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OTC 7963

Using 3D seismic and attribute analysis to track fluid movement in thesubsurface

M.N. Luheshi, S.G. Ahsvihare and J. Evans, BP Exploration

COPWWt l-. OFFSHORE TECHNOLOGY CONFERENCE

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Abstract

In 1993, there was a well control incident whiledrilling a prospect offshore Vietnam. This incidentnecessitated the implementation of an extensiveseismic monitoring programme. It became rapidlyapparent that conventional high resolution seismictechniques for identifying and monitoring gas in thesubsurface were inadequate due to loss of signal powerthrough the system.

This problem drove the need for an innovativeapproach to acquiring a “Medium Resolution 3DSuwey”. This had two important characteristics,namely significantly higher bandwidth thanconventional 3D data and also having the penetrationnecessary in order to image the target reservoir,through a disturbed overburden.

This paper describes the technology used inacquiring and processing the data, as this has relevanceto use of this type of acquisition for conventionalreservoir description purposes. It also describes theattribute analysis developed to compare multiplevintages of seismic in order to be able to track fluidmovement in the subsurface.

The enhanced resolution achieved has relevance toenabling better reservoir characterisation in general.The attribute analysis is an example of how these typeof data can then be conditioned in order to allowtracking of fluid in the subsurface, in appropriatesituations.

Introduction/background

Using seismic data to track fluid movement is a fairlywell known technique, but one that still needs

considerable development for routine use. In certainsituations the rock properties of an interval changesignificantly enough to create a measurably differentseismic response. [n order to apply this technology it isnecessary to establish an understanding of

● The distribution of rock properties in the intervalof interest

● The scale of the change in seismic responsecaused by different fluid fills (water, oil, gas)

● Whether the quality of the available seismic datahas the amplitude, time and depth resolutionsrequired to detect these changes

● And, for monitoring cases, whether the quality ofthe seismic is controlled sufficiently to enablecomparisons between successive surveys

In the case presented here, the interest was inmonitoring possible subsurface gas movementsfollowing a well control incident. The technicalproblems and the approach, however, are quitegeneric; in that the same principles apply for fluidmonitoring experiments in conventional productionsituations.

For the particular problem presented here, it wasnecessary to establish the history of gas movement inthe subsurface using a series of seismic data sets whichhad different acquisition and processing histories. Theneed was to understand to what extent it was possibleto measure the extent of gas charged sands using theseseismic data sets.

The general analytical process was to build anunderstanding of the seismic signature by

constructing a detailed analysis of the rockproperties distribution in the intervals of interestt%omthis, use seismic modelling to predict thelikely signature of reservoir quality sands undervarious conditions of fluid charge and depthanalyse the quality of the various surveys acquiredbefore and after the incident to establish theirrepeatabilityperform a detailed geophysical mapping andattribute analysis exercise to aHow comparison ofspecific sequences using the various seismic datasetscondition the resulting maps to allow comparisonof results from the different seismic vintages

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2 USING 3D SEISMIC AND AITRIBUTE ANALYSIS TO TRACK FLUID MOVEMENT OTC 7963

● Interpret the resulting attribute maps in terms ofgas distribution and possible changes of these overtime.

Sei8mic Quality Issues

In order to appreciate the scale of the technicalchallenge, it is worth reviewing the characteristics ofthe data available for this study.

There were several vintages of seismic acquired inthe area of interest. Five surveys were conventionalshallow high resolution seismic (one pre-drill and fourpost drill). in order to understand the pre-drillsubsurface condition, data from conventional 2D and3D seismic were also analysed.

The high resolution seismic had very highbandwidth but relatively shallow signal penetration(the prospect is at about 1.6 km depth). Theconventional 2D and 3D seismic haa good depthpenetration but low resolution. There was a need toconstruct an image that had resolution approachingthat of “shallow surveys” and penetration equivalent tothat of conventional deep 2D or 3D seismic.

Medium resolution 3D seismic. It was thus decided toacquire a special 3D seismic data set usingconventional 3D technology, with very large sourcesand long cables, but with parameters selected so as toget greatly enhanced vertical resolution (shallow gunand cable depths were used). This is refereed to as theMedium Resolution 3D (or Mid Res 3D).

The Mid Res 3D succeeded in achieving thedesign goals of enhanced resolution and depth ofpenetration. This was achieved in spite of very poorweather conditions. An example of the outcome isshown in Figs. 1a - d. These figures show acomparison of the different vintages of data. 1ssparticular a comparison of Fig. ld with the remainingpanels in Fig. 1 shows that the Mid Res 3D has

good depth of penetration

excellent lateral resolution

and vertical resolution about twiceconventional 3D

The impact of this has been a greatly enhancedunderstanding of the stratigraphy and of thedepositional systems in the interval of interest. This isbest illustrated by an example of a time slice (see Fig.2) which shows very detailed depositional features.‘i%istype of information is invaluable in producing thegeological description needed for understandingpotential fluid migration pathways.

