Reservoir prediction method and device, equipment and storage medium

Abstract

The invention discloses a reservoir prediction method and device, equipment and a storage medium, and belongs to the technical field of seismic exploration. According to the method provided by the invention, first-time pre-stack inversion is carried out on the seismic data which is partially stacked through the angle gather, a target reflection coefficient body is obtained through the first-time pre-stack inversion, then a coal seam reflection coefficient body is extracted from the target reflection coefficient body, convolution is carried out on the coal seam reflection coefficient body and seismic wavelets, and coal seam seismic data is obtained. And then removing the seismic data of the coal seam from the seismic data of which the angle gathers are partially superposed, namely stripping the coal seam, thereby obtaining the seismic data without the coal seam. And carrying out second-time pre-stack inversion based on the coal seam-removed seismic data to obtain a plurality of elastic parameters. According to the method, when the elastic parameters are obtained through inversion, the coal seam seismic data are removed, and the shielding effect of the coal seam is removed, so that the elastic parameters can be accurately obtained, the gas-bearing sandstone reservoir under the coal can be accurately identified, and the reservoir prediction accuracy is improved.

Description

Technical Field

[0001] This application relates to the field of seismic exploration technology, and in particular to a reservoir prediction method, apparatus, equipment and storage medium. Background Technology

[0002] In the Turpan-Hami Basin, Lower Jurassic coal-bearing strata are widely developed. With the continuous improvement of exploration, Jurassic coal-bearing sandstone oil and gas reservoirs have become a key area for oil and gas exploration.

[0003] In related technologies, when predicting reservoirs in gas-bearing sandstone formations of coal-bearing strata, pre-stack inversion is performed on seismic data to obtain elastic parameters, and then reservoir prediction is made based on these elastic parameters.

[0004] However, seismic data contains a large amount of coal seam reflection information, and coal seams have a strong shielding effect on reservoirs. Therefore, the elastic parameters obtained by directly using seismic data for pre-stack inversion are not accurate, making it difficult to accurately identify the distribution of gas-bearing sandstones, resulting in low reservoir prediction accuracy. Summary of the Invention

[0005] This application provides a reservoir prediction method, apparatus, device, and storage medium, which can improve the accuracy of reservoir prediction. The technical solution is as follows:

[0006] On the one hand, a reservoir prediction method is provided, the method comprising:

[0007] Acquire the first seismic data of the target work area. The first seismic data is the pre-stack common reflection point gather seismic data.

[0008] The first seismic data is converted from the offset seismic profile to the corner gather seismic profile to obtain multiple second seismic data, which are data volumes of different corner gather parts superimposed.

[0009] Based on the multiple second seismic data, seismic wavelets are extracted respectively;

[0010] Pre-stack inversion is performed based on the multiple second seismic data and their corresponding seismic wavelets to obtain the target reflection coefficient volumes corresponding to the multiple second seismic data respectively;

[0011] For each target reflectance coefficient volume, extract the coal seam reflectance coefficient volume from the target reflectance coefficient volume;

[0012] Based on the coal seam reflection coefficient volume and its corresponding seismic wavelet, convolution is performed to obtain coal seam seismic data;

[0013] The coal seam seismic data is removed from the second seismic data to obtain the coal seam-free seismic data;

[0014] Pre-stack inversion was performed based on multiple seismic data from coal seams to obtain multiple elastic parameters;

[0015] Reservoir prediction is performed based on the aforementioned multiple elastic parameters.

[0016] In one possible implementation, the conversion of the first seismic data from the offset seismic profile to the corner gather seismic profile to obtain multiple second seismic data sets includes:

[0017] Based on the maximum incident angle, multiple angle intervals are divided, and there is overlap between two adjacent angle intervals;

[0018] Based on the multiple angle intervals, the first seismic data is subjected to angle-separated partial superposition processing to obtain the multiple second seismic data.

[0019] In another possible implementation, the step of performing pre-stack inversion based on the plurality of second seismic data and their corresponding seismic wavelets to obtain the target reflection coefficient volumes corresponding to the plurality of second seismic data respectively includes:

[0020] Acquire stratigraphic data and well logging data, wherein the well logging data includes well logging sub-data of multiple well logging parameters;

[0021] Data interpolation is performed based on the logging sub-data and stratigraphic data of the multiple logging parameters to obtain multiple data volumes;

[0022] Based on the multiple second seismic data and their corresponding seismic wavelets, the well logging data, and the multiple data volumes, pre-stack inversion is performed to obtain multiple target reflection coefficient volumes.

[0023] In another possible implementation, extracting the coal seam reflectance volume from the target reflectance volume includes:

[0024] Acquire drilling data;

[0025] Based on the stratigraphic data, the reflectance volume of the main coal seam is extracted from the target reflectance volume;

[0026] Based on the drilling data, a first reflection coefficient threshold and a second reflection coefficient threshold are determined, wherein the first reflection coefficient threshold is greater than the second reflection coefficient threshold;

[0027] Based on the first reflection coefficient threshold and the second reflection coefficient threshold, the coal seam reflection coefficient volume is extracted from the main coal seam reflection coefficient volume.

[0028] In another possible implementation, the pre-stack inversion based on multiple coal seam-free seismic data yields multiple elastic parameters, including:

[0029] For each logging parameter, the logging sub-data of the logging parameter is processed to remove the coal seam, resulting in the coal seam-removed logging sub-data of the logging parameter.

[0030] Based on the coal seam removal logging sub-data and the coal seam removal seismic data, pre-stack inversion is performed to obtain multiple elastic parameters.

[0031] In another possible implementation, the step of processing the logging sub-data of the logging parameters to obtain the coal seam-free logging sub-data of the logging parameters includes:

[0032] The logging sub-data of the logging parameters are fitted with a normal distribution;

[0033] Based on the fitting results, the distribution range of the logging parameters is determined;

[0034] By replacing the value at the coal seam in the well logging sub-data with any value from the distribution interval, the coal seam-free well logging sub-data of the well logging parameters is obtained.

[0035] On the other hand, a reservoir prediction apparatus is provided, the apparatus comprising:

[0036] The acquisition module is used to acquire the first seismic data of the target work area, which is the pre-stack common reflection point gather seismic data;

[0037] The conversion module is used to convert the first seismic data from the offset seismic profile to the corner gather seismic profile to obtain multiple second seismic data, wherein the multiple second seismic data are data volumes superimposed from different corner gather parts;

[0038] The first extraction module is used to extract seismic wavelets based on the multiple second seismic data.

[0039] The first inversion module is used to perform pre-stack inversion based on the plurality of second seismic data and their corresponding seismic wavelets to obtain the target reflection coefficient volume corresponding to the plurality of second seismic data respectively.

[0040] The second extraction module is used to extract the coal seam reflectance volume from the target reflectance volume for each target reflectance volume;

[0041] The convolution module is used to perform convolution based on the coal seam reflection coefficient volume and its corresponding seismic wavelet to obtain coal seam seismic data;

[0042] The removal module is used to remove the coal seam seismic data from the second seismic data to obtain coal seam-free seismic data;

[0043] The second inversion module is used to perform pre-stack inversion based on multiple coal seam-removed seismic data to obtain multiple elastic parameters.

[0044] The prediction module is used to predict the reservoir based on the multiple elastic parameters.

[0045] In one possible implementation, the conversion module is used to divide multiple angle intervals based on the maximum incident angle, with overlap between adjacent angle intervals; and to perform angle-separated superposition processing on the first seismic data based on the multiple angle intervals to obtain the multiple second seismic data.

[0046] In another possible implementation, the first inversion module is used to acquire stratigraphic data and well logging data, wherein the well logging data includes well logging sub-data of multiple well logging parameters; data interpolation is performed based on the well logging sub-data of multiple well logging parameters and stratigraphic data to obtain multiple data volumes; pre-stack inversion is performed based on the multiple second seismic data and their corresponding seismic wavelets, the well logging data and the multiple data volumes to obtain multiple target reflection coefficient volumes.

