW-SE direction in the central area

Fig 3 Geophysic-geological cross section along NW-SE direction in the Southern area

Fig 4 Geophysic-geological cross section along NE-SW direction in the Western margin

Fig 5 Geophysic-geological cross section along NE-SW direction in the cetral area

Fig 6 Geophysic-geological cross section along NE-SW direction in the Eastern margin

petroleum EXPLORATION & PRODUCTION

Fig 7 Distribution map of BI.2 thickness

Fig 9 Distribution map of BI.2 grain size

Fig 11 Distribution map of BI.2 matrix

and cement content

Fig 12 Distribution map of BI.1 matrix

and cement content

Fig 10 Distribution map of BI.1 grain size Fig 8 Distribution map of BI.1 thickness

Fig 13 Distribution map of BI.2 porosity

Fig 15 Distribution map of BI.2 Sw Fig 16 Distribution map of BI.1 Sw

Fig 17 Distribution map of BI.2 NTG Fig 18 Distribution map of BI.2 NTG

Fig 14 Distribution map of BI.1 porosity

Determination of fractured basement meability in White Tiger oil field from well log data by artificial neural network system using zone permeability as desired output

per-MSc Tran Duc Lan

R&E Institute - Vietsovpetro

Abstract

Recently, some authors have suggested using the Artificial Neural Network (ANN) method

to determine permeability from log data An ANN is built from the permeability of cores This method is highly applicable in sedimentary rocks[1], [4] However, due to the limitation in coring methods and laboratory measurements, core permeability is not truly representative for the per- meability in the fractured basement rock in well bores.

In our study about fractured basement in White Tiger Field, offshore Vietnam, instead of using core permeability, we have used zone permeability as desired outputs of an ANN to cal- culate permeability profile in wells Zone permeability, which was estimated from built-up pres- sures and production log test data has high reliability and directly represents for the permeabil- ity of the well bore rock.

We have determined the permeability for 16 wells in the field where both zone

permeabili-ty and well log data are available For qualipermeabili-ty control, the calculated permeabilipermeabili-ty is compared reversely to zone permeability The correlation coefficients are very high, commonly greater than 0.98

Having calculated permeability and input well log data i.e samples (more than 55,000 ples) in these 16 wells, we have been building a system of dozens of ANNs (called ANN sys- tem) to determine permeability for wells which have only well log data.

sam-p

In White Tiger and Dragon fields, offshore

Vietnam, the main reservoirs are in fractured and

vuggy basement Permeability is one of the most

important reservoir properties This property has a

significant impact on petroleum fields operations

and reservoir management Joint Venture

Vietsovpetro Company has been conducting

sever-al researches on determination of permeability

using the rate of drilling penetration (ROP), mud

loss data, core data, drilling stem testing data

(DST), built-up pressures analysis (BUP) and

pro-duction logging data (PLT) The results are

promis-ing and applicable in sedimentary rocks However,

these traditional methods encounter limitations

when applied to fractured basement rocks Due to

the difficulty in taking core in fractured and faulted

zones, we normally core in fresh rock intervals, the

core permeability hence are not representing for the

permeability in reservoir targets Using BUP and

PLT data, permeability are determined for wide

intervals from 4m to several hundred meters These

spare zone permeability values are not enough for

oil reserve calculation and for planning of

produc-tion and development strategy Following, we will

present a more detailed and reliable method to

determine permeability (in the sense of

permeabili-ty sections – permeabilipermeabili-ty profile)

Artificial neural network introduction

The first artificial neuron was produced in

1943 by the neurophysiologist Warren McCulloch

and the logician Walter Pits But the technology

available at that time did not allow them to do too

much The ANN theory then was developed further

by Minsky and Papert In 1969, they published a

book to summarize criticized issues in ANN theory

and presented valuable study for later

develop-ment of ANN Since then, ANN was restored and

applied widely [3]

In fact, ANN is a computer program It is

pro-grammed based on mathematical models using

ANN theory An ANN has abilities to learn and to

run In another word, an ANN has two main

processes These are training and running

processes [2]

A model of an ANN is shown in Figure 1 It is

used to determine permeability from well log data

There are three layers in the ANN, which are input

layer, hidden layer and output layer The input layer

contains 6 nodes corresponding to logging curves

of GR, DT, NPHI, RHOB, LLD and LLS (or MSFL)

The hidden layer contains 7 nodes and the output

layer contains 1 node representing permeability.Between layers, there are connections and weights

In the model, an ANN divides complex lems into simpler tasks Each task is solved at a rel-evant node by a so-called processing element (PE)

prob-A PE receives value with sum weighted inputs andreturns outputs by solving the corresponding activa-tion function We used sigmoid functions as activa-tion functions in hidden and output layers especial-

ly for discrete input data Sigmoid function is ear and derivative in the whole determination field.Its output varies from 0 to 1 We have also tried tan-gent hyperbolic functions for calculation This type

nonlin-of activation function has the output varies from -1

to 1 However, the calculated permeability fromthese two functions appears to be similar

An example formula of a sigmoid function is asequation (1) and Figure 2 below:

The permeability in Figure 1 is calculated asequation (2)

Where ƒ is simplified sigmoid function without

parameter b In this case b is replaced by wb[in-o]

and wb[h-out] which are weighting factors mined from the input-hidden layer connection andhidden-output layer connection, respectively.After the training process, weighting factors areset up for each connection using least mean squareerror method and back propagation algorithm.Having weighting factors, we can easily calculate

deter-permeability (Perm) using equation (2).

