System and method of high definition tomography and resolution for use in generating velocity models and reflectivity images

A system and method are provided for determining a broadband high definition reflectivity based image for a geographical area of interest (GAI). The system and method generate a conventional reflectivity image based on acquired seismic data for the GAI, generate a high frequency (HF) velocity model of the GAI based on the acquired seismic data, convert the HF velocity model into a low frequency (LF) reflectivity image, and adaptively merge the LF reflectivity image with the conventional reflectivity image to form the broadband HD reflectivity image of the GAI.

TECHNICAL FIELD

The embodiments relates generally to both land and marine seismic exploration, and more specifically to systems and methods for using high definition tomography to develop enhanced velocity models.

BACKGROUND

A widely used technique for searching for oil or gas is the seismic exploration of subsurface geophysical structures. Reflection seismology is a method of geophysical exploration to determine the properties of a portion of a subsurface layer in the earth, which information is especially helpful in the oil and gas industry. Marine-based seismic data acquisition and processing techniques are used to generate a profile (image) of a geophysical structure (subsurface) of the strata underlying the seafloor. This profile does not necessarily provide an accurate location for oil and gas reservoirs, but it may suggest, to those trained in the field, the presence or absence of oil and/or gas reservoirs. Thus, providing an improved image of the subsurface in a shorter period of time is an ongoing process.

The seismic exploration process consists of generating seismic waves (i.e., sound waves) directed toward the subsurface area, gathering data on reflections of the generated seismic waves at interfaces between layers of the subsurface, and analyzing the data to generate a profile (image) of the geophysical structure, i.e., the layers of the investigated subsurface. This type of seismic exploration can be used both on the subsurface of land areas and for exploring the subsurface of the ocean floor.

Marine reflection seismology is based on the use of a controlled source that sends energy waves into the earth, by first generating the energy waves in or on the ocean. By measuring the time it takes for the reflections to come back to one or more receivers (usually very many, perhaps in the order of several dozen, or even hundreds), it is possible to estimate the depth and/or composition of the features causing such reflections. These features may be associated with subterranean hydrocarbon deposits.

For a seismic gathering process, as shown inFIG. 1, a data acquisition system10includes a ship2towing plural streamers6that may extend over kilometers behind ship2. Each of the streamers6can include one or more birds13that maintains streamer6in a known fixed position relative to other streamers6, and the birds13are capable of moving streamer6as desired according to bi-directional communications birds13can receive from ship2. One or more source arrays4a,bmay be also towed by ship2or another ship for generating seismic waves. Source arrays4a,bcan be placed either in front of or behind receivers14(shown inFIG. 2), or both behind and in front of receivers14. The seismic waves generated by source arrays4a,bpropagate downward, reflect off of, and penetrate the seafloor, wherein the refracted waves eventually are reflected by one or more reflecting structures (not shown inFIG. 1) back to the surface (seeFIG. 2, discussed below). The reflected seismic waves propagate upwardly and are detected by receivers14provided on streamers6. This process is generally referred to as “shooting” a particular seafloor area, and the seafloor area can be referred to as a “cell”.

FIG. 2illustrates a side view of the data acquisition system10ofFIG. 1. Ship2, located on ocean surface46of ocean water40, tows one or more streamers6, that is comprised of cables12, and a plurality of receivers14. Shown inFIG. 2are two source streamers, which include sources4a,battached to respective cables12a,b. Each source4a,bis capable of transmitting a respective sound wave, or transmitted signal20a,b. For the sake of simplifying the drawings, but while not detracting at all from an understanding of the principles involved, only a first transmitted signal20awill be discussed in detail (even though some or all of source4can be simultaneously (or not) transmitting similar transmitted signals20). First transmitted signal20atravels through ocean40and arrives at first refraction/reflection point22a. First reflected signal24afrom first transmitted signal20atravels upward from ocean floor42, back to receivers14. As those of skill in the art can appreciate, whenever a signal—optical or acoustical—travels from one medium with a first index of refraction n1and meets with a different medium, with a second index of refraction n2, a portion of the transmitted signal is reflected at an angle equal to the incident angle (according to the well-known Snell's law), and a second portion of the transmitted signal can be refracted (again according to Snell's law).

Thus, as shown inFIG. 2, first transmitted signal20agenerates first reflected signal24a, and first refracted signal26a. First refracted signal26atravels through sediment layer16(which can be generically referred to as first subsurface layer16) beneath ocean floor42, and can now be considered to be a “new” transmitted signal, such that when it encounters a second medium at second refraction/reflection point28a, a second set of refracted and reflected signals32aand30a, are subsequently generated. Further, as shown inFIG. 2, there happens to be a significant hydrocarbon deposit44within a third medium, or solid earth/rock layer18(which can be generically referred to as second subsurface layer18). Consequently, refracted and reflected signals are generated by the hydrocarbon deposit, and it is the purpose of data acquisition system10to generate data that can be used to discover such hydrocarbon deposits44. As further seen inFIG. 2, second refracted signal32aencounters hydrocarbon deposit44, at third refraction/reflection point34a, generating third refracted signal38a, and third reflected signal36a. Further, second transmitted signal20bgenerates first reflected and refracted signals (from second transmitted signal)24b, and26b, respectively, at first reflection/refracting point22b. Second refracted signal26bencounters solid earth/rock layer18at second reflection/refraction point28b, thereby generating second reflected signal30b, and second refracted signal32b. Second refracted signal32btravels through second layer18and encounters hydrocarbon deposit44and third reflection/refraction point34b, and generates third reflected signal36band third refracted signal38b. As those of skill in the art can appreciate, though it appears that this process can continue ad infinitum, such may be technically true and possible, but with each reflection/refraction, only a certain percentage of the energy from the impinging signal is reflected and refracted, and so the strength of the signal diminishes quickly, and can, in fact, after only a few encounters with such interfaces, diminish to the point that the sensitivity of receivers14is not large enough to distinguish the signals over other noise in the system. Nonetheless, it is an important part of seismic signal processing to discern different refracted/reflected signals from the noise to the greatest extent possible.

The signals recorded by seismic receivers14vary in time, having energy peaks that may correspond to reflectors between layers. In reality, since the sea floor and the air/water are highly reflective, some of the peaks correspond to multiple reflections or spurious reflections that should be eliminated before the geophysical structure can be correctly imaged. Primary waves suffer only one reflection from an interface between layers of the subsurface (e.g., first reflected signal24a). Waves other than primary waves are known as multiples, and more strictly, are events that have undergone more than one reflection. Typically, multiples have a much smaller amplitude than primary reflected waves, because for each reflection, the amplitude decreases proportionally to the product of the reflection coefficients of the different reflectors (usually layers or some sort). As shown inFIG. 3, discussed below, there are several ways for multiples to be generated.

As illustrated inFIG. 3, seismic source4produces first transmitted wave20athat splits into a primary transmitted wave26a(referred to also as first refracted signal) penetrating inside first subsurface layer16(referred to also as “sediment layer” though that does not necessarily need to be the case) under ocean floor42, and first reflected signal24athat becomes surface multiple signal50after it interfaces with ocean surface46(or fourth interface). Second transmitted wave20bis reflected once at second interface48and becomes second reflected signal24b, and then is reflected down again from ocean floor42to become internal multiple signal51. Internal multiple signal51and surface multiple signal50also reaches receiver14, but at different times. Thus, receiver14can receive at least several different signals from the same transmitting event: second reflected signal30a, surface multiple signal50, and internal multiple signal51. Multiples can also be classified as short path multiples, and long path multiples (e.g., surface multiples and internal multiples). Short path multiples are those whose travel path is short compared to the primary reflections, and long path multiples are those whose travel path is long compared to the primary reflections. One type of short path multiples include ghosts52, in which the seismic energy or wave is transmitted upwards first towards a reflecting boundary layer, then down, and up again to the receiver. As seen inFIG. 3, ghost52leaves source4, travels upwards and reflects nearly perfectly off ocean surface46, then down to ocean floor42, and up to receiver14. Because of the near perfect reflectivity of ocean surface46, the magnitude of ghosts52rivals that of “true” reflected signals24and thus are typically very important to marine seismic exploration. As such, ghosts52can be very strong.