[0047] In another possible implementation, the second extraction module is used to acquire drilling data; extract the reflection coefficient volume of the main coal seam from the target reflection coefficient volume based on the stratigraphic data; determine a first reflection coefficient threshold and a second reflection coefficient threshold based on the drilling data, wherein the first reflection coefficient threshold is greater than the second reflection coefficient threshold; and extract the coal seam reflection coefficient volume from the reflection coefficient volume of the main coal seam based on the first reflection coefficient threshold and the second reflection coefficient threshold.

[0048] In another possible implementation, the second inversion module is used to perform coal seam removal processing on the well logging sub-data of each well logging parameter to obtain coal seam removal well logging sub-data of the well logging parameter; and to perform pre-stack inversion based on the coal seam removal well logging sub-data of the multiple well logging parameters and the multiple coal seam removal seismic data to obtain multiple elastic parameters.

[0049] In another possible implementation, the second inversion module is used to fit the logging sub-data of the logging parameters to a normal distribution; based on the fitting result, determine the distribution range of the logging parameters; and replace the value at the coal seam in the logging sub-data with any value in the distribution range to obtain the coal seam-free logging sub-data of the logging parameters.

[0050] On the other hand, an electronic device is provided, comprising a processor and a memory, wherein the memory stores at least one piece of program code, which is loaded and executed by the processor to implement the reservoir prediction method described in any of the preceding claims.

[0051] On the other hand, a computer-readable storage medium is provided, wherein at least one piece of program code is stored in the computer-readable storage medium, the at least one piece of program code being loaded and executed by a processor to implement the reservoir prediction method described in any of the preceding claims.

[0052] On the other hand, a computer program product is provided, wherein at least one piece of program code is stored in the computer program product, the at least one piece of program code being loaded and executed by a processor to implement the reservoir prediction method described in any of the preceding claims.

[0053] This application provides a reservoir prediction method. The method first performs a pre-stack inversion using partially stacked seismic data from corner gathers to obtain the target reflection coefficient volume. Then, the coal seam reflection coefficient volume is extracted from the target reflection coefficient volume and convolved with a seismic wavelet to obtain coal seam seismic data. Next, the coal seam seismic data is removed from the partially stacked seismic data from the corner gathers, i.e., the coal seam is stripped out, resulting in coal seam-free seismic data. A second pre-stack inversion is then performed based on the coal seam-free seismic data to obtain multiple elastic parameters. This method uses coal seam-free seismic data to obtain the elastic parameters, removing the shielding effect of the coal seam. Therefore, it can accurately obtain the elastic parameters, thereby accurately identifying gas-bearing sandstone reservoirs beneath coal and improving the accuracy of reservoir prediction.

[0054] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this disclosure. Attached Figure Description

[0055] Figure 1 This is a schematic diagram of the implementation environment of a reservoir prediction method provided in an embodiment of this application;

[0056] Figure 2 This is a flowchart of a reservoir prediction method provided in an embodiment of this application;

[0057] Figure 3 This is a schematic diagram of a predicted reservoir provided in an embodiment of this application;

[0058] Figure 4 This is a schematic diagram of a cross-section of near, middle and far gather partially superimposed seismic data before coal seam removal, provided in an embodiment of this application.

[0059] Figure 5 This is a schematic diagram of a cross-section of near, middle and far gather partial superimposed seismic data after removing the coal seam, provided in an embodiment of this application;

[0060] Figure 6 This is an impedance plane diagram obtained by inversion before and after coal seam removal, provided in an embodiment of this application;

[0061] Figure 7This is a schematic diagram of a reservoir prediction device provided in an embodiment of this application;

[0062] Figure 8 This is a structural block diagram of a terminal provided in an embodiment of this application;

[0063] Figure 9 This is a structural block diagram of a server provided in an embodiment of this application. Detailed Implementation

[0064] To make the technical solution and advantages of this application clearer, the embodiments of this application will be described in further detail below.

[0065] The terms "first," "second," "third," and "fourth," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0066] It should be noted that all information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in this application have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the seismic data, drilling data, stratigraphic data, and well logging data involved in this application were all obtained with full authorization.

[0067] Figure 1 This is a schematic diagram of the implementation environment of a reservoir prediction method provided in an embodiment of this application. See also... Figure 1 The implementation environment includes an electronic device, which can be provided as terminal 101, or as a combination of terminal 101 and server 102. If the electronic device is provided as terminal 101 and server 102, terminal 101 and server 102 can be connected via a wireless or wired network. In this embodiment, the electronic device is not specifically limited.

[0068] If the electronic device is provided as terminal 101, then reservoir prediction is performed by terminal 101.

[0069] If the electronic device provides a terminal 101 and a server 102, then the terminal 101 sends seismic data to the server 102, the server 102 performs reservoir prediction based on the seismic data, and then returns the prediction result to the terminal 101. A target application is installed on the terminal 101, which is used for reservoir prediction. The server 102 is the backend server for the target application, providing background services for it.

[0070] The terminal 101 can be at least one of the following: mobile phone, tablet computer, PC (Personal Computer) device, intelligent voice interaction device, and vehicle terminal. The server 102 can be at least one of the following: a single server, a server cluster consisting of multiple servers, a cloud server, a cloud computing platform, and a virtualization center.

[0071] Figure 2 This is a flowchart of a reservoir prediction method provided in an embodiment of this application, executed by an electronic device. See also... Figure 2 The method includes:

[0072] Step 201: The electronic device acquires the first seismic data of the target work area.

[0073] The first set of seismic data consists of pre-stack common reflection point (CRP) gather seismic data, and the target study area is the seismic research area, which is also the area where reservoirs are predicted.

[0074] Electronic devices can acquire locally stored first earthquake data, or they can acquire first earthquake data sent by other electronic devices; there are no specific limitations on this.

[0075] Step 202: The electronic device converts the first seismic data from the offset seismic profile to the corner gather seismic profile, and obtains multiple second seismic data.

[0076] Among them, multiple second earthquake data are data volumes superimposed from different angle gathers.

[0077] This step can be achieved through the following steps (1) to (2), including:

[0078] (1) Electronic devices are divided into multiple angle intervals based on the maximum incident angle.

[0079] The maximum incident angle is the maximum incident angle that the target layer can completely receive, and there is overlap between two adjacent angle intervals.

[0080] The number of angle intervals can be set and changed as needed; for example, the number of angle intervals can be 2, 3, or 4, without specific limitation. In this embodiment, only an example with 3 angle intervals is used for illustration.

[0081] If the angle is divided into three intervals, the electronic device can determine the angle corresponding to 1 / 3 of the maximum angle of incidence, obtaining the first angle; determine the angle corresponding to 2 / 3 of the maximum angle of incidence, obtaining the second angle; for the first and second angles, the electronic device can determine the first angle interval centered on the first angle, and the second angle interval centered on the second angle. For the maximum angle of incidence, the third angle interval is determined using the maximum angle of incidence as the maximum boundary value of the interval. The first angle may or may not be the center value of the first angle interval; similarly, the second angle may or may not be the center value of the second angle interval.

[0082] For example, if the maximum incident angle is 24°, the electronic device can divide the data into three angle ranges: 6°~16°, 10°~20°, and 14°~24°, while ensuring that the signal-to-noise ratio of the superimposed data in each part is consistent.

[0083] (2) The electronic device performs angular partial superposition processing on the first seismic data based on the multiple angle intervals to obtain multiple second seismic data.

[0084] For example, if there are three angle intervals: 6°–16°, 10°–20°, and 14°–24°, the electronic equipment will perform angle-separated stacking processing on the first seismic data according to these three angle intervals, corresponding to the near, intermediate, and far gather portion stacking of seismic data, respectively. (See [reference needed]). Figure 3 .

