Economically Improving Oil Recovery by Advanced Reservoir Management

Journal of Petroleum Science and Engineering, 8 (1992) 205-219 205 Elsevier Science Publishers B.V., Amsterdam

Economically improving oil recovery by advanced reservoir management*

Gian Luigi Chierici Faculty of Engineering, University of Bologna, Bologna, Italy

(Received November 25, 1991; revised version accepted May 22, 1992)

ABSTRACT

Chierici, G.L., 1992. Economically improving oil recovery by advanced reservoir management. J. Pet. Sci. Eng., 8: 205- 219.

In oil fields exploited by water or immiscible-gas injection the application of the advanced reservoir management (ARM) technique allows high volumetric flooding efficiencies (and therefore high oil recovery factors) with a moderate increase in cost and without making recourse to enhanced oil recovery (EOR) processes. Following a short analysis of the characteristics and drawbacks of the various EOR processes, the ARM technique is described in detail. This technique calls for the use, all through field life, of the most advanced well and interwell testing and logging methods. The geological, or "static" model of the reservoir (which is built from outcrop studies and well data gathered during the field development phase) is modified and validated based on interwell connectivity data and displacing fluid front position recorded throughout the field life. The "dynamic" numerical model of the reservoir obtained in this way is continuously updated and is used for engineering remedial action (including the drilling of infill wells) aimed at maximizing reservoir rock coverage by the injected fluid(s). A mandatory prerequisite for oil field exploitation by the ARM technique is the availability of a team of experts covering all disciplines related to reservoir geology and engineering. The experts work synergetically together from the very moment the field is discovered up to its abandonment.

Introduction

It is well known that the crude oil is now, and will be for some decades to come, the most important source of energy to mankind. Oil reserves contained in the oil fields discovered so far approximately amount to (Table 1 ) 160 km 3 ( 1 km3= 109 m3= 1 billion m3). This figure is based on the current average estimate of 30 percent for the fraction ofoi l (ER) that can

*Text of a conference given at the "Petroleum Reservoir Characterization" workshop held on May 25, 1991 in Florence, Italy, in the framework of the 3rd Conference of the European Association of Petroleum Geoscientists. Correspondence to: G.L. Chierici, Faculty of Engineering, Inst. Earth Sciences, Viale del Risorgimento 2, 1-40136 Bologna, Italy.

be economically produced using established technologies.

In addition, the experts evaluate undiscovered oil reserves at 95 to 120 km 3 (Jacquard, 1991).

In total, the currently discovered reserves plus not-yet-discovered reserves represent some 70-80 years ofoi l production at the current rate.

Worldwide, the cumulative amount of oil that will remain trapped in the oil fields when they are completely exhausted (that is, when no more oil can be economically produced using the currently available technologies) is evaluated (Table 1 ) at some 840-890 km 3, which represent some 250 years ofoi l production at the present rate.

This enormous amount of oil is equivalent

0920-4105/92/$05.00 © 1992 Elsevier Science Publishers B.V. All rights reserved.

206

TABLE 1

World resources and reserves of conventional oil (billion m 3 stock-tank oil); from Jacquard ( 1991 )

G.L. CH1ERICI

Resources Reserves

Original Cumulative Identified reserves production as of reserves as of

1.1.1991 1.1.1991

Oil remaining in the reservoir at abandonment

In identified 880 265 oilfields

In undiscovered 320- 400 95-120 deposits

Total 1200-1280 360-385

105 160 615

95-120 225-280

105 255-280 840-895

to 100,000 billion dollars at the prevailing ap- proximate sale price of $ 18/bbl of crude oil. Over the last 40 years this has drawn the attention and the R&D efforts of most oil compa- nies worldwide.

In this paper we will discuss the following points:

( 1 ) Why the approaches used so far for improving oil recovery have resulted (with few exceptions) in little success, at least from an economic point of view?

(2) Which new technologies must be developed for a significant improvement in oil recovery within the economic constraints im- posed by the world free-market?

1. The enhanced oil recovery (EOR) processes: their characteristics and limitations

The list of the enhanced oil recovery (EOR) processes which have so far undergone field testing, either on a pilot or on a fieldwide scale, is presented in Fig. I where the main compo- nents, the fluids injected into the reservoir and the classification (thermal, or miscible, or chemical) are shown for each process.

