exploration for gas hydrates in deepwater northern gulf of mexico

lable at ScienceDirect

Marine and Petroleum Geology 25 (2008) 845–859

Contents lists avai

Marine and Petroleum Geology

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

Exploration for gas hydrates in the deepwater, northernGulf of Mexico: Part II. Model validation by drilling

Jianchun Dai*, Niranjan Banik, Diana Gillespie, Nader DuttaSchlumberger, 10001 Richmond Avenue, Houston, TX 77042, USA

a r t i c l e i n f o

Article history:Received 31 January 2007Received in revised form 3 September 2007Accepted 15 February 2008

Keywords:Gas hydrateBSRInversionSeismic detectionCalibrationGulf of Mexico

* Corresponding author.E-mail address: [email protected] (J. D

0264-8172/$ – see front matter Published by Elsevierdoi:10.1016/j.marpetgeo.2008.02.005

a b s t r a c t

This study examines the accuracy of the predictions of gas hydrate saturations made based on five-stepanalysis of 3D seismic data prior to 2005 drilling, logging, conventional coring, and pressure coresampling through the gas hydrate stability zone at two focus sites in the northern Gulf of Mexico. Thesepredictions are detailed in Part I (2008). Here we conduct a detailed analysis of the gas hydrate saturationusing both resistivity and P-wave velocity log data and analyze the pre-drilling predictions, which weremade almost exclusively on the basis of seismic data, with no local logging control. Well log measure-ments, core data analysis, and pressure core-degas experiments all indicated general agreement with thepre-cruise analysis regarding the location and approximate concentration of gas hydrates in the sedi-ments. We find that seismic predictions are generally consistent with log-based estimates after upscalingto seismic frequencies. We recalibrated the pre-drill model based on the new field data so that a refinedversion of the model could be used for future work.

Published by Elsevier Ltd.

1. Introduction

In recent years, the detection and delineation of gas hydrateshave drawn significant attention from the scientific communityworldwide due to its potential as an alternative energy source,cause for drilling hazards, and being an agent for global climatechanges. Reflection seismic technology, being the principal methodin hydrocarbon exploration, has been extensively used for hydratedetection (e.g., Collett et al., 1999a; Dai et al., 2004; Diaconescuet al., 2006; Ecker et al., 1998; Kvenvolden and Barnard, 1983;Shipley et al., 1979; Xu et al., 2004). In Part I, we presented results ofgas hydrates detection and characterization based on a seismicapproach termed the five-step workflow (Dai et al., 2004; Xu et al.,2004).

Inherent in seismic prediction are uncertainties. In the case ofgas hydrate exploration in the Gulf of Mexico (GoM), the sources ofuncertainty are mainly due to a general lack of ‘‘ground-truth’’ in-formation about the formation and accumulation of gas hydrates.Since the prediction is model based, the validation of a particularmodel and calibration of model parameters through drilling areimportant first steps to improve the accuracy of seismic prediction.Additionally, the properties of near-seafloor sediments vary con-siderably, and well log data in such shallow sections are often not

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Ltd.

reliable due to borehole conditions. Lack of quality shallow-log datain a given hydrate-exploration basin poses additional constraintson the reliability of gas hydrate saturation predictions based ona seismic approach.

In 2005, the DOE–Chevron Joint Industry Project (JIP) drillingand coring program completed seven holes for the study of gashydrates in the northern GoM. One objective of the program wasto collect sediment cores and obtain well logs in areas where therewas substantial seismic evidence of the occurrence of gas hydratesand thus aid in the verification of the exploration model. The JIPtargeted Keathley Canyon (KC) 151 and Atwater Valley (AT) 13 and14, both at water depths of about 1300 m. These sites are shown inFig. 1 of Part I (Dai et al., 2008). The Atwater Valley site hasa prominent gas hydrate mound in a high fluid flux area (Elliset al., 2008; Wood et al., 2008), and Keathley Canyon site hada regional bottom simulating reflector (BSR; Hutchinson et al.,2008). The lithologies in both locations consisted of clays and siltswith small amount of sands. In both locations, the inferred in situgas hydrate concentrations were low and highly variable.

Five holes were drilled in AT13 and 14, and two holes weredrilled in close proximity to each other in KC151 (holes KC151-2 andKC151-3). KC151-2 was drilled first and Logging-While-Drilling(LWD) and Resistivity-at-the-Bit (RAB) were run in this well duringdrilling. Following completion of KC151-2, coring started at KC151-3. wireline dipole sonic/gamma ray (GR)/general purpose in-clinometer Tools (GPIT) were successfully run after the completionof the KC151-3 hole. Two possible substantial hydrate zones (Fig. 1)

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were penetrated in these holes at depths between 200 and 300meters below seafloor (mbsf). The depth interval corresponds totwo-way time interval between 2020 ms and 2120 ms from themean sea level.