[0085] See Figure 4 , Figure 4 (a) is a schematic diagram of the cross-section of the near-gathering partial superimposed seismic data before removing the coal seam. Figure 4 (b) is a schematic diagram of the cross-section of the partially stacked seismic data before removing the coal seam. Figure 4 (c) is a schematic diagram of the cross section of the superimposed seismic data from the far-field set before removing the coal seam. It can be seen that the characteristics of the superimposed seismic data from the near-field, middle-field, and far-field sets are generally similar.

[0086] In this embodiment, gather data is fundamental to pre-stack inversion. Therefore, it is necessary to perform deduplication, energy compensation, denoising, and gather flattening on the gathers to fundamentally ensure the reliability of the stacked data used for pre-stack inversion. Furthermore, based on the stacking velocity, this application performs incident angle-based stacking processing on the pre-stack CRP gather seismic data, achieving a conversion from seismic reflection wave amplitude varying with offset to seismic reflection wave amplitude varying with incident angle.

[0087] Step 203: The electronic device extracts seismic wavelets based on multiple second seismic data.

[0088] This step can be achieved through the following steps (1) to (4), including:

[0089] (1) For each second earthquake data, the electronic device performs spectral analysis on the second earthquake data to determine the dominant earthquake frequency.

[0090] Electronic devices can determine the dominant frequency and bandwidth of the target layer's seismic data by performing spectral analysis on the second seismic data.

[0091] (2) The electronic equipment determines the number of wells that meet the conditions in the target work area. For each well, the electronic equipment determines the reflection coefficient based on the logging data of that well.

[0092] The reflection coefficient includes the transverse wave reflection coefficient and the longitudinal wave reflection coefficient.

[0093] Electronic equipment acquires logging data, which includes logging data from multiple wells within the target area. For each well, the electronic equipment determines whether the logging data is complete and whether any data is missing. If the logging data is complete and not missing, the well is deemed to meet the criteria, thus determining the number of wells within the target area that meet the criteria. For example, there may be 10 wells within the target area with complete logging data.

[0094] For each well, the logging data includes logging curves, which include sonic logging curves. Electronic equipment determines the P-wave velocity and S-wave velocity based on the sonic logging curves, determines the P-wave reflection coefficient based on the P-wave velocity, and determines the S-wave reflection coefficient based on the S-wave velocity.

[0095] (3) Electronic equipment performs convolution based on reflection coefficient and Rack wavelet to determine synthetic seismic data.

[0096] Among them, the dominant frequency of the Ricker wavelet is the same as the dominant frequency of the earthquake. That is, the electronic equipment determines the dominant frequency and bandwidth of the Ricker wavelet based on the dominant frequency and bandwidth of the earthquake.

[0097] The reflection coefficient includes the reflection coefficient of the shear wave and the reflection coefficient of the p-wave. The electronic equipment convolves the p-wave reflection coefficient with the Ricker wavelet to obtain the p-wave composite seismic data, also known as the p-wave composite record; and convolves the shear wave reflection coefficient with the Ricker wavelet to obtain the shear wave composite seismic data, also known as the shear wave composite record.

[0098] (4) The electronic equipment calibrates the synthetic seismic data of each well based on the second seismic data, and extracts the seismic wavelet based on the calibration results.

[0099] For each well, the second seismic data includes the well-side seismic trace data. Based on this trace data, electronic equipment performs coarse calibration on the P-wave composite record (PWR) for that well, aiming to match the PWR with the well-side seismic trace data as closely as possible and establishing a rough time-depth relationship. Building upon this coarse calibration, a deterministic wavelet extraction method is used to extract seismic wavelets from the well-side seismic trace data and well logging data. The PWR is then recalculated and recalibrated, with the time-depth relationship finely adjusted using wave impedance correspondence. This process of extracting seismic wavelets and performing fine well-seismic calibration is repeated until the PWR and well-side seismic trace data achieve a high correlation, such as a correlation coefficient greater than a preset correlation threshold.

[0100] After the P-wave composite record and well-side seismic trace data are calibrated, the S-wave composite record is calibrated following the steps described above until the calibration achieves the desired effect. This process can be as follows: Electronic equipment performs coarse calibration of the well's S-wave composite record based on the well-side seismic trace data, ensuring the S-wave composite record matches the well-side seismic trace data as closely as possible and establishing a rough time-depth relationship. Based on the coarse calibration, a deterministic wavelet extraction method is used to extract seismic wavelets from the well-side seismic trace data and well logging data. The S-wave composite record is then recalculated and calibrated again, with the time-depth relationship finely adjusted using wave impedance correspondence. This process of repeatedly using the deterministic wavelet extraction method to extract seismic wavelets and performing fine well-seismic calibration continues until the S-wave composite record and well-side seismic trace data achieve a high degree of correlation.

[0101] After the electronic equipment calibrates the P-wave synthesis record and S-wave synthesis record of each well and the seismic trace data next to the well in the manner described above, it determines the average value of the seismic wavelets corresponding to each of the multiple wells, obtains the seismic average wavelet, and determines the seismic average wavelet as the seismic wavelet corresponding to the second seismic data.

[0102] Correspondingly, for the near, middle and far gathers of partially superimposed seismic data, the electronic equipment determines the seismic wavelets corresponding to the near, middle and far gathers of partially superimposed seismic data in the manner described above.

[0103] In seismic inversion, the degree of matching between the seismic wavelet and the seismic data directly affects the accuracy of the inversion results. Therefore, to obtain high-quality inversion results, it is necessary to find a seismic wavelet extraction scheme suitable for the seismic data of the work area and extract accurate seismic wavelets. In the embodiments of this application, a deterministic wavelet extraction method is adopted, and well-seismic calibration is performed on the partially superimposed seismic data of different angle gathers to accurately extract the seismic wavelet.

[0104] Step 204: The electronic device performs pre-stack inversion based on multiple second seismic data and their corresponding seismic wavelets to obtain the target reflection coefficient volumes corresponding to the multiple second seismic data respectively.

[0105] This step can be achieved through the following steps (1) to (3), including:

[0106] (1) Electronic equipment acquires stratigraphic data and well logging data.

[0107] The logging data includes sub-data for multiple logging parameters, and includes logging data for each well within the target area. The multiple logging parameters include P-wave velocity, S-wave velocity, density, and other logging parameters.

[0108] For stratigraphic data, the electronic equipment can obtain the stratigraphic data of the target area from the first seismic data. For well logging data, the electronic equipment has already obtained it in step 203; therefore, it is not necessary to obtain well logging data again in this step.

[0109] (2) The electronic equipment performs data interpolation based on the logging sub-data and stratigraphic data of multiple logging parameters to obtain multiple data volumes.

[0110] For each logging parameter, the logging sub-data includes the logging curve for that logging parameter. For example, if the logging parameter is P-wave velocity, then its corresponding logging curve is the P-wave velocity curve.

[0111] If multiple logging parameters include P-wave velocity, S-wave velocity, and density, the electronic equipment determines the P-wave impedance curve based on the P-wave velocity curve and the density curve. The P-wave impedance is the product of the P-wave velocity and the density. Similarly, the electronic equipment determines the S-wave impedance curve based on the S-wave velocity curve and the density curve. The S-wave impedance is the product of the S-wave velocity and the density.

[0112] The transverse wave impedance curve and the longitudinal wave impedance curve are vertical, while the stratigraphic data are horizontal. Under the constraint of the stratigraphic data, the electronic equipment performs spatial data interpolation on the transverse wave impedance curve and the longitudinal wave impedance curve according to the horizontal trend of the stratigraphic data, thereby obtaining the corresponding data volume.

