Seismic Processing for Geohazards - Onshore Nigeria Case Study

A.ADEBAYO*, J.OBIEKWE, CGGVeritas

Summary

Geo-hazards in oil exploration principally

result from shallow gas (hydrates) found

both onshore and offshore, which may

adversely impact well placement and

drilling activities. They can result in loss

of well control and ‘blow-outs’ which can

lead to fatality. Seismic data, when

properly processed, provide a means with

which these gases could be identified.

Offshore, new generation broadband

marine and seabed techniques exist for

their identification. Onshore (and offshore)

geo-hazards identification using seismic

data is the best available tool and requires

accurate processing. The main processing

steps to achieve this goal are the main

focus of this paper.

In this paper we present an adapted

processing flow for geo-hazard

identification on a seismic survey onshore

Niger Delta. A focus on three main steps is

needed to reveal shallow gas on land

seismic data:

1. Attention to proper near surface

modelling for refraction statics

computation.

2. Use of robust suit of preserved

amplitude processing algorithms.

3. Accurate velocities.

Introduction

The near surface of land surveys where

shallow gases are located is often masked

with high-frequency noise, low-fold data

and low-velocity layer effect on refraction

statics computation. The low-fold is also a

draw-back for velocity analysis.

A point of advantage from field data is the

acquisition sampling rate which is

normally at 2ms; this is adequate for this

study.

We have therefore focused the adapted

processing flow on addressing these issues

that affect the ‘visibility’ of near surface

information on a 3D land survey from the

onshore Niger Delta.

The data used in this paper was acquired in

2009 using orthogonal geometry with the

following acquisition and recording

parameters:

Bin size: 25m x 25m

Source line spacing: 350m

Receiver line spacing: 300m

Shot-point interval: 50m

Receiver Station interval: 50m

Active receiver lines/shot: 8

Maximum active channels/shot: 1344

Maximum active receivers/line: 168

Nominal full fold: 48

Shots/salvo: 24

Max. Trace offset (m): 4480

Min. trace offset (m): 36.35

Sampling interval (ms): 2

Swaths: 8

Receiver lines: 36

Total Shots: 5565

Full fold area (km2): 52

Location

The survey area is located in the north

central part of onshore Niger Delta,

Nigeria, as shown in Figure 1.

Figure 1: survey location map

Initially, prospects in the area were

identified on the basis of 2D seismic lines

of very old vintage. The first well was

drilled, based on that dataset, in 1972

which resulted in a blow-out. Following

this, three relief wells were drilled from a

nearby location to control the blowout.

The blowout however continued for more

than two years before it subsided. A

significant amount of water, mud and

cement were injected into suspected

reservoirs which were understood to have

caused the blowout by the initial operator.

No appraisal activities took place in the

block until 2007. The acquisition in 2009

was therefore aimed at studying any

possible charged high pressure sand in the

shallow section as part of exploration

activity by the subsequent operator of this

lease area.

The geology, typical in Niger Delta, is

characterised with large scale growth

faults dipping to the south/southwest and

occurring as rollover anticlines generally

trending in northwest/southeast direction.

The target for this study is between 0ms -

1000ms.

Processing Strategy

In achieving the objectives in processing,

we focussed attention on three main areas

that enhance signal in the shallow,

discussed below.

1. Thoroughly quality-checked near-

surface model for refraction statics

computation and application.

Geostar, a CGGVeritas integrated solution

for near-surface modelling and 3D

refraction statics tools is used in this work.

It builds and refines a near-surface model

and creates weathering statics corrections

by using seismic first arrival information.

An accurate refraction solution begins with

precise first break picks which are the

input as depicted in Figure 2 below. The

picks are double-checked as overlay after

auto-picking.

Figure 2: refraction statics workflow

The initial model was built from field up-

hole data and refined manually to fit

reasonable geological expectations; for

example, regional trending are carefully

removed and smoothed where necessary.

The first breaks and the initial models are

validated by travel conformance displays

which are expected to be flattened with the

modelled velocities from the up-hole data.

The degrees of correctness of the updated

(or final) models are confirmed with how

flat the first arrivals have become in

subsequent travel conformance displays.

We updated the models using a cascade of

iterated linear model inversion (LMI)

technique.

We, more importantly, checked that the

final refraction solution indeed solves the

topographic and the low-velocity-layer

problems. Several sections through the

final model and the computed statics

solution showed surface consistency with

the surface elevation map, shown in Figure

3a and b. A sample section through the

model is shown in Figure 4 below.

Figure 3a: survey surface elevation map

Figure 3b: Final refraction statics map

Figure 4: Near surface model for

refraction statics computation showing the

travel conformance display from the initial

model and below after final updated

model.

Of course, the final check is the actual

application on seismic. The resulting

refraction statics are then applied on the

data volume, stacked and quality-checked.

The results are shown in Figures 5 and 6,

where near surface reflections have

become better aligned with consequent

higher stack response.

