Quantifying and predicting naturally fractured reservoir behavior with continuous fracture models

Quantifying and predicting naturally fractured reservoir behavior with continuous fracture models Creties Jenkins, Ahmed Ouenes, Abdel Zellou, and Jeff Wingard ABSTRACT This article describes the workflow used in continuous fracture modeling (CFM) and its successful application to several projects. Our CFM workflow consists of four basic steps: (1) interpreting key seismic horizons and generating prestack and poststack seismic attributes; (2) using these attributes along with log and core data to build seismically constrained geocellular models of lithology, porosity, water saturation, etc.; (3) combining the derived geocellular models with prestack and poststack seismic attributes and additional geomechanical models to derive high-resolution three-dimensional (3-D) fracture models; and (4) validating the 3-D fracture models in a dynamic reservoir simulator by testing their ability to match well performance. Our CFM workflow uses a neural network approach to integrate all of the available static and dynamic data. This results in a model that is better able to identify fractured areas and quantify their impact on well and reservoir flow behavior. This technique has been successfully applied in numerous sandstone and carbonate reservoirs to both understand reservoir behavior and determine where to drill additional wells. Three field case studies are used to illustrate the capabilities of the CFM approach. INTRODUCTION The characterization of naturally fractured reservoirs is a recurring challenge for oil and gas companies. Reservoir models built to assist with field development planning and depletion optimization need to accurately incorporate the effects of natural fractures in the nearwellbore regions and to also predict their distribution in interwell areas. Copyright 2009. The American Association of Petroleum Geologists. All rights reserved. Manuscript received February 10, 2009; provisional acceptance April 16, 2009; revised manuscript received May 31, 2009; final acceptance July 13, 2009. DOI:10.1306/07130909016 AUTHORS Creties Jenkins DeGolyer and MacNaughton, Dallas, Texas; cjenkins@demac.com Creties Jenkins is a senior staff geologist for De- Golyer and MacNaughton where he specializes in reservoir characterization, geocellular modeling, and resource estimation in clastic reservoirs, including coalbed methane and shale gas accumulations. He received an M.S. degree in geology and a B.S. degree in geological engineering from the South Dakota School of Mines and Technology. Ahmed Ouenes Prism Seismic, Greenwood Village, Colorado; ouenes@prismseismic.com Ahmed Ouenes is the president of Prism Seismic. Previously, he was the chief reservoir engineer at (RC) 2 where he developed the first commercial software for the CFM technology. Ahmed smain interest is the development of improved reservoir characterization technologies especially for fractured reservoirs. Ahmed graduated from Ecole Centrale de Paris and holds a Ph.D. in petroleum engineering from New Mexico Tech. Abdel Zellou Prism Seismic, Centennial, Colorado; abdel@prismseismic.com Abdel M. Zellou is director of consulting at Prism Seismic. He has worked as a consultant on numerous fractured reservoirs all over the world and contributed to the drilling of many successful wells. He codeveloped ReFract, a leading fractured reservoir software using patented technology. Abdel graduated from New Mexico Tech with an M.Sc. degree in petroleum engineering. Jeff Wingard DeGolyer and MacNaughton, Dallas, Texas; jwingard@demac.com Jeff Wingard is a senior staff reservoir engineer at DeGolyer and MacNaughton where he has developed and evaluated geocellular and simulation models for waterflood, miscible gas, and thermal Enhanced Oil Recovery projects. He earned a B.S. degree in chemical engineering from the Massachusetts Institute of Technology in 1980 and a Ph.D. in petroleum engineering from Stanford University in 1988. ACKNOWLEDGEMENTS The AAPG Editor thanks the following reviewers for their work on this paper: Soren Christensen, Stuart D. Harker, Tony Morland, Ronald A. Nelson, and an anonymous reviewer. AAPG Bulletin, v. 93, no. 11 (November 2009), pp. 1597 1608 1597

Many attempts have been made in the past to achieve this, but most of them have proven to be inaccurate and unreliable. For example, many companies are still using simple methods such as reservoir curvature to identify areas that are likely to be fractured. Such a simplified approach ignores lithology, bed thickness, and other factors that are generally known to influence fracture development. Similarly, geostatistical techniques such as kriging and sequential Gaussian simulation (SGS) are commonly inadequate for distributing fracture properties in interwell areas because these properties do not change in a regular manner away from well control. Geomechanical models attempt to construct a fracture description and mechanical property distribution from the tectonic history, but these models commonly fail to capture the complex heterogeneity and anisotropy of the fracture system (Mace et al., 2004). Discrete fracture modeling (Dershowitz et al., 1998; Sabathier et al., 1998), which has become very popular in recent years, is constrained by the assumption that a limited number of macroscale fractures, as observed in cores and image