[0113] For the P-wave impedance curve, the electronic equipment performs data interpolation on the P-wave impedance curve under the constraint of the stratigraphic data to obtain the P-wave impedance data volume. For the S-wave impedance curve, the electronic equipment performs data interpolation on the S-wave impedance curve under the constraint of the stratigraphic data to obtain the S-wave impedance data volume. For the density curve, the electronic equipment performs data interpolation on the density under the constraint of the stratigraphic data to obtain the density data volume.

[0114] (3) The electronic equipment performs pre-stack inversion based on multiple second seismic data and their corresponding seismic wavelets, well logging data and multiple data volumes to obtain multiple target reflection coefficient volumes.

[0115] In this embodiment, after obtaining multiple data volumes, the electronic device can perform low-pass filtering on these data volumes to obtain multiple low-frequency models. The low-frequency models can reflect the spatial distribution of the corresponding data in the low-frequency region. For example, the low-frequency model of longitudinal wave impedance can reflect the spatial distribution of longitudinal wave impedance in the low-frequency region. If the multiple data volumes are respectively longitudinal wave impedance data volumes, transverse wave impedance data volumes, and density data volumes, then the corresponding low-frequency models are respectively the low-frequency model of longitudinal wave impedance, the low-frequency model of transverse wave impedance, and the low-frequency model of density.

[0116] Low-frequency information is indispensable in pre-stack inversion. Due to the limited bandwidth of seismic data, both high-frequency and low-frequency information are missing. The lack of high-frequency information reduces the vertical resolution of seismic inversion, making it difficult to identify thinner strata. Conversely, the lack of low-frequency information affects the accuracy of identifying thicker strata and increases the difficulty of quantitative interpretation. In actual seismic data acquisition, defects in geophones can lead to inaccurate or missing low-frequency information, especially below 10Hz. Since the missing low-frequency information cannot be calculated from existing seismic data during seismic inversion, well logging data includes this missing information. Therefore, this application uses well logging data interpolation to establish a low-frequency model, providing low-frequency information for seismic inversion.

[0117] For example, if information below 8.2Hz is missing in the seismic data of the study area, the low-frequency model constructed is mainly for the information of the 0-8Hz part of the absolute wave impedance in the inversion. By selecting some wells in the study area, a low-frequency model of the P-wave impedance, S-wave impedance and density in this frequency band was constructed for the subsequent pre-stack inversion.

[0118] Pre-stack inversion assumes that the volume of reflection coefficients in a formation is composed of a series of sparse and discontinuous large reflection coefficients and Gaussian-distributed small reflection coefficients. Geologically, large reflection coefficients represent subsurface discontinuities and lithological boundaries. First, based on the fundamental assumption of constrained sparse pulses, target optimization parameters are obtained. Constrained sparse pulse inversion adjusts the wave impedance calculated from the objective function for each seismic trace, including adjusting the reflection coefficients, ultimately yielding elastic parameters and the volume of reflection coefficients.

[0119] The approximate relationship between the reflection coefficient R(θ) and the underground medium parameters can be expressed by the following formula (1):

[0120] R(θ) = A + Bsin 2 θ-Csin 2 θtan 2 θ

[0121] in, θ is the incident angle of the reflected wave, V p V s Let represent the P-wave velocity and S-wave velocity of the subsurface medium, respectively, and ρ represent the density of the subsurface medium. ΔV p ΔV represents the longitudinal wave velocity difference between the upper and lower elastic bodies. s Δρ represents the difference in transverse wave velocity between the upper and lower elastic bodies, and Δρ represents the difference in density between the upper and lower elastic bodies.

[0122] Equation (1) above is the reflection coefficient related to velocity, which can be rewritten as the reflection coefficient related to impedance, as shown in the following formula (2):

[0123] R pp (θ1)=aR AI +bR SI +c′R d

[0124] Where, a = 1 + tan 2 θ, θ1 represents the longitudinal wave incident angle, R AI R SI R d These represent the reflection coefficients of the longitudinal wave impedance, the transverse wave impedance, and the density, respectively. This represents the mean of the squares of the ratio of the longitudinal and transverse wave velocities.

[0125] And because The reflection coefficient R of the longitudinal wave impedance can be derived from it. AI and transverse wave impedance reflection coefficient R SI See formulas (3) and (4) below:

[0126]

[0127] Among them, R p P represents the velocity reflection coefficient, density coefficient, or impedance reflection coefficient. p The type of reflection coefficient corresponding to AI = V. p ρ, SI = V s ρ, These represent the average impedance of the reflected longitudinal wave and the average impedance of the transverse wave, respectively, of the upper and lower elastic bodies. ΔV p ρ、ΔV s ρ represents the difference in longitudinal wave impedance and the difference in transverse wave impedance between the upper and lower elastic bodies, respectively.

[0128] The electronic device is equipped with a pre-stack inversion application, and the principle of the pre-stack inversion application can be reflected by the above formulas (1) to (4).

[0129] The electronic device inputs multiple second seismic data and their corresponding seismic wavelets, well logging sub-data of multiple well logging parameters, and multiple low-frequency models into the pre-stack inversion application. Pre-stack inversion is then performed using the application to obtain multiple target reflection coefficient volumes. The well logging sub-data of these multiple well logging parameters includes P-wave velocity curves, S-wave velocity curves, and density curves.

[0130] For partially stacked seismic data from near, intermediate, and distant gathers, pre-stack inversion yields three volumes of reflection coefficients for near, intermediate, and distant sources. (See also...) Figure 3 .

[0131] It should be noted that the core of pre-stack inversion is the optimization of inversion parameters, which mainly involves the following steps:

[0132] (1) Set the seismic signal-to-noise ratio parameter. Under the constraint of this parameter, the inversion results can achieve a high correlation with the seismic data. The higher the signal-to-noise ratio, the better the correlation.

[0133] (2) Test the sparsity constraint factor. The sparsity constraint factor is used to indicate the sparsity of the reflectance volume. Its value is positively correlated with the sparsity of the reflectance volume.

[0134] (3) Generally speaking, when the incident angle of the seismic reflected wave is less than 30°, it is difficult to invert stable elastic density data using the AVA inversion method. In order to obtain stable density data volume inversion results as much as possible, the third step is to test the singular value decomposition parameters. First, a low value of uncertainty needs to be given. By adjusting the relevant singular value decomposition threshold, the relationship between the transverse wave impedance and the longitudinal wave impedance can be controlled, which is conducive to obtaining stable transverse wave impedance and density data.

[0135] (4) In pre-stack inversion, to obtain high-precision density inversion results, it is generally necessary to perform fine angle gather division on the large-angle seismic data to obtain as many angle gather partial superimposed seismic data as possible.

[0136] (5) Test the wavelet scaling factor. The wavelet scaling factor directly affects the range of the inversion results. If the wavelet scaling factor is too small relative to the characteristics of the seismic data, it may cause the inversion impedance values ​​to be too dispersed, which may easily cause a sudden change in the impedance values. The opposite is also true.

[0137] (6) Low frequencies are very important for seismic inversion, so low frequencies need to be merged in seismic inversion. Merging low frequencies first requires confirming the range of low frequencies involved in the inversion calculation. If too high a merging frequency is selected, the model frequency band will cover the seismic frequency band, which will artificially reduce the seismic information. If too low a merging frequency is selected, noise may be artificially introduced into the seismic inversion.

[0138] (7) The seventh step is to conduct a mudstone uncertainty test. This parameter is mainly used to constrain the relationship between P-wave impedance and S-wave impedance, and this constraint relationship is more obvious for strata with a compaction tendency.

[0139] (8) The eighth step is to perform filtering tests. Some parameters are calculated under low-pass filtering. Therefore, in order to avoid the influence of high-frequency information on the application of parameters, it is necessary to set filtering parameters. These parameters are usually set higher than the merging frequency.