As well known the oil recovery factor, ER, is the product of two quantities, that is the volumetric flooding efficiency, Ev, and the microscopic displacement efficiency, ED. The rela- tive influence of the various EOR processes on Ev and ED is presented in Table 2.

Miscible processes can only improve the ED value, while they do not have any beneficial effect (and sometimes they have a negative effect!) on Ev.

A sizable improvement in the Ev value can be obtained by thermal processes (which also have some beneficial effect on ED) and by the injection of polymer solutions.

Chemical processes, which are based on the injection of a cushion of micellar solution followed by a polymer solution, show a beneficial effect on ED and a limited effect on Ev. Their drawbacks are the high cost of the chemicals and their adsorption by the reservoir rock.

From the above-described influence of the various EOR processes on the Ev and ED val- ues it is obvious that, before considering an EOR process for a field, the main question to be answered is: "how the residual oil in this field is split between oil trapped in the rock streaks not swept by the displacing fluid (Nr,v) and oil remaining in the pores in those parts of the reservoir which came in contact with the displacing fluid (Nr.D)?"

This is not an academic question: it is one of the most important questions which must re- ceive an answer before one may even consider an EOR process for a field.

In fact, if most of the residual oil is of the Nr, V type (poor Ev) there is no point in using a miscible process with a gas, or a micellar/ polymer flooding. These fluids will preferen-

ECONOMICALLY IMPROVING OIL RECOVERY BY ADVANCED RESERVOIR MANAGEMENT

C O M P O N E N T F L U I D P R O C E S S

207

[I STEAM DRIVE ]~9[..-.--

STEAM SOAK (HUFF'N PUFF) ~1---

~ 1 IN SITU COMSUS¥ION I

r I WET COMBUSTION ~ -

- - ~ MISCIBLE DISPLACEME NT I

- I WAG-CARBON OIOXZD E ~ - -

VAPORIZING GAS DRIVE

_ [ CONDENSING GAS DRIVE

I J WAG-NATUBA'-GAS =1 NITROGEN MISCIBLE I

I DISPLACEMENT

I FLUE GAS MISCIBLE DISPLACEMENT

OIL. WATER

POLYMERS

ALKALIS

Fig. 1. E n h a n c e d oil r ecovery (EOR) processes .

MICELLAR-POLYMER FLOODING

POLYMER FLOODING

CAUSTIC FLOODING

w ch c~

I" g

i

to J W

L

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tially flow along the same paths as the water (or gas) which displaced the oil before, and only the tiny amount ofoil left in the pores will be displaced.

On the other hand, if most of the residual oil is of the Nr, D type (poor ED) there is no point in using a process designed for increasing the volumetric flooding efficiency, such as a thermal process or a polymer flooding.

Only in the last few years it has become

common practice to run campaigns of spe- cially-tailored pulsed neutron capture (PNC) logs (Corwith and Mengel, 1990) in oil fields being considered for EOR projects, with the aim of determining the spatial distribution of residual oil. Lack of information on this vital topic has resulted in the technical failure of a large number of EOR projects.

Unfortunately, little attention has so far been paid to the fact that, in order to reach a high

208

TABLE ~ z .

Effect of EOR processes on Ev and Eo improvement

EOR Process Ev ED Improvement Improvement

Thermal Steam drive *** *

Steam foam flood *** * Steam soak ** Very hot water ** **

In-situ combustion *** **

Miscible

Chemical

Hydrocarbon gas ***

Carbon dioxide *** Nitrogen ***

Polymer ***

Surfactant I polymer * ** Caustic * **

Performance Indicator: - none, * little, ** significant, *** major.

volumetric efficiency, in any flooding process (based on water, or gas, or an EOR fluid as displacing agent) it is mandatory that the larg- est possible percentage of the reservoir rock volume be connected to at least a couple of production/injection wells. This matter, which seems trivial at first look, will be discussed in detail in the next chapter.

The main drawbacks of the EOR processes

G.L. CHIERICI

are the high investments they involve (which, in most cases, are front-end investments with returns deferred in time) and their high operating costs. Some information on these points is presented in Table 3 (M6o, 1988) where a comparison is made between the costs of the various EOR processes and those of oil field exploitation by water injection or by conventional depletion, in different countries.

Although some cost reduction is expected to be provided by future technological improve- ments, the cost of EOR processes still remains too high when compared with the current selling price of the crude.

