Al-Anazi & Babadagli 2010

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Automatic fracture density update using smart well data and articialneural networksA. Al-Anazi, T. Babadagli n

University of Alberta, Department of Civil and Environmental Engineering, School of Mining and Petroleum Engineering, 3-112 Markin CNRL-NREF, Edmonton, AB, Canada T6G 2W2

a r t i c l e i n f o

Article history:Received 19 November 2008

Received in revised form4 August 2009Accepted 9 August 2009

Keywords:Smart wellsFracture networksStatic dataProduction dataStatic modelANN

a b s t r a c t

This paper presents a new methodology to continuously update and improve fracture network models.We begin with a hypothetical model whose fracture network parameters and geological information areknown. After generating the exact fracture network with known characteristics, the data wereexported to a reservoir simulator and simulations were run over a period of time. Intelligent wellsequipped with downhole multiple pressure and ow sensors were placed throughout the reservoir andput into production. These producers were completed in different fracture zones to create arepresentative pressure and production response.

We then considered a number of wells of which static (cores and well logs) and dynamic(production) data were used to model well fracture density. As new wells were opened, historical staticand dynamic data from previous wells and static data from the new wells were used to update thefracture density using Articial Neural Networks (ANN). The accuracy of the prediction model dependssignicantly on the representation of the available data of the existing fracture network. Theimportance of conventional data (surface production data) and smart well data prediction capabilitywas also investigated. Highly sensitive input data were selected through a forward selection scheme totrain the ANN. Well geometric locations were included as a new link in the ANN regression process.Once the relationship between fracture network parameters and well performance data wasestablished, the ANN model was used to predict fracture density at newly drilled locations. Finally,

an error analysis through a correlation coefcient and percentage absolute relative error performancewas performed to examine the accuracy of the proposed inverse modeling methodology.It was shown that fracture dominated production performance data collected from both

conventional and smart wells allow for automatically updating the fracture network model. Theproposed technique helps in generating another readily available at no cost data source for fracturecharacterization as a supplement to limited 1D data obtained from well logs and cores.

& 2009 Elsevier Ltd. All rights reserved.

1. Introduction

Naturally fractured reservoirs (NFR) are characterized by theirfracture network properties, such as fracture density, orientation,location, dimension, and connectivity. The network propertiescontrol the uid ow in the reservoir and therefore accurate

prediction of those parameters is essential in generating the staticmodel (fracture network) to be used for performance prediction.Several different approaches have been utilized based onstatistical, fractal, and articial neural network (ANN) methodsto build conditioned stochastic fracture network models. Fracturestatistics and distribution functions in this process are tradition-ally extracted from core, well log, outcrop, and seismic data.

Intelligent elds in which reservoir surveillance data arecontinually measured using permanently installed downhole

completion devices are becoming increasingly popular. Multi-phase production data are transmitted to engineers to monitoreld operations and make effective decisions. The readilyavailable conventional and smart well production information,referred to as dynamic data, could also be useful in generatingstatic models of NFRs. In our previous attempt, we showed that a

non-linear relationship exists between dynamic data and fracturenetwork characteristics ( Al-Anazi and Babadagli, 2007 ). In thatstudy, ANN was used to detect the underlying relationship.

Our inverse problem consists of predicting fracture networkcharacteristics using limited static (well logs and cores) andreadily available historical well performance (dynamic) data. Thesolution to such an inverse problem is ill-posed in general andcannot uniquely constrain the detailed variations in reservoirstatic properties.

The objective of this study was to investigate the importanceof smart (permanent downhole devices) and conventional(well surface devices) dynamic data in generating the fracturenetwork maps.

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Contents lists available at ScienceDirect

journal homepage: www .elsevier.com/locate/cageo

Computers & Geosciences

0098-3004/$- see front matter & 2009 Elsevier Ltd. All rights reserved.

doi: 10.1016/j.cageo.2009.08.005

n Corresponding author: Tel.: +1 780 492 9626; fax: +1 780 492 0249.E-mail address: [email protected] (T. Babadagli) .

Computers & Geosciences 36 (2010) 335347
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2. Literature review

Forward modeling is traditionally applied in reservoir char-acterization and fracture network modeling. Inverse modeling, onthe other hand, is an approach that attempts to extract a fracturenetwork that honors all available static and dynamic data.