Figure 5: An inline stack before refraction

statics application

Figure 6: an inline stack after refraction

statics application

2. Use of Robust suit of preserved

amplitude processing algorithms.

Preserved amplitude algorithms assure

easy detection and preservation of changes

which are due to changes in geology. All

of the algorithms employed here are

normally used in seismic processing

prospecting for deep targets; two of these

stand out which aid near-surface imaging.

The two are:

a. Adaptive Ground-roll Attenuation

algorithm for both aliased and

dispersive ground-roll noise

attenuation.

b. REVIVE 5D Interpolation for data

regularisation.

a. Adaptive Ground-roll Attenuation is a

preserved amplitude processing algorithm

used to remove aliased and dispersive

ground-roll noise.

Where ground-roll characteristics vary

across a survey, standard methods such as

FK and radon techniques which use fixed

parameterisations and regular grids

become ineffective. CGGVeritas AGORA-

Adaptive Ground Roll Attenuation

provides accurate attenuation by adapting

to the ground-roll characteristics in 2D and

3D domains.

The ground-roll is elastically modelled and

adapted to the data in a least-squares sense

before subtraction. This provides robust

and effective attenuation to the ground-

roll, even on external cables and in

challenging conditions where noise is

aliased or dispersive, whilst ensuring

excellent preservation of primaries.

AGORA is also used to attenuate guided

waves allowing wider mutes, improved

velocity picking, increased signal-noise

ratio in the stack and providing more far-

offset data for full AVO studies. This is

appropriate in removing noise that masks

near-surface information.

Figure 7: shot and FK spectrum before

AGORA noise attenuation

Figure 8: shot and FK spectrum after

AGORA noise attenuation

Figures 7 and 8 show a shot before and

after AGORA application. Figures 9 and

10 show an inline stack without and with

AGORA noise attenuation.

Figure 9: inline stack without AGORA

noise attenuation

Figure 10: inline stack with AGORA noise

attenuation

b. REVIVE 5D Interpolation for data

regularisation has been employed in this

work. In this algorithm, full interpolation

and regularisation to bin centre is achieved

in FK domain after data has been

transformed using multi-dimensional

Fourier transformation. The reverse

transform reconstructs the energy to bin

centre (or any specified coordinates)

resulting in reduced gaps and much higher

signal-to-noise in the stack. Figure 11

depicts these transforms.

Figures 12 and 13 show an inline stack

without and with REVIVE 5D

interpolation.

Figure 11: data interpolation and

regularisation with REVIVE 5D

Figure 12: inline stack without REVIVE

5D interpolation

Figure 13: inline stack with REVIVE 5D

interpolation

3. Accurate velocity analysis was

performed, which was aided greatly due to

increased data continuity provided by

REVIVE 5D, resulting in precise velocity

picking which was used for pre-stack time

migration. The precision in the velocity

field was also helped with the temporal

sampling of 2 ms recorded from the field.

Great care was taken to estimate the best

velocity possible at an early stage. The

brute velocity analysis was prepared with

scans of several CDP gathers of a sub-line.

These were used as references in

subsequent analyses. The final migration

velocity was performed at a grid of 250m x

250m. Residual move-out velocity was

done on a grid of 100m x 100m for final

stack.

Figure 14 compares stack images of the

raw data and final migration.

Figure 14: stack inline: raw vs. final

migrated image

Discussion of Results

Important feedback was received on the

interpretation of this processed dataset and

how it was used in delineating geo-hazards

and well placement, these are discussed

here.

Regarding the reported blowout of 1972,

its current extent was easily seen and

delineated at times 220ms and 850ms and

easily mapped spatially as shown in

Figures 15 and 16.

Comparison with the originally processed

section is shown in Figure 17, which was

also interpreted. This does not map

properly the spatial extent of the geo-

hazards at ~850ms.

Figure 15: spatial delineation of extent of

blowout at time 220ms

Figure 16: spatial delineation of extent of

blowout at time 850ms – mid Benin section

Figure 17: blowout effect on seismic:

original vs reprocessed

This interpretation consequently helped in

well placement as depicted in Figure 18.

Figure 18: well placement aided by easy

delineation of blowout effect

Conclusion

The adapted processing flow used in this

work has helped in identifying and

delineating the extent of the blowout

which happened in 1972 on this survey

area. It has therefore helped in properly

placing the well and avoiding risks to the

equipment and personnel. This is fully in

line with the set objectives.

Actual well drilling has been started

without any fatality.

Acknowledgments

The authors thank Sterling Energy and

Exploration Company for permission to

publish this paper. We also thank Debo

Adewusi, Philippe Lamant, Luke Twigger

and Roger Taylor of CGGveritas for their

contributions.

References

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Volume 1 (SEG)

David Le Meur, et al, 2008, Adaptive

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EAGE,

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Perkings, C. and Zwaan, M. Ground-roll

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abstract, session L0021

Sheng Xu, Yu Zhang and Gilles Lambare.

2012. Antileakage Fourier transform for

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