The enhanced resolution achieved is due to theshallow gun and cable depths used (4m & Smrespectively). Generally these depths are set lower to

avoid adverse weather conditions. However, thisexperience shows that in spite of the poor weatherduring acquisition, the 3D process is very robust atnoise reduction, so that it is feasible to obtain higherbandwidths. The ultimate limitation on this will be thenatural earth absorption effects which will eventuallylimit the bandwidth achieved.

The lesson tlom this example is that it is worthchecking under local condhions whether for the giventarget depth this “Mid Res” acquisition configurationcould provide greater resolutions, Certainly theexperience reported here, would indicate that thismethod is very much worth consideration given thegreat added value that can be obtained from such data.The added value in improved reservoir description canbe quite considerable, depending on target depth.

Rock Properties Analyde

Understanding the geophysical acoustic properties ofthe rocks through which the seismic signal propagatesis key to using attribute techniques for fluid orIithology prediction. Here the intent was to use anseismic amplitude based technique for detecting gaspresence. This immediately raises the question of therange of amplitudes one would expect in the section ofinterest and how different these reflections would be ifbrine tilled sands were replaced with gas,

Seismic sections will generaily have a wide rangeof reflection amplitudes; some of which will be due topurely Iithological changes and others will be affectedby the presence of hydrocarbons. The goal of the rockproperties analysis is to understand the range ofpossible reflection coefficients using well data andwhere necessary fluid substitution techniques.

The analysis is based mostly on fairly standardpetrophysical techniques. From the weil data it ispossible to generate distributions of, for example,velocity, density and acoustic impedance of variousIithologies and plot a trend curve with depth (see Fig.3), From such trend curves it is then possible toconstruct generic property curves for each Iithologywhich can be used as input to seismic modelling (seebelow),

The need here is to understand the impact of gason the different sands penetrated in this section. Fluidsubstitution is used to estimate the effect of gassaturation on the acoustic impedance of potentialreservoir sands, Fig. 4 shows an example for one sand,of the impact of gas saturation on acoustic impedance(Al). This shows the typical behaviour of a rapidreduction of AI as gas saturation is increased fromzero to about 10°4 and then the curve becomesrelatively insensitive to saturation.

100

OTC 7963 M.N, LUHESHI, S.G. ALUVIHARE, J.EVANS 3

Seismic Calibrstionlmodelling

The interesting information that is needed from therock properties analysis, is the impact of Iithology andfluid saturation changes on the likely reflectivity thatwill be observed on a seismic section. Anunderstanding of this will help identifi the range ofamplitudes that can be used to uniquely identify gaspresence and those where the interpretation is likely tobe more ambiguous.

The technique used here was to build simple“wedge” models consisting of three layers (se Fig. 5),with the middle layer being sand of a given set ofproperties and the encasing layers being shales. Thewedge geometry allows the investigation to includethickness variations, since these also have a significanteffect on the seismic response.

The next step is to calculate the seismic responseof each model. Two examples are shown in Figs. 6a &6b. Fig. 6a shows the case where the sand is brinefilled and 6b is gas filled. There are quite cleardifferences between these two cases, both in terms ofamplitude and also in terms of the phase of the TopSarrdreflector.

Clearly it would be possible to use such models asa tool to hunt for anomalous seismic amplitudes (bothin terms of brightness and phase). However, in thiscase, there is a large volume of data to be scanned inorder to try and identifi significant anomalies and thento interpret these. Hence it was necessary to construct asimpler less Iabour intensive method to identi~anomalies. A method based on average Root MeanSquare (RMS) energy in a defined interval was used.

This RMS attribute process was based on analysisof several examples of the types of models shown inFigs. 5 & 6. ‘fire technique consisted of defining aninterval between two geological horizons, as pickedhorn seismic, Then the RMS energy for this interval atevery trace location was calculated. If this is done for arange of wedge models using the properties defined inthe petrophysical analysis, then the results can beshown in RMS plots such as Fig. 7. This figure showsthe RMS energy calculated from a variety of wedgemodels using several different combinations of sandand shale properties, with and without gas charge.

Fig. 7 shows the very clear impact of gas chargeon the RMS attribute. There is a very large change inbrightness almost regardless of thickness. For therange of depths of interest it was possible to quantifythe “brightening” effect of the gas. This brighteningwas found to be in the range of 300°A to 700%. Thisgives us the essential quantification of the impact ofgas charge on the seismic attribute of interest (here,interval RMS energy for each sequence).

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Thus the general conclusions from the rockproperties and seismic calibration analyses is that

- gas presence can be detected using intervalRMS attribute

– gas “brightens” RMS by 300% to 700%

- seismic is insensitive to changes in gassaturation above IOO/O.

Clearly the amplitude effect will reduce withincreasing depth. Eventually it will be very difticrrlt tounambiguously identi~ gas charged sand. The key inthis type of analysis is to understand the scale of theeffect and how it varies with depth. This is a genericrequirement for alI fluid detection techniques based onseismic and requires very careful methodical analysisof the geophysical rock properties using well data.

