Hyperspectral remote sensing uses sensors that collect data across a wide range of electromagnetic wavelengths, with more than 100 contiguous bands that provide detailed spectral signatures. This allows identification of subtle mineral and material differences that can indicate oil and gas deposits. Seeps at the surface cause alterations detectable by hyperspectral analysis, like calcite, pyrite and clay changes. A Hydrocarbon Index highlights absorption peaks related to hydrocarbons. Classification algorithms like Spectral Angle Mapper can map hydrocarbon-bearing zones by comparing spectra to known samples. Soil tonal anomalies from bleaching or iron/clay changes also indicate subsurface structures and seepage areas for exploration.

1. HYPERSPECTRAL REMOTE
SENSING FOR OIL
EXPLORATION
PRESENTED BY
JOSHUA . R
MPE 007

2. Remote Sensing - Basic Principles
 “Remote sensing is the science and art of obtaining information about an
object, area or phenomenon through sensing and analysis of the data
acquired by a device which is not in physical contact with the object, area or
phenomenon under investigation by recording reflected or emitted energy
and processing, analysing, and applying that information for various
applications”
 Remote sensing of earth's environment comprises measuring and recording of
electromagnetic energy reflected from or emitted by the earth’s surface and
atmosphere from a· vantage point above the surface by Sensors mounted on
satellite platforms which measure the amounts of energy reflected from or
emitted by the earth's surface.

5. Electromagnetic Remote Sensing
Process
 The generalised processes involved in electromagnetic remote sensing system or
passive remote sensing system, namely, data acquisition and data analysis are
outlined below and a schematic diagram of electro-magnetic remote sensing
process .The data acquisition process comprises distinct elements, namely,
 (i) energy sources, (ii) propagation of energy through the atmosphere, (iii)
energy interactions with earth's surface features (iv) airborne/space borne
sensors to record the reflected energy and (v) generation of sensor data in the
form of pictures or digital information.

7. Types of remote sensing
 Passive: source of energy is
either the Sun or
Earth/atmosphere.
 Sun
- wavelengths: 0.4-5 µm
 Earth or its atmosphere
- wavelengths: 3 µm -30 cm
 Active: source of energy is part
of the remote sensor system.
 Radar
- wavelengths: mm-m
 Lidar
- wavelengths: UV, Visible, and
near infrared
Camera takes photo as example, no flash and flash

11. HYPERSPECTRAL
REMOTE SENSING

12. INTRODUCTION
 The term “multi” is derived from the Latin word for “many” and “hyper” is the Greek
word for “over,” “above,” or an “exaggerated amount.”
 These, combined with “spectral,” which relates to colors, are combined to form
“multispectral” and “hyperspectral,” which figuratively mean “many colors.”
 It Differs from other remote sensing in that it covers many narrowly defined spectral
channels, where as, conventional remote sensing looks at several broadly defined
spectral regions.
 Hyperspectral remote sensing is the science of acquiring digital imagery of earth materials
in many narrow contiguous spectral bands.
 The simultaneous acquisition of images of the same area in many (usually 100 or more),
narrow, contiguous, spectral bands. The preferred term is “imaging spectroscopy”.

13. The spectral signature
 In order to correctly interpret the hyperspectral data, the retrieved spectral signatures
must be correlated to specific materials. Therefore specific spectral libraries,
containing the spectral signature of the materials to be detected, must be built up.
 This requires that highly accurate reflected light measurements of samples of the
investigated material must be performed in the lab or in the field.
 Any given material will reflect, absorb or transmit the electromagnetic (EM)
radiation at different wavelengths in a unique and specific way. The specific
combination of reflected and absorbed EM radiation at varying wavelengths is
called the “spectral signature”.

14. As an example, the Figure
shows the reflectance spectra
(i.e., the percentage of
reflected EM radiation)
measured by laboratory
spectrometers for three
materials:
A green bay laurel leaf, the
mineral talc and a Silty loam
soil.
Field and laboratory
spectrometers usually measure
reflectance at many narrow,
closely spaced wavelength
bands, so that the resulting
spectra appear to be
continuous curves.

15. Hyperspectral Data
 Like the laboratory
spectroradiometers,
hyperspectral sensors can record
about 100 to 200+ contiguous
selected wavelengths of
reflected and emitted energy,
with high spectral resolution
(5-10 nm), enabling the
construction of an effective, and
continuous reflectance spectrum
for every pixel scene.

