10: Structural Inversion
A recorded seismic wavefield represented by a shot gather has two components – traveltimes and amplitudes. Direct inversion of a seismic wavefield to estimate elastic parameters of the earth demands numerically intensive computations. Instead, most practical methods of inversion are applied to seismic traveltimes and amplitudes, separately. In Chapter 9, we discussed methods of layer velocity estimation – Dix conversion, stacking velocity inversion, and coherency inversion, which essentially are based on inversion of traveltimes. In Chapter 8, we discussed prestack Kirchhoff migration, which again, essentially is based on computing diffraction traveltimes. Traveltime inversion thus yields a structural model of the earth represented by a set of layer velocities and reflector geometries, which can then be used to derive a structural image of the earth by depth migration. The term structural inversion may be appropriately used to describe the process of structural modeling and imaging by way of inversion of traveltimes.
In Chapter 11, we shall discuss poststack amplitude inversion to estimate an acoustic impedance model of the earth and prestack amplitude inversion to derive the amplitude variation with offset (AVO) attributes. Amplitude inversion thus yields a stratigraphic model of the earth represented by a combination of acoustic impedance and AVO attribute changes within the layers themselves. The term stratigraphic inversion may be appropriately used to describe the processes of estimating the acoustic impedance and AVO attributes by way of inversion of amplitudes.
In this chapter, we shall discuss case studies in structural inversion of 2-D and 3-D seismic data. These case studies relate to structural complexities caused by
(a) extensional tectonism as in the cases of salt diapirs of the North Sea and the Gulf of Mexico,
(b) compressional tectonism as in the cases of overthrust belts of the Middle East and Rocky Mountains, and
(c) wrench tectonism as in the cases of the pull-apart basins of offshore Indonesia and Venezuela.
Figures & Tables
The Classical Greeks had a love for wisdom –
It came down to us as philo sophia.
And I have a passion for the seismic method –
Let this be an ode to philo seismos.
O how sweet it is –
Listening to the echos from the earth.
The seismic method has three principal applications:
(a) Delineation of near–surface geology for engineering studies, and coal and mineral exploration within a depth of up to 1 km: The seismic method applied to the near-surface studies is known as engineering seismology.
(b) Hydrocarbon exploration and development within a depth of up to 10 km: The seismic method applied to the exploration and development of oil and gas fields is known as exploration seismology.
(c) Investigation of the earth’s crustal structure within a depth of up to 100 km: The seismic method applied to the crustal and earthquake studies is known as earthquake seismology.
This book is devoted to application of the reflection seismic method to the exploration and development of oil and gas fields.
Conventional processing of reflection seismic data yields an earth image represented by a seismic section which usually is displayed in time. Figure I-1 shows a seismic section from the Gulf of Mexico, nearly 40 km in length. Approximate depth scale indicates a sedimentary section of interbedded sands and shales down to 8 km. Note from this earth image a salt sill embedded in the sedimentary sequence. This allocthonous salt sill has a rugose top and a relatively smooth base. Note the folding and faulting of the sedimentary section above the salt.
The reflection seismic method has been used to delineate near-surface geology for the purpose of coal and mineral exploration and engineering studies, especially in recent years with increasing acceptance. Figure I-2a shows a seismic section along a 500-m traverse across a bedrock valley with steep flanks. The lithologic column based on borehole data indicates a sedimentary sequence of clay, sand, and gravel deposited within the valley. The bedrock is approximately 15 m below the surface at the fringes of the valley and 65 m below the surface at the bottom of the valley. The strong reflection at the sediment-bedrock boundary is a result of the contrast between the low-velocity sediments above and the high-velocity Precambrian quartz pegmatite below.
The reflection seismic method also has been used to delineate the crustal structure down to the Moho discontinuity and below. Figure I–2b shows a seismic section recorded on land along a 15-km traverse. Based on regional control, it is known that the section consists of sediments down to about 4 km. The reflection event at 6.5–7 s, which corresponds to a depth range of 15–20 km, can be postulated as the crystalline basement. The group of reflections between 8–10 s, which corresponds to a depth range of 25–35 km, represents a transition zone in the lower crust – most likely, the Moho discontinuity, itself.
Common-midpoint (CMP) recording is the most widely used seismic data acquisition technique. By providing redundancy, measured as the fold of coverage in the seismic experiment, it improves signal quality. Figure I–3 shows seismic data collected along the same traverse in 1965 with single-fold coverage and in 1995 with twelve-fold coverage. These two different vintages of data have been subjected to different treatments in processing; nevertheless, the fold of coverage has caused the most difference in the signal level of the final sections.
Seismic data processing strategies and results are strongly affected by field acquisition parameters. Additionally, surface conditions have a significant impact on the quality of data collected in the field. Part of the seismic section shown in Figure I-4 between midpoints A and B is over an area covered with karstic limestone. Note the continuous reflections between 2 and 3 s outside the limestone-covered zone. These reflections abruptly disappear under the problem zone in the middle. The lack of events is not the result of a subsurface void of reflectors. Rather, it is caused by a low signal-to-noise (S/N) ratio resulting from energy scattering and absorption in the highly porous surface limestone.
Surface conditions also have an influence on how much energy from a given source type can penetrate into the subsurface. Figure I-5 shows a seismic section along a traverse over a karstic topography with a highly weathered near-surface. In data acquisition, surface charges have been used to the right of midpoint A, and charges have been placed in holes to the left of midpoint A. In the absence of source coupling using surface charges, there is very little energy that can penetrate into the subsurface through the weathered near-surface layer. As a result, note the lack of coherent reflections to the right of midpoint A. On the other hand, improved source coupling using downhole charges has resulted in better penetration of the energy into the subsurface in the remainder of the section.
Besides surface conditions, environmental and demographic restrictions can have a significant impact on field data quality. The part of the seismic section shown in Figure I-6 between midpoints A and B is through a village. In the village, the vibroseis source was not operated with full power. Hence, not enough energy penetrated into the earth. Although surface conditions were similar along the entire line, the risk of property damage resulted in poor signal quality in the middle portion of the line.
Other factors, such as weather conditions, care taken during recording, and the condition of the recording equipment, also influence data quality. Almost always, seismic data are collected often in less-than-ideal conditions. Hence, we can only hope to attenuate the noise and enhance the signal in processing to the extent allowed by the quality of the data acquisition.
In addition to field acquisition parameters, seismic data processing results also depend on the techniques used in processing. A conventional processing sequence almost always includes the three principal processes – deconvolution, CMP stacking, and migration.