Reservoir burial depth prediction method based on cooperation of multiple fields, multiple offsets and multiple wave modes

Abstract

The invention discloses a reservoir burial depth prediction method based on cooperation of multiple field domains, multiple offsets and multiple wave modes. According to the method provided by the invention, the accuracy and reliability of reservoir burial depth prediction are remarkably improved by integrating multi-offset observation data and a multi-waveform collaborative analysis technology. Specifically, an observation mode combining multi-azimuth non-zero offset and zero offset is adopted, so that the space coverage range of the underground structure is effectively expanded, and system errors caused by a single observation angle are reduced. A wave field separation technology is further utilized to extract multiple effective wave modes such as longitudinal waves, transverse waves, converted waves and reflected waves, an extended data set containing rich wave field information is constructed, extrapolation models are established for different target stratums through a multi-wave-mode propagation path intersection algorithm, and synchronous accurate prediction of the multi-target reservoir stratums is achieved.

Description

Technical Field

[0001] This invention relates to the technical field of downhole reservoir exploration and development, specifically to a reservoir depth prediction method based on multi-field, multi-offset, and multi-wave pattern coordination. Background Technology

[0002] In the exploration and development of resources such as oil and gas, coalfields, and minerals, accurate prediction of the burial depth of downhole reservoirs is a core element determining the success or failure of drilling projects and controlling exploration costs. Vertical seismic profiling (VSP) technology, due to its direct time-depth correspondence, has a natural advantage over surface seismic exploration techniques in downhole reservoir detection, and has become the core technological foundation for reservoir location, widely applied in various resource exploration scenarios.

[0003] However, traditional VSP technology has gradually revealed many industry pain points that urgently need to be addressed in its long-term application: First, its application scenarios are limited. Traditional technologies mostly focus on exploration in single resource areas such as oil and gas fields, and are not adaptable enough to other types of resources such as coal fields, metallic minerals, and non-metallic minerals. Its technical versatility is limited, making it difficult to meet the diversified needs of cross-domain resource exploration. Second, its data acquisition dimensions are limited. Traditional VSP technology mostly adopts a single zero-bias observation mode, relying solely on zero-bias data to estimate reservoir depth. A single observation angle is prone to introducing systematic errors, leading to incomplete data analysis. The shortcomings directly affect the prediction accuracy; third, the wave type is not fully utilized. Traditional technologies often rely only on data inversion of a single wave type, such as P-wave or S-wave, without fully exploring the propagation characteristics and velocity differences of multiple effective wave types, such as converted waves. It is difficult to build a comprehensive wave field model, resulting in limited reliability of reservoir depth prediction; fourth, the multi-target layer prediction capability is weak. The algorithm design of traditional technologies is mostly aimed at a single target layer and lacks adaptability to parallel prediction of multiple target layers. When facing the multi-target exploration needs under complex geological conditions, the efficiency is low and the prediction accuracy is difficult to guarantee.

[0004] To address these issues, some technical solutions attempt to improve performance by optimizing the layout of observation points or improving data processing algorithms, but a systematic solution has yet to be formed: existing improvement solutions are mostly limited to adjustments in a single dimension (such as simply increasing the number of observation points or optimizing a single waveform analysis algorithm), failing to achieve three-dimensional collaborative innovation in acquisition mode, waveform utilization, and application scenarios, and thus failing to fundamentally solve the core pain points of insufficient accuracy, limited reliability, and narrow applicability of traditional technologies.

[0005] As resource exploration and development extend to deeper and more complex geological conditions, and the demand for cross-domain resource exploration grows, the industry is placing higher demands on the accuracy, reliability, and versatility of reservoir depth prediction technologies. Against this backdrop, developing an innovative VSP (Very-Surveyed Surface Prediction) technology that can overcome the limitations of traditional technologies and achieve multi-scenario adaptability, high-precision data acquisition, and multi-wavelength collaborative prediction has become an urgent need for the resource exploration industry. This technology is of great significance for reducing ineffective drilling costs and improving the economic benefits of exploration and development.

[0006] In summary, among related technologies, in scenarios requiring high-precision prediction of reservoir depth, the traditional Vertical Seismic Profile (VSP) technology suffers from insufficient prediction accuracy and poor reliability because it relies solely on single offset observation data and a single type of seismic wave for inversion. Summary of the Invention

[0007] The technical problem this invention aims to solve is that, in scenarios requiring high-precision reservoir depth prediction, traditional vertical seismic profiling (VSP) techniques suffer from insufficient prediction accuracy and poor reliability due to relying solely on single offset observation data and a single type of seismic wave for inversion. The purpose is to provide a reservoir depth prediction method based on multi-field, multi-offset, and multi-wavelength coordination, thereby resolving the issues of insufficient prediction accuracy and poor reliability.

[0008] This invention is achieved through the following technical solution:

[0009] In a first aspect, the present invention provides a reservoir depth prediction method based on multi-field, multi-offset, and multi-wave pattern coordination, comprising:

[0010] By configuring preset parameters, well seismic data suitable for multi-resource exploration scenarios are collected synchronously; wherein, the well seismic data includes: zero offset data and non-zero offset data at at least three different azimuth angles;

[0011] The well seismic data is processed using a wavefield separation algorithm to obtain an extended wavefield dataset; wherein, the extended wavefield dataset includes: P-wave wavefield data, S-wave wavefield data, converted wave wavefield data, P-wave reflected wavefield data, P-wave-converted wave wavefield data, and converted wave reflected wavefield data;

[0012] For at least two target layers, extrapolation models are established based on the extended wavefield dataset, and the predicted burial depth of each target layer is determined by calculating the intersection of the multi-wave propagation paths. Specifically, the first target layer establishes a first extrapolation model based on a combination of P-wave wavefield data, P-wave reflected wavefield data, and P-wave-converted wavefield data, and the second target layer establishes a second extrapolation model based on a combination of converted wavefield data and converted wave reflected wavefield data.

[0013] Furthermore, the step of synchronously acquiring well seismic data suitable for multi-resource exploration scenarios through preset parameter configuration includes:

[0014] Based on the resource type of the target exploration scenario, the corresponding set of seismic data acquisition parameters is determined; the resource types include: oil and gas fields, coal fields, metallic minerals and non-metallic minerals.

[0015] Acquire raw seismic signals from an observation system deployed on-site; wherein the raw seismic signals are acquired by the observation system based on a set of seismic data acquisition parameters; wherein the observation system includes: multiple excitation points deployed on the ground, surrounding the target well area and covering a distance from the wellhead ranging from zero offset to the maximum effective detection offset, and a three-component geophone deployed at preset intervals inside the well to form a three-dimensional downhole observation network from the wellhead to below the deepest reservoir of the target;

[0016] The original seismic signal is processed to generate the well seismic data.

[0017] Further, the step of processing the original seismic signal to generate the well seismic data includes:

[0018] The original seismic signal is sequentially subjected to time alignment correction, amplitude normalization, and deconvolution to generate the well seismic data.

[0019] Furthermore, the step of processing the well seismic data using a wavefield separation algorithm to obtain an extended wavefield dataset includes:

[0020] Different wave types in the well seismic data are identified and their features are extracted to obtain the propagation characteristics of different wave types;

[0021] Based on the propagation characteristics of the different wave types, the well seismic data is separated into multiple types of wavefield data in the extended wavefield dataset.

[0022] Furthermore, the step of identifying and extracting features from different wave types in the well seismic data to obtain the propagation characteristics of different wave types includes:

[0023] Velocity analysis was performed on the well-ground seismic data to determine the propagation velocity ranges of P-waves and S-waves, and polarization analysis was performed on the well-ground seismic data to distinguish the polarization characteristics of P-waves and S-waves.

[0024] The step of separating the well seismic data into multiple types of wavefield data in the extended wavefield dataset based on the propagation characteristics of the different wave types includes:

[0025] Based on the wave equation, and utilizing the propagation speed range and polarization characteristics, a wave field separation model is constructed.

[0026] Using the Kirchhoff integral method, wavefield extension and separation are performed on the well seismic data based on the wavefield separation model to obtain the multi-type wavefield data.

[0027] Furthermore, the step of establishing extrapolation models based on the extended wavefield dataset for at least two target layers, and determining the predicted burial depth of each target layer by calculating the intersection point of the multi-wavelength propagation paths, includes:

[0028] For each target layer, wavefield data of multiple preset wave pattern combinations corresponding to that target layer are selected from the extended wavefield dataset;

[0029] Based on the propagation speed and time relationship of each wave type in the preset wave type combination, the corresponding wave field propagation path equations are constructed respectively;

[0030] The predicted burial depth of the target layer is determined by solving the intersection point of the wave field propagation path equation in space.

[0031] Furthermore, the step of constructing corresponding wavefield propagation path equations based on the propagation speed-time relationship of each wave type in the preset wave type combination includes:

[0032] For each waveform in the preset waveform combination, based on its propagation speed and travel time from the excitation point to the detector, a spatial propagation path equation from the ground surface to the underground target point is constructed to form an extrapolation equation for describing the waveform propagation path.

