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40 Oilfield Review

Satellite Sensing: Risk Mapping for Seismic Surveys

Stephen Coulson Ola GråbakEuropean Space AgencyFrascati, Italy

Andrew CuttsDenis SweeneyGatwick, England

Ralph Hinsch Martin SchachingerRohöl-Aufsuchungs AG Vienna, Austria

Andreas LaakeCairo, Egypt

David J. MonkApache CorporationHouston, Texas, USA

Jeff TowartApache EgyptCairo, Egypt

Oilfield Review Winter 2008/2009: 20, no. 4. Copyright © 2009 Schlumberger.For help in preparation of this article, thanks to StevenCovington, US Geological Survey (USGS) and Darrel Williams,National Aeronautics and Space Administration (NASA),Greenbelt, Maryland, USA; and David Morrison, Abu Dhabi, UAE.Petrel is a mark of Schlumberger. Google is a mark of Google Inc.

Satellite imaging of the Earth’s surface provides an invaluable view from on high. The

colorful and sometimes artistic images result from combinations of data from different

portions of the electromagnetic spectrum. Geoscientists use these to discriminate land

use, type of vegetation, lithology, elevation and surface roughness. By evaluating these

remotely sensed attributes, they establish risk factors for seismic source and receiver

signal quality, for vehicular and personnel access and for potential survey damage to

the environment.

Remote sensing by orbiting satellites providesinput to seismic survey planning for all fourcomponents of QHSE: quality, health, safety andenvironment. Data from satellite surveys givemap and elevation views of features on and justbelow the surface, as well as an indication of rocktype. These images from above replace detailedground evaluations, a key benefit in remote orhazardous locations.

The risk of low-quality seismic data because ofpoor coupling between the ground and a seismicsource or receiver is inferred from satelliteimagery using a rock physics model of the inter -preted lithology. The ability to locate dangerousterrain is essential for protection of the healthand safety of survey personnel. That information,

along with interpretations of terrain stability,determines safe deployment of seismic acquisi -tion vehicles and associated equipment. Finally,remote-sensing data can identify environ mentallysensitive areas and, through their use in surveyplanning, minimize the negative impact ofseismic acquisition on these areas.

Satellite images of the Earth’s surface havebecome familiar to many people through Webservices such as Google Earth. However, remotesensing is more than just a map image: Satelliteimages present a continuous view across an areain multiple spectral bands. Typically, theseinclude reflected radiation in the visible, infra -red and microwave bands. Some satellites alsoobtain radar images to map tectonic elements or

> Lithology map overlain on a digital elevation map obtained by remote sensing. The arid region of Ghazalat in the Egyptian Western Desert has limestoneheights over a sandstone plateau. A steep escarpment separates a depression with a sabkha base (bottom right ) from the plateau.

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Winter 2008/2009 41

moisture. Time-lapse satellite images allowmapping of seasonal or longer-term changes or ofsubsidence over a reservoir.

Several satellites have surveyed the Earth’ssurface, with a variety of frame or viewing sizesand resolutions. Resolution varies both bysatellite and by portion of the spectral bandsampled. Although the resolution of mostsatellites is insufficient to discriminate indivi -dual features such as bushes or boulders,remote-sensing maps can differentiatevegetation-covered regions from boulder fieldsbecause of their different spectral reflections.Since satellite images can encompass an entireland seismic survey area, this technology is auseful tool for hazard screening and for planningdeployment and acquisition logistics.

The most important factor affecting how aremote-sensing evaluation proceeds is theterrain: whether it is flat, rocky, sandy, popu lated,farmed, covered with vegetation or icy (previouspage). The type of maps produced can differgreatly by survey location because differentcombinations of spectral bands optimizediscrimination of different specific risks.

In a land seismic survey, the most efficientand repeatable acoustic source is a vibrator, suchas a vibroseis truck. However, vibrator trucks arelarge and heavy; their deployment requirescareful logistical planning. In steep terrain, thereis a danger of rolling over, and in soft terrain, ofthe truck getting stuck in sand or mud.

Other risks arise from the contact andcoupling between a vibrator pad and the surface.Although a vibrator truck might be supported ina sabkha or a dry riverbed, the crust mightappear stable yet not sustain the additional forcefrom the vibrator, causing the truck to fallthrough.1 Also, soft sediments may attenuate theacoustic signal strongly. At the other texturalextreme, a hard, rock-strewn surface may notallow proper coupling because the vibrator padcontacts only a few high points on the rocks—point loading.

Evaluating risk of poor source and receivercoupling to the Earth’s surface and of energylosses related to seismic-wave propagation in thenear surface is important for planning a seismicsurvey. These two factors account for themajority of the degradation of the seismic signalintended for hydrocarbon exploration andreservoir characterization. Remote sensing canhelp develop a risk assessment for data acqui -sition by densely characterizing the near surfaceusing optical and radar data.

This article describes remote sensing andincludes two case studies in very different typesof geography. The first, from a desert environ -ment in Egypt, shows the general approach toremote sensing, describing how the variousspectral bands combine to deliver useful plan -ning information. The second involves thedetermination of glacial features in Austria.These field examples illustrate the broad but not exhaustive scope of the utility of remoteimaging today.

RGB and BeyondTelevision screens and computer monitorsdeliver an impressive variety of color to thehuman eye by combining only three colors: red,green and blue (RGB). Based on just the RGBportion of the spectrum, people commonlyperform the kind of discrimination done bysatellite sensing. We tend to associate green with vegetation and blue with water, and manyrocks are shades of tan and gray.