In one of the earliest studies, Ouenes (2000) devised amethodology to characterize fractured reservoirs using ANN. Itbegan with ranking all available geologic drives and eldobservations such as structure, lithology, and bed thickness toevaluate the impact on the fracture indicator using a fuzzy neuralnetwork. Then, multiple realizations were stochastically gener-ated using neural networks that are evaluated through probabilitymaps. The methodology was illustrated using an actual tight gasfractured sandstone reservoir, and the production-based fracturedensity was successfully predicted. The resulting 3D fracturedensity volume map or probability constraints used in buildingthe discrete-fracture network can be used to estimate directionalfracture permeability for further reservoir modeling and manage-ment ( Ouenes and Hartley, 2000 ; Ouenes et al., 1995 ). Later,Boerner et al. (2003) predicted fracture intensity by integratingseismic data and 3D model attributes such as porosity andlithology and the rst and second derivatives of the structuralsurfaces. Since fracture intensity is spatially distributed anddifcult to obtain, the expected ultimate recovery was used as aproxy for fracture intensity.

Forward modeling studies also utilized the fractal theory tocharacterize the fracture networks. Babadagli (2001) applied thefractal theory to a geothermal reservoir to gure out the precisefractal dimensions of fracture properties, such as fracture length,orientation, density, length, spatial distribution, and connectivity.Park et al. (2005) characterized and generated a 3D fracturemodel for a fractured basement reservoir based on statistical andfractal analyses using static data such as FMI logs, outcrop, andseismic data.

Inverse modeling studies used either static (pressure transienttests) or dynamic (production) data. In an attempt to characterizea reservoir using pressure data, Aydinoglu et al. (2002) proposedan inverse solution methodology to characterize an anisotropicfaulted reservoir. Synthetic pressure transient data using ANNtechnology was used to determine reservoir permeability,porosity, distance to the fault, orientation of the fault withrespect to ow directions, and the sealing characteristics. He et al.(2002) presented a streamline approach to identify reservoircompartmentalization and ow barriers during primary produc-tion. In addition to those, Athichanagorn et al. (1999) developed amultistep procedure to process and interpret long term pressuredata using simulated and eld data sets. It was shown thathistorical pressure data could be used to obtain the distributionsof reservoir properties. Later, Tamagawa et al. (2002) constructeda fracture network model using static (borehole images) anddynamic data (represented by well test pressure derivativecurves). Recently, Tran et al. (2007) presented an integratedapproach utilizing object-based modeling, stochastic simulation,

and global optimization. Initially, the target fracture network was

formulated from observed eld data. Then, a stochastic simulationwas used to create an initial estimate of the fracture networkmodel. An objective function was statistically formulated be-tween the initial and the target fracture network. Finally, asimulated annealing algorithm was used to minimize theobjective function to reproduce the target network. The metho-dology was applied to an actual outcrop map and producedsatisfactory results.

Studies using production data in inverse modeling are alsoavailable. Jansen and Kelkar (1996; 1997) presented a simplecross-correlation approach using production data to examine theinterwell communication and interference of a mature wateroodin order to rank areas for subsequent development. Chugh et al.(2000) analyzed production data using Inverted Decline Curvesand the Reciprocal Productivity Index to estimate the megascopicreservoir permeability. Spatial permeability distribution wasclassied based on different scale measurements. Later, Will etal. (2003) developed a technique based on an objective functionfor gradient-based optimization of fracture-system parametersincorporating seismic anisotropic attributes and reservoir produc-tion performance data. It is a parallel workow for effective elasticand permeability elds modeling from an initial preconditioneddiscrete-fracture model. The objective function was minimizedusing a systematic update of selected fracture parameters. Thesimultaneous technique allowed fast convergence of both fracturetrend and intensity.

Reproducing the principle fracture parameters that control theow behavior has been considered an ill-posed problem and therehave been limitations to solely using well performance data topredict fracture static data. Cobenas et al. (1998) systematicallyexamined the nature of the objective function during multiphaseproduction data integration to explore the source of the non-uniqueness and the impact of some proposed remedies. Theyshowed that the continuous non-linear trade-off between para-meters is the major source of non-uniqueness during dynamicdata integration. They demonstrated the danger associated withusing dynamic data in isolation.

As seen, using ANN and inverse modeling in reservoircharacterization is not a new idea, though their applications forfracture networks are limited. However, using ANN and inverse

modeling through conventional production data in fracturenetwork modeling is uncommon. We introduced this concept inthis paper and added specic smart well data in this analysis.