As is apparent fromFIG. 3, the timing of the received signals will depend on the depth of the ocean40, its temperature, density, and salinity, the depth of sediment layer16, and what it is made of. Thus, receiver14can become “confused” as to the true nature of the subsurface environment due to reflected signals30, and multiple signals50,51, and52. As briefly discussed above, other multiples can also be generated, some of which may also travel through the subsurface. A multiple, therefore, is any signal that is not a primary reflected signal. Multiples, as is known by those of ordinary skill in the art, can cause problems with determining the true nature of the geology of the earth below the ocean floor. Multiples can be confused by data acquisition system10with first, second or third reflected signals. Multiples do not add any useful information about the geology beneath the ocean floor, and thus they are, in essence, noise, and it is desirable to eliminate them and/or substantially reduce and/or eliminate their influence in signal processing of the other reflected signals so as to correctly ascertain the presence (or the absence) of underground/underwater hydrocarbon deposits.

Internal multiple signals51typically arise due to a series of subsurface impedance contrasts. They are commonly observed in seismic data acquired in various places, such as the Santos Basin of Brazil. They are often poorly discriminated from the primaries (i.e., the first, second and third reflected signals, among others), because they have similar movement, dips and frequency bandwidth, thereby making attenuation and/or elimination of internal multiple signals51(as well as surface multiples50) one of the key issues in providing clear seismic images in interpreting areas of interest. Over time, various methods have been developed to address this difficult problem and most of them rely on the ability to identify the multiple generators.

The acquisition of data in marine-based seismic methods usually produces different results in source strength and signature based on differences in near-surface conditions. Further data processing and interpretation of seismic data requires correction of these differences in the early stages of processing. Surface-Related Multiples Elimination (SRME) is a technique commonly used to predict a multiples model from conventional flat streamer data. Attenuating the surface-related multiples is based on predicting a multiples model, adapting the multiples model and subtracting the adapted multiples model from the input streamer data.

FIG. 39depicts schematically a land seismic exploration system (system)70for transmitting and receiving vibro-seismic waves intended for seismic exploration in a land environment. At least one purpose of system70is to determine the absence, or presence of hydrocarbon deposits44, or at least the probability of the absence or presence of hydrocarbon deposits44. System70comprises a source consisting of a vibrator71(source and vibrator being interchangeable terms for the same device) operable to generate a seismic signal (transmitted waves), a plurality of receivers72(or geophones) for receiving seismic signals and converting them into electrical signals, and seismic data acquisition system200′ (that can be located in, for example, vehicle/truck73) for recording the electrical signals generated by receivers72. Source71, receivers72, and data acquisition system200′, can be positioned on the surface of ground75, and all interconnected by one or more cables72.FIG. 39further depicts a single vibrator71, but it should be understood that source71can actually be composed of multiple or a plurality of sources71, as is well known to persons skilled in the art. As can be further appreciated by those of skill in the art, land related acquisition methods include all land acquisition methods, and in particular land acquisition schemes where receivers and sources (natural or man-made) can be either on or close to the surface (topography) or in the subsurface (in particular along well paths).

In operation, source71is operated so as to generate a vibro-seismic signal. This signal propagates firstly on the surface of ground75, in the form of surface waves74, and secondly in the subsoil, in the form of transmitted ground waves76that generate reflected waves78when they reach an interface77between two geological layers. Each receiver72receives both surface wave74and reflected wave76and converts them into an electrical signal in which are superimposed the component corresponding to reflected wave78and the one that corresponds to surface wave74, the latter of which is undesirable and should be filtered out as much as is practically possible.

An example of a vibratory source71is shown inFIG. 40. Source71can include base plate88that connects to rod80. Rod80includes piston82inside reaction mass84. Insulation devices86can be provided on base plate88to transmit weight90of vehicle73to base plate88. Base plate88is shown inFIG. 40as lying on ground75. The force transmitted to ground75is equal to the mass of base plate88times its acceleration, plus the weight of reaction mass84times its acceleration. The weight of vehicle73(shown inFIG. 39) prevents base plate88from losing contact with ground75. Many designs for vibratory sources71exist on the market, and any one of them can be used with the novel features discussed herein.

Velocity model building remains a crucial step in seismic depth imaging for both land and marine seismic imaging. As those of ordinary skill in the art can appreciate, in order to provide a representative image of the geographical area of interest (GAI), i.e., in order to properly interpret the seismic waves to provide accurate seismic images, it is necessary to have a well-defined velocity model of the general area. However, in order to create a well-defined velocity model of the general area, it is also sometimes, perhaps always, necessary to have an accurate description of the geological physical structures in the area; this presents the classic problem of what is developed first, and how can it be trusted to provide the correct information? A general drawback of conventional tomographic approaches is that the estimated velocity models do not conform enough to the structures (i.e., the geological physical structures underwater and/or underground, including even different layers of subsurface areas).

There are certain problems, however, with determining accurate velocity models using current methods and system, especially when knowledge of the underwater and/or underground structures is not known. Accordingly, it would be desirable to provide methods, modes and systems for using high definition tomography to develop enhanced velocity models for geographical areas of interest.

SUMMARY

An object of the embodiments is to substantially solve at least one or more of the problems and/or disadvantages discussed above, and to provide at least one or more of the advantages described below.

It is therefore a general aspect of the embodiments to provide a system and method for determining a broadband high definition reflectivity based image for a geographical area of interest that will obviate or minimize problems of the type previously described.

According to a first aspect of the embodiments, a method is provided for determining a broadband high definition reflectivity based image for a geographical area of interest (GAI), the method comprising generating a conventional reflectivity image based on acquired seismic data for the GAI, generating a high frequency (HF) velocity model of the GAI based on the acquired seismic data, converting the HF velocity model into a low frequency (LF) reflectivity image, and adaptively merging the LF reflectivity image with the conventional reflectivity image to form a broadband HD reflectivity image of the GAI.

According to the first aspect, the conventional reflectivity image is bandwidth limited, the LF reflectivity image is a PreStack Depth Migration image, and the LF reflectivity image can also be a PreStack Time Migration image. Still further according to the first aspect, the steps of generating a convention reflectivity image and generating a HF velocity model include generating a series of seismic signals by a plurality of source transmitters, and receiving raw data at a plurality of receivers based on the generated series of seismic signals and saving the same as said acquired seismic data.

According to the first aspect, the step of adaptively merging comprises determining amplitude as a function of frequency for the LF reflectivity image to create a first spectral signal, determining amplitude as a function of frequency for the conventional reflectivity image to a create a second spectral signal, determining portions of overlapping frequency between the LF reflectivity image and the conventional reflectivity image with respect to the first and second spectral signals, and combining said first and second spectral images in said overlapping frequency portions along with said first and second spectral images in non-overlapping frequency portions to generate said broadband HD reflectivity image.

According to the first aspect, the step of adaptively merging comprises combining the LF reflectivity image and the conventional reflectivity image using spectral balancing techniques, and the step of combining comprises summing the LF reflectivity image and the conventional reflectivity image in the frequency range wherein an overlap occurs in regard to a spectral content of each of the LF reflectivity image and the conventional reflectivity image.

According to the first aspect, the step of summing comprises determining amplitude as a function of frequency for the LF reflectivity image to create a first spectral signal, determining amplitude as a function of frequency for the conventional reflectivity image to a create a second spectral signal, performing spectral shaping of the first spectral signal and of the second spectral signal to enable summation of the first and second spectral signals, geometrical shaping the LF reflectivity image and the conventional reflectivity image to further facilitate summation of the LF reflectivity image and the conventional reflectivity image, and summing the LF reflectivity image and the conventional reflectivity image based on the outputs of the spectral shaping and warping steps. Still further according to the first aspect, the step of geometric shaping comprises compensating at least one of or both time and space variant differences in the LF reflectivity image and conventional reflectivity image in order to sum the LF reflectivity image and the conventional reflectivity image.

According to the first aspect, the step of spectral shaping comprises determining a regularity of properties of a combined signal and using said determined regularity to combine the LF reflected image and the conventional reflectivity image that have different phase and amplitude spectra. According to the first aspect, the step of geometrical shaping comprises image shaping the LF reflectivity image and the conventional reflectivity image using at least one of warping, shaping, and morphing. Still further according to the first aspect, the step of generating a high frequency (HF) velocity model of the GAI based on the acquired seismic data comprises obtaining a conventional velocity model of the GAI using said acquired seismic data, determining density of the conventional velocity model data, determining spatial wavelengths of the velocity variations within each of the velocity model layers, determining a size of the velocity model grid mesh according to the determined velocity model layer data density and spatial wavelengths of the velocity variations within each of the velocity model layers, developing residual move out data and dip data from the velocity model grid mesh according to the size of the velocity model grid mesh, eliminating residual move out and dip data outliers that exceed a first parameter to generate a first set of retained residual move out and dip data, and inverting the first set of retained residual move out and dip data to generate the HF velocity model of the GAI.

