Oil & Natural Gas Corporation Ltd.

Seismic Data Interpretation

What is seismic interpretation?

Interpretation is telling the geologic story contained in seismic data.

It is correlating the features we see in seismic data with elements of geology as we know them. Depositional environments and depositional

history Structure

Anticline, syncline Faults Stratigraphy

unconformity Pinch-out Channels, reefs, salt domes

Objective

Prospect generation and identification of suitable locations for drilling by interpreting subsurface geo-data Petroleum system

Reservoir rock Porous and permeable sandstone, limestone Any other rock type forming trap, e.g., fractured shales

Source rock Shale/carbonates

Cap rock Shale/carbonates

Reservoir characterization Estimation of reservoir parameters

Area, thickness, porosity, saturation etc. The primary goal of seismic interpretation is

to make maps that provide geologic information (reservoir depth structure, thickness, porosity, etc.).

Petroleum System

Source rock

oil

Cap rock

reservoir rock

Traps

Interpretation functionalities

Project and data management Data conditioning (interpretive processing)

Scaling, filtering, wavelet processing etc. Integration of various types of data (seismic, well etc.) Calibration (well to seismic tie)

Synthetic seismogram generation and correlation with seismic Visualization Horizon and fault Correlation Generation of time maps Depth conversion and generation of depth maps Special studies

Seismic Attributes Direct hydrocarbon indicators (DHI) and AVO Seismic Inversion Time lapse reservoir monitoring

Integration of structure, attributes, impedance, geologic model etc.

Prospect generation and location identification Reports/proposals

Pre-requisite of interpretation

Knowledge of geology and geophysical processes Type of data and type of information which can be

extracted Objective of interpretation and amenability of seismic data

Elements of seismic trace data Modes of display Amplitude, time and frequency Role of colours and colour bar

Contrasting and gradational colour scheme Polarity and phase conventions Resolution and detectability Seismic to well tie

Character based matching between synthetic and seismic

Depth to time conversion (T-D curves) Check shots, VSP, Synthetics

A seismic section showing colour convention and other display elements

Elements of display

Positive amplitude Blue

Negative amplitude Red

Horizon

Elements of display- max + max

Variable area wiggle display

+/- zero crossing

-/+ zero crossing

3-D Cube of seismic data

Elements of display

Time slice

In-linecross-line

Successive time slices depict the anticlinal closures

Time 2430

Time 2390

Elements of display

Vertical section and time slices

Top and bottom of a gas reservoir (low impedance zone) in (a) American polarity and (b) European polarity

Polarity Convention

American polarity isdescribed as: An increase inimpedance yields positive amplitude normally displayed in blue. A decrease in impedance yields negative amplitude normally displayed in red.

European (or Australian)polarity is described as thereverse, namely: An increase inimpedance yields negative amplitude normally displayed in red. A decrease in impedance yields positive amplitude normally displayed in blue.

American

European

Phase

The task of tying seismic data and well data together and hence identifying seismic horizons is often oversimplified. The task requires knowledge of velocity, phase, polarity and tuning effects. Zero phase data makes all aspects of interpretation easier and everyone knows that zero phase is desirable. However, zero phase is difficult to accomplish and often it is not achieved in processing. Hence interpreters always need to check the phase and polarity of their data.

ResolutionThe ability to separate two features that are close togetherThe minimum separation of two bodies before their individual identities are lost on resultant map or cross-sectionThe resolving power of seismic data is always measured in terms of seismic wavelength (λ=V/F) Limit of separability = λ/4The predominant frequency decreases with depth because the higher frequencies in the seismic signal are more quickly attenuated. Wavelength increases with depth.Resolution decreases with depthFor thinner intervals amplitude is progressively attenuated until Limit of visibility=λ/25 is reached when reflection signal becomes obscured by the background noise

Limit of Separability

Age of rocks Very young

young medium old Very old

Depth Very shallow

shallow medium deep Very deep

Velocity (m/s) 1600 2000 3500 5000 6000

Predominant frequency

70 50 35 25 20

Wavelength 23 40 100 200 300

Separability 6 10 25 50 75

Limit of visibility

Factors affecting the visibility Impedance contrast of

the geologic layer of interest relative to the embedding material

Random and systematic noise in the data

Phase of the data or shape of seismic wavelet

It may be less than 1 m to more than 40 m

Limit of visibility

S/N Example Limit

Poor Water sand poor data

λ/8

Moderate Wayer or oil sand fairly good data

λ/12

High Gas sand good data

λ/20

Outstanding Gas sand excellent

data

λ/30

STRUCTURAL INTERPRETATIONCalibration (Well-to-seismic-tie)Correlation (horizons and faults) Map generation (gridding and contouring)

