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SPE 30722 Improved Fractured 3-D Seismic

Reservoir Characterization

Using Neural Networks,

Geomechanics

and

A.M. Zellou, A. Ouenes, SPE, A.K. Banik, SPE, New Mexico Petroleum Recovery Research Center

combmes the curvature method and a neural network to describe the subsurface fracture intensity. As a second step, the subsurface fracture network is obtained from the fracture intensity map using the “weighting method. ” This new method provides a new tool

Copyright 1995, Society of Petroleum Engineers, Inc. This paper was prapamd for presentation ●t the SPE Annual Tachnical Conference & Exhibition held in Dallas, TX, U.S.A., 22-25, October 1995. This per was aafactadfw p+aaantationby ●n SPE Program Committaa following review of m “~ mation containad in ●n ●batract submitted by the authw(s). Contants of th- pa. per, as praaantad, have not bean reviewed by the Society of Patmkaum Enginaast ●nd am aubjactad to correction by the uthor(s). The mate$al, ●s praaantad, does not nacasaarily mfkactany poa-kionof the Sociaty of Patmleum Engwmara,its officasa,or members. Pa em mad ●t SPE maatin~ am subject to pubficatmn review by Ediil Committees #the &atyofPatmle.m En&naara. P.rmiaaic.ntoco~J: ~~~&fi:tn:&ra:~ than 300 swds. Illuatratmns m not ba copiad. ack!mwkadgamantof where ●nd whom tha paper was pmaamtad.Wrii Lilwariin, SPE, p.~. S= K~SS&, ~ch~M, T.. %“ 75QQ U.S.A., fax 01.214-9524435.

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1. For the geologic interpretation of two types of fractures: fold-related fractures in the Young Deep Unit [YDU) and regional fractures 2. For the simulation of fractured reservoirs described in a companion paperl

Abstract In this paper, a new approach which uses seismic at-

3. For the exploration and the development of fractured reservoirs.

tributes in a quantitative manner to enhance the characterization of fractured reservoirs is presented. The new approach uses the seismic travel time to identify the reservoir structure and the thickness of the fractured producing formation. Using these data, a quantitative geomechanical model is constructed. When comparing the geomechanical mode~ dIX&ed from seismic data and mapping methods, it becomes apparent that many structure details may be misrepresented and/or missed when interpolation methods are used for defining resqrvoir structure. Using a Neural Network and the available well data, the geomechanical model is correlated with the oil production. This model is compared to the seismic amplitude which appears to provide the best indication of fkacture intensity in the case of the field studied. Given the seismic data, whkh are available over the entire reservoir, and the fracture model found by the neural network, the overall reservoir ihcture network is predicted.

FractureMapping Background. During the last few years, the characteri-

zation of naturally fractured reservoirs haa been a challenging task for geologists and petroleum engineers. A good understanding of the fracture network in the subsurface and on the outcrop implies the knowledge of fracture genesis. A first classification of natural ftacture systems proposed by Stearns and Friedman2 consisted of two major categories of fractures 1. regional orthogonal fractures 2. structure-related fractures (tectonic fractures). The fixture classification was further systemized by Nelson3 and two other types of fractures were added: contraction or diagenetic fractures and surface-related fractures. This paper, and the companion paper focus on regional orthogonal fractures and structure-related fractures. Regional fractures are those that pervade over large areas with little or no change in orientation and are always perpendicular to the bedding surface. The constant orientation of regional fractures ia due to a constant state of stress over a large area. Zoback4 et al. deacnbed the state of stress for the North American and parts of the Pacific plate. The description includes the Permian Basin, where the reservoirs addressed in this paper are located. Structur&related fractures, or tectonic fractures, are those related to a local tectonic event. There are two