[0033] The step of determining the predicted depth of the target layer by solving the intersection point of the wave field propagation path equation in space includes:

[0034] Multiple wavefield propagation path equations constructed for the same target layer are solved simultaneously. The intersection point of the paths of multiple equations in space is calculated, and the depth value corresponding to the intersection point is determined as the predicted burial depth of the target layer.

[0035] Secondly, the present invention provides a reservoir depth prediction device based on multi-field, multi-offset, and multi-wave pattern coordination, comprising:

[0036] The synchronous acquisition module is used to synchronously acquire well seismic data suitable for multi-resource exploration scenarios through preset parameter configuration; wherein, the well seismic data includes: zero offset data and non-zero offset data at at least three different azimuth angles;

[0037] The wavefield separation module is used to process the well seismic data using a wavefield separation algorithm to obtain an extended wavefield dataset; wherein the extended wavefield dataset includes: P-wave wavefield data, S-wave wavefield data, converted wave wavefield data, P-wave reflected wavefield data, P-wave-converted wave wavefield data, and converted wave reflected wavefield data;

[0038] The prediction module is used to establish extrapolation models for at least two target layers based on the extended wavefield dataset, and to determine the predicted burial depth of each target layer by calculating the intersection of the multi-wave propagation paths; wherein, the first target layer establishes a first extrapolation model based on a combination of P-wave wavefield data, P-wave reflected wavefield data and P-wave-converted wavefield data, and the second target layer establishes a second extrapolation model based on a combination of converted wavefield data and converted wave reflected wavefield data.

[0039] Thirdly, the present invention provides an electronic device, comprising: a memory, and one or more processors communicatively connected to the memory; the memory stores instructions executable by the one or more processors, the instructions being executed by the one or more processors to cause the one or more processors to implement the method described above.

[0040] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0041] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0042] The method provided by this invention significantly improves the accuracy and reliability of reservoir depth prediction by integrating multi-offset observation data with multi-waveform collaborative analysis technology. Specifically, it employs an observation mode combining multi-azimuth non-zero offset and zero offset, effectively expanding the spatial coverage of subsurface structures and reducing systematic errors caused by single observation angles. Furthermore, it utilizes wavefield separation technology to extract multiple effective waveforms, including P-waves, S-waves, converted waves, and their reflections, constructing an extended dataset containing rich wavefield information. Then, through a multi-waveform propagation path intersection algorithm, extrapolation models are established for different target layers, achieving simultaneous and accurate prediction of multiple target reservoirs. This method not only overcomes the dependence of traditional techniques on single waveforms and offsets, improving the comprehensiveness of data interpretation and anti-interference capabilities, but also enhances adaptability to complex geological conditions. It provides more reliable data support for drilling target location in oil and gas fields, coalfields, and mineral resource exploration, effectively reducing the risk of ineffective drilling and improving exploration and development efficiency. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0044] Figure 1 A flowchart illustrating a reservoir depth prediction method based on multi-field, multi-offset, and multi-wave pattern coordination, provided in the embodiments of this specification;

[0045] Figure 2 The images show the three-component raw records of the VSP (Volume SP) seismic data provided in the embodiments of this specification. The horizontal axis represents the formation depth in meters (m), and the vertical axis represents the recording time in millimeters (ms). Figure a shows the downhole vertical Z-component record, Figure b shows the downhole horizontal component H1 record, and Figure c shows the downhole horizontal component H2 record.

[0046] Figure 3 This is a schematic diagram of zero-well-distance VSP P-wave first arrival pickup provided in the embodiments of this specification. The recording time is in ms; the vertical axis represents the formation depth in meters.

[0047] Figure 4 This is one of the VSP multi-wavelength depth prediction principle diagrams provided in the embodiments of this specification. The horizontal axis represents the formation depth in meters (m), and the vertical and horizontal axes represent the recording time in milliseconds (ms).

[0048] Figure 5 This is the second schematic diagram of the VSP multi-wavelength depth prediction principle provided in the embodiments of this specification. The horizontal axis represents the formation depth in meters (m), and the vertical and horizontal axes represent the recording time in milliseconds (ms).

[0049] Figure 6 These are schematic diagrams illustrating the six wavefield intersection methods for depth prediction provided in the embodiments of this specification.

[0050] Figure 7 The P and R components after rotation are provided in the embodiments of this specification. The horizontal axis represents the formation depth in meters (m), and the vertical axis represents the recording time in millimeters (ms). Figure a shows the P component recording, and Figure b shows the R component recording. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0052] Among related technologies, Vertical Seismic Profiling (VSP) is a downhole geophysical exploration method used in resource exploration. Its basic principle is to generate seismic waves at the surface and directly receive seismic wave signals from various subsurface interfaces using a geophone array deployed at different depths within the well. Because the geophones are placed in the well, the seismic wave propagation path is closer to vertical, avoiding interference from complex near-surface structures. Therefore, VSP can establish a more accurate correspondence between seismic wave travel time and formation depth, offering significant inherent advantages over surface seismic exploration in determining formation velocities and calibrating seismic reflection interface depths.

[0053] In related technologies, a simplified observation mode is employed to streamline data acquisition and processing. Specifically, during the data acquisition phase, a single seismic source is typically deployed directly above the wellhead or at a very close offset (i.e., zero offset VSP) to obtain a set of approximately vertically propagating seismic wave data. During the data processing and interpretation phase, time-depth conversion and stratigraphic calibration are primarily performed based on the first arrival time or waveform characteristics of a single wave type (often a P-wave).

[0054] However, as exploration targets expand to deeper and more complex oil and gas reservoirs and various mineral resources, the limitations of the aforementioned technical architecture become increasingly apparent, directly leading to bottlenecks in the accuracy and reliability of reservoir depth prediction. The fundamental reasons are: First, a single observation angle introduces systematic bias. Relying solely on zero-offset data is equivalent to observing the subsurface structure from only a fixed perspective, making it impossible to effectively constrain and verify velocity models and anisotropy through multi-angle observations. When there are lateral variations or inclined interfaces in the subsurface medium, time-depth conversion based on a single vertical path assumption will produce systematic errors that are difficult to correct. Second, insufficient utilization of single wave type information leads to multiple interpretations. Seismic waves propagate underground, generating multiple wave types such as P-waves (P-waves), S-waves (S-waves), and various converted waves (e.g., Ps-waves), which carry different information about lithology, fluids, and fractures. Related technologies only utilize P-waves, abandoning the independent constraint information provided by S-waves and converted waves, making the inversion problem ill-conditioned and the interpretation results multiple-answer, especially in reservoirs with complex lithology or difficult fluid identification, where the reliability of the prediction results is difficult to guarantee.

[0055] Therefore, in scenarios requiring high-precision prediction of reservoir depth, the traditional vertical seismic profile (VSP) technique suffers from insufficient prediction accuracy and poor reliability because it relies solely on single offset observation data and a single type of seismic wave for inversion.

[0056] This invention addresses the aforementioned technical problems by proposing an innovative solution. Its core concept lies in overcoming the shortcomings of traditional VSP technology, which suffers from insufficient prediction accuracy and reliability due to limitations in data dimensionality and inadequate utilization of wave types, by employing multi-offset observation data and combining it with the collaborative analysis of multiple seismic wave types.

[0057] like Figure 1 As shown, this embodiment provides a reservoir depth prediction method based on multi-field, multi-offset, and multi-wave pattern collaboration. The execution subject of the method can be a seismic processing server, a geological interpretation and reservoir prediction workstation, etc.

[0058] The method includes:

[0059] Step S12: Synchronously acquire well seismic data suitable for multi-resource exploration scenarios by configuring preset parameters; wherein, the well seismic data includes: zero offset data and non-zero offset data at at least three different azimuth angles.

[0060] In this embodiment, the preset parameter configuration can be represented as the various technical parameters for seismic data acquisition set according to the geological conditions and resource types of the target exploration area. The multi-resource exploration scenario can include different geological environments such as oil and gas fields, coalfields, metallic minerals, and non-metallic minerals. It is understood that the reservoir geological conditions (e.g., burial depth, lithology, structural complexity) corresponding to different resource types vary significantly, therefore, it is necessary to adjust the acquisition parameters to optimize signal quality.

[0061] In this embodiment, the synchronous acquisition can be described as the execution entity receiving data from a pre-deployed field observation system. This observation system completes the acquisition of data from all excitation points in a predetermined sequence during a unified exploration project, and ensures that all data have a unified time reference for joint processing.