In Part I we presented a detailed pre-drill analysis and pre-diction results for the drilling areas based on the five-step seismicapproach (Dai et al., 2004; Xu et al., 2004). In this paper, we eval-uate hydrate concentrations based on drilling results in the KC151gas hydrate wells using resistivity from KC151-2 and dipole sonicdata from KC151-3. We compare these results to predictions de-tailed in Part I (Dai et al., 2008) and derived from prestack wave-form inversion (PSWI) (Mallick, 1995, 1999; Dutta, 2002) onexisting 3D seismic data. We then present an updated seismicmodel based on the current drilling data. We also present resultsusing a fast, yet robust simultaneous prestack seismic inversiontechnology (Rasmussen et al., 2004).

2. KC151 log evaluation and well ties to seismicmeasurements

As shown in Fig.1, the KC151 suite of logs (combination of KC151-2 and KC151-3) includes gamma ray (GR) and photoelectric ab-sorption factor (PEF); shallow, medium and deep resistivity averages(BSAV, BMAV, BDAV); differential caliper average (DCAV); P-wavevelocity (Vp); S-wave velocity (Vs); bulk density (RHOB); neutronporosity (NPHI); and nuclear magnetic resonance (NMR). The

Fig. 1. Combined log suite from KC151-2 and 3, including gamma ray (GR), photoelectricBDAV), differential caliper average (DCAV), P-wave velocity (Vp); S-wave velocity (Vs), bulkvolume of shale (VCL) is estimated using a simple transform of the GR log. The BSR is mar

volume of shale (VCL) is estimated using a simple transform of theGR log. It shows a trend of increasing shale content with depth. Itmust be noted, however, that the GR log contains a major baselineshift at 125 mbsf (Fig.1), and we lack good qualitycoring informationto define the GR trend for pure sands. Therefore, the accuracy of VCLestimation may be questionable.

Two anomalously elevated resistivity zones are indicated by theresistivity logs in Fig. 1: one between 220 and 240 mbsf and theother between 260 and 300 mbsf. The Vp log also shows highvelocities at the upper zone, but the velocities are not high and,almost constant with depth in the lower zone. These two anoma-lous zones may be indicative of appreciable hydrate concentration.Detailed discussions of these potential gas hydrate zones will begiven in later sections of this paper.

The Vs log does not show any coherency with Vp or other logs.A review of the up and down logging runs of acoustic and shearsonic velocities shows high levels of consistency in Vp, but noisyand poor consistency in Vs (Fig. 2). We used the up-going Vp afterminor editing. Because of the poor consistency of the shear logs,we considered the Vs log to be unsuitable and therefore did notuse this log for gas hydrate estimation in the current work. Thedensity and the neutron porosity logs appear to be reliable belowapproximately100 mbsf. They also extend to depths below theBSR. At depths shallower than 100 mbsf, these logs are of poorquality. This is evident from the caliper data through this shallowsection.

absorption factor (PEF), shallow, medium and deep resistivity averages (BSAV, BMAV,density (RHOB), neutron porosity (NPHI), and nuclear magnetic resonance (NMR). Theked by the black line.

Fig. 2. KC151-3 up and down logging runs of acoustic and shear sonic velocities.

J. Dai et al. / Marine and Petroleum Geology 25 (2008) 845–859 847

Well-to-seismic ties provide a basis for ascertaining hydrateevents on seismic data and for the calibration of a hydrate-estimation model. We created a well-based, zero-offset syntheticseismogram and compared it with the reprocessed poststack seis-mic trace (Fig. 3) and prestack gather (Fig. 4) at the well location.The seismic data reprocessing was done to optimize hydrate de-tection and quantification as described in Part I (Dai et al., 2008).Two sources of time–depth relations were available to aid the well-to-seismic tie. One is the time–depth curve from pre-drill PSWI,and the other is the checkshot data from the KC151-3 well. Thefocus of the synthetic is at the interval where dipole sonic logs areavailable, between 1900 ms and above the level of the BSR (Figs. 3and 4). For the synthetics, the impedance was calculated from theproduct of Vp and bulk density. At shallower and deeper zones

Fig. 3. Well-to-seismic tie for stack data. The seismic wiggle traces in between the synthtrajectory.

where the Vp is not available, Gardner’s equation (Gardner et al.,1974) was used to estimate Vp for these zones. The application ofGardner-type velocity transformation may be questionable for theunconsolidated rocks. Also, the quality of the shallower density ispoor due to bad hole conditions as indicated by the enlarged caliper(Figs. 3 and 4). Nonetheless, this does not affect the well-to-seismictie in general since we focus on the interval where Vp observationsare available.