With the exception of the thermal process of steam drive, which can be profitably applied to heavy-oil reservoirs shallower than 900 m, and of miscible processes in areas where cheap injection gas is available, all other EOR processes are, at the best, at the borderline between profit and loss: this makes them partic- ularly vulnerable to oil price slumps.

The 1990 worldwide production of crude oil by EOR processes, subdivided by countries, is presented in Table 4 (Jacquard, 1991 ). It must be noted that the EOR oil represents 2.7% only of world oil production.

TABLE 3

Oil production costs and investments; from M6o ( 1988 )

Production costs Investments US $ / m 3 US $ / m 3 / d

Conventional oil Mean value Middle East Non-OPEC countries

High-e~st oil Rough offshore conditions Deep offshore Improved and enhanced oil recovery Waterflooding Thermal processes Miscible gas /carbon dioxide f looding Chemical flooding- polymer

- surfactant Extra-heavy crude

30 25 ,000- 50,000 6 3000- 20,000

50 20 ,000- 75,000

60-125 + 65,000-155,000 + 60-190 + 95,000-220,000 +

12- 60 6000- 30,000 60-160 50,000-160,000 60-190 + 65,000-160,000 60-125 65,000-190,000

125-315 + 95,000-190,000 125-190 190,000-250,000

ECONOMICALLY IMPROVING OIL RECOVERY BY ADVANCED RESERVOIR MANAGEMENT 209

TABLE 4

EOR production worldwide (thousand m3/day); from Jacquard (1991)

Type of EOR process Total

Thermal Miscible Chemical

U.S.A. 72,200 30,400 1900 104,500 Canada 1300 20,200 2700 24,200 Venezuela 17,200 1700 18,900 U.S.S.R. 12,400 2400 2 2 , 3 0 0 37,100 Others (estimated) 27,000* 44,500** 300 71,800

Total 130,100 99 ,200 2 7 , 2 0 0 256,500

*Mainly from Douri field (Indonesia). **Mainly from Hassi Messaoud (Algeria) and Itisar C (Libya) fields.

Percentagewise, the highest contribution of the EOR processes to oil product ion occurs in the U.S.A. Being a mature area, with a large number of depleted and almost-depleted oil fields, a high technology, a stable political situation and a current reserve-to-yearly-production ratio of about 10 only, the U.S.A. are, and have been for the last 30 years, the ideal testing ground for all EOR processes. In spite of that, EOR oil contributes by only 8.6% to the U.S.A. oil production; some 70% of the EOR production is heavy oil from shallow fields exploited by steam drive and steam soaking.

2. Well spacing: its influence on the volumetric flooding efficiency

An obvious conclusion arising from what has been presented in the previous pages is that, no matter what displacing fluid is used, high volumetric efficiencies can be attained only when a very high percentage of the porous and permeable rock "islands" constituting the pay is connected to at least a couple of production + injection wells.

This brings into the picture the influence of pay heterogeneity on oil recovery factor.

It is well known that, in addit ion to the heterogeneity resulting from the presence of faults, almost all reservoir rocks are intrinsically het-

lOO%

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F- Z ~ DELTA PLAto

tu ~COA... " i ~ Q" ~ T A R Y CHAN O /POINT llAmi

SO0 1 0 0 0 1 5 0 0 2 0 0 0 ft

LENGTH OF SHALE INTERCALATION

Fig. 2. Continuity of shale intercalations as a function of depositional environment (from Weber, 1982).

/d.~A! Existing Well SpHIng#

lB. With Blanket Inflll Drilling#

/C. With Strategic Inflll Drlllln~

~ii:. ::~:~ " . ~ i:" ~:.:~!~'":!~i~

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Fig. 3. Recovery of uncontacted mobile oil (UMO) by infill drilling (from Ray, 1990).

erogeneous at macro- and megascopic scale as a result of the depositional processes.

The classical diagram by Weber ( 1982 ) presented in Fig. 2 shows how the statistical distribution of the length of shale intercalations depends on the energy of the depositional environment: the higher the energy the smaller is the average length of shale bodies. The same applies to the length of the porous and perme-

210 G.L. CHIERIC1

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0 ~ 00

LI.