Introduction Naturally fractured reservoirs represent a significant percentage of oil reservoirs throughout the world. Because of their specificity and heterogeneity, naturally fractured reservoirs have been the subject of many studies. Basically, these studies deal with the prediction of the subsurface fracture network. Indeed, a good understanding of the fracture network, i.e., a good understandhg of fkcture connectivity, orientation, and location is the key point to fractured reservoir characterization. A new method in fractured reservoir characterization ia presented in this paper. As a first step, this new method

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IMPROVED FRACTURED RESERVOIR CHARACTERIZATION USING NEURAL NETWORKS. GEOMECHANICS AND 3-D SEISMIC

types of structure-related fhctures: fractures associated with faults, and fractures associated with folds. These fractures form networks with specific spatial relationships to folds and faults. Faults are, by definition, a state of shear. The majority of fractures associated in the vicinity of faults are shear fractures parallel to the fault, shear fractures conjugate to the fault, and extension fractures bisecting the acute angle between these two shear duections. Fractures associated with folds are complex and are more dficult to interpret than fractures associated with faults. However, the fracture pattern is strongly reia’ti to the shape of the fold. Stearns and Friedman2 gave a schematic illustration of most common fractures associated with folds (Fig. 1). It is important to note that conjugate shear fractures form in such a way that the angle between them is approximately 60 degrees. These different types of fractures have been investigated both at the outcrop and subsurface level. Outcrop. Currently, cores, wireline logs, FMS (Formation MlcroScanner), and outcrop analysis are the best tools for describing fractures. The purpose of measuring fractures on the outcrop is to establish the characteristics of the fractures in terms of length, aperture, and spacing. It is now well established that the hktograms of h~w a ln@-IIQrmd &ktribUtiO11.5 +h... +k._ harnt.t.ria+ire UUWG..=.. CA*LMA -.”. .“”.- - . . - .0

These outcrop studks may provide a complete and accurate description of the fracture network.5-7 The surface measurements are then speculatively projected into the -..L-..-- ILL:.....lu~y . .. h,. ..m”fi.+m;n u= ~~~=t u-. d the su’bsw=ixe. ml-~ne exmqJunmwII modeling of the subsurface iiacture network using outcrop information may be misleading because of 1. The important diiTerencebetween burial and surface states of stress 2. The existence of more fractures on the outcrop than in the subsurface as a result of stress release and weathering. Therefore, if drilling locations are the objective, one needs to examine the subsurface fractures for a realistic description of the reservoir. Subsurface. A complete and accurate description of the fracture network can be obtained from the outcrop as mentioned earlier. Several methods have been used to get ss &X.Was passible to such a description of the subsurface fracture network. Core analysis is commonly used to describe the subsurface features of fractures such as dip, strike, and aperture. Core samples from vertical wells give little information on the degree of reservoir fracturing. The probability of a vertical well intercepting fractures is very small, and not representative of the entire reservoir. Core samples from slanted or horizontal wells provide better information on fracturea.s However, the information obtained from the core analysis is liiited by

SPE 30722

1. The dimension of the core, as compared to the size of the reservoir. The core data can only be used in 1-D models and are little help for 2-D or 3-D models. 2. The uncertainty of the valuea due to the unloading, whkh generates surface-related fractures, and artiiicially induced fractures during drilliig. These two limitations make the core analysis difiicult to interpret. Nevertheless, several methods have been used to describe theoretically the fracture network. Among them are two recent methods 1. The application of stochastic simulation to fracture network deacriptiong 2. The application of fractal geometry to the study of fracture networks.l” The stochastic estimation of a fracture network in the reservoir is based on geostatistics. In other words, the fracture network in the reservoir ia simulated using the available core data at difTerent points in the field. The fractal geometry has been applied to simulate a fracture network: using pressure transient tests.l”