[0140] (9) The ninth step is to test other parameters. The method of parameter selection and the principles to be followed are the same as above.

[0141] Another point to note is that while this inversion can also yield elastic parameters, the elastic parameters obtained are inaccurate because the second seismic data includes coal seam seismic data. Therefore, after removing the coal seam seismic data, an inversion is performed again to obtain accurate elastic parameters.

[0142] Step 205: For each target reflectance volume, the electronic device extracts the coal seam reflectance volume from the target reflectance volume.

[0143] This step can be achieved through the following steps (1) to (4), including:

[0144] (1) Electronic equipment acquires drilling data.

[0145] The drilling data includes drilling data from multiple wells within the target work area. Electronic devices can acquire locally stored drilling data or data transmitted from other electronic devices; there are no specific limitations on this.

[0146] (2) The electronic equipment extracts the reflectance volume of the main coal seam from the target reflectance volume based on the stratum data.

[0147] Based on stratigraphic data, electronic equipment can determine the distribution of the main coal seam and other types of strata, and extract the reflection coefficient volume of the main coal seam from the target reflection coefficient volume based on the distribution of the main coal seam and other types of strata.

[0148] (3) The electronic device determines the first reflection coefficient threshold and the second reflection coefficient threshold based on the drilling data.

[0149] The first reflection coefficient threshold is greater than the second reflection coefficient threshold, and the first reflection coefficient threshold and the second reflection coefficient threshold are opposite numbers.

[0150] Electronic equipment uses drilling data to determine the reflection coefficient, which includes the reflection coefficient of coal seams and other types of formations. Based on the difference between the reflection coefficients of coal seams and other types of formations, a first reflection coefficient threshold and a second reflection coefficient threshold are determined.

[0151] By comparing the reflection coefficient profile and drilling data, the reflection coefficient results are in good agreement with the well data, which can reflect the spatial distribution of the geological body.

[0152] (4) The electronic device extracts the coal seam reflectance volume from the reflectance volume of the main coal seam based on the first reflectance threshold and the second reflectance threshold.

[0153] The reflection coefficients greater than the first reflection coefficient threshold or less than the second reflection coefficient threshold are the reflection coefficients of the coal seam. The electronic device extracts the reflection coefficients greater than the first reflection coefficient threshold and the reflection coefficients less than the second reflection coefficient threshold from the main coal seam reflection coefficient volume, and combines the reflection coefficients greater than the first reflection coefficient threshold and the reflection coefficients less than the second reflection coefficient threshold to form the coal seam reflection coefficient volume.

[0154] For example, if the first reflection coefficient threshold is 0.14 and the second reflection coefficient threshold is -0.14, then the reflection coefficients greater than 0.14 and less than -0.14 are combined to form the coal seam reflection coefficient volume.

[0155] In this embodiment of the application, the top and bottom reflection coefficients of the coal seam are extracted from the target reflection coefficient volume by means of layer constraints and reflection coefficient threshold constraints, thereby obtaining the coal seam reflection coefficient volume.

[0156] Step 206: The electronic device performs convolution based on the coal seam reflection coefficient volume and its corresponding seismic wavelet to obtain coal seam seismic data.

[0157] The electronic device convolves the coal seam reflection coefficient volume and its corresponding seismic wavelet, starting from the main peak of the seismic wavelet to eliminate the boundary effects caused by the wavelet sidelobes, thus obtaining the coal seam seismic data. This process can be represented by the following formula (5):

[0158] S*(t)=R(t)*W(t)

[0159] Where S*(t) represents the coal seam seismic data, R(t) represents the coal seam reflection coefficient volume, and W(t) represents the seismic wavelet.

[0160] Step 207: The electronic device removes the coal seam seismic data from the second seismic data to obtain the coal seam-free seismic data.

[0161] Electronic devices can obtain seismic data from coal seams using the following formula (6):

[0162] Sw(t) = S(t) - α × S*(t)

[0163] Where Sw(t) represents the seismic data after coal seam removal, S(t) represents the second seismic data, and α represents the wavelet scaling factor, where 0 < α ≤ 1. The wavelet scaling factor depends on the parameter settings during reflection coefficient inversion. For example, the wavelet scaling factor is 0.45.

[0164] When the wavelet scaling factor is 1, the difference between the second seismic data and the coal seam seismic data is the coal seam-free seismic data.

[0165] See Figure 5 , Figure 5 (a) is a schematic diagram of the cross-section after coal seam removal from the superimposed seismic data of the near-gathering section. Figure 5 (b) is a schematic diagram of the cross-section after coal seam removal from the partially superimposed seismic data in the middle set. Figure 5 (c) is a schematic diagram of the cross-section after coal seam removal from the overlaid seismic data in the far-field set. Combined with... Figure 4 (a) Figure 4 (b) Figure 4 (c) It can be seen that before the coal seam is removed, the location of the sandstone under the coal seam is occupied by the strong reflection of the coal seam, and the weak reflection waveform of the sandstone is difficult to identify intuitively; after the coal seam is removed, the strong reflection of the coal seam is stripped off, and the reflection waveform of the sandstone under the coal seam is separated, so that a more realistic sandstone reflection waveform can be obtained.

[0166] In the embodiments of this application, coal seams are stripped out using a pre-stack inversion-based coal seam removal technique, thereby highlighting gas-bearing reservoirs.

[0167] Step 208: The electronic device performs pre-stack inversion based on multiple coal seam removal seismic data to obtain multiple elastic parameters.

[0168] This step can be achieved through the following steps (1) to (2), including:

[0169] (1) Electronic equipment processes the logging data to remove the coal seam, and obtains the logging data without coal seam.

[0170] For each logging parameter, the electronic device fits a normal distribution to the logging sub-data of that parameter; based on the fitting result, the distribution range of that logging parameter is determined; any value in the distribution range is used to replace the value at the coal seam in the logging sub-data to obtain the coal seam-free logging sub-data of that logging parameter.

[0171] In this implementation, the logging sub-data includes logging curves. Electronic equipment performs normal distribution PDF fitting on the logging curves. Based on the fitting results, the parameters of the distribution function are determined. After determining the parameters of the distribution function, the distribution function itself can be determined. Then, based on the distribution function, the distribution interval of the logging parameters is determined. Any value within the distribution interval is used to replace the value at the coal seam in the logging curve, thus obtaining the coal seam-free logging curve for the logging parameters.

[0172] For example, if the logging parameter is density, after determining the density distribution function, the density distribution interval can be determined based on this function. Any value within this distribution interval can be used to replace the density value at the coal seam location in the density curve. This removes the coal seam from the density curve, effectively performing a coal seam-free density curve.

[0173] In this embodiment, removing the coal seam from the well logging data maintains consistency between the seismic and well logging data, thereby improving the quality of the secondary inversion. Subsequent calibration using the seismic composite record after coal seam strong reflection stripping yielded good results, showing a slight improvement compared to the original well-seismic calibration, demonstrating the feasibility of stripping coal seam information from well logging.

[0174] (2) The electronic equipment performs pre-stack inversion based on coal seam logging data and multiple coal seam seismic data to obtain multiple elastic parameters.

[0175] The coal seam removal logging data includes coal seam removal logging sub-data with multiple logging parameters, including multiple elastic parameters such as P-wave velocity, S-wave velocity, and density.

[0176] For ease of distinction, the seismic wavelet extracted in step 203 is referred to as the first seismic wavelet, the data volume in step 204 is referred to as the first data volume, and the low-frequency model is referred to as the first low-frequency model. Correspondingly, the process can be as follows: the electronic device extracts the second seismic wavelet based on multiple coal seam-removed seismic data sets; the electronic device performs pre-stack inversion based on the multiple coal seam-removed seismic data sets and their corresponding second seismic wavelets to obtain multiple elastic parameters.

[0177] The process of extracting the second seismic wavelet based on multiple coal seam seismic data is similar to step 203 and will not be repeated here.