~ 4o u,I

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I N T E R W E L L D I S T A N C E

Fig. 4. Recovery efficiency as a function of interwell distance, Slaughter field, Texas (from Chierici, 1990a).

TABLE 5

Composition of Texas oil reserve additions (1973-1982); from Fisher (1987)

Volume % of (million m 3) total

103.4 11 New field wildcat discovery, with appreciation

Reserve growth from existing fields Extension and inf'fll drilling 696.4 73 New-pool discovery 95.4 10 Tertiary projects 39.8 4 Delayed abandonment 22.3 2

Total 957.3 100

able bodies (sand, sandstone, carbonates) which constitute the pay of the petroleum reservoirs.

It is obvious that, for a given depositional environment, the smaller the well spacing the higher is the number of porous and permeable bodies connected to at east a couple of production + injection wells, and therefore the higher is the volumetric flooding efficiency and the oil recovery. The situation is schematically shown in Fig. 3.

The influence of well spacing on oil recovery efficiency had already been brought to the at-

tention of the people in the oil patch back in the 1920s by Cutler (1924), with his rule stat- ing that, in each area of an oil field, oil recovery is proportional to the reciprocal of interwell distance. Unfortunately this empirical rule faded out, with time, in the mind of reservoir engineers.

A proof of the validity of Cutler's rule is presented in Fig. 4 (Chierici, 1990a), where the oil recovery percentage in the huge Slaughter field, TX (which has been exploited by waterflooding) is plotted versus the reciprocal of interwell distance. Figure 4 is based on data presented in the classical paper by Van Everdingen and Kriss (1980): the average straight line drawn through the cloud of data points shows how well Cutler's rule holds in this case.

Among the proofs of the influence of well spacing on oil recovery we mention here those provided by a study of the Bureau of Eco- nomic Geology of the University of Texas at Austin (Fisher, 1987) evidencing that:

(1) Some 73% of oil reserve additions in Texas from 1973 to 1982 (Table 5 ) are due to infill and extension drilling, against a tiny 11% provided by new-field-wildcat discoveries;

(2) Some 16% of the original oil in place (OOIP) in the U.S.A. fields (that is some 12.7 billion m 3 ofoil) is mobile oil contained in unswept zones of developed reservoirs (Fig. 5 ) and should therefore be considered for (geologically targeted) infill drilling. This com- pares with a meagre 3% of the OOIP (i.e., 2.6 billion m 3 of oil) which could be produced from U.S.A. oil fields if and when the oil price reaches 30 $ ( 1987 ) per barrel.

Finally, a recent study sponsored by the US Department of Energy (Ray, 1990) on oil recovery additions that can be achieved in a number of oil fields in Texas, Oklahoma and New Mexico, either by the EOR process of polymer flooding, or by permeability profile modification through in-situ formation of gels, or bly blanket inf'fll drilling, shows (Fig. 6 ) how infill drilling provides the highest increase in

E CONOMICALLY I M P R O V I N G OIL RECOVERY BY A D V A N C E D RESERVOIR M A N A G E M E N T 211

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I LE O I L \X' ", , ' , \

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TARGET FOR CURRENTLY ~ TARGET FOR "ADVANCED CASE" ] IMPLEMENTED EOR PROCESSES EOR PROCESSES F (OIL PRICE UP TO 3 0 $ / b b l ) (OIL PRICE ABOVE 30 $ / b b l )

Fig. 5. U.S. oil distribution and additional oil recovery from geologically targeted inffll drilling and enhanced oil recovery (EOR) (from Fisher, 1987).

/ Pollqnet" Floodlt~ // / Infll l Dri l l l l~

Fig. 6. Uncontacted mobile oil (UMO) reserve additions in individual process projects. Analyzed reservoirs in Texas, Oklahoma and New Mexico (from Ray, 1990).

reserve additions independent of the selling price of crude oil.

Can we conclude that waterflooding, enhanced by blanket infill drilling, is the panacea for economically improving oil recovery? The answer is no.

Obvious economic constraints limit the number of wells that can be drilled in an oil field containing a given amount of oil in place.

As a consequence, it is mandatory that infill well locations be carefully selected so as to op- timize reservoir performance using the mini- m u m number of wells.

To this end advanced techniques of reservoir management must be used: this theme will be treated in detail in the following chapters.