logs, control fluid flow. A review of discrete fracture modeling methods and their characteristics can be found in the works of Dershowitz et al. (2004) and Bourbiaux et al. (2005). To capture fracture effects at multiple scales and simultaneously integrate all of the available information, including core, log, seismic, and well test data, an alternative technique (Ouenes et al., 1995; Ouenes, 2000) called continuous fracture modeling (CFM) can be used. This article provides a review of the CFM approach by describing its key characteristics and illustrating these with three case studies. In each of these studies, objective criteria are used to evaluate the predictive capability of the generated model, which is critical to ensure its reliability. This can be done by comparing the model properties to the actual properties found in new wells or in existing wells that were not used to construct the model (also known as blind wells). Another method of comparison is to test the validity of the model in a numerical simulator to see if well performance can be reasonably matched without significant model adjustments. CONTINUOUS FRACTURE MODELING APPROACH The CFM approach does not focus on the fractures themselves but instead on the factors that control where fracturing occurs. Lithology, structure, proximity to faults, and other geological factors are commonly known to control the location and intensity of fracturing. These factors, known as fracture drivers, can be identified not just from wellbore data but also from seismic data, which is the key to predicting fracture occurrence throughout the reservoir. Through the CFM approach, these fracture drivers are related to fracture indicators, which include the interpretation of fractures from core descriptions, image logs, and production logs that demonstrate the existence of a fracture at a specific location. Once this relationship is established, the fracture drivers can be used to predict the location of fractures elsewhere in the reservoir. Common geological fracture drivers include facies types, porosity, and reservoir zonation. Geomechanical fracture drivers include deformation, slopes and curvatures of structural surfaces in multiple directions, and proximity to faults. Seismic fracture drivers include elastic properties, acoustic impedance, and spectral imaging attributes. In combination, these fracture drivers are a much more powerful tool for characterizing the fracture system than if used alone. The CFM approach can be applied in both two and three dimensions (2-D and 3-D). When working in 2-D, the fracture indicator is represented by a single value at each well, and the fracture drivers are a series of 2-D contour maps. Ouenes et al. (1998) and Zellou and Ouenes (2003) presented a 2-D CFM example where Dakota Formation sweet spots were identified in the San Juan Basin. When working in 3-D, fracture indicators are assigned to model cells intersected by wellbores, and the fracture drivers consist of distributed properties conditioned to log, core, and seismic data. Table 1 lists those fracture drivers most commonly used in building a geocellular model for CFM and some of the methods used to obtain the seismic fracture drivers. The table also contains a list of common fracture indicators available at the wellbore. A neural network is used to find possible relationships between the fracture drivers and fracture indicators observed at the wellbore. The neural network approach first ranks all the fracture drivers according to how reliably they correlate with the fracture indicators. The modeler then reviews the results in light of what is known about the significance and robustness of each fracture driver, and how the ranking compares to what is understood about the physical distribution of fractures in the reservoir. The best-correlated fracture drivers that make the most sense are then subjected to a training and validation process before being used to 1598 Continuous Fracture Modeling (CFM)

Table 1. Summary of the Most Common 3-D Fracture Drivers Used in the CFM Approach along with Possible Fracture Indicators Available at the Wellbore Geological Fracture Drivers Structural and Geomechanical Fracture Drivers Seismic Attributes Used as Fracture Drivers Fracture Indicators in the Wellbore Core- and log-derived porosity and permeability Structural curvature (dip and slope) Amplitude and amplitudebased attributes Fracture count from image logs Lithofacies volumes (shale, dolomite, calcite) Structure and reservoir depth Impedance derived from poststack inversions Fracture count from core descriptions Log data (gamma ray, density, resistivity, etc.) Deformation Frequency-dependent spectral imaging attributes Fluid entry or exit locations from production logs Fluid saturations Stress and strain fields Statistical spectral imaging attributes Well-test permeabilities Correlation framework Distance to faults Elastic properties derived from prestack inversions Difference between deep and shallow resistivity-log values Vertical and lateral trends Bed or layer thickness Azimuthal anisotropy attributes Drilling losses generate a suite of equiprobable fracture-density realizations. These realizations can then be screened and calibrated to permeability-thickness (kh) data or directional permeability data obtained from well tests. The resulting effective permeability model can then be exported for use in numerical