According to the first aspect, the method still further comprises displaying the broadband HD reflectivity image of the GAI.

According to a second aspect of the embodiments, a system is provided for determining a broadband high definition reflectivity based image for a geographical area of interest (GAI), the system comprising a processor configured to—generate a conventional reflectivity image based on acquired seismic data for the GAI, generate a high frequency (HF) velocity model of the GAI based on the acquired seismic data, convert the HF velocity model into a low frequency (LF) reflectivity image, and adaptively merge the LF reflectivity image with the conventional reflectivity image to form a broadband HD reflectivity image of the GAI.

According to the second aspect, the conventional reflectivity image is bandwidth limited, the LF reflectivity image is a PreStack Depth Migration image, and the LF reflectivity image can also be a PreStack Time Migration image.

According to the second aspect, the system further comprises a plurality of transmitters configured to generate a series of seismic signals, a plurality of receivers configured to receive raw data based on the generated series of seismic signals, and a memory configured to save the received raw data as said acquired seismic data.

According to the second aspect, the system is further configured to determine amplitude as a function of frequency for the LF reflectivity image to create a first spectral signal, determine amplitude as a function of frequency for the conventional reflectivity image to a create a second spectral signal, determine portions of overlapping frequency between the LF reflectivity image and the conventional reflectivity image with respect to the first and second spectral signals, and combine said first and second spectral images in said overlapping frequency portions along with said first and second spectral images in non-overlapping frequency portions to generate said broadband HD reflectivity image.

According to the second aspect, the system is further configured to combine the LF reflectivity image and the conventional reflectivity image using spectral balancing techniques. Still further according to the second aspect, the processor is further configured to sum the LF reflectivity image and the conventional reflectivity image in the frequency range wherein an overlap occurs in regard to a spectral content of each of the LF reflectivity image and the conventional reflectivity image.

According to the second aspect, the processor is further configured to determine amplitude as a function of frequency for the LF reflectivity image to create a first spectral signal, determine amplitude as a function of frequency for the conventional reflectivity image to a create a second spectral signal, perform spectral shaping of the first spectral signal and of the second spectral signal to enable summation of the first and second spectral signals, geometrically shape the LF reflectivity image and the conventional reflectivity image to further facilitate summation of the LF reflectivity image and the conventional reflectivity image, and sum the LF reflectivity image and the conventional reflectivity image based on the outputs of the spectral shaping and warping steps.

According to the second aspect, the processor is further configured to compensate at least one of or both time and space variant differences in the LF reflectivity image and conventional reflectivity image in order to sum the LF reflectivity image and the conventional reflectivity image, and still further according to the second aspect, the processor is further configured to determine a regularity of properties of a combined signal and using said determined regularity to combine the LF reflected image and the conventional reflectivity image that have different phase and amplitude spectra. According to the second aspect, the processor is further configured to image shape the LF reflectivity image and the conventional reflectivity image using at least one of warping, shaping, and morphing.

According to the second aspect, the processor is further configured to obtain a conventional velocity model of the GAI using said acquired seismic data, determine density of the conventional velocity model data, determine spatial wavelengths of the velocity variations within each of the velocity model layers, determine a size of the velocity model grid mesh according to the determined velocity model layer data density and spatial wavelengths of the velocity variations within each of the velocity model layers, develop residual move out data and dip data from the velocity model grid mesh according to the size of the velocity model grid mesh, eliminate residual move out and dip data outliers that exceed a first parameter to generate a first set of retained residual move out and dip data, and invert the first set of retained residual move out and dip data to generate the HF velocity model of the GAI.

According to the second aspect, the system further comprises a display configured to display the broadband HD reflectivity image of the GAI,

DETAILED DESCRIPTION

Used throughout the specification are several acronyms, the meaning of which are provided as follows: universal serial bus (USB); geographical area of interest (GAI); two dimensional (2D); three dimensional (3D); pre-stack depth migrations (PreSDM); residual move-out (RMO); pre-stack time migration (PreSTM); tilted transverse isotropy (TTI); low definition (LD); high definition (HD); low frequency (LF); high frequency (HF); conventional reflectivity image (CRI); high definition velocity reflectivity image (HDVRI); and common image gathers (CIG).

As generally discussed above, the main purpose of seismic exploration is to render the most accurate possible graphic representation of specific portions of the Earth's subsurface geologic structure (also referred to as a GAI). The images produced allow exploration companies to accurately and cost-effectively evaluate a promising target (prospect) for its oil and gas yielding potential (i.e., hydrocarbon deposits44).FIG. 41illustrates a general method for seismic exploration (method400). There are five main steps: a detailed discussion of any one of the process steps would far exceed the scope of this document, but a general overview of the process should aid in understanding where the different aspects of the embodiments can be used. Step402of method400involves positioning and surveying of the potential site for seismic exploration. In step404, a determination of what type of seismic energy source should be used, and then causing seismic signals to be transmitted. While method400applies equally to both marine and land seismic exploration systems, each will use different types of equipment, especially in generating seismic signals that are used to develop data about the Earth's subsurface geologic structure. In step406, data recording occurs. In a first part of this step, receivers14,64receive and most often digitize the data, and in a second part of the step406, the data is transferred to a recording station. In step408, data processing occurs. Data processing generally involves enormous amounts of computer processing resources, including the storage of vast amounts of data, multiple processors or computers running in parallel. Finally, in step410, data interpretation occurs and results can be displayed, sometimes in two-dimensional form, more often now in three dimensional form. Four dimensional data presentations (a 3D plot or graph, over time (the fourth dimension) are also possible, when needed to track the effects of other processes, for example.

Method for Determining a High Definition Tomography Velocity Model of a Geographical Area of Interest According to an Embodiment

According to an embodiment, several applications of an innovative high resolution tomography system and method are presented that inverts densely picked dip and residual move-out data to reveal detailed structurally conformable velocities. Application of the method to synthetic 2D Marmousi II dataset demonstrates its ability to produce structurally conformable velocity models with a level of detail that promotes velocity attributes as an aid to geological interpretation. As such, it is complementary to full waveform inversion for the interpretation of reflected waves. An application to actual 3D marine dataset is shown wherein obtained higher resolution velocity model results in improved focusing of migrated images and an improved match to well velocities. As those of ordinary skill in the art can appreciate, wells provide a means for ascertaining actual velocity measurements as a means for comparison to theoretically developed velocity model data.

In recent developments, successive step changes in tomography-based migration velocity systems and methods have resulted in much improved seismic imaging over time. As for any inversion-based method, these step changes fall in two categories: a first one concerns the data to invert and a second family relates to the model-space representation and the inversion algorithms. As those of skill in the art can appreciate, inversion based systems, at their most basic level, determine input data based on expected output data and expected models.

For those methods that relate to improvements concerning ascertainment of the data, high density rich azimuth acquisition geometries have greatly increased the angular redundancy/diversity of wave-paths that constitutes the main velocity discriminator. Systems and methods for removing and/or isolating noise coupled with signal enhancement techniques have also significantly contributed to improve reliability of dense automated picking tools (see, Siliqi R., et al., 2009, “Structurally Coherent Wide Azimuth Residual Move Out Surfaces,” 79th annual SEG meeting, SEG Expanded Abstracts, 4039-4043).

For those method that relate to improvements concerning model-space representation and the inversion algorithms, significantly larger linear systems and associated methods have been implemented wherein enormous amounts of data can be processed to provide enhanced velocity models.