We used an 18 Hz central frequency Ricker wavelet to createthe synthetic seismic traces. This wavelet was consistent with thewavelet extracted from the seismic data near the well location. Thetie between the synthetic and the seismic data at the well locationwas reasonably good in the zone of interest (1900 ms–2220 mstwo-way travel time). The poor tie for the shallower section is dueto a poor quality density log and the absence of Vp and Vs logs (Figs.3 and 4). The characteristic peak at the top of the high P-wavevelocity zone at 2020 ms as shown in Figs. 3 and 4 greatly facili-tated matching the events in synthetic and seismic data. The arti-fact in the synthetic data arising from the absence of good-qualitywell data at and near the water bottom was addressed in lateranalyses through the use of PSWI (see below), where we demon-strate 90% goodness of fit between the PSWI synthetic gather andthe real gather at the well. This also underscores the value ofpseudo-well data derived from prestack inversion for hydratedetection and quantification.

Fig. 5 shows the overlay of Vp and resistivity curves on theseismic section based on the updated time–depth relation. The highVp anomaly ties to the seismic peak at w2020 ms, but Vp is prac-tically constant at the seismic peak at 2070 ms (Fig. 5, top panel). Incomparison, the resistivity seems to tie to the seismic data at bothplaces (Fig. 5, bottom panel).

3. Gas hydrate concentration (Sgh) estimation from logs

There is an extensive literature on the use of well logs for esti-mation of gas hydrate concentration (e.g., Collett et al., 1984, 1999b;

etic and the seismic section are the extracted seismic response along the borehole

Fig. 4. Well-to-seismic tie for prestack gather. The seismic wiggle traces in between the synthetic and the seismic gather are the stacked seismic trace of the gather.


Collett, 2001; Collett and Lee, 2004; Kleinberg et al., 2003, 2005;Lee and Collett, 2008; Mathews, 1986; Guerin et al., 1999; Hyndmanet al., 1999). The most commonly used logs for hydrate concen-tration estimation include resistivity, sonic, and NMR logs. Below,we discuss the gas hydrate concentration estimates based on re-sistivity and sonic measurements at the KC151 wells. These esti-mated results are then compared with the prediction from seismicmeasurements as presented in Part I.

3.1. Sgh estimation from resistivity logs

Resistivity-based Sgh estimation includes two steps. It first in-volves solving for water saturation using Archie’s (1942) equation

Sw ¼�

aRw

4mRt

�1n

(1)

The second step is to solve for Sgh on the assumption that theporous medium contains only brine water and hydrates

Sgh ¼ 1� Sw (2)

In Eq. (1), 4 is the rock porosity, Rw is the formation water re-sistivity, Rt is the formation resistivity, m is the cementation ex-ponent, n is the saturation exponent, and a is a parameter related torock tortuosity.

3.1.1. Rw estimationRw is estimated through Arps’ (1953) formula

Rw ¼ RrTr þ 21:5T þ 21:5

(3)

where Rr (0.35 Um) and Tr (4 �C) are the seafloor water resistivity(in Um) and reference temperature (in �C), respectively. T is theformation temperature and it is calculated from

T ¼ Tr þ Tg*Depth (4)

where Tg is the shallow geothermal gradient. Fig. 6 shows the for-mation water resistivity profiles with the thermal gradient chang-ing from 2.6 to 4.0 �C/100 m. We chose a Tg of 3.8 �C/100 m,consistent with the near-seafloor shallow depth thermal gradient(Hutchinson et al., 2008; Fig. 6).

3.1.2. Porosity estimationDensity-derived porosity and neutron porosity are available for

the studied well. Both porosities show coherent trends with depth,but the neutron porosity is systematically higher than the densityporosity (Fig. 1), possibly due to high shale content of the shallowunconsolidated rocks. In this work we calibrate the neutron po-rosity trend against the density porosity trend and use the porosityas the average of the two porosities

4 ¼ 12½4d þ 4n� (5)

where 4d and 4n are the density-derived porosity and calibratedneutron porosity, respectively. 4d is calculated as given below

4d ¼rma � rb

rma � rw(6)

where rma, rb, and rw are the densities for matrix (2650 kg m�3),bulk, and pore water (1036 kg m�3), respectively. In the presence ofgas, the bulk density overestimates porosity, and the neutron dataunderestimate it. Thus, the averaging process helps reduce proba-ble gas effects in porosity estimation.

