Reservoir prediction method and device, electronic equipment and storage medium

Abstract

The application discloses a reservoir prediction method and device, electronic equipment and a storage medium, and belongs to the technical field of seismic exploration. The method determines acoustic time difference relationship data, density relationship data, porosity relationship data and water saturation relationship data according to first logging data, then reconstructs impedance relationship data according to the relationship data, determines a seismic wavelet according to first seismic data and the first logging data, and obtains a first data volume and a second data volume based on the reconstructed impedance relationship data, the first seismic data and the seismic wavelet, and fuses middle-low frequency data in the first data volume with high frequency data in the second data volume to obtain a target data volume. The method supplements the missing high frequency data in the seismic data by the high frequency data in the logging data, can predict thin sand layers with relatively thin thickness, and thus improves the prediction effect on the thin sand layers with relatively thin thickness.

Description

Technical Field

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

[0002] Currently, in oil and gas basins, strata dominated by thin interbedded sandstone and mudstone are widely present. Within these strata lie relatively thin sand layers, which contain abundant oil and gas resources. Therefore, predicting the location of these thin sand layers has become an urgent problem to be solved.

[0003] The relevant technologies primarily involve acquiring well logging and seismic data, and then using these data to perform inversion to predict thin sand layers. Inversion refers to the process of using seismic data, constrained by known geological patterns and well logging data, to image the spatial structure and physical properties of subsurface rock strata. The thickness of a thin sand layer is generally one-quarter of the wavelength of a seismic wave; therefore, the shorter the wavelength of the seismic wave, the smaller the predictable thickness of the sand layer. With a fixed seismic wave velocity, wavelength and frequency are negatively correlated; the higher the frequency, the shorter the wavelength, meaning the higher the frequency, the smaller the predictable thickness of the sand layer.

[0004] However, due to the lack of high-frequency data in seismic data, related technologies cannot utilize high-frequency data when performing inversions using seismic data. They can only predict thin sand layers with a minimum thickness of about 15m, and cannot predict thinner sand layers, resulting in poor prediction results for thinner sand layers. Summary of the Invention

[0005] This application provides a reservoir prediction method, apparatus, electronic device, and storage medium, which can improve the prediction effect for thin sand layers. The specific technical solution is as follows:

[0006] On one hand, embodiments of this application provide a reservoir prediction method, the method comprising:

[0007] Acquire first seismic data and first well logging data, wherein the first seismic data is seismic data collected in the seismic work area by exciting seismic waves, and the first well logging data is well logging data collected by detecting target wells in the seismic work area;

[0008] Based on the first logging data, the acoustic transit time relationship data, density relationship data, porosity relationship data, and water saturation relationship data of the seismic work area are determined. The acoustic transit time relationship data is used to represent the relationship between the time required for the seismic wave to propagate a preset distance in the formation and the depth. The porosity relationship data is used to represent the relationship between porosity and depth.

[0009] Based on the first seismic data and the first well logging data, a seismic wavelet is determined, which is used to represent the propagation form of the seismic wave in the stratum.

[0010] Based on the acoustic time difference data, the density data, the porosity data, and the water saturation data, the impedance data is reconstructed to obtain the reconstructed impedance data. The reconstructed impedance data is used to reflect the relationship between the impedance information of the formation and the depth.

[0011] Based on the reconstructed impedance relationship data, the first seismic data, and the seismic wavelet, an inversion is performed to obtain a first data volume. The first data volume is used to reflect the distribution of reservoirs with a thickness greater than a first preset thickness in the formation.

[0012] Based on the reconstructed impedance relationship data, random modeling is performed to obtain a second data volume, which is used to reflect the distribution of reservoirs with a thickness less than the first preset thickness in the formation.

[0013] Data with a frequency lower than a first preset frequency is extracted from the first data body to obtain the first data;

[0014] Extract data with a frequency greater than the first preset frequency from the second data volume to obtain the second data;

[0015] The first data and the second data are fused in the frequency domain to obtain a target data volume, which is used to reflect the distribution of reservoirs in the formation with a thickness greater than the first preset thickness and a thickness less than the first preset thickness.

[0016] Reservoir prediction is performed based on the target data volume.

[0017] In one possible implementation, the step of reconstructing the impedance relationship data based on the acoustic time difference relationship data, the density relationship data, the porosity relationship data, and the water saturation relationship data to obtain the reconstructed impedance relationship data includes:

[0018] For each depth location, the acoustic velocity of the fluid is obtained based on the type of fluid corresponding to that depth location in the formation;

[0019] Based on the water saturation at the depth location and the acoustic velocity of the fluid, the acoustic velocity of the pore portion at the depth location in the formation is determined.

[0020] Based on the acoustic velocity, acoustic transit time, density, and porosity of the pore portion corresponding to each depth location, the impedance relationship data is reconstructed to obtain the reconstructed impedance relationship data.

[0021] In another possible implementation, the impedance relationship data is reconstructed based on the acoustic velocity, acoustic transit time, density, and porosity of the pore portion corresponding to each depth location, resulting in the reconstructed impedance relationship data, including:

[0022] For each depth position, the product of the acoustic velocity and porosity of the pore portion corresponding to the depth position is determined to obtain the first acoustic velocity.

[0023] The difference between the acoustic velocity in the pore portion and the first acoustic velocity is determined to obtain the second acoustic velocity.

[0024] The first value is obtained by determining the product of the sound wave velocity and the sound wave time difference in the pore portion;

[0025] The difference between the first value and the porosity is determined to obtain the second value;

[0026] The ratio of the second acoustic velocity to the second value is determined to obtain the third acoustic velocity, which is used to reflect the acoustic velocity of the rock skeleton corresponding to the depth position in the stratum.

[0027] The product of the third acoustic velocity and density at each depth position is determined to obtain the reconstructed impedance relationship data.

[0028] In another possible implementation, determining the seismic wavelet based on the first seismic data and the first well logging data includes:

[0029] Based on the first logging data, a first reflection coefficient sequence is determined;

[0030] Based on the logging acoustic velocity in the first logging data, the first reflection coefficient sequence is converted from the depth domain to the time domain to obtain the second reflection coefficient sequence;

[0031] Create a first wavelet, and convolve the second reflection coefficient sequence with the first wavelet to obtain the second seismic data;

[0032] Based on the first matching degree between the first seismic data and the second seismic data, time-depth relationship data is determined, which is used to represent the relationship between the seismic waves in time and depth;

[0033] Based on the time-depth relationship data and the second reflection coefficient sequence, seismic wavelets are extracted from the first seismic data.

[0034] In another possible implementation, the method further includes:

[0035] Based on the reconstructed impedance relationship data, the seismic wavelet, and the time-depth relationship data, a third seismic data is synthesized.