Using zone permeability as desired output of an ANN to determine fractured basement perme- ability from well log data

Determination of permeability profile for the wells having well log data and zone permeability

One of the most important characteristics intraining process of an ANN is the statistical analysisbased on the majority of samples It ensures thatthe output values are closest to the desired outputvalues of predominant samples

An example is in the Table 1 The table tains 2 groups of samples Group A contains sam-ples from 1 to 5 Group B contains samples from the

con-6 to 10 Sample 11 has the input value similar to that

(1)

(2)

petroleum EXPLORATION & PRODUCTION

in group A but the desired output is similar to the

desired output in group B After training and running

processes, the output value of sample 11 will be

similar to the out put values in group A

By using zone permeability values as desired

outputs for the statistical analysis in an ANN, we

suggest a procedure to determine permeability from

well log data as in Figure 3

We calculated permeability for 16 wells, which

have enough both zone permeability and well log

data, belong White Tiger oil field Figure 4 and

Figure 5 shows the relation of calculated

permeabil-ity and data

Figure 6 shows the zone permeability from

ANN in Y axis and zone permeability form BUP-PLT

in X axis The correlation coefficients, which

indi-cates the similarity between X and Y data, are as

high as 0.98

Determination of permeability for the wells

having only well log data

Having calculated permeability and input well

log data i.e samples (more than 55,000 samples) in

these 16 wells, we can calculate permeability for

other wells in White Tiger field which have only well

log data However, this amount of samples is too

big for the training process of an ANN According to

us, this number should be less than 5,000 In order

to optimize the training and re-training processes,

we divide these samples into smaller groups Each

group has a specific value range and each sample

is assigned to only one group For samples, which

do not belong to any existing groups, they will be

omitted during calculation After classification step,

training process is done for each group separately

In together, we will have a system of ANNs

The ultimate test for any technique that bears

the claim of permeability prediction from well logdata, is accurate and verifiable prediction of perme-ability for wells from which only the well log data isavailable We built the ANN system including 44ANNs For developing this ANN system, we used 68zones for training and 16 zones for cross-validationtesting Figure 7 is the cross plot of zone permeabil-ity which predicted by ANN system (kz-ANN) oncross-validation testing data set against actual zonepermeability (kz-PLT) The correlation coefficient isgreater than 0.89

We used this ANN system to calculate the meability profile for 85 wells in the fractured base-ment reservoir of White Tiger oil field Figure 8shows the permeability profiles, which were predict-

per-ed by the ANN system, of some wells having onlywell log data

Conclusions

The success in using zone permeability asdesired output for statistical analysis in an artificialneural network to determine permeability from welllog data has opened a new trend in application ofANN Desired output data is not set separately for asingle sample, instead we use averaged number tofor a group of samples This averaging method ishighly practical, especially when we can not choosethe desired outputs for each particular input data

By dividing input data i.e samples into smallergroups, we can manage a huge amount of inputdata for training processes An ANN is designed forone specific group of input samples Depends onthe number of groups, we will have an ANN system.This ANN system is flexible It is easy to add newinput data to save running time in re-trainingprocesses

References

[1] Lê Hải An, 2000, Phương pháp tính độ

thấm từ tài liệu địa vật lý giếng khoan bằng mạng nơron, Hội nghị Khoa học lần thứ 14, quyển 4 Dầu

khí, Hà Nội, tr 5-7

[2] Trần Đức Lân, 2005, Giải pháp nơron nhân

tạo và phương pháp làm giàu các tham số trong nghiên cứu Địa chất, 3, Tạp chí DK, Tr.23-31.

[3] Christos Stergiou, 2004, NeuroSolution,NeuroDimension, Inc 1800 N Main Street, SuiteD4 Gainesville, FL 32609.http://www.nd.com.[4] Mohaghegh, S., Balan, B., Ameri, S.(1995),

State-Of-The-Art in Permeability Determination From Well Log Data, SPE 30979.

Table 1

Fig 1 The model of an ANN [6-7-1] is used to determine permeability from well log data

Fig 2 Graphic of a sigmoid function given a = 0.5 and b = - 8

Fig 3 Procedure to determine permeability from well log data

petroleum EXPLORATION & PRODUCTION

Fig 4 Prediction models permeability values (K-AN) vs zone permeability (K-TV)

for wells belong White Tiger oil field Fractured Section

Fig 5 Comparison of determined permeability by ANN (PerAnn) and other well log data

Fig B is a zoom in of fig A at zone 3600-3660m

Fig 6 Zone permeability from BUP-PLT (K-TV)

vs zone permeability from ANN (K-AN1z)

Fig 7 Comparison between actual zone permeability

kz-PLT and prediction zone permeability kz-ANN with 16 zones of testing data set

Fig 8 Permeability profile prediction by ANN system from the wells having only well log data

Y Matsumoto

Japan Oil Engineering (JOE)

N H Long, D A Thanh, Cao Huu Binh

Petrovietnam Exploration & Production Corporation (PVEP)

Y Ueda, T Uchiyama

Nippon Oil Exploration Limited (NOEX)

H Kawahara, H Okabe, S Takagi, and H Mitsuishi

Japan Oil, Gas and Metals National Corporation (JOGMEC)

Introduction

As an injection gas for EOR (Enhanced Oil Recovery), CO 2 (carbon dioxide) has a number of

advan-tages, such as a high density at reservoir condition, high ability of extracting heavier components,

relative-ly low MMP (Minimum Miscibility Pressure) with oil, and high solubility in oil To reproduce those special

properties of CO 2 in reservoir simulation and evaluate its EOR process properly, a compositional reservoir

model is required, where fluid properties are calculated with EOS (Equation of State).

