mapping thin subsurface reservoir sandsfrom spectral and...

European Journal of Scientific Research

ISSN 1450-216X / 1450-202X Vol. 150 No 2 September, 2018, pp. 116-125

http://www. europeanjournalofscientificresearch.com

Mapping Thin Subsurface Reservoir Sandsfrom

Spectral and Textural Seismic Attribute Analyses over

“Mayehun” Field, Niger Delta, Nigeria

Olatunbosun Adedayo Alao

Faculty of Science

Department of Geology

Obafemi Awolowo University, Ile-Ife, Nigeria

Sunday JephthahOlotu

Faculty of Science

Department of Geology,


Ibukun Olorunniwo

Faculty of Science



Adekunle Abraham Adepelumi

Faculty of Science



Bankole Dayo Ako

School of Earth and Mineral Science

Department of Applied Geophysics

Federal University of Technology Akure, Nigeria

Samuel Bakare Ojo

School of Earth and Mineral Science

Department of Applied Geophysics

Federal University of Technology Akure, Nigeria

Abstract

This work aimed at mapping and characterizing thin subsurface reservoirs in

“Mayehun” field, Niger Delta employing narrow-band spectral analyses and seismic

texture attributes which measure amplitude variations from trace-to-trace and are also

responsive to lateral variations within a reservoir. RokDoc and OpendTect software were

used to analysethe well logs and 3D seismic data volume. The data were structurally and

stratigraphically analysed to isolate the different hydrocarbon reservoirs. Properties such as

lithology, fluid content, structure and texture of the reservoirs were characterized using

responses from generated Narrow-band seismic spectral and texture attributes. The data

interpretation comprised structural, spectral, texture and instantaneous spectral analyses of

Mapping Thin Subsurface Reservoir Sandsfrom Spectral and Textural Seismic

Attribute Analyses over “Mayehun” Field, Niger Delta, Nigeria 117

the thin subsurface reservoir sands. Seismic textural analysis attributes such as

homogeneity and energy were deployed, while the spectral analysis attributes employed

were maximum spectral amplitude, dominant response and average response frequencies.

The results obtained revealed distinct features of high reflection strength on seismic

amplitude sections for the thin subsurface reservoir sands, but are enhanced in both spectral

and textural attribute domains. The spectral attribute shows the channel features as

lenticular in shape with intercalations of embedded shaly formation. The study showed the

plausibility of texture and narrow-band spectral analysesas a robust tool for mapping thin

subsurface reservoirs.

1. Introduction Widess (1973) defined a “thin bed” as a bed whose thickness is below half a quarter (1/8) of the

dominant wavelength (λ�) of the seismic pulse propagating through the bed. In discussing the effect of

bed thickness on reflection character and timing using a symmetrical wavelet, Widess (1973) suggested

that “ λ� 8⁄ be the resolution limit, or the minimum distance at which a composite waveform stabilized

as the derivative of the waveform from an individual reflection”. However, this definition has more

theoretical than practical impact because of the difficulties in judging waveform stabilization. “A more

workable and widely accepted definition of resolution limit corresponds to Rayleigh’s criterion of peak

to trough separation at λ� 4⁄ ” (Kallweit and Wood, 1982). This point is also a “tuning point,” at which

composite amplitude reaches a maximum if an opposite polarity (at top and bottom) thin bed is

involved. Because it uses peak to trough time separation in conjunction with amplitude, applicable

spectral method is dependent on careful seismic processing to establish the correct wavelet phase and

true trace to trace amplitudes (Zeng, 2009).

Seismic attributes are quantitative measures of seismic characteristics of interest which play a

vital part in exploration and exploitation of oil and gas. Seismic attributes extract information from

seismic reflection data that areuseful for qualitative and quantitative interpretation (Chopra and

Marfurt, 2005, 2006).