simulation and development planning. A simplified workflow summarizing the entire 3-D CFM approach is shown in Figure 1. VALUE OF SEISMIC DATA IN CONTINUOUS FRACTURE MODELING The CFM approach relies heavily on the use of seismic data to provide key fracture drivers. Structural fracture drivers such as dip magnitude and interpreted faults can be derived from basic seismic interpretation, and seismic attributes can be derived from seismic volumetric curvatures (Al-Dosari and Marfurt, 2006). Bed thickness variations can be determined from isochrons or from the tuning frequency derived from spectral imaging. Lithology, porosity, and other rock properties can be derived indirectly from high-resolution seismic attributes obtained in prestack and poststack inversions calibrated against log and core data from wells. Under good conditions, these seismic attributes can be generated at a vertical resolution of 2 3m(6 10 ft), which, in our experience, is comparable to the thickness of intervals that control fluid flow in fractured reservoirs and to the vertical scale at which geocellular models are built. The key to deriving attributes at a 2 3-m (6 10-ft) scale is to integrate the seismic traces with the log data through high-resolution seismic stochastic inversion (Haas and Dubrule, 1994). In this process, synthetic seismograms are generated from the pseudoimpedance log and compared to the actual seismic trace at a given well location. The synthetic seismogram that results in the best match to the actual seismic trace is retained as the inversion solution at that location. The vertical resolution of the simulated data is determined by the selection of the vertical cell size (determined by the end user of the model) and not by the frequency content of the seismic data. The result of the stochastic seismic inversion is a 3-D volume with a seismic-like areal resolution and a loglike vertical resolution that honors both the log data and the seismic data. An example showing the soundness of this technique comes from a recent work in a Hungarian reservoir (Zellou et al., 2006) where the data from five wells were used to generate a high-resolution seismic impedance volume with a sample rate of 0.5 ms (2 3 m[6 10 ft]). The resulting impedance volume was then compared to the actual impedance values at four blind wells resulting in a correlation coefficient of 0.77 (Figure 2). This goodness of fit illustrates the potential ability of the high-resolution inversion to accurately predict impedance values in the 3-D seismic volume. For the fracture modeling effort in this example, a high-resolution impedance was derived using all nine wells. The predictive capabilities of CFM depend, in large part, on the quality of the seismic and well-log data used to extract the relevant seismic attributes. A good-quality 3-D seismic survey and a large number of available wells with good-quality sonic and density logs recorded over a long interval provide the best input data. Jenkins et al. 1599

Figure 1. Simplified workflow showing the key components of the 3-D CFM approach, which includes the generation of seismic attributes, the generation of geologic and geomechanical fracture drivers constrained by seismic data, the construction of a fracture indicator log at the wells, and the quantitative integration of all these data with artificial intelligence tools (neural networks). The quality of the seismic data also has a significant impact on the quality of the structural framework and the resulting 3-D geocellular grid, which are built in the time domain using interpreted horizons and faults. For example, poor seismic data may result in the inability to capture critical faults, which will negatively impact the acoustic impedance inversion. Figure 3 shows a cross section of the impedance for the Hungarian reservoir (Zellou et al., 2006) described previously. Without a proper structural framework that considers all the faults and an inversion algorithm that is able to honor the sharp offsets near each of the faults, the derived impedance will not be very useful in the CFM workflow. High-resolution seismic attributes have been produced for almost 15 yr using Haas and Dubrule s stochastic or geostatistical inversion techniques (Haas and Dubrule, 1994; Dubrule et al., 1998). Many geoscientists have been using seismic attributes simulated at a res1600 Continuous Fracture Modeling (CFM) olution of 2 3 m (6 10 ft) for more than a decade (Lo and Bashore, 1999; Torres-Verdin et al., 1999; Dasgupta et al., 2000; Robinson, 2001; Sullivan et al., 2004; Francis, 2005; Raguwanti et al., 2005). Commercial software using this approach has also been created. Despite these advances, many geoscientists are still unfamiliar with the technique and erroneously believe that seismic attributes are limited to a one-quarter wavelength resolution. In the CFM approach, many seismic attributes are computed near the seismic detection limit (2 3 m [6 10 ft]), which could be five to seven times smaller than the seismic resolution (15 20 m [49 66 ft]). The use of high-resolution attributes is critical to achieve good predictive capabilities with the CFM approach. Boadu (1998) showed that, in theory, a neural network can accurately predict the fracture density using limited seismic attributes. The following case studies each include the use of one or more high-resolution seismic attributes in the CFM approach.