Some satellites capture sunlight reflectedfrom the surface of the Earth in these threespectral bands; the intensity of each band—given as a gray-scale value—can be assigned asintensity values for each respective color andrecombined to generate a familiar color image.Most satellites designed for remote sensing havesensors for additional bands in other parts of theelectromagnetic spectrum; these bands add awider range of information (above). As anexample, sensors on the Landsat 7 satellitecapture intensity data from seven spectral bandsplus a panchromatic, or pan, band.2 Three bandsin the visible (VIS) spectrum roughly cover red,green and blue colors. A very near-infrared(VNIR) band helps differentiate types ofvegetation, while one in the near infrared (NIR)is sensitive to the amount of water in plants, orturgidity. Surface geology is discriminated byusing a short-wave infrared (SWIR) band. Inaddition, the Landsat 7 pan sensor covers most of

1. A sabkha is a salt flat.2. Landsat satellites are launched by NASA and operated

by the USGS. For more information: http://landsat.usgs.gov/ (accessed February 11, 2009).

> Landsat 7 Enhanced Thematic Mapper Plus (ETM+) spectral bands and selected uses of bandinformation. The Landsat 7 satellite has sensors for three visible-spectrum bands and four infraredbands, plus a panchromatic, or pan, band spanning the visible and very near-infrared bands (top).Since the detected bands respond either strongly or weakly to different surface features, combiningthem is useful in discriminating such features (bottom).

0 1 2 3 10 12 14

Visible to very nearinfrared

Water

Infrastructure;terrain feature

mapping

Wavelengthrange

Surface featureinterpretation

Vegetation

Seismicapplication

Logistics planning;environmental-impact estimate

Near to short-wave infrared

Burned vegetation

Sedimentary rocks;alluvial and eolian deposits

Wavelength, μm

Thermal infrared

Moisture in ground and voids

Metamorphic, volcanic andmagmatic rocks

Data-quality estimation;near-surface modeling

Nor

mal

ized

am

plit

ude

VIS–visible; VNIR–very near infrared; Pan–panchromatic; NIR–near infrared; SWIR–short-wave infrared; TIR–thermal infrared

TIRVNIR SWIRNIR

VISBlue, Green, Red

Pan

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the visible spectrum and some of the VNIR. It hasa higher resolution than the other bands, whichhelps sharpen final images. These sensors—forsix spectral bands plus the pan band—detectsunlight reflected from the Earth’s surface.

The last Landsat 7 sensor detects heatradiated in the thermal-infrared (TIR) band,which has a significantly longer wavelength thanthe other bands. The surface thermal propertiesfrom the TIR band distinguish mineralogy. Manyrocks—and tar—that are black in the visibleand SWIR bands are differentiated by theirresponse in the TIR range because the mineralsthat compose the rocks radiate heat at differentintensities. The TIR response from cool surfacefeatures such as ice and water is low. Similarly,cooling induced by evaporation in wadi beds,open faults and karst features is alsocharacterized by a low-energy TIR response.

Other remote-sensing satellites detectdifferent bands; some have more bands thanLandsat 7, and others have fewer. Thus, thespecific methodology applied to distinguishsurface and near-surface features is somewhatdependent on the satellite’s capabilities.

The area included in an image and theresolution of the image also depend on the datasource. For example, the Landsat 7 satellite has alarge frame size of 185 km [115 mi] by 180 km[112 mi]. Its resolution in the thermal band is60 m [197 ft]; in the visible and infrared bands, itis 30 m [98 ft]. The highest resolution comes froma pan band: 15 m [49 ft]. At the other extreme, ahigh-resolution satellite with a small viewing area,QuickBird, has a square frame size of 16.5 km[10.3 mi] and a resolution of 61 cm [2 ft] in its panband and 2.4 m [8 ft] in an infrared band.3

A few satellites obtain radar images of thesurface. Imaging radar uses an active illumi -nation system, in contrast to the passive optical-imaging systems just described that rely onillumination from the Sun. This mode of opera -tion gives radar systems the ability to imagethrough clouds and at night, distinct advantagesover systems relying on natural light.

An antenna mounted on an airplane orspacecraft transmits the radar signal. Termedside-looking radar, it hits the Earth’s surfaceobliquely and scatters. The same antennareceives the reflected signal, known as the echo.

Echo amplitude is recorded, and when used forcoherent radar processing such as syntheticaperture radar (SAR), the phase of the receivedecho is also recorded.

The amplitude of the captured signal withineach pixel represents the radar backscatter forthat area on the ground, with bright areasindicating a significant amount of the radarenergy reflected back to the antenna. Thisreflected energy depends on several conditions ofthe target area, such as its electrical proper ties,moisture content and perhaps most importantly,the physical size of the scatterers in the area.Generally, a brighter backscatter on the imageindicates a rougher surface, while dark areasrepresent flat surfaces.

Radar imaging can also be used to obtainsurface height using interferometric SAR. Onemethod to obtain height generates parallax byusing two separated antennas mounted on thesame platform. The resulting stereoscopic imageis used to create a digital elevation model(DEM). One common source of the parallax viewused for topographic interpretation is a USNational Aeronautics and Space Administration(NASA) space shuttle mission performed in2000.4 Its SAR antenna obtained images withlateral resolution of 30 m in the USA and 90 m[295 ft] in the rest of the world. The nominalvertical resolution is 30 m, but it is stronglydependent on topography; in flat terrain, it canhave an accuracy of about 1 m [about 3 ft].Another source for DEMs is the ASTER packageon the Terra satellite, which has two VNIRcameras that can be arranged to obtain astereoscopic image.5 The resulting DEM has a 30-mvertical resolution and a 15-m lateral resolution.