FracturedReservoirCharacterizationUsinginterpolated ReservoirStructure Background. The intensity and dwection of the fractures in the reservoir is a function of the state of stress, which is strongly related to the depth, the thickness, the tectonic history, and the structure of the reservoir. Murrayll and Lisle12 took into account the fo%rth parameter by intrducing the use of curvatures. Murray’s model includes an unrealistic assumption that ihctures occur only in the direction perpendicular to the radius of the folding, meaning the shear properties of the state of stress are not considered. The idea of using the curvature of the structure to estimate fracture intensity is a sound approach as the stress in the rock depends on the curvature. However, the stress state is described by a tensor and, therefore, has directional properties. 0thersls~14 generW the curvature method to include the curvature in many directions. Those curvature values in four directions are obtained from an interpolated structure map, created by using well log data and a mapping method.1 Those curvature values represent one set of inputs for the neural nstwork. The other inputs are the depth, the thickness, and the cii-mlative production of the reservoir. A precise description of the neural network, i.e., inputs, outputs, number of layers, ia given in the companion paper. A fracture intensity map is obtained after training the neural network, by using all the known geomechanical parameters, the structure, and the bed thickness. This approach was applied to three diiferent type9 of actual oil reservoirs.

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A.M. ZELLOU, A. OUENES, A.K. BANIK

3

due to the regiomd stress in the N60E duection, and the other orthogonal and structure-related, perpendicular to the first trend. Furthermore, we can see on Fig. 8 that the fracture intensity histogram ia simiiar to the fracture length and fracture spacing histograms, with a log-normal distribution, just as found in the literatures–s With th~ new method to characterize fractured reservoirs, it becomes possible to classify the fractures and distinguish between high and low intensity zones. The most important fractures, those that aflect fluid flow, are the ones with high intensity. That is to say, the hcture directions are strongly rekited to

Results and Discussions. The Young Deep Unit (YDU) is one of the eight Bone Springs reservoirs located at the edge of the Northwest Shelf in southeast New Mexico. The Unit area is about two square miles (33 x 23 gridblock of 350 feet), with 30 wells. Fig. 3 shows the fracture intensity map of the YDU where depth, curvatures, and cumulative oil production are the inputs of this model. The neural network provides a value of the fracture intensity for each grid block. This fracture intensity is a combination of the length, the aperture, and the population of the fractures in the considered gridblock. Using the “weighting method” (see Appendix), a fracture network map, Fig. 4, ia created from this hcture intensity map. Analysis of this map shows clearly that the fracture system is structure-related (Fig. 5). Note ‘that- the ~“U is “the”nose of a fold. One can see the fracture strip, from the west through the center back to the northeast, following the shape of the folding nose and the conjugate shear fractures, making an angle of approximately 60 degrees.

1. The state of stress in the case of the Formation AA for Unit A and B. One maximum principal stress direction in thk region can be detected as one fracture dwection trend in the rose diagram. These fractures can be classified as regional fractures.2’3 2.. The structure of the formation. If we refer to the Young Deep Unit, the fracture system is foldrelated (tectonic fracturea3) and we notice the con- jugate shear fractures, making an angle of about 60 degrees.2

In this paper and in its companion, two other actual oil reservoirs were considered in the application of th~ new approach. The first unit, referred to as Unit A is located in a large fractured West Texas reservoir. Sixteen sections of th~ unit were considered for the study. For Unit A, the fracture intensity was represented by the cumulative oil produced at 78 wells drilled prior to 1960. In Unit A, we are interested in mapping the ffacture intensity of a 900 to 1000 ft thick shaley sandstone formation, which we will describe as Formation AA. For Unit A, little inform+ tion was available at the time of the study. Besides the estimated cumulative production of 78 wells, the tops of Formation AA and a 3-D seismic survey were available. For Unit A, the average density of wells per section is WDA = 4.9wells/sq.mile. Another unit, referred to as Unit B encompasses 22 wells that produced on primary before water injection into Formation AA started. Tops of Formation AA were available at 55 wells located in 4 sections. The average well density per section in Unit B is WDB = 13.7wells/sq.mile.