[0062] In this embodiment, the zero-offset data can be represented as seismic data acquired when the source point is extremely close to the wellhead (less than 100 meters). In this observation method, the seismic wave propagation path is approximately vertical. Non-zero offset data can be represented as seismic data acquired when there is a certain horizontal distance (offset) between the source point and the wellhead. This embodiment requires at least three non-zero offset data points at different azimuth angles, which is crucial for achieving multi-dimensional observation. Specifically, the offset range can be from hundreds to thousands of meters (e.g., 500-5000 meters), depending on the depth of the exploration target and geological conditions. The three different azimuth angles are set to achieve three-dimensional spatial sampling. For example, the first azimuth angle can be arranged along the main structural strike, the second azimuth angle can be perpendicular to the main structural strike, and the third azimuth angle can be arranged obliquely to obtain more comprehensive wavefield coverage.

[0063] In one possible and specific implementation, the field observation system may include:

[0064] Excitation point array: Multiple excitation points are deployed on the ground, arranged in a ring or radial pattern around the target wellhead. The distances between these multiple excitation points and the wellhead range from zero offset (which can be less than 100 meters) to a maximum effective detection offset range preset according to the depth of the exploration target (e.g., 500 meters to 5000 meters). This is used to ensure that seismic waves can be incident on the underground target from different azimuths and angles.

[0065] Geophone array: Multiple high-sensitivity three-component geophones are deployed in the target well at a preset interval (e.g., 10-50 meters), extending from the wellhead to a certain depth (e.g., 100-200 meters) below the deepest reservoir of the target, thereby forming a three-dimensional observation network distributed along the wellbore to receive seismic wave signals from all directions.

[0066] In one possible and specific implementation, the preset parameters may include preset values ​​for: source excitation parameters (e.g., excitation energy, dominant frequency), detector receiving parameters (e.g., sampling rate, gain), and observation system geometric parameters (e.g., maximum offset, channel spacing). For example, for deep, low signal-to-noise ratio oil and gas reservoirs, a higher excitation energy and a lower receiving frequency can be configured to enhance the penetration of low-frequency effective signals; for shallow, high-resolution coalfield exploration, a higher dominant frequency and a denser channel spacing can be configured.

[0067] Step S14: Process the well seismic data using a wavefield separation algorithm to obtain an extended wavefield dataset; wherein the extended wavefield dataset includes: P-wave wavefield data, S-wave wavefield data, converted wave wavefield data, P-wave reflected wavefield data, P-wave-converted wave wavefield data, and converted wave reflected wavefield data.

[0068] In this embodiment, the wavefield separation algorithm can be represented as a processing method for identifying and extracting different types of seismic wavefield signals from raw seismic data. Specifically, it can be based on wave equation theory, using velocity analysis and polarization analysis techniques to identify different wave types. Velocity analysis can distinguish between P-waves and S-waves by calculating the differences in the propagation speed of seismic waves in different strata. Polarization analysis can further identify wave type characteristics based on the differences in the vibration direction of seismic waves. On this basis, the Kirchhoff integral method can be used for wavefield extrapolation calculations to separate the mixed seismic signals into independent wavefield components.

[0069] In this embodiment, the P-wave field data can be represented as an independent data volume separated from the mixed wave field, consisting only of the P-wave component (which may simultaneously include the down-current P-wave traveling directly from the seismic source to the detector and the up-current reflected P-wave traveling upwards from the subsurface interface to the detector). A P-wave, also known as a P-wave (Primary-wave), is a body wave in which the direction of particle vibration is parallel to the direction of wave propagation.

[0070] In this embodiment, the shear wave field data can be represented as a separate, independent data volume comprising only shear wave components (which may also include down-going direct shear waves and up-going reflected shear waves). A shear wave (S-wave) is a body wave in which the direction of particle vibration is perpendicular to the direction of wave propagation.

[0071] In this embodiment, the converted wave field data can be represented as a separate, independent data volume with converted waves (e.g., Ps waves, Sp waves) as the main components. The propagation path and time-distance relationship of converted waves differ from those of pure P-waves or pure S-waves, providing an additional observation perspective. Converted waves are seismic waves that undergo wave mode conversion during propagation; for example, they are P-waves (P) at incidence and convert to S-waves (S) upon interface reflection or transmission, denoted as Ps waves.

[0072] In this embodiment, the P-wave reflected wavefield data is a subset of the P-wave field data. That is, it refers to the P-wave that has undergone reflection. The P-wave generated by the seismic source, which returns as a P-wave after reflection at the subsurface interface, is called a PP wave.

[0073] In this embodiment, the longitudinal wave-converted wave field data belongs to a type of converted wave, but has a more complex propagation path. For example, the incident wave is a longitudinal wave (P), which is converted into a transverse wave (S) when reflected at one interface. This transverse wave is reflected again at another interface and is finally received in the form of a transverse wave.

[0074] In this embodiment, the converted wave reflected wave field data can be a wave that has undergone reflection after being formed. For example, a Ps wave, after being formed, may be reflected again at another interface and remain a transverse wave (S), denoted as a PsP wave, or it may undergo another conversion.

[0075] In one possible and specific implementation, firstly, velocity analysis (e.g., velocity spectrum analysis) can be performed on the data to determine the propagation velocity ranges of P-waves (P-waves) and S-waves (S-waves) in the strata of the work area. Simultaneously, polarization analysis is performed to distinguish P-waves, whose energy is primarily along the propagation direction, and S-waves, whose energy is perpendicular to the propagation direction, using the directional characteristics of the particle vibration vectors in the three-component data. Then, based on the extracted wave pattern characteristics (velocity, polarization), a wavefield separation model can be constructed. This model can be based on the wave equation theory of seismic wave propagation to describe the propagation behavior of different wave patterns in the medium. Next, using wavefield extension techniques such as the Kirchhoff integral method, numerical simulation and back-calculation of the original wavefield are performed under the guidance of the model, thereby separating the aliased wavefields one by one. After the above separation process, the final result is a set of clean wave pattern data including six types (but not limited to six types), namely the extended wavefield dataset. Specifically, it can include: longitudinal wave field data (P-wave), transverse wave field data (S-wave), converted wave field data (e.g., P-wave incident, S-wave reflected Ps-wave), longitudinal wave reflected wave field data (PP-wave), longitudinal wave-converted wave field data (e.g., PPS-wave), and converted wave reflected wave field data (e.g., PsP-wave).

[0076] Step S16: For at least two target layers, extrapolation models are established based on the extended wave field dataset, and the predicted burial depth of each target layer is determined by calculating the intersection of the multi-wave propagation paths; wherein, the first target layer establishes a first extrapolation model based on a combination of P-wave wave field data, P-wave reflected wave field data and P-wave-converted wave field data, and the second target layer establishes a second extrapolation model based on a combination of converted wave field data and converted wave reflected wave field data.

[0077] In this embodiment, when selecting wave mode combinations for different target layers, the most suitable wave mode combination can be determined based on the geological characteristics and wavefield response features of each target layer. For example, for the first target layer, a combination of P-wave, P-wave reflection, and P-wave-converted wave can be selected. For the second target layer, a combination of converted wave and converted wave reflection can be selected.

[0078] In this embodiment, the extrapolation model is constructed based on the propagation characteristics of the selected wave type. For each wave type, a propagation path equation from the excitation point to the underground target point can be established according to its propagation velocity and travel time information. This equation describes the propagation law of seismic waves in different media and provides a mathematical basis for subsequent intersection point calculations.

[0079] In this embodiment, the intersection point of multiple wave pattern propagation paths can be calculated by solving the path equations of multiple wave patterns corresponding to the same target layer to find their spatial intersection location. The depth value corresponding to this intersection point is the predicted burial depth of the target layer. This multi-wave pattern intersection method can effectively offset the errors that may exist in the prediction of a single wave pattern and improve the reliability of the results.

[0080] In one possible and specific implementation, the implementing entity can select a set of wave pattern data that best reflects the characteristics of each reservoir (target layer) from the extended wavefield dataset and construct a corresponding mathematical prediction model. The extrapolation model can be a mathematical relationship (i.e., a wavefield propagation path equation) describing the propagation path prediction of a specific wave pattern from a known receiver location to an unknown subsurface target point (i.e., the target layer reflection point or conversion point). Specifically, for the first target layer (which may be the main reservoir), a combination of P-wave wavefield data (P), P-wave reflected wavefield data (PP), and P-wave-converted wavefield data (PPS) can be selected. Based on the known propagation velocity of each wave pattern and its travel time observed in the corresponding data, path equations describing the propagation of P-waves, PP waves, and PPS waves from their respective surface excitation points to the target layer and back to the well receiver are constructed. For the second target layer (which may be another reservoir or marker layer), a combination of converted wavefield data (Ps) and converted wave reflected wavefield data (PsP) can be selected. Similarly, a specific path equation can be constructed based on the propagation laws and velocity model of converted waves.