A second mode uses a single antenna withimages taken on separate passes of the airplaneor spacecraft over the target. This method ofdetermining small changes in elevation over aperiod of time detects surface movement assmall as 1 cm [0.4 in.], which can be used tomonitor subsidence over reservoirs.6

A different method uses a laser scannermounted on an airplane, referred to as laser-induced detection and ranging (LiDAR). Sincethe plane’s altitude is much lower than that of aradar-equipped satellite, LiDAR yields a higher-resolution DEM; typical resolution is 10 cm

[4 in.] vertically and about 20 to 100 cm [8 to 39 in.] laterally. LiDAR service must beordered specifically for the area of interest.

The first steps in remote-sensing evalua tionare determining what information is required andwhat is available. Since its launch in 1999, theLandsat 7 satellite—with its multispectralcapabilities—has scanned the planet on a 16-daycycle. Other satellite databases are also avail able.

Knowledge of the topography of a regionunder study helps determine which combina -tions of spectra will be of greatest utility. Inaddition, accurate field surveys obtain detailedinformation at specific locations to provideground truth for the remotely sensed data.

Satellite images have a wide variety ofapplications. Map views and 3D surface modelingare important tools in designing infrastructureand assessing flood risks. Remote sensingdiscriminates some surface mineral deposits,provides input for planning and monitoring CO2

storage projects and enables reconstruction ofglacial activity through evaluation of moraines.Comparison of older satellite images with newones—the Landsat program began in 1972—reveals changes in land use or condition. Remoteevaluation also helps determine and monitorgroundwater levels—important input for seismicstudies because the water table is often the firstrefractor encountered by the seismic signal. Oneobjective for use of satellite images within theE&P industry is to determine the risks associatedwith conducting a seismic survey.

Seismic Survey EvaluationGeologists select a seismic survey locationbecause of what may be in the subsurface, for themost part irrespective of surface conditions.Therefore, the survey planners must cope withthe challenges inherent in an area’s geographyand topography to find the best specific locationsfor seismic source and receiver placement.

In heavily forested areas, vibrators and othervehicles have limited access. The same is true ofswampy or marshy ground. In desert climates,loose sand dunes limit access for vibrators. Steepslopes also prevent support-vehicle access. Othergeographic features present their own logisticalproblems, which can be detected using combina -tions of remote-sensing methods (next page).

42 Oilfield Review

3. QuickBird is owned by DigitalGlobe. For additional information: http://www.digitalglobe.com/index.php/85/QuickBird (accessed February 11, 2009).

4. The Shuttle Radar Topography Mission (SRTM) data areadministered by the Jet Propulsion Laboratory of the California Institute of Technology, Pasadena, USA. See

6. Van der Kooij M: “Land Subsidence Measurements at theBelridge Oil Fields from ERS InSAR Data,” presented at the3rd ERS Symposium, Florence, Italy, March 14–21, 1997.See http://earth.esa.int/workshops/ers97/papers/vanderkooij1/ (accessed February 5, 2009). For more on subsidence: Doornhof D, Kristiansen TG,Nagel NB, Pattillo PD and Sayers C: “Compaction and Sub-sidence,” Oilfield Review 18, no. 3 (Autumn 2006): 50–68.

www2.jpl.nasa.gov/srtm/ (accessed February 11, 2009).5. ASTER stands for Advanced Spaceborne Thermal

Emission and Reflection Radiometer. Terra is the flagshipsatellite of the Earth Orbiting System, a series of NASAspacecraft. For more information: http://asterweb.jpl.nasa.gov/ (accessed February 11, 2009).


Winter 2008/2009 43

> The impacts of satellite imagery on seismic data quality and survey logistics. Surface features (top) often encountered in land seismicsurveys can be differentiated using combinations of spectral bands (bottom).

Surface faults(TIR, radar)

Basalt flows(TIR)

Wadis, buried wadis(TIR, radar)

Sand dunes(SWIR, DEM)

Rivers(VIS)

Infrastructure(VNIR, SWIR)

Sea(VIS)

Forests(VNIR, SWIR)

Screes, alluvial fans(SWIR, DEM)

Sabkhas, salt lakes(DEM, SWIR)

Farmland(SWIR)

Lakes(VIS)

Moraine ridges(SWIR, DEM)

Rock types(SWIR)

Topography(DEM)

Swamps(VIS–NIR) Beaches

(VNIR)

Marshes, meadows(VIS–NIR)

River terraces(DEM, radar)

Rough surfaces(Radar)

Buried river beds(TIR, radar)

Impact on LogisticsImpact on Seismic Data QualityType of Satellite DataSurface FeatureSurface Class

Topography andtexture

Escarpments, riverterraces

DEM, radar Noise from scattered surface waves Severe risk to vehicles for 15% to 25% slope,and no access beyond 25% slope

Rough surfaces Radar Baseplate point loading; poorreceiver coupling

Severe risk of tire damage for vehicles

Surface faults TIR, radar Noise from scattered surface waves None unless escarpment present

Land use Farmland SWIR Possible permitting restrictions Possible permitting restrictions

Forests VNIR, SWIR Low seismic velocity and highattenuation if located on dry glacial till

Limited vibrator and vehicle accessin dense forest

Infrastructure VNIR, SWIR High broadband noise level,limitation to vibrator drive level

Limited vibrator and vehicle access

Swamps, marshes,meadows

VIS–NIR Resonance; mud roll; substantialvelocity statics

If wet, no access for vibrators andvehicles; hand-carry recording equipment

Water features VIS Transition-zone equipment required No vehicle access; transition-zoneequipment required

Lithology Basalt flows TIR Poor vibrator coupling; strongscattering from basalt texture