For geologic information from seismic data, nearby wells are correlated to seismic reflectors. Synthetic seismograms (synthetics) provide this link by converting rock properties from well logs to a synthetic trace. Synthetics make “rocks look like wiggles,” using the convolution model (T = RC * W), which states that traces (T) are the result of convolving (*) the reflection coefficient series (RC) with the wavelet (W). When seismic data are acquired, a source wavelet is sent into the earth, reflected back (convolved) to the surface at geologic boundaries (RC), and recorded as a trace (T). RC is calculated from velocity and density logs

Well to seismic tie Synthetic Seismograms

RC for normal incidence isRC = (r1v1- r2v2) /(r1v1+ r2v2)where v1, v2 are P-wave velocities (sonic log) and r1, r2 are densities (density log) in the layer above (1) and below (2) the reflecting boundary. The normal incidence assumption is generally valid, except where velocity and density contrasts are very large (gas sands, coal, hard streaks, etc.). When these exceptions are critical to the interpretation, RC needs to be calculated as a function of angle (AVA) from more complex equations and a third parameter, S-wave velocity (shear log).

Well to seismic tie Synthetic Seismograms

(1) r1,v1

(2) r2,v2

Synthetic to seismic matching before starting the interpretation. Understand geologic elements (model) and their geophysical responses (Trace).

DT RHOB IMP GR LLD RC

Black traces

Seismic

Red traces

synthetic

Seismic signatures of pay sands in B12-11. Sandstone pays are marked by troughs. Pay1 and Pay3 high negative amplitudes

Overlay of synthetic and logs on seismic sections. Here synthetic is shown by yellow trace

Well to seismic tie Synthetic Seismograms

Horizon Correlation Identification of sequence boundaries

Reflection configuration and termination pattern, Onlap, Top lap and Down Lap

Tracking Auto and manual mode

Auto dip and correlation 3D tracking

Fault correlation Picking on vertical and horizontal sections Fault plane correlation Computing throw of faults

Map generation Base map generation

Depicting seismic Well location Cultural data Block boundaries Scale, coordinates and legends

Griding Various method with faults and without

faults Incorporating heaves and throw

Contouring Over lay of attributes Map analysis

Time map generated from horizon and fault correlation

Seismic attributes

Seismic attributes

Attributes are derivatives of basic seismic measurements/Information Seismic attributes extract information from seismic

data that is otherwise hidden in the data These information can be used for predicting,

characterizing, and monitoring hydrocarbon reservoirs Basic information

Time Amplitude Frequency Attenuation Phase

Most attributes are derived from normal stacked and migrated data volume

Can be derived from Pre-stack data (AVO)

Seismic attributes

Attribute Information

Time-derived Structural information

Amplitude-derived Stratigraphic and reservoir

Frequency-derived Stratigraphic and reservoir

Attenuation Permeability

Direct hydrocarbon indicator (DHI)

DHI

Bright spot Water sand has lower

impedance than embedding medium and impedance of gas sand is further reduced.

Top and base reflections show natural pairing

If sand is thick enough, flat spot or fluid contact reflection should be visible between gas sand and water sand

Flat spot will have opposite polarity than bright spot at top

More common in shallower sandstone reservoirs ( Mio-Plio)

High impedance

Low impedance

High impedance

High impedance

Med impedance

High impedance

Pay sands

Water sands

Peak on synthetic

seismogram

DHI

Low impedance

Med impedance

Low impedance

Low impedance

High impedance

Low impedance

Pay sands

Water sands

Dim spot Water sand has higher

impedance than embedding medium and water is replaced by water impedance is reduced

Contrast is reduced at upper and lower boundaries and reservoir is seen as dim spot

Flat spot can be expected at the point where the dimming occurs.