[0081] In one possible and specific implementation scheme, it is understood that, for the same underground reflection point, although the propagation paths of different wave types are different, their reflection points (or conversion points) at the target layer should spatially converge at a single point. Therefore, in this implementation scheme, this geometric principle can be used for inversion. The implementing entity can simultaneously solve multiple (e.g., three) wavefield propagation path equations constructed for the same target layer. This solution process can be mathematically finding a point in three-dimensional space that simultaneously satisfies (ideally satisfies in the least squares sense) the path constraints of all equations. The spatial coordinate point obtained by the solution is the predicted path intersection point. Finally, through time-depth conversion (using the seismic wave travel time and formation mean velocity corresponding to the intersection point), the spatial location of the intersection point can be converted into a specific predicted burial depth (which can be expressed as sea level or depth below the surface).

[0082] The method provided in this embodiment significantly improves the accuracy and reliability of reservoir depth prediction by integrating multi-offset observation data with multi-waveform collaborative analysis technology. Specifically, it employs an observation mode combining multi-azimuth non-zero offset and zero offset, effectively expanding the spatial coverage of subsurface structures and reducing systematic errors caused by single observation angles. Furthermore, it utilizes wavefield separation technology to extract multiple effective waveforms, including P-waves, S-waves, converted waves, and their reflections, constructing an extended dataset containing rich wavefield information. Then, through a multi-waveform propagation path intersection algorithm, extrapolation models are established for different target layers, achieving simultaneous and accurate prediction of multiple target reservoirs. This method not only overcomes the dependence of traditional techniques on single waveforms and offsets, improving the comprehensiveness of data interpretation and anti-interference capabilities, but also enhances adaptability to complex geological conditions. It provides more reliable data support for drilling target location in oil and gas fields, coalfields, and mineral resource exploration, effectively reducing the risk of ineffective drilling and improving exploration and development efficiency.

[0083] In some implementations, the step of synchronously acquiring well seismic data suitable for multi-resource exploration scenarios through preset parameter configuration includes:

[0084] Step S122: Determine the corresponding set of seismic data acquisition parameters based on the resource type of the target exploration scenario; wherein, the resource type includes: oil and gas field, coal field, metallic minerals and non-metallic minerals.

[0085] In this embodiment, a pre-established correlation logic or experience database between resource types and acquisition parameters can be created for major resource exploration scenarios such as oil and gas fields, coalfields, metallic minerals, and non-metallic minerals. Specifically, after receiving the resource type identifier of the target exploration scenario input by the user or automatically identified, the executing entity can invoke the built-in adaptation logic to generate or retrieve a set of seismic data acquisition parameters. This set of seismic data acquisition parameters can be a set of instructions including multiple types of parameters.

[0086] More specifically, the set of seismic data acquisition parameters may include:

[0087] The source excitation parameters may include, but are not limited to, excitation energy, excitation method (explosive, controllable source scanning frequency and length), and center frequency.

[0088] The detector receives parameters, which may include the sampling rate (e.g., 0.5 ms, 1 ms), the recording length, and the gain settings for each channel.

[0089] The observation system's geometric parameters can specify the recommended values ​​for the expected maximum effective detection offset, minimum non-zero offset, and preset spacing of the in-well geophones.

[0090] In one possible specific implementation, the configuration logic for this seismic data acquisition parameter set could be:

[0091] Excitation Energy: Before VSP field acquisition, a fine-grained source excitation parameter test can be conducted. By comparing the effects of different schemes such as source output, source station combination, scan length, and scan frequency, the optimal excitation parameters can be screened, and the best excitation energy suitable for the target resource type can be determined.

[0092] Center frequency: Based on specific design requirements and combined with the results of previous source scanning frequency comparison tests, the center frequency suitable for reservoir detection is determined comprehensively.

[0093] Excitation method: Selected according to surface conditions and exploration accuracy requirements. Explosive source is suitable for deep, low signal-to-noise ratio reservoir scenarios. Controllable source can be adapted to the reservoir burial characteristics of different resource types by adjusting the scanning frequency and length.

[0094] Sampling rate: Determined by technical requirements, it can be adapted to the seismic wave propagation characteristics of the target resource reservoir to ensure effective capture of high-frequency or low-frequency signals.

[0095] Step S124: Acquire the raw seismic signal collected by the observation system deployed on site; wherein the raw seismic signal is collected by the observation system based on the seismic data acquisition parameter set; wherein the observation system includes: multiple excitation points deployed on the ground, surrounding the target well area and covering the range from zero offset to maximum effective detection offset distance from the wellhead, and a three-component geophone deployed in the well at preset intervals to form a downhole three-dimensional observation network from the wellhead to below the deepest reservoir of the target.

[0096] In this embodiment, the excitation point array may include multiple excitation points. These multiple excitation points (seismic sources) have been pre-deployed on the ground. They are distributed around the target wellhead, forming a spatial sampling network. The distances between these multiple excitation points and the wellhead cover a range from zero offset (where the excitation point is directly above or very close to the wellhead, less than 100 meters, to obtain a near-vertical incident wave field) to a preset maximum effective detection offset (e.g., ranging from 500 meters to 3000 meters). This layout ensures that the underground target can be illuminated by seismic waves from multiple different azimuths and incident angles.

[0097] In this embodiment, the downhole three-dimensional observation network may include multiple three-component geophones to form a geophone array. Specifically, the three-component geophones can be firmly deployed within the target wellbore at preset intervals (e.g., a basic interval of 10-50 meters, which can be increased to 5-10 meters in key sections). Their deployment range can start near the wellhead and extend to a certain depth (100-200 meters) below the deepest reservoir of the target. This forms a three-dimensional receiving network distributed along the wellbore, used to receive upgoing and downgoing waves from various excitation points in all directions.

[0098] In this embodiment, the executing entity can send the seismic data acquisition parameter set determined in step S122 to the recording unit of the observation system. Then, the observation system can sequentially excite at each excitation point according to a preset time sequence and synchronously record the vibration signals received by all in-well geophone channels, forming a raw, unprocessed seismic signal. Finally, this raw seismic signal can be received by the executing entity through a data transmission network.

[0099] Step S126: Process the original seismic signal to generate the well seismic data.

[0100] In this embodiment, the step of processing the original seismic signal to generate the well seismic data includes:

[0101] Step S1262: The original seismic signal is sequentially subjected to time alignment correction, amplitude normalization processing and deconvolution processing to generate the well seismic data.

[0102] In this embodiment, time alignment correction is used to eliminate the problem of time asynchrony between data channels caused by instrument startup delay, transmission delay, or slight differences in clock speeds of different detectors, to ensure that all signals have a unified zero point. In one possible and specific implementation, firstly, the accurate arrival time of the first arrival (i.e., the earliest arrival) of the seismic wave on each original seismic signal channel can be automatically identified. This can be done by detecting sudden increases in signal energy or by using a preset automatic picking algorithm (e.g., an energy ratio-based method). Then, the first arrival time of each signal channel can be compared and calculated with a reference time. This reference time can be a theoretically calculated value (based on known source-detector distances and a preset velocity model) or the first arrival time of a selected channel (e.g., the zero-offset channel with the highest signal-to-noise ratio) can be used as a benchmark. Finally, correction is performed; for example, for each data channel, the entire time series can be cyclically shifted forward or backward based on the calculated time shift. For example, if the first arrival time of a channel is Δt later than the reference time, then Δt is subtracted from the timestamps of all sampling points of that channel, thereby achieving time alignment with the reference channel.

[0103] In this embodiment, amplitude normalization is used to compensate for inter-channel amplitude variations caused by differences in excitation energy, different propagation paths, and inconsistent detector sensitivity. In one possible and specific implementation, based on the spherical diffusion theory of seismic wave propagation, the inherent energy attenuation curve of each channel signal due to different propagation distances (inversely proportional to the square or higher power of the travel time) can be calculated, and each channel data can be multiplied by a time-dependent gain function to compensate for geometric diffusion effects. In another possible and specific implementation, the root mean square (RMS) amplitude or mean absolute amplitude of each channel data can be calculated within a selected time window (e.g., including the time window of the main reflected wave). Then, the amplitudes of all channels are scaled to a common target level (e.g., to make the RMS amplitudes of all channels equal).

[0104] In this embodiment, a deconvolution algorithm can be used to compress seismic wavelets, suppressing short-period multiples and reverberation interference, thereby improving the temporal resolution of seismic records and making reflected pulses sharper, facilitating subsequent wavefield identification. In one possible and specific implementation, the deconvolution algorithm can be least-squares deconvolution, that is, assuming the seismic wavelet is of minimum phase and the reflection coefficient sequence is white noise, the optimal inverse filtering operator is solved under the least-squares error criterion. The deconvolution algorithm can be a predictive deconvolution algorithm, a homomorphic deconvolution algorithm, or an iterative deconvolution algorithm.

[0105] In some implementations, the step of processing the well seismic data using a wavefield separation algorithm to obtain an extended wavefield dataset includes:

[0106] Step S142: Identify and extract features from different wave types in the well seismic data to obtain the propagation characteristics of different wave types.

[0107] In this embodiment, components of different wave types, such as P-waves, S-waves, and various converted waves, can be identified from mixed well seismic data, and features describing their propagation behavior can be extracted to provide constraints for subsequent accurate separation.