This paper describes a compositional simulation study conducted to evaluate the applicability of CO 2

-EOR to Lower Miocene Reservoir of Rang Dong field The study consists of the following steps:

(1) Construction and tuning of an EOS (Equation of State) fluid model on the basis of laboratory data including the results of CO 2 swelling test and CO 2 slim-tube test

(2) Construction and history-matching of a compositional reservoir model for the field

(3) Evaluation of CO 2 -EOR by field-scale compositional reservoir simulation.

In the study, the applicability of CO 2 flooding was discussed through the evaluation of oil increment

over waterflood and CO 2 utilization factors.

To cross-check the results, two commercially-available simulation software systems, listed below, were

used in this study.

Comprehensive CO 2 EOR study - Study on Applicability of CO 2 EOR to Rang Dong field

Part II: Compositional simulation study

EOS Modeling of Fluid

Available Data and Modeling Plan

For this study, new fluid samples (surface

samples) were collected at the selected producer

(Part I) and recombined to make a reservoir oil

sample Subsequently, compositional analysis,

conventional PVT experiments, CO2 swelling test,

and CO2slim-tube test were conducted; CO2

-relat-ed tests were perform-relat-ed for the first time for this

reservoir

In addition to the new data, compositional

analysis data of 10 fluid samples and conventional

PVT experiment data (constant composition

expan-sion, differential liberation, separator test, viscosity

measurement, etc.) of 8 fluid samples were

avail-able for this study With these data, the validity of

the new fluid data was discussed: Not only the

con-sistency of the data but also the suitability for the

simulation use as a representative of the Lower

Miocene Reservoir fluid

It was concluded that the volumetric behavior

of the new sample was typical of this reservoir but

the measured viscosity was slightly higher than

most of the others Therefore, it was decided that

the EOS modeling and tuning would be conducted

with the new data (the selected producer’s data),

and then viscosity re-tuning would be performed in

the stage of field-scale history matching

EOS Modeling/Tuning

The Peng-Robinson EOS (1978) was used for

this study For the EOS model for E-04P, C7+ was

split into 4 pseudo-components, assuming the

2-stage exponential distribution Light and

intermedi-ate components were grouped into 4

pseudo-com-ponents This splitting/lumping scheme and the

resulting pseudo-component system are shown in

Table 1 CO2was left alone as one

pseudo-nent because it was to be used as the main

compo-nent of injection gas

For PVT matching, the following EOS

parame-ters were tuned

(1) Volumetric Behavior Matching

- Critical Temperatures (Tc) for C7+

- Critical Pressures (Pc) for C7+

- Binary Interaction Parameters between CO2and Hydrocarbons

- Volume Shift Parameters for C7+

(2) Viscosity Matching

- Critical Volumes for Viscosity (VcVis) for C7+

- LBC CoefficientsThe LBC coefficients were modified becauseotherwise good match for oil viscosity was notattained

After the parameter tuning, the EOS simulationresults successfully matched with experimental data

of constant composition expansion, differential ation, separator test, viscosity measurement, CO2swelling test, and CO2slim-tube test The compari-son between the measured and the calculated forthe separator test and differential liberation is shown

liber-in Table 2; very good matches were achieved.Experimental and calculated results of GOR and rel-ative oil volume in differential liberation are shown inFig 1

For the phase behavior in CO2 swelling test,the comparison between the measured and thecalculated is shown in Fig 2 The figure showsliquid volume fractions for the pressure regionbelow 4,500 psia (the maximum pressureassumed in operation/simulation scenarios); avery good match was achieved Also a goodmatch was achieved for oil viscosity measured inthe swelling test

The PVT matching process was performedwith CMG WinProp and Schlumberger PVTi.Results of the two PVT simulators agreed accurate-

ly when both the simulators attained convergence.For the CO2 slim-tube test, comparisonbetween the measured and the calculated is shown

in Fig 3; oil recovery at 1.2 PV-injected for eachpressure setting is plotted A sufficiently good matchwas attained The relative permeability model ofCoats (1980) was used to incorporate the effect ofinterfacial tension change

Bubble Point Adjustment

The main area of the reservoir originally had abubble point pressure of 2,514 psia, while the bub-ble point of the E-04P sample was 2,102.7 psia.Therefore, the initial fluid composition for the EOS inthe field-scale model must be different from thepresent sample composition Consequently, the