Often risk for vibrator and vehicle tires

Caliche, mineralizationhorizons

NIR, SWIR, radar Narrow-band resonance; strongabsorption

No risk for vibrator and vehicle access

Claypans DEM, NIR Resonance If wet, no access for vibrators

Hard rock outcrops NIR, SWIR, radar Baseplate point loading; poorreceiver coupling

Limited risk of access for vibrators

Sabkhas, salt lakes DEM, SWIR Resonance; mud roll; velocity statics;high attenuation

Severe risk for vibrator and vehicle access

Wadis, buried wadis,buried river beds

TIR, radar Groundwater table for P-wave statics;poor coupling in wadis

No risk for vibrator and vehicle access

Geomorphology Moraine ridges SWIR, DEM Low seismic velocity and highattenuation in dry glacial till

No risk

Sand dunes SWIR, DEM Elevation statics; strong attenuation;trapped surface-wave modes

Access for vibrators severely limited;preparation of track required

Screes, alluvial fans SWIR, DEM Low seismic velocity and highattenuation

Limited vibrator access in steep terrain

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Moving the vibrators and personnel on site isonly part of the planning required. The topogra -phy also impacts the quality of the couplingbetween source or receiver and ground and mayaffect signal propagation in the near surface. Arough or rocky surface may cause point loading ofthe baseplate, which highly distorts the trans -mitted signal. Good source coupling can beachieved in soft sediments, as long as the groundsupports the baseplate load. However, if thebaseplate breaks through a hard ground surface,the result is again poor coupling, a distortedsignal and possibly a cutoff of the high-frequencycomponent of the generated signal (above).

The most significant contribution to surface-related noise in seismic acquisition is groundroll, which is a surface wave, or more precisely aRayleigh wave, that travels at the ground/airinterface (below). Rayleigh waves travel more

slowly than the compressional waves that travelthrough the subsurface rock formations; thesebody waves are the desired signals for seismicsurveys of the subsurface. The surface Rayleighwaves also attenuate more slowly than the body waves. This lack of attenuation exacerbatesnoise caused by scattering from surface features,an effect geophysicists try to mitigate by proper planning.

In wet environments such as swamps, marshesand some sabkhas, the surface wave couples withthe liquid and is termed a mud wave.7 A mud waveis often much slower than a Rayleigh wavebecause of the weak particle coupling in thewater-saturated solid near the surface.

Variations in ground elevation require staticcorrections to the measured seismic signals.Determining the corrections may be particularlydifficult in near-surface, weathered soils. Signalsin the surface materials may have radicallyslower velocities than those in the hard rockbeneath. If the weathered layer has significantlocalized variations in thickness, this mayrequire static corrections that change rapidly,both vertically and laterally, within a small area.Sand dunes, sabkhas and marshes pose thisproblem for seismic acquisition.

In addition to the static correction problem,in sand dunes, body waves may reflect from thebottom of the dune, becoming trapped within thedune itself. In wadis, the top of the water tableaffects the first breaks in the seismic signal, sothe water level is important to discern. Softmaterials, such as unconsolidated sand, sabkhasand dry glacial till, also cause high attenuation ofthe body-wave signal within the surface layer.

Boundaries often scatter the seismic energy,creating noise. These may be topographicchanges, such as escarpments, or lithological ormineralization boundaries. The risk of noise fromscattering is higher in hard ground such ascarbonates and basalt. Resonance of seismicwaves occurs in areas that are enclosed bymaterials of greater acoustic impedance. Forexample, once a surface wave from hard rockenters a softer claypan, it may become trapped,reflecting back at another boundary with thehard rock. Similar observations are often made in swamps.

Mapping risks to a land seismic party beforeits deployment is one way to assess potentialproblems for personnel and equipment. A satel -lite survey that discriminates surface features indetail gives this option. For example, a DEM isparticularly useful for identifying structure at ascale of 10 m [33 ft] and larger. It can locateescarpments and highlight other features thathave a common elevation signature, either flat(such as claypans, sabkhas, floodplains, swampsand marshes) or varied (such as wadis, sanddunes and glacial moraines). At smaller scales ofcentimeters to decimeters, radar imagery illumi -nates surface microstructure and textureinformation by distinguishing diffuse andspecular reflections. This provides informationabout rock structure, fractures and ripples. Inaddition, minerals have different responses in theinfrared range, so those bands are included instudies of lithology.

In most cases, remote-sensing analysisincorpo rates information from one or moresatellites, from ground observations and mapsincluding infrastructure, and when available,from subsurface geology. Integration of datausing a geographic information system (GIS) iscritical. A GIS is a tool for storing, visualizing andprocessing data in a common geographicalworkspace to help model the world as accuratelyas possible. The system allows a user tointeractively query and analyze data and createmaps. Within a GIS, for example, an image froma radar satellite overlain with a combination ofvisible and infrared bands can be mapped in acommon space with the traverse and observa -tions of a ground survey. The GIS software alsoallows the viewer to see the combined data fromany angle or to “fly” through the space. Bycombining the remotely sensed data withphysical models, such as wave propagation andsource and receiver coupling to various surfacematerials, and using logical rules, such as safeslope angle for vehicles, the GIS system displays

44 Oilfield Review

> Baseplate breakthrough. The sabkha hadinsufficient strength to support the vibratingbaseplate, which broke the surface.

> Surface modes in seismic acquisition. A vibrator truck directs seismicenergy to deep formations as body waves (black). However, significantenergy is scattered from this wave or trapped near the surface. Some isrefracted at formation boundaries (light blue). Rayleigh waves (purple)travel along the surface and may scatter from escarpments, as shownhere, or at changes in lithology (not shown). Other seismic energy may betrapped in soft sediments between harder layers (orange) or reflected atinterfaces (red).