Review of literature indicates a rather large range of valuespossible for various parameters needed in these calculations. Themost commonly used combination of parameter values are theHumble values, i.e., a¼ 0.62; m¼ 2.15. Collett et al. (1999b) usedthe Humble values and n of 1.9386 (Pearson et al., 1983) for the

Fig. 5. Left panel shows Vp overlay on seismic section. Right panel shows resistivity overlay on seismic section. The red arrow indicates the possible gas hydrate zone associatedwith the strong seismic amplitude event on the right side of the well.


Mallik 2L-38 Sgh estimation, and the result matches well with thecore measurement. Hacikoylu et al. (2006) also published empiricalvalues for different lithologies, including shaly sand (a¼ 1.65,m¼ 1.33) and unconsolidated sand (a¼ 0.62 and m¼ 2.15). In thisstudy, we adjust parameters a, m, and n to calculate Sw. We find thata¼ 0.90, m¼ 1.90, and n¼ 1.9386 produce Sw that follows thebackground water-saturated intervals (in which Sw¼ 1). Wetherefore use this set of values for the estimation of gas hydratesaturation.

3.1.3. Sgh estimationThe Sgh is calculated using Eq. (2). The Sgh curve (Fig. 7) reveals

two major Sgh zones in the interval from 2020 ms to 2120 ms, with

Sgh values as high as 30%. The result of upscaling to the seismic scaleshows more general hydrate anomalies, with the majority of Sgh

lower than 20%. We will use the upscaled results for the compari-son with the pre-drill seismic gas hydrate concentration predictionmade in Dai et al. (2008, Part I).

3.2. Sgh estimation from P-wave velocity log

To estimate gas hydrate saturation from Vp and Vs data, twopieces of information are needed: knowledge of the elastic prop-erties of the background rocks and a rock physics model that ac-counts for the effect of gas hydrates on the host rocks. In thissection, we use P-wave velocity measurements from the KC151 well

Fig. 6. Formation water resistivity estimation using Arps’ relation. The water resistivitycurves with depth shift to the left as geothermal gradient changes from 2.6 to 4.0 �C/100 m, with an increment of 0.4 �C/100 m.


log and effective medium theory model to account for the gas hy-drate effect. Dai et al. (2004) and Xu et al. (2004) provide moredetailed discussions of this subject.

With the modeling, we estimated Sgh from the P-wave velocityof KC151-3 (Fig. 8). Note that the Sgh estimated from P-wavevelocity ranges from 0 to 15% in seismic scale which is little lowerbut, in general, consistent with those estimated from resistivity.

4. Discussion on log-based hydrate concentration

The Sgh derived from resistivity data reveals two major zones ofpotential hydrate concentration in the interval between 2020 msand 2120 ms, with Sgh ranging from 0 to 30% on the log scale and 0–15% in seismic scale (Fig. 7). The Sgh estimated from the P-wavevelocity implies the presence of gas hydrates in both zones. How-ever, the P-wave-based estimate in the lower zone is considerablylower than that obtained by the resistivity method. The discrepancybetween the two estimates may suggest that the upper hydratezone can be more confidently assessed as hydrate bearing than thelower zone. A close look at the RAB images of these two possiblehydrate zones may indicate a probable increase of shale contentfrom the upper hydrate zone (A) to the lower hydrate zone (B)

Fig. 7. Resistivity-derived Sgh (fraction of pore volume) versus two-way travel time(ms). The solid curve is the original estimation and the dashed curve shows the resultsupscaled to seismic scale.

(Figs. 9 and 10). The sand in zone B may contain more shale lami-nations. This shale lamination effect may have caused a lowering ofthe background Vp that, in turn, lowered the estimated hydratesaturation in this interval. In this case, the lower zone is stillprobably hydrate bearing, but without much velocity increase dueto the laminated nature of the shale-prone host rocks. Anotherpossibility is that the rock properties at these twin holes may varydue to heterogeneity, and the lower gas hydrate zone as diagnosedfrom resistivity from KC151-2 may not exist at the KC151-3 locationmay have been missed by sonic logging in this hole.

5. Comparison and calibration of seismic prediction withlog-derived data

5.1. Comparison with pre-drill seismic results

Fig. 11 shows the comparison of the log-based Sgh estimates withthe pre-drill seismic prediction (dashed lines). The log-based esti-mates are upscaled to the seismic scale as shown in Figs. 7 and 8.