The initial forays into tomographic approaches involved iterative processes with several iterations of pre-stack depth migrations (PreSDM), residual move-out (RMO) picking, and linear updates of velocity (see, Liu, Z., 1997, An Analytical Approach to Migration Velocity Analysis,” Geophysics 62, 1238-1249; and Woodward, M., et al., 1998, Automated 3D Tomographic Velocity Analysis of Residual Move-out in Pre-stack Depth Migrated Common Image Point Gathers,” 68th Annual International Meeting, SEG, Expanded Abstracts, 1218-1221). Non-linear systems and methods have also been developed, include non-linear tomography (see, Guillaume, P., et al., 2001. “3D finite-offset Tomographic Inversion of CRP-Scan Data, With or Without Anisotropy,” 71st annual SEG meeting, SEG Expanded Abstracts 20, 718-721), and more recently non-linear slope tomography (see, Billette F., et al., 2003, “Practical Aspects and Application of 2D Stereotomography,” Geophysics, Vol. 68, No. 3, pages 1008-1021; Lambaré, G., 2008, “Stereotomography,” Geophysics, 73, 5, VE25-VE34; and see, Guillaume, P., et al., 2008, “Kinematic Invariants: An Efficient and Flexible Approach for Velocity Model Building,” 78th annual SEG meeting, SEG workshop “Advanced Velocity Model Building Techniques for Depth Imaging”) that uses a local focusing criterion with no assumptions about the shape of the reflectors or of the RMO curves. Moreover, the physics of wave propagation is more accurately taken into account with tilted transverse isotropy (TTI) velocity models. Despite these advances, velocity models updated with such approaches remain smooth and poorly conform to structures. For example, in regard toFIG. 4, which illustrates a pre-stack data migration image of an underground structure of a geographic area of interest (GAI) andFIG. 5, which illustrates an image of the same geographic area of interest as inFIG. 4, but using a densely picked residual move-out (RMO) and dip process, there isFIG. 6that illustrates a velocity model image of the same geographic area of interest as inFIGS. 4 and 5, but with much less velocity resolution and matching to the geologic structures than desired. This appears as a serious drawback considering velocity structures revealed by full waveform inversion (see, Plessix, et al., 2010, “Application of Acoustic Full Waveform Inversion to a Low-frequency Large-offset Land Data Set,” 81st annual SEG meeting, SEG Exp. Abstracts 30, 930-934).FIG. 4exhibits nice detailed features corresponding to geological structures, and in comparison between the illustration ofFIGS. 5 and 6(FIG. 6being the velocity model of the same GAI), the rapid changes in RMO in the gently dipping thin beds and in the structured area do not translate into detailed velocities after conventional tomography.

Embodiments described herein provide an innovative high definition (HD) tomography system and method that can estimate detailed structurally-conformable velocity models. The capability of the system and method according to embodiments to reveal detailed spatial variations of velocity and to improve seismic imaging is first illustrated using Marmousi II synthetic dataset. In addition, use of the system and method for high definition tomography systems according to embodiments presented herein is used with an actual marine dataset to illustrate its improvements over conventional systems and methods.

FIG. 7illustrates a flowchart of method100for determining a high definition (HD) tomography velocity model of a geographical area of interest (GAI) according to an embodiment. According to an embodiment, a HD velocity model means a velocity model that contains vertical frequencies higher than about 3 to about 6 Hz, thus filling in the usual gap found between reflectivity and velocity, as shown and described in reference toFIG. 19. High definition velocity models are obtained by HD tomography that starts with best model from conventional tomography. HD tomography looks for smaller velocity model Eigen values of a linear system to solve while introducing specific constraints derived from inversion data in order to avoid inversion instability. Method100begins with step102, wherein a reasonably accurate velocity model is obtained by conventional means. Such conventional velocity model can include, for example, a multi-layer velocity model representation into which layer boundaries describing strong velocity contrasts can be introduced.

In step104the density of the data within each of the velocity models is determined. The density of each velocity model layer will be used in determining the size of a grid mesh, as discussed below. Following step104, in step106, method100determines the spatial wavelengths of the velocity variations within each of the velocity model layers. Then, in step108, the size of the velocity grid mesh is determined according to the previously determined velocity model layer data density and spatial wavelengths of the velocity variations within each of the velocity model layers. Of course, each of steps106-108is performed for each velocity model layer (of which there could be dozens or even hundreds). According to an embodiment, the grid mesh, which essentially determines the nature of the high definition velocity model, is determined to provide a much higher definition of the different velocities that was previously obtained in the convention velocity model.

In step110, RMO and dip data are developed from the grid mesh data in a substantially continuous manner such that significant amounts of data are generated. In step112, method100reviews the RMO and dip data for outliers and disregards the same. In step114, the now densely picked RMO and dip data are inverted, to develop the HD velocity model. According to an embodiment, there are at least two methods for performing RMO picking In a first method, detailed RMO information can be obtained by tracking local coherency in multi-dimension along common image gathers (CIG) (see, Traonmilin, Y., et al., 2009, Multi-dip Estimation in ‘N’ Dimensions,” EAGE 71st Conference, EAGE Extended abstract, P083) or scanning parametric curves along common image gathers (CIG) (Siliqi et al., 2009). The multi-dimensional tracking approach is more accurate when the signal to noise ratio is sufficiently high, while the curve/surface picking methods can be preferred when signal to noise ratio is low. Because tomography from surface seismic experiments tries to solve a quite ill-posed inversion problem in some kind of least squares sense, it is important to make it as well-conditioned as possible and to reject outliers in RMO picks as much as possible. As can be seen inFIG. 6, HD velocity model building tomography performed according to the embodiment discussed above accurately translates the validated small spatial variations of RMO into localized perturbations of velocity, i.e., much greater resolution in the differences of velocity in corresponding structures.

Application of the System and Method for Determining a High Definition Tomography Velocity Model of a Geographical Area of Interest According to an Embodiment Using Synthetic Field Data

Attention is directed toFIGS. 8-9.FIG. 8illustrates a non-linear slope tomography velocity model determined according to conventional methods using well-known Marmousi II synthetic data (Martin, G. S., et al., 2006, “Marmousi 2: An Elastic Upgrade for Marmousi,” The Leading Edge 25, 156-166.),FIG. 9illustrates a HD velocity model using well-known Marmousi II synthetic data determined in accordance with the method according to an embodiment described herein, andFIG. 10illustrates an actual velocity model based on the well-known Marmousi II synthetic data. The water column has a height (or depth) of about 460 m. The Marmousi II model is interesting because it exhibits velocity discontinuities that can be expected to be recovered with the HD tomography velocity model according to an embodiment. Synthetic seismic data developed in the Marmousi II model is computed by an acoustic wave equation using a finite differences scheme for a marine type acquisition with a maximum offset of 3 km.

FIG. 8illustrates the velocity model using a conventional method (non-linear slope tomography), and this produces a relatively smooth velocity model. InFIG. 9, a full data PreSDM is run beginning with the velocity model shown inFIG. 8, i.e., using the method according to an embodiment discussed herein. As detailed above in method100shown inFIG. 7, RMO is picked again on high density CIGs and is inverted using high definition tomography. The obtained high definition model shown inFIG. 9can readily be compared to a slightly smoothed version of the exact model shown inFIG. 10; not that improvement over the conventional method shown inFIG. 9, and the closeness between the results using the method according to an embodiment (FIG. 9) and the actual velocity model shown inFIG. 10. The results betweenFIGS. 9 and 10are much more alike, and the improvement over the conventional method ofFIG. 8is substantial inFIG. 9.

As is evident fromFIGS. 8-10, even if the velocity model obtained by a conventional tomography approach provides good focusing, it is quite smooth and poorly conforms to the geological structures. The HD tomography velocity model obtained according to an embodiment slightly improves the focusing and the positioning, but greatly improves the structural conformity of the velocity model which now nicely fits to the PreSDM image. Both in shallow and deeper parts of the velocity model thin velocity layers now appear and nicely match those of the exact model (i.e.,FIGS. 9 and 10are substantially similar).

FIG. 11illustrates a superimposition of the actual seismic data and HD tomography velocity model data developed according to an embodiment fromFIGS. 8-10in one diagram, andFIGS. 12A-Dillustrate comparisons of the velocity model data profile at four locations in the superimposed diagram ofFIG. 11. Four logs—or “slices” of data—are extracted fromFIG. 11and shown with greater resolution inFIGS. 12A-D(log/slice A=FIG. 12A, log/slice B=FIG. 12B, and so on), so that a comparison between the conventional tomography, high definition tomography and exact velocity models can be shown explicitly. According to an embodiment, improvements in terms of resolved spatial frequency of vertical variations of velocity of between 6-8 Hz has been realized, whereas conventional processes provide only about 2 Hz of the same. InFIGS. 12A-D, the exact vertical velocity profile are shown as lines1206and are smoothed in order to fit with the resolution of the high definition tomographic result, the conventional tomography is shown as lines1202, and the HD tomography velocity model is shown as line1204. As can be appreciated by those of skill in the art, the variations in velocity as illustrated by the HD tomography velocity model according to an embodiment are very well detected and quantified, especially in the shallow parts.