Figure 2. Predicted (x axis) versus actual (y axis) impedance at four blind wells not used in a seismic inversion. The predicted impedance at the wells was extracted from an impedance cube computed with a resolution of 0.5 ms or 2 3 m(6 10 ft) and compared to the actual logs averaged at 2 m (from Zellou et al., 2006; reprinted by permission of the Society of Petroleum Engineers). FIELD CASE STUDIES OF CONTINUOUS FRACTURE MODELING South Arne Chalk Field, Danish North Sea In this project (Christensen et al., 2006), the CFM approach was used to predict the 3-D distribution of fracture density and to create reservoir simulation models in the South Arne field, a complex chalk reservoir. Numerous fracture drivers, including a high-resolution acoustic impedance inversion and several spectral imaging attributes, were combined with porosity log values at 15 wells to construct a porosity model. The resulting model compared very favorably with three blind Figure 3. Cross section along an inline showing the derived impedance at a resolution of 0.5 ms. Because the structural interpretation captures the existing faults and their sharp offsets, the derived seismic attributes are more representative and therefore will be more useful in the CFM workflow (from Zellou et al., 2006; reprinted by permission of the Society of Petroleum Engineers). Jenkins et al. 1601

Figure 4. Validation of predicted porosities in the South Arne field of the North Sea. The red curves are the predicted porosity values from the seismic attributes. The black curves are the upscaled porosity values from the logs (Christensen et al., 2006; reprinted by permission of the Society of Petroleum Engineers). MD = measured depth. wells that were not included in the model construction (Figure 4). Four vertical appraisal wells drilled subsequent to model construction were used to further validate the inverted impedance and derived porosity models. After validation, a fracture-density model was generated using porosity, seismic data, and geomechanical fracture drivers. Two fracture indicators were used to calibrate the fracture model and quantify the effects of fractures: core permeability and the fracture density estimated from image logs. A matrix permeability model was also generated using the porosity model as the dominant fracture driver. This matrix permeability model was subsequently combined with the fracture-density model to generate an effective permeability model for numerical simulation. The resulting effective permeability from fracture permeability enhancement for a matrix volume with N fractures in the same direction was described by the following equation. K eff ¼ K m þ f a 3 h 84:44 10 6 ð1þ where K eff is the effective permeability of the combined matrix and fracture flow, (in millidarcies), K m is the matrix permeability (in millidarcies), f is the fracture density (in number of fractures per meter), and a h is the average fracture aperture open for flow (in microns). The four models discussed above (porosity, matrix permeability, fracture density, and effective permeability) are individual property models that were each generated within a single geocellular modeling project. The effective permeability and porosity models were subsequently exported for use in numerical simulation. As shown for two wells (Figure 5), the resulting simulation runs matched the individual well performance without making any substantive adjustments. The matches in these two wells, which had moderately simple completions, indicate that the derived fracturedensity model was able to quantify the complex fracture network at the appropriate resolution. Other wells in the field, with much more complex completions, were more difficult to match. Nonetheless, the CFM-derived effective permeability model used in the simulation work was far better than the model previously used and required smaller adjustments to obtain matches. The history match was further optimized by varying the initially estimated average fracture aperture to find the best overall match to effective permeability. The history match approach resulted in a geologically meaningful result using apertures ranging from 30 to 40 mm, which provides a good starting point for future history matching once more well tests become available. The fracture-density model was validated on a wellby-well basis by comparing fracture density in the model with the actual counts of conductive fractures. The results are shown in Figure 6 for three random horizontal wells that were available during the study. The comparison shows fair to good agreement between the predicted 1602 Continuous Fracture Modeling (CFM)