Fkom these three reservoirs, we can conclude that the interpolated structure map method accurately describes the fracture network if enough wells are available over the study area in the case of a flat lying reservoir. Siiaremap accuracy can be obtained if the structure of the reservoir presents enough variability. In order to obtain more accurate structural tops in areas where well control ia limited, such as in Unit A, seismic attributea may also be used.

Fractured Reservoir Characterization using 3-D Seismic Background. In this investigation, 3-D seismic data from the naturally fractured shaley sands of Formation AA were used for enhancing the fracture map. Two horizons (upper and lower Formation AA) were selected based on seismic two-way travel time. Interpretation was made using an autotracking procedure, available in the seismic software.zi The autotracking procedure usea selected aeedpointa and searches adjacent traces for picks with similar or identical seismic attributea. The autotracking modes used in this present work are the standard or default modes provided by the software. The upper and lower Formation AA horizons were mapped and the plain average amplitude between the upper and lower Formation AA has been calculated. Fractures in the subsurface are affected by several geological parameters; structure, bed thkkness, composition, main size, and =–. Dorosity.3Jo The previous method using ~._.––– ––– the interpolated structure method takes into account the first two parameters, the bed thkk.ness and the structure, through the curvature. Tine two 3-D seismic attriiiutes used in the study are the plain average amplitude and the tw-way travel time. The 3-D seismic travel time provides

The presence of a major fracture trend haa been welldocurnented4J5 in this area. The rose diagram, Fig. 9, obtained from the fracture network map of Unit B (Fig. 7) gives a good indication of the fracture trend in the N60E dwection, the general trend observed for Formation AA.15 However, considering the sparse well density of Unit A, it ia hard to obtain a good fracttie intensity map (Fig. 12) using the interpolated structure map shown in Fig. 10. This map that covers 16 sections was obtained by using the 78 known tops and the mapping method described in the companion paper. The lack of well data does not redt — in —.-—— a realistic of the the same _–-–-=-—_ –—--– --- —-.—.- Using --.4 --- cxtimate -. .----—-.----- structure. methodology in Unit B, the neural network provided the fracture intensity map shown in Fig. 6. Given the higher weii density in Unit B, the interpolated structure map used by the neural network leads to a meaningful fracture interpolated map. Notice two hcture trends (Fig. 9): one

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TS +t.~ :~.m~+i~” +~OkWMX is la.rrp pnnll~h. ~-wave data AlSOIl.”.* . . AU..-.”-.“ -— ~-----0--, IL VIIGIV. provides a good indication of fracture intensity. These new tools should enhance the usefulness of seismic data in locating natural fractures. Since horizontal wells are , .—..—--.:--- -..4.. aepencient on inwr~epun~ um u--.1 ~ Lh..+....n. u-~ U1c=, prcper Rservoir description is imperative. Application of thk research should lead to the improved success of horizontal driiling in fractured reservoirs.

information on the structure and the bed thickrress of the Thd amnlitlwle indicates ----— litholon ii% f3T’VGil-. AU= YM.M.. U. ~.u~w .---= ---------------—--w However, in Formation AA, the lithologies variation. 17-18 do not vary greatly and the average amplitude is affected by the contrast between rock and fluids. A highly fractured zone should contain a large amount of fluids that will be represented in a seismic trace by a high amplitude. The average amplitude map created from this data will be compared with the fracture intensity map generated by the neurai network. ml.

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Conclusions A new method in fractured reservoir characterization has been described in th~ paper. Bssed on the results presented, we arrive at the following conclusions:

3-D Seismic Travel Time Instead of Structure Top.