3. Fracture network stochastic simulation

In our previous work, conventional well production data wasused to map fracture orientation, density, dimension, andconductivity using an articial neural network (ANN). Thefracture network model was stochastically generated usinglimited well static data in a single-layer reservoir with a totalof thirty wells. Well performance drivers were ranked based ona sequential forward regression scheme to select the best

ANN prediction model. A complex non-linear relationship was

Nomenclature

ErrL learning data set errorErrV validation data set errorErrT testing data set errorICV intelligent control valveMPFM multiphase ow meter

PDHM permanent downhole monitoring system

PI productivity index, sm 3 /barP/T pressure/temperature gaugeqo oil ow rate, sm

3 /dayq g gas ow rate, sm

3 /dayqw water ow rate, sm

3 /dayQ o oil cumulative production, sm

3

Q g gas cumulative production, sm3

Q w water cumulative production, sm3

A. Al-Anazi, T. Babadagli / Computers & Geosciences 36 (2010) 335347 336


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captured with high prediction accuracy between well perfor-mance and fracture characteristics such as density, length,conductivity, dip and azimuth ( Al-Anazi and Babadagli, 2007 ).

This work was extended to encompass smart well data in thepresent study. A two-layer reservoir model separated by a shalelayer was built using a commercial software package (FRACA)with the grid system built using the MATLAB software. Smallvalues of porosity and permeability were assigned to the matrix

to ensure its low contribution to reservoir ow behavior. Adiscretized geo-cellular model was built with 45 45 3 gridblocks. The two layers measure a uniform thickness of 15 m andare separated by a 5 m shale layer.

Fracture network maps were generated using MATLAB bydistributing fractures along the completion depth of all the wells.The code written for this purpose facilitates assigning a certaindistribution to all fracture characteristics. The prepared data lesfor a hundred wells including fracture, facies, and well trajectorywere loaded into FRACA. Fracture orientation including fracturedip and dip-azimuth was assumed not to change signicantlyover the eld. Fractures were placed randomly along eachwellbore. Similarly, fracture dimension and conductivity wereassumed to be xed over the entire eld. Two different fracturedensity distributions were assigned to the rst layer and thesecond layer. Fracture density was normally distributed repre-senting a reservoir with different well performances. Also,fracture orientation, dimension, and conductivity were xeddifferently for both layers.

Well fracture data were loaded into the static model and afracture network was generated stochastically for both layers. Thestructural model with equivalent directional porosity and perme-ability was exported to the ECLIPSE simulator for dynamicsimulation.

A black-oil model was adopted for ow in a CornerPointgeometric petroleum reservoir where geometry, porosity, anddirectional permeability values were extracted from the importednetwork maps. Hypothetical multiphase PVT properties, relativepermeability curves, and rock properties were assigned. Thereservoir was initialized by a static pressure of 500 bar and GasOil Contact (GOC) and Water Oil Contact (WOC) were assumedto be 1000 and 1535 m, respectively. The wells were placed

throughout the reservoir constrained by their original locationduring the FRACA modeling stage to obtain their correspondingfracture characteristics imbedded in the imported maps.

The well production was controlled by assigning sequentiallydifferent owing bottom hole pressures to generate different wellperformance responses. A well production plan was allocated toserve our modeling purpose in predicting the fracture density atnewly drilled wells as discussed below. The well production plan

included producing oil at a certain rate and pressure using smartwell facilities by opening and closing wells at different zones. Forexample, high fracture density zones were closed for a period of time to improve the productivity of the low density zone afterreaching a certain water cut.

In this study, we incorporated well smart data into our fracturedensity prediction model. The smart data was generated from theowing blocks of the well completion. In other words, the well owdata across the completion intervals in the rst and the third layerswere generated through ECLIPSE completion keywords. This is tosimulate intelligent well completion pressure and ow devices thatare permanently installed in smart wells. The well ow dataincludes oil, water, gas rates, and pressure for particular layers.

Fig. 1 shows a schematic representation of a smart well designused in this study. Surface measurements are done throughpressure and temperature gauges ( P /T ) and multiphase owmeters (MPFM) are used to measure multiphase ow rates,pressure, temperature, gas/oil ratio, and water cut. Also, smartpressure data across the upper and the lower productive layersare continuously monitored through permanent downholemonitoring systems (PDHMS). The ow is fully controlled byintelligent control valves (ICVs).

After the ow model simulation is performed, three simulateddynamic data were obtained: (a) the whole well (conventionaldata), (b) only the rst layer (smart well data), and (c) only thesecond layer (smart well data) performances. The data generatedfor the three scenarios are: (a) multiphase production perfor-mances that include the production rate, (b) cumulative produc-tion, and (c) the productivity index. Well performance representsthe conventional data while layer performance represents thesimulated smart data generated from smart well downholecompletion devices. This hypothetical and exact model was used

ICV

ChokeValve

P/T Gauge

ProductionPacker

PDHMS

Smart Data -1Upper Layer

(Current Study)

ConventionalData

(IPTC 11492)

Smart Data -2Lower Layer

(Current Study)

MPFM

To ProductionLine

Shale Zone

Fig. 1. Schematic diagram of smart well completion used in this study.



