Distinguishing gas-bearing sandstone reservoirs within mixedsiliciclastic-carbonate sequences using extendedelastic impedance: Nile Delta — Egypt

Ahmed Hafez1 and John P. Castagna2

Abstract

In the Abu Madi Formation of the Nile Delta Basin, false bright spots may be misinterpreted as being indica-tive of hydrocarbons due to mixed clastics and carbonates. However, rock-physics analysis of well logs in aparticular prospect area where such ambiguity exists suggests that attributes derived using extended elasticimpedance (EEI) inversion may help identify hydrocarbons because they better show anomalous behaviorin particular directions that are readily related to pore fluids and lithology. The EEI attributes calculated fromwell logs correlate extremely well to lithology and fluid properties, thereby differentiating amplitude anomaliescaused by gas-bearing sandstones encased in shale from similar amplitudes caused by juxtaposition of high-impedance carbonates over lower impedance water-filled sandstones. Comparing seismically derived EEIattributes to well logs from a productive well and a nonproductive well indicates that seismic inversion cansuccessfully identify lithologies such as shales, sandstones, carbonates, and anhydrite and distinguish gas-bearing from water-bearing sandstones. The technique can thus potentially be used to better delineate and riskprospects in the area, as well as assisting exploration efforts in other locations where similar ambiguities inamplitude interpretation exist.

IntroductionThe Nile Delta Basin is considered a world-class pro-

lific gas province in which more than 300 exploratorywells have been drilled to target hydrocarbon-bearingreservoirs. Several multitrillion-cubic-feet gas fieldshave been discovered in the deepwater of the Nile Delta(Samuel et al., 2003). To date, approximately 132 pro-ducing gas/condensate fields exist in the Nile cone area.In total, 19% out of these fields are producing from theLate Miocene (Messinian) Abu Madi Formation sand-stone reservoirs that are located in onshore and off-shore portions of the Nile Delta Basin (Dolson et al.,2001). Use of seismic direct hydrocarbon indicators(DHIs) such as bright spots, flat spots, shadow zones,and polarity change at the pore fluid contact, play akey role in exploration and development in this basinespecially for the Pliocene and Late Miocene reservoirs.However, drilling results indicate that false bright spotsmay result from unanticipated lithologic variations. Inthis paper, we investigate the use of prestack seismicanalysis and inversion to improve the reliability in seis-mic indication of hydrocarbons in the Nile Basin wherenonclastic sediments occur.

Geologically, in the study area, the Messinian storystarted with forced regression of the relative sea level

due to intense tectonic movements, which resulted increation of many incised valleys and lagoonlike depres-sions on the shelf. Two giant fluvial incised valleys havebeen created: the Abu Madi to the east and Abu Qir tothe west (El-Barkooky and Helal, 2002). These fluvialreservoirs are mainly sourced from mudstone beds inthe Abu Madi Formation and shale beds of the Sidi SalemFormation (Middle Miocene) (Abdel Aal et al., 2000;Keshta et al., 2012). This stage was followed by a slowrising in the relative sea level that resulted in floodingof the shelf with relatively warm and shallow water ac-companied with a development of the sand shoals at theshelf edge. Such conditions were suitable to “turn on” thecarbonate factory and deposition of thick limestone sec-tion within the lagoonal system. Afterward, rapid trans-gressions associated with high rates of base-level risetook place, resulting in the drowning of the carbonateplatform (i.e., water depth exceeding the photic limit),which shuts down the carbonate deposition. Drowningrepresents the final stage in the evolution of a carbonateplatform, prior to the return to a clastic-dominated envi-ronment. Afterward, the desiccation event of the Neo-Te-thys (old Mediterranean Sea) deposited anhydrite of upto 70m in thickness that mainly was deposited within thelagoonlike depressions (El-Barkooky and Helal, 2002).

