Method, apparatus, device, and storage medium for predicting hydrocarbon reservoirs

Abstract

The application discloses a method, device, equipment and storage medium for predicting an oil and gas reservoir, and belongs to the technical field of oil and gas exploration. The method comprises the following steps: determining a gamma threshold value for distinguishing sandstone and mudstone, a resistivity threshold value for distinguishing the oil and gas reservoir from other layers, and an effective sample number and an optimal cutoff frequency for inversion. The inversion prediction model after inversion is filtered in sequence according to the gamma threshold value and the resistivity threshold value, so that the inversion prediction model for representing the positions of each oil and gas reservoir corresponding to a target area in the ground is obtained. The application can improve the accuracy of predicting the distribution positions of the oil and gas reservoir in the ground.

Description

Technical Field

[0001] This application relates to the field of oil and gas exploration technology, and in particular to a method, apparatus, equipment and storage medium for predicting the distribution of oil and gas reservoirs. Background Technology

[0002] As oil and gas exploration becomes increasingly difficult, oil and gas reservoirs are gradually developing towards thinner vertical sand bodies and poorer lateral continuity, thus requiring higher accuracy in reservoir prediction.

[0003] Reservoir prediction is a technique for predicting the underground distribution of oil and gas reservoirs within a given region. One related technique involves obtaining lithology, resistivity, gamma values, and other parameters from well logging data (logging data) of all wells within a specific region and performing inversion to obtain the prediction results. These inversion results represent the predicted underground distribution of oil and gas reservoirs.

[0004] In the process of developing this application, the inventors discovered that the related technology has at least the following problems:

[0005] The accuracy of predicting the underground distribution location of oil and gas reservoirs largely depends on the data used for inversion. However, in related technologies, the only data used for inversion is well logging data, which is relatively limited and leads to low accuracy in predicting the underground distribution location of oil and gas reservoirs. Summary of the Invention

[0006] This application provides a method, apparatus, device, and storage medium for predicting oil and gas reservoirs, which can improve the accuracy of predicting the underground distribution location of oil and gas reservoirs. The technical solution is as follows:

[0007] On the one hand, a method for predicting oil and gas reservoirs is provided, the method comprising:

[0008] Based on the lithological and gamma data included in the logging data of each well in the target area, a gamma threshold for distinguishing sandstone and mudstone is determined.

[0009] Based on the resistivity data and gamma data included in the well logging data, a resistivity threshold for distinguishing oil and gas reservoirs from other layers is determined.

[0010] Obtain seismic data corresponding to the target area, and determine the effective number of samples and the optimal cutoff frequency for inversion based on the seismic data and the location information of each well in the target area;

[0011] Based on the effective number of samples, the optimal cutoff frequency, the gamma data, and the seismic data, an inversion is performed to obtain a first inversion prediction model, wherein the first inversion prediction model is used to represent the lithology at various locations underground in the target area;

[0012] Based on the gamma threshold, the first inversion prediction model is filtered out to obtain the second inversion prediction model after filtering.

[0013] Based on the effective number of samples, the optimal cutoff frequency, the resistivity data, the seismic data, and the second inversion prediction model, an inversion is performed to obtain a third inversion prediction model, wherein the third inversion prediction model is used to represent the oil and gas content at various locations underground in the target area.

[0014] The third inversion prediction model is filtered out based on the resistivity threshold to obtain a fourth inversion prediction model after filtering out the resistivity threshold. The fourth inversion prediction model is used to represent the underground location of each oil and gas reservoir corresponding to the target area.

[0015] Optionally, the determination of the gamma threshold for distinguishing sandstone and mudstone based on the lithological and gamma data included in the logging data of each well in the target area includes:

[0016] Based on the lithological data and gamma values ​​included in the logging data of each well in the target area, the content values ​​of sandstone and mudstone under different gamma values ​​were determined.

[0017] Based on the content values ​​corresponding to sandstone and mudstone under the different gamma values, a gamma threshold for distinguishing sandstone and mudstone is determined.

[0018] Optionally, determining the resistivity threshold for distinguishing oil and gas reservoirs from other layers based on the resistivity data and gamma data included in the well logging data includes:

[0019] Based on the resistivity data corresponding to oil and gas reservoirs and other layers in the well logging data and the gamma data, a resistivity threshold for distinguishing oil and gas reservoirs from other layers is determined.

[0020] Optionally, the step of acquiring seismic data corresponding to the target area, and determining the effective number of samples and the optimal cutoff frequency for inversion based on the seismic data and the location information of each well in the target area, includes:

[0021] Based on the earthquake data and the location information of each well in the target area, a first correlation index is determined for each well and a different number of surrounding wells;

[0022] The effective sample size is determined based on the first correlation index between each well and a different number of surrounding wells.

[0023] Based on the earthquake data and the location information of each well in the target area, a second correlation index between the earthquake waveform and the well logging waveform corresponding to the location of each well at different earthquake frequencies is determined.