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as the base case in this paper to check and/or to validate the ANNmodels as explained below.

4. ANN modeling

An articial neural network (ANN) is a data mining techniquethat has the ability to capture the underlying complex non-linear

relationship in the data structure ( Haykin, 1994 ). One of the mostimportant uses of ANN is its ability to correlate a multiscalehistorical database and extrapolate the knowledge to newlyemployed input data by self-tuning its parameters to perfectlygenerate representative models.

The well performance data including multiphase productionrates, cumulative productions, and productivity indices wereloaded into the ANN modeling software . Production rates, as wellas their cumulatives at different times and at the end of theproduction life, were the inputs to the ANN model. Fracturedensities at each well were a single value in this analysis. Eachrecord contains all modeling wells that are open at the time withtheir location coordinates, performance data, and the correspond-ing fracture density values. Each record was divided into threesets including learning (60%), validation (20%), and testing (20%).The validation set was used to cross-validate the relationshipestablished during the training process and the testing set wasused to test the model quality.

A completely connected perceptron (CCP) was selected toprevent the oversizing network problem. The range of hiddenunits started at zero neurons representing a linear regression andended at 20 neurons representing a highly complex non-linearstructure. Growing architecture was selected to optimize thenetwork size in which each generation has one more hidden unitthan the previous generation. Validation error was selected as astopping criterion during training since overtraining causes thenetwork to memorize results rather than to generalize. Inaddition, a total of ten ANNs were simultaneously trained toprevent them from trapping in local minima. Finally, the bestnetwork was selected based on the lowest validation error.

5. Forward regression

The reservoir production response is nonlinearly regressed tocapture the fracture signature presented as fracture density.Performance drivers such as well locations, production rates (oil,gas, water) at selected time intervals and their cumulatives, and theproductivity indices of the wells were used in the forward regressionprocess. Certain performance drivers are highly sensitive in predict-ing fracture density. Therefore, performance input data has to beranked to optimize the ANN training process. According toFruhwirth et al. (2006) , there exists no exact solution to test thecontribution of input drivers to model quality. Several differentcombinations of input drivers should be trained and the validationerror is used as the selection criterion for the best model.

In our study, a total of fourteen drivers were submitted toneural network training considering different combinations of well location coordinates, rates, cumulative, and the productivityindex of the training wells. During network construction, thesedrivers were classied into four groups and different combina-tions were modeled.

6. Static data-driven models

Reservoir fracture network models are normally characterized

and updated using static data generated from seismic, subseismic,

and microseismic sources as well as well data such as cores andlogs. During initial eld development, there is a scarcity of staticdata to constrain the generated model which in turn increases thefracture network uncertainty.

In this part of the study, the directional permeability andporosity maps were continuously updated as more well fracturedata become available from newly added wells to demonstratethe importance of the additional static data. Several fracture

network updates were carried out by ne tuning the maps usingthe additional data and comparing the results using historymatching to eld cumulatives of the base case. The base casemodel was described in the section Fracture Network StochasticSimulation and it is used as the case to check the updated modelsagainst throughout the analyses done in this paper.

Initially, the base fracture network model was stochasticallygenerated by uploading the well fracture data of 100 wells. Thedrilling was conducted in six stages starting from drilling 16 wellsup to 80 wells. Simultaneously, a total of six networks weregenerated based on a different number of wells. Due to increase innumber of wells (and their static data) at each step, the reliabilityof the fracture network of the whole reservoir presumablyincreased.

The six network maps along with the initial base model wereexported to the dynamic simulator and history matching tothe base case for these six different realizations was conducted.The same number of wells with the same locations was used inthe ow simulations to obtain a consistent performance compar-ison. Field liquid and gas cumulative performances shown inFigs. 2 and 3 , respectively, were compared to the base case (100well case represented by triangles).

The eld performance of the initial model (16 wells) does notmatch the base case (100 wells) over the time windowconsidered. On the other hand, the last model (80 wells) presentsthe best agreement with the base case. However, looking at theother models (2464 wells) and examining the eld performancebehavior leads to the conclusion that there is no unique fracturenetwork realization that could be derived from the limited staticdata. The results also indicate that data from a large number of wells have to be obtained to generate a possible representativefracture network model. In addition, well distribution signi-cantly affects the capability of drilled wells to capture the fracturenetwork distribution. However, the data needed to constrain wellplacement may not be known a priori which makes modelingtotally based on static data limited. Hence, the addition of dynamic data to the fracture network generation process can bethought of as a potential tool to reduce the uncertainty of thegenerated fracture network. This will be the main objective of thispaper and discussed in the next sections.