Application of the System and Method for Determining a High Definition Tomography Velocity Model of a Geographical Area of Interest According to an Embodiment Using Actual Field Data

FIG. 13illustrates a conventionally obtained velocity model superimposed on an initial PreSDM image using actual data from a known GAI, andFIG. 14illustrates a HD velocity tomography model obtained using the method according to an embodiment discussed herein superimposed on an a final PreSDM image using actual data from the same GAI as shown inFIG. 13.FIG. 13illustrates a conventional velocity model building as discussed by Guillaume, P., et al., 2008, “Kinematic Invariants: An Efficient and Flexible Approach for Velocity Model Building,” 78th annual SEG meeting, SEG workshop “Advanced Velocity Model Building Techniques for Depth Imaging.” The use of Guillaume's method provided for imaging of complex structures and used TTI to fit the image and velocity model to the wells thanks to the introduction. As understood by those of skill in the art, the “image” is the cross-sectional view of the GAI and its geological features (i.e., layers, or strata), that can be most advantageously obtained using a velocity model and further data processing. Thus,FIG. 13is both a view of a conventional “Guillaume” velocity model, and the determined image of the same GAI.

As discussed above, bothFIGS. 13 and 14illustrate their respective velocity models being superimposed with the corresponding depth migrated images. As can be understood and seen by one of skill in the art, the high definition velocity model (FIG. 14) nicely conforms to the geological structures but also nicely improves the structures in the PreSDM image (see the bottom flat reflector inFIG. 14).

Referring now toFIGS. 15 and 16,FIG. 15illustrates a common image gather of a different marine geographical area of interest,FIG. 16Aillustrates a close up view of the CIG in the box ofFIG. 15prior to implementation of the method according to an embodiment, andFIG. 16Billustrates a close up view of the CIG in the box ofFIG. 15following implementation of the method according to an embodiment. In comparingFIG. 16BtoFIG. 16A, it can be seen that flattening of the CIGs has been significantly improved as expected when performing HD velocity tomography according to an embodiment.

FIG. 17illustrates a larger view of the marine data shown inFIG. 15with a HD velocity tomography model determined according to an embodiment superimposed on the marine data (represented as a final PreSDM stack), andFIG. 18illustrates a comparison of preconditioned well log velocity at a well location, a conventionally obtained velocity model data at the well location, and HD velocity model data determined according to an embodiment also at the well location, the well location being located in the marine GAI shown inFIG. 17.

FIG. 17illustrates the HD velocity model data determined according to an embodiment discussed herein superimposed with the final PreSDM stack image data. As can be appreciated by those of skill in the art, the results illustrated inFIG. 17are very representative of what can be expected from HD tomography velocity model developed according to an embodiment as an aid to interpretation.FIG. 17shows a clear and nice delineation of the velocity structures along the layers. The velocity definition appears suitable for pore pressure prediction and interpretation of fast velocity layers (carbonates). Compared to conventional pore pressure prediction done in time domain, the HD tomography velocity model is part of a physically valid pure depth workflow, which improves the accuracy of estimated velocities and the focusing of final PreSDM images.FIG. 18illustrates a comparison of preconditioned well log velocity at a well location (line1806), a conventionally obtained velocity model data at the well location (line1802), and HD tomography velocity model data determined according to an embodiment also at the well location (line1804), the well location being located in the marine GAI shown inFIG. 17.

Described and shown herein are two applications of an innovative HD tomography velocity model according to an embodiment that inverts densely picked RMO data for revealing detailed structurally conformable velocities. The application of the HD tomography velocity model according to an embodiment to the synthetic 2D Marmousi II dataset demonstrates the ability to produce structurally conformable velocity models with a level of detail that promotes velocity attributes as an aid to geological interpretation. As such the HD tomography velocity model according to an embodiment can offer an alternative to full waveform inversion for the interpretation of reflected waves. The application of the HD tomography velocity model according to an embodiment to a 3D marine dataset further demonstrates the capability of the method in presence of noise. The data obtained from the HD tomography velocity model according to an embodiment results in improved focusing as well as an improved match to the well velocities.

Method for Developing a Broadband High Definition Reflectivity Based Image for a GAI Using a HD Tomography Velocity Model and a Conventional Reflectivity Based Image for the GAI as a Basis According to a Further Embodiment

As those of skill in the art can appreciate, the system and method discussed above provide for improved velocity models that can be the starting basis for improved seismic reflectivity images of a GAI. However, while the improved velocity models could be used therewith conventional seismic reflectivity image creating processes, according to a further embodiment, further discussed herein are improved systems and methods that use the high definition velocity models described herein, or other high definition velocity models as its basis for determining broadband high definition pre-stack time migration or pre-stack data migration reflectivity images of a geographical area of interest according to an embodiment. As those of skill in the art can appreciate, the term “broadband” is a relative term; the initial signal can be broadband and it becomes even more broadband when combined with low frequency (LF) velocity information

As those of skill in the art can appreciate, the aim of inverting seismic waveforms is to obtain the “best” earth model, which can roughly be defined as the one producing seismograms that best match (usually under a least-squares criterion) those recorded. Previously published articles have discussed these ideas in great detail, and one preeminent scholar posited an idea, later verified, that earth structure's wavelengths could be resolved from seismic reflection data (see, J. Claerbout, “Imaging the Earth's Interior,” 1985, p47,Fig.1.4-3,Reliability of information obtained from surface seismic measurements). This concept was described and summarized in a simple, but meaningful graph, shown inFIG. 19.FIG. 19illustrates reliability of information obtained from surface seismic measurements using conventional processes. As discussed in Mr. Claerbout's book,[n]ote that there is an information gap from 2-10 Hz. Even presuming that rock physics can supply us with a relationship between P and K, the gap seriously interferes with the ability of a seismologist to predict a well log before the well is drilled. What seismologists can do somewhat reliably is predict a filtered log.The observational situation described above has led reflection seismologists to a specialized use of the word velocity. To a reflection seismologist, velocity means the low spatial frequency part of “real velocity.” The high-frequency part of the “real velocity” isn't called velocity: it is called reflectivity. Density is generally disregarded as being almost unmeasurable by surface reflection seismology.(http://sepwww.stanford.edu/sep/prof/iei/xrf/paper_html/node19.html#SECTION00122300000000000000)

Thus, debate on wavelengths of earth structures resolved by reflection seismics continues to evolve. While long wave length components of the velocity model can be solve by tomography, short wave length components are solved from the reflectivity. As seen inFIG. 19, the graph created by J. Claerbout in 1985, between these two domains the mid wavelength could hardly be obtained, i.e. there exists a mid-wavelengths' gap.

FIG. 20illustrates a method300for developing a broadband high definition reflectivity based image for a GAI using a HD tomography velocity model and a conventional reflectivity based image for the GAI as a basis according to a further embodiment.

Method300begins with step302wherein bandwidth limited seismic data is recorded and used to create a reflectivity image of a subsurface in a GAI using conventional processing means. This reflectivity image can be referred to as a “conventional reflectivity image” (CRI). In step304, a HD or high frequency (HF) tomography velocity model is generated using the same data, according to, for example, method100as discussed above in regard toFIG. 7. However, as those of ordinary skill in the art can appreciate, method300for constructing the broadband HD reflectivity image of the GAI according to an embodiment is not limited to use of method100that develops a HF tomography velocity model as described in great detail above; other methods, both known and unknown, can be used in method300according to an embodiment.

Method300then proceeds to step306wherein the HF tomography velocity model developed in step304is then converted into an image of the reflectivity of the subsurface of the GAI, or “low frequency (LF) Reflectivity Image” (LFRI). According to an embodiment, the LF reflectivity image can be at least one of either a PreStack Time Migration image, and a PreStack Depth Migration image. In step308, the LFRI and CRI are adaptively merged according to the following equation to obtain a broadband HD reflectivity image (BBHDRI):
BBHDRFI=A×LFRI(x,y,z)+B×CRI(x,y,z)  (1),

wherein A and B are operators, developed empirically in order to adapt time and frequency dependent phases and amplitudes to both LFRI and CRI signals. As those of the art can appreciate, although the figures associated with the embodiments discussed herein are all shown in two dimensions, the embodiments can also be realized in three dimensions, as Equation (1) indicates. According to an embodiment, the reflectivity estimates or images from the HD velocity models are very low frequency estimates. These are then added to the reflectivity images developed from the previously obtained seismic data, which are relatively “high frequency” in comparison: the result, in effect, is to complement the image that normally would have been obtained by making use of the HD velocity model to estimate low-frequency reflectivity. The broadband HD reflectivity image can then be displayed to portray with substantially greater clarity and resolution the subsurface structure of the GAL

According to an embodiment, the adaptive merge between the HD reflectivity image and the conventional reflectivity image comprises combining two signals with disparate signal spectra according to known combination techniques. According to a further embodiment, a spectral balancing approach is used. As shown inFIG. 26, line A and line B, which represent HD reflectivity image and the conventional reflectivity image spectra, respectively, a summation is tuned in the frequency range where the two signals overlap; the result is that the geological content is enhanced that facilitates interpretation of the image. According to a further embodiment, the tuned summation can include spectral shaping as needed of each components to facilitate summation of the two signals, and warping, or geometrical shaping of the images (as shown inFIGS. 23 and 24) to the extent necessary to obtain a best superimposition of the images (shown inFIG. 25). Thus, the adaptive merge includes both signal processing of two signals and data processing of the two images that provide the signals shown inFIG. 26. According to a further embodiment, adaptively merging the HD reflectivity image and the conventional reflectivity image comprises combining the two reflectivity images not only in overlapping frequency portions, but also in non-overlapping frequency portions.