16. Cntd…
 Hyperspectral imagery provides an
opportunity for more detailed
image analysis. Using
hyperspectral data, spectrally
similar (but unique) materials
can be identified and
distinguished, and sub-pixel
scale information can be
extracted.

17. Impact of less bands in satellite
sensor

18. Hyperspectral Remote sensing for
Oil exploration
 Remote sensing can potentially provide a wide array of information not easily acquired from
surface observations. In recent years, hyperspectral remote sensing has opened up newer
opportunities to identify minerals remotely.
 Hyperspectral remote sensing also known as imaging spectrometry / imaging spectroscopy is
the acquisition of an image in many very narrow, contiguous spectral bands.
 Though, multispectral sensors, record the target radiance at a handful of wavelengths with
broad bandwidth (20-200 nm), hyperspectral data can record about 100 to 200+ contiguous
selected wavelengths of reflected and emitted energy, with high spectral resolution (5-10
nm), enabling the construction of an effective, and continuous reflectance spectrum for every
pixel scene.
 Recently, with advancing technology, imaging spectroscopy has begun to focus on the
geological applications. Hyperspectral imagery has been particularly effective for mapping the
alteration minerals.

19. How it works ?
 Oil and gas reservoirs usually leak. As a result, large quantities of oil and gas from
these reservoirs reach the surface and form seeps. The vertical migration of oil and gas
along fractures is termed as the chimney effect.
 Oil seeps that can be detected by naked eye are refer to macroseeps, whereas seeps that
can only be detected by special instruments are termed as microseeps.
 Macroseeps, have been extensively studied. Seeps are relevant to the oil and gas
industry as a potential source of information for exploration.
 Since oil and gas seeps have been documented to alter surface minerals, it may also be
possible to identify macro- and micro seepages of oil and gas by mapping mineral
assemblages associated with such alterations.

21. Surface Expression of
Seepages
Hydrocarbon microseepage gives rise to surface geochemical expression can take many forms…
 Anomalous concentrations methane and ethane in sediment, soil, water, and even
atmosphere
 Microbiological anomalies and the formation of "paraffin dirt"
 Anomalous non-hydrocarbon gases such as helium and radon.
 Mineralogical changes such as the formation of calcite, pyrite, uranium.
 Elemental sulfur, and certain magnetic iron oxides and sulphides clay mineral alterations
 Radiation anomalies, Geothermal and hydrologic anomalies.
 Geobotanical anomalies, electrical, and magnetic properties of soils and sediments.

22. Hyperspectral data for oil
exploration
 Hydrocarbon-bearing substances show characteristics absorption peaks at 1730 and 2310
nm, i.e., in the SWIR. Thus, focusing hyperspectral remote sensing observation specifically
on this region, hydrocarbons can be detected efficiently.
 The absorption peaks (or radiance minimum) could be recognized in the HyMap pixel
spectra, despite noise produced by the atmosphere between the scanner and the ground.
 Although less prominent, the peaks were still significant enough for hydrocarbon bearing
materials to be detected when the pixel spectra were evaluated.
 However, efficient mapping of the locations of hydrocarbons required image processing
capable of accentuating all pixels with such absorption maxima. Following the above
considerations, scientist have developed a Hydrocarbon Index (HI) focused on the 1730
nm absorption peak.

23. HYDROCARBON INDEX (HI)
 Where, RA : λA , RB : λB , and RC : λC, are Radiance/ Wavelength pairs of each
‘Index Point’. For Hyperion (sensor) data the radiance values of RA, RB, RC are
1699.4nm, 1729.7nm, and 1749.79 nm respectively.
 If, hydrocarbon bearing material is present at the surface, the value of HI >
0. If, hydrocarbon-bearing material is not present, HI=0. It can be assumed that the
larger the index value, the larger the hydrocarbon concentration. Nevertheless, the
above estimate of oil abundance is only qualitative and not quantitative. Kuehn F.
and B. Heorig, American Society for testing Materials,GERMANY.

24. Cntd..
 Training the classification of satellite imagery with spectral inputs of samples collected over
previously defined areas of hydrocarbon micro seepage resulted in the successful
identification of hydrocarbon bearing zone.
 Spectral Angle Mapper (SAM) as well as Mixture Tuned Matched Filter (MTMF)
techniques were utilized for classification of images.
 Spectral Angle Mapper (SAM) is a method for directly comparing image spectra to known
spectral end members input by the user .
 Mixture Tuned Matched Filter (MTMF) is a classification method that also provides a means
of detecting specific materials based on matches to user end member input or image-derived
end member spectra.