As a first step, only the 3-D seismic travel time is used aa an input parameter in the neural network to describe the structural top and the thkkness of the Unit A. The rlknlmmd in -TNu .+-..Ubucl. -+. .=1 by picking mu. cu +nm .“y U-=.-J “- .-~. ~~ ~Sohtdned

1. A new tool has been developed to describe the subsuflace fracture network.

the horizon corresponding to the upper Formation AA in terms of milheconds. The time imaging of the top of Unit A ia close enough to the depth imaging in this case, since no subsalt, thrust belt, or other fault shadows are present in this area.lg The main limitation on the success of depth imaging is the accuracy of the velocity model,19 whk.h was not available. The thickness of the formation is the time difference between the two horizons corresponding to the lower and upper Formation AA. Those two parameters are used as input parameters in the neural network. The fracture intensity map in Fig. 13 more accurately depicts the highly fractured zones and illustrates several well known C!:...,. .- !i-- TT-.:A nA. Old? ml “a+zxrm.lr ,,QOQ 111 IJlle u IIIIJ tk ~lt?’ub W.” V. AX w.,. Swsd spots

2. This fracture network provides geologic information on the type of fracture ( structurerelated fractures or regional fractures). 3. The location and the direction of natural fractures ia obtained from both the fracture intensity map and the fracture network map. 4. The curvature method used with a neural network provides a good estimation of fracture intensity and d:rection.

structure properties and the zones of intense fracturing are located around major structural changes (Fig. 11), we suggest that the fracture network in Unit A is not only a result of regional stress but also is structure-related.

5. The addition of seismic attributes (travel time and average amplitude) improves the quantitative characterization of naturally fractured reservoirs, eap~ cially when well coverage is poor.

3-D Seismic Amplitude. In the second step, the plain vertical average amplitude is analyzed. Thk parameter provides information on the lateral variation of petrog~ ological parameters such as lithology and porosity.20 The amplitude of a P-wave reflection is directly related to the magnitude of the acoustic impedance contrast, which is the product of the rock density and the P-wave velocity. The average amplitude contrast has been mapped in Fig. 14, and clearly shows the location of highly fractured zones. Thk validates the fracture intensity map obtained from the neural network using the 3-D seismic time. Thk also confirms that the *D, P-wave survey can contribute significantly in locating highly fractured zones.18

6. Thk new approach was tested on three actual fractured reservoirs located in the Permian Basin. Seismic data provided more accurate predictions than ones obtained using mapping methods. 7. The 3-D seismic average amplitude alone provides enough information on highly fractured zones for very thkk formations such as Formation AA.

Acknowledgments We would like to thank Martha Cather, Bill Weiss, John Cot6 and David Schechter from the Petroleum Recovery Research Center for their contributions to this work. We extend our thanka to Tom Sheffield, Paul McDonald from Parker & Parsley, and HEYCO for providing field data. Finally, we would like to thank Jack Gawron, Sharaon Cama, David Shimbo, and Scott Evans with Landmark Graphics Corporation for their assistance and for prvialing seismic interpretation software and the necessary hardware. Special thanka to Adwait Chawath6 and Mark Valenzuela for reviewing this paper. This research effort was funded by the State of New Mexico.

Application to Field Development and Exploration The application of this research to field problems is apparent and should have wide spread utility in locating infill drilling sites and development opportunities where the well density and production history are suf%cient. Ty@cally 2-D seismic and regional geology are used to define exploration prospects whkh are then refined with 3-D seismic.

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A.M. ZELLOU, A. OUENES, A.K. BANIK

References

5

Murray, G.H., Jr.: “Quantitative Fracture Study-Sanish Pool, McKenzie County, North Dakota,” AAPG Bulletin, (Jan. 1968), V.52,

[1] Ouenes, A., Richardson, S. and Weiss, W.W; “l%ctured Reservoir Characterization ad Performance Forecasting using Geomechanics and Artificial Intelligence,” paper SPE 30572 to be presented at the 1995 SPE Annual Technical Conference and Exhibition, Dallas, Texas, oct.22-25.

No.lj

57-65.

iisie, J.L.: “Detection of Zones of Aiinormai Strains in Structures Using Gaussian Curvature Analysis,” AAPG BuZletin, (Dee.1994), V.78, No.12, 1811-1819.