“Seismic texture, as opposed to other image textures, is defined as a reflection amplitude

pattern that is characterized by the magnitude and variation of neighboring acoustic samples at a

specific location in a seismic volume” (Gao, 1999a, b, 2001a, b, 2002). “Seismic textures work in an

analogous manner with elevation replaced by amplitude, and the probing finger by a rectangular or

elliptical analysis window oriented along the structure” (Yenugu et al., 2010).

“Seismic texture attributes are statistical measures of amplitude (or other attributes) extracted

along a dipping horizon. While the internal framework of geologic elements may fall below seismic

vertical resolution, lateral variation within these elements may give rise to a unique texture that can be

detected and recognized” (Yenugu et al., 2010).

Spectral analysis (Fourier analysis) involves finding the amplitude of frequency components for

a waveform. It is the analytical representation of a waveform as a weighted sum of sinusoidal

functions. Determining the amplitude and phase of cosine (or sine) waves of different frequencies into

which a waveform can be decomposed. Fourier analysis (opposite of Fourier synthesis) can be thought

of as a subset of the Fourier transform (Sheriff, 2002).

Farfour and Yoon (2014) delineated the F39 Frio reservoir from South Texas to be ultra-thin

beds using seismic attributes. They also confirmed compartmentalization occurrence within the

reservoir.

2. Theory and/or Method Chopra and Alexeev (2005, 2006) and Chopra et al. (2017),described high-amplitude,low-frequency

anomalies as generally depictive of hydrocarbon accumulation, and exhibit low contrast, high energy

118 Olatunbosun Adedayo Alao, Sunday JephthahOlotu, Ibukun Olorunniwo,

Adekunle Abraham Adepelumi, Bankole Dayo Ako and Samuel Bakare Ojo

and low entropy, compared to non-hydrocarbon deposits. High-amplitude continuous reflections,

generally associated with marine shale sediments, have relatively high contrast, low entropy, andlow

energy. Turbiditesediments havinglow contrast, high homogeneity and high energy are generally

identified as low-amplitude discontinuous reflections (Gao, 2003).

In this study, we selected spectral attributes which are called the response frequencies (which

are the variability/tuning point of the windows of frequencies as they are useful to capture the essence

of the spectrum that is as a result of thin-bed tuning) and the seismic spectral attributes that are

considered include:

a) Peak (maximum) frequency, f �� (t): This is the frequency with the highest amplitude. It

returns the dominant frequency from the frequency spectrum.

b) Trough (minimum) frequency, f ��(t): This is the frequency with the lowest

amplitude.

c) Mean frequencies frequency, f ��(t): This returns the arithmetic mean of the frequency

spectrum.

Amplitude at the maximum frequency,��(�): This is the amplitude of the dominant

frequency. It is also referred to as maximum spectral amplitude and returns the maximum amplitude of

the frequency spectrum.

Additionally, the instantaneous attributes are frequency response displayed in time amplitude of

the envelope.

Also, of all 15 texture attributes, homogeneity, contrast, entropy, and energy generate the

desired discrimination without any redundancy: (Chopra and Alexeev, 2006). The equations used for

individual texture measure calculations are given below:

1) Energy is the degree of textural uniformity of an image. Mathematically, it is given as

(1)

where P� denotes the ith row and the jth column of the Gray-Level Co-occurrence Matrix

(GLCM). When all elements in the GLCM are equal,energy is low, and it is useful for

highlighting continuity and geometry (Chopra and Marfurt, 2007).

2) Entropy is the degree of disordeliness of the image.

(2)

Texturally uniform imageshave large Entropy. Many GLCM elements are characterized by

low values in such a case(Chopra and Marfurt, 2007).

3) Contrast is the measure of confined divergence present in an image or a measure of the

image contrast.

(3)

Contrasted pixels exhibit high inertia or contrast with associatedlow homogeneity. When

combined, both homogeneity and inertia give discriminating information (Chopra and

Marfurt, 2007).

4) Homogeneity is magnitude of the general smoothness of an image.

(4)

For GLCMs with elements restricted to the diagonal, homogeneity which is a measure of

similarity of pixel is high. Thus, homogeneity is helpful in quantifying reflection continuity (Chopra

and Marfurt, 2007).