petroleum EXPLORATION & PRODUCTION

composition corresponding to the initial bubble point

was determined by a trial-and-error process

assum-ing equilibrium-gas swellassum-ing for the sample oil in

simulation

Slim-Tube Test Simulation for Impurity Effect

Using the final EOS for laboratory experiments,

the impurity effect on slim-tube test recovery was

estimated by simulation Slim-tube test simulations

were conducted with mixtures of CO2and

associat-ed gas for various mixing ratios

The calculated oil recoveries (at 1.2

PV-inject-ed) for those injectants are compared in Fig 4 It is

shown that the high ability of CO2is not much

dete-riorated by the impurity of 10 mole% When the

impurity is lower than 20 mole%, the oil recovery is

higher than 95% at 3,400 psig

Field-Scale Simulation

Model Construction and Initialization

The latest field-scale black-oil model

(Eclipse-100 model) of Rang Dong Lower Miocene, already

history-matched, was used as the base for the

com-positional reservoir model (Eclipse-300 model) The

plane view of the grid system for the model is shown

in Fig 5 The model is composed of 97x116x40

(corresponding to divisions in the x, y, and z

direc-tions) grid-blocks

The fluid property data of black-oil type was

replaced with the EOS data corresponding to the

EOS model tuned for the selected producer’s new

data The initialization run of the compositional

model resulted in an initial state sufficiently close to

that of the black-oil model

History Matching

Simulation was performed for the history

peri-od The compositional model run resulted in higher

water cuts and higher GORs than those of the

his-tory-matched black-oil model The major cause was

the difference in oil viscosity: The compositional

model was equipped with the EOS model tuned for

the selested producer’s new data, which had a

rela-tively high oil viscosity, while the black-oil model

was set up with a lower oil viscosity

Therefore, the viscosity-related parameters

(critical volumes for viscosity calculation) of the

EOS model were slightly tuned and the oil viscosity

of the compositional model was lowered As a

result, the final compositional model sufficiently

matched the black-oil model in history production

and pressure performances The oil viscosity

calcu-lated by the final EOS model for field-scale tion is consistent with the existing viscosity data ofthis reservoir as shown in Fig 6

simula-Preparation for Prediction and EOR Evaluation

In the course of this study, the amount of CO2available to the project was evaluated by a site sur-vey and the base case supply was set at 1.00 mil-lion ton/year of pure CO2, which is equivalent to51.72 MMSCF/D of CO2

To determine the base case settings, trial diction runs were performed with various case set-tings and their results were compared; differentinjector locations (for both peripheral and dispersedinjection) and different injection schedules for WAG(Water-Alternating-Gas) and CO2continuous injec-tion were tested As a result, a peripheral WAGinjection case with 6 newly-drilled injectors, whichattained a higher oil recovery than most cases, wasselected for the base case Increasing the number

pre-of injectors beyond this case brought only aninsignificant increment in oil recovery for the predic-tion period (up to 1 Jan 2026)

Base Case Settings for CO 2 Injection Process

The well locations for the base case of CO2

injection are shown in Fig 7 The settings are marized as follows:

sum The number of WAG injectors: 6 (groupedinto 3 pairs)

- One WAG cycle: 3-month CO2injection + month water injection

6 The number of WAG cycles for each pair: 10 Scheduling of WAG cycles: 3-month phase-shifts between pairs (to flatten/lower the field total

- The number of newly-drilled producers: 2

An existing water injector and 6 newly-drilledinjectors conduct water injection to raise the reser-voir pressure prior to the CO2injection since a high-

er reservoir pressure promotes the development ofmiscibility or near-miscibility The field total CO2injection rate is specified to be 51.72 MMSCF/D.The injection gas composition is 100% CO2

Base Case Results

The calculated oil production and injection formance of the base case CO2 WAG is shown in

per-Fig 8, compared with that of waterflood per-Fig 9

shows the change of oil saturation distribution in

simulation for a layer of the model

The base case CO2 WAG brought a field

cumulative oil production equivalent to 41.8% of

OOIP at 1/1/2026 The outcome of CO2flooding is

evaluated as follows:

- CO2Injected: 7.5 million ton

- Oil Increment against Natural Depletion:

The gross CO2 utilization factor calculated is

close to those of the best cases of actual oil fields in

U.S.A

About 29.3% of the injected CO2was produced

from the wells in the period from 1/1/2012 to 1/7/2019

(i.e the duration of CO2 injection) If the produced

CO2is ideally recycled for the injection of this project,

the net CO2utilization is evaluated as follows

Net CO2Utilization Factor (against Waterflood):

3.92 MSCF/STB

Oil Increment vs CO 2 Quantity

The relation between the oil increment and the

total amount of CO2 injected was investigated by

changing only the CO2injection rate under the base

case settings The results are shown in Fig 10 The

calculation results showed some worsening of CO2

utilization factor as the total amount of CO2 was

increased

Several variational cases were derived from

the base case and evaluated by simulation

Considering those results, the improvement of oil

recovery by the optimization of drilling and

produc-tion/injection plans appears to be limited:

Remarkable improvement over the oil-increment

curve in Fig 10 seems unlikely

Impurity Effect

To evaluate the effect of impurity in injectant, a

variation of the base case was simulated Under the

base case settings, the injectant composition was

replaced with a mixture composition of CO2 90

mole% and C1 (methane) 10 mole% The surface

gas injection rate was not changed The oil

incre-ment against waterflood decreased by only 0.4% of

recovery volume It appears that the impurity of this

magnitude does not significantly deteriorate the oil

recovery process

Comparison with Hydrocarbon Gas Injection

Assuming the use of a sales hydrocarbon-gas(C179 mole%, C2-C318 mole%, C4-C63 mole%) asinjectant, 3 prediction cases with different field-injection rates were performed Under the settings

of CO2WAG cases, only the injectant compositionwas replaced The results were compared withthose of the CO2injection Fig 11 shows the com-parison in oil increment (against waterflood) Asshown in the figure, the hydrocarbon WAG attained

a fairy good result in this simulation

Concluding Remarks

(1) The gross CO2utilization factor calculatedwith compositional simulation was close to (or a lit-tle better than) those of the best cases of actual oilfields in U.S.A This result encourages a further dis-cussion on the feasibility of CO2-EOR in this field,though it may be hard to surmount the problemsrelated to cost and risk