[0036] Determine the matching degree between the first seismic data and the third seismic data to obtain the second matching degree;

[0037] In response to the second matching degree being greater than the second preset threshold, the step of performing inversion based on the reconstructed impedance relationship data, the first seismic data, and the seismic wavelet to obtain the first data volume is executed.

[0038] In another possible implementation, determining the acoustic transit time relationship data, density relationship data, porosity relationship data, and water saturation relationship data of the seismic work area based on the first well logging data includes:

[0039] The first logging data is preprocessed to obtain the second logging data, wherein the preprocessing includes at least one of environmental correction, outlier removal, and standardization.

[0040] Based on the second logging data, the sonic transit time relationship data, the density relationship data, the porosity relationship data, and the water saturation relationship data are determined.

[0041] On the other hand, embodiments of this application provide a reservoir prediction apparatus, the apparatus comprising:

[0042] The acquisition module is used to acquire first seismic data and first well logging data. The first seismic data is seismic data collected in the seismic work area by exciting seismic waves, and the first well logging data is well logging data collected by detecting target wells in the seismic work area.

[0043] The first determining module is used to determine, based on the first well logging data, the acoustic transit time relationship data, density relationship data, porosity relationship data, and water saturation relationship data of the seismic work area. The acoustic transit time relationship data is used to represent the relationship between the time required for the seismic wave to propagate a preset distance in the formation and the depth. The porosity relationship data is used to represent the relationship between porosity and depth.

[0044] The second determining module is used to determine a seismic wavelet based on the first seismic data and the first well logging data, wherein the seismic wavelet is used to represent the propagation form of the seismic wave in the stratum;

[0045] The reconstruction module is used to reconstruct impedance relationship data based on the acoustic time difference relationship data, the density relationship data, the porosity relationship data and the water saturation relationship data, to obtain the reconstructed impedance relationship data, which is used to reflect the relationship between the impedance information of the formation and the depth.

[0046] The inversion module is used to perform inversion based on the reconstructed impedance relationship data, the first seismic data and the seismic wavelet to obtain a first data volume. The first data volume is used to reflect the distribution of reservoirs with a thickness greater than a first preset thickness in the formation.

[0047] The modeling module is used to perform random modeling based on the reconstructed impedance relationship data to obtain a second data volume. The second data volume is used to reflect the distribution of reservoirs with a thickness less than the first preset thickness in the formation.

[0048] The first extraction module is used to extract data with a frequency lower than a first preset frequency from the first data body to obtain the first data.

[0049] The second extraction module is used to extract data with a frequency greater than the first preset frequency from the second data body to obtain the second data.

[0050] The fusion module is used to fuse the first data and the second data in the frequency domain to obtain a target data volume. The target data volume is used to reflect the distribution of reservoirs in the formation with a thickness greater than the first preset thickness and a thickness less than the first preset thickness.

[0051] The prediction module is used to perform reservoir prediction based on the target data volume.

[0052] In one possible implementation, the reconstruction module is configured to, for each depth location, obtain the acoustic velocity of the fluid based on the type of fluid corresponding to that depth location in the formation; determine the acoustic velocity of the pore portion corresponding to that depth location in the formation based on the water saturation and the acoustic velocity of the fluid; and reconstruct impedance relationship data based on the acoustic velocity, acoustic transit time, density, and porosity of the pore portion corresponding to each depth location, thereby obtaining the reconstructed impedance relationship data.

[0053] In another possible implementation, the reconstruction module is configured to, for each depth location, determine the product of the acoustic velocity and porosity of the pore portion corresponding to the depth location to obtain a first acoustic velocity; determine the difference between the acoustic velocity of the pore portion and the first acoustic velocity to obtain a second acoustic velocity; determine the product of the acoustic velocity of the pore portion and the acoustic time difference to obtain a first value; determine the difference between the first value and the porosity to obtain a second value; determine the ratio of the second acoustic velocity to the second value to obtain a third acoustic velocity, the third acoustic velocity being used to reflect the acoustic velocity of the rock skeleton corresponding to the depth location in the stratum; and determine the product of the third acoustic velocity and density corresponding to each depth location to obtain the reconstructed impedance relationship data.

[0054] In another possible implementation, the second determining module is configured to: determine a first reflection coefficient sequence based on the first logging data; convert the first reflection coefficient sequence from the depth domain to the time domain based on the logging acoustic velocity in the first logging data to obtain a second reflection coefficient sequence; create a first wavelet; convolve the second reflection coefficient sequence and the first wavelet to obtain second seismic data; determine time-depth relationship data based on a first matching degree between the first seismic data and the second seismic data, the time-depth relationship data being used to represent the relationship between the seismic waves in time and depth; and extract a seismic wavelet from the first seismic data based on the time-depth relationship data and the second reflection coefficient sequence.

[0055] In another possible implementation, the device further includes:

[0056] The synthesis module is used to synthesize third seismic data based on the reconstructed impedance relationship data, the seismic wavelet, and the time-depth relationship data;

[0057] The third determining module is used to determine the matching degree between the first seismic data and the third seismic data to obtain a second matching degree.

[0058] The inversion module is also used to perform an inversion based on the reconstructed impedance relationship data, the first seismic data, and the seismic wavelet in response to the second matching degree being greater than the second preset threshold, to obtain the first data volume.

[0059] In another possible implementation, the first determining module is used to preprocess the first logging data to obtain second logging data, wherein the preprocessing includes at least one of environmental correction, removal of abnormal data, and standardization processing; based on the second logging data, the sonic transit time relationship data, the density relationship data, the porosity relationship data, and the water saturation relationship data are determined.

[0060] 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.

[0061] 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 the processor to implement the reservoir prediction method described in any of the preceding claims.

[0062] 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.

[0063] The beneficial effects of the technical solutions provided in this application are:

[0064] This application provides a reservoir prediction method. The method first reconstructs impedance relationship data, then obtains a first data volume and a second data volume based on the reconstructed impedance relationship data. Next, it fuses the low- and mid-frequency data from the first data volume with the high-frequency data from the second data volume to obtain a target data volume. Since the high-frequency data in the target data volume is obtained from the second data volume, which is based on well logging data, and well logging data has high vertical resolution, supplementing the missing high-frequency data in seismic data with high-frequency data from well logging data can predict thin sand layers, thereby improving the prediction effect for thin sand layers. Attached Figure Description

[0065] Figure 1 This is a schematic diagram of the implementation environment of a reservoir prediction method provided in an embodiment of this application;

[0066] Figure 2 This is a flowchart of a reservoir prediction method provided in an embodiment of this application;

[0067] Figure 3 This is a schematic diagram of a predicted reservoir provided in an embodiment of this application;

[0068] Figure 4 This is a schematic diagram of the structure of a reservoir prediction device provided in an embodiment of this application;

[0069] Figure 5 This is a structural block diagram of a terminal provided in an embodiment of this application;

[0070] Figure 6 This is a structural block diagram of a server provided in an embodiment of this application. Detailed Implementation

[0071] To make the technical solution and advantages of this application clearer, the embodiments of this application will be described in further detail below.