According to an embodiment, “warping,” or “geometrical shaping” techniques aim at compensating small time or space variant differences between the images to combine. Spectral shaping techniques aim at combining signals having different phase and amplitude spectra according to some assumptions on the regularity of the properties of the combined signal according to a further embodiment. According to a further embodiment, geometrical shaping includes image shaping the LF reflectivity image and the conventional reflectivity image using at least one of warping, shaping, and morphing, all of which are image shaping techniques known to those of skill in the art.

As a result of the method described in regard toFIG. 20, a new graph can be created that indicates an improvement in imaging obtained by reflection seismology.FIG. 21illustrates reliability of information obtained from surface seismic measurements using the method for determining broadband high definition pre-stack time migration or pre-stack data migration reflectivity images of a geographical area of interest according to an embodiment. InFIG. 21, line A represents the original reliability of information as described and provided by conventional processes and systems, according to Mr. Claerbout. Line B, however, indicates the substantial increase in reliability or accuracy of the information obtained from surface seismic measurements according to the embodiments described herein. The difference betweenFIG. 19, the original reliability model, andFIG. 21, the reliability model derived according the methods discussed herein according to an embodiment, result from an appreciation and subsequently derived mathematical processes as discussed in regard to method300, discussed above in regard toFIG. 20. The appreciation and subsequently developed method illustrate that an overlap exists between low frequency (LF) broadband reflectivity (i.e., seismic data) and the HF velocities. The overlap and consequent mathematical combination, described herein as an adaptive merge, make it possible to adjust the gains or amplitudes in order to combine both partial images (see, e.g.,FIGS. 26 and 35).

Application of the System and Method for Developing a Broadband High Definition Reflectivity Based Image for a GAI Using a HD Tomography Velocity Model and a Conventional Reflectivity Based Image for the GAI as a Basis According to a Further Embodiment Using Synthetic Field Data

FIGS. 22 through 26illustrate the processes and improvements of method300described above in regard toFIG. 19when used on synthetic field data referred to as the Marmousi II model. In method300, data is first recovered from receivers14as a result of signals or seismic waves transmitted by sources4; however, such is not the case when using the synthetic data.FIG. 24illustrates a conventional PreSDM reflectivity image developed using conventional processes using the well-known Marmousi II synthetic data in accordance with step302of second method300according to an embodiment.FIG. 22illustrates a HD velocity model using well-known Marmousi II synthetic data determined in accordance with first method100and step304of second method300illustrated in flowchart form inFIG. 20according to an embodiment. InFIG. 22, method300has performed step304such that a HD velocity model using well-known Marmousi II synthetic data has been created.FIG. 23illustrates a LF reflectivity image developed using conventional processes using the HD velocity model data ofFIG. 22determined in accordance with step306of second method300according to an embodiment.FIG. 25illustrates an adaptive merge of the conventional PreSDM reflectivity image developed in accordance with step302of second method300and the HD reflectivity image developed in accordance with step306of second method300as performed in step308of second method300according to an embodiment.

FIG. 26illustrates an effective spectra image derived from seismic reflectivity and velocity data, in regards to the image shown inFIG. 23(reflectivity derived from HD velocity) and the PreSDM reflectivity image shown inFIG. 24according to an embodiment. The graph shown inFIG. 26shows the effective spectra derived from seismic (reflectivity) and velocity data, and, according to an embodiment, that both spectra are overlapping. As a result, in the overlapping/common part of reflectivity and velocity distribution, these attributes can be combined, compared and processed to derive physical properties, as has been shown and described herein according to the embodiments. The graph from Claerbout 1985 (FIG. 19) shows the historical problem; the fact that the velocity and reflectivity distributions were not overlapping prevented conventional processes from mixing these quantities. The embodiments disclosed herein, especially as discussed in view of the methods100and200discussed in reference toFIGS. 7, and 20, respectively, has overcome those issues and the result is the generation of a high definition broadband reflectivity image that provides enhanced velocity models for GAIs.

Application of the System and Method for Developing a Broadband High Definition Reflectivity Based Image for a GAI Using a HD Tomography Velocity Model and a Conventional Reflectivity Based Image for the GAI as a Basis According to a Further Embodiment Using Actual Field Data

FIGS. 27 through 30illustrate the processes and improvements of method300described above in regard toFIG. 19when used on a first example of actual field data.FIG. 29illustrates a conventional PreSDM reflectivity image developed using conventional processes using the first example of actual field data in accordance with step302of second method300according to an embodiment.FIG. 27illustrates a HD velocity model using the first example of actual field data determined in accordance with first method100and step304of second method300illustrated in flowchart form inFIG. 20according to an embodiment.FIG. 28illustrates a LF reflectivity image developed using conventional processes using the HD velocity model data ofFIG. 27determined in accordance with step306of second method300according to an embodiment.FIG. 30illustrates an adaptive merge of the conventional PreSDM reflectivity image developed in accordance with step302of second method300and the LF reflectivity image developed in accordance with step306of second method300as performed in step308of second method300according to an embodiment.

FIGS. 31 through 35illustrate the processes and improvements of method300described above in regard toFIG. 19when used on a second example of actual field data.FIG. 33illustrates a conventional PreSDM reflectivity image developed using conventional processes using the second example of actual field data in accordance with step302of second method300according to an embodiment.FIG. 31illustrates a HD velocity model using the second example of actual field data determined in accordance with first method100and step304of second method300illustrated in flowchart form inFIG. 20according to an embodiment.FIG. 32illustrates a LF reflectivity image developed using conventional processes using the HD velocity model data ofFIG. 31determined in accordance with step306of second method300according to an embodiment.FIG. 34illustrates an adaptive merge of the conventional PreSDM reflectivity image developed in accordance with step302of second method300and the LF reflectivity image developed in accordance with step306of second method300as performed in step308of the second method according to an embodiment.FIG. 35, similarly toFIG. 26, illustrates effective spectra derived from seismic (reflectivity) and velocity data in regards to the HD reflectivity image developed using conventional processes shown inFIG. 32and the conventional PreSDM reflectivity image developed using conventional processes shown inFIG. 33.

System for Determining a High Definition Tomography Velocity Model of a GAI and for use in Developing a Broadband High Definition Reflectivity Based Image for a GAI using a HD Tomography Velocity Model and a Conventional Reflectivity Based Image for the GAI as a Basis According to a Further Embodiment

FIG. 36illustrates a seismic data acquisition system200suitable for use in implementing the method for determining a high definition tomography velocity model of a geographical area of interest according to an embodiment and suitable for use in implementing the method for developing a broadband high definition reflectivity based image for a GAI using a HD tomography velocity model and a conventional reflectivity based image for the GAI as a basis according to a further embodiment. System200includes, among other items, server201, source/receiver interface202, internal data/communications bus (bus)204, processor(s)208(those of ordinary skill in the art can appreciate that in modern server systems, parallel processing is becoming increasingly prevalent, and whereas a single processor would have been used in the past to implement many or at least several functions, it is more common currently to have a single dedicated processor for certain functions (e.g., digital signal processors) and therefore could be several processors, acting in serial and/or parallel, as required by the specific application), universal serial bus (USB) port210, compact disk (CD)/digital video disk (DVD) read/write (R/W) drive212, floppy diskette drive214(though less used currently, many servers still include this device), and data storage unit232. Data storage unit232itself can comprise hard disk drive (HDD)216(these can include conventional magnetic storage media, but, as is becoming increasingly more prevalent, can include flash drive-type mass storage devices224, among other types), ROM device(s)218(these can include electrically erasable (EE) programmable ROM (EEPROM) devices, ultra-violet erasable PROM devices (UVPROMs), among other types), and random access memory (RAM) devices220. Usable with USB port210is flash drive device224, and usable with CD/DVD R/W device212are CD/DVD disks234(which can be both read and write-able). Usable with diskette drive device214are floppy diskettes237. Each of the memory storage devices, or the memory storage media (216,218,220,224,234, and237, among other types), can contain parts or components, or in its entirety, executable software programming code (software)236that can implement part or all of the portions of the method described herein. Further, processor208itself can contain one or different types of memory storage devices (most probably, but not in a limiting manner, RAM memory storage media220) that can store all or some of the components of software236.