(2) The peripheral CO2 injection works ciently for this reservoir model, recovering theremaining oil extensively spread in the field.However, when the CO2injection for the actual field

effi-is to be designed, the reservoir continuitiesassumed in this reservoir model should be carefullyexamined and investigated with acquisition of relat-

ed field data

(3) The peripheral CO2 injection has someunfavorable characteristics: A risk of losing CO2tothe outside and a time-lag of recovering the injected

CO2 The CO2 recycling plan should be taken intoconsideration when injector locations are discussed

in a further study for a design of CO2flooding.4) In the case of gas flooding, the sweep direc-tion (vertical or horizontal) significantly affects the oilrecovery process; the viscous fingering and thegravity effects have strong influence The relativepermeability data should be reviewed in a furtherstudy from the viewpoint of correspondencebetween the phenomena in the actual reservoir andthose in simulation

(5) Simulation runs with the field-scale modelshowed that the hydrocarbon gas injection processmay bring a fairly good result A severe gravity-over-ride did not occur in the model, at least partly owing

to the small kv/kh (vertical permeability/horizontalpermeability ratio), which was on the order of1/1,000 in the main area To evaluate the hydrocar-bon gas injection process properly and compare itwith CO2 injection, a further study with a sectormodel is recommended, where the gravity segrega-

petroleum EXPLORATION & PRODUCTION

tion, partial dissolution of injectant (into reservoir

oil), viscous fingering, and channeling can be

repro-duced more accurately than in the field-scale

model

Acknowledgements

The authors thank the management of PVN for

a full support to this study Technical contributions ofJOGMEC, NOEX, VPI, PVEP and JVPC teammembers are also highly acknowledged

Table 2 Comparison of observed and calculated

results for conventional PVT experiments

Fig 1 Experimental and calculated results for GOR

and relative oil volume in differential liberation

Fig 2 Experimental and calculated results for

liquid volume fraction in CO 2 swelling test

Fig 3 Experimental and calculated results for oil recovery

in CO 2 slim-tube test

Fig 4 Simulation results of slim-tube tests with

mixtures of CO 2 and associated gas for various mixing ratios

Fig 5 Plane view of the grid system for the field-scale model

Fig 6 Oil viscosity calculated by the final EOS model for field-scale simulation,

compared with experimental viscosity data of this reservoir

Fig 7 Well locations for CO 2 WAG (Base Case)

petroleum EXPLORATION & PRODUCTION

Fig 8 Calculated field production and injection

performances for Waterflood Case and CO 2 WAG Case (Base Case)

Fig 9 Change of oil saturation distribution in CO 2 WAG (Base Case), Layer-09

Fig 10 Relation between the oil increment

and the total amount of CO 2 injected

Fig 11 Comparison of CO 2 WAG and hydrocarbon WAG for oil increment (against waterflood)

Actually, the Dragon oil field area consists of a cluster of medium and smalloil fields such as South – Eastern Dragon, Eastern Dragon, North – EasternDragon and Central Dragon New discoveries are the Southern Dragon andCentral Southern Dragon based on the drilling and formation testing results ofwells R.20 and R.15.

Dragon structure is a positive 3rd class structure located in the South –Western part of the Central uplifted zone of Cuu Long basin Beside the CentralDragon uplifted zone there still exist single positive 4th class structures likeEastern Dragon, South – Eastern Dragon The tectonic destructions having theparallel orientation, North-Eastern – South-Western, divide the Central Dragonuplifted zone into the 4thclass uplifted blocks such as North – Eastern Dragon,Central Dragon, South Central Dragon and Saddle

In the Dragon oil field area the Lower Oligocene deposits are fully present,maybe, only in the central low flank of the area On the tops of the structures theLower Oligocene deposits are absolutely absent in South Dragon, South CentralDragon, Saddle and South – Eastern Dragon They are partially encountered inNorth-Eastern Dragon, Eastern Dragon Upper Oligocene deposits are most pres-ent in North – Eastern Dragon, Eastern Dragon and are not fully present in South– Eastern Dragon, Central Dragon and Southern Dragon Here Upper Oligocenedeposits are formed filling up the deep zones for the morphology to become razed

in the Early Miocene period

In difference to the White Tiger field area the Dragon field area is destroyed

by the tectonic fault system mainly of latitude orientation because the whole ment here is mostly not affected by the West-Eastern revolving horizontal tensionfield of the central uplifted zone but the stretching tension field parallel to the ori-entation line of Conson uplifted zone

base-The Dragon oil field area and the planning of small oil field development under complicated geological and technological conditions

Dr Tran Le Dong Eng Tran Van Hoi

Dr Hoang Van Quy

Vietsovpetro

Abstract

The Dragon oil field area consists of a number of medium and small oil fields having a very plicated geological structure with small, scattered oil and gas reservoirs Their crude oil characteristics are unfavourable for production and transportation Hence planning of effective development of Dragon oil field has become one of the most important and urgent problems, which have been discussed by

com-JV Vietsovpetro.