3. Advanced reservoir management (ARM): a technology for oil recovery improvement

3. I. A R M : what is it?

A good pragmatic definition (Chierici, 1990b) of reservoir management is "maximizing the economic value of petroleum reservoirs by optimizing production rate and hydrocarbon recovery, while minimizing capital investments and operating expenses".

Advanced reservoir management techniques must be used in order to improve oil recovery over and above the value obtained by standard waterflooding and immiscibile gas injection, without making recourse to expen- sive and sometime questionable EOR processes.

212 G.L. CHIERICI

In a nutshell, advanced oil recovery (ARM) is based on the following points:

( 1 ) The appraisal, development and exploitation of the oil reservoir are studied, planned and followed by a complete and efficient team of specialists including geologists, geophysi- cists, geostatisticians, log analysts, reservoir and production engineers;

(2) The synergetic activity of the team starts at the very moment the reservoir is discovered and is continuously carried out up to field abandonment;

(3) During the reservoir appraisal and development phases the main (but not only) task of the team is to provide a detailed and reliable geologic (or "static") model of the reservoir. The model is used during the development and early-production phases of field life, as well as for initializing the numerical simulation model of the field. The most sophisti- cated survey, logging and statistical treating techniques are used in order to provide data to the geological model;

(4) As soon as the reservoir is under production, frequent and regular campaigns of surveys and tests are planned and carried out with the aim of determining the evolution in space and in time of displacing fluid (s) fronts and of the basic reservoir parameters (pressure, well productivity, water/oil and gas/oil ratios). The information gathered is synergetically interpreted by the team of specialists supervising field life;

(5) All through the reservoir exploitation phase a continuous feedback process between data gathered from reservoir performance and numerical model simulations is established. The numerical model is continuously validated by adjusting its parameters so as to ob- tain a "dynamical" model matching field performance. This model is used in planning rate and pattern changes in production and injection wells and well remedial works (recomple- tions, fracturing, acidizing, etc. );

( 6 ) Based on field performance and numerical model forecasts, the team of specialists su-

pervising field life periodically plans and pro- poses to the management large field remedial works aimed at improving interwell connectivity, and therefore the volumetric efficiency of reservoir flooding (drilling of infill wells, large scale changes in production/injection well pattern, improvement in reservoir permeability profile by polymers cross-linked in situ, etc. ).

It should be noted that the ARM approach provides a geological/engineering model of the reservoir which, being validated by matching (in steps) the complete life of the field, is the most reliable model on which different EOR processes can be studied and numerically tested when the reservoir is exhausted by water- or gas-flooding.

Moreover, at the end of field life the maxi- mum of connectivity is achieved between injection and production wells. This is a mandatory prerequisite for the success of any subsequent EOR project.

3.2. ARM." the "'static" geological model

As mentioned in the previous section, the first step in the ARM technology is the con- struction of the geological (or "static") model of the reservoir based on well data gathered during the appraisal and development phases. As a consequence, at this stage only a limited number of control points are available in the reservoir.

Direct information on reservoir characteristics available at this point are only "static" data (porosity, permeability, initial pressure, fluid compositions) provided by cores cut, logs recorded and tests performed in the wells.

But what about reservoir characteristics in the interwell areas? This is the most important question the reservoir geologist must answer before even starting to think of the geological reservoir model.

Let us suppose a well has been cored and the permeability profile from cores presented in the upper part of Fig. 7 has been obtained. It


PAY Z O N A T I O N

P E R M E A B I L I T Y B L O W - UP OF Z O N E C

/ / / C2

/ / / C3 , ,~

C5

a. MULT ILAYER b. RANDOMLY HETEROGENEOUS C. LENT ICULAR

0 III Z 0 N

® ® ® ® ® ®

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® ® ® ~)

® ® ®

® u ®

= ®1 z o ® N ®[ ®

® 1

1® I®[

I I I ®

Fig. 7. Permeability variations within a reservoir zone, as shown by core analyses; (a)-(c) different geological models to explain and extrapolate them to the well drainage area (from Chierici, 1985).

shows that within each zone of the pay there are some variations in permeability.

But what about the lateral continuity of each streak with different permeability? (Chierici, 1985). Are they continuous layers, as shown in Fig. 7a? Or is this a stochastically heterogeneous unit of sedimentation, as shown in Fig. 7b? Or is the pay made of intercommunicat ing streaks with different permeability and different areal extension, as shown in Fig. 7c? And what about the presence of faults and of in- terbedded shales, which are so common in sand/shale sequences deposited in high-energy environments?