7. Dynamic data-driven models

With the availability of extensive production history andlimited static data, the underlying relationship between wellperformance and fracture density can be captured and the datacan be used to generate a representative fracture network model.Conventional and smart well data were integrated to examinetheir prediction capability of the fracture density in this section.

7.1. Importance of smart production data

The conventional well data was used to map fracture densityof the two layers all together and on a one-by-one basis toinvestigate the advantages of smart data over conventional data.In mapping the fracture density, the static and dynamic well data

were used and initially the fracture density (or population)



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around each well was determined. Using the software package, itwas distributed in the whole reservoir. Obviously, increase in thenumber of wells would yield a more reliable description of thefracture network. The procedures are explained as follows:

7.1.1. Use of conventional well data to map well fracture densityWell conventional data was dened as the data obtained at the

surface using multiphase ow meter and surface pressure andtemperature gauges. Modeling was started by selecting theoptimum well performance parameters to minimize the numberof input channels and enhance ANN prediction efciency. A totalof seven models were used to select the most signicantparameters that contribute in generating well fracture density,and regression using ANN was carried out over eight years of production. Among the seven models, the seventh model was

selected as the best to predict well fracture density due to the

lowest validation error ( Table 1 ). The ANN model presents highaccuracy as seen in Fig. 4. The correlation coefcient and thepercentage absolute relative error show that well conventionaldata is a useful source of data for well fracture density mapping.

7.1.2. Use of well conventional data to map rst layer fracturedensity

In this case, our objective is to investigate the possibility of mapping fracture density in the rst productive layer using wellconventional data and to compare it with the result generatedusing the smart data of the rst layer.

Initially, the well completion data was used to map the rstlayer fracture density. A forward selection scheme was used toselect the best model. A total of fourteen models were regressedover eight years of production and the second model was selected

as the best as it gives the minimum validation error ( Table 2 ).

0.E+00

1.E+06

2.E+06

3.E+06

4.E+06

5.E+06

6.E+06

7.E+06

0Time, years

F i e l d C u m u

l a t i v e

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i d ,

S T B

16-Well-Based Model24-Well-Based Model36-Well-Based Model48-Well-Based Model64-Well-Based Model80-Well-Based Model100-Well-Based Model

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Fig. 2. Field liquid cumulative performance of updated fracture network.

0.E+00

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16-Well-Based Model24-Well-based Model

36-Well-Based Model48-Well-Based Model64-Well-Based Model80-Well-based Model100-Well-Based Model

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Fig. 3. Field cumulative gas performance of updated fracture network.



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Then, the smart well data for the rst layer was used to mapfracture density over eight years of production. A forwardselection scheme was applied to select the optimum inputchannels and nd the model with the most efcient predictioncapability. Among seven models, the second model showed theminimum validation error. The second ANN model that has wellproduction cumulatives with well geometric location was se-lected based on its lower validation error ( Table 3 ).

The correlation coefcients for both conventional and smartwell data cases show a good match over eight stages of production ( Fig. 5). Similarly, the percentage of absolute relative

error performance for both conventional and smart data yielded a

good match. This indicates that well conventional data can beused successfully to map the rst layer fracture density. It meansthat the fracture density signature was recognized in both typesof data.

7.1.3. Use of well conventional data to map second layer fracturedensity

This case was devoted to investigating the possibility of mapping fracture density in the second productive layer usingwell conventional data and to compare it with the result

generated using the smart data in the second layer. The only

0.000.10

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Absolute Relative Error,%

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2.0 3.0 4.0 5.0 6.0 7.0 8.0

Fig. 4. ANN model error prole using well conventional data.

Table 2ANN model optimization using forward selection scheme (layer-1 modeling using well conventional data) .

ErrL ErrV ErrT Model Well location qo q g qw Q o Q g Q w PI

0.6337 0.6515 0.6653 1 x x0.5352 0.6413 0.7435 2 x x x x0.8191 0.8928 1.0306 3 x x x x0.8312 1.0088 1.0603 4 x x x x x0.6829 0.9622 1.1254 5 x x x x x x x0.8384 0.9938 1.1040 6 x x x x x x x x0.7969 0.8223 1.2429 7 x x x x x3.6803 4.3225 4.0267 8 x x x x x x x2.9654 3.9129 3.5032 9 x x x x x x4.7476 5.0758 5.2060 10 x x x x5.0194 5.2213 5.1002 11 x x x3.9729 4.6276 5.1298 12 x x x3.7909 4.4240 4.4413 13 x x x x

5.3629 5.5320 5.4683 14 x

Table 1ANN model optimization using forward selection scheme (well conventional data).