Baseplate


Winter 2008/2009 45

the risk information in an easy-to-understandformat. This is best seen by example in the casestudies that follow.

Geomorphology in an Arid TerrainIn the Egyptian Western Desert, almost 700 km[435 mi] west of Cairo, Apache Egypt planned aseismic study in the Ghazalat basin, whichcontains plateaus and steep escarpments. Priorto running the survey, WesternGeco includedsatellite remote sensing as part of a multiphysics,near-surface characterization to establish therisk for logistics and acquisition.8

The near surface comprises two formations,the Moghra and the overlying Marmarica (right).The lowest portion of the Marmarica formationhas alternating hard limestone and softgypsiferous marl layers, which transitions into amassive limestone in the upper part of theMarmarica formation. The underlying Moghraformation consists of an alternating sequence ofsandstone and claystone layers. Both formationsoutcrop in the Ghazalat prospect area.

A DEM image of the area was available fromASTER satellite data with lateral and verticalresolutions of 30 m, sharpened to about 17 m[56 ft] using the higher-resolution pan band.About 10% of the study area is in the QattaraDepression about 80 m [260 ft] below sea level,bordered by an escarpment of 100 to 120 m[330 to 390 ft] that reaches a plateau at a heightof 50 to 60 m [165 to 195 ft]. The plateau makesup about 50% of the study area, with elevationsgreater than 200 m [660 ft] above sea level in the north (below right).

In addition to the large escarpment boundingthe depression, other escarpments are present.These were determined using an eight-directionedge-detection algorithm.9 The escarpments canbe overlain on the terrain-height map to obtain atopographic classification map.

7. The mud wave is also known as a Stoneley-Scholtewave, or just a Scholte wave.

8. Laake A and Zaghloul A: “Estimation of Static Correctionsfrom Geologic and Remote-Sensing Data,” The LeadingEdge 28, no. 2 (February 2009): 192–196. Cutts A and Laake A: “An Analysis of the Near SurfaceUsing Remote Sensing for the Prediction of Logistics andData Quality Risk,” paper presented at the 4th NorthAfrican/Mediterranean Petroleum and Geosciences Con-ference and Exhibition, Tunis, Tunisia, March 2–4, 2009.For information on data quality characterization in aridregions: Laake A, Strobbia C and Cutts A: “IntegratedApproach to 3D Near Surface Characterization in DesertRegions,” First Break 26 (November 2008): 109–112.

9. The method used is called a Sobel edge-detection algorithm. It is often used in north-south and east-westdirections, but because of the complicated lobes of themesas and other features, the eight-direction methodused here provided smoother, continuous lines for the escarpments.

M E D I T E R R A N E A N S E A

WesternDesert

Qattara Depression

Nile

Rive

r

Cairo

E G Y P T

Ghazalatstudy area

200

km0 200

0 mi

Limestone Claystone and marl

Sandstone

Elev

atio

n, m

Mar

mar

ica

Form

atio

nM

ogrh

a

Desc

riptio

n

Limestone

Softgypsiferous

marl

Alternatingsandstone

and claystone

250

200

150

100

50

0

–50

Lith

olog

y

> Topographic map of Ghazalat showing escarpments. A digital elevationmodel (DEM) shows a part of the Qattara Depression (blue, bottom right )bounded by a steep and tall escarpment. A broad plateau with mesas(green) makes up about half the study area, bounded on the north byhighlands (yellow to brown). An edge-detection algorithm determined thelocations of escarpments (black).

5

km0 5

0 mi 2001000Elevation, m

–80

> Ghazalat geography and geology. Ghazalat is in Egypt’s WesternDesert, bordering on the Qattara Depression (map). The areacomprises mesas and tablelands to the south (photograph) andheights to the north. The formations are layers of limestone,sandstone, and claystone and marl (right ).


The lithology classification was taken from aLandsat 7 Enhanced Thematic Mapper Plus(ETM+) satellite survey over the area. Thisclassification was mapped along with the ASTERDEM data using GIS methodologies.

Experience across a broad range of satelliteimaging applications has shown that certaincombinations of spectral bands discriminatespecific types of surface features, and these areoften the first ones examined. Although all sevenbands can be examined in any combination, it ismore convenient to combine three bands tomake maps for visual examination. Data fromeach band are essentially gray-scale data. Thesegray-scale data can be assigned to one of thethree RGB colors, with gray-scale data from two other bands assigned to the other two colors.One common Landsat 7 ETM+ presentation is742 RGB, in which Band 7 (SWIR) is representedby red, Band 4 (VNIR) by green, and Band 2 (VIS green) by blue.

46 Oilfield Review

> Ghazalat lithostructural map. A combination of several bands from theLandsat 7 satellite provided good differentiation of lithology in the aridGhazalat area. Lithologic discrimination came from one thermal and twoSWIR bands; additional image details and colors resulted from the overlayof two visible bands.

Sandstone Limestone 1 Limestone 2Sabkha, clay

5

km0 5

0 mi

> Ghazalat lithology classification and ground observations. To create this map, geoscientists optimized combinations of satellite bands, independentlyfor each lithology class, including two limestone classes (dark blues), a class encompassing marl, loess and sand (yellow), two sandstone classes(orange), and a class for clay and sabkha (light blue). Mixed colors on this map indicate mixed lithologies within an area. This map was used to planlimited ground traversals, which validated the remotely sensed mapping (photographs, corresponding to circles on map).