25. SOIL TONALANOMALIES
THROUGH HYPERSPECTRAL DATA
 Anomalous soil mineralogy can indicate buried geologic structures and zones
experiencing oil seepage. Hyperspectral data can be used to map these anomalies in
hydrocarbon exploration efforts.
 Hydrocarbons that escape from underground reservoirs cause oxidation-reduction
reactions either in situ or along vertical migration paths and result in
anomalies in sediments and soils.
 The surface expression of hydrocarbon-induced alteration of soils and sediments
can take many forms, including the following.

26. Cntd….
 Microbiological anomalies and the formation of “paraffin dirt”
 Mineralogical changes such as formation of calcite, pyrite, uranium, sulphur, and
certain magnetic iron oxides and sulphides.
 Bleaching of red beds
 Clay mineral alteration
 Radiation anomalies
 Biogeochemical and Geobotanical anomalies.

27. Cntd…
 Among these anomalies, bleaching of red beds, enrichment of ferrous iron, alterations of
clay minerals and carbonates and botanical anomalies exhibit diagnostic spectral features
that allow detection by remote sensing techniques.
 Remote sensing has the potential to detect hydrocarbon-induced alteration in rocks,
soils and vegetation.
 Extensive studies have been performed on the reduction of ferric iron (red-bed bleaching),
the conversion of feldspars and mixed-layer clays to Kaolinite, the increase of
carbonate content and the anomalous spectral reflectance of vegetation.
 The attraction of remote sensing is that it offers a rapid and cost-effective means of
conducting reconnaissance for hydrocarbon-induced alteration.

28. HYDROCARBON-INDUCED
DIAGENETIC ALTERATION
BLEACHED BEDS:
 The presence of bleached and discoloured red sandstones at the surface above petroleum
accumulations has been widely noted.
 Bleaching of red beds occurs whenever acidic or reducing fluids are present to remove
Ferric oxide (Fe2O3 - Hematite).
 Such conditions also favour the formation of Pyrite (FeS2) and Siderite (FeCO3) from
the iron released during the dissolution of Hematite.
 Leakage from petroleum accumulations of reducing agents such as hydrocarbons, H2S
and CO2 could be responsible for bleaching overlying red beds.

29. Cntd…
 The reflectance characteristics of various ferric and ferrous iron minerals, clay minerals and calcite are
shown below. Ferric iron (in hematite) exhibits its strongest reflectance at wavelengths greater than 1.0 µm
at progressively shorter wavelengths there is first a distinct absorption feature at 0.9 µm, then an increase
in reflectance at 0.8 µm and finally, at still shorter wavelengths, reflectance falls off sharply.
Hunt et al., 1973
 These characteristics can be used in remote sensing data-processing to separate bleached red
beds from their unbleached equivalents.

31. Clay Mineral Alteration
 The production of CO2, H2S, and organic acids resulting from the microbial
oxidation of hydrocarbons in near surface soils and sediments can create reducing,
slightly acidic conditions that promote the diagenetic weathering of Feldspars to
produce clays and may lead to the conversion of normally stable Illitic clays to
Kaolinite.
 Clays thus formed remain chemically stable unless their environment is changed.
Kaolinite exhibits a very strong absorption feature centred at 2.2 µm along with a
subordinate absorption feature at 2.16 µm, forming a diagnostic doublet. This can
be picked out in remote-sensing imagery and used to indicate areas enriched in
Kaolinite.

32. Abdelhamid & Rabba Ratio
R(4/2):G(6/7):B(5/6)
Abdelhamid and Rabba Ratio (1994)
This is the Color composite image
derived from above band ratios
(R[4/2]:G[6/7]:B[5/6]), were use to
map clay mineral alterations zones
(Abdelhamid & Rebba, 1994). Clay
minerals alteration areas are displayed
in dark blue to violet blue pixels

33. VEGETATION STRESS
 Hydrocarbon microseepage creates a reducing environment in the soil and overburden
at depths shallower than would be expected in the abs
 The presence of hydrocarbons stimulates the activity of hydrocarbon-oxidising
bacteria, which decreases oxygen content of the soil whille increasing its contents of
carbon dioxide and organic acids.
 These changes affect pH and Eh of soil, which in turn affect the plant nutrients and to
their healthy vegetation. This may affect the root structure of vegetation and ultimately
influence its physical strength and good health and hence also its spectral reflectance
properties.