In addition to the above described components, system200also comprises user console234, which can include keyboard228, display226, and mouse230. All of these components are known to those of ordinary skill in the art, and this description includes all known and future variants of these types of devices. Display226can be any type of known display or presentation screen, such as liquid crystal displays (LCDs), light emitting diode displays (LEDs), plasma displays, cathode ray tubes (CRTs), among others. User console235can include one or more user interface mechanisms such as a mouse, keyboard, microphone, touch pad, touch screen, voice-recognition system, among other inter-active inter-communicative devices.

User console234, and its components if separately provided, interface with server201via server input/output (I/O) interface222, which can be an RS232, Ethernet, USB or other type of communications port, or can include all or some of these, and further includes any other type of communications means, presently known or further developed. System200can further include communications satellite/global positioning system (GPS) transceiver device238, to which is electrically connected at least one antenna240(according to an embodiment, there would be at least one GPS receive-only antenna, and at least one separate satellite bi-directional communications antenna). System200can access internet242, either through a hard wired connection, via I/O interface222directly, or wirelessly via antenna240, and transceiver238.

Server201can be coupled to other computing devices, such as those that operate or control the equipment of ship2, via one or more networks. Server201may be part of a larger network configuration as in a global area network (GAN) (e.g., internet242), which ultimately allows connection to various landlines.

According to a further embodiment, system200, being ostensibly designed for use in seismic exploration, will interface with one or more sources4a,band one or more receivers14. These, as previously described, are attached to streamers6a,b, to which are also attached birds13a,bthat are useful to maintain positioning. As further previously discussed, sources4and receivers14can communicate with server201either through an electrical cable that is part of streamer6, or via a wireless system that can communicate via antenna240and transceiver238(collectively described as communications conduit246).

According to further embodiments, user console235provides a means for personnel to enter commands and configuration into system200(e.g., via a keyboard, buttons, switches, touch screen and/or joy stick). Display device226can be used to show: streamer6position; visual representations of acquired data; source4and receiver14status information; survey information; and other information important to the seismic data acquisition process. Source and receiver interface unit202can receive the hydrophone seismic data from receiver14though streamer communication conduit248(discussed above) that can be part of streamer6, as well as streamer6position information from birds13; the link is bi-directional so that commands can also be sent to birds13to maintain proper streamer positioning. Source and receiver interface unit202can also communicate bi-directionally with sources4through the streamer communication conduit248that can be part of streamer6. Excitation signals, control signals, output signals and status information related to source4can be exchanged by streamer communication conduit248between system200and source4.

Bus204allows a data pathway for items such as: the transfer and storage of data that originate from either the source sensors or streamer receivers; for processor208to access stored data contained in data storage unit memory232; for processor208to send information for visual display to display226; or for the user to send commands to system operating programs/software236that might reside in either the processor208or the source and receiver interface unit202.

FIG. 42illustrates a portion of land seismic data acquisition system (land system)200′ that is also suitable for use to implement a method for determining a high definition tomography velocity model of a geographical area of interest according to an embodiment and suitable for use in implementing the method for developing a broadband high definition reflectivity based image for a GAI using a HD tomography velocity model and a conventional reflectivity based image for the GAI as a basis according to a further embodiment. As those of skill in the art can appreciate, while the seismic data signals themselves can represent vastly different types of underground structure, and while the signal processing can, therefore, be vastly different as a consequence, the basic equipment remains essentially the same, and thus,FIG. 42closely resemblesFIG. 36, and includes many of the same components. As a result, in fulfillment of the dual goals of clarity and brevity, a detailed discussion of land system200′ will be omitted (as like objects inFIG. 42have been referenced similarly to those inFIG. 36), other than to note that the source of the signal source/vibrators62and receivers6a-ncommunicate to source/receiver interface202via cables80/246, but these are similar to streamers6/246in terms of command, control and communications functions.

Systems200and200′ can be used to implement method100and300for use in implementing the method for determining a high definition tomography velocity model of a geographical area of interest according to an embodiment and suitable for use in implementing the method for developing a broadband high definition reflectivity based image for a GAI using a HD tomography velocity model and a conventional reflectivity based image for the GAI as a basis according to a further embodiment. Hardware, firmware, software or a combination thereof may be used to perform the various steps and operations described herein. According to an embodiment, software236for carrying out the above discussed steps can be stored and distributed on multi-media storage devices such as devices216,218,220,224,234, and/or237(described above) or other form of media capable of portably storing information (e.g., universal serial bus (USB) flash drive426). These storage media may be inserted into, and read by, devices such as the CD-ROM drive414, the disk drive412, among other types of software storage devices.

The above embodiments were discussed without specifying what type of seismic receivers14are used to record the seismic data. In this sense, it is known in the art to use, for a marine seismic survey, streamers6that are towed by one or more vessels/ships2and the streamers6include seismic receivers/detectors14. The streamers6can be horizontal or slanted or having a curved profile as illustrated inFIG. 37.

The curved streamer6ofFIG. 37includes a body or cable12having a predetermined length; plural detectors14provided along the body12; and plural birds13provided along body12for maintaining the selected curved profile. Curved streamer6is configured to flow underwater when towed such that the plurality of detectors14are distributed along the curved profile. The curved profile can also be described by as parameterized curve, e.g., a curve described by (i) a depth z0of a first detector14(measured from the water surface46), (ii) a slope s0of a first portion T of body12with an axis54parallel with water surface46, and (iii) a predetermined horizontal distance hcbetween the first detector14aand an end of the curved profile. It should be noted that not the entire streamer6has to have the curved profile. In other words, the curved profile should not be construed to always apply to the entire length of streamer6. While this situation is possible, the curved profile may be applied only to a first portion56of streamer6. In other words, streamer6can have (i) only a first portion56having the curved profile or (ii) a first portion56having the curved profile and a second portion58having a flat profile, the two portions being attached to each other.

FIG. 38illustrates a multi-level source for use with the marine seismic exploration system shown inFIG. 1according to an embodiment. Further, the above embodiments may be used with multi-level source60.FIG. 38illustrates multi-level source60for use with marine seismic exploration system10shown inFIG. 1according to an embodiment. Multi-level source60has one or more sub-arrays62. The first sub-array62has a float64that is configured to float at the water surface46or underwater at a predetermined depth. Plural source points66a-dare suspended from the float64in a known manner. A first source point66amay be suspended closest to the head64aof the float64, at a first depth z1. A second source point66bmay be suspended next, at a second depth z2, different from z1. A third source point66cmay be suspended next, at a third depth z3, different from z1and z2, and so on.FIG. 38shows, for simplicity, only four source points66a-d, but an actual implementation may have any desired number of source points66. In one application, because source points66can be distributed at different depths, the source points66at the different depths are not simultaneously activated. In other words, the source array is synchronized, i.e., a deeper source point66is activated later in time (e.g., 2 ms for 3 m depth difference when the speed of sound in water is 1500 m/s) such that corresponding sound signals produced by the plural source points66coalesce, and thus, the overall sound signal produced by the source array appears as being a single sound signal.

The depths z1to z4of the source points of the first sub-array62can obey various relationships. In one application, the depths of source points66increase from head64atoward the tail64bof float64, i.e., z1<z2<z3<z4. In another application, the depths of source points66decrease from head64ato tail64bof float66. In another application, source points66are slanted, i.e., provided on an imaginary line68. In still another application, line68is a straight line. In yet another application, line68is a curved line, e.g., part of a parabola, circle, hyperbola, etc. In one application, the depth of the first source point66afor the sub-array62is about 5 m and the largest depth of the last source point66dis about 8 m. In a variation of this embodiment, the depth range is between about 8.5 and about 10.5 m or between about 11 and about 14 m. In another variation of this embodiment, when line68is straight, the depths of the source points66increase by 0.5 m from a first source point to an adjacent source point. Those skilled in the art would recognize that these ranges are exemplary and these numbers may vary from survey to survey. A common feature of all these embodiments is that source points66have variable depths so that a single sub-array62exhibits multiple-level source points66.