[0072] 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.

[0073] 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 and well logging data involved in this application were obtained with full authorization.

[0074] 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 a terminal 101, a server 102, or both. If the electronic device is provided as a terminal 101 and a server 102, the terminal 101 and the server 102 can be connected via a wireless or wired network. In this embodiment, the electronic device is not specifically limited.

[0075] If the electronic device is provided as terminal 101, then terminal 101 acquires the first seismic data and the first well logging data, performs reservoir prediction based on the first seismic data and the first well logging data, and displays the prediction results.

[0076] If the electronic device is provided as server 102, then terminal 101 obtains the first seismic data and the first well logging data, and then sends the first seismic data and the first well logging data to server 102. Accordingly, the implementation environment also includes: terminal 101, server 102 performs reservoir prediction based on the first seismic data and the first well logging data, and then returns the prediction results to terminal 101, and terminal 101 displays the prediction results.

[0077] If the electronic device provides a terminal 101 and a server 102, the terminal 101 acquires the first seismic data and the first well logging data, and then sends the first seismic data and the first well logging data to the server 102. The server 102 performs reservoir prediction based on the first seismic data and the first well logging data, and then returns the prediction result to the terminal 101. The terminal 101 displays the prediction result.

[0078] 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.

[0079] In the embodiments of this application, the method can be applied to the prediction of thin sand layers. The method can characterize thin sand layers of 2-10m. Compared with related technologies that can only characterize thin sand layers of more than 15m, the method can meet the geological requirements for characterizing thin sand layers of less than 10m, which is a major breakthrough in the prediction of thin interbedded sandstone and mudstone reservoirs.

[0080] 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:

[0081] Step 201: The electronic device acquires the first seismic data and the first well logging data.

[0082] The first set of seismic data consists of seismic data collected by stimulating seismic waves in the seismic work area, and the first set of well logging data consists of well logging data collected by probing the target wells in the seismic work area. The seismic work area refers to the region where the reservoir to be predicted is located.

[0083] In this step, the first seismic data and the first well logging data can be stored in the same database or in different databases. If the first seismic data and the first well logging data are stored in the same database, the electronic device retrieves the first seismic data and the first well logging data from the database respectively. If the first seismic data and the first well logging data are stored in different databases, the electronic device retrieves the first seismic data from the first database and the first well logging data from the second database.

[0084] Step 202: Based on the first well logging data, the electronic equipment determines the acoustic transit time relationship data, density relationship data, porosity relationship data, and water saturation relationship data of the seismic work area.

[0085] Among them, the acoustic time difference data is used to represent the relationship between the time required for seismic waves to propagate a preset distance in the stratum and the depth; the density data is used to represent the relationship between the density of the stratum and the depth; the porosity data is used to represent the relationship between the porosity and the depth; and the water saturation data is used to represent the relationship between the water saturation and the depth.

[0086] This step can be achieved through the following steps (1) to (2), including:

[0087] (1) The electronic equipment preprocesses the first logging data to obtain the second logging data.

[0088] Preprocessing includes at least one of environmental correction, outlier removal, and standardization. Here, we will only use environmental correction, outlier removal, and standardization as examples for explanation.

[0089] If the preprocessing includes environmental correction, removal of outlier data, and standardization, the electronic equipment can first perform environmental correction on the first logging data, then remove outlier data from the corrected logging data, and finally perform standardization on the logging data after removing outlier data.

[0090] Among them, environmental correction is mainly to eliminate the phenomenon of well logging curve enlargement caused by the collapse of mudstone sections during drilling, while standardization is mainly to eliminate measurement errors caused by factors such as instruments, environment, and compaction, and to standardize the same type of data from different wells in the seismic work area to the same numerical range.

[0091] (2) The electronic equipment determines the acoustic time difference relationship data, density relationship data, porosity relationship data and water saturation relationship data based on the second logging data.

[0092] In this step, the electronic equipment can acquire acoustic time difference relationship data and density relationship data based on the second logging data.

[0093] Furthermore, the process by which the electronic equipment determines porosity relationship data based on the second well logging data can be as follows: The electronic equipment uses histograms, cross-plots, etc., based on the second well logging data to determine characteristic curves sensitive to the reservoir. Based on these characteristic curves, a first cutoff value is determined. Curve values ​​greater than the first cutoff value correspond to non-reservoirs, while curve values ​​not greater than the first cutoff value correspond to reservoirs. Then, the porosity of non-reservoirs is set to 0, and the porosity relationship data of the reservoirs is obtained.

[0094] Specifically, the electronic equipment can determine whether to use the same characteristic curve as a guide to obtain porosity and water saturation relationship data based on actual conditions. If the same characteristic curve is used, the electronic equipment sets the porosity of the non-reservoir layer to 0 and obtains the porosity relationship data of the reservoir layer, and also sets the water saturation of the non-reservoir layer to 0 and obtains the water saturation relationship data of the reservoir layer. If different characteristic curves are used as guides to obtain porosity and water saturation relationship data, the electronic equipment sets the porosity of the non-reservoir layer to 0 based on the first characteristic curve and obtains the porosity relationship data of the reservoir layer, and sets the water saturation of the non-reservoir layer to 0 based on the second characteristic curve and obtains the water saturation relationship data of the reservoir layer.

[0095] It should be noted that in sandstone and mudstone areas, gamma curves provide a good indication of reservoir and non-reservoir conditions. When obtaining porosity relationship data, the gamma curve can be used as a guide, constraining the porosity of the non-reservoir (mudstone) section to 0, and only obtaining the porosity relationship data for the reservoir (sandstone) section. In the embodiments of this application, the electronic device can use appropriate formulas to obtain porosity relationship data and water saturation relationship data according to actual conditions; no specific limitations are imposed on this.

[0096] Step 203: The electronic device determines the seismic wavelet based on the first seismic data and the first well logging data.

[0097] The process by which the electronic device determines the seismic wavelet based on the first seismic data and the first well logging data is as follows: The electronic device determines the first reflection coefficient sequence based on the first well logging data; based on the logging acoustic velocity in the first well logging data, the first reflection coefficient sequence is converted from the depth domain to the time domain to obtain the second reflection coefficient sequence; a first wavelet is created, and the second reflection coefficient sequence and the first wavelet are convolved to obtain the second seismic data; based on the first matching degree between the first seismic data and the second seismic data, the time-depth relationship data is determined; based on the time-depth relationship data and the second reflection coefficient sequence, the seismic wavelet is extracted from the first seismic data.

