illuminating the shadows: tomography, attenuation and pore pressure processing in the south caspian...

Ž .Journal of Petroleum Science and Engineering 24 1999 1–12www.elsevier.nlrlocaterjpetscieng

Illuminating the shadows: tomography, attenuation and porepressure processing in the South Caspian Sea

Stan Lee a,), Jesse Shaw a, Rachel Ho a, Jane Burger a, Sudarshan Singh b,Bill Troyer b

a Mobil Technology, Dallas, TX, USAb Mobil New Exploration and Producing Ventures, USA

Abstract

A new approach was successfully applied to produce better images through seismic shadow zones. These zones arecaused by the existence of shallow low velocity zones and are a problem to interpretation efforts in the South Caspian Sea. Acombination of careful wavelet processing with offset domain amplitude compensation and prestack tomographic inversionproduced significantly improved images compared to conventional processing. Visco-acoustic modeling was successfullyapplied to investigate the effects of seismic attenuation in the seismic shadow zones. Forward modeling results with differentQ values were compared to the seismic data and the best model fit to the seismic data determined. The final results indicatethat the seismic shadow zones have a low Q in the order of 30 compared to 120 outside the anomaly. Q values of 175 werepredicted for deeper zones in the seismic data. A detailed interval velocity model derived from the tomographic inversionanalysis was also successfully used to make accurate pore pressure and fracture pressure predictions from the seismic data.After calibration to available well information, the pressure predictions were used to construct a mud weight and mudcirculation warning display. The information provides drilling with pressure estimates that allow for better planning that canaffect both cost and safety in drilling. Knowledge of the difference in pore pressure and rock fracture pressure is useful indesigning casing points and deciding on mud weights to use during drilling. Pore pressures in the medium overpressurerange which can possibly represent drilling hazard were predicted in this study. q 1999 Elsevier Science B.V. All rightsreserved.

Keywords: tomography; South Caspian Sea; visco-acoustic modeling

1. Introduction

The South Caspian Sea is a major oil and gasproducing area which is characterized by thick sedi-mentary deposits and complex tectonic structure

) Corresponding author. Tel.: q1-214-951-2982; fax: q1-214-951-2098.

Ž .E-mail address: stan [email protected] S. Lee–

Ž .Karayev et al., 1996 . Structural trends in the SouthCaspian Sea include anticlinal folds, steep dips, andmud volcanoes. Many large anticlinal folds containworld class producing fields. But interpretation ofseismic data of these fields is challenging due toseismic low amplitude. Conventional processing failsto correctly image through seismic shadow zones

Ž .frequently observed in this fold region see Fig. 1 .These seismic shadow zones are characterized bylow amplitude events or low signal-to-noise ratio,

0920-4105r99r$ - see front matter q 1999 Elsevier Science B.V. All rights reserved.Ž .PII: S0920-4105 99 00019-4

( )S. Lee et al.rJournal of Petroleum Science and Engineering 24 1999 1–122

Fig. 1. Conventional imaging methods result in significantly reduced amplitudes due to the existence of the shallow low velocity zone. Thenew imaging method produces better imaging quality inside and below the low amplitude zone.

arrival time delay, attenuation of high frequencyinformation, and distortion of underlying reflectors,making interpretation difficult to impossible in sev-eral cases.

To better understand the seismic shadow zoneproblem, a frequency domain amplitude analysis wascarried out to quantify amplitude attenuation in theanomaly. Our analysis shows that an amplitude at-tenuation of 4 to 6 dB occurs inside the low ampli-tude anomaly zone. The observed arrival time delayswere found to be due to the existence of low velocitylayers. To investigate the nature of the shadow zone,we also performed visco-acoustic modeling. The ve-locity model used in this study was determined witha prestack travel time inversion or tomographic in-version approach. This velocity model was assigneddifferent attenuation factors for shot record simula-tion. Comparing the field records with synthetic shot

records, it was possible to construct an attenuationdistribution.

