HYPERSPECTRAL REMOTE SENSING FOR OIL

EXPLORATION

PRESENTED BY JOSHUA . R

MPE 007

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.

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.

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

HYPERSPECTRAL REMOTE SENSING

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”.

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”.

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.

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.

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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.

Impact of less bands in satellite sensor

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.

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.

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.

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.

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.

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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.

SOIL TONAL ANOMALIES 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.

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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.

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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.

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.

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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.

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.

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

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.

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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

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.

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.

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.

PHYSIOGRAPHY

LINEAMENTS

1. ENE – WSW2. NNE – SSW3. N – S4. NW – SE

1

2

1.

2.

Fig 12 : Gravity Data

Fig 13 : Gravity Anomaly Map

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.

Applications of Hyperspectral Remote sensing

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

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.

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

Thank You…