1GDF Suez, Cairo, Egypt. E-mail: ahmed_geophy@yahoo.com.2University of Houston, Houston, Texas, USA. E-mail: jcastagnaou@yahoo.com.Manuscript received by the Editor 23 December 2015; revised manuscript received 15 April 2016; published online 5 August 2016. This paper

appears in Interpretation, Vol. 4, No. 4 (November 2016); p. T435–T449, 18 FIGS.http://dx.doi.org/10.1190/INT-2015-0223.1. © 2016 Society of Exploration Geophysicists and American Association of Petroleum Geologists. All rights reserved.

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Technical papers

Interpretation / November 2016 T435Interpretation / November 2016 T435

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Case study descriptionIn the study area, two wells (A and B) have been

drilled to the Messinian section to target the gas-bearingsandstone reservoirs within it, with a lateral distancebetween the wells of approximately 22 km. These twowell locations were selected based on seismic DHI re-sponses (i.e., bright spots on the full-stack seismic datasets) that indicated to be gas-bearing sand as proved byseveral nearby gas/condensate producing fields withinthe Pliocene and Messinian sequences such as AbuMadi,Sequoia, Simian, Scarab, and Saffron fields (Samuel et al.,2003; Katamish et al., 2005; Cross et al., 2009; Hafez et al.,2014). The well (A) results indicate that the Messiniansection consists of siliciclastics (sandstone-shale interca-lation), inwhich approximately 25m of gas-bearing sand-stone overlies approximately 50 m of water-bearingsandstone and is sealed by a thick shale. According towell (A) logs, the gas-bearing sandstone is characterizedby a lower bulk density and slightly higher P- and S-wavevelocities than the encasing shale (Figure 1); however,the P-wave acoustic impedance of the gas-bearing sand-stone and the encasing shale are very close. On the otherhand, well (B) logs indicate that the Messinian sectionconsists of mixed siliciclastics carbonates and anhydritelithologies that were not predicted before drilling (Fig-ure 2). All the sandstone reservoirs in this well are con-sidered water bearing. The integrated geologic and

geophysical studies applied to the Messinian section inthe study area indicate that the sandstone reservoirs en-countered by well (A) were deposited in the incised flu-vial valley of the Abu Madi Formation that formed as aresult of the forced regression of sea level during falling-stage systems tracts (Catuneanu, 2006) (Figure 3a). Thissandstone reservoir is stratigraphically equivalent to thebasal sandstone in well (B), which also could be inter-preted as fluvial-fill deposits. The carbonate section en-countered at the well (B) location is mainly composed oftight mudstone with wackestone and packstone inter-vals. This carbonate platform formed in a lagoonal envi-ronment in a shelf setting (Figure 3b). After the drowningof the shelf by relative sea level rise, the system switchedfrom carbonate to clastic-dominated deposition (Fig-ure 3c). Seismic data interpretation indicates that well(A) is located on the upthrown side of the Rosetta faultsystem whereas well (B) is located on the downthrownside of the Rosetta fault within rotated fault blocksettings. The thickness of the Messinian sequences in-creases on the downthrown side of that fault. The thick-ness of the Messinian section increases toward the faultplane, which could suggest that these sequences weredeposited during the fault movement (Figure 4).

The well-to-seismic tie at well (A) indicates that thetop of the gas-bearing sandstone is represented on thefull-stack seismic data by a trough (the seismic data is

Figure 1. Well-log expression and lithologyof the Messinian section at the well (A) loca-tion showing that the gas-bearing sandstonereservoir is characterized by lower bulk den-sity and slightly higher P- and S-wave veloc-ities than the encased shale.

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SEG-normal polarity) whereas the top of the water-bearing sandstone is represented by a peak (Figure 5).The well-to-seismic tie at well (B) indicates that the topof the anhydrite encased by shale is represented by apeak, the top of the carbonate encased by shale is rep-resented by a peak, and the top of water-bearing sand-stone encased by the carbonates is represented by atrough (Figure 6).