The picture of the continuity of permeable streaks versus distance, resulting from well information, obviously depends on data density, that is on the number of wells drilled in the reservoir.

A typical case history is that of the Means San Andres reservoir in Andrews County, (Tex.). The San Andres Unit is a very heterogeneous carbonate formation with a compli-

cated pattern of streaks and lenses with large differences in permeability. The Means San Andres reservoir was developed in steps, initially on a 40-acre spacing, then on a 20-acre, and finally on a 10-acre spacing.

Figure 8 shows how the picture of the continuity vs. interwell distance of the permeable carbonate streaks (that is, the information on the distribution of their size) changed when the data gathered from the wells drilled on a 20- acre, and then on a 10-acre spacing, were added to the information initially obtained from the wells drilled on a 40-acre spacing (Barber et al., 1983).

The smaller the well spacing, and, therefore, the higher the density of control points, the more heterogeneous the reservoir rock ap- pears to be. This is a common fact resulting from the experience gained in a number of reservoir studies.

From the above considerations it follows that the direct information gathered during well drilling and completion is not sufficient

214 G.L. CHIERICI

@

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z

z o o

100

80

60

40

20

_ _ _ _ I I l _ _ I

~ ' ~ R 10. 4 CRE

0 =6o 46o .;o .6o

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1000

DISTANCE BETWEEN WELLS, meter

Fig. 8. Interwell connectivity progression, Means San Andres Unit, Texas (from Barber et al., 1983).

for constructing a detailed and reliable model of the reservoir. To this end other information and data treatments must be added, these are:

( 1 ) detailed paleogeographical, sedimentological and petrophysical studies of reservoir rock outcrops (if any) completed with basin- wide studies taking into account the data gathered from all wells drilled in the basin. Studies of this type are being carded out, on different depositional environments, both in Europe (Ravenne et al., 1989; Weber and Van Geuns, 1990) and in the U.S.A. (Honarpour et al., 1989; Kittridge et al., 1990);

(2) 3D seismic surveys interpreted in stratigraphic mode (Justice et al., 1991). Al- though the vertical resolution attained so far by these techniques is limited, when the surveys are jointly interpreted with outcrop and well data they provide valuable indirect information on the variation of reservoir rock parameters in the interwell areas;

( 3 ) statistical treatment of all "hard" (measured quantities) and "soft" (expert knowl- edge) information available. Mainly in the appraisal and initial development phases, the number of control points (wells) in a reservoir, and of measured physical data, is not sufficient for building a wholly deterministic model of reservoir architecture. As a conse-

quence, recourse must be made to a statistical treatment of the available information.

To this end various approaches have been used. The most recent approach, which has currently gained a wide consensus (Omre et al., 1992 ) is the so called two-stage stochastic modelling of reservoir heterogeneities.

In stage one of the process the large-scale heterogeneities which are associated with different geological bodies, or facies, are modelled either deterministicaUy (if density and quality of the "hard" data is sufficient to de- fine the spatial continuity and extension of the different sedimentary bodies or flow units) or by a discrete stochastic model. Marked-point processes (Delhomme and Giannesini, 1979 ), Markov fields (Farmer, 1988 ), truncated random functions (Gurrillot et al., 1989), two- point histograms (Farmer, 1989) are the most common methods for stochastically generat- ing the discrete model, which is appropriately conditioned so as to honor known ("hard") information from wells, outcrops and 3D seismics.

Simple layer cake architectures can usually be handled deterministically, while jigsaw puzzle and labyrinth (Weber and Van Geuns, 1990) reservoir types can only by modelled stochastically.


In stage two of the stochastic modelling technique the spatial variation of the petrophysical characteristics within each sedimentary body, or flow unit, is modeled using a stochastic model of continuotis type. The indicator kriging (Journel and Alabert, 1990), Gaussian random fields (Matheron, 1973) and fractals (Hewett, 1986 ) are the most common methods used for estimating parameter distribution in continuous models.

Recent examples of the application of two- stage stochastic modelling have been presented by Alabert and Massonnat (1990), Damsleth et al. (1990) and Rudkiewicz et al. (1990).