34. Cntd…
 Remote sensing of anomalous (or stressed) vegetation takes two forms. One is the
mapping of the distribution of different species of vegetation and the
differences in health and morphology within each species.
 The second approach is to determine differences in spectral characteristics
between healthy and stressed vegetation.
 Some type of vegetation have been classified as “Hydrocarbon indicator Plants”
such as,
1. Anabasis Salsa
2. Allium

35. Spectral reflectance of vegetation
 The spectral signatures of vegetation
associated with hydrocarbon
microseepage used for detection of
hydrocarbon-induced vegetation
anomalies by remote sensing.
 The main targets of attention are the
green peak (at 0.56 µm), the red trough
(at 0.67 µm). This indicates the soil is
affected with HC microseeps,
regardless of factors such as climate,
geology, soil type, soil moisture.

36. ANALYSIS OF DEEP SEATED
GEOLOGICAL STRUCTURES
 Analysis of deep seated geological structures may provide important clues for hydrocarbon
exploration leads and also for identifying potential locales for oil and gas reservoirs. But, deep
seated geological structures cannot be identified directly from the earth surface and have to be
inferred from other indirect indicators.
 Field studies carried out in several regions have established that often there exists a considerable
relationship between surface lineaments / fractures and deep seated structures and has further
proven that the lineaments and fractures observed at the surface can be projected into the
subsurface to correctly infer possible subsurface geological structure.
 Based on these observations, researchers have proposed that regional fracture are first
established in basement and/or deep subsurface rocks due to large-scale tectonic activities.

37. LINEAMENT ANALYSIS
By using Satellite Images and with the help of Remote Sensing techniques scientist
have studied various relations in between subsurface structures such as follows..
 Relation between the Lineaments and Hydrocarbon deposits.
 Relation between the Structural, Tectonic, and Petroleum
Reservoirs.
 Solving various problems of Exploration and Prospecting.
 Detection of Low-Amplitude faults and Fractures zones as zone of
high Permeability in Productive Zones.
 Basin Boundaries.
 Neo Tectonic activities.

38. PHYSIOGRAPHY

39. LINEAMENTS
1. ENE – WSW
2. NNE – SSW
3. N – S
4. NW – SE

41. 1
2
1.
2.

43. Fig 12 : Gravity Data

44. Fig 13 : Gravity Anomaly Map

45. Inference
 Lineament analysis in a sedimentary basin for oil exploration using
satellite images through Remote Sensing will give us,
 Direct detection of local anticlinal folds
 Anomaly in Lineament density and gravity anomaly.
 Detection of Mesofractures (Conduit for Oil and Gas migration both vertical and
lateral)
 Recognition of Faults both Gravity and normal.
 Buried folds effects on overlaying sediments (topographic expression)
 Tonal changes due to Mineralogical Alteration.
 Drainage Anomaly etc.

46. Applications of Hyperspectral
Remote sensing

47. List of main
Hyperspectral
Sensor mounted on
Satellite platforms
(both Air/Space
borne)

48. Reference
Satellite image sources :
 Hyperion on Earth Observer -1 , NASA Guddard Space Flight Centre (0.40-2.5
spectral resolution).
 LANDSAT THEMATIC MAPPER 7 – USGS ( United States Geological
Survey)-(30m resolution)
 IRD – 1D , Indian Remote Sensing Satellites. (5.8 m resolution).
Papers refered:
1. Advance applications of Hyperspectral Remote Sensing.
Evans, M.E.
2. Reddy, Anji (et.ll) , Remote Sensing and GIS.
3. Hyvista Corpotation, Mapping Natural Hydrocarbon Seeps, USA.

49. 4. Jupp, David LB, Discussion around Hyperion Data,
CSIRO- office of space science and application.
5. Clouster E (et.all) , Spectral Properties of Hydrocarbon (1989)
6. Kuehn F. and B. Heorig, “Environmental Remote S sensing of Military Exercise Areas in
Germany”, Remote Sensing and GIS for site characterization.
7. Saravanavel J, Centre For Remote Sensing, Bharathidasan University.
8. Study of oil and gas Reservoirs using Hyperspectral Remote Sensing,
Qingjiu Tian (et.all). IIOESS (2012).
9. Advance Hyperspectral Remote Sensing for geologic mapping and exploration, Frad , A Kruse

50. Thank You…