[0024] The optimal cutoff frequency is determined based on the second correlation index corresponding to the location of each well at different seismic frequencies.

[0025] On the other hand, an apparatus for predicting oil and gas reservoirs is provided, the apparatus comprising:

[0026] The determination module is used to determine the gamma threshold for distinguishing sandstone and mudstone based on the lithological and gamma data included in the logging data of each well in the target area; to determine the resistivity threshold for distinguishing oil and gas reservoirs from other layers based on the resistivity data included in the logging data and the gamma data; to acquire the seismic data corresponding to the target area; and to determine the effective number of samples and the optimal cutoff frequency for inversion based on the seismic data and the location information of each well in the target area.

[0027] The inversion module is used to perform inversion based on the effective number of samples, the optimal cutoff frequency, the gamma data, and the seismic data to obtain a first inversion prediction model, wherein the first inversion prediction model is used to represent the lithology at various locations underground in the target area;

[0028] The processing module is used to filter the first inversion prediction model based on the gamma threshold to obtain the second inversion prediction model after filtering.

[0029] The inversion module is used to perform inversion based on the effective number of samples, the optimal cutoff frequency, the resistivity data, the seismic data, and the second inversion prediction model to obtain a third inversion prediction model, wherein the third inversion prediction model is used to represent the oil and gas content at various locations underground in the target area.

[0030] The processing module is used to filter the third inversion prediction model based on the resistivity threshold to obtain a fourth inversion prediction model after filtering, wherein the fourth inversion prediction model is used to represent the underground location of each oil and gas reservoir corresponding to the target area.

[0031] Optionally, the determining module is used to:

[0032] Based on the lithological data and gamma values ​​included in the logging data of each well in the target area, the content values ​​of sandstone and mudstone under different gamma values ​​were determined.

[0033] Based on the content values ​​corresponding to sandstone and mudstone under the different gamma values, a gamma threshold for distinguishing sandstone and mudstone is determined.

[0034] Optionally, the determining module is used to:

[0035] Based on the resistivity data corresponding to oil and gas reservoirs and other layers in the well logging data and the gamma data, a resistivity threshold for distinguishing oil and gas reservoirs from other layers is determined.

[0036] Optionally, the determining module is used to:

[0037] Based on the earthquake data and the location information of each well in the target area, a first correlation index is determined for each well and a different number of surrounding wells;

[0038] The effective sample size is determined based on the first correlation index between each well and a different number of surrounding wells.

[0039] Based on the earthquake data and the location information of each well in the target area, a second correlation index between the earthquake waveform and the well logging waveform corresponding to the location of each well at different earthquake frequencies is determined.

[0040] The optimal cutoff frequency is determined based on the second correlation index corresponding to the location of each well at different seismic frequencies.

[0041] In another aspect, a computer device is provided, the computer device including a processor and a memory, the memory storing at least one instruction, the at least one instruction being loaded and executed by the processor to perform the operations performed by the method for predicting oil and gas reservoirs as described above.

[0042] In another aspect, a computer-readable storage medium is provided, wherein at least one instruction is stored therein, the at least one instruction being loaded and executed by a processor to perform the operations performed by the method for predicting oil and gas reservoirs as described above.

[0043] The beneficial effects of the technical solutions provided in this application are:

[0044] In this embodiment, an inversion model representing lithology is obtained by using effective sample size, optimal cutoff frequency, gamma data, and seismic data. The first inversion prediction model is then filtered out based on a determined gamma threshold to obtain a second inversion prediction model. This second model is then inverted to obtain a third inversion prediction model that uses resistivity to represent the underground locations of various oil and gas reservoirs in the target area. Finally, the third inversion prediction model is filtered out based on a resistivity threshold to obtain a fourth inversion prediction model representing the underground distribution of various oil and gas reservoirs in the target area. It is evident that this application incorporates effective sample size, optimal cutoff frequency, and seismic data as references during the inversion process, and filters out the obtained inversion prediction models based on gamma and resistivity thresholds respectively. The inversion prediction model representing the underground locations of various oil and gas reservoirs in the target area is then determined based on the filtered inversion prediction model, which improves the accuracy of predicting the underground distribution of oil and gas reservoirs. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart of a method for predicting oil and gas reservoirs provided in an embodiment of this application;

[0047] Figure 2 This is a schematic diagram of a method for predicting oil and gas reservoirs provided in an embodiment of this application;

[0048] Figure 3 This is a schematic diagram of a method for predicting oil and gas reservoirs provided in an embodiment of this application;

[0049] Figure 4 This is a schematic diagram of a method for predicting oil and gas reservoirs provided in an embodiment of this application;

[0050] Figure 5 This is a schematic diagram of a method for predicting oil and gas reservoirs provided in an embodiment of this application;

[0051] Figure 6 This is a schematic diagram of a device for predicting oil and gas reservoirs provided in an embodiment of this application;

[0052] Figure 7 This is a schematic diagram of the device structure of the computer equipment provided in the embodiments of this application. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0054] The method for predicting oil and gas reservoirs provided in this application can be implemented by a terminal. The terminal can run a seismic inversion application, such as thin-layer seismic inversion software. The terminal can have a processor and a memory. The memory can store execution data and programs for predicting oil and gas reservoirs, such as seismic data corresponding to a certain area and well logging data from various wells within that area. The processor can run the execution program in the memory and, based on the execution data, such as seismic data and well logging data, implement the method for predicting oil and gas reservoirs provided in this application.