FIGS. 43A through 45Eillustrate a configuration of at least two streamers6a,6bfor use in the marine seismic exploration system10shown inFIG. 1. InFIGS. 43A through 43E, a particular configuration of first and second streamers6a,6bare shown that illustrate several exemplary devices that assist in maintaining directional control and stability of streamers6in marine exploration system10. The devices include spread ropes94, that separate streamers6, bend restrictors96that join spread ropes94to streamers6, and spurline98, which connects streamer6bto 3-Eye splice144, which attaches to bridle block150and deflector148. At least one purpose of deflector148is to provide a force to said plurality of streamers6to maintain directional stability and control. A close up view of bridle block150is shown inFIG. 43E. A close up view of 3-Eye splice is shown inFIG. 43D. A close up view of bend restrictor96is shown inFIG. 43B. Head buoys92a,92bprovide a visual indication of the location of streamers6, and they are connected to streamers6by restrictors156, a close up view of which is shown inFIG. 43C.

FIG. 44illustrates tail-buoy100for use with marine seismic exploration system10shown inFIG. 1with ballasted keel162shown in the extended position, andFIG. 45illustrates tail-buoy100for use with marine seismic exploration system10shown inFIG. 1with ballasted keel shown162in the retracted position. The purpose of tail-buoy100is to (a) provide a visual indicator of the end of streamers6, and (b) to assist in maintaining directional stability and control of streamers6. This is especially true with Broadseis streamer configurations. In order to accomplish both functions, it is necessary to maintain directional control of tail-buoy100in much the same manner as is done with birds13. Therefore, ballasted keel162with pitch and yaw stabilizers160,158have been added. Yaw stabilizer158comprises most of ballasted keel162, as it is shown to be the vertical component that can be controlled much in the same manner as a rudder for a boat. That is, when it is determined to have tail-buoy100turn to the left, directional controls are sent to it and received at navigation mast154(which contains power sources, signal processing circuitry, and so on, a detailed description of which has been omitted for the dual purposes of clarity and brevity), so that yaw stabilizer158turns to the left, causing the nose of tail-buoy100to swing to the left as water passes around yaw stabilizer158, as those of ordinary skill in the art can appreciate. The same general principles apply when it is desired to turn tail-buoy100to the right. Pitch stabilizer160assists in maintaining direction control in much the same manner, but is used to impart a down-ward or up-ward force on the body of tail-buoy100with respect to the surrounding water. According to an alternate embodiment, pitch stabilizer160can be made fixed and not controllable by remote command. When not needed, or for storage purposes, ballasted keel164can be stored in a retracted position, as shown inFIG. 45. Additional motors, servos, and appropriate command and control circuitry can be provided to effectuate those functions, or the same can be accomplished manually, without additional circuitry and so on; when stored, ballasted keel162is folded up and a pin keeps in the retracted condition, and when placed in the water, the pin is removed, ballasted keel162folds down, the ballast drives ballasted keel162in the down position.

It should be noted in the embodiments described herein that these techniques can be applied in either an “offline”, e.g., at a land-based data processing center or an “online” manner, i.e., in near real time while on-board the seismic vessel, i.e., in marine applications. For example, for determining a high definition tomography velocity model of a geographical area of interest according to an embodiment and suitable for use in implementing the method for developing a broadband high definition reflectivity based image for a GAI using a HD tomography velocity model and a conventional reflectivity based image for the GAI as a basis according to a further embodiment can occur as the seismic data is recorded on-board the seismic vessel. In this case, it is possible for broad band HD reflectivity image data to be generated as a measure of the quality of the sampling run.

As also will be appreciated by one skilled in the art, the various functional aspects of the embodiments may be embodied in a wireless communication device, a telecommunication network, as a method or in a computer program product. Accordingly, the embodiments may take the form of an entirely hardware embodiment or an embodiment combining hardware and software aspects. Further, the embodiments may take the form of a computer program product stored on a computer-readable storage medium having computer-readable instructions embodied in the medium. Any suitable computer-readable medium may be utilized, including hard disks, CD-ROMs, digital versatile discs (DVDs), optical storage devices, or magnetic storage devices such a floppy disk or magnetic tape. Other non-limiting examples of computer-readable media include flash-type memories or other known types of memories.

Further, those of ordinary skill in the art in the field of the embodiments can appreciate that such functionality can be designed into various types of circuitry, including, but not limited to field programmable gate array structures (FPGAs), application specific integrated circuitry (ASICs), microprocessor based systems, among other types. A detailed discussion of the various types of physical circuit implementations does not substantively aid in an understanding of the embodiments, and as such has been omitted for the dual purposes of brevity and clarity. However, as well known to those of ordinary skill in the art, the systems and methods discussed herein can be implemented as discussed, and can further include programmable devices.

Such programmable devices and/or other types of circuitry as previously discussed can include a processing unit, a system memory, and a system bus that couples various system components including the system memory to the processing unit. The system bus can be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. Furthermore, various types of computer readable media can be used to store programmable instructions. Computer readable media can be any available media that can be accessed by the processing unit. By way of example, and not limitation, computer readable media can comprise computer storage media and communication media. Computer storage media includes volatile and non-volatile as well as removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CDROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the processing unit. Communication media can embody computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and can include any suitable information delivery media.

The system memory can include computer storage media in the form of volatile and/or non-volatile memory such as read only memory (ROM) and/or random access memory (RAM). A basic input/output system (BIOS), containing the basic routines that help to transfer information between elements connected to and between the processor, such as during start-up, can be stored in memory. The memory can also contain data and/or program modules that are immediately accessible to and/or presently being operated on by the processing unit. By way of non-limiting example, the memory can also include an operating system, application programs, other program modules, and program data.

The processor can also include other removable/non-removable and volatile/non-volatile computer storage media. For example, the processor can access a hard disk drive that reads from or writes to non-removable, non-volatile magnetic media, a magnetic disk drive that reads from or writes to a removable, non-volatile magnetic disk, and/or an optical disk drive that reads from or writes to a removable, non-volatile optical disk, such as a CD-ROM or other optical media. Other removable/non-removable, volatile/non-volatile computer storage media that can be used in the operating environment include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM and the like. A hard disk drive can be connected to the system bus through a non-removable memory interface such as an interface, and a magnetic disk drive or optical disk drive can be connected to the system bus by a removable memory interface, such as an interface.

The embodiments discussed herein can also be embodied as computer-readable codes on a computer-readable medium. The computer-readable medium can include a computer-readable recording medium and a computer-readable transmission medium. The computer-readable recording medium is any data storage device that can store data which can be thereafter read by a computer system. Examples of the computer-readable recording medium include read-only memory (ROM), random-access memory (RAM), CD-ROMs and generally optical data storage devices, magnetic tapes, flash drives, and floppy disks. The computer-readable recording medium can also be distributed over network coupled computer systems so that the computer-readable code is stored and executed in a distributed fashion. The computer-readable transmission medium can transmit carrier waves or signals (e.g., wired or wireless data transmission through the Internet). Also, functional programs, codes, and code segments to, when implemented in suitable electronic hardware, accomplish or support exercising certain elements of the appended claims can be readily construed by programmers skilled in the art to which the embodiments pertains.

The disclosed embodiments provide a source array, computer software, and a method for determining a high definition tomography velocity model of a geographical area of interest according to an embodiment and suitable for use in implementing the method for developing a broadband high definition reflectivity based image for a GAI using a HD tomography velocity model and a conventional reflectivity based image for the GAI as a basis according to a further embodiment. It should be understood that this description is not intended to limit the embodiments. On the contrary, the embodiments are intended to cover alternatives, modifications, and equivalents, which are included in the spirit and scope of the embodiments as defined by the appended claims. Further, in the detailed description of the embodiments, numerous specific details are set forth to provide a comprehensive understanding of the claimed embodiments. However, one skilled in the art would understand that various embodiments may be practiced without such specific details.

The above-described embodiments are intended to be illustrative in all respects, rather than restrictive, of the embodiments. Thus the embodiments are capable of many variations in detailed implementation that can be derived from the description contained herein by a person skilled in the art. No element, act, or instruction used in the description of the present application should be construed as critical or essential to the embodiments unless explicitly described as such. Also, as used herein, the article “a” is intended to include one or more items.

All United States patents and applications, foreign patents, and publications discussed above are hereby incorporated herein by reference in their entireties.