Overpressure can occur when the escape of porefluid is blocked because of low permeability in thesediment, obstructing the fluid migration pathwaysŽ .Gurevich et al., 1994 . Over-pressured zones in theSouth Caspian Sea are believed to be related to thefast deposition of sediments throughout geologicalhistory. The generation of overpressure is caused bythe inability of pore pressure fluid to escape at a ratecommensurate with sediment deposition. Rapid sedi-mentation is commonly associated with faulting and

Žthe vertical mobility of the mud diapirs Smale et al.,.1997 . The over-pressured boundaries are sometimes

associated with hydrocarbon deposits. Overpressurezone detection is very important in mapping reser-

Ž .voirs Kerimov et al., 1996 . In field drilling opera-tions, accurate pressure prediction helps drilling

( )S. Lee et al.rJournal of Petroleum Science and Engineering 24 1999 1–12 3

engineers to achieve an efficient cement design withcost savings and safety.

In the discussion that follows the nature of theseismic shadow zone is quantified. A processingapproached is applied to improve the structural imag-ing quality using a prestack model-based technique.The improved structural imaging results are thenused to better delineate the potential prospect. Esti-mates of seismic attenuation and formation pressureare determined to assist the reservoir characterizationand drilling design.

2. Structural imaging

As seen in Fig. 1, conventional imaging methodsusing DMO and time migration result in significantlyreduced amplitudes and large arrival time delaysbelow the shallow low velocity zones. A structuralinterpretation using conventional processing is diffi-cult in these ‘‘shadow zones’’. The new approachapplied in this study produced significantly improvedimages beneath the shadow zone. This approachincludes amplitude balancing and interval velocityanalysis using tomographic inversion. In addition tothe conventional processing, steps and the specialattention to detail are needed in this project. Thespecial processing steps were as follows:

Ø Offset domain amplitude balancing,Ø CDP domain residual moveout, andØ Velocity correction for the low velocity zone.

Offset domain amplitude balancing compensatesfor energy loss in the low amplitude zone. Theamplitude compensation is a temporal and spatialfilter. We apply a smoothly varying minimum phasefilter to correct the effects of inelastic attenuation.Using tomographic inversion we can calculate thedelay time due to the low velocity zone. Tomo-graphic inversion is a model-based procedure togenerate a geologically and geophysically consistentvelocity model. Tomographic inversion uses seismicray tracing to calculate the travel time with a giveninitial velocity model. The travel time errors arecalculated by subtracting the seismic event arrivaltimes observed in the seismic data from those pre-dicted from the tomographic inversion. The velocity

model and horizon positions are updated to minimizethe travel time errors for all the rays. Tomographyuses the flatness of the reflectors in the migratedCDP gathers as a quality control to insure the veloc-ity model is correct. When the estimated intervalvelocities are not correct, reflection events on the

Ž .migrated common-reflection-point CRP gathers arenot flat, producing a poor image. This is because thedepth error varies with offset. After several iterationsof updating the velocity model and horizon positions,a final optimal result with flattened events is ob-tained. The combination of the offset domain ampli-tude balancing and the residual velocity analysis wasperformed to produce the high fidelity structuralimages.

On the right side of Fig. 1, the quality of theimage, produced by the new processing technique, ismuch better than the conventional processing method.The faults inside the seismic shadow zone are noweasier to identify. These imaging improvements al-lowed interpreters to delineate and map the targethorizons more accurately with a higher degree ofconfidence. In conventional processing, which as-sumes that events have a smooth hyperbolic move-out, the shadow zones cannot be correctly imagedwhere strong lateral velocity variations producenon-hyperbolic effects.

3. Visco-acoustic modeling

When seismic waves propagate through a gas-saturated zone, amplitudes are intrinsically attenu-ated. This amplitude reduction results from the lossof elastic energy due to transmission, attenuation andother processes. The energy dissipation is a functionof rock properties, physical state, and the nature ofthe fluid saturation. A dimensionless parameterknown as a quality factor or Q is used to describethe attenuation of seismic waves in the earth. Thequality factor Q is defined as follows:

Qs2pv1Ž .

Dv

where vselastic energy stored at maximum stressand strain, and Dvsenergy loss of a harmonicexcitation.


A larger Q value means the medium attenuatesŽ .elastic energy less. Huages 1981 used VSP data for

Q testing and concluded that low Q zones may bedependent upon the degree of sedimentation andpore pressure. Low Q values occurred often in poorlyconsolidated porous sands. Q estimation using apower spectral ratio of the reflections from two time

Žintervals one shallow and one deeper in the surface.seismic data is prone to errors. These errors arise

from the sampling fluctuations inherent in powerspectral estimates as well as changes in the signal-to-noise ratio from any finite segment of data. To

Ž .avoid such errors, White 1992 suggests estimatingthe seismic wavelets in the two intervals by match-ing the seismic data to well-log synthetic seismo-gram. Q can then be estimated from the ratio of thetwo amplitude spectra.