[0108] In this embodiment, step S142 may include:

[0109] Step S1422: Perform velocity analysis on the well seismic data to determine the propagation velocity range of P-waves and S-waves, and perform polarization analysis on the well seismic data to distinguish the polarization characteristics of P-waves and S-waves.

[0110] In one possible and specific implementation, the first-arrival time inversion method can be used to perform velocity analysis on the well seismic data to determine the propagation velocity ranges of P-waves and S-waves. Specifically, the first-arrival times of the direct P-waves (P-waves) and direct S-waves (S-waves) can be picked up. Using the determined source-detector distance (approximately detector depth for zero bias) and travel time, the layer velocity or average velocity at each detector point is directly calculated. Through iteration or layer stripping methods, the layer velocity model of the formation is obtained through inversion.

[0111] In one possible and specific implementation, velocity spectrum analysis can be used to analyze the velocity of the well seismic data to determine the propagation velocity ranges of P-waves and S-waves. Specifically, for non-zero bias VSP data, the execution entity can generate a velocity spectrum (e.g., a constant velocity scan of CVP gathers). By scanning a series of possible Vp and Vs values, the correlation (or superposition energy) of correcting non-zero bias gathers to zero offset gathers at different velocities can be calculated. The velocity pair with the highest energy is the most probable velocity at that depth.

[0112] In one possible and specific implementation, the vector wave field information recorded by the three-component detector can be used to distinguish between longitudinal waves (particle vibration direction parallel to the wave propagation direction) and transverse waves (particle vibration direction perpendicular to the wave propagation direction) based on the difference in particle vibration direction (polarization). Specifically, the executing entity can perform the following analysis on the three-component data at each sampling time or within a time window: First, for a segment of three-component data within a selected analysis time window, its 3x3 covariance matrix is ​​calculated. This matrix represents the energy and interrelationships between the three component signals. Then, eigenvalue decomposition is performed to decompose the covariance matrix into three eigenvalues ​​and corresponding eigenvectors. The eigenvector corresponding to the largest eigenvalue indicates the polarization direction of the main vibration energy within that time window. Finally, wave type discrimination is performed. The polarization direction can be determined by combining the wave propagation direction (the ray direction from the source to the detector). If the angle between the polarization direction and the propagation direction is very small (close to 0° or 180°), then the energy is determined to be mainly a longitudinal wave (P-wave). If the polarization direction is nearly perpendicular to the propagation direction, it is determined to be a transverse wave (S-wave).

[0113] Step S144: Based on the propagation characteristics of the different wave types, the well seismic data is separated into multiple types of wavefield data in the extended wavefield dataset.

[0114] In this embodiment, step S144 may include:

[0115] Step S1442: Based on the wave equation and utilizing the propagation speed range and polarization characteristics, construct a wave field separation model.

[0116] In this embodiment, a mathematical model that can accurately simulate the propagation of seismic waves (including P-waves, S-waves, and converted waves) in a specific medium can be established as a theoretical framework and numerical calculation basis for wavefield separation.

[0117] In this embodiment, firstly, the executing entity can use the propagation velocity range (Vp, Vs) obtained in step S1422 as input to construct a two-dimensional or three-dimensional velocity model. This model can be based on the assumption of a layered or meshed medium. Polarization characteristic information can be used to guide the setting of anisotropic parameters in the model (considering anisotropy). Then, the corresponding wave equation is selected according to the accuracy requirements and computational efficiency. For a complete elastic wave simulation, the elastic wave equation can be used, or the acoustic wave equation can be used to approximate the P-wave and S-wave respectively. Next, the executing entity can discretize the selected wave equation in the spatial and temporal domains (e.g., finite difference method, finite element method) to form a discrete system that can be used for numerical solution. Finally, the parameterized velocity model, density model (which can be obtained from well logging data or derived from velocity through empirical formulas), and boundary conditions (e.g., free surface, absorbing boundary) are integrated into the discretized wave equation framework to form a complete wavefield separation model. This wavefield separation model can simulate seismic waves generated from any excitation point and record the complete wavefield (including all wave types) at any receiver location.

[0118] Step S1444: Using the Kirchhoff integral method, the wavefield is extended and separated from the well seismic data based on the wavefield separation model to obtain the multi-type wavefield data.

[0119] In this embodiment, the constructed wavefield separation model can be used as a background, and the integral method can be applied to back-infer the mixed wavefield data observed on the surface / in the well into the underground space. During the back-inferration process, filtering and separation are performed according to the wave pattern characteristics, and finally, various types of pure wavefield data are extracted.

[0120] In this embodiment, wavefield extension can be performed based on the Kirchhoff integral. Specifically, the Kirchhoff integral can be expressed as an integral representation based on the Green's function solution of the wave equation. The executing entity can use the Green's function (or approximation) corresponding to the model constructed in step S1442 to propagate (extend) the well seismic data (as known boundary values) back to various points underground using the Kirchhoff integral formula. This process essentially traces the wavefield recorded by the detector back to the reflection or scattering points underground.

[0121] In this embodiment, wavefield separation can be performed as follows. Specifically, one method of wavefield separation is to utilize the differences in propagation speeds of different wave types. More specifically, due to their different speeds, longitudinal waves (P-waves), transverse waves (S-waves), and various converted waves will have different time-distance curves for their corresponding phase axes in non-zero offset VSP data. Based on this principle, by applying time-difference corrections (e.g., normal time-difference correction, NMO) corresponding to the velocity field of each wave type, the phase axis of the target wave type can be flattened, thereby separating it from other wave types on the corrected gather.

[0122] In this embodiment, the polarization characteristics extracted in step S1422 can also be used to apply polarization filtering during the wavefield extension process or to the extension results. For example, at each underground imaging point, only the energy whose vibration direction is parallel to the P-wave propagation direction can be retained to obtain the P-wave wavefield. Similarly, the S-wave wavefield can be obtained by retaining the energy whose polarization direction is consistent with that of the S-wave. Through the above extension and separation operations, independent P-wave wavefield data (P), S-wave wavefield data (S), converted wavefield data (Ps, etc.), and multiple reflections and converted reflections of the above wavefields (e.g., PP, PsP, PPS, etc.) can be extracted from the original mixed data, which together constitute the extended wavefield dataset.

[0123] In some implementations, the step of establishing extrapolation models based on the extended wavefield dataset for at least two target layers, and determining the predicted burial depth of each target layer by calculating the intersection points of multi-wavelength propagation paths, includes:

[0124] Step S162: For each target layer, select wavefield data from the extended wavefield dataset that includes multiple preset wave pattern combinations corresponding to that target layer.

[0125] In this embodiment, the preset waveform combination can be represented as a combination of multiple waveforms pre-defined for different types or depths of target layers based on prior geological and geophysical knowledge or empirical rules before data processing. This selection action is automatically completed by the executing entity based on the mapping relationship between the target layer identifier and the waveform combination.

[0126] In this embodiment, the executing entity can retrieve preset waveform combinations defined for each type of layer from a pre-stored rule base or configuration file based on the user-input identifiers of each target layer (e.g., main oil layer, coal seam A, marker layer B, etc.). Then, it extracts all wavefield data volumes specified by the combination from the generated extended wavefield dataset. For example, for a layer identified as the first target layer (e.g., a sandstone-mudstone reservoir), the selected waveform combination could be: P-wave wavefield data (P), P-wave reflected wavefield data (PP), and P-wave-converted wavefield data (PPS). For a layer identified as the second target layer (e.g., another interface or a different type of reservoir), the selected waveform combination could be: converted wavefield data (Ps) and converted wave reflected wavefield data (PsP).

[0127] Step S164: Based on the propagation speed and time relationship of each wave type in the preset wave type combination, construct the corresponding wave field propagation path equations respectively.

[0128] In this embodiment, this step is used to abstract the propagation process of seismic waves into a computable mathematical model, and to establish an equation describing the geometric path and time relationship of each type of wave used for prediction, which is propagated from the surface excitation point through the underground target point to the geophone in the well.

[0129] In this embodiment, step S164 may include:

[0130] Step S1642: For each waveform in the preset waveform combination, based on its propagation speed and travel time from the excitation point to the detector, construct its spatial propagation path equation from the ground surface to the underground target point to form an extrapolation equation for describing the waveform propagation path.

[0131] In this embodiment, for a selected waveform (e.g., P-wave), the executing entity can obtain: the propagation velocity model of the waveform above and below the corresponding target layer (obtained from the velocity analysis in step S142, which may be layer velocity or average velocity); and the precise travel time of the waveform from each non-zero offset excitation point to each downhole detector (which can be extracted from the corresponding wavefield data by methods such as first arrival picking or cross-correlation).