petroleum EXPLORATION & PRODUCTION

This is the most important

feature that makes the

destruc-tion zones here have a lower

density than the basement of

White Tiger field

North – Eastern Dragon

field (1) was discovered by the

exploration well R.6 Here there

are oil pools in the Lower

Oligocene, Upper Oligocene and

Lower Miocene deposits The

total in place oil reserves are

13964 thousand tons,

correspon-ding to recoverable 2070

thou-sand tons However, the main

production here is concentrated

in the Lower Miocene oil pool

Central Dragon field

-RP1(2) - was discovered by the

exploration well R.6 Here the

oil pools are distributed mainly

in the upper part of the Lower

Miocene deposits The total in

place oil reserves are 6116

thousand tons, corresponding

to recoverable 556 thousand

tons However, on 01.09.2007

the total production from this

area achieved 591 thousand

tons This number can still be

raised up to more than 1 million

tons due to the successful

application of the method of

cut-ting the 2ndbores of some wells

for the exploitation of the

remaining oil pools

Eastern Dragon field (3) was

discovered by well R.11: A

com-mercial oil flow was received

from the Lower Oligocene

sedi-mentary rock sequence and the

Pre-Cenozoic basement In

com-parison with other oil

accumula-tions of the Dragon field area the

Eastern Dragon oil, being well

screened and trapped, has lower

density and viscosity, higher gas

content that is fairly convenient

for production and transportation

The total in place oil reserves are

22779 thousand tons,

correspon-ding to recoverable 6129 sand tons

thou-South – Eastern Dragonfield (4) was discovered by wellR.14: A commercial oil flow wasreceived from the Pre-Cenozoicbasement section This is amedium oil pool The total inplace oil reserves are 32756thousand tons, corresponding torecoverable 8745 thousand tons,including 10702 thousand tons of

in place reserves This is an oilpool in fractured basement hav-ing water at the bottom - one ofthe important features forenhancing oil recovery On01/09/2007 the total productionfrom this oil pool achieved

5315707 tons which is muchmore than prediction due to therather successful application of

the production regulation regimeand putting gaslift into produc-tion On this basis the total pro-duction of South - EasternDragon field can be much morethan the calculated number.Southern Dragon - Turtlefield (5) is located on the bound-ary of the activity zones of JVVietsovpetro and VRJ, consisting

of oil pools in the Pre-Cenozoicbasement and Lower Oligocene

to Lower Miocene sedimentaryrock sequence This is a medi-um-sized oil field with the total inplace oil reserves reachingapproximately 50-60 million cubicmeters Beside the above statedoil fields there exist other smallones such as South-CentralDragon (6), Central-CentralDragon (7) and Saddle (8) Their

total in place oil reserves fluctuate around

10 million tons

One of the important features of theDragon oil field area is that the reservesare not large and scattered in a number ofobjects from the Pre-Cenozoic basement

to Lower Miocene sedimentary The oilpools, being lenses or pointed bevel layers,have narrow developing range, heavy oilwith high viscosity and low gas content

Formation oil density varies from0.65g/cm3(North-Eastern Dragon, EasternDragon) to 0.769 g/cm3 (South-EasternDragon), 0.79-0.80 g/cm3(Central Dragon)and more, up to 0.803 g/cm3 (SouthernDragon – Turtle) The gas content of forma-tion oil decreases from 217 m3/ton (North-Eastern Dragon, Eastern Dragon) to 50

m3/ton (South-Eastern Dragon), 40 m3/ton(Central Dragon) and less than 30 m3/ton(Southern Dragon – Turtle) Formation oilviscosity is one of the characteristics of oilfrom Dragon field area that is inconvenientfor production and transportation The low-est value has been observed in formationoil from the North-Eastern Dragon andEastern Dragon areas (0.618 MPa.s) andthe highest value – in the Central Dragonarea This value reaches approximately 2MPa.s in the South-Eastern DragonSouthern Dragon

Up to now only the Central Dragonand South-Eastern Dragon areas inDragon field have been put into production

Due to the characteristics such asvery complicated geological structure, oilpool scattering, not large reserves, forma-tion oil characteristics inconvenient forproduction the planning for economicaldevelopment of Dragon oil field is a com-plicated question under discussion It isdifficult to effectively develop such areas

as North-Eastern Dragon, Central Dragon,Eastern Dragon and South-CentralDragon if they stand independently So JVVietsovpetro has taken up the decision tobuild a integrated production system forthe whole area of Dragon field consisting

of a processing center, a piping and age system, and to connect the productionsystem of Dragon field to that of WhiteTiger field This production system has thefollowing characteristics:

stor The processing center is installed

- All piping is thermoisolated

- To reduce the oil congelation perature chemical agents must be added

tem-to the oil being trasported in the pipeline insuch an amount depending on the flowrate, congelation temperature, pipelinelength

- In case production flow does notreach the minimum level sea water must

be added to transport the products

- Due to the greater viscosity of oil

in comparison with that of the injectedwater injection program and solutions toadjust it have their own characteristics

Conclusion

Dragon oil field is an area with a verycomplicated geological structure Oil reser-voirs are small and scattered, havingunfavourable reservoir and screeningproperties that result in heavy oil, low gascontent, small flow rate To effectivelydevelop these small oil reservoirs it is nec-essary to consider and build a integratedproduction system

During the last years the application ofthe above stated point of view of develop-ing clusters of small fields has broughtgreat effect to JV Vietsovpetro It is also inuse for other fields such as Ca Ngu Vang,Southern Dragon-Turtle There exists anexpectation that during its application theproduction system of this cluster of smalloil fields will be continuously adjusted andimproved so that it will contribute to theproduction efficiency increase

Reference

Tran Le Dong, Tran Van Hoi, Hoang

Van Quy and other The genaral scheme of

Dragon oil field development Vietsovpetro,

2005

Using domestic bioethanol for blending high octane gasohol - phase 3

Eng Nguyen Huynh Hung My MSc Huynh Minh Thuan Eng Le Hong Nguyen Eng Le Duong Hai

Vietnam Petroleum Institute

As the trends on development of the alternative energy to ensure energy security and tal safety continue, the use of biofuels to replace gradually conventional fuel has become indispensable and has spread all over the world In this paper, the authors present two main contents of the gasohol phase 3 research namely the durability and wearing level of truck engines running E5 and conventional gasoline and the stability of E5 stored in underground storage tank (UST) at similar conditions as tanks

environmen-in conventional gas stations.