When a stochastic approach is used in building the geological model of a reservoir, each "realization" (that is, each distribution of reservoir parameters obtained by repeatedly ap- plying the stochastic model to the simulation of reservoir architecture) has, by definition, the same probability of reproducing the actual internal structure of the reservoir.

Unfortunately, when such "equiprobable" geological models are used in forecasting (via numerical reservoir models) the production performance of the reservoir, different results are usually obtained. While in this way the in- determination range of reservoir performance can be evaluated, the problem remains of how to select the most probable behaviour of the reservoir on which investments, and overall economics in general, of field development and exploitation should be based.

This is the reason why more and more "hard" (that is, well, outcrop and seismic) information must be gathered during field development and exploitation. Action should be continued up to the point when the amount and quality of information available make completely deterministic at least stage one of the above sketched two-stage process.

By this approach the uncertainty range of future field performance and the economic risk involved in field exploitation are considerably reduced.

TABLE 6

Information used in constructing the "static" geological model

( 1 ) study of the outcrops of the reservoir rock (2) sedimentology, tectonics and diagenesis studies at basin

scale (3) sedimentological, petrographical and petrophysical

analyses on cores (4) 3D seismic surveys interpreted in both structural and

stratigraphic modes (5) crosswen seismic surveys (an emerging tool) (6) well logs interpreted in terms of lithology, porosity and

fluid saturations (7) drill-stem and production test results (8) two-stage deterministic/stochastic mapping of reservoir

heterogeneities (9) information on interwell connectivity as a function of

well spacing

A list of the most important data to be gathered and studies to be performed in the prep- aration of the geological model is shown in Ta- ble 6.

All the available data and studies must be jointly and synergetically examined by the specialists of the team supervising field life: usually at each meeting spirited discussions take place with occasional crossfires. The geological model of the reservoir can be drawn only after each point is cleared and agreement is reached.

3.3. ARM: the "dynamical" model

The geological model is intrinsically a "static" model of reservoir architecture, as it does not imbed any information on fluid flow and front (s) displacement performance in the reservoir.

As soon as the oil field is under production (and, if it is the case, water or gas is injected in it) the team of specialists supervising field life must conceive, plan in detail and supervise campaigns of surveys to be carried out at regular time steps all through fieM life in order to get information on its "dynamic" performance.

In addition to the standard well tests which are supposed to provide information on the

216 G.L. CH1ER1CI

dynamic performance of each well (flowing and shut-in bottom-hole pressure; evolution in time of the productivity and of gas/oil and water/oil ratios ), the team must plan in detail and supervise campaigns of special tests aimed at determining:

(1) The degree of continuity between the wells and each permeable rock streak in the reservoir;

(2) The evolution in time and in space of fluid saturations in the reservoir, and therefore the position of the displacing fluid(s) front (s) at each point in time.

The most important tests and logs to be run to this end are (Table 7 ):

For the evaluation of interwell continuity: ( 1 ) Pulse tests between wells (Ehlig-Econ-

omides et al., 1990; Bunn et al., 1991 ); (2) Single-well vertical pulse tests (Hira-

saki, 1974; Bremer et al., 1985 ); ( 3 ) Interference tests; (4) Tracer tests (Brigham and Abbasza-

deh-Dehgani, 1987; Mishra et al., 1991 );

For the determination of fluid distribution and front(s) position in time:

( 1 ) Production (PLT) logs;

TABLE 7

Information on reservoir performance needed for getting the "dynamic" model from the "static" one

(l

(2

(3

Well behaviour production data production (PLT) logs pressure drawdown and buildup tests vertical pulse tests between layers Interwell connectivity pulse tests interference tests tracer tests Fluid distribution in the reservoir and front(s) advancement neutron-density (CNL+FDC) logs pulsed neutron capture (PNC) logs induced gamma-rays spectral (IGRS) logs nuclear magnetism (NML) logs

(2) Neutron-density logs (Galford et al., 1989);

(3) Pulsed-neutron capture logs (Corwith and Mengel, 1990 );

(4) Induced gamma-ray spectral logs (Hertzog et al., 1989);

( 5 ) Nuclear magnetism logs (Freedman and Rouault, 1989).

In particular, the information on fluids distribution behind well casing obtained from pulsed-neutron and nuclear magnetism logs, and the information on point (s) of water and free-gas entry into production wells obtained from PLT logs, are of vital importance for determining the evolution in time of the displacing fluid front (s).