Figure 5. Initial history matching results for two wells showing good agreement between predicted and actual data for water production (blue), pressure (orange), and gas-oil ratio (red). The oil rate curve (green) is specified in the model (Christensen et al., 2006; reprinted by permission of the Society of Petroleum Engineers). Jenkins et al. 1603

Figure 6. Count of actual conductive fractures (black) from image logs in horizontal boreholes compared to the predicted fracture density (red) from CFM modeling (Christensen et al., 2006; reprinted by permission of the Society of Petroleum Engineers). and actual occurrence of fractures. Subsequently, two horizontal wells were drilled in 2007 after the completion of the study. Fracture-density logs extracted from the model showed a favorable comparison to the actual fracture-density logs from these wells. Maloichskoe Carbonate Reservoir, Western Siberian Basin Unlike the South Arne field, where image logs and core permeability were used as the key fracture indicators, this project (Pinous et al., 2006) used a simple difference in the values of the shallow and deep resistivity logs as the key fracture indicator. Fracture drivers included seismic inversion and spectral imaging volumes, curvature in multiple directions, distance to faults, porosity, and deformation. Overall, 27 fracture drivers were evaluated and ranked based on how accurately each was correlated to the fracture indicator. The CFM approach was used to quantify the relationship between the fracture drivers and the fracture indicator, and then to predict fracture density in every cell of the model. Fifty equiprobable fracture model realizations were generated, and three of these were selected as the base case, downside case, and upside case for further analysis. A connectivity analysis based on displaying the CFM results as discrete fractures and pipes was then conducted to identify those areas most likely to contain connected fractures. A well was subsequently drilled using this information, and based on a comparison of the image log from this well to the modeling results, a good correspondence between the predicted and actual location of fractured intervals existed (Figure 7). The predicted fracture model showed that the upper interval would be highly fractured, the middle interval would be poorly fractured, and the lower interval would be moderately fractured. This is very similar to 1604 Continuous Fracture Modeling (CFM)

Figure 7. Comparison between the model-derived fracture density, derived from the difference between the shallow and deep resistivity logs, and the measured Formation Micro- Scanner (FMS) log in the Maloichskoe field. Note that, at a depth of 2850 m (9350 ft), the model predicted a 2-m (6-ft)- thick interval with no fractures that was confirmed by the FMS log (Pinous et al., 2006; reprinted by permission of the Society of Petroleum Engineers). MD = measured depth. the interpretation subsequently derived from the image log in this well. Although variations between the predicted and actual fracture density are present, the comparison is quite good given that the key fracture indicator was the difference between a shallow and a deep resistivity log. The fracture-density model was built using a geocellular grid containing cells 2 m (6 ft) thick, and thus it was possible to generate a predicted fracturedensity log having a 2-m (6-ft) resolution, as shown in Figure 7. Sabria Quartzite Field, Tunisia This project began with a high-resolution inversion and the generation of spectral imaging cubes. A structural framework was then built in both time and depth, and two key seismic attributes, impedance and tuning frequency, were resampled to the geocellular grid. Geostatistical techniques were then used to distribute porosity, permeability, water saturation, and shale content into this grid. These four geological parameters, the two seismic attributes, and structural information were used as fracture drivers. The fracture indicator log was provided by core descriptions from three wells. The CFM approach was used to rank the fracture drivers according to how reliably they matched this fracture