[0098] The first reflection coefficient sequence includes multiple reflection coefficients. Different layers correspond to different reflection coefficients. The reflection coefficient is the amplitude ratio of the reflected wave to the incident wave. The time-depth relationship data is used to represent the relationship between seismic waves in time and depth.

[0099] The electronic device, based on the second well logging data obtained after preprocessing the first well logging data, determines a first reflection coefficient sequence. All reflection coefficients in this first reflection coefficient sequence are depth-domain reflection coefficients. The electronic device obtains the logging acoustic velocity from the second well logging data and converts multiple reflection coefficients in the first reflection coefficient sequence from the depth domain to the time domain based on the logging acoustic velocity, obtaining a second reflection coefficient sequence. Then, a first wavelet is created, and the second reflection coefficient sequence and the first wavelet are convolved to obtain second seismic data, which is depth-domain seismic data. The electronic device converts the second seismic data from the depth domain to the time domain and matches the converted second seismic data with the first seismic data to determine a first matching degree between the converted second seismic data and the first seismic data. If the first matching degree is greater than a first preset threshold, the electronic device determines time-depth relationship data based on the converted second seismic data and the unconverted second seismic data.

[0100] Since seismic data can be obtained by convolution of wavelet and reflection coefficient sequence, after determining the time-depth relationship data, electronic equipment can extract the second wavelet from the first seismic data based on the time-depth relationship data and reflection coefficient sequence, and use the second wavelet as the seismic wavelet.

[0101] In the embodiments of this application, the electronic device may also determine the seismic wavelet in other ways, and no specific limitation is made on the way of determining the seismic wavelet.

[0102] It should be noted that after the electronic equipment determines the seismic wavelet, it can perform fine calibration of the reservoir based on the seismic wavelet to accurately locate the reservoir. High-quality reservoir calibration results are the foundation for establishing a high-quality initial inversion model and help improve prediction accuracy. The fine calibration method can be set and changed as needed, and no specific limitations are imposed.

[0103] Another point to note is that the electronic device can execute step 202 first and then step 203, or it can execute step 203 first and then step 202, or it can execute step 202 first, then step 204, and then step 203. There is no specific limitation on the order of these steps.

[0104] Step 204: The electronic device reconstructs the impedance relationship data based on the acoustic time difference relationship data, density relationship data, porosity relationship data and water saturation relationship data to obtain the reconstructed impedance relationship data.

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

[0106] (1) For each depth location, the electronic device obtains the acoustic velocity of the fluid based on the type of fluid corresponding to that depth location in the stratum.

[0107] The acoustic velocity is the speed at which seismic waves propagate in a fluid. Seismic waves travel at different speeds in different types of fluids. Based on this, electronic devices can obtain the acoustic velocity of seismic waves in a fluid corresponding to each depth location during the drilling process, thereby obtaining the acoustic velocity of the fluid at each depth location.

[0108] Formations generally contain water, and in addition to water, they also contain oil or gas. Therefore, the type of fluid is generally water and oil, or water and gas. Based on this, the sound wave velocity of the fluid includes the sound wave velocity of water and the sound wave velocity of oil, or it includes the sound wave velocity of water and the sound wave velocity of gas.

[0109] (2) The electronic device determines the acoustic velocity of the pore portion at the depth location in the formation based on the water saturation and the acoustic velocity of the fluid at the depth location.

[0110] For each depth location, if the fluid corresponding to that depth location is water and oil, the electronic device determines the oil saturation based on the water saturation, then determines the product of the water saturation and the water saturation at that depth location to obtain a first value, determines the product of the oil saturation and the oil saturation at that depth location to obtain a second value, and determines the sum of the first and second values ​​to obtain the pore velocity at that depth location.

[0111] The sum of water saturation and oil saturation is 1. Therefore, the electronic device determines that oil saturation = 1 - water saturation.

[0112] If the fluid at that depth is water and gas, the electronic device determines the gas saturation based on the water saturation, then determines the product of the gas acoustic velocity at that depth and the gas saturation to obtain a third value, and determines the sum of the first and third values ​​to obtain the acoustic velocity of the pore portion at that depth.

[0113] The sum of water saturation and gas saturation is 1. Therefore, the electronic device determines that gas saturation = 1 - water saturation.

[0114] Therefore, for any depth location, if the water saturation at that depth location is 100%, then the sound wave velocity in the pore portion corresponding to that depth location is the sound wave velocity of water.

[0115] (3) The electronic device reconstructs the impedance relationship data based on the acoustic velocity, acoustic time difference, density and porosity of the pore portion corresponding to each depth position, and obtains the reconstructed impedance relationship data.

[0116] This step can be achieved through the following steps (3-1) to (3-6), including:

[0117] (3-1) For each depth position, the electronic device determines the product of the acoustic velocity and porosity of the pore portion corresponding to that depth position to obtain the first acoustic velocity.

[0118] Electronic devices can pre-establish a rock physical volume model, then determine the corresponding interpretation equation based on the established rock physical volume model, and reconstruct the impedance relationship data based on the interpretation equation to obtain the reconstructed impedance relationship data.

[0119] In this embodiment, the rock physical volume model can be a pure rock volume physical model, a muddy rock volume physical model, or other models, without specific limitations. The pure rock physical volume model consists of a rock skeleton and a porous portion, while the muddy rock volume physical model consists of mud, a rock skeleton, and a porous portion. Furthermore, the pure rock physical volume model can be an oil and gas-bearing pure rock physical volume model or a water-bearing pure rock physical volume model. Here, we will only use a water-bearing pure rock physical volume model as an example for illustration.

[0120] If the rock physical volume model is a pure rock physical volume model containing water, its corresponding interpretation equation is as follows:

[0121]

[0122] Where Δt represents the acoustic transit time at any depth, φ represents the porosity at that depth, v1 represents the third acoustic velocity, and v2 represents the acoustic velocity in the pore portion at that depth.

[0123] In this explanatory equation, Δt, φ, and v2 are known data, while v1 is unknown data. Based on this, the electronic device transforms the explanatory equation to obtain...

[0124] Based on this, the electronic device determines the product of the acoustic velocity and porosity of the pore portion corresponding to the depth position, and obtains the first acoustic velocity, which can be expressed as: v2φ.

[0125] (3-2) The electronic device determines the difference between the sound wave velocity in the pore section and the first sound wave velocity to obtain the second sound wave velocity.

[0126] The velocity of the second sound wave can be expressed as: v2 - v2φ.

[0127] (3-3) The electronic device determines the product of the acoustic velocity and the acoustic time difference in the pore section to obtain the first value.