Biot-Gassmann fluid substitution (Gassmann, 1951)and seismic amplitude-variation-with-offset (AVO) mod-eling was applied to the gas-bearing sandstone reservoirencased by shale at well (A) to investigate the changes inthe seismic signature with changing of reservoir porefluid at subsurface conditions. The synthetic AVO mod-els are then compared to the real seismic common depthpoint (CDP) gathers. Despiked and depth-shifted P-wavevelocity, S-wave velocity, and bulk density logs wereused to generate AVO synthetic gather models using theZoeppritz equation (Zoeppritz, 1919) with a seismicwavelet extracted from the seismic data at/around thewell location and assuming primaries dominate the seis-mic response. As spectral balancing versus offset proc-ess was applied prior the wavelet extraction, the waveletwas insensitive to offset and an offset-constant waveletwas used. However, Hampson-Russell software wasused for AVO synthetic modeling and analysis. Analysis

of the resulting synthetics indicates that the top of thegas-bearing sandstone is represented on the seismic dataas a weak trough at the near angles with increasing am-plitude with increasing angle (Figure 7a and 7b). On theintercept-gradient crossplot, the top of the gas-bearingsandstone reservoir is located within quadrant III andis characterized by negative intercept and gradient. Ac-cordingly, the top of the gas-bearing sandstone reservoircould be classified as AVO class II according to Ruther-ford and Williams (1989). There is a good qualitativematch between the AVO model and the real seismicgather; however, the real gather shows larger gradientthan the model (Figure 7a–7c). This may be related toinaccuracies in the local angle of incidence calculationor in the well-log measurements. On the other hand, thegas-bearing sandstone is replaced by the fully saturatedwater-bearing sandstone with the same rock frame prop-erties using the Biot-Gassmann fluid substitution ap-proach. The P-wave velocity and bulk density of thewater-bearing sandstone are higher than that of thegas-bearing sandstone, whereas the S-wave velocityis slightly lower than in the gas case. The AVO modelingof the replaced water-bearing sandstone encased byshale shows that the top of this reservoir is representedby a weak peak that decreases with offset. The AVA plotshows that this contrast is characterized by a small

Figure 2. Well-log expression and lithologyof the Messinian section at the well (B) loca-tion showing that the Messinian composed ofmixed siliciclastics-carbonates and anhydritesequences.

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positive intercept and an amplitude decrease with theoffset (i.e., a small negative gradient). The intercept-gra-dient crossplot shows that the top of this reservoir islocated in quadrant IV and imbedded in the background

trend (as would result from a background linear rela-tionship between P- and S-wave velocities such as themudrock line of Castagna et al., 1985; Figure 7a–7c).The Biot-Gassmann fluid substitution modeling is alsoapplied to the basal water-bearing sandstone encasedby tight carbonates in well (B) to replace it with fullysaturated gas-bearing sandstone with the same rockframe properties. The introduction of gas into this sand-stone reservoir causes an obvious decrease in the bulkdensity and P-wave velocity with a slight increase in theS-wave velocity. The AVO modeling shows that the topof the water-bearing sandstone encased by tight carbon-ates is characterized by a weak amplitude seismictrough that slightly increases with offset. The AVA plotshows that this contrast is characterized by a small neg-ative intercept and the amplitude increases slightly withoffset (i.e., small negative gradient). The intercept-gra-dient crossplot shows that the top of this reservoir islocated in quadrant III near to the top of the gas-bearingsandstone encased by shale and also could be classifiedas AVO class-II (Figure 8a–8c). The AVO modeling ap-plied to the replaced gas-bearing sandstone encased bytight carbonates shows that the top of this hypotheticalgas reservoir exhibits a relatively strong (high-amplitude)trough that slightly increases with offset. The AVA plotshows that this contrast is characterized by a small neg-ative intercept and amplitude slightly increasing withthe offset. The intercept-gradient plot shows that thetop of this reservoir is located also in quadrant III andcould be classified as AVO class-II (Figure 8a–8c). Un-fortunately, there are no seismic prestack gathers avail-able to compare with the AVO models at well (B). How-ever, according to these models, it is obvious that thegas-bearing sandstone encased by shale, water-bearingsandstone encased by tight carbonates, and gas-bearingsandstone encased by tight carbonates all have similarAVO behaviors. It is evident from Figure 8c that the pri-mary effect of fluid substitution is to change the AVOintercept. With tight geologic constraints, careful inter-pretation of the AVO intercept could potentially be afruitful DHI method. However, with variable and unex-pected lithology changes, such an approach could beperilous. A more reliable method is needed.