indicator and to generate multiple fracture-density models. An average model was then chosen, and data from three well tests were used to convert the model into fracture permeability. This was then used as an input for numerical modeling. During the history matching process, a match was achieved by making adjustments to relative permeability. The fracture permeability distribution itself was not altered. After history matching was completed, various new drilling locations were planned and tested with the model. Based on the results of this work, the Sabria 11 well was drilled and completed in 2007. A comparison between the actual and predicted porosity for this well (Figure 8) shows good agreement, as does the actual and predicted impedance for this well. In addition, the actual oil rate of Sabria 11 is in line with the predicted range of rates forecasted by the numerical model. IMPACT OF NEW SEISMIC TECHNOLOGIES ON CONTINUOUS FRACTURE MODELING Since its inception in 1995 (Ouenes et al., 1995), the main focus of the CFM approach has been to incorporate multiple seismic attributes to improve the 3-D description of fractures. The technique has been applied to multiple fields, and its value has been demonstrated by the ability of the resulting models to predict the fracture Jenkins et al. 1605

Figure 8. Comparison between actual and predicted porosity values in a new well in the Sabria Quartzite field showing a higher porosity interval from 3814 to 3930 m (12,513 to 12,894 ft). The well (left) was only logged to 3930 m (12,894 ft), but the model (right) extracted values along the entire borehole and thus shows predicted values of lower porosity deeper than 3930 m (12,894 ft) (Ouenes et al., 2008; reprinted by permission of the Society of Petroleum Engineers). density in new wells and closely match the performance of individual wells. Because of the flexibility of the CFM approach, it will continue to benefit from new seismic technologies that provide better descriptions of subsurface properties. The initial application of CFM technology used only the seismic amplitude (Zellou et al., 1995), but this was later improved by adding coherency (Gauthier et al., 2000). Improvements were also made by adding impedance (Laribi et al., 2003; Zellou et al., 2003) and prestack azimuthal attributes (Boerner et al., 2003; Wong, 2003) to the list of fracture drivers. In many instances, the addition of spectral imaging attributes (Ouenes et al., 2004; Christensen et al., 2006; Pinous et al., 2006; Zellou et al., 2006) to the previous seismic attributes greatly improved the predictability of the models. Recently, the use of volumetric curvature (Al-Dosari and Marfurt, 2006), which is a significant improvement over curvature methods applied to surfaces and coherency-type seismic attributes, provided some of the best seismic attributes for fractured reservoirs where faults are major factors. The use of elastic properties derived from high-resolution prestack inversion is one of the best seismic inputs for the CFM approach. Unfortunately, deriving such seismic attributes requires the shear component from dipole sonic logs, which is rarely available in fractured reservoirs. The absence of seismic attributes or the use of poorquality attributes reduces the predictive capabilities of the CFM models. Although a CFM model can be built without seismic data, it will likely do a poor job of predicting fracture density at undrilled locations. This may also be the case if the seismic data are of poor quality, especially if this leads to an inaccurate structural interpretation that fails to include some of the faults. If a large number of wells are present, this will help compensate for poorer quality seismic data and the resulting model will have better predictive capabilities. The presence of good seismic data alone is not sufficient to ensure a predictive CFM model, and other important conditions must be satisfied. One of these is the need to extract high-resolution attributes capable of capturing the vertical variations at a scale of a few meters. A seismic attribute at an initial seismic resolution of 4 ms (which represents 15 to 25 m [49 to 82 ft] of thickness) will not be very useful for the CFM approach. Another important condition is that enough good-quality data exist to generate an accurate fracture indicator log. Ideally, this would include cores, logs, well tests, and production logs with each demonstrating the existence of productive fractures at the same location in a given wellbore. With less data and/or conflicting information, confidence in the calibration of fracture indicators to fracture drivers is reduced as is the predictive capability of the CFM approach. 1606 Continuous Fracture Modeling (CFM)