[0128] This first value can be represented as: v2Δt.

[0129] (3-4) The electronic device determines the difference between the first value and the porosity to obtain the second value.

[0130] This second value can be expressed as: v2Δt-φ.

[0131] (3-5) The electronic device determines the ratio of the second sound wave velocity to the second value to obtain the third sound wave velocity.

[0132] The third acoustic velocity is used to reflect the acoustic velocity of the rock skeleton corresponding to that depth in the stratum, and this third acoustic velocity is v1.

[0133] (3-6) The electronic device determines the product of the third acoustic velocity and density at each depth position to obtain the reconstructed impedance relationship data.

[0134] The reconstructed impedance data is used to reflect the relationship between the impedance information of the formation and its depth.

[0135] For each depth position, the impedance corresponding to that depth position is the product of the third sound wave velocity and density. Based on this, the electronic device determines the product of the third sound wave velocity and density corresponding to each depth position, thereby obtaining the reconstructed impedance relationship data, that is, the reconstructed impedance curve.

[0136] In this embodiment, impedance curve reconstruction mainly addresses the problem that the original acoustic waves cannot distinguish lithology. By using a rock physical volume model, the influence of pores and fluids on acoustic wave velocity is eliminated, and impedance relationship data is reconstructed. The characteristic curves corresponding to the reconstructed impedance relationship data reflect the lithological information of the formation skeleton, distinguishing the acoustic wave velocity information of the reservoir from that of mudstone, and providing more effective basic data for inversion.

[0137] It should be noted that after obtaining the reconstructed impedance relationship data, the electronic device can verify it to determine the accuracy of the reconstructed impedance relationship data. Accordingly, the process can be as follows: the electronic device synthesizes third seismic data based on the reconstructed impedance relationship data, seismic wavelet, and time-depth relationship data; determines the matching degree between the first and third seismic data to obtain a second matching degree; and executes step 205 in response to the second matching degree being greater than a second preset threshold.

[0138] In this implementation, the electronic device re-determines the reflection coefficient sequence based on the reconstructed impedance relationship data. Based on the re-determined reflection coefficient sequence, seismic wavelet, and time-depth relationship data, it synthesizes third seismic data. Then, it determines a second matching degree between the synthesized third seismic data and the originally acquired first seismic data. If the second matching degree is greater than a second preset threshold, step 205 is executed. If the second matching degree is not greater than the second preset threshold, the electronic device re-determines the impedance relationship data until the second matching degree is greater than the second preset threshold.

[0139] Step 205: The electronic device performs inversion based on the reconstructed impedance relationship data, the first seismic data, and the seismic wavelet to obtain the first data volume.

[0140] In this step, after the electronic equipment performs fine calibration of the reservoir, it establishes an initial inversion model based on the reconstructed impedance relationship data. Then, the first seismic data and seismic wavelet are input into the initial inversion model, and the inversion is performed through the initial inversion model to obtain the first data volume. The first data volume is used to reflect the distribution of reservoirs with a thickness greater than the first preset thickness in the formation.

[0141] The electronic device can establish an initial inversion model by performing inter-well interpolation along the stratigraphic plane, or it can establish an initial inversion model by other means, without specific limitations. In addition, the method used by the electronic device to perform inversion using the initial inversion model can be set and changed as needed. In this embodiment, the adaptive broadband constrained inversion method is used as an example for explanation.

[0142] If the inversion method is the adaptive broadband constrained inversion method, the electronic device first determines the forward equation of the wave impedance perturbation, then determines the posterior probability density function of the wave impedance perturbation according to Bayesian theory, and then solves the maximum posterior probability, which is equivalent to minimizing the objective function. Finally, the iterative expression of wave impedance inversion is obtained, and the data volume that makes the residual of the iterative expression meet the standard is the first data volume.

[0143] In this embodiment, compared to other inversion methods, the adaptive broadband constrained inversion method has advantages such as direct inversion of absolute impedance, avoidance of error propagation, adaptive variation of damping factor, stable inversion results, and high resolution. Furthermore, since the reconstructed impedance relationship data can directly distinguish between reservoirs and non-reservoirs, and the adaptive broadband constrained inversion method can directly characterize reservoirs, the inversion results obtained in this step can preliminarily characterize the inversion results of sand layers above 10m.

[0144] Step 206: The electronic device performs random modeling based on the reconstructed impedance relationship data to obtain the second data volume.

[0145] In this step, the electronic device can use the principles of geostatistics to randomly model the reconstructed impedance relationship data to obtain a second data volume. The second data volume is used to reflect the distribution of reservoirs with a thickness less than the first preset thickness in the formation.

[0146] In this implementation, the electronic device can statistically analyze the reservoir thickness and its variation pattern in the vertical direction, the thickness distribution pattern, and the variation pattern of the reservoir in the horizontal direction. Then, based on the variogram, it can perform random modeling on the reconstructed impedance relationship data. This modeling result can improve the bullseye effect of conventional models in the plane and improve the model quality.

[0147] Step 207: The electronic device extracts data with a frequency lower than a first preset frequency from the first data body to obtain the first data.

[0148] The electronic device extracts low- and mid-frequency information from the first data volume to obtain the first data.

[0149] The first preset frequency can be set and changed as needed, for example, the first preset frequency is 70Hz or 80Hz. If the first preset frequency is 70Hz, the electronic device extracts data with a frequency less than 70Hz from the first data body to obtain the first data.

[0150] Step 208: The electronic device extracts data with a frequency greater than the first preset frequency from the second data body to obtain the second data.

[0151] The electronic device can directly extract data with a frequency greater than the first preset frequency from the second data body to obtain the second data, or it can extract data with a frequency greater than the first preset frequency but less than the second preset frequency from the second data body to obtain the second data, wherein the second preset frequency is greater than the first preset frequency.

[0152] The second preset frequency can be set and changed as needed. For example, if the second preset frequency is 150Hz, and the first preset frequency is 70Hz, the electronic device can extract data with a frequency between 70 and 150Hz from the second data body to obtain the second data.

[0153] Step 209: The electronic device fuses the first data and the second data in the frequency domain to obtain the target data volume.

[0154] The electronic device fuses low- and medium-frequency information with high-frequency information to obtain a target data volume, which is used to reflect the distribution of reservoirs with a thickness greater than a first preset thickness and a thickness less than a first preset thickness in the formation.

[0155] In this embodiment of the application, considering the characteristics of low vertical resolution and high horizontal resolution of seismic data and high vertical resolution and low horizontal resolution of well logging data, the electronic device uses seismic impedance inversion results and combines them with well logging data to constrain the heterogeneity between wells. It integrates the low- and mid-frequency information in the inversion results with the high-frequency information in the stochastic modeling results, and combines the inversion results with the stochastic modeling results to improve the vertical resolution of the inversion results.