Applied workflowThe current study shows an approach that could be

used to distinguish the gas-bearing sandstone withinmixed siliciclastic and carbonate sequences usingrock-physics analysis and inversion of the extendedelastic impedance (EEI) seismic reflectivity to produceabsolute EEI with correct spectral characteristics. Gen-erally, the applied workflow consists of four integratedsteps: (1) rock-physics analysis, (2) EEI correlation andreflectivity generation, (3) colored inversion of the EEIreflectivities and conversion of the relative EEI into ab-solute EEI, and (4) interpreting the absolute invertedEEI cubes in terms of lithology and pore-fluid contents(Figure 9).

Figure 3. Possible depositional systems show architecturalelements of the Messinian sequences that are interpreted fromthe integrated geologic and geophysical studies.

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Figure 4. (a) Noninterpreted full-stack seismic cross section between the A and B wells. (b) Interpreted seismic cross section thatcropped to the Messinian sequences is showing the lateral variations in the amplitude and the structure elements that affectedthese sequences.

Figure 5. Well-to-seismic tie at well (A) location is showing that the gas-bearing sandstone reservoir is represented by the troughseismic loop whereas the water-bearing sandstone reservoir is represented by the peak seismic loop.

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Rock-physics analysisThe applied workflow starts with well-based rock-

physics analysis in which the conditioned well logs areused to generate the acoustic and elastic logs that willbe used to create crossplots. By using these plots, sev-eral lithology and pore fluid clusters could be defined.Figure 10a shows the crossplot between the VP∕VS ra-tio and P-wave acoustic impedance (IP) of the Messi-nian section of wells (A and B). Analysis of this plotindicates that the IP of the gas-bearing sandstone is veryclose to that of the shale, which explains the AVO class-II behavior of the top of this reservoir. The VP∕VS ratioof the gas-bearing sandstone significantly overlaps withthe water-bearing sandstone cluster. However, the tightwater-bearing carbonate cluster has the highest IP. Thecrossplot between the bulk modulus (κ) and Poisson’sratio (PR) indicates that the gas-bearing sandstone haslow κ, but there is a significant overlap with that of theshale. It is also noted that there is an obvious overlapof the PR between the gas-bearing sandstone and thewater-bearing sandstone (Figure 10b). The crossplot be-tween the Lambda-Rho (LR) and Mu-Rho (MR) showsthat the gas-bearing sandstone cluster has the lowest LR.There is also an obvious separation between the gas-bearing sandstone and the water-bearing sandstone res-ervoirs. The tight water-bearing carbonate and the water-bearing calcareous sandstone clusters have the highestLR and MR (Figure 10c). Analysis of the crossplot be-tween the κ and LR indicates that the gas-bearing sand-

stone could be distinguished from the water-bearingsandstone aswell as from the tight water-bearing carbon-ates (Figure 10d). Nevertheless, there is overlap betweenthe shale and the water-bearing sandstone reservoir. Ac-cording to these analyses, the crossplot of the LR-MRattribute pair could be a useful tool to distinguish thegas-bearing sandstone in this mixed lithology settingif, because these are absolute moduli derived from logsand thus have a known low-frequency component, thelow-frequency background model for seismically in-verted moduli is correct. Therefore, the inversion of theseismic data into the LR and MR, or other seismicallyderived moduli combinations, could potentially be help-ful to delineate the gas-bearing sandstone in three dimen-sions. The LR and MR could be extracted from theseismic data through several technical approaches suchas simultaneous inversion of the angle gathers (Ma,2002), independent inversion of the P- and S-impedances(Russell et al., 2003) or through the EEI (Whitcombeet al., 2002). In this study, we will use the concept of theEEI as a practical means of identifying rotations re-sponding to variations in specific rock characteristicsas well as inverting for elastic parameters such as LRand MR.