SUMMARY A successful modeling approach for naturally fractured reservoirs needs to predict where naturally fractured intervals will be encountered in undrilled wells and do so at a scale (2 3 m[6 10 ft]) that is comparable to core, log, and well-test data. A successful approach must also be able to provide an effective permeability model for numerical simulation that will match the performance of individual wells. The CFM approach meets these criteria based on the results of multiple case studies. The approach provides a method for geoscientists to quantitatively integrate all of the available data by simultaneously relating multiple fracture drivers to any type of fracture indicator. This integration is achieved by the quantitative and simultaneous use of multiple 3-D property volumes, especially seismic attributes, to predict the interwell occurrence of fractures and build effective permeability models that incorporate them. As new seismic processing and interpretation technologies continue to evolve, the resulting seismic attributes will be used to further improve the ability of the CFM approach to quantify the location and effects of fractures on well performance. REFERENCES CITED Al-Dosari, S., and K. Marfurt, 2006, 3D volumetric multispectral estimates of reflector curvature and rotation: Geophysics, v. 71, no. 5, p. 41 51, doi:10.1190/1.2242449. Boadu, F. K., 1998, Inversion of fracture density from field seismic velocities using artificial neural networks: Geophysics, v. 63, no. 2, p. 534 545, doi:10.1190/1.1444354. Boerner, S., D. Gray, D. Todorovic-Marinic, A. Zellou, and G. Schnerck, 2003, Employing neural networks to integrate seismic and other data for the prediction of fracture intensity: Society of Petroleum Engineers Paper No. 84453, 13 p. Bourbiaux, B., R. Basquet, J. M. Daniel, L. Y. Hu, S. Jenni, A. Lange, and P. Rasolofosaon, 2005, Fractured reservoirs modeling: A review of the challenges and some recent solutions: First Break, v. 23, no. 9, p. 33 40. Christensen, S. A., T. E. Dalgaard, A. Rosendal, J. W. Christensen, G. Robinson, A. Zellou, and T. Royer, 2006, Seismically driven reservoir characterization using an innovative integrated approach: Syd Arne field: Society of Petroleum Engineers Paper No. 103282, 11 p. Dasgupta, S., M. Hong, P. La Croix, L. Al-Mana, and G. Robinson, 2000, Prediction of reservoir properties by integration of seismic stochastic inversion and coherency attributes in super giant Ghawar field (abs.): Society of Exploration Geophysicists Expanded Abstracts, v. 19, p. 1501 05, doi:10.1190/1.1815691. Dershowitz, B., P. Lapointe, T. Eiben, and L. Wei, 1998, Integration of discrete feature network methods with conventional simulator approaches: Society of Petroleum Engineers Paper No. 49069, 9p. Dershowitz, W., P. La Pointe, and T. Doe, 2004, Advances in discrete fracture network modeling: Proceedings of the U.S. Environmental Protection Agency/National Groundwater Association Fractured Rock Conference, p. 882 894. Dubrule, O., M. Thibaut, P. Lamy, and A. Haas, 1998, Geostatistical reservoir characterization constrained by 3D seismic data: Petroleum Geoscience, v. 4, no. 2, p. 121 128. Francis, A., 2005, Limitations of deterministic and advantages of stochastic seismic inversion: Canadian Society of Exploration Geophysicists Recorder, v. 30, no. 2, p. 5 11. Gauthier, B. D. M., A. M. Zellou, A. Toublanc, M. Garcia, and J. M. Daniel, 2000, Integrated fractured reservoir characterization: A case study in a North Africa field: Society of Petroleum Engineers Paper No. 65118, 11 p. Haas, A., and O. Dubrule, 1994, Geostatistical inversion: A sequential method of stochastic reservoir modeling constrained by seismic data: First Break, v. 12, no. 11, p. 561 569. Laribi, M., et al., 2003, Integrated fractured reservoir characterization and simulation: Application to Sidi El Kilani field, Tunisia.: Society of Petroleum Engineers Paper No. 84455, 13 p. Lo, T.-W., and W. Bashore, 1999, Seismic constrained facies modeling using stochastic seismic inversion and indicator simulation: A North Sea example (abs.): Society of Exploration Geophysicists Expanded Abstracts, v. 18, p. 923 926. Mace, L., L. Souche, and J.