[0156] See Figure 3 ,from Figure 3 As can be seen, the electronic equipment acquires the first seismic data and the first well logging data, then performs seismic wavelet extraction and fine calibration on the first seismic data, preprocesses the first well logging data to obtain the second well logging data, performs rock physical analysis on the second well logging data, then reconstructs the impedance curve, performs stochastic modeling and inversion based on the reconstructed impedance curve, and merges the high-frequency information in the stochastic modeling with the mid- and low-frequency information in the inversion result to obtain the final inversion result.

[0157] Step 210: The electronic device performs reservoir prediction based on the target data volume.

[0158] Electronic equipment images the target data volume to obtain the imaging results. Compared to mudstone, sandstone has higher impedance; therefore, the parts with higher impedance and thinner thickness in the imaging results are thin sand layers.

[0159] Through practical application in actual blocks, the method provided in this application embodiment has been proven to effectively improve the vertical resolution of the inversion results. Comparative analysis with drilling data confirmed that the inversion results can effectively characterize thin sand layers of 2-10m, and the consistency rate between the inversion results and drilling lithology is over 80%. Compared with related technologies that can only characterize reservoirs larger than 15m, the method provided in this application embodiment represents a significant breakthrough in predicting thin interbedded sandstone and mudstone reservoirs.

[0160] This application provides a reservoir prediction method. The method first reconstructs impedance relationship data, then obtains a first data volume and a second data volume based on the reconstructed impedance relationship data. Next, it fuses the low- and mid-frequency data from the first data volume with the high-frequency data from the second data volume to obtain a target data volume. Since the high-frequency data in the target data volume is obtained from the second data volume, which is based on well logging data, and well logging data has high vertical resolution, supplementing the missing high-frequency data in seismic data with high-frequency data from well logging data can predict thin sand layers, thereby improving the prediction effect for thin sand layers.

[0161] Figure 4 This is a schematic diagram of the structure of a reservoir prediction device provided in an embodiment of this application. See also... Figure 4The device includes:

[0162] The acquisition module 401 is used to acquire first seismic data and first well logging data. The first seismic data is seismic data collected in the seismic work area by exciting seismic waves, and the first well logging data is well logging data collected by detecting target wells in the seismic work area.

[0163] The first determining module 402 is used to determine, based on the first well logging data, the acoustic transit time relationship data, density relationship data, porosity relationship data and water saturation relationship data of the seismic work area. The acoustic transit time relationship data is used to represent the relationship between the time required for seismic waves to propagate a preset distance in the stratum and the depth. The porosity relationship data is used to represent the relationship between porosity and the depth.

[0164] The second determining module 403 is used to determine the seismic wavelet based on the first seismic data and the first well logging data. The seismic wavelet is used to represent the propagation form of seismic waves in the strata.

[0165] The reconstruction module 404 is used to reconstruct impedance relationship data based on acoustic time difference relationship data, density relationship data, porosity relationship data and water saturation relationship data, and obtain the reconstructed impedance relationship data. The reconstructed impedance relationship data is used to reflect the relationship between the impedance information of the formation and the depth.

[0166] The inversion module 405 is used to perform inversion based on the reconstructed impedance relationship data, the first seismic data and the seismic wavelet to obtain the first data volume. The first data volume is used to reflect the distribution of reservoirs with a thickness greater than the first preset thickness in the formation.

[0167] Modeling module 406 is used to perform random modeling based on the reconstructed impedance relationship data to obtain a second data volume. The second data volume is used to reflect the distribution of reservoirs with a thickness less than the first preset thickness in the formation.

[0168] The first extraction module 407 is used to extract data with a frequency lower than a first preset frequency from the first data body to obtain the first data.

[0169] The second extraction module 408 is used to extract data with a frequency greater than the first preset frequency from the second data body to obtain the second data.

[0170] The fusion module 409 is used to fuse the first data and the second data in the frequency domain to obtain the target data volume. The target data volume is used to reflect the distribution of reservoirs with a thickness greater than the first preset thickness and a thickness less than the first preset thickness in the formation.

[0171] The prediction module 410 is used to predict reservoirs based on the target data volume.

[0172] In one possible implementation, the reconstruction module 404 is used to obtain the acoustic velocity of the fluid at each depth location based on the type of fluid at that depth location in the formation; determine the acoustic velocity of the pore portion at the depth location in the formation based on the water saturation and the acoustic velocity of the fluid at the depth location; and reconstruct the impedance relationship data based on the acoustic velocity, acoustic transit time, density, and porosity of the pore portion at each depth location to obtain the reconstructed impedance relationship data.

[0173] In another possible implementation, the reconstruction module 404 is used to, for each depth location, determine the product of the acoustic velocity and porosity of the pore portion corresponding to the depth location to obtain a first acoustic velocity; determine the difference between the acoustic velocity of the pore portion and the first acoustic velocity to obtain a second acoustic velocity; determine the product of the acoustic velocity of the pore portion and the acoustic time difference to obtain a first value; determine the difference between the first value and the porosity to obtain a second value; determine the ratio of the second acoustic velocity to the second value to obtain a third acoustic velocity, the third acoustic velocity being used to reflect the acoustic velocity of the rock skeleton corresponding to the depth location in the stratum; and determine the product of the third acoustic velocity and the density corresponding to each depth location to obtain the reconstructed impedance relationship data.

[0174] In another possible implementation, the second determining module 403 is used to determine a first reflection coefficient sequence based on the first well logging data; convert the first reflection coefficient sequence from the depth domain to the time domain based on the logging acoustic velocity in the first well logging data to obtain a second reflection coefficient sequence; create a first wavelet; convolve the second reflection coefficient sequence and the first wavelet to obtain second seismic data; determine time-depth relationship data based on a first matching degree between the first seismic data and the second seismic data, the time-depth relationship data being used to represent the relationship between seismic waves in time and depth; and extract a seismic wavelet from the first seismic data based on the time-depth relationship data and the second reflection coefficient sequence.

[0175] In another possible implementation, the device also includes:

[0176] The synthesis module is used to synthesize third-party seismic data based on the reconstructed impedance relationship data, seismic wavelet, and time-depth relationship data.

[0177] The third determination module is used to determine the matching degree between the first and third seismic data to obtain the second matching degree.

[0178] The inversion module is also used to perform inversion based on the reconstructed impedance relationship data, the first seismic data, and the seismic wavelet in response to the second matching degree being greater than the second preset threshold, to obtain the first data volume.