Energy = * * Pi,j2 ji

Entropy = * * Pi,jlogPi,j ji

Contrast = * *(i − j)2Pi,j ji

Homogeneity = * * 11 + (i − j)2 Pi,j

ji



3. The Case of “Mayehun”Field, Niger Delta 3.1 Seismic Textural Analysis:

Energy and similarity (or homogeneity) attributes were selected for subsurface mapping of

hydrocarbon from seismic textural analysis. These attribute maps are presented in figures1 and 2 with

maps of its seismic amplitude and mean. The bluish portion of the seismic amplitude and mean maps

correspond to the thin-sand BC (figures 1a and 1d)and thin-sand D (figures 2a and 2d) in the study area

since they are locations with highest values of seismic reflection strength. The seismic amplitude is

perhaps irregular but the mean is a filtered version (running/moving average) of seismic amplitude, in

line with concept of image processing, as the mean was computed with a low pass filter. For instance,

the seismic amplitude maps indicated thatthe structures (closures) are tightly packed due to higher

spatial frequency, but they are more regularly separated in the seismic mean maps.Additionally, as

observed on the energy maps (figures 1b and 2b for the two thin-sands), the red portions have the

highest values of energy throughout the maps, indicative of hydrocarbon accumulation.The whitish

parts of these maps, lowest values of energy, are indicative of marine shale deposits. Therefore, this

result can be used with much confidence since the high energy of hydrocarbon sediments was

corroborated with another textural attribute of homogeneity (similarity) as its highest zones represented

by the heavy red colorations in figures 1c and 2c (maps of homogeneity for these thin-sands).

Moreover, all the existing wells in “Mayehun” field are observed to be located in these high regions of

energy and homogeneity. Observably, there are locations on the study area with major high values of

energy and homogeneity where there are no existing wells, as shown in figures 1 and 2. These areas are

indicated on the energy and homogeneity maps for the two thin-sands (BC and D) as prospective areas

of hydrocarbon accumulation.

3.2 Seismic Spectral Analysis

For the two thin-sands, the spectral analysis attributes considered included average response frequency,

dominant response frequency, and maximum spectral amplitude, as compared with the seismic

amplitude maps (figures 3 and 4). The seismic amplitude maps (figures 3a and 4a) show the channel

(indicated by a white oval) as lenticular in shape. These features are not only noticeable on the spectral

attributes maps for the two thin-sands (figures 3b–d and 4b–d), since they gave additional information

that the channel (as depicted by the outer oval) is not totally homogeneous but with holes of shaly

formation (pinpointed by the inner oval) within the sand lenses (channel). These portions have shades

of red, blue and dark brown (encircled by the lens-shaped channel of yellow, green, and light brown

coloration respectively) as displayed on the spectral attributemaps. Therefore, spectral attributes clearly

and precisely define the lithological differentiation of shale and the productive sand/sandstone

formations in the study area.



Figure 1: Attribute Maps of Seismic Textural Analysis for Thin-Sand BC (a) Seismic Amplitude, (b) Energy,

(c) Similarity (Homogeneity), and (d) Mean [derived from OpendTect Software]

Figure 2: Attribute Maps of Seismic Textural Analysis for Thin-Sand D (a) Seismic Amplitude, (b) Energy,

(c) Similarity (Homogeneity), and (d) Mean [derived from OpendTect Software]



Figure 3: Attribute Maps of Seismic Spectral Analysis for Thin-Sand BC (a) Seismic Amplitude, (b) Average

Response Frequency, (c) Dominant Response Frequency, and (d) Maximum Spectral Amplitude.

The inner oval indicates a shaly formation within the channel (outer oval) in “Mayehun”field

[derived from OpendTect Software]

Figure 4: Attribute Maps of Seismic Spectral Analysis for Thin-Sand D (a) Seismic Amplitude, (b) Average

Response Frequency, (c) Dominant ResponseFrequency, and (d) Maximum Spectral

Amplitude: The inner oval indicates a shaly formation within the channel (outer oval) in “Mayehun”field[derived from

OpendTect Software].