Because simple acoustic modeling fails to accountfor the effects of attenuation, a visco-elastic model-ing is an ideal method to simulate the wavefield. Wedid not have shear wave information for the model.Therefore, we represented the medium by a visco-acoustic model. This model neglects the effects ofconverted waves and other elastic scattering. A moreaccurate synthesis of the data would be achievedwith a visco-elastic model that we leave to futureinvestigations.

According to Hooke’s law for elastic media, stressis directly proportional to instantaneous strain butindependent of rate of strain. The energy is storedwithout dissipation. For a viscous liquid, stress isproportional to the rate of strain and independent ofstrain itself; however, energy is dissipated. Combin-ing characteristics of elastic solids and viscous liq-uids, we can produce more realistic synthetic seismo-grams from the viscous modeling than can be ob-

Ž .tained from acoustic modeling. Carcione et al. 1988used the following convolutional model to describethe visco-elastic behavior of a material:

d™ ™P x,t sy e x,t ) MŽ . Ž . Rd t

=L t t´ i1y 1y exp y 2Ž .Ý ž / ž /t ts sis1 i i

™ ™Ž .where xsn-dimensional spatial vector, e x,t sdilatation in an n-dimensional visco-elastic medium,

™Ž .P x,t spressure in an n-dimensional visco-elastic

medium, M sacoustic module of the medium, tR s i

smaterial relaxation times for t -th mechanism, ti ´ i

smaterial relaxation times for ´ -th mechanism,i

Ls the number of iterations of relaxation, and )sconvolutional operator.

This study used a 3D finite difference visco-Ž .acoustic modeling code based on Eq. 2 to generate

synthetic seismograms and wavefield snap shots.This modeling code uses a 4th order finite differenceapproach in the space domain. The source functionused in the modeling was a normal polarity Rickerwavelet. To get the related relaxation time for eachQ, the relaxation time of strain and stress are storedin the memory for each Q value. For the non-viscouscase, a linearly distributed stress and strain was usedfor each relaxation time.

Fig. 2 shows the measured P wave velocities andQ values from the South China Sea. The model isobserved to have a gas chimney or volume with a Qfactor of 50 and a background with a Q of 200. Theinterval velocity of the chimney is 2500 mrs whichis 17% lower than the surrounding velocity. Thesource using the Ricker wavelet was injected on thesurface near the middle of the model. Fig. 3 showsthe synthetic shot records calculated from the model.On the left a Q value of 50 was used in the chimney

Fig. 2. 3D velocity–Q model for testing visco-acoustic behavior.


Fig. 3. Central profile of the 3D visco-acoustic modeling with Q factors of 50 and 200 in the gas chimney.

volume and, looking at events with arrival timesgreater than one second, lower amplitudes were ob-served than when a Q value of 200 was used. Theamplitude ratio between the two Q cases is about1:4.

Ž .Liao and McMechan 1996 applied a full-waveform inversion to synthetic data to obtain estimatesof Q values. We applied the visco-acoustic model-ing method to estimate the Q value of the seismicattenuation zone in the South Caspian Sea. Thevelocity model was derived from a tomographicinversion. The input seismic data was carefullywavelet processed to preserve amplitude and phase.To derive the Q factors of the low amplitude zone, aforward modeling approach were used. Based on aspectrum analysis in the ‘gas chimney’ zone, a Q of30 was estimated. Fig. 4 shows the initial Q modelconsisting of a Q of 30 inside the ‘gas chimney’Ž .1100 m–1350 m and Q’s of 120 outside the gaschimney. On the top of Fig. 4, the input trace is fromthe center of the seismic shadow zone. Using thevelocity model from tomographic inversion, the re-sulting zero offset trace of the synthetic shot recordis shown on the bottom of Fig. 4. Comparing thesynthetic amplitudes with field amplitudes, there is asignificant difference in arrival times of 0.5 to 2.5 s.

These amplitude differences are due to the fact thatQ factors were not considered. By comparing fieldrecords with synthetic shot records, it was possible toobtain an optimal Q distribution. Fig. 5 shows thefinal inversion results from seven iterations of com-parisons. Fig. 5 shows the synthetic amplitudesmatching well with the field amplitude. The final Qinversion results show that the Q values range from30 to 175. The ‘gas chimney’ has the lowest Q of30, and at deeper depths, Q values range from 100to 175. Both the Q value and predicted pore pressureinside the ‘‘gas chimney’’ indicate the presence of alow velocity zone.