Of the two major hydrate anomaly zones inferred to have beenpenetrated by the KC wells at the interval between 2020 ms and2120 ms, the pre-drill prediction matches well with the lower zoneB that is centered at about 2080 ms, especially through the re-sistivity method, but only shows very small hydrate saturation inthe upper zone A that is centered at 2020 ms (Fig. 11). In addition,the pre-drill model also predicts high hydrate saturations at shal-lower intervals (1900–2000 ms), where the log-based estimationonly shows small concentrations. The poor ties in the shallow in-tervals (<2000 ms) may have been caused both by the errors inseismic prediction due to noise and unreliable log data in shallowinterval due to enlarged borehole conditions. The poor ties in theupper zone A centered at 2020 ms may be due to the absence of anycalibration and a good low-frequency background model in thepre-drill prediction. A re-examination of the pre-drill PSWI (Fig. 12)does show a small peak in Vp at 2020 ms (zone A). However, theabsolute value of PSWI Vp at this event is not prominent, resultingin a possible under estimation of hydrate saturation in pre-drillpredictions. We also note that the pre-drill model yields an ap-preciable hydrate saturation event above the BSR at 2200 ms, butpost-drill estimation from the resistivity log does not show anysuch indication (Fig. 11). The velocity logs were not available at thisdepth. It is possible that hydrate may be concentrated just abovethe BSR, but it may be on the right side of the well, as indicated bythe strong reflection shown by the arrow in Fig. 5. The hydrateanomaly above the BSR might not have been picked up by theresistivity log due to its limited penetration of sediments away fromthe borehole.

In summary, the comparison between pre-drill prediction andlog data based estimates suggests that the seismic prediction cancapture hydrate anomalies even with modest saturation, as low as10–20% in this case. However, seismic prediction of low saturationhydrates may be quite uncertain due to errors in seismic inversion,in rock model of gas hydrates, in background rock properties, andin well-positioning. The log-based calculation of gas hydrateconcentration also needs calibration of parameters, which is, in thiscase, difficult to do due to low concentration of gas hydrates ingeneral and the lack of direct evidence of gas hydrates from coringin particular.

5.2. Post-drill calibration of PSWI results

Because of the band-limited nature of seismic data, the resultsbased on seismic inversion alone may be ambiguous and uncertain.A major source of such uncertainty is the lack of a low-frequencybackground trend that accounts for major lithologic variations. Dueto lack of local well data during the pre-drill model construction

Fig. 8. Sgh estimation from P-wave velocity log at KC151-3. The left panel shows the input Vp log (solid) and the background trend (dash). The nomogram in the middle panel showsthe modeled P-wave velocities with hydrate saturations ranging from 0 to 50% with an increment of 5% from left to right. The right panel shows the estimated hydrate saturation(solid curve for log scale and dashed curve for seismic scale).

Fig. 9. KC151-2 logs with RAB images. Bright color in the upper RAB image for zone A reveals high resistivity, possibly due to both hydrate concentration and sandier sediments. Thesediments may become more shaly in the lower section as shown in the lower RAB image.


Fig. 10. KC151-2 logs with RAB images. The color of the RAB images become more bright in the lower portion of zone B, possibly due to both gas hydrate concentration and moresands in the clay-dominated environment.


phase, we used a generic lithology-independent trend based ona rock physics model that is typical of deepwater sediments in theGoM (Dai et al., 2008, Part I). This may be a major source of error inour model prediction. To understand this further and to createa reliable predictive model, we next calibrated the inversion resultsusing the well data. This entailed creating a new suite of low-fre-quency Vp, Vs, and density models that more accurately describethe earth model. This is shown in the well data in Fig. 12. Werecomputed the PSWI with the new low-frequency model as theinitial model.

Fig. 11. Comparison of Sgh estimated from log data with pre-drill seismic prediction. Theblue and red curves are the Sgh estimates in seismic scale from resistivity and P-wavevelocity, respectively. The black dots are the result of pre-drill prediction as discussed Daiet al. (2008).

The results from calibrated PSWI are shown in Fig. 13. We termthese the ‘‘post-drill inversion’’ results. The correlation coefficientbetween the actual seismic angle gather and the synthetic anglegather is w0.91, suggesting a reliable match. We caution, however,that the high correlation coefficient may not directly reflect theerror/uncertainty of the solution due to the highly non-uniquenature of the methodology. The result should therefore always becalibrated against log measurement if available. The post-drill PSWIresults now do show the presence of a high peak at event A, whichmatches with the log measurement.

The highs and lows in the P-wave velocity data from inversionreflect possible lithologic or fluid variations. At the shallow depth,sandy sediments usually possess higher velocity than shale becauseof lower porosity as well as higher grain velocity. Gas hydrate zoneshave been known to possess high velocities above the BSR and lowvelocity beneath the BSR. Based on these observations, we inferthat shallow sand and shale sequences, the BSR, and possible gashydrate anomalies can be recognized in the pseudo-well logs fromPSWI provided a reliable low-frequency trend is used to guide theinversion process.