[2] Stearns, D.W and lMedman, M.: “Reservoirs in Fractured Rock,” AAPG Memoir 16,82-106.

Ouenea, A., Weiss, W.W., Richardson, S., Suk tan, A.J., Gum, T. and Brooks, L.C.: “A New Method to Characterize Fractured Reservoirs: Applications to Infill Drilling: paper SPE/DOE 27799 presented at the SPE/DOE 1994 Symposium on Improved Oil Recovery, Tulsa, Oklahoma, April 17-20.

[3] Nelson, R.A.: “Natural Fracture Systems: De scription and Classification,” AAPG Builetin, (Dee.1979), V. 63, No. 12,22142221. [4] Zoback, M.L., Zoback, M.D. and ScMltz M.E.: “Index of Stress Data for the North American and Parts of the Pacific Plate,” United States nf ~~~~:~~. , Gnlmical _.-. -o--— ~IWVqj -lle=nti.rn~m~ .~- .-.-.. .- t.h~ --Open-File Report 84157, 1984.

[14]

Richardson, S.: Genemlization of the CurvaA __l;.._a:a_ ..4 Na.. -l NA...,LnLn L-. — IV’ll..sI. -.J U1bU . ..-.JA~lJ6WiWIW16 bU7C CW6UU UJ iv GU1 w L v GUWUt w to Ructunxi Reservoir Chamcterization, Master Thesis, New Mexico Td, Socorro, NM (1995).

r-~ 7.. .--– [OJ wrenz,

J .C. and Lauhch, SE.: “DeS@tkXi and Interpretation of Natural Fhcture Patterns in Sandatones of the I%ontier Formation Along the Hogsback, Southwestern Wyoming? Gas F@ search Institute Topical Report, GIW940020, Drilling and Completion Group, January 1994.

[6] Lorenz, J. C., Teufel, L.W. and Warpinski, N.R.: “Regional l%ctures I: A Mechanism for the Formation of Regional Fkactures at Depth in FlatLying Reservoirs,” AAPG Bulletin, (Nov. 1991), V.75, No.13, 17141737.

[15]

Elkina, L.F. and Skov, A.M.: ‘Determination of Ihcture Orientation from Pressure Interference: 7hms. AIME (1960) 219,301.

[16]

Bunch, A.H. and Dromgoole, P.W.: “Llthology and Fluid Predktion from Seismic and Well Data: paper presented at the EAPG Conference, Stavanger, June 1993.

r.

[7] Lorenz, J.C. and Finley, S.J.: “Regional Fractures II: Fracturing of Meaaverde Reservoirs in the Piceance Basin, Colorado: AAPG BuZletin, (Nov. 1991), V.75, No.13, 1738-1757.

-1 [1(J

Lynn, H.B.: %eisrnk Ik%@cctionof Grknted Fracturea? Oil and Gas Joumalj Aug.4, 1986.

[18]

Lynn, H.B., Batea, R., Layman, M. and Jones, M.: “Natural lkcture Characterization Using Pw-.= n~~;.. ”.”.. “w—. %;.-;. -..”s-, 11.t. vSp, lkrrahnl~ -.. —-.. vv-v G .--”” Loga, and the In-Situ Stress Field Determination,” Paper SPE 29595 presented at the 1995 SPE Rocky Mountain Regional/LowPermeabllity Reservoirs Symposium, Denver, CO, March 20-22.

Imaging

[8] Lorenz, J.C. and Hill, R.E.: “Subsurface lhcture Spacing Comparison of Interferences from Slant/Horizontal and Vertical Cores? paper SPE 21877 presented at the 1991 SPE Rocky Mountain Regional Meeting, Denver, April 15-17. [19]

[9] GUOG. and Evans R,D.: “Geolo@c and Stochastic Characterization of Naturally Fkactured Reaervoirs~ paper SPE 27025 presented at the III Latin American/Caribbean Petroleum Engineering Conference, Buenos Aires, Argentina, 2729 April 1994.