[0179] In another possible implementation, the first determining module 402 is used to preprocess the first logging data to obtain the second logging data, wherein the preprocessing includes at least one of environmental correction, removal of abnormal data and standardization processing; based on the second logging data, the sonic transit time relationship data, density relationship data, porosity relationship data and water saturation relationship data are determined.

[0180] This application provides a reservoir prediction device. The device first reconstructs impedance relationship data, then obtains a first data volume and a second data volume based on the reconstructed impedance relationship data. Next, it fuses the low- and mid-frequency data from the first data volume with the high-frequency data from the second data volume to obtain a target data volume. Since the high-frequency data in the target data volume is obtained from the second data volume, which is based on well logging data, and well logging data has high vertical resolution, supplementing the missing high-frequency data in seismic data with high-frequency data from well logging data can predict thin sand layers, thereby improving the prediction effect for thin sand layers.

[0181] If the electronic device is provided as a terminal, refer to Figure 5 , Figure 5 This illustration shows a structural block diagram of a terminal 500 provided in an exemplary embodiment of this application. The terminal 500 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 500 may also be referred to as a user device, portable terminal, laptop terminal, desktop terminal, or other names.

[0182] Typically, terminal 500 includes a processor 501 and a memory 502.

[0183] Processor 501 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 501 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 501 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 501 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the screen. In some embodiments, processor 501 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0184] Memory 502 may include one or more computer-readable storage media, which may be non-transitory. Memory 502 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 memory 502 is used to store at least one piece of program code, which is executed by processor 501 to implement the operations performed by the terminal in the reservoir prediction method provided in the method embodiments of this application.

[0185] In some embodiments, the terminal 500 may also optionally include a peripheral device interface 503 and at least one peripheral device. The processor 501, memory 502, and peripheral device interface 503 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 503 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of the following: a radio frequency circuit 504, a display screen 505, a camera assembly 506, an audio circuit 507, and a power supply 508.

[0186] Peripheral device interface 503 can be used to connect at least one I / O (Input / Output) related peripheral device to processor 501 and memory 502. In some embodiments, processor 501, memory 502 and peripheral device interface 503 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 501, memory 502 and peripheral device interface 503 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0187] The radio frequency (RF) circuit 504 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 504 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 504 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. Optionally, the RF circuit 504 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 504 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 504 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0188] Display screen 505 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 505 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 501 for processing. In this case, display screen 505 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 505, disposed on the front panel of terminal 500; in other embodiments, there may be at least two display screens, disposed on different surfaces of terminal 500 or in a folded design; in still other embodiments, display screen 505 may be a flexible display screen, disposed on a curved or folded surface of terminal 500. Furthermore, display screen 505 may be configured as a non-rectangular irregular shape, i.e., a non-rectangular screen. Display screen 505 may be made of materials such as LCD (Liquid Crystal Display) or OLED (Organic Light-Emitting Diode).

[0189] The camera assembly 506 is used to acquire images or videos. Optionally, the camera assembly 506 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 506 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.

[0190] The audio circuit 507 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 501 for processing, or input to the radio frequency circuit 504 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each located at a different part of the terminal 500. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert the electrical signals from the processor 501 or the radio frequency circuit 504 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 507 may also include a headphone jack.

[0191] Power supply 508 is used to power the various components in terminal 500. Power supply 508 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 508 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, and 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.

[0192] In some embodiments, the terminal 500 further includes one or more sensors 509. The one or more sensors 509 include, but are not limited to, an accelerometer 510, a gyroscope 511, a pressure sensor 512, an optical sensor 513, and a proximity sensor 514.

[0193] Accelerometer 510 can detect the magnitude of acceleration along the three axes of a coordinate system established by terminal 500. For example, accelerometer 510 can be used to detect the components of gravitational acceleration along the three axes. Processor 501 can control display screen 505 to display the user interface in either a landscape or portrait view based on the gravitational acceleration signal acquired by accelerometer 510. Accelerometer 510 can also be used for games or for acquiring user motion data.

[0194] The gyroscope sensor 511 can detect the orientation and rotation angle of the terminal 500. The gyroscope sensor 511, in conjunction with the accelerometer sensor 510, can collect 3D motion data from the user on the terminal 500. Based on the data collected by the gyroscope sensor 511, the processor 501 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.

[0195] The pressure sensor 512 can be disposed on the side bezel of the terminal 500 and / or on the lower layer of the display screen 505. When the pressure sensor 512 is disposed on the side bezel of the terminal 500, it can detect the user's grip signal on the terminal 500, and the processor 501 can perform left / right hand recognition or quick operation based on the grip signal collected by the pressure sensor 512. When the pressure sensor 512 is disposed on the lower layer of the display screen 505, the processor 501 can control the operable controls on the UI interface based on the user's pressure operation on the display screen 505. The operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0196] An optical sensor 513 is used to collect ambient light intensity. In one embodiment, the processor 501 can control the display brightness of the display screen 505 based on the ambient light intensity collected by the optical sensor 513. Specifically, when the ambient light intensity is high, the display brightness of the display screen 505 is increased; when the ambient light intensity is low, the display brightness of the display screen 505 is decreased. In another embodiment, the processor 501 can also dynamically adjust the shooting parameters of the camera assembly 506 based on the ambient light intensity collected by the optical sensor 513.

[0197] The proximity sensor 514, also known as a distance sensor, is typically located on the front panel of the terminal 500. The proximity sensor 514 is used to detect the distance between the user and the front of the terminal 500. In one embodiment, when the proximity sensor 514 detects that the distance between the user and the front of the terminal 500 is gradually decreasing, the processor 501 controls the display screen 505 to switch from a screen-on state to a screen-off state; when the proximity sensor 514 detects that the distance between the user and the front of the terminal 500 is gradually increasing, the processor 501 controls the display screen 505 to switch from a screen-off state to a screen-on state.

[0198] Those skilled in the art will understand that Figure 5 The structure shown does not constitute a limitation on terminal 500, and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0199] If the electronic device is provided as a server, see [link to relevant documentation]. Figure 6 , Figure 6This is a schematic diagram of a server structure provided in an embodiment of this application. The server 600 can vary significantly due to different configurations or performance. It may include a central processing unit (CPU) 601 and a memory 602. The memory 602 stores at least one line of program code, which is loaded and executed by the processor 601 to implement the operations performed by the server in the aforementioned reservoir prediction method. Of course, the server 600 may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input and output. The server 600 may also include other components for implementing device functions, which will not be elaborated here.

[0200] If the electronic device is provided as a terminal and a server, the block diagrams of the terminal and server can be found separately. Figure 5 and Figure 6 .

[0201] 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.

[0202] 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.