[0132] Based on ray propagation theory, the executing entity can establish an equation describing the propagation path for each pair (excitation point i, detector j) and its corresponding travel time Tij. This equation can take the form: Distance (excitation point i -> target point X) / Velocity 1 + Distance (target point X -> detector j) / Velocity 2 = Tij. Here, target point X is the spatial point (reflection point or conversion point) to be determined on the target layer, and velocities 1 and 2 are the propagation velocities of the wave on the corresponding path segments. For reflected waves, velocities 1 and 2 can be the same (e.g., Vp); for converted waves (e.g., Ps), velocities 1 and 2 can be different (e.g., Vp and Vs). This equation constitutes a set of extrapolation equations describing the propagation path of this wave type.

[0133] Step S166: Determine the predicted burial depth of the target layer by solving the intersection point of the wave field propagation path equation in space.

[0134] In this embodiment, the spatial location of the target layer can be deduced by solving a system of equations using the independent geometric constraints provided by the multi-wavelength model.

[0135] In this embodiment, step S166 may include:

[0136] Step S1662: Solve the multiple wavefield propagation path equations constructed for the same target layer simultaneously. Calculate the intersection point of the paths of the multiple equations in space and determine the depth value corresponding to the intersection point as the predicted burial depth value of the target layer.

[0137] In this implementation, the executing entity can simultaneously solve multiple sets of path equations (each set including equations from multiple offsets and detectors) established for the same target layer but based on different waveforms (e.g., P-wave, PP-wave, PPS-wave). This is a mathematical inversion problem, aiming to find one or more three-dimensional spatial points (X, Y, Z) such that the point simultaneously (or optimally in the least squares sense) satisfies the constraints of all equations. The solution algorithm can be a linear algorithm, a nonlinear least squares method, a grid search method, or an iterative inversion algorithm (e.g., the Gauss-Newton method). The optimal spatial point coordinates obtained are the intersection point of the multi-wave propagation paths. The Z coordinate of this point (which can be elevation depth or vertical depth) directly gives the predicted burial depth of the target layer at that well location. In some implementations, if the intersection point is a time-domain result directly obtained through travel time inversion, a time-depth conversion can also be performed, using a velocity model to convert travel time into depth.

[0138] In one possible specific implementation, when performing the specific solution, the depth prediction calculation formula can be substituted into the solution:

[0139] D0=(T1-T2) / V1+D1

[0140] In the formula, D0 is the predicted depth of the target layer; T1 is the two-way time of the target layer; T2 is the two-way time of the VSP bottom depth; V1 is the layer velocity of the target layer; and D1 is the VSP bottom depth.

[0141] like Figures 2 to 7 As shown, in a specific implementation scheme, a high-precision reservoir depth prediction method based on multi-field, multi-offset, and multi-wavelength collaboration is provided. This implementation scheme addresses the industry pain points of traditional VSP technology in reservoir exploration, such as limited application scenarios, insufficient prediction accuracy, and limited reliability. It proposes "in-well VSP multi-field, multi-offset, and multi-wavelength downhole oil and gas reservoir prediction technology," constructing a complete technical system of "three-dimensional technical dimensional collaboration + standardized process implementation." Its core innovation concept lies in breaking through the unidirectional optimization limitations of traditional VSP technology. Through three-dimensional linkage of "multi-field extended application, multi-offset precise acquisition, and multi-wavelength collaborative prediction," it achieves a triple upgrade in the accuracy, reliability, and applicability of reservoir depth prediction, providing high-precision and high-reliability technical support for the exploration of various resource types and reducing exploration and development risks.

[0142] (I) Multi-field extended application module

[0143] Technological breakthrough direction: Break through the limitations of traditional VSP technology, which is only applicable to the exploration of a single oil and gas field, and build a universal application system that spans resource fields.

[0144] Specific implementation method: By optimizing seismic wave excitation parameters, detector adaptability design and data analysis algorithm, the technology can be widely adapted to exploration scenarios of various types of resources such as oil and gas fields, coal fields, metallic minerals and non-metallic minerals. There is no need for large-scale technical transformation for different resource types. Differentiated exploration needs can be met by simply fine-tuning the parameters.

[0145] Core value: Significantly enhance the versatility and engineering value of the technology, expand the application boundaries of VSP technology, and provide a unified high-precision detection solution for the multi-resource exploration industry.

[0146] (II) Multi-offset precision acquisition module

[0147] Innovative acquisition mode: A collaborative acquisition mode of "zero bias + multiple non-zero bias" is adopted to construct a multi-dimensional observation network.

[0148] Key implementation steps:

[0149] Seismic source deployment: Multiple triggering points at different locations and distances are pre-set on the ground to ensure that seismic waves fully cover the well area and avoid blind spots in observation;

[0150] Detector arrangement: High-sensitivity detectors are deployed at different depths in the well at preset intervals to form a three-dimensional downhole observation array, ensuring comprehensive signal reception;

[0151] Synchronous data acquisition: Using dedicated seismic exploration instruments, “zero-bias VSP data volume + multiple sets of non-zero-bias VSP data volumes” are acquired simultaneously to achieve complete capture of effective signals at different offsets.

[0152] Technical advantages: By using complementary verification of multiple sets of non-zero bias data and zero bias data, the systematic errors caused by a single observation angle are offset, providing a multi-dimensional and highly reliable original data foundation for subsequent reservoir prediction.

[0153] (III) Multi-wave pattern collaborative prediction module

[0154] Effective Waveform Integration: It fully integrates multiple effective waveforms, including downlink direct wave, uplink longitudinal wave (P wave), uplink and downlink transverse wave (S wave), and converted wave (Ps wave, Psv wave, PPs wave, PsP wave, etc.), breaking through the limitation of traditional technology that relies on only a single waveform.

[0155] Core algorithm innovation: The "multi-wave extrapolation and convergence method" is proposed, which constructs a multi-wave field collaborative extrapolation model based on the propagation path characteristics, velocity differences and conversion rules of different wave types.

[0156] Multi-purpose layer prediction logic:

[0157] For the first target layer: Based on the propagation characteristics of P-wave, PP-wave, and PPs-wave, a three-dimensional extrapolation model is established, and the precise intersection point is obtained through wavefield path intersection calculation to determine its burial depth;

[0158] For the second target layer: Utilizing the conversion characteristics and propagation laws of Ps wave, PsP wave, and PsPs wave, a dedicated extrapolation equation is designed. The intersection point is obtained through multi-wave intersection verification to complete the burial depth prediction.

[0159] Extension and Expansion: Supports flexible expansion to synchronous prediction of more target layers by adding corresponding waveform combinations, adapting to the multi-target detection needs in complex exploration scenarios.

[0160] This implementation plan establishes a standardized three-tiered process: "field data acquisition - data preprocessing - reservoir depth prediction," ensuring the standardization and repeatability of technology implementation.

[0161] Field data acquisition phase: By scientifically deploying seismic sources and geophones, a ground-downhole collaborative observation network is constructed to simultaneously acquire zero-bias and multi-non-zero-bias VSP data volumes, ensuring data coverage and integrity;

[0162] Data preprocessing stage: Deconvolution is used to suppress noise interference, and combined with a high-precision wave field recognition algorithm, target wave types such as P wave, PP wave, Psv wave, and Sv wave are accurately separated to provide a clean data foundation for subsequent prediction;

[0163] Reservoir depth prediction stage: Classify by target layer, construct a dedicated prediction model based on the corresponding wave pattern combination, and achieve accurate inversion of burial depth through multi-wave intersection calculation to ensure the reliability of prediction results for each layer.

[0164] In this implementation plan, through collaborative optimization of multiple fields, multiple offsets, and multiple waveforms, a synergy of technological innovation is formed to break through the single optimization bottleneck of traditional VSP technology; a full-process standard from data acquisition to prediction output is established to ensure the consistency and replicability of technology application; and adaptation across multiple resource fields such as oil and gas, coalfields, and metal minerals is achieved to enhance the engineering value and promotion potential of the technology.

[0165] This implementation scheme breaks through the limitations of traditional VSP technology with its single offset and single waveform. By using complementary verification of multi-offset (zero offset + non-zero offset) data and extrapolation and intersection of multiple waveforms (P-wave, PP-wave, Psv-wave, sv-wave, etc., a total of 6 types of wave fields), it achieves accurate positioning of the true burial depth of the target reservoir, significantly reducing the prediction deviation caused by single data or waveform, and providing high-precision depth data support for drilling operations.

[0166] This implementation scheme adopts the core method of multi-wave extrapolation and intersection. The intersection point is obtained by extrapolating different wave groups (P-wave type, SV-wave type) and cross-validating the reservoir burial depth data. This avoids the problem of multiple solutions in the interpretation of a single wave field, makes the prediction results closer to the actual situation of underground reservoirs, and improves the credibility and persuasiveness of the data.

[0167] This implementation plan relies on multi-field collaborative technology to break through the limitation of traditional VSP technology being used only for oil and gas field exploration, and expands to multiple fields such as coalfield and mineral resource exploration, to build a cross-resource type exploration application system and enhance the versatility and industrialization value of the technology.