More common in deeper sandstone reservoirs where shale impedance is lower than sandstone impedance

DHI Polarity reversal

Water sand is of higher impedance than enclosing medium and gas sand impedance is lower than enclosing medium

The polarity of reflections from water-sand-shale interface and gas-sand-shale interface have opposite sign and thus polarity reversal

Flat spot from GWC may show bright amplitude .

More common in medium depth range sandstone reservoirs

Med impedance

Low impedance

Med impedance

Med impedance

High impedance

Med impedance

Pay sands

Water sands

Validation of DHI

AVO In many practical cases gas sands show an

increase of amplitude with offset Many difficulties of theoretical and practical

nature Data is pre-stack hence lower S/N

Pitfalls in DHI

Exploration prospects based on a sound geologic model and supported by seismic amplitude anomalies are highly prospective and are usually assigned a high probability of success. However, a fraction of such prospects, perhaps 10-30%, result in dry holes. Postdrill appraisal can usually assign these results to one or more of the following factors:

• Unusually strong lithologic variations• Fizz water and low gas saturation• Superposition of seismic reflections• Contamination of the seismic signal by multiples or other undesired energy

In seismic gather reflection coefficient at an incidence angle θ is given by: R(θ ) = Ro + BSin2θ

Where: Ro: RC at zero offset and R (θ) : RC at angle θ B is a gradient term which produces the AVO effect. It is dependent on changes in density, ρ, P-wave velocity, VP, and S-wave velocity, Vs.

Principle of AVO Analysis

Principle of AVO AnalysisThe AVO response is dependent on the properties of P-wave velocity (VP), S-wave velocity (VS), and density (ρ) in a porous reservoir rock. This involves the matrix material, the porosity, and the fluids filling the pores.

Poisson’s Ratio

K = the bulk modulus, or the reciprocal of compressibility.μ = The shear modulus, modulus of rigidity

Increase of amplitude with offset

Principle of AVO Analysis

Stack

Gather

Principle of AVO Analysis

S-wave

P-wave

Water Saturation

Vel

ocit

y (m

/s)

With increase in gas saturation, P-wave velocity drops dramatically, but S-wave velocity only increasesslightly.

• Rutherford and Williams (1989) derived the following classification scheme for AVO anomalies, with further modifications by Ross and Kinman (1995) and Castagna (1997):

• Class 1: High impedance gas sand with decreasing AVO• Class 2: Near-zero impedance contrast • Class 2p: Same as 2, with polarity change• Class 3: Low impedance gas sand with increasing AVO• Class 4: Low impedance sand with decreasing AVO

Rutherford/Williams Classification

The generic AVO curves at the top of the gas sand

Inversion

Inversion

Inversion is the process of extracting, from the seismic data, the underlying geology which gave rise to that seismic.

Traditionally, inversion has been applied to post-stack seismic data, with the aim of extracting acoustic impedance volumes.

Recently, inversion has been extended to pre-stack seismic data, with the aim of extracting both acoustic and shear impedance volumes. This allows the calculation of pore fluids.

Another recent development is to use inversion results to directly predict lithologic parameters such as porosity and water saturation.

Inversion Non-Uniqueness

All inversion algorithms suffer from “non-uniqueness”.

There is more than one possible geological model consistent with the seismic data.

The only way to decide\between the possibilities is to use other information, not present in the seismic data.

This other information is usually provided in two ways: The initial guess model constraints on how far the final result may deviate

from the initial guess The final result always depends on the “other

information” as well as the seismic data

Acoustic Impedance

The definition of the zero-offset reflection coefficient, shown in the figure above. R0 , the reflection coefficient, is the amplitude of the seismic peak shown and represents relative impedance contrast.

1122

11220 VV

VVR

Seismic raypath

Interface at depth = d

r1 V1

r2 V2

t

Reflection at time t = 2d/V1

Geology SeismicSurface

SeismicWavelet

Shale

Gas Sand

Impedance Reflectivity

Wavelet

Seismic trace

The common forward model for all inversions

General Forward Model for Inversion

ImpedanceReflectivity

InverseWavelet

Seismic

Inversion tries to reverse the forward model

Inverse Model

QC plot showing accuracy of inversion process. Error between synthetic generated from real and inverted impedance is insignificant

Differentiating different lithology types through acoustic impedance inversion

Impedance profile distinguishing between different rock types

Limestone

Sandstone

Basement

Thanks