0.1812 1.9993 1.9901 1 x x0.2995 1.9560 2.1173 2 x x x x0.3570 1.8017 2.0394 3 x x x x0.2912 2.0577 2.3193 4 x x x x x0.2847 1.7169 2.0378 5 x x x x x x x

0.2104 1.9436 2.1262 6 x x x x x x x x0.4189 1.6494 1.7647 7 x x x x x



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difference here is that the lower productive layer has lowerfracture density than the upper one.

Initially, the well completion data was used to map thesecond layer fracture density and a forward selection scheme wasapplied to select the best model. A total of seven models wereregressed over eight years of production and the sixth model wasfound to be the best as it gives the minimum validation error(Table 4 ).

The smart well data for the second layer was used to map thefracture density over the simulated period of production. Aforward selection scheme was applied to select the optimuminput channels and a model with the most efcient predictioncapability. Out of seven models, the fourth showed the minimumvalidation error. The ANN model that contains cumulative wellproductions with the geometric location of the wells was selected

based on its lower validation error ( Table 5 ).

The correlation coefcient and the percentage absolute relativeerror performance values for conventional and smart data show agood match over the eight stages of production ( Fig. 6).

In summary, well conventional data and smart data allowequally high fracture density prediction capability. However, thesmart completion is important to obtain static and dynamicpressure data to calculate the productivity index, which wasshown as a critical parameter in constructing the fracturenetwork models ( Tables 25 ).

7.2. Fracture density prediction

The objective here is to examine the uniqueness of dynamicdata capability to predict well fracture density at newly drilled

wells. Several different cases at different levels of static data, i.e.,

0.90

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Correlation Coefficient Using Conventional Data

Correlation Coefficient Using Smart Data-1

Absolute Relative Error, % Using ConventionalData

Absolute Relative Error, % Using Smart Data-1

2 3 4 5 6 7 8

Fig. 5. ANN model error prole comparison of rst layer smart data and well conventional data.

Table 4ANN model optimization using forward selection scheme (layer-2 modeling using well conventional data).


0.1456 1.6310 1.9953 1 x x0.1988 1.4461 1.5009 2 x x x x0.1019 1.9090 2.1178 3 x x x x0.2612 1.4818 1.3759 4 x x x x x0.1969 1.8924 1.9883 5 x x x x x x x0.1835 1.3597 1.4141 6 x x x x x x x x0.1412 1.4582 1.7380 7 x x x x x

Table 3ANN model optimization using forward selection scheme (layer-1 modeling using smart data -1).


0.2690 0.6488 0.7402 1 x x0.3106 0.6349 0.7267 2 x x x x0.2684 0.9417 0.8986 3 x x x x0.2586 0.9454 1.0263 4 x x x x x0.3126 0.9268 0.9379 5 x x x x x x x

0.2801 1.2934 1.9651 6 x x x x x x x x0.2106 0.8746 0.7943 7 x x x x x



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easily seen through the comparison of the 20, 40 and 70-wellcases.

The correlation coefcient was generated as an average valueover time. One can observe that there is a positive contributionfrom dynamic data ( Fig. 8). Initially, the correlation is too weakand as more production data is included, the correlation becomesstronger.

Combining the static and dynamic data and looking at thecases with anomalies, the addition of more wells as shown inthe case of the 50-well might have interrupted the ANNconstructed model and hence, the prediction drifted off from generating a better prediction at the newly drilled wells.Although the high correlation model that is ne tuned bydynamic data does not produce a decrease in prediction errortrend when the production data of newly drilled wells were fed tothe trained model, the prediction error started to atten off at

values less than 10% error. This is not surprising since we are

using a data-driven model that honors the existing data whichin turn guides us to focus on data preprocessing before modelingis initiated.

CASE II: The objective is to examine the prediction capabilityconsistency over time using several different initial numbers of wells and comparing the result with CASE I. A total of 5 wells weredrilled nine years after the initial eld start-up and the samemodeling procedure explained in the previous case was carriedout here. The locations of those 5 wells are totally different fromthe previous case.