Experimental results showed that the wearing level of a new truck engine running E5 was lent to that in a new engine using conventional gasoline within the route of 20.000 km Moreover, the qual- ity of E5 was still in compliance with Vietnamese gasoline standard within two months of storing in the UST In this paper, the authors will highlight the advantages and disadvantages of E5 over conventional gasoline in terms of compatibility and performance of the test engine These experimental results are sci- entific basis for the petroleum companies to decide on blending and distribution options of gasohol.

equiva-I Introduction

The main objective of the “Project on

develop-ment of biofuels by 2015, vision to 2025” is to

devel-op biofuels, a new and renewable energy, to use as

an alternative fuel to partially replace conventional

fossil fuels, and to contribute to assure energy

secu-rity and environmental protection According to the

roadmap of the project, in 2010-2015, the

govern-ment will introduce to the market an amount of

bio-fuels accounting for from 0.4% to 1.0% of total

petroleum demand in the country According to

fore-cast, the total quantity of gasoline consumed in

2010 is 4.1 MM tonnes and 5.6 MM tonnes in 2015,

respectively Therefore, the total amount of biofuels

need to be produced to meet the roadmap scheme

is not insignificant

Phase I and phase II of the project “Study of

using domestic bioethanol for blending high octanegasohol” for which Petrovietnam R&D Center forPetroleum Processing (PVPro - VPI) conductedfrom 2003 to 2007 have not evaluated the durabilityand wearing level of engines running ethanol blend-

ed gasoline comparing with engines running ventional gasoline Therefore, phase III was thefinal phase of the project which aimed to collect suf-ficient scientific facts and obtain experimental data

con-on the impacts of ethanol blended gasoline to cles engines and to the existing operating, storageand distribution system

vehi-II Content of the research

1 Objectives

Conventional gasoline 92 RON blended with 5vol% of anhydrous bioethanol (99.6%) and 1.2 vol%

Table 1 Properties of E5 gasohol measured in storing period

of VpCl-705 additive (E5) was stored and preserved

in the model dispenser storage tank to evaluate

sta-bility for two months Tests of engine durasta-bility and

wear was conducted using a Suzuki truck running

E5 was compared to the truck running with

conven-tional gasoline One of the objectives of the project

is to study the feasibility of the upgrade of the 90

RON gasoline product from the Dung Quat Refinery

by blending 5 vol% of anhydrous bioethanol to

boost the RON value to 92

2 Establishment of a storage model and

assessment of quality stability of E5 in real

con-dition

a Storage model

Underground storage tank (UST) system was

designed to contain petroleum product following

Vietnamese (TCVN) standard:

- Dimension: 1,800 mm × 1,250 mm × 3 mm

(Net volume: 2,208 metric liters);

- Tank construction material: CT3 plated steel;

- Linings outside covered by 2 layer of fiberglass

b Methodology and experimental results

Periodically, once per week, technicians fromPetroleum Lab (Petrolimex V) sampled and meas-ured basic specifications of E5 gasohol Analyticalresults at the end of 1st, 4th, and 8thare presented inTable 1

Figure 1 Placing E5 gasohol storage tank system

was constructed to be put underground

petroleum PROCCESING

Table 2 Characteristic of auto vehicles used for testing

Table 3 Comparison of engine power for engines running E5 and M92 gasoline

Experimental results in Table 1 showed that:

- Temperature of E5 samples in USTdecreased gradually from 280C at 1st week to

approximate 240C at 8thweek;

- After adding 5 vol% of anhydrous ethanolinto 95 vol% of based gasoline (M92 - conventional

gasoline 92 RON) with an existing concentration of

oxygenated compounds of 1.97 %wt, the total

oxy-genated content increased to 2.86 %wt;

- Octane number and copper strip corrosionspecifications of E5 were not changed after 8 weeks

of preservation RON had a constant value of 93.9

while copper strip corrosion specification was still in

1A grade;

- Oxygenate, water content, distillation perature, RVP and density of E5 changed slightly

tem-after 8 preserving weeks;

- Existent gum content increased slightly InM92 based gasoline, gum content was

1.5mg/100ml Right after blending, existent gum

content of E5 sample was 1.84mg/100ml After 8

week of preservation, it was 1.90mg/100 ml;