3.3 Instantaneous Spectral Analysis

Figures 5 to 8 show the results of instantaneous spectral analysis for the two thin-sands. The figures

comprise combined maps of instantaneous amplitude (reflection strength), instantaneous phase,

instantaneous frequency, envelope weighted phase, envelope weighted frequency, and thin-bed

indicator as compared with its seismic amplitude maps.The instantaneous amplitude maps (shown in

figures 5b and 7b) resemble the smoothened energy maps (figures 1b and 2b) for the corresponding

thin-sand. For each seismic sub-volumes considered, envelope weighted phase maps (figures 5d and

7d) are the smoothened version of instantaneous phase maps (figures 5c and 7c). In each case, the

instantaneous phase maps (which emphasizes the continuity of events) and envelope weighted phase

show considerable amplitude variation and mimic the variation seen on the seismic amplitude maps

(shown in figures 5a and 7a). Envelope weighted phase maps show an internal inconsistency, that is,

discontinuity (smearing at a part) of the amplitude/sand lenses (indicated by an oval).

Figure 5: Attribute Maps of Instantaneous Spectral Analysis for Thin-Sand BC (a) Seismic Amplitude, (b)

Instantaneous Amplitude, (c) Instantaneous Phase, and (d) Envelope Weighted Phase. The bigger

oval indicates the channel in “Mayehun”field [derived from OpendTect Software]

Instantaneous frequency (figures 6b and 8b) and/or envelope weighted frequency (figures 6c

and 8c) display the channel as lens-shaped body as highlighted seismic amplitude maps (figures 6a and

8a) and also show that it is not just one body of sand (pinpointed by two ovals).

The difference between a corresponding instantaneous frequency attribute and weighted-

average frequency attribute gives the thin-bed indicator attribute in each case (figures 6d and 8d). The

images in figures 6d and 8d are plotted using a color bar that highlights the extreme values. Thin-bed

indicators show the boundary of the two bodies that indicated that the channel (shown by the outer

oval) in “Mayehun” field is not totally homogeneous but with holes of shaly formation (identified by

the inner oval) within the sand lenses (channel).



Figure 6: Attribute Maps of Instantaneous Spectral Analysis for Thin-Sand BC (contd.) (a) Seismic

Amplitude, (b) Instantaneous Frequency, (c) Envelope Weighted Frequency, and (d) Thin bed

Indicator. The inner oval indicates a shaly formation within the channel (outer oval) in the study

area [derived from OpendTect Software].

Figure 7: Attribute Maps of Instantaneous Spectral Analysis for Thin-Sand D (a) Seismic Amplitude, (b)

Instantaneous Amplitude, (c) Instantaneous Phase, and (d) Envelope Weighted Phase. The bigger

oval indicates the channel in “Mayehun” field[derived from OpendTect Software]



Figure 8: Attribute Maps of Instantaneous Spectral Analysis for Thin-Sand D (contd.) (a) Seismic Amplitude,

(b) Instantaneous Frequency, (c) Envelope Weighted Frequency, and (d) Thin bed Indicator. The

inner oval indicates a shaly formation within the channel (outer oval) in “Mayehun”field [derived

from Opend Tect Software]

The instantaneous amplitude (amplitude of the envelope or reflection strength) maps in figures

5 and 7 have one-to-one correlation with the energy and similarity maps displayed in figures 1 and 2,

and the instantaneous frequency, envelope weighted frequency, and thin bed indicator in figures 6 and

8. The red portion of the instantaneous amplitude maps are areas with high reflection strength that are

associated with hydrocarbon accumulations (especially gas).

4. Conclusions Thesuperiority and efficiency of application of both seismic texture and narrow-band spectral analyses

as a powerful interpretation tool for mapping reservoirs thinner than one-quarter wavelength was

demonstrated in this study. Distinct zones of structural closures were delineated using this approach.

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mapping thin subsurface reservoir sandsfrom spectral and...

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