4. Pressure imaging

In recent years, industry has increased explorationand production in over-pressured areas. Over-pres-sured zones can act as good reservoir seals, but canalso cause drilling difficulties, particularly in main-taining an adequate mud weight safety margin. Infield drilling operations, the weight of the mud should

Ž .be at least 1 pound per gallon ppg larger than thepore pressure to keep mud circulating smoothly. If


Fig. 4. The initial Q model and synthetic modeling results from initial Q model.

the difference between the fracture and pore pressureis less than 1 ppg, drilling can be very difficult.Accurate overburden, pore, and fracture pressureprediction can assist the drilling engineers by provid-ing the means to prepare an effective casing and muddesign. This can also help control drilling costs.

Pore pressure is defined as the fluid pressurewithin the pore space of a sedimentary rock. Over-burden pressure is the total vertical stress exerted bythe weight of the overlying rocks and their containedfluids. Fracture pressure is the stress to fracture a

formation, which relates to the overburden pressure,horizontal stress, and the pore pressure. Normally theoverburden is supported by the rock matrix. In over-pressured situations, the overburden is partially sup-ported by fluid or pore pressure. When a compactinglaw is provided, the overburden pressure can beobtained using the follow equation:

D Dsw s

P sC r dwqC r 1yf hŽ .Ž .�H Hob sw gr0 0

qr f h dh 3Ž . Ž .4f


Fig. 5. The final Q inversion from seven iterations of updating the Q model.

where, P soverburden pressure, r s fluid den-ob f

sity, r ssea water density, f sgrain density,sw grŽ .f h sporosity, hsvertical depth, Csconstant

coefficient, D ssediment thickness, and D ss sw

water depth.Ž .From Eq. 3 , the overburden pressure is related

to the height of the water column and the lithostaticŽ .column. The first integral term of Eq. 3 varies

relative to sea level, and the second integral termvaries relative to sea floor. The density can be

obtained from bulk density log measurement. If di-rect density measurement is not available, it can beestimated from the other well or seismic data. Thereare a number of ways to predict pore pressure fromdrilling mud weights, resistivity, conductivity, sonicand seismic interval velocity. For exploration areassuch as the South Caspian Sea with seismic shadowzones, where very little or no pressure information isavailable, interval velocity method is valuable. Tradi-tionally, the industry has used the Hottman and


Ž . Ž .Johnson 1965 , Foster and Whalen 1966 , EatonŽ . Ž .1969 and Eaton and Eaton 1997 methods. Amongthese method, the Eaton method is most widely usedin industry. The Eaton equation is listed as follows:

ND tn

P sP y P yP 4Ž . Ž .pp ob ob nrD to

where, P spredicted pore pressure, P snormalpp nr

pore pressure, D t snormal shale travel time, D t sn o

observed shale travel time, Nsexponential coeffi-cient.

The Eaton method predicts pore pressure usingthe shale compacting assumption. Thus the method isappropriate in sand-shale sequence only. For ourstudy area, the geology fits the Eaton assumption. In

Ž .Eq. 4 , the exponential coefficient or N is deter-mined by the regional geological basins and effsetwells. The typical N value of the Gulf of Mexico is

Fig. 6. The pore pressure distribution in low amplitude zone shows the pressure increases with depth. The lighter shading represents apressure of 10 ppg.


Ž .3. From Eq. 4 , the overburden pressure is critical tothe accuracy of pore pressure. In vertical wells,fracture pressure is related to the overburden pres-sure, horizontal stress, and the pore pressure. Tofracture a formation would require a mud weightpressure at least equal to the formation pressure. Anyadditional pressure required must be related to over-coming horizontal stress and the cohesive strength of

Ž .the rock matrix. Mathews and Kelly 1967 devel-oped an equation to calculate the fracture pressure:

P sP q P yP ) K 5Ž .Ž .fp pp ob pp

where P s fracture pressure, Kshorizontal stressrfp

vertical stress.To obtain accurate fracture pressure requires

knowledge of not only the pore pressure, but also the

Fig. 7. The fracture pressure distribution in low amplitude zone shows the increases with depth.


horizontal stress and the cohesive strength of theŽ .rock matrix. In Eq. 5 , the K parameter is based on

an empirical relationship and is specific to geo-graphic area.