EEI correlation analysisWhitcombe et al. (2002) introduce EEI as a general-

ized impedance function based on the two-term AVOequation with end-member acoustic impedance (AI)

Figure 6. Well-to-seismic tie at well (B) location is showing that the top of anhydrite, tight water-bearing carbonates are rep-resented by peak seismic loops, whereas the water-bearing sandstone reservoir encased by tight carbonates is represented by theweak trough seismic loop.

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and gradient impedance (GI), where GI is the imped-ance equivalent of the AVO gradient term B. Whitcombeand Fletcher (2001) show how the EEI parameter Chiangle (χ) could be viewed as the rotation angle in theAI-GI plane, which is the impedance equivalent of theAB (intercept-gradient) plane; χ is related to the AVOincidence angle by tanðχÞ ¼ sin2ðθÞ (Connolly et al.,2002). Accordingly, the second step in the presentedworkflow is to analyze the correlation, as a functionof χ, between EEI logs and the available petrophysicallogs such as calcite volume (Vcal) as well as lithologylogs such as gamma ray. In addition, the optimal EEIChi angles for predicting rock physics parameters suchas PR, LR, MR, κ, and Young’s modulus (E) are alsochecked. The EEI curves are generated at different χranging from þ90° to −90° using the well logs and thencorrelated with the target logs. The analysis of this cor-relation indicates that the EEI logs at anglesþ19°, −52°,þ13°, −26°, þ38° exhibit an excellent match with theLR, MR, κ, E, and PR logs, respectively, with correlationcoefficients of 98% and 99%, 99%, 99%, and 97% respec-

tively. The analysis of this correlation also indicates thatEEI log of angle χ þ 17 exhibits a good match with theVcal with a correlation coefficient of 72%. On the otherhand, the correlation of EEI logs and the GR shows thatthere is no correlation with any Chi angle (Figure 11).The EEI logs at angle χ of −26°, þ13°, þ19°, and −52°with the calculated E, κ, LR, and MR logs, respectively,at well (A). Figure 12 indicates that there is a goodmatch between these logs especially in the gas zone.The same comparison is also made for well (B) andshows a good match in the clastics and carbonates in-tervals (Figure 13). This suggests that it may be possibleto generate EEI reflectivity cubes that correlate with E,κ, LR, and MR using the χ of −26°,þ13°,þ19°, and −52°,respectively, assuming all wave propagation effectshave been properly corrected. Because the LR and MRattribute pairs have been demonstrated to be a good dis-criminator of the gas-bearing sandstone reservoirs, twoEEI reflectivity cubes corresponding to LR and MR gen-erated at angles of χ of þ19° and −52° are of particularinterest.

Figure 7. Seismic AVO models of different pore fluid with the comparison with the real data at well (A) location.

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Colored inversion of extended elastic reflectivityThe fast-track colored inversion technique was intro-

duced by Lancaster and Whitcombe (2000). The seismicdata are inverted into band-limited relative impedanceimplicitly accounting for the seismic wavelet and using

the well logs. One of the benefits of applying the coloredinversion is to modify the spectrum of the seismic data,such that the wavelet used for inversion is more stableand less influenced by the reflectivity spectrum. The col-ored inversion is not requiring building of low-frequency

Figure 8. Seismic AVO models of different pore fluid at well (B) location comparing with AVO models at well (A).

Figure 9. Workflow steps that applied to thestudy area to distinguish the gas-bearing sand-stone reservoirs within mixed siliciclasticsand carbonates sequences in the study area.

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Figure 10. Well-based rock-physics analysis of wells A and B showing the characteristics of different lithologies and pore fluidsbased on their acoustic and elastic properties.