[0168] This implementation scheme supports simultaneous prediction of multiple target layers. Different intersection points are obtained through extrapolation of different waveform combinations, each corresponding to the depth of a different target reservoir. This addresses the efficiency bottleneck of traditional single-target prediction methods and adapts to the needs of joint evaluation of multiple reservoirs in complex exploration scenarios. Through the synergy of multi-field expansion, multi-offset complementarity, and multi-waveform intersection technologies, this implementation scheme achieves a quadruple upgrade in reservoir depth prediction: accuracy, reliability, applicability, and multi-target capability. This provides a more reliable theoretical basis and data support for drilling operations in oil and gas, coalfield, and mineral resource exploration.

[0169] In one specific implementation plan, a high-precision reservoir depth prediction method based on multi-field, multi-offset, and multi-wave pattern coordination is provided.

[0170] (1) Well seismic data acquisition:

[0171] As the foundation of the entire technical system, well-drilled seismic data acquisition is designed with the core principles of "multi-field adaptation, multi-offset coordination, and multi-wavelength full coverage" to construct a comprehensive and high-precision data acquisition system. The specific implementation details are as follows:

[0172] Acquisition Scenario Adaptation Design: Breaking through the limitations of traditional VSP technology's single-oil and gas field application, this design customizes acquisition parameters for exploration scenarios of different resource types, including oil and gas fields, coalfields, metallic minerals, and non-metallic minerals. Based on the burial characteristics and geological structural complexity of different resource reservoirs, the design optimizes source energy, detector sensitivity, and observation network density to ensure technical adaptability and achieve efficient data acquisition across resource domains.

[0173] Seismic source deployment scheme: A multi-directional, multi-distance, three-dimensional excitation layout is adopted. Multiple excitation points are deployed on the ground around the target well area, with the distance between the excitation points and the wellhead covering the range from zero offset to the maximum effective detection offset (which can be set from 0-5000m depending on the exploration depth requirements). The type of seismic source is selected according to surface conditions and exploration needs, including explosive sources and controlled sources, to ensure that seismic waves can cover the underground reservoir of the well area in all directions and avoid detection blind spots.

[0174] Downhole geophone deployment: High-sensitivity three-component geophones are selected and deployed in the well according to the principle of "equal spacing + denser coverage in key layers". The basic spacing is set at 10-50m. For sections with known potential reservoir development or complex geological structures, the spacing is reduced to 5-10m, forming a complete downhole three-dimensional observation network from the wellhead to 100-200m below the deepest target reservoir. The geophones are fixed using cement coupling or mechanical clamping to ensure a tight fit with the wellbore wall and improve signal reception sensitivity.

[0175] Multi-offset collaborative acquisition mode: Employing a synchronous acquisition strategy of "zero-offset + multiple non-zero-offset" data. During zero-offset acquisition, the excitation point is within 100m of the wellhead, acquiring seismic wave data that propagates vertically close to the wellbore. During multiple non-zero-offset acquisition, preset excitation points at different azimuths and distances on the ground are sequentially excited, simultaneously recording multiple sets of non-zero-offset VSP data volumes (typically 3-8 sets of non-zero-offset observation points with offset gradient distribution). Through complementary acquisition of zero-offset data and multiple sets of non-zero-offset data, a multi-dimensional observation perspective is constructed, laying the data foundation for offsetting systematic errors and improving prediction accuracy.

[0176] Full-wavelength signal acquisition: Utilizing a dedicated high-precision seismic exploration instrument (sampling rate ≤ 0.5ms, dynamic range ≥ 120dB), the system simultaneously receives various effective wavelengths, including first arrival waves, downlink direct waves, uplink P-waves (P-waves), uplink and downlink S-waves (S-waves), and converted waves (Ps-waves, Psv-waves, SvP-waves, etc.). During acquisition, the signal-to-noise ratio is monitored in real time. By adjusting the excitation energy and detector coupling state, the integrity and effectiveness of the zero-bias VSP data volume and multiple sets of non-zero-bias VSP data volumes are ensured, achieving complete acquisition of all wavelength signals.

[0177] (2) Well seismic data processing:

[0178] The data processing stage focuses on "signal optimization, wavefield separation, and data purification." Through multi-step refined processing, it provides high-quality data support for subsequent reservoir prediction. The specific process is as follows:

[0179] Raw data preprocessing: First, the acquired raw VSP data is standardized to unify the data storage format and coordinate system. Then, deconvolution processing is performed, using an iterative deconvolution algorithm to suppress surface noise, instrument noise, and environmental interference, compensating for energy attenuation during seismic wave propagation, and highlighting the amplitude and frequency characteristics of the effective wavefield signal. Simultaneously, data consistency correction is performed, including time synchronization correction (eliminating time differences between different detectors and sources) and amplitude normalization correction (unifying the amplitude magnitudes of signals with different offsets and waveforms), ensuring data reliability and comparability.

[0180] High-precision wavefield separation: A wavefield identification and separation algorithm based on the wave equation is employed, combining the spatial characteristics of multi-offset data with the propagation patterns of multiple wave types to achieve accurate separation of target wave types. First, the propagation velocity ranges of P-waves and S-waves are determined through velocity analysis, and the polarization characteristics of wave types are used to initially distinguish between longitudinal and transverse waves. Then, considering the differences in the propagation paths of converted waves (Ps waves, Psv waves, SvP waves, etc.), a wavefield separation model is constructed. Through Kirchhoff's integral method or forward modeling of the wave equation, iterative optimization is performed to accurately separate various target wave types, including P-waves, PP waves, Psv waves, Sv waves, SvP waves, SvSv waves, Ps waves, and PsP waves. After separation, the purity of each wave type data is verified to ensure that the interference from other wave types in a single wave type data is less than 5%, providing a clean data foundation for subsequent predictions.

[0181] Data quality control and screening: A multi-dimensional data quality evaluation system was established to comprehensively assess the processed data from aspects such as signal-to-noise ratio, waveform integrity, amplitude stability, and phase consistency. Data segments that did not meet the quality standards underwent secondary processing (such as re-deconvolution and supplementary wavefield separation), and data segments that could not be repaired were marked and removed. Simultaneously, zero-bias data and multiple non-zero-bias data were cross-validated. By comparing the propagation characteristics of the same waveform at different offsets, the consistency and reliability of the data were verified, ultimately forming a high-quality multi-offset, multi-waveform VSP processing dataset.

[0182] (3) Conduct a comprehensive interpretation to obtain the prediction results of oil and gas reservoirs.

[0183] The comprehensive interpretation phase is based on the core logic of "multi-wave pattern coordination, multi-target layer parallel processing, and accurate burial depth inversion." Through the construction of three-dimensional models and the fusion analysis of multi-dimensional data, high-precision prediction of oil and gas reservoirs is achieved. The specific process is as follows:

[0184] Interpretation Foundation Preparation: Integrate various wave type data (P-wave, PP-wave, Ps-wave, etc.) obtained during the data processing stage, and combine them with regional geological data (such as stratigraphic lithology data, structural outline maps, and existing drilling data) and logging data (such as sonic logging and density logging data) to establish an interpretation foundation database. Simultaneously, clarify the prediction objectives, including the number of main target layers, the expected burial depth range, and reservoir lithology and physical property prediction indicators, thus defining a clear direction for the interpretation work.

[0185] Construction of multi-wave collaborative inversion model: For the geological characteristics of different target layers, a dedicated multi-wave extrapolation convergence model is constructed.

[0186] For the first target layer: Based on the propagation velocity, polarization characteristics, and path patterns of P-waves, PP-waves, and PPs-waves, and combined with the wave velocity-lithology relationship calibrated from well logging data, a three-dimensional extrapolation model is constructed. The propagation paths of various wave types from different offset excitation points to downhole detectors are calculated through numerical simulation, and path equations are established. Using the principle of multi-wave path convergence, the convergence point 1 of different wave type propagation paths is solved. Combined with the time-depth conversion formula (depth = wave velocity × propagation time / 2), the burial depth corresponding to convergence point 1 is accurately calculated, and the reservoir depth range of the first target layer is preliminarily determined.

[0187] For the second target layer: Utilizing the conversion characteristics (such as conversion point location and wave velocity abrupt change patterns) and propagation laws of Ps waves, PsP waves, and PsPs waves, combined with the possible lithological combinations of this target layer, the extrapolation model parameters are corrected, and a dedicated extrapolation equation is established. Through cross-calculation of the converted wave paths corresponding to multiple sets of non-zero bias data, intersection point 2 is obtained, thus completing the accurate prediction of the burial depth of the second target layer.

[0188] Multi-target layer extension prediction and validation: To address complex exploration needs, the extrapolation model can be extended to simultaneous prediction of more target layers by adding corresponding wave pattern combinations (such as SvP wave, SvPp wave, etc.). After calculating the burial depth of each target layer, cross-validation is required, combining regional geological structural patterns and reservoir validation data from existing wells. By comparing the deviation between the predicted burial depth and the actual well-revealed depth (with a deviation rate ≤3%), model parameters are optimized to ensure the reliability of the prediction results.