A comparison of the pre- and post-drill PSWI is given in Fig. 14.Overall, both solutions are comparable, especially in P-wavevelocities, but there are also significant differences as discussedabove.

Similarly, the hydrate concentration estimate based on P-wavevelocity from post-drill PSWI is given in Fig. 15. It shows a high peakof w20% of hydrate concentration at event A and a smaller peakof w10% concentration at event B. A comparison of log-basedhydrate saturation with all versions of seismic estimates is given inFig. 16, which shows coherency in zones A, B, and the interval

Fig. 12. Pre-drill PSWI at KC151-3. The blocky curves in the left three panels are the inverted P-wave, Poisson’s ratio, and density profiles, respectively, resulting from PSWI. Thesmooth curves are the corresponding input backgrounds. Red arrows show peaks in the seismic data, and the blue arrow indicates the BSR. The right two panels, respectively,display the actual seismic angle gather and the synthetic angle gather after convergence during the iteration process. The correlation coefficient of the two gathers is about 0.90.


immediately above the BSR at about 2200 ms. The Sgh from log datadoes not show a significant hydrate response near 2200 ms; how-ever, the seismic estimates show the anomaly consistently. In-spection of the seismic section does reveal a significant event at the2200 ms level just at the north side of the well, and we postulatethat the well may have missed this hydrate-bearing zone.

5.3. ISIS simultaneous inversion for Sgh estimation

ISIS�-based simultaneous inversion (Rasmussen et al., 2004)(hereafter referred to as simultaneous inversion) is a seismicmethod for inverting prestack seismic data for elastic parameters.The method uses multiple traces and a global error minimizationalgorithm to robustly invert for elastic parameters. Preconditionedseismic data are input as multiple angle stacks. Prior models for Vp,Vs (or Poisson’s ratio), and density are the initial low-frequencybackground models for elastic parameters and form a basis for theobjective and cost functions for inversion. The prior models arederived from seismic velocity, interpreted seismic horizons, andavailable well information. A simulated annealing method is usedto generate and update model parameters. The forward modeling isdone using the linearized Zoeppritz equation-based reflectioncoefficient series and convolution of wavelets. The wavelets mayvary spatially and temporally for each angle stack.

Although both simultaneous inversion and PSWI assume a local1D horizontally layered earth model for inversion of earth’s elastic

model parameters, they differ substantially in forward modeling.While the PSWI forward modeling relies on the full elastic wavepropagation method using the Kennett propagator matrix (Mallick,1995; Kennett, 1983), the forward modeling in simultaneousinversion is based on the Linearized Zoeppritz equation. TheZoeppritz equation-based forward modeling renders the simulta-neous inversion method very fast. There are also other additionaldifferences, including differences in the optimization proceduresand wavelet extraction. The optimization in simultaneous inversionis done over a 3D volume while the optimization in PSWI is fora single CMP. Mallick (2007) compared PSWI results with those ofZoeppritz based 2-term and 3-term synthetic seismograms usingwell data containing gas hydrates.

Because simultaneous inversion is fast and robust, it is verysuitable for efficiently scanning the elastic parameters in a 3Dseismic volume and for subsequent quantification of hydrates. Inthe present study, we used simultaneous inversion to generateacoustic and shear impedance volumes at and around well KC151-3with both raw and preconditioned seismic data. We then comparedthe results at the well with those of the well data and PSWI resultsand transformed the inverted acoustic impedance profile at thewell location to gas hydrate concentration. The results are shown inFigs. 17 and 18.

The post-drill PSWI and simultaneous inversion-based Vp andSgh are generally consistent with each other and with the sonic Vp.Thus simultaneous inversion results can be used for rapid scans of

Fig. 13. Post-drill PSWI at KC151-3. The blocky curves in the left three panels are the inverted P-wave, Poisson’s ratio, and density profiles, respectively, resulting from PSWI. Thesmooth curves are the corresponding input backgrounds, and the pink curves are the actual logs from the wells. Red arrows show peaks in the seismic data, and the blue arrowindicates the BSR. The right two panels, respectively, display the actual seismic angle gather and the synthetic angle gather after convergence during the iteration process. Thecorrelation coefficient of the two gathers is about 0.91.


seismic data for hydrate detection and quantification. Comparisonof the hydrate concentrations predicted from surface seismic mea-surements with the results obtained during drilling indicates thatpre-drill seismic prediction can capture hydrate anomalies even atmodest saturation of w10% (Fig. 18). Although possible ambiguitiesand uncertainties involved in each estimation step increase at lowsaturation levels, meaningful relative changes may be captured.