The addition of dynamic performance data has generallyresulted in a decrease in trend in the prediction error. This isobvious for the cases of 30, 50, and 70 wells ( Fig. 9). Two otherwell cases (40 and 64) also showed a decrease in trend untila point where the addition of more dynamic data becomesless important and the trend showed no change with time. The

20-well case showed an almost uniform trend with the minimum

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E r r o r ,

%

20-well Case30-Well Case40-Well Case50-Well Case64-Well Case70-Well CaseBest fit (20-well case)Best fit (30-well case)Best fit (40-well case)Best fit (50-well case)Best fit (64-well case)Best fit (70-well case)

2 3 4 5 6 7 8 9 10

Fig. 7. CASE I: ANN prediction performance using staticdynamic data.

50

55

60

65

70

75

80

85

90

95

100


C o r r e

l a t i o n

C o e

f f i c i e n

t , %

20-well Case

30-Well Case

40-Well Case50-Well Case

64-Well Case

70-Well Case

2 3 4 5 6 7 8 9 10

Fig. 8. CASE I: Change of correlation coefcient with training time for different number of wells.



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prediction error. In general, the dynamic data reduced theprediction error to a maximum of 10% which is a remarkableresult and increased the correlation coefcient as illustrated inFig. 10 . When the effect of static data is examined, once can seethat having the least error with 20 wells and the highest with 70wells is an indication of the non-unique character of the fracturenetwork construction process. It is highly likely that the randomselection of 20 wells turned out to be highly representative of themodel or highly correlated to the 5 newly drilled wells. At rstsight, this could be attributed to the random nature of fracturenetworks. Randomness is involved in the fracture networkgeneration process, which is normally not the case in naturalfracture patterns. Therefore, it should be emphasized that, inthese limited runs, there is an anomaly whereas a model with 20wells is better than the one using 70 wells and additional work

will be needed to investigate this issue in some future work. The

effect of dynamic data, however, is more consistent and similar inboth CASES I and II.

This case shows the potential of integrating dynamic data intocases where large static data sets are not good enough toprovide better predictions. The prediction error in the 70-well casestarted high and having more production data reduced the errorsignicantly. This might also lead us to further select the optimumnumber of wells for prediction based on dynamic data analysis.

CASE III: The objective of this case is to examine the predictioncapability consistency of fracture density of 5 different wells overa shorter period of time compared to the previous two cases. Toachieve this, the new wells were drilled ve years after the initialeld start-up. As in the previous case, the ANN model wasconstructed using a different initial numbers of wells to examinethe potential use of static and dynamic (cumulative production)

data to predict the well fracture density at the newly drilled wells.

0

5

10

15

20

25

30

35


A N N M o

d e

l P r e d

i c t i o n

E r r o r ,

%

20-well Case30-Well Case40-Well Case50-Well Case64-Well Case70-Well CaseBest fit (20-well case)Best fit (30-well case)Best fit (40-well case)Best fit (50-well case)Best fit (64-well case)Best fit (70-well case)

2 3 4 5 6 7 8 9 10

Fig. 9. CASE II: ANN prediction performance using staticdynamic data.

50

55

60

65

70

75

80

85

90

95

100

1

Training Time Cycles, years

C o r r e

l a t i o n

C o e

f f i c i e n

t , %

20-well Case

30-Well Case

40-Well Case

50-Well Case

64-Well Case

70-Well Case

2 3 4 5 6 7 8 9 10

Fig. 10. CASE II: Change of correlation coefcient with training time for different number of wells.



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Clearly, the addition of dynamic data for all study cases, exceptthe 30-well, has enhanced the prediction capability to a certainlevel ( Fig. 11 ). The prediction error was minimized over a shortperiod of time using additional production data for the 64- and70-well cases. It might also be recognized that for two well cases(50 and 70), the initial prediction error was low and there was nofurther noticeable reduction. Analyzing the predictionperformance, the error would have been further reduced if continuous additional dynamic data were available to bring itdown to acceptable values as in the 20-well case. The anomalous30-well case reveals that the addition of more dynamic data hasdrifted the prediction off which might be attributed to the factthat the additional data has manipulated the ANN model. This islikely due to using a data-driven model that can highlightabnormal trends. The correlation coefcient was plotted for allcases in Fig. 12 . The dynamic data improved the correlation to the

values higher than 90% for all cases.

All three cases given above show the potential of dynamic andstatic data to be employed in the prediction of fracture density ina newly opened well. There were cases where anomalous trendswere detected; however, the prediction is a function of how muchthe historical static and dynamic data are related to the dynamicand static nature of the predicted wells. The study eventuallyguides us to select the optimum number of wells to enhance theprediction capability through data preprocessing based on welldynamic data.