[0189] Comprehensive Reservoir Evaluation and Result Output: Based on the determined burial depth of each target layer, and combining characteristic parameters such as amplitude, frequency, and phase of multi-wavelength data, the continuity, thickness, and physical properties (such as porosity and hydrocarbon potential) of the reservoir are analyzed. The hydrocarbon reservoir potential of each target layer is comprehensively assessed, and favorable, moderately favorable, and unfavorable reservoir areas are delineated. The final output is a hydrocarbon reservoir prediction report, including burial depth data for each target layer, a reservoir planar distribution map, a longitudinal profile interpretation map, and reservoir potential evaluation conclusions, providing precise target location data for drilling operations.

[0190] The application of multi-field methods can broaden the scope of VSP technology, enabling its application in oil and gas fields, coalfields, and mineral resource exploration. Multiple offsets (including zero offset and non-zero offset) can more comprehensively reflect the structure and characteristics of underground reservoirs, improve prediction accuracy, and avoid the prediction limitations caused by the single offset of traditional methods. The extrapolation and convergence prediction method of multiple waves (upward and downward P-waves, upward and downward S-waves, converted waves, etc.) combines more information, thus predicting the true burial depth of the target reservoir more reliably and closely. At the same time, it can also predict multiple target layers, which has multi-target characteristics.

[0191] According to embodiments of the present invention, a reservoir depth prediction device based on multi-field, multi-offset, and multi-wave pattern coordination is provided, comprising:

[0192] The synchronous acquisition module is used to synchronously acquire well seismic data suitable for multi-resource exploration scenarios through preset parameter configuration; wherein, the well seismic data includes: zero offset data and non-zero offset data at at least three different azimuth angles;

[0193] The wavefield separation module is used to process the well seismic data using a wavefield separation algorithm to obtain an extended wavefield dataset; wherein the extended wavefield dataset includes: P-wave wavefield data, S-wave wavefield data, converted wave wavefield data, P-wave reflected wavefield data, P-wave-converted wave wavefield data, and converted wave reflected wavefield data;

[0194] The prediction module is used to establish extrapolation models for at least two target layers based on the extended wavefield dataset, and to determine the predicted burial depth of each target layer by calculating the intersection of the multi-wave propagation paths; wherein, the first target layer establishes a first extrapolation model based on a combination of P-wave wavefield data, P-wave reflected wavefield data and P-wave-converted wavefield data, and the second target layer establishes a second extrapolation model based on a combination of converted wavefield data and converted wave reflected wavefield data.

[0195] According to an embodiment of the present invention, an electronic device is provided. The electronic device in this embodiment may include one or more of the following components: a processor, a network interface, memory, non-volatile memory, and one or more application programs, wherein the one or more application programs may be stored in the non-volatile memory and configured to be executed by one or more processors, and the one or more programs are configured to perform the methods described in the foregoing method embodiments.

[0196] According to embodiments of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a computer, causes the computer to perform the method described in any of the above embodiments.

[0197] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A reservoir depth prediction method based on multi-field, multi-offset, and multi-wave pattern coordination, characterized in that, include: By configuring preset parameters, well seismic data suitable for multi-resource exploration scenarios are collected synchronously; wherein, the well seismic data includes: zero offset data and non-zero offset data at at least three different azimuth angles; The well seismic data is processed using a wavefield separation algorithm to obtain an extended wavefield dataset; wherein, the extended wavefield dataset includes: P-wave wavefield data, S-wave wavefield data, converted wave wavefield data, P-wave reflected wavefield data, P-wave-converted wave wavefield data, and converted wave reflected wavefield data; For at least two target layers, extrapolation models are established based on the extended wavefield dataset, and the predicted burial depth of each target layer is determined by calculating the intersection of the multi-wave propagation paths. Specifically, the first target layer establishes a first extrapolation model based on a combination of P-wave wavefield data, P-wave reflected wavefield data, and P-wave-converted wavefield data, and the second target layer establishes a second extrapolation model based on a combination of converted wavefield data and converted wave reflected wavefield data.

2. The method according to claim 1, characterized in that, The step of synchronously acquiring well seismic data suitable for multi-resource exploration scenarios through preset parameter configuration includes: Based on the resource type of the target exploration scenario, the corresponding set of seismic data acquisition parameters is determined; the resource types include: oil and gas fields, coal fields, metallic minerals and non-metallic minerals. Acquire raw seismic signals from an observation system deployed on-site; wherein the raw seismic signals are acquired by the observation system based on a set of seismic data acquisition parameters; wherein the observation system includes: multiple excitation points deployed on the ground, surrounding the target well area and covering a distance from the wellhead ranging from zero offset to the maximum effective detection offset, and a three-component geophone deployed at preset intervals inside the well to form a three-dimensional downhole observation network from the wellhead to below the deepest reservoir of the target; The original seismic signal is processed to generate the well seismic data.

3. The method according to claim 2, characterized in that, The step of processing the raw seismic signal to generate the well seismic data includes: The original seismic signal is sequentially subjected to time alignment correction, amplitude normalization, and deconvolution to generate the well seismic data.

4. The method according to claim 1, characterized in that, The step of processing the well seismic data using a wavefield separation algorithm to obtain an extended wavefield dataset includes: Different wave types in the well seismic data are identified and their features are extracted to obtain the propagation characteristics of different wave types; Based on the propagation characteristics of the different wave types, the well seismic data is separated into multiple types of wavefield data in the extended wavefield dataset.

5. The method according to claim 4, characterized in that, The steps of identifying and extracting features from different wave types in the well seismic data to obtain the propagation characteristics of different wave types include: Velocity analysis was performed on the well-ground seismic data to determine the propagation velocity ranges of P-waves and S-waves, and polarization analysis was performed on the well-ground seismic data to distinguish the polarization characteristics of P-waves and S-waves. The step of separating the well seismic data into multiple types of wavefield data in the extended wavefield dataset based on the propagation characteristics of different wave types includes: Based on the wave equation, and utilizing the propagation speed range and polarization characteristics, a wave field separation model is constructed. Using the Kirchhoff integral method, wavefield extension and separation are performed on the well seismic data based on the wavefield separation model to obtain the multi-type wavefield data.

6. The method according to claim 5, characterized in that, The step of establishing extrapolation models based on the extended wavefield dataset for at least two target layers, and determining the predicted burial depth of each target layer by calculating the intersection point of the multi-wavelength propagation paths, includes: For each target layer, wavefield data of multiple preset wave pattern combinations corresponding to that target layer are selected from the extended wavefield dataset; Based on the propagation speed and time relationship of each wave type in the preset wave type combination, the corresponding wave field propagation path equations are constructed respectively; The predicted burial depth of the target layer is determined by solving the intersection point of the wave field propagation path equation in space.

7. The method according to claim 6, characterized in that, The step of constructing the corresponding wavefield propagation path equation based on the propagation speed and time relationship of each wave type in the preset wave type combination includes: For each waveform in the preset waveform combination, based on its propagation speed and travel time from the excitation point to the detector, a spatial propagation path equation from the ground surface to the underground target point is constructed to form an extrapolation equation for describing the waveform propagation path. The step of determining the predicted burial depth of the target layer by solving the intersection point of the wave field propagation path equation in space includes: Multiple wavefield propagation path equations constructed for the same target layer are solved simultaneously. The intersection point of the paths of multiple equations in space is calculated, and the depth value corresponding to the intersection point is determined as the predicted burial depth of the target layer.

8. A reservoir depth prediction device based on multi-field, multi-offset, and multi-wave pattern coordination, characterized in that, include: The synchronous acquisition module is used to synchronously acquire well seismic data suitable for multi-resource exploration scenarios through preset parameter configuration; wherein, the well seismic data includes: zero offset data and non-zero offset data at at least three different azimuth angles; The wavefield separation module is used to process the well seismic data using a wavefield separation algorithm to obtain an extended wavefield dataset; wherein the extended wavefield dataset includes: P-wave wavefield data, S-wave wavefield data, converted wave wavefield data, P-wave reflected wavefield data, P-wave-converted wave wavefield data, and converted wave reflected wavefield data; The prediction module is used to establish extrapolation models for at least two target layers based on the extended wavefield dataset, and to determine the predicted burial depth of each target layer by calculating the intersection of the multi-wave propagation paths; wherein, the first target layer establishes a first extrapolation model based on a combination of P-wave wavefield data, P-wave reflected wavefield data and P-wave-converted wavefield data, and the second target layer establishes a second extrapolation model based on a combination of converted wavefield data and converted wave reflected wavefield data.

9. An electronic device, characterized in that, include: A memory, and one or more processors communicatively connected to the memory; The memory stores instructions that can be executed by the one or more processors to cause the one or more processors to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 7.

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