Fagin, S. and McCann, D.: Petm Systems Imaging: uary/February, 1995.

“Seismic Depth JanWorldj

[20] Shultz, P.S., Ronen, S., Hattori, M., Mantran, P.,

Hoakina, J., and Corbett, C.: “Seismic-Guided Estimation of Reservoir Properties,” paper SPE 28386 presented at the 1994 SPE Annual Technical Conference and ExhMion, New Orleans, LA., USA, Sept. 25-28.

[10] Acuna, J.A. and Yortsos, Y. C.: “Application of Fractal Geometry to the Study of Networks of Fractures and Their Pressure Thnsient? Water Resoumes Resenmh, (March 1995), V.31, No.3, 527-540.

[21]

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Seis WorksTM/3D, LandmarkGraphicsCorporation.

IMPROVED FRACTURED RESERVOIR CHARACTERIZATION USING NEURAL NETWORKS, GEOMECHANICS AND 3-D SEISMIC

6

Appendix: The Weighting Method

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The fracture network map discussed in this paper is obtained from the fracture intensity map predicted by the neural network. 1 The main trends of fracturing can also be seen. A model hss been developed to derive the fracture network based on these two observations. Thk+model is ..L.,..L.,06. UUGD+hc,%-41A”, U.ZG.“..”i”m “ .u~ based on the “weightiiig Tbetkd, “ .wAllul assumptions: b

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The angle of the fracture, O, for each grid block is found by using the eight surrounding gridblock.

Each grid block has a fracture intensity ~~i,j, (Fig. 2) These eight gridblock associated with their intensity ar considered as eight weighted points on a circle of radius I (IXo) L {Kl~. a).

~~e

is @.ve~ by th AA “.,.. ”.. nf “. the ---- fractama -------..

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vector Afi,j 7:

Each grid block on the fracture intensity map is associated with a fracture, i+l

This fracture has an angle and an intensity represented by a line. The length of thk lime is related to the fracture intensity (Fli,j ). Furthermore the duection of thk lime corresponds to the hcture orientation measured by the angle 0.

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FIk,i ~i,jMk,l

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k=a-1 i=j–1

Knowing the coordinates of the eight weighted points, the vector M~

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can be written as:

The fracture intensity as represented by a line is a combination of 1. The aperture 2. The fracture population in the gridblock ‘1 The fracture !e@h. “.

Based on the previous assumptions, the two pararneters smociated w~h each mid block, which are the ~gle and the length associated with the fracture intensity, are determined as follows: ●

The length representing the fracture intensity is a function of the gridblock fracture intensity (F1i,j ). In order to distinguish the dtierent sizes of fractures and to focus on high fractured zones, a power function has been used in the graphical representation. length = 1.510x*

A

=

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=

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. . . . . . . . . . . . . . . . . . . . ..(A.l)

210

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Fig. l-Schematic

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A.M. ZELLOU, A. OUENES, A.K. BANIK

illustration of fractures associated with the nose of a fold (after Stearns and Fkiedman2).

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IMPROVED FRACTURED RESERVOIR CHARACTERIZATION USING NEURAL NETWORKS, GEOMECHANICS AND 3-D SEISMIC

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212

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A.M. ZELLOU, A. OUENES, A.K. BANIK

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IMPROVED FRACTURED RESERVOIR CHARACTERIZATION USING NEURAL NETWORKS, GEOMECHANICS AND 3-D SEISMIC

10

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. A.M. ZELLOU, A. OUENES, A.K. BANIK

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Fig. 12—Unit A Fhcture Intensity Map (using the interpolated map).

~.5

i

Fig. 13—Unit A Fracture Intensity Map (using 3-D seismic travel time).

I

AverqeAn@lude

Fig. 14-Unit

A Average Amplitude Map.

215

11