[0178] The pre-stack inversion process of the electronic equipment based on multiple coal seam-removing seismic data and their corresponding second seismic wavelets is as follows: The electronic equipment performs data interpolation based on coal seam-removing well logging sub-data and stratigraphic data with multiple logging parameters to obtain multiple second data volumes; low-pass filtering is applied to the multiple second data volumes to obtain multiple second low-frequency models; the multiple coal seam-removing seismic data and their corresponding second seismic wavelets, the coal seam-removing well logging sub-data with multiple logging parameters, and the multiple second low-frequency models are input into the pre-stack inversion application, and pre-stack inversion is performed through the pre-stack inversion application to obtain multiple elastic parameters. Among these, the coal seam-removing well logging sub-data with multiple logging parameters includes the coal seam P-wave velocity curve, the coal seam S-wave velocity curve, and the coal seam density curve.

[0179] The process of obtaining multiple second data volumes by electronic devices is the same as the process of obtaining multiple data volumes in step 204. The process of obtaining multiple second low-frequency models is the same as the process of obtaining multiple low-frequency models in step 204. The process of inputting multiple coal seam-removed seismic data and their corresponding second seismic wavelets, multiple well logging parameters' coal seam-removed well logging sub-data, and multiple second low-frequency models into the pre-stack inversion application for pre-stack inversion is the same as the process of inputting multiple second seismic data and their corresponding seismic wavelets, multiple well logging parameters' well logging sub-data, and multiple low-frequency models into the pre-stack inversion application for pre-stack inversion in step 204 for pre-stack inversion, and will not be repeated here.

[0180] In this embodiment, pre-stack inversion is performed using seismic data and well logging data after coal seam removal, which can effectively remove the shielding effect of coal seams and accurately identify gas-bearing sandstone reservoirs beneath coal.

[0181] See Figure 6 , Figure 6 (a) is the impedance plane diagram obtained from the first inversion before coal seam removal. Figure 6 (b) shows the impedance plane obtained from the second inversion after coal seam removal. It can be seen that the high impedance zone representing the sand layer obtained from the first inversion before coal seam removal is distributed throughout the entire work area without obvious regularity; the high impedance zone representing the sand layer obtained from the second inversion after coal seam removal has stronger regularity. The southern wing of the work area is the inner delta front facies, with less sand and more mud, and the sand bodies are mainly distributed in a channel-like pattern. The northern wing of the work area is the outer delta front facies, with more sand and less mud, and the sand bodies are mainly distributed in a large area with multiple fan-shaped superpositions, which is more in line with the understanding of geological sedimentation.

[0182] In this embodiment, seismic CRP gather data is divided into multiple angle-stacked data volumes according to multiple angle intervals. Pre-stack inversion is performed to obtain reflection coefficient volumes corresponding to the multiple angle-stacked data volumes. Through stratigraphic constraints and reflection coefficient threshold constraints, only the top and bottom reflection coefficients of the coal seam are extracted. Seismic wavelet convolution is then performed on these coefficients to obtain only the seismic data of the coal seam. Seismic data of the coal seam is extracted from the multiple angle-stacked data volumes to obtain multiple angle-stacked seismic data after removing the coal seam. These angle-stacked seismic data after removing the coal seam are used as input for a second pre-stack inversion to obtain multiple elastic parameters such as P-wave velocity, S-wave velocity, and density, thereby predicting the distribution of gas-bearing sandstone reservoirs. This method innovatively forms a coal seam stripping method based on pre-stack inversion, followed by a second iterative inversion to predict the distribution of gas-bearing reservoirs beneath the coal seam. This is of great significance for realizing the efficient exploration and large-scale discovery of gas-bearing sandstone oil and gas reservoirs in coal-bearing strata, pointing the way for the next stage of lithological exploration in the block, and providing a basis for increasing reserves and production in the region.

[0183] Step 209: Electronic equipment performs reservoir prediction based on multiple elastic parameters.

[0184] Electronic equipment can determine P-wave impedance, S-wave impedance, P-wave / S-wave velocity ratio, and other parameters based on P-wave velocity, S-wave velocity, and density, and can perform reservoir prediction based on these parameters.

[0185] This application provides a reservoir prediction method. The method first performs a pre-stack inversion using partially stacked seismic data from corner gathers to obtain the target reflection coefficient volume. Then, the coal seam reflection coefficient volume is extracted from the target reflection coefficient volume and convolved with a seismic wavelet to obtain coal seam seismic data. Next, the coal seam seismic data is removed from the partially stacked seismic data from the corner gathers, i.e., the coal seam is stripped out, resulting in coal seam-free seismic data. A second pre-stack inversion is then performed based on the coal seam-free seismic data to obtain multiple elastic parameters. This method uses coal seam-free seismic data to obtain the elastic parameters, removing the shielding effect of the coal seam. Therefore, it can accurately obtain the elastic parameters, thereby accurately identifying gas-bearing sandstone reservoirs beneath coal and improving the accuracy of reservoir prediction.

[0186] It should be noted that in related technologies, when predicting reservoirs, the seismic response of the coal seam can be removed from post-stack seismic data to highlight the weak reflection information of the reservoir. Then, attribute analysis or post-stack inversion is performed using the seismic data after removing the coal seam information to characterize the reservoir distribution features. However, post-stack inversion using post-stack seismic data after removing the coal seam only yields a single elastic parameter (P-wave impedance), which is insufficient to distinguish between gas-bearing and non-gas-bearing sandstone reservoirs. The method provided in this application removes the shielding effect of the coal seam in the pre-stack domain, thereby accurately identifying gas-bearing sandstone reservoirs beneath the coal.

[0187] In summary, the method provided in this application for reservoir prediction has the following advantages:

[0188] (1) It accurately identifies strong coal seam reflections in seismic data, quickly and effectively removes strong coal seam reflections, has good coal seam stripping effect, and is highly universal.

[0189] (2) After removing the coal seam from the logging curves using statistical methods, rock physics modeling of the coal seam-removed curves was carried out to obtain curves of longitudinal wave velocity, transverse wave velocity, density, etc., providing a data basis for pre-stack secondary inversion.

[0190] (3) The results of the second inversion show that the correspondence between the reflection axis of the sand layer after coal seam removal and the well is better than that before coal seam removal. The ratio of longitudinal and transverse wave velocities can better distinguish between sand layers and gas-bearing sand, which confirms the feasibility of the pre-stack coal seam removal and gas-bearing sand prediction technology.

[0191] (4) The method provided in this application was applied to the Qiudongji 7 well area and achieved good results. The new inversion results showed a high consistency rate with the drilling data, increasing from 75% to 87.5%. Using this method, seven sweet spots were predicted in the favorable strata at the bottom of Xishanyao, covering an area of ​​28.4 km². 2 This supported the drilling of Well Ji 10. A sweet spot area of ​​184 km² was identified across three sand layers in the Sangonghe Formation. 2 Three appraisal wells and two development wells were deployed, among which wells Ji702H and Ji703H achieved high production. The concentrated exploration of tight gas in Qiudong has yielded good results, further consolidating the resource base of hundreds of billions of cubic meters and providing technical support for new oil and gas discoveries.

[0192] Figure 7 This is a schematic diagram of the structure of a reservoir prediction device provided in an embodiment of this application. See also... Figure 7 The device includes:

[0193] The acquisition module 701 is used to acquire the first seismic data of the target work area. The first seismic data is the pre-stack common reflection point gather seismic data.

[0194] The conversion module 702 is used to convert the first seismic data from the offset seismic profile to the corner gather seismic profile to obtain multiple second seismic data, which are data volumes of different corner gather parts superimposed.

[0195] The first extraction module 703 is used to extract seismic wavelets based on multiple second seismic data.