In the past, seismic stacking velocities, convertedto depth domain interval velocities using the Dixequation, were used for pressure prediction. Pressure

prediction from the Dix equation velocities can be inerror when lateral velocity variations and dippingstructures exist, and can become unstable whenstacking velocity decreases. Both of these problemsoccur in over-pressured areas. The method used inthis study improved the quality of the geopressureestimation by increasing the accuracy of the interval

Fig. 8. The differential pressure is obtained by fracture pressure subtracts pore pressure. The range of the differential pressure is between 1and 6 ppg.


velocity measurement. Our improvement uses tomo-graphic inversion to yield more accurate lateral inter-val velocities in the depth domain. This techniquealso allows the integration of pressure prediction,AVO indicators, and reservoir depth imaging.Color-coded geopressure is then overlain on seismicdepth sections to indicate variations in geopressuredistribution. Additionally, reservoir boundary map-ping can be enhanced by overlaying color-codedgeopressure distribution charts on AVO sections.

Use of the tomographic inversion yields moreaccurate velocities both laterally and with depth. Thefinal interval velocity model from the tomographicinversion was used to make the pore and fracturepressure predictions. The sediment compacting trendwas determined by analyzing two seismic events,one shallow and one deeper. Based on the derivedinterval velocities and well information, we adjustedthe normal compacting trend to allow the calculatedpore pressure to match the actual pressure at the welllocation. Pore and fracture pressures were estimatedfrom interval velocities through the low amplitudezone as well as deeper in the section.

The geopressure estimation was then overlain onthe depth migration sections providing a useful reser-voir interpretation tool. Fig. 6 shows a depth domainpore pressure distribution. The pore pressure distri-bution shows that the pressure remains a hydrostaticpressure to a depth of 2500 m. The pressure starts toincrease with depth and reaches 10 ppg at a depth of2800 m. In Fig. 6 the lighter shading with a pressureof 10 ppg represents the top of an over pressurezone. The fracture pressure was calculated based on

Ž .Eq. 5 and the results were plotted in Fig. 7. Fig. 7shows the fracture pressure distribution in the lowamplitude zone also increases with depth. At a depthof 2800 m from the top of overpressure zone, thefracture pressure reaches 15 ppg. A differential pres-sure between the fracture pressure and the porepressure was calculated to detect the mud circulation.If differential pressure is less than 1 ppg, drilling canbe very difficult. Fig. 8 shows the differential pres-sure distribution of fracture pressure and pore pres-sure. In Fig. 8, the differential pressure ranges from1 to 6 ppg. These results indicate that the mudcirculation should be smooth in the studied lowamplitude zone.

5. Conclusions

A new method for imaging through and below aseismic shadow zone of the South Caspian Sea wassuccessfully applied. The quality of the image, pro-duced by the new processing technique, is muchbetter than the conventional processing method. Thefaults inside the seismic shadow zone are easier toidentify. These imaging improvements allowed inter-preters to delineate and map the target horizons moreaccurately with a higher degree of confidence. Im-provements were due to the application of offsetdomain amplitude compensation and prestack tomo-graphic inversion. Simple acoustic modeling fails toaccount for the effects of attenuation, a visco-acous-tic modeling approach was used to simulate thewavefield of the seismic shadow zones. The visco-acoustic modeling results suggest Q values rangefrom 30 to 175. The ‘gas chimney’ has the lowest Qvalue of 30, and at deeper depths, Q values rangefrom 100 to 175. Tomographic inversion velocitieswere used to predict pore pressure which were foundto range between hydrostatic to medium pressure.Pore pressures in the medium overpressure rangewhich can possibly represent drilling hazard. Thestudy also produces a useful mud circulation warningdisplay by color coding the difference between thecalculated fracture and pore pressure.

Acknowledgements

The authors gratefully acknowledge Mobil NewExploration and Producing Ventures for supportingthe study, and Mobil for permission to present thispaper. Special thanks go to the State Oil Company of

Ž .Azerbaijan SOCAR and Kevin Woller for valuablediscussions and Caspian Geophysical for acquisitionof excellent quality of 2D speculative data. We alsoappreciate Bill Soroka, Bob Keys and Stew Levin forreviewing the manuscript.

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illuminating the shadows: tomography, attenuation and pore pressure processing in the south caspian...

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