Seismic Data Considerations

Having established the scale of the signal that needs tobe detected, then for time lapse analysis, the nextquestion is whether the various vintages of seismicsurvey are comparable. To detect fluid movementusing seismic it is necessary to compare a surveyacquired after the movement has occurred with onethat predates it.

A direct numerical comparison requires that theonly difference between the two surveys must be dueto changes in fluid distribution. This condition isgenerally extremely difticult to achieve, even in caseswhere the data are acquired in precisely the samefashion. Seismic products are sensitive to bothacquisition and processing methodologies and toachieve rigorous repeatability requires extremely closequality control, down to the need for controllingversions of processing sothvare as well as the obviousrequirements of repeatable acquisition and processingparameters.

In the case described here, the operationalconsiderations were such that there was an explicitneed to be able to compare radically different seismicdata sets, This then required a method to render thesedata sets comparable to the extent that anomalousevents due to gas charge could be identified.

A method for conditioning the data was developedto allow this comparative analysis. This method reliedon the simple observation that the reflectivity of asection of the subsurface wilI be invariant with timeunless there has been some movement of fluids in thetime between the acquisition of the various surveys.Hence if one could identifi an area of subsurface and adepth range within which no change was expected, itwould be possible to condition the RMS attribute forthis zone so that it was repeatable between thedifferent surveys.

4 USING 3D SEISMIC AND ATTRIBUTE ANALYSIS TO TRACK FLUID MOVEMENT OTC 7963

This is a variant on the technique of balancingseismic amplitudes using a reference horizon.

As an example of the extent of the variabilityobserved on the data sets used in this study, Fig. 8shows the RMS energy for a 500ms thick interval foran area of seismic from four data sets. These datasegments were selected from an area where there wasconvincing evidence of an undisturbed subsurface.Hence each bar in Fig. 8 should be identical in theoryif the seismic had been acquired and processed inexactly the same way. Clearly there are verysignificant amplitude differences between thesesurveys, which could mask the effects that need to bedetected.

The RMS attribute analysis based on the syntheticmodels discussed above, suggests a possible practicalconditioning technique. If the RMS energy for aninterval is estimated from a survey and then mapped,then the frequency distribution of the mappedamplitudes will be approximately normal (see Fig. 9a).The points at the high end of the distribution (thosewith RMS values greater than, say, 3 times the meanvalue) are very likely to be due to gas charged sand(based on the rock properties analysis). If the samecalculation is performed using another seismic data setthen a similar distribution will be found (see Fig. 9b).

The main part of both these distributions (those pointsaround the mean) should be due to reflections frommud/mud interfaces or mud!brine sand interfaces.

Geophysical attribute analysis.

Hence as a pragmatic tool for conditioning thedifferent data seta, a process which causes thefrequency distributions of RMS maps estimated fromdifferent data sets to overlay, should be robust enoughfor our needs, The process that was used in this case isas follows

●

●

●

●

●

●

Interpret the same horizons from the differentseismic data setsEstimate interval RMS energy for each sequencefrom every seismic data setProduce an RMS map for each sequence ofinterest fkom each seismic surveyCalculate the frequency distribution of the RMSvalues from the mapsScale the RMS maps so that the RMS range tlommean vaiue to mean value plus 3 times standarddeviation, was identical for each map ( see Fig.1o)Plot the resulting maps

Fig. 11 shows an example for a particularsequence. This figure shows a comparison of RMSmaps produced from 2 surveys (one was a high

resolution survey and one was the Mid Res 3D). The

significant anomalies in each of these maps are closelyrepeatable, which implies little gas movement in thissequence. The significance of this figure is that in spiteof the observed non-repeatability of the surveys, theconditioning process described above, has managed tosubstantially “cure” these differences. The observabledifferences are well below the threshold needed forindicating gas presence at this level (namely an RMSbrightening of about 300’Yo).

Key Lessons for Dstectlon of Fluid Movement

The ideal situation for detecting the movement of fluidfronts in the subsurface from seismic data are

1. Ensure exactly repeatable time lapse seismic2. The hydrocarbon effect on a seismic attribute is

significantly higher than noise and/or purelylithoIogicaI events

The work described here, shows a way ofunderstanding at least to what extent (2) is ttue, for agiven situation. This is a systematic rigorousmethodology for assessing the scale of thehydrocarbon signal and the ability of a seismicexperiment to detect this size of change.

As to the issue of repeatability of the seismic, thiswork has shown one pragmatic way of overcoming theinevitable variability that appears in practice. [t is asimple robust method that can be applied efficiently tolarge volumes of data.

The key lessons can be summarised as follows

1. Understand the rock properties/seismicrelationship

2. Understand the quality of your seismic

●HOWrepeatable was the experiment

‘Find a conditioning function to allow timelapse comparisons

3. For both fluid detection and reservoir descriptionwork, go for maximum lateral and verticalresolution using 3D seismic

“Consider High Resolution 3D

●3D technology is a very robust andpowerful noise reduction tool

The enhanced resolution from surveys of the 3D MidResolution type, have a very high value in geologicaldescription.

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