Figure 11. Correlation analysis of the EEI atdifferent Chi angles with the petrophysical andacoustic/elastic logs at the wells (A and B) lo-cations.

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Figure 12. Correlation analysis between the E, κ, LR, and MR logs that are calculated from the recorded from the well (A) wirelinelogs; the EEI logs at certain Chi angles are showing a good match with these logs.

Figure 13. Correlation analysis between the E, κ, LR, MR, and calcite volume logs that are calculated from the recorded from thewell (B) wireline logs; the EEI logs at certain Chi angles are showing a good match with these logs.

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initial model prior the inversion, which is highly sensitiveto the well-log quality, provided impedance statisticsmatch expected trends. In this technique, an operator isdesigned to map the seismic spectrum onto an earthspectrum typically derived from the well logs (Francis,2002). The combination between the colored impedanceand the EEI gives a product that is optimal for given pet-rophysical or lithological properties (Francis, 2002). Ac-cordingly, the third step in the presented workflow is toapply the fast-track colored inversion technique to theEEI reflectivities. In this step, the colored inversion op-erator is designed using the reflectivity spectrum fromwell logs and then applied to the EEI reflectivities ofLR and MR to invert them into relative impedance. Afterinverting the EEI reflectivity cubes of LR and MRinto relative impedances via applying colored inversion,

a model-based inversion is applied to convert the relativeimpedance to the absolute impedance through a creationof an initial impedancemodel. This initial model includesinterpolated impedance data from the well logs guidedby a stratigraphic framework defined by the pickedseismic horizons. The low-frequency component of thisinterpolation can result in errors in the absolute imped-ances and must be determined with great care. The in-tegration between the relative EEI and the initial modelsresults in absolute EEI cubes of LR and MR (Figure 14).This integration is accomplished in the same way be-cause it is done in conventional band-limited inversion,by adding the low-frequency band from the well logs tothe band-limited output of the process that is otherwisedeficient in those low frequencies. The absolute EEIcubes and the direct model-based inversions pass the

Figure 14. (a) Full-stack seismic section along wells (A and B); (b and c) seismic absolute colored inversion products from theEEI reflectivities of LR and MR, and (d) possible lithology interpretation from the integration between the LR and MR.

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low frequencies directly from the well logs to the output.The low-frequency component of the inversion outputis thus the same for both methods. A comparison be-tween the inverted EEI for LR and MR extracted atthe well locations and the frequency-filtered LR andMR logs (Figure 15 for well A) shows a good match be-tween the LR and MR logs and the traces extracted fromthe inverted EEI cubes at the well location in the gas andwater zones as well as in the shale intervals. Also thecomparison made for well (B) shows that there is a goodmatch between the logs and extracted traces in the clas-tics, carbonates, and even in the anhydrite intervals(Figure 16).

Interpretation of the inverted EEI cubesThe fourth step of the presented workflow is to in-

terpret the inverted cubes in terms of lithology and porefluid using the LR and MR cubes. In this step, the cross-plot between the absolute colored inversions of EEI forLR and MR is generated and the polygon templates ex-tracted from the well-based rock physics are applied(Figure 17). There is a small shift between the polygonstemplates extracted from the well-based rock physics

analysis, and the ones used to interpret the inversionresults may be due to noise present in the input seismicdata, inadequate wavelet extraction/stabilization, or im-perfect amplitude compensation, among other possibleexplanations. However, the similarity of the polygonsincreases the confidence that they are approximatelycorrectly located. Via this plot, the gas- and water-bear-ing sandstone and the shale-, and water-bearing lime-stone clusters are delineated and then projected tothe seismic data to investigate the lateral variationsof the defined lithologies and pore fluids (Figure 18).Generally, the interpreted lithologies and pore fluidtypes (i.e., gas-bearing versus water-bearing reservoirs)show a good match with the well data and they alsoshow spatial variations in the three dimensions. It isobvious that the water-bearing sandstone could bedistinguished without confusion from the gas-bearingsandstone even if it is encased by tight carbonates. Thetight water-bearing carbonates are also delineated andmapped. As a result, the extension of the gas-bearingsandstone reservoir encountered by the well (A) isdelineated and mapped, and the volume of the discov-ered gas resources is re-estimated.