In order to get information on the dynamic behaviour of the reservoir, a suitable early- production phase should be carried out through a limited number of wells.

After each batch of special tests, the data recorded are jointly and synergetically interpreted by the team of specialists supervising field life, with the usual spirited discussions and crossfire.

The resulting information is fed to the numerical model, and its parameters are ad- justed so as to match calculated versus actual field performance.

In making the adjustments, the usual "garbage in, garbage out" approach must be avoided: for each parameter an accurate check of the consistency of the modified value with the geological model must be made.

Every time the information gathered from the field and matched on the model evidences an irregular advancement of the displacing fluid front(s), with the formation of fingers and "pockets" of unswept rock, the team must plan appropriate remedial action (s) aimed at maximizing the volumetric flooding efficiency, and therefore the oil recovery factor.

Reservoir reaction to remedial works (such as well recompletion, reperforation and stim- ulation, permeability profile modification by


polymers crosslinked in situ, change in the production/injection rates and well pattern, drilling of infill wells targeted at draining mobile oil contained in unswept rock "pockets") is evaluated on the numerical model before the remedial work is carded out and the results obtained in the field are fed back to the model as input data.

The repeated process of feeding the numerical model with new information on interwell connectivity and field performance, adjusting the model and feeding back to field operations new production strategies and schedules (Fig. 9) upgrades the initially stochastic numerical model (Haldorsen and Damsleth, 1990; Dam- sleth et al., 1990; MacDonald et al., 1991 ) into a deterministic one, where rock parameter distributions approach the actual internal structure of the reservoir.

Moreover, and most important, by the continuous tracking of the displacing fluid front (s) and its repeated adjustment process,

a high volumetric flooding efficiency (and therefore a high oil recovery factor) is achieved.

At the end of field life by waterflooding, or by immiscible gas injection, the following ARM by-products are available for use in any subsequent EOR project:

( 1 ) a good connection between production and injection wells; and

(2) an accurately validated deterministic numerical model of the reservoir, on which different EOR processes can be numerically tested before proceeding to a field pilot test.

Conclusions

If continuously and properly applied from oilfield discovery all through its exploitation by water- or immiscible gas injection, the ARM technique may result in a volumetric flooding efficiency, and therefore in an oil recovery

I DISCOVERY

WELL r P ELMNARY On LLNOO i

RESERVOIR EVALUATION APPRAISAL WELLS

GEOLOGICAL MODEL I

"' FIELD

I A"AND°NMENT PRIMARY DEPLETION

÷ IOR/EOR

UPOATING THE

GEOLOGICAL MODEL

EVALUATION OF

FIELD PERFORMANCE

PLANNING OF REMEDIAL ACTIONS

I DRILLING OF

DEVELOPMENT W E L L S I I I

UPDATING THE ~ _ ~

GEOLOGICAL MODEL /

Fig. 9. Reservoir management: the iterative (or "feedback") approach (from Chierici, 1990b).

DEVELOPMENT L

PLANNING

218 G.L. CHIERICI

percentage, which makes recourse to EOR processes no longer necessary.

The availability of a complete and efficient team of specialists in all disciplines related to reservoir appraisal, development and exploitation, and its synergetic activity, is the basic prerequisite for the application of the ARM technique to a field.

ARM calls for a lot of well and interwell tests to be run all through field life. The data gathered provide information on the internal structure of the reservoir, allow tracking the advancement of displacing fluid front (s) and locating "pockets" of unswept rock, thus al- lowing appropriate remedial action aimed at maximizing reservoir rock coverage by the displacing fluid (s).

Obviously, the ARM approach calls for additional costs. Top management should be made aware that the extra time and money spent to run and process data from special tests by teams of specialists constitute results which are a significant asset of the oil company.

Nomenclature

ED microscopic displacement efficiency, dimensionless

ER oil recovery factor, dimensionless Ev volumetric flooding efficiency,

dimensionless Nr.D volume of stock-tank oil remaining in the

pores of the fraction of reservoir rock which was swept by the displacing fluid(s) (m 3)

Nr.v volume of stock-tank oil trapped in the reservoir rock fraction not swept by the displacing fluid (s) (m 3 )

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Economically Improving Oil Recovery by Advanced Reservoir Management