-L. Mallet, 2004, 3D fracture characterization based on geomechanics and geologic data uncertainties: 9th European Conference on the Mathematics of Oil Recovery: Houten, Netherlands, European Association of Geoscientists and Engineers, 8 p. Ouenes, A., 2000, Practical application of fuzzy logic and neural networks to fractured reservoir characterization: Computers and Geosciences, v. 26, no. 9, p. 953 962. Ouenes, A., S. Richardson, and W. Weiss, 1995, Fractured reservoir characterization and performance forecasting using geomechanics and artificial intelligence: Society of Petroleum Engineers Paper No. 30572, 12 p. Ouenes, A., A. M. Zellou, P. M. Basinski, and C. F. Head, 1998, Practical use of neural networks in tight gas fractured reservoirs: Application to the San Juan Basin: Society of Petroleum Engineers Paper No. 39965, 8 p. Ouenes, A., G. Robinson, and A. Zellou, 2004, Impact of pre-stack and post-stack seismic on integrated naturally fractured reservoir characterization: Society of Petroleum Engineers Paper No. 87007, 11 p. Ouenes, A., G. Robinson, D. Balogh, A. Zellou, D. Umbsaar, H. Jarraya, T. Boufares, L. Ayadi, and R. Kacem, 2008, Seismically driven characterization, simulation and underbalanced drilling of multiple horizontal boreholes in a tight fractured quartzite reservoir: Application to Sabria field, Tunisia: Society of Petroleum Engineers Paper No. 112853, 12 p. Pinous, O., E. P. Sokolov, S. Y. Bahir, A. Zellou, G. Robinson, T. Royer, N. Svikhnushin, D. Borisenok, and A. Blank, 2006, Application of an integrated approach for the characterization of a naturally fractured reservoir in the West Siberian basement (example of Maloichskoe field): Society of Petroleum Engineers Paper No. 102562, 7 p. Raguwanti, R., A. Sukotjo, B. Wikan, and H. Adibrata, 2005, Innovative approach using geostatistical inversion for carbonate reservoir characterization in Sopa field, south Sumatra, Indonesia (abs.): 2005 AAPG International Conference and Exhibition, Paris, France, http://www.searchanddiscovery.net/documents /abstracts/2005intl_paris/raguwanti.htm. Robinson, G. C., 2001, Stochastic seismic inversion applied to reservoir characterization: Canadian Society of Exploration Geophysicists Recorder, v. 26, no. 1, p. 36 40. Sabathier, J. C., B. J. Bourbiaux, M. C. Cacas, and S. Sarda, 1998, A Jenkins et al. 1607

new approach of fractured reservoirs: Society of Petroleum Engineers Paper No. 39825, 11 p. Sullivan, C., E. Ekstrand, T. Byrd, D. Bruce, M. Bogaards, R. Boese, and P. Mesdag, 2004, Quantifying uncertainty in reservoir properties using geostatistical inversion at the Holstein field, deepwater Gulf of Mexico (abs.): Society of Exploration Geophysicists Expanded Abstracts, v. 23, p. 1491 1494, doi:10.1190/1.1851128. Torres-Verdín, C., M. Victoria, G. Merletti, and J. Pendrel, 1999, Tracebased and geostatistical inversion of 3-D seismic data for thin-sand delineation, an application in San Jorge Basin, Argentina: The Leading Edge, v. 18, no. 9, p. 1070 1076, doi:10.1190/1.1438434. Wong, P. M., 2003, A novel technique for modeling fracture intensity: A case study from the Pinedale anticline in Wyoming: AAPG Bulletin, v. 87, no. 11, p. 1717 1727, doi:10.1306/07020303005. Zellou, A., and A. Ouenes, 2003, Integrated fractured reservoir characterization using neural networks and fuzzy logic: Three case studies, in M. Nikravesh, L. A. Zadeh, and F. Aminzadeh, eds., Soft computing and intelligent data analysis in oil exploration: Developments in Petroleum Geoscience 51, p. 583 602. Zellou, A., L. Hartley, E. Hoogerduijn-Strating, S. Al Dhahab, W. Boom, and F. Hadrami, 2003, Integrated workflow applied to the characterization of a carbonate fractured reservoir: Qarn Alam field: Society of Petroleum Engineers Paper No. 81579, 8 p. Zellou, A., G. Robinson, T. Royer, P. Zahuczki, and A. Kiraly, 2006, Fractured reservoir characterization using post-stack seismic attributes: Application to a Hungarian reservoir (abs.): European Association of Geoscientists and Engineers Annual Conference, Vienna, Austria, 4 p. Zellou, A. M., A. Ouenes, and A. K. Banik, 1995, Improved fractured reservoir characterization using neural networks, geomechanics, and 3-D seismic: Society of Petroleum Engineers Paper No. 30722, 11 p. 1608 Continuous Fracture Modeling (CFM)