Figure 15. Correlation between frequency-filtered LR and MR logs, and the invertedEEI traces of LR and MR extracted at the well(A) location.

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Figure 16. Correlation between frequency-filtered LR and MR logs, and the invertedEEI traces of the LR and MR extracted atthe well (B) location.

Figure 17. Crossplot between the absolute colored inverted LR and MR cubes and the possible lithology and pore-fluid inter-pretation that calibrated from the well-based rock-physics analysis.

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ConclusionsIn the study area, the Messinian section consists of

mixed sequences of siliciclastics and carbonates. Seis-mically, using stacked amplitudes alone to indicate gas-bearing sandstone may be highly uncertain because theamplitude signature is affected by variation in lithologyand rock frame properties in addition to fluid proper-ties. This paper shows an established workflow thatcan be used to delineate the variations in the lithologiesand pore fluids within the Messinian sequences throughthe integration between rock physics and EEI inver-sion. In the study area, the application of this workflowsucceeded in differentiating the gas-bearing sandstonefrom the water-bearing reservoirs regardless of the en-casing lithology (i.e., either shale or carbonates) as evi-denced by comparison of seismically derived quantitiesto well logs. Also, this workflow was used to reevaluatethe discovered gas resources by delineating the exten-sion of the reservoir. This workflow can potentially beused to reassess nearby prospects to reduce explora-tion risk and could be applied to other areas where am-plitude analysis is ambiguous for similar reasons.

AcknowledgmentsThe authors thank Egyptian general petroleum corpo-

ration for permission to publish this work. The thanksalso extend to S. Al-Dossary from Saudi Aramco andA. Hussein from Crescent Petroleum Company for re-viewing this paper and their valuable recommendationsthat resulted in significant enhancement of this paper.

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Figure 18. Lithology and pore-fluid interpreted from the inverted LR and MR cubes at the wells (A and B) locations are showing agood match between the well data and the interpreted lithology.

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Ahmed Hafez received a B.S. (2006)(excellent and honor degrees) in geo-physics from Al-Azhar University anda master's degree (2013) in appliedgeophysics. He joined Rashpetco sub-surface team at the end of 2006 wherehe was responsible for the develop-ment of large gas fields in the Mediter-ranean as well as for delineation of

new prospects. In 2010, he transferred to GDF Suez wherehe is currently working with the subsurface team to de-velop gas discoveries in the Mediterranean and the explo-ration of onshore Egypt. His main technical interest is tointerpret the geologic aspects using seismic techniquessuch as seismic sequence stratigraphy, rock physics, andseismic inversion.

John P. Castagna received a B.S.(1976) in geology and a master's de-gree (1981) in high-temperature geo-chemistry from Brooklyn College, andhe received a doctoral degree (1983)in exploration geophysics from theUniversity of Texas at Austin. Hespecializes in exploration geophysicsresearch and development. He is

widely known for his work in direct hydrocarbon detectionand reservoir characterization. During his career, he hasheld several positions in ARCO research and Vastar resour-ces. In 2000 and 2010, he founded Fusion Geophysical andLumina Geophysical, respectively. He was named distin-guished lecturer for SEG, delivering the fall lecture on “Ap-plied AVO analysis: Use and abuse of amplitude variationwith offset.” He has served SEG in various other capacitiesincluding chairman of the TLE editorial board, first vicepresident, and technical program chairman for the 2003Annual Convention in Dallas. His book, Offset-dependentreflectivity: Theory and practice of AVO analysis, is anSEG bestseller. He has also served as an associate editorfor GEOPHYSICS. He currently holds the Robert Sheriff Chairof Geophysics at the University of Houston.

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