7.3. Geometric well input parameter for ANN prediction model

It is important to encompass all input drivers that are relatedto the predicted parameter in specic and to the predictionmechanism in general. The spatial distribution of well perfor-

mance data is a characteristic of the fractured-based reservoir and

0

2

4

6

8

10

1214

16

18

20

22

24

26

1


A N N M o

d e

l P r e d

i c t i o n

E r r o r ,

%

20-Well Case30-Well Case40-Well Case50-Well Case64-Well Case70-Well CaseBest fit (20-well case)Best fit (30-well case)Best fit (40-well case)Best fit (50-well case)Best fit (64-well case)Best fit (70-well case)

2 3 4 5 6

Fig. 11. CASE III: ANN prediction performance using staticdynamic data.

50

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65

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75

80

85

90

95

100

1


C o r r e

l a t i o n

C o e

f f i c i e n

t , %

20-Well Case

30-Well Case

40-Well Case50-Well Case

64-Well Case

70-Well Case

2 3 4 5 6

Fig. 12. CASE III: Change of correlation coefcient with training time for different number of wells.



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well performance, as shown earlier, was well mapped to predictfracture density. The prediction model can be signicantlyimproved if well geometrical parameters, i.e., well coordinates,are considered. The ANN model errors given in Table 2 were

plotted in Fig. 13. There are two classes of data as can be clearly

seen: the lower ANN model error class and the higher ANN modelerror class. The distinction class criterion is the presence orabsence of well locations. The model error range was drasticallyreduced from 3.915.53 to 0.641.01 with the addition of welllocations into the ANN modeling.

To further visualize the mapping potential of this new parameter,plots of two cases were randomly selected from Table 2 . The secondmodel was chosen as the best due to its minimum validation error,which uses well cumulative productions and well locations as inputdrivers for the ANN model. This model involves a total of 100 wellsand modeling was conducted over eight years of production. First,the well location was removed from the ANN model and the cross-plot in Fig. 14 was obtained using only production performancedata. The cross-plot for the case with well locations is shown inFig. 15 . The correlation coefcient of the former model was as low as29% compared to the latter one, which was 98%. The predictedmodel considering well location was able to capture well fracturedensity at all eight stages of production with a high correlation. Thisexercise clearly indicates the importance of this critical parameter(well location) and emphasis should be given to that parameter infurther analyses.

8. Conclusions

The study shows the potential of using dynamic data infracture network parameter prediction. Our observations and

conclusions can be summarized as follows:

Dynamic data allows prediction of fracture density with a priorilimited well static data. articial neural networks (ANN) is auseful tool to be used in this exercise.

Multiphase production data integration, well cumulativeproduction, and productivity index showed a high potentialto predict fracture density.

Well conventional and smart data have equally high capabilityfor fracture density prediction.

Inverse modeling using dynamic data becomes complex whenwell fracture-related distinction criterion is absent.

A strong mapping potential of well fracture density wasobserved through the use of well geometric location as an

input to the ANN model.

0.89 0.96 0.99

4.32

3.91

5.085.22

4.634.42

5.53

1.01

0.65 0.640.82

0.0

1.0

2.0

3.0

4.0

5.0

6.0

1

Training Model Number

A N N T r a

i n i n g

V a l i d a

t i o n

E r r o r ,

%

2 3 4 5 6 7 8 9 10 11 12 13 14

Fig. 13. Fracture density models validation error comparison using data given in Table 2 .

55

60

65

70

75

80

85

90

55 Actual Fracture Density

A N N P r e

d i c t e d F r a c t u r e

D e n s i

t y

60 65 70 75 80 85 90

Fig. 14. Cross-plot of ANN predicted and actual fracture density at a stage of aproduction without well location.

55

60

65

70

75

80

85

90

55

Actual Fracture Density

A N N P r e

d i c t e d F r a c t u r e

D e n s i

t y

60 65 70 75 80 85 90

Fig. 15. Cross-plot of ANN predicted and actual fracture density at a stage of aproduction with well location.



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The prediction model loses its capability of prediction whensolely using well performance data.

The proposed methodology can be easily integrated intointelligent eld technology to approximate fracture densityaround targeted well locations.

Acknowledgements

We would like to thank Beicip Inc. for providing the FRACAsoftware and Ms. Pascale Neff of Beicip for technical support. Weare also thankful to Schlumberger for providing the ECLIPSEsoftware. We are grateful to Dr. Rudolf K. Fruhwirth (Neuro GeneticSolutions GmbH) for providing the cVision (ANN) software package.The rst author (AA) also thanks Saudi Aramco for nancial supportthrough its scholarship program during the course of this study. Thispaper is the revised and improved version of SPE 113282 presentedat the 2008 SPE Europec/EAGE Annual Conference and Exhibitionheld in Rome, Italy, 912 June 2008.

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Al-Anazi & Babadagli 2010