[0196] The first inversion module 704 is used to perform pre-stack inversion based on multiple second seismic data and their corresponding seismic wavelets to obtain the target reflection coefficient volume corresponding to the multiple second seismic data respectively.

[0197] The second extraction module 705 is used to extract the coal seam reflectance volume from the target reflectance volume for each target reflectance volume;

[0198] Convolution module 706 is used to perform convolution based on the coal seam reflection coefficient volume and its corresponding seismic wavelet to obtain coal seam seismic data;

[0199] The removal module 707 is used to remove coal seam seismic data from the second seismic data to obtain coal seam-free seismic data.

[0200] The second inversion module 708 is used to perform pre-stack inversion based on multiple coal seam-removed seismic data to obtain multiple elastic parameters.

[0201] Prediction module 709 is used for reservoir prediction based on multiple elastic parameters.

[0202] In one possible implementation, the conversion module 702 is used to divide multiple angle intervals based on the maximum incident angle, with overlap between adjacent angle intervals; and to perform angle-separated superposition processing on the first seismic data based on the multiple angle intervals to obtain multiple second seismic data.

[0203] In another possible implementation, the first inversion module 704 is used to acquire stratigraphic data and well logging data, the well logging data including well logging sub-data of multiple well logging parameters; data interpolation is performed based on the well logging sub-data of multiple well logging parameters and stratigraphic data to obtain multiple data volumes; pre-stack inversion is performed based on multiple second seismic data and their corresponding seismic wavelets, well logging data and multiple data volumes to obtain multiple target reflection coefficient volumes.

[0204] In another possible implementation, the second extraction module 705 is used to acquire drilling data; extract the reflection coefficient volume of the main coal seam from the target reflection coefficient volume based on the stratigraphic data; determine a first reflection coefficient threshold and a second reflection coefficient threshold based on the drilling data, wherein the first reflection coefficient threshold is greater than the second reflection coefficient threshold; and extract the coal seam reflection coefficient volume from the reflection coefficient volume of the main coal seam based on the first reflection coefficient threshold and the second reflection coefficient threshold.

[0205] In another possible implementation, the second inversion module 708 is used to perform coal seam removal processing on the well logging sub-data of each well logging parameter to obtain coal seam removal well logging sub-data of the well logging parameter; and to perform pre-stack inversion based on the coal seam removal well logging sub-data of multiple well logging parameters and multiple coal seam removal seismic data to obtain multiple elastic parameters.

[0206] In another possible implementation, the second inversion module 708 is used to fit the logging sub-data of logging parameters to a normal distribution; based on the fitting result, the distribution range of logging parameters is determined; any value in the distribution range is used to replace the value at the coal seam in the logging sub-data to obtain the coal seam-free logging sub-data of logging parameters.

[0207] This application provides a reservoir prediction device. The device first performs a pre-stack inversion using seismic data partially superimposed from corner gathers to obtain the target reflection coefficient volume. Then, it extracts the coal seam reflection coefficient volume from the target reflection coefficient volume and convolves it with a seismic wavelet to obtain coal seam seismic data. Next, it removes the coal seam seismic data from the seismic data partially superimposed from the corner gathers, effectively stripping the coal seam, thus obtaining coal seam-free seismic data. A second pre-stack inversion is then performed based on the coal seam-free seismic data to obtain multiple elastic parameters. This device uses coal seam-free seismic data when obtaining elastic parameters, removing the shielding effect of the coal seam. Therefore, it can accurately obtain elastic parameters, thereby accurately identifying gas-bearing sandstone reservoirs beneath coal and improving the accuracy of reservoir prediction.

[0208] refer to Figure 8 , Figure 8 This illustration shows a structural block diagram of a terminal 800 provided in an exemplary embodiment of this application. The terminal 800 can be a portable mobile terminal, such as a smartphone, tablet computer, MP3 player (Moving Picture Experts Group Audio Layer III), MP4 player (Moving Picture Experts Group Audio Layer IV), laptop computer, or desktop computer. The terminal 800 may also be referred to as a user device, portable terminal, laptop terminal, desktop terminal, or other names.

[0209] Typically, terminal 800 includes a processor 801 and a memory 802.

[0210] Processor 801 may include one or more processing cores, such as a quad-core processor or an octa-core processor. Processor 801 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 801 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 801 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 801 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0211] The memory 802 may include one or more computer-readable storage media, which may be non-transitory. The memory 802 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 802 are used to store at least one line of program code, which is executed by the processor 801 to implement the reservoir prediction method provided in the method embodiments of this application.

[0212] In some embodiments, the terminal 800 may also optionally include a peripheral device interface 803 and at least one peripheral device. The processor 801, memory 802, and peripheral device interface 803 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 803 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of the following: a radio frequency circuit 804, a display screen 805, a camera assembly 806, an audio circuit 807, and a power supply 808.

[0213] Peripheral device interface 803 can be used to connect at least one I / O (Input / Output) related peripheral device to processor 801 and memory 802. In some embodiments, processor 801, memory 802 and peripheral device interface 803 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 801, memory 802 and peripheral device interface 803 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0214] The radio frequency (RF) circuit 804 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 804 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 804 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. Optionally, the RF circuit 804 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The RF circuit 804 can communicate with other terminals through at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 804 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0215] Display screen 805 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 805 is a touch display screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 801 for processing. In this case, display screen 805 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there may be one display screen 805, disposed on the front panel of terminal 800; in other embodiments, there may be at least two display screens, disposed on different surfaces of terminal 800 or in a folded design; in other embodiments, display screen 805 may be a flexible display screen, disposed on a curved or folded surface of terminal 800. Furthermore, display screen 805 may be configured as a non-rectangular irregular shape, i.e., a non-rectangular screen. Display screen 805 may be made of materials such as LCD (Liquid Crystal Display) or OLED (Organic Light-Emitting Diode).

[0216] The camera assembly 806 is used to acquire images or videos. Optionally, the camera assembly 806 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is located on the front panel of the terminal, and the rear-facing camera is located on the back of the terminal. In some embodiments, there are at least two rear-facing cameras, which are any one of a main camera, a depth-sensing camera, a wide-angle camera, and a telephoto camera, to achieve background blurring by fusion of the main camera and the depth-sensing camera, panoramic shooting by fusion of the main camera and the wide-angle camera, VR (Virtual Reality) shooting, or other fusion shooting functions. In some embodiments, the camera assembly 806 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm-light flash and a cool-light flash, which can be used for light compensation at different color temperatures.

[0217] The audio circuit 807 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting the sound waves into electrical signals that are input to the processor 801 for processing, or input to the radio frequency circuit 804 to achieve voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each located at a different part of the terminal 800. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert the electrical signals from the processor 801 or the radio frequency circuit 804 into sound waves. The speaker may be a conventional diaphragm speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, the audio circuit 807 may also include a headphone jack.

[0218] Power supply 808 is used to supply power to the various components in terminal 800. Power supply 808 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 808 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged via a wired line, while a wireless rechargeable battery is a battery that is charged via a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0219] In some embodiments, the terminal 800 further includes one or more sensors 809. The one or more sensors 809 include, but are not limited to, an accelerometer 810, a gyroscope 811, a pressure sensor 812, an optical sensor 813, and a proximity sensor 814.

[0220] Accelerometer 810 can detect the magnitude of acceleration on the three coordinate axes of a coordinate system established by terminal 800. For example, accelerometer 810 can be used to detect the components of gravitational acceleration on the three coordinate axes. Processor 801 can control display screen 805 to display the user interface in either a landscape or portrait view based on the gravitational acceleration signal acquired by accelerometer 810. Accelerometer 810 can also be used for games or for acquiring user motion data.