5

km0 5

0 mi

Sandstone

Sandstone

Claypan

Marl, loess

Limestone

Sandstone

Limestone Marl, loess, sand Sandstone Clay, sabkha


Winter 2008/2009 47

Bands can also be compared by ratio or bydifference of their gray scales. Some of thecommon combinations were not appropriate inthe Ghazalat study because they specificallyinclude bands sensitive to vegetation, and this isa dry desert area. Although several of thecombina tions discriminated between thesandstone of the plateau and the limestone of thehighlands, the combination of a thermal and twoSWIR bands had the best differentiation betweentwo limestone types. This RGB image wassharpened by using a multiband difference thatincludes responses in two of the visible bands toshow texture within the limestone and sand -stone. The resulting image highlights theclaypans and details of the layers on theescarpments (previous page, top).

A different way to classify lithology usesseparate criteria specific for each rock type. Inthe Ghazalat area, several band ratios wereevaluated to distinguish two types of limestone,two types of sandstone, marl, loess and sand, andsabkha or clay (previous page, bottom). This maphelped guide a field validation of the data.Traverses through the area by foot and off-roadvehicle confirmed the interpretation obtained byremote sensing.

With lithology and topography determined,an estimate of risk for a seismic study can bequantified (right). Logistics risks are associatedwith access and movement. The steepescarpments and terrain edges limit vehicleaccess. The limestone highlands have roughtopography and sharp edges, making maneu -vering difficult but not impossible. The clay andsabkha areas also limit access because there is adanger of falling through the top crust into softsediments. In contrast, the sandstone areas, forthe most part, have no access limitations.

Other risks are associated with the quality ofseismic signals. The escarpments, including thoseat formation boundaries, present topographicscattering risks. The rough surface of thelimestones increases the risk of point-loadingproblems with the vibrator pads. The twolimestone formations have different levels of thisrisk, with the western limestone having less. Thesoft clay and sabkha have an increased risk ofsignal attenuation and resonance.

Acoustic velocities in the lithological unitscan be modeled to estimate source and receiverstatic corrections. In Ghazalat, this yields a goodcomparison with the lower-resolution estimateobtained from picking the first break inrefraction statics. The risks were verified in a

> Risk maps for the Ghazalat area. The logistics risks include access andmaneuvering risks for vehicles (top). The sandstone areas are generally lowrisk (pale blue, which is a mix of white coding for low-risk areas and thegeographic background information), but the highlands presentmaneuverability difficulties (red). Soft surfaces, such as the large sabkha inthe depression, limit access (blue). Large escarpments (black) areimpossible for the trucks to access. In the surface-velocity risk map(bottom), escarpments also pose severe risks for seismic-signal scattering(black). The rough surfaces of the highland limestones result in moderatescattering risk (red); these areas also have increased risk for point loading ofa vibrator baseplate. Claypans and sabkhas have high signal-attenuation risk(blue). Sandstone areas generally have low surface-velocity risk.

Logistics Risk Map

5

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Surface-Velocity Risk Map

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shot-point gather in the southern plateau near amesa (below). The shot gather shows the effectsof scattering from a mesa edge and from aboundary between lithologies, confirming thepredictive value of the remote survey.

Glacial Moraines in Cultivated HillsIn the foothills of the Austrian Alps, Rohöl-Aufsuchungs AG (RAG) has a concession toexplore an area with a complex geologicalhistory. While the Alps advanced from the south,

sediments eroded from these forming mountainswere shed into a Tertiary foreland basin, theMolasse basin. A deep-marine channel system upto 200 km [120 mi] long formed parallel to thebasin axis. Late-stage folding and thrusting

48 Oilfield Review

> Risk classifications confirmed by common shot gather. This small section is in the southern part of the survey area. It includes mesas and outcropfeatures, as seen in the elevation profile and the QuickBird high-resolution satellite image. All of the traces from a single seismic shot (red circle inelevation profile) are shown in the shot gather. The ridge (left of center ) and lithology change at the outcrop (right of center ) result in variations in gatherintensity (yellow to green transitions). This confirms the prediction (red) of the scatter risk map. The QuickBird image confirms the locations ofescarpments (scarps) and edges as defined in the logistics risk map and which influence the scatter risk map.

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Winter 2008/2009 49

partly affected the southern Molasse sedimentsbut left most of the basin to the northundeformed. From south to north, geologicaldeposits comprise a limestone fold belt, andimbricated and folded, as well as undeformed,Molasse sediments. The whole area is largelycovered by glacial deposits such as moraines andincludes partly postglacial erosional features.

The area is highly cultivated on the flatterrain, with dense forest on hills and steepslopes, and swamps in former glacial lakes andalong rivers. Glacial moraines, which are gravelridges deposited at the maximum advance of aglacier, remain, along with remnants of overflowand drainage channels that formed when theglaciers melted (right). It is densely covered withinfrastructure, including villages and cities. Thesatellite survey compares well with an extensiveground-based survey reported in 1957, whichlinks the locations of seismic acquisition risksinterpreted from the satellite images to a groundtruth (below).10

> Austrian moraines. A LiDAR DEM survey shows the variation in elevation in this area of the Alpinefoothills. The escarpments (black) were located using an edge-detection algorithm. The two morainesindicate glacial extent. In addition, overflow and drainage channels are apparent in the image. Theseand the former lake developed as the glacier melted. The white rectangle indicates the location ofthe more-detailed image in the figure on page 51.

Drainage channelDrainage channels

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10. See geologic map included in Aberer F: “Die Molassezone im westlichen Oberösterreich und inSalzburg,” Mitteilungen der geologischen Gesellschaft inWien 50 (1957): 23–94 (in German).