- Therefore, it can be concluded that someproperties (RON, density, oxygenated content) can

be considered constant with time because E5

gaso-hol was not phase-separated in the storage tank.When phase separation occurs, there will be anupper layer of gasoline and a milky layer of ethanoland water right below In many cases, a third layer

of only water exits at the bottom (i.e ethanol cules were pulled out of gasoline-ethanol bonds andtransferred into ethanol-water phase which tends tosettle at the bottom of the tank) When phase sepa-ration occurs, ethanol content of ethanol blendedgasoline will decrease, leading to a decrease inoctane number, density, and oxygenate content;

mole Moreover, the based gasoline M92 conmole tained MTBE compound This oxygenate agent isconsidered as a co-solvent that could increase thesolubility of water in mixture of ethanol-gasoline.Consequently MTBE can help decrease tempera-ture of phase separation and increase water toler-ance In frame of the testing, results focused only onthe stability of E5 within storage time but did not sur-veyed the anti-separation ability of MTBE in ethanolblended gasoline

con-3 Evaluation of durability and wearing level of engines running E5 in comparison with conven- tional gasoline

a Auto vehicles used for testing

b Methodology

Tests were conducted using an European modern measurement equipment with a consistent dure for both engines (brand new) running 02 type of fuel: E5 gasohol and conventional gasoline M92.The economic and technical summary index of the engines, the pressure of combustion chamber ofthe engines, and the content of wear metals in lubricating oil formed were measured following a commontesting procedure for both engines running E5 gasohol and conventional gasoline M92

proce-Operation values of the 1stengine running conventional gasoline M92 through testing process (455 hrs)was taken as standard data for comparison with the 2ndengine running ethanol blended gasoline (E5)

c Modes of measurement – test results

* Mode 1: Measurement of economic - technical parameter of engine

Measurement of engine power

Table 4 Fuel consumption of engines running E5 and M92 gasoline

Table 5 Average fuel consumption of engine running E5 and M92 gasoline

Table 6 Cylinders wear of the engines running E5 and M92 gasoline

Table 7 Valve wear of the engines running E5 and M92 gasoline

Table 8 Level of gas leaking of the engines running E5 and M92 gasoline

- Data in Table 3 show that after 455 consecutive working hours, power reduction of engine running E5(1.887%) was lower than of that running M92 (2.242%)

 Measurement of fuel consumption

- Comparison of fuel consumption between 2 engines at maximum speed after 455 testing hours showsthat the fuel consumption of engine running E5 was from 2.496% to 2.955% higher than that of the enginerunning conventional gasoline M92 The increase in fuel consumption may due to to the lower heating value

of ethanol compared with conventional gasoline

- However, when considering average fuel consumption of comprehensive testing process for tion of durability and wearing level of engines, the above results indicate that average fuel consumption ofthe two engines are about 6.9kg/h, equivalent to 10.9 liters/100km and average speed converted into actu-

evalua-al road condition achieved around 79km/hr Table 5 evalua-also indicates that average fuel consumption of engineusing E5 in testing process was about 0.653% lower than that of engine running conventional gasoline M92

* Mode 2: Measurement of engine pressure

 Verification of cylinder and valve size of the engines

- Experimental results of cylinder wear of the engine after 455 testing hours show that cylinders wear ofthe engine running E5 decrease 0.018% - 0.064% in comparison with that of engine running M92 gasoline

- Experimental results on valve wear of the engines after 455 testing hours indicate that the valveswear of the engine using E5 is essentially similar to that of the engine running M92 gasoline

 Test of gas leaking from combustion chamber downward to crankcase:

Table 9 Compression pressure of engines running E5 and M92 gasoline

Table 10 Kinetics viscosity of lubricant in engines running E5 and M92 gasoline

- After running the engines for up to 220 hours, gas leaking of two engines decreased because theworking surface between cylinder and compression rings was sealed;

- After that, level of gas leaking of both engines increased because the wear began to increase withtesting time Gas leaking in the engine running E5 increased faster than the one running conventional gaso-line M92 after 400 hours The reason is that inside the combustion chamber of the engine using E5 exists

an amount of completely unevaporated ethanol This ethanol can act as a detergent in lubricant layerbetween cylinder and piston wall which increases wear of compression rings, resulting in increasing of gasleaking and decreasing of compression pressure; This wearing behavior was clearer when running theengine at maximum speed (5,500 rpm) and high load (95-100% maximum load) as shown in Table 8

 Measurement of compression pressure of combustion chamber

- Initially, compression pressure in the cylinders of the two test engines increased slightly (about 1.277%) which indicates that the engines in run-in period created good tightness in combustion chamber(up to 200 hours);

1.119 After the initial period, compression pressure inside the cylinders of both engines decreased due tothe wear of compression rings and cylinder Especially, after about 400 hours, compression pressure insidecylinder of the engine running E5 decreased to lower than 14 bars (about 13%) due to high wear Meanwhilethe engine using M92 gasoline still had high compression pressure of more than 15 bars

* Mode 3: Measurement of lubricant quality

 Measurement of lubricant quality (kinetics viscosity)

- Despite of a run-in time of 15 hours, the gas

leaking downward to crankcase still existed

because the working surface between compression

rings and cylinder was not sealed tightly After 100

hours running, when the working surfaces were

sealed tightly restricting gas leaking to the

crankcase of the engines, the kinetics viscosity of

the lubricant increased about 7%;

- Up to 370 hours of running, the kinetic

viscos-ity of the lubricant in the engine running E5 showed

a remarkable decrease while that of the engine

run-ning M92 gasoline still remained at high value

 Measurement of content of metal formed byengine wear in lubricant

- Impact of E5 on engine wear was also ated through analyzing the formation density of met-als in the lubricant

evalu-Compression rings wear: Initially the formation

of Cr and Mo in the lubricant of the engine runningM92 gasoline was higher than that in the enginerunning E5 because of the higher initial degree ofcone and oval of the compression rings of enginerunning M92 After the compression rings havebeing leveled and sealed, this trend reversed