6. Seismic detectability of hydrates in the GoM

We have showed that seismic methods that rely on a properlycalibrated low-frequency background model can be used for gashydrate detection and quantification. However, predictions basedon seismic data do have a degree of uncertainty, particularly in theabsence of local well log data. This is particularly true if the inferredgas hydrate saturation is small, as is the case for this present study.In this section we discuss possible errors in the estimation of Sgh

and try to find a minimum value of Sgh for which seismic-basedestimation might be reliable for the GoM.

The error in the estimation of gas hydrate saturation may arisefrom many factors, including data acquisition geometry, seismicdata processing, elastic inversion, estimation of porosity profiles,and gas hydrates modeling. The estimation of error due to in-adequate data acquisition geometry is beyond the scope of thepresent study. We started out with data that were already acquiredand partially processed for exploration and production of hydro-carbon reservoirs in the area. The objective of reprocessing of

existing data was to provide resolutions higher than that availablethrough the standard processing schemes and to make sure that allsignificant processing steps were amplitude preserving. When welldata are available, we usually validate amplitude characteristicsand scales through seismic-to-well synthetic ties. This is an im-portant step to limit processing errors and pitfalls, but seldomavailable in an exploration setting and within the zone of gashydrate stability. For the analysis of possible errors we confineourselves to those arising from the elastic inversion, rock modelsfor shallow sand and shale, and the corresponding gas hydratemodels for shallow sand and shale.

As discussed in Part I (Dai et al., 2008) and Sections 4 and 5 ofthis paper, the most important elastic parameter in seismicallyestimating Sgh is the P-wave velocity, Vp (or impedance Ip). The S-wave velocity, Vs (or impedance, Is) may be also used eitherindependently or jointly with Vp (or Is). Usually the error in Vsinversion is more (wroughly 1.5–2 times) than that in Vp or (Ip). Inour experience the error in Vp or Ip in deepwater sediments nearthe mudline is about 2–5%. By the same token, the error in porositymodeling, when well calibration is available, is about 3–4%. In theabsence of well calibration, the porosity error may be higher. Thedensity and porosity functions that are needed are usually modeledto be consistent with the P-wave velocity. From our experiencewith seismic data and from rock physics principles, we know thatthe low-frequency trends for shale and sand differ substantially atshallow depths. For example, in the GoM, the velocity of sands ata given depth in the shallow section is typically higher than those

Fig. 14. Comparison between logs and PSWI results. The blue and red curves are the pre-drill and post-drill PSWI-derived Vp and Vs, respectively. The black and green curves in thethree panels are the initial input models and corresponding log measurements, respectively. Both solutions are comparable especially in P-wave velocities. The high Vp below2000 ms from post-drill PSWI (red curve in the left panel) corresponds to possible hydrate in zone A.


for shales. Since the estimation of Sgh is based on the deviation ofthe P-wave velocity from the low-frequency trend, the main errormay come from the modeling of low-frequency trends for shalesand sands. Thus in the sand-shale environment in the GoM the

Fig. 15. Hydrate concentration estimation based on P-wave velocity from post-drill PSWI. ThThe middle panel plots Vp and its background trend against the nomogram of modeled P-wfrom left to right. The right panel shows the estimated hydrate saturation (Sgh).

estimation of Sgh is expected to be reliable when the inverted P-wave velocity exceeds that of the sand trend. Any reversal in thetrend may be due to changes in the lithology at that depth. On thisbasis we calculate a threshold Sgh value above which the estimation

e left panel shows the input Vp from PSWI (blocky) and the smooth background trend.ave velocities with hydrate saturations ranging from 0 to 50% with an increment of 5%

Fig. 16. Comparison of hydrate concentration estimates from both pre- and post-drillPSWI (Sgh–Seis-Pre-Drill, Sgh–Seis-Post-Drill) with those estimated from log mea-surements (Sgh–R from resistivity, and Sgh-Vp from P-wave velocity). The comparisonshows coherency in zones A, B, and the interval immediately above the BSR.


may be considered reliable within the context of possible errors inthe estimation of velocity or impedance.

We first obtain the porosity, density, and P-wave velocity trendsof shale based on the measurements in the KC151 wells (Fig. 19).

Fig. 17. Acoustic impedance inversion results from simultaneous inversion. The blue curvecorresponding result from post-drill PSWI. The green curves are the initial prior models. Thdata.