> Ground truth. The interpreted satellite results (color) overlay an extensive ground-based topographic survey (gray scale) reported in 1957 (Aberer,reference 10, used with permission). The remarkable match in the overlay area gives confidence in the location of satellite interpretations.


50 Oilfield Review

In frontier areas, satellite data are oftenbrought into play during the earliest stages ofexploration, well before seismic survey plan-ning and layout. There, satellite imagery isused to prioritize areas likely to contain oiland gas prospects. Employing a variety of sen-sors, satellites are especially suited for grossreconnaissance of remote regions and largesurvey areas. The data from these differenttypes of sensors are useful far beyond theircapability to map topography, regional geology,lineaments and structural trends.

Satellite data acquired over land are ana-lyzed to infer the presence of hydrocarbonsthrough indirect signs, such as chemical,physical or microbiological changes in soil andvegetation. For example, when gas seeps tothe surface, it partially displaces oxygenwithin the soil to create an oxygen-poor envi-ronment. This also affects the reduction-oxidation potential and pH of the soil. Thesechanges are manifested as alterations in soilmineralogy such as the formation of new min-erals (calcite, pyrite and uranium), bybleaching of red-bed outcrops or by electro-chemical changes.1

Such permutations are, in turn, reflected inthe health or type of vegetation surrounding agas seep. Not only is oxygen depleted from thesoil, but accompanying changes in soil-nutrientsolubility result in a deficiency or excess ofnutrients taken up by plants. These effects may register in the plant’s spectral responsedetected by satellite optical sensors. Thereflectance of stressed plants is often higher in the visible region and lower in the near-infrared.2 The pattern and intensity of suchindicators may be important for delineating

fractures or other characteristics of subsurfaceaccumulations, and gas has been detected alongcertain linear features seen on satellite images.3

Offshore, satellite imagery is useful fordeveloping exploration leads through identifi-cation of possible oil seeps. Oil, emanatingfrom natural seeps on the seafloor, rises to thesurface of the ocean where it may bedetectable through visible, near-infrared andradar imagery. Synthetic aperture radar(SAR), in particular, is highly successful indetecting oil on the sea surface. This side-looking radar transmits signals at an obliqueangle to the Earth, and thus it is sensitive tobackscatter produced by tiny capillary waveson the ocean’s surface.4

Oil tends to dampen waves on the ocean sur-face, producing a smooth surface that reflectsmost of the signal away from the SAR receiver.The backscatter intensity is anomalously lowover a smooth surface compared to the sur-rounding area. However, numerous factorsaffect the interpretation and location of surfaceslicks relative to source vents on the seafloor.Factors that can move or obscure the presenceof a smooth ocean surface include wind velocityand direction, currents, cloud cover, meteoro-logical conditions and marine vegetation.5

More importantly, the damping of surfacewaves may be attributed to numerousprocesses that require further investiga-tion—many slicks have nothing to do withthe presence of oil. Rain cells, wind shadowsand current flow can smooth local areas ofthe sea surface. Algal mats and even coralspawn also affect sea motion. Bathymetricslicks are generated by localized accelerationof currents flowing over submarine channels.

These slicks have suggested the presence ofuncharted channels that were subsequentlyverified by high-resolution multibeam swathbathymetric surveys.6

Off the North West Shelf of Australia, SARhas detected slicks during the ebb of nocturnalneap tides, five nights after a full moon occur-ring between March and April and betweenOctober and November. These annular- to cres-cent-shaped areas of low backscatter, foundover coral reefs and carbonate shoals in thesouthern Timor Sea, have been interpreted ascoral spawn slicks.7 Restricting SAR acquisi-tion to predictable nonspawning times hasavoided the misinterpretation of slicks causedby coral spawn as those caused by oil. Thisapplication highlights further potential forSAR as a tool for biological research.

The identification of natural oil seeps isinstrumental in revealing undiscoveredresources. However, the ability to determinewhich SAR slicks are caused by oil requirescareful analysis of ancillary data. Recognizinglinks between SAR slicks and oceanographicor biological processes makes it possible toimprove the assessment of potential explo-ration targets.

Overall, satellite remote-sensing techniquesare valuable in rapidly screening large or inac-cessible areas. They can be used to prioritizeprospects for further investigation by other technologies, such as coring, airborne laserfluorescence and seismic surveys.8 As with allsensing from a distance, this approach is bestused selectively and proves its worth when verified by ground-truth measurements. —MV

Prospecting by Satellite

1. Red beds are reddish sedimentary strata, such assandstone, siltstone or shale, which have accumulatedunder oxidizing conditions; the red color comes fromspecks of iron oxide minerals.

2. Noomen MF, Skidmore AK and van der Meer FD: “Detecting the Influence of Gas Seepage on Vegeta-tion, Using Hyperspectral Remote Sensing,” inHabermeyer M, Mülle A and Holzwarth S (eds): Pro-ceedings, The 3rd EARSeL Workshop on ImagingSpectroscopy. Herrsching, Germany: ERSeL (2003):252–255.

3. Jones VT, Matthews MD and Richers DM: “Light Hydrocarbons for Petroleum and Gas Prospecting,” in Hale M (ed): Handbook of Exploration Geochemistry:

Geochemical Remote Sensing of the Sub-Surface, vol.7. Amsterdam: Elsevier (2000): 133–212.

4. A capillary wave is a ripple or small surface-waterwave with a maximum wavelength of 1.73 cm [0.68 in].This wavelength is so short that the surface tension ofthe water itself exerts a restoring force to its motion.