[0203] 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 first seismic data and first well logging data, wherein the first seismic data is seismic data collected in the seismic work area by exciting seismic waves, and the first well logging data is well logging data collected by detecting target wells in the seismic work area; Based on the first logging data, the acoustic transit time relationship data, density relationship data, porosity relationship data, and water saturation relationship data of the seismic work area are determined. The acoustic transit time relationship data is used to represent the relationship between the time required for the seismic wave to propagate a preset distance in the formation and the depth. The porosity relationship data is used to represent the relationship between porosity and depth. Based on the first seismic data and the first well logging data, a seismic wavelet is determined, which is used to represent the propagation form of the seismic wave in the stratum. Based on the acoustic time difference data, the density data, the porosity data, and the water saturation data, the impedance data is reconstructed to obtain the reconstructed impedance data. The reconstructed impedance data is used to reflect the relationship between the impedance information of the formation and the depth. Based on the reconstructed impedance relationship data, the first seismic data, and the seismic wavelet, an inversion is performed to obtain a first data volume. The first data volume is used to reflect the distribution of reservoirs with a thickness greater than a first preset thickness in the formation. Based on the reconstructed impedance relationship data, random modeling is performed to obtain a second data volume, which is used to reflect the distribution of reservoirs with a thickness less than the first preset thickness in the formation. Data with a frequency lower than a first preset frequency is extracted from the first data body to obtain the first data; Extract data with a frequency greater than the first preset frequency from the second data volume to obtain the second data; The first data and the second data are fused in the frequency domain to obtain a target data volume, which is used to reflect the distribution of reservoirs in the formation with a thickness greater than the first preset thickness and a thickness less than the first preset thickness. Reservoir prediction is performed based on the target data volume.

2. The method according to claim 1, characterized in that, The process of reconstructing impedance relationship data based on the acoustic time difference relationship data, the density relationship data, the porosity relationship data, and the water saturation relationship data to obtain reconstructed impedance relationship data includes: For each depth location, the acoustic velocity of the fluid is obtained based on the type of fluid corresponding to that depth location in the formation; Based on the water saturation at the depth location and the acoustic velocity of the fluid, the acoustic velocity of the pore portion at the depth location in the formation is determined. Based on the acoustic velocity, acoustic transit time, density, and porosity of the pore portion corresponding to each depth location, the impedance relationship data is reconstructed to obtain the reconstructed impedance relationship data.

3. The method according to claim 2, characterized in that, The impedance relationship data is reconstructed based on the acoustic velocity, acoustic transit time, density, and porosity of the pore portion corresponding to each depth location, resulting in the reconstructed impedance relationship data, including: For each depth position, the product of the acoustic velocity and porosity of the pore portion corresponding to the depth position is determined to obtain the first acoustic velocity. The difference between the acoustic velocity in the pore portion and the first acoustic velocity is determined to obtain the second acoustic velocity. The first value is obtained by determining the product of the sound wave velocity and the sound wave time difference in the pore portion; The difference between the first value and the porosity is determined to obtain the second value; The ratio of the second acoustic velocity to the second value is determined to obtain the third acoustic velocity, which is used to reflect the acoustic velocity of the rock skeleton corresponding to the depth position in the stratum. The product of the third acoustic velocity and density at each depth position is determined to obtain the reconstructed impedance relationship data.

4. The method according to claim 1, characterized in that, The step of determining the seismic wavelet based on the first seismic data and the first well logging data includes: Based on the first logging data, a first reflection coefficient sequence is determined; Based on the logging acoustic velocity in the first logging data, the first reflection coefficient sequence is converted from the depth domain to the time domain to obtain the second reflection coefficient sequence; Create a first wavelet, and convolve the second reflection coefficient sequence with the first wavelet to obtain the second seismic data; Based on the first matching degree between the first seismic data and the second seismic data, time-depth relationship data is determined, which is used to represent the relationship between the seismic waves in time and depth; Based on the time-depth relationship data and the second reflection coefficient sequence, seismic wavelets are extracted from the first seismic data.

5. The method according to claim 4, characterized in that, The method further includes: Based on the reconstructed impedance relationship data, the seismic wavelet, and the time-depth relationship data, a third seismic data is synthesized. Determine the matching degree between the first seismic data and the third seismic data to obtain the second matching degree; In response to the second matching degree being greater than the second preset threshold, the step of performing inversion based on the reconstructed impedance relationship data, the first seismic data, and the seismic wavelet to obtain the first data volume is executed.

6. The method according to claim 1, characterized in that, The step of determining the acoustic transit time relationship data, density relationship data, porosity relationship data, and water saturation relationship data of the seismic work area based on the first well logging data includes: The first logging data is preprocessed to obtain the second logging data, wherein the preprocessing includes at least one of environmental correction, outlier removal, and standardization. Based on the second logging data, the sonic transit time relationship data, the density relationship data, the porosity relationship data, and the water saturation relationship data are determined.

7. A reservoir prediction device, characterized in that, The device includes: The acquisition module is used to acquire first seismic data and first well logging data. The first seismic data is seismic data collected in the seismic work area by exciting seismic waves, and the first well logging data is well logging data collected by detecting target wells in the seismic work area. The first determining module is used to determine, based on the first well logging data, the acoustic transit time relationship data, density relationship data, porosity relationship data, and water saturation relationship data of the seismic work area. The acoustic transit time relationship data is used to represent the relationship between the time required for the seismic wave to propagate a preset distance in the formation and the depth. The porosity relationship data is used to represent the relationship between porosity and depth. The second determining module is used to determine a seismic wavelet based on the first seismic data and the first well logging data, wherein the seismic wavelet is used to represent the propagation form of the seismic wave in the stratum; The reconstruction module is used to reconstruct impedance relationship data based on the acoustic time difference relationship data, the density relationship data, the porosity relationship data and the water saturation relationship data, to obtain the reconstructed impedance relationship data, which is used to reflect the relationship between the impedance information of the formation and the depth. The inversion module is used to perform inversion based on the reconstructed impedance relationship data, the first seismic data and the seismic wavelet to obtain a first data volume. The first data volume is used to reflect the distribution of reservoirs with a thickness greater than a first preset thickness in the formation. The modeling module is used to perform random modeling based on the reconstructed impedance relationship data to obtain a second data volume. The second data volume is used to reflect the distribution of reservoirs with a thickness less than the first preset thickness in the formation. The first extraction module is used to extract data with a frequency lower than a first preset frequency from the first data body to obtain the first data. The second extraction module is used to extract data with a frequency greater than the first preset frequency from the second data body to obtain the second data. The fusion module is used to fuse the first data and the second data in the frequency domain to obtain a target data volume. The target data volume is used to reflect the distribution of reservoirs in the formation with a thickness greater than the first preset thickness and a thickness less than the first preset thickness. The prediction module is used to perform reservoir prediction based on the target data volume.

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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