[0221] The gyroscope sensor 811 can detect the orientation and rotation angle of the terminal 800. The gyroscope sensor 811 can work in conjunction with the accelerometer sensor 810 to collect 3D motion data from the user on the terminal 800. Based on the data collected by the gyroscope sensor 811, the processor 801 can perform the following functions: motion sensing (e.g., changing the UI based on the user's tilt), image stabilization during shooting, game control, and inertial navigation.

[0222] The pressure sensor 812 can be disposed on the side bezel of the terminal 800 and / or the lower layer of the display screen 805. When the pressure sensor 812 is disposed on the side bezel of the terminal 800, it can detect the user's grip signal on the terminal 800, and the processor 801 can perform left / right hand recognition or quick operation based on the grip signal collected by the pressure sensor 812. When the pressure sensor 812 is disposed on the lower layer of the display screen 805, the processor 801 can control the operable controls on the UI interface based on the user's pressure operation on the display screen 805. The operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0223] An optical sensor 813 is used to collect ambient light intensity. In one embodiment, the processor 801 can control the display brightness of the display screen 805 based on the ambient light intensity collected by the optical sensor 813. Specifically, when the ambient light intensity is high, the display brightness of the display screen 805 is increased; when the ambient light intensity is low, the display brightness of the display screen 805 is decreased. In another embodiment, the processor 801 can also dynamically adjust the shooting parameters of the camera assembly 806 based on the ambient light intensity collected by the optical sensor 813.

[0224] The proximity sensor 814, also known as a distance sensor, is typically located on the front panel of the terminal 800. The proximity sensor 814 is used to detect the distance between the user and the front of the terminal 800. In one embodiment, when the proximity sensor 814 detects that the distance between the user and the front of the terminal 800 is gradually decreasing, the processor 801 controls the display screen 805 to switch from a screen-on state to a screen-off state; when the proximity sensor 814 detects that the distance between the user and the front of the terminal 800 is gradually increasing, the processor 801 controls the display screen 805 to switch from a screen-off state to a screen-on state.

[0225] Those skilled in the art will understand that Figure 8 The structure shown does not constitute a limitation on terminal 800 and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0226] For a server structure diagram, please refer to [link / reference]. Figure 9The server 900 can vary considerably depending on its configuration or performance. It may include a central processing unit (CPU) 901 and a memory 902. The memory 902 stores at least one line of program code, which is loaded and executed by the processor 901 to implement the aforementioned reservoir prediction method. Of course, the server 900 may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input and output. The server 900 may also include other components for implementing device functions, which will not be elaborated upon here.

[0227] In an exemplary embodiment, a computer-readable storage medium is also provided, which stores at least one piece of program code that is loaded and executed by a processor to implement the reservoir prediction method in the above embodiments.

[0228] In an exemplary embodiment, a computer program product is also provided, which stores at least one piece of program code that is loaded and executed by a processor to implement the reservoir prediction method in the above embodiments.

[0229] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0230] The above description is only for the purpose of enabling those skilled in the art to understand the technical solution of this application, and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A reservoir prediction method, characterized in that, The method includes: Acquire the first seismic data of the target work area. The first seismic data is the pre-stack common reflection point gather seismic data. The first seismic data is converted from the offset seismic profile to the corner gather seismic profile to obtain multiple second seismic data, which are data volumes of different corner gather parts superimposed. Based on the multiple second seismic data, seismic wavelets are extracted respectively; Pre-stack inversion is performed based on the multiple second seismic data and their corresponding seismic wavelets to obtain the target reflection coefficient volumes corresponding to the multiple second seismic data respectively; For each target reflectance coefficient volume, extract the coal seam reflectance coefficient volume from the target reflectance coefficient volume; Based on the coal seam reflection coefficient volume and its corresponding seismic wavelet, convolution is performed to obtain coal seam seismic data; The coal seam seismic data is removed from the second seismic data to obtain the coal seam-free seismic data; Pre-stack inversion was performed based on multiple seismic data from coal seams to obtain multiple elastic parameters; Reservoir prediction is performed based on the aforementioned multiple elastic parameters.

2. The method according to claim 1, characterized in that, The process of converting the first seismic data from the offset seismic profile to the angle gather seismic profile yields multiple second seismic data sets, including: Based on the maximum incident angle, multiple angle intervals are divided, and there is overlap between two adjacent angle intervals; Based on the multiple angle intervals, the first seismic data is subjected to angle-separated partial superposition processing to obtain the multiple second seismic data.

3. The method according to claim 1, characterized in that, The step of performing pre-stack inversion based on the plurality of second seismic data and their corresponding seismic wavelets to obtain the target reflection coefficient volumes corresponding to the plurality of second seismic data includes: Acquire stratigraphic data and well logging data, wherein the well logging data includes well logging sub-data of multiple well logging parameters; Data interpolation is performed based on the logging sub-data and stratigraphic data of the multiple logging parameters to obtain multiple data volumes; Based on the multiple second seismic data and their corresponding seismic wavelets, the well logging data, and the multiple data volumes, pre-stack inversion is performed to obtain multiple target reflection coefficient volumes.

4. The method according to claim 3, characterized in that, Extracting the coal seam reflectance volume from the target reflectance volume includes: Acquire drilling data; Based on the stratigraphic data, the reflectance volume of the main coal seam is extracted from the target reflectance volume; Based on the drilling data, a first reflection coefficient threshold and a second reflection coefficient threshold are determined, wherein the first reflection coefficient threshold is greater than the second reflection coefficient threshold; Based on the first reflection coefficient threshold and the second reflection coefficient threshold, the coal seam reflection coefficient volume is extracted from the main coal seam reflection coefficient volume.

5. The method according to claim 3, characterized in that, The pre-stack inversion based on multiple coal seam-free seismic data yields several elastic parameters, including: For each logging parameter, the logging sub-data of the logging parameter is processed to remove the coal seam, resulting in the coal seam-removed logging sub-data of the logging parameter. Based on the coal seam removal logging sub-data and the coal seam removal seismic data, pre-stack inversion is performed to obtain multiple elastic parameters.

6. The method according to claim 5, characterized in that, The process of removing the coal seam from the logging sub-data of the logging parameters to obtain the coal seam-removed logging sub-data of the logging parameters includes: The logging sub-data of the logging parameters are fitted with a normal distribution; Based on the fitting results, the distribution range of the logging parameters is determined; By replacing the value at the coal seam in the well logging sub-data with any value from the distribution interval, the coal seam-free well logging sub-data of the well logging parameters is obtained.

7. A reservoir prediction device, characterized in that, The device includes: The acquisition module is used to acquire the first seismic data of the target work area, which is the pre-stack common reflection point gather seismic data; The conversion module is used to convert the first seismic data from the offset seismic profile to the corner gather seismic profile to obtain multiple second seismic data, wherein the multiple second seismic data are data volumes superimposed from different corner gather parts; The first extraction module is used to extract seismic wavelets based on the multiple second seismic data. The first inversion module is used to perform pre-stack inversion based on the plurality of second seismic data and their corresponding seismic wavelets to obtain the target reflection coefficient volume corresponding to the plurality of second seismic data respectively. The second extraction module is used to extract the coal seam reflectance volume from the target reflectance volume for each target reflectance volume; The convolution module is used to perform convolution based on the coal seam reflection coefficient volume and its corresponding seismic wavelet to obtain coal seam seismic data; The removal module is used to remove the coal seam seismic data from the second seismic data to obtain coal seam-free seismic data; The second inversion module is used to perform pre-stack inversion based on multiple coal seam-removed seismic data to obtain multiple elastic parameters. The prediction module is used to predict the reservoir based on the multiple elastic parameters.

8. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing at least one piece of program code, which is loaded and executed by the processor to implement the reservoir prediction method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one piece of program code, which is loaded and executed by a processor to implement the reservoir prediction method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product stores at least one piece of program code, which is loaded and executed by a processor to implement the reservoir prediction method as described in any one of claims 1 to 6.

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