5. Hood KC, Wenger LM, Gross OP and Harrison SC:“Hydrocarbon Systems Analysis of the Northern Gulfof Mexico: Delineation of Hydrocarbon Migration Path-ways Using Seeps and Seismic Imaging,” inSchumacher D and LeSchack LA (eds): Surface Explo-ration Case Histories: Applications of Geochemistry,Magnetics, and Remote Sensing, AAPG Studies inGeology no. 48 and SEG Geophysical ReferencesSeries no. 11. Tulsa: AAPG (2002): 25–40.

6. Jones AT, Thankappan M, Logan GA, Kennard JM,Smith CJ, Williams AK and Lawrence GM: “CoralSpawn and Bathymetric Slicks in Synthetic ApertureRadar (SAR) Data from the Timor Sea, North-WestAustralia,” International Journal of Remote Sensing27, no. 10 (May 2006): 2063–2069.

7. Jones et al, reference 6.8. The airborne laser fluorescence (ALF) technique

measures fluorescence of aromatic hydrocarbons thathave been excited by a laser fired at the sea surface. ALF surveys can detect the presence of micron-thickhydrocarbon accumulations.


Winter 2008/2009 51

These ecological and cultural observations areindicators of operational difficulties and risks. Inaddition, there are data-quality risks for seismicacquisition: The glacial moraines limit propercoupling, requiring large static corrections;swamps will generate resonance from trappedsurface waves; multiple source types may benecessary since vibrators cannot be used inswamps; and the surface features may generatesubstantial levels of noise from surface wavesscattered from escarpments.

RAG invested in a LiDAR DEM study toidentify and mitigate potential problems beforebeginning seismic acquisition. Of the availableremote-sensing sources, this aircraft-basedsurvey provides the most accurate surface map (above left). It identified locations ofincreased scattering risk from abrupt changesin elevation. The steep slopes represent bound -aries that scatter energy in seismicsurface-wave modes. Identifying the type andlocation of such surface changes helpsgeoscientists design a filter that eliminatesnoise scattered from a specific direction.

Using the LiDAR survey and working withgeoscientists from WesternGeco, RAG recon -structed the glacial and postglacial history of thesurvey area. From this survey, the geoscientistsdeveloped an elastic model for layer depths,thicknesses, velocities and attenuation, and then computed model-based surface staticcorrections and coupling corrections for thesources and receivers.

In general, local fluctuations of the seismicsignal resulting from variations in couplingconditions are corrected by amplitudecompensation. However, variations in couplingconditions are limited to certain frequencies,meaning that a general amplitude correctionmay introduce noise rather than attenuate it.The RAG study used a surface-consistent methodthat also included the correction of the spectraldistortion at source and receiver resulting fromthe variations in coupling conditions here. Toperform this task, RAG loaded the high-resolution DEM from the LiDAR, the seismicsurvey and the field data into a comprehensiveGIS database.

Geomorphology maps from the remote-sensing study provided information about thelocal near-surface geology such as glacialmoraines and swamps. These attributes derivedfrom remote sensing were found to correlate withthe frequency content of seismic attributescomputed from surface-consistent spectraldeconvolutions for the source, receiver andcommon midpoint (CMP) terms for one half ofthe survey (above right). From the remote-sensing attributes and the spectral seismicattributes, geoscientists predicted the seismicresponse for the other half of the survey, whichcompared well with the data and validated theprocedure. Because of the detail and areal extentof the remote-sensing study, the company wasable to ensure the consistency of correctionsacross the entire concession.

The Richness of Remote SensingWithin the E&P industry, the use of remotesensing by satellite is not restricted to seismicsurvey planning. It is also used to find clues tothe presence of hydrocarbons (see “Prospectingby Satellite,” previous page), and in reservoirsurveillance, such as for subsidence monitoringand for planning and monitoring CO2 injection.

The results of remote-sensing analysis arestored in a GIS database. These can be combinedwith subsurface information and models togenerate 3D representations of the study area.Subsurface information and formation propertiesare often incorporated in modeling packages suchas the Petrel seismic-to-simulation software.11

Integration of the surface and subsurfaceinformation into one package allows assessmentof surface constraints within the context of ashared 3D space. As this article describes, suchintegration provides valuable insights for aseismic acquisition program. It helps linksubsurface structure to its surface expression offaults and folds. Planning of drilling andproduction facilities and pipelines accounts forboth surface and subsurface needs, includingenvironmental constraints.

Satellite images that help locate businessesand friends’ homes are becoming useful tools inour daily lives because of their easy accessibilityon the Internet. Similarly, the richer images frombands extending deep into the infrared spectrumare becoming increasingly indispensable for E&P activities. —MAA

> DEM resolution comparison of LiDAR and SRTM. The resolution of theaircraft-borne LiDAR data (left ) is significantly better than that from the2000 space shuttle mission (right ). This small area corresponds to the whiterectangle in the top figure on page 49.

> Correlations between geomorphology andspectral attributes. For example, where morainesand hard rock were present, the source andcommon midpoint (CMP) terms calculated byspectral deconvolution exhibited low frequencies.

0.5

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11. Gras R and Stanford N: “Integration of Surface Imagerywith Subsurface Data,” paper P-115, presented at theEAGE 62nd Conference and Technical Exhibition, Glasgow, Scotland, May 29–June 2, 2000.

GeomorphologyAttribute fromRemote Sensing

Surface-ConsistentSpectral SeismicAttribute

Moraines, hard rocksin fold belt

Low-frequency sourceand CMP attributes

Locally compactedareas, swamps

High-frequency sourceand CMP attributes

River marshes,swamps

Low-frequency receiverattributes

Infrastructural noisefrom built-up areas

High-frequency receiverand CMP attributes


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