Due to the lack of good-quality logs for sand events, we obtainporosity and density trends for sand based on the regional GoMdata from Gregory (1977). We then calculate sand velocity based ona mixed shale (50%) and sand (50%) properties to account for theshaliness.

We then model the P-wave velocity responses of hydrate-bearing shales. The colored curves in the left panel of Fig. 20 area nomogram with hydrate concentration ranging from 0 to 50%with an increment of 5%. The threshold hydrate concentration is thecalculated hydrate concentration at which the hydrate-bearingshale will possess a P-wave velocity that is equal to that of sand.Hydrate saturations larger than this threshold saturation could notbe caused merely by lithologic change, providing that the modelsused are relevant. Our modeling shows that the threshold satura-tion varies from about 30% near the seafloor to about 20% at a depthof 500 mbsf.

7. Summary

We presented a detailed analysis of estimation of gas hydrateconcentration based on log data in KC151 wells and compared theseestimates with pre-drill and post-drill seismic predictions. Our log-based estimation implies that low hydrate saturation (<20%) zoneswere penetrated by drilling in KC151 at the interval between 2.0and 2.1 s. The log estimates are based on resistivity and sonic log(Vp) information. The resistivity-based estimates yield a larger Sgh

than those based on sonic logs alone. Also, sonic-based results are

s are the inverted acoustic impedance at the well location, and the red curves are thee panels show the consistent inversion results from both raw and conditioned seismic

Fig. 18. Hydrate concentration (Sgh) estimation based on P-wave velocity from simultaneous inversion. The left panel shows the input Vp curve from simultaneous inversion and thesmooth background trend. The middle panel plots Vp and its background trend against the nomogram of modeled P-wave velocities with hydrate saturations ranging from 0 to 50%with an increment of 5% from left to right. The right panel shows the estimated hydrate saturation based on the input Vp curve.


not consistent with the possible presence of hydrates in zone B, oneof the two zones inferred to contain hydrate based on an analysis ofthe resistivity logs.

Two pressure core samples were recovered within the intervalof interest. Degassing experiments released methane consistentwith 6% and 1.5% hydrate by volume at depths of 236 mbsf and383 mbsf, respectively (JIP coring update in 2006). These resultsare, in general, consistent with seismically based post-drill esti-mates discussed in this paper.

Fig. 19. Background models for shale and sand constrained by the KC151 drilling result. Thecurves are the corresponding models for sand. The back dashed lines are from the well log

Comparison of log-based estimates with the pre-drill seismicprediction and subsequent post-drill model calibration is encour-aging. It indicates that the current seismic method can capture gashydrate concentration anomalies, even at low to moderate satu-rations. However, quantitative seismic assessment of gas hydratesin low hydrate-concentration zones may be quite error-prone.

Gas hydrate saturations from seismic estimation are systemat-ically lower than those estimated from downhole logs. We believethat this is mainly due to thin layer effects. When the log is upscaled

blue curves are the porosity, density, and P-wave velocity models for shale, and the reds.

Fig. 20. Seismic detectability of natural hydrates. In the left panel, the dashed line with lower values shows the P-wave velocity for the shale trend, and the dashed line with largervalues displays the sand trend. The group of solid curves represents hydrate concentrations from 0 to 50% with an increment of 5% from left to right, starting with the shalebackground. The solid curve in the right panel shows hydrate concentration interpolated from the sand P-wave velocity trend.


to the seismic scale, the agreement with the estimated hydratesaturations is found to be much better. Post-drill analysis indicatesthat PSWI results agree with those based on analysis of the well logVp measurements after upscaling the sonic data to seismicfrequencies. In addition, adjusting the hydrate nomogram to thebackground trend of the input velocity data enhances the reliabilityof hydrate saturation estimation from seismic measurements.

We also tested simultaneous inversion technology and pro-duced P- and S-wave impedance volumes. The results from thisprocess matched those from the PSWI approach and the sonic log atthe well. This implies that the inversion technology used here isadequate for seismic characterization of gas hydrates. Because thesimultaneous inversion technology does not use full waveformmatching in the inversion algorithm, it is very fast and well-suitedfor inversion of large volume of 3D seismic data.

Finally, inference of significant (w10–20% Sgh) hydrates in twozones (A and B) in the well coincided with sand-rich intervals ofa predominantly shale section. Out of these two intervals, zone Aprobably has a higher percentage of sand and, based on our results,is more likely to contain hydrate than zone B.

Acknowledgements

We thank A. Dev and M. Eissa for their help with the study. Thispaper was prepared with the support of the U.S. Department ofEnergy. However, any opinions, findings conclusions, or recom-mendations expressed herein are those of the authors and do notnecessarily reflect the views of the DOE.

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