[0055] Figure 1 This is a flowchart illustrating a method for predicting oil and gas reservoirs provided in an embodiment of this application. See also... Figure 1 This embodiment includes:

[0056] Step 101: Based on the lithological and gamma data included in the logging data of each well in the target area, determine the gamma threshold used to distinguish between sandstone and mudstone.

[0057] The well logging data can include acoustic wave, acoustic impedance, gamma ray, resistivity, and density measurements at different depths for each well. The gamma ray threshold can be determined based on the content of sandstone and mudstone in the corresponding reservoir at different gamma ray values ​​included in the well logging data. Specifically:

[0058] Based on the lithological data and gamma values ​​included in the logging data of each well in the target area, the content values ​​of sandstone and mudstone at different gamma values ​​are determined; based on the content values ​​of sandstone and mudstone at different gamma values, the gamma threshold used to distinguish between sandstone and mudstone is determined.

[0059] In practice, a lithological distribution histogram for the first region can be determined based on the proportion of sandstone and mudstone at different gamma values ​​included in the logging data of each well within that region. For example... Figure 2 As shown, in the lithology distribution histogram, the vertical axis represents percentages and the horizontal axis represents gamma values. Technicians can determine the gamma threshold used to distinguish between sandstone and mudstone based on the percentages of sandstone and mudstone at different gamma values ​​in the histogram. For example... Figure 2 In the study, the percentage of sandstone is higher when the gamma value is less than 100, and the percentage of mudstone is higher when the gamma value is greater than 100. Therefore, 100 can be determined as the gamma threshold for distinguishing between sandstone and mudstone.

[0060] Step 102: Based on the resistivity and gamma data included in the well logging data, determine the resistivity threshold used to distinguish oil and gas reservoirs from non-oil and gas reservoirs.

[0061] Non-oil and gas reservoirs can include mudstone, poor gas layer, poor oil layer, dry layer, water-bearing gas layer, oil-water layer, suspected layer, gas layer, steam-water layer, water layer, sandstone, oil-water layer, etc. Well logging data can include the resistivity and gamma values ​​corresponding to oil and gas reservoirs and non-oil and gas reservoirs within the target area. A resistivity threshold for distinguishing between oil and gas reservoirs and non-oil and gas reservoirs can be determined based on the corresponding resistivity and gamma values.

[0062] In practice, a GR-lnR cross plot (gamma-resistivity cross plot) can be determined based on the gamma and resistivity values ​​of different reservoirs included in the corresponding well logging data within the first region. Then, based on the resistivity of different reservoirs in the GR-lnR cross plot, a resistivity threshold for distinguishing oil and gas reservoirs from other layers can be determined. For example... Figure 3 In the GR-lnR cross plot shown, the resistivity value corresponding to the oil and gas layer is generally greater than 5, while the resistivity value of other layers is generally less than 5. Therefore, 5 can be used as the resistivity threshold to distinguish the oil and gas reservoir from other layers.

[0063] It should be noted that before performing steps 101 and 102, technicians can optimize the various logging parameters in the logging data, such as sonic parameters, density, gamma ray, and resistivity. Additionally, during steps 101 and 102, time-depth calibration and wavelet estimation of the inversion method can be performed; the specific processing flow is existing technology and will not be elaborated here.

[0064] Step 103: Obtain the seismic data corresponding to the target area. Based on the seismic data and the location information of each well in the target area, determine the effective number of samples and the optimal cutoff frequency for inversion.

[0065] The effective sample number is used to estimate the number of effective samples in the inversion results of the predicted points. It can be used to represent the degree of influence of spatial variations in seismic waveforms on the reservoir and can improve the lateral resolution of the inversion. When the reservoir variation in the study area is small and the heterogeneity is weak, the effective sample number can be appropriately increased; when the reservoir variation in the study area is rapid and the heterogeneity is strong, the sample number can be appropriately decreased.

[0066] The optimal cutoff frequency is used to determine the maximum frequency of the inverted volume. The optimal cutoff frequency can be used to adjust the determinism and stochasticity of the inverted volume. A smaller optimal cutoff frequency results in lower longitudinal resolution and stronger determinism in the inverted volume. Conversely, a larger optimal cutoff frequency results in higher longitudinal resolution and stronger stochasticity.

[0067] During implementation, the effective number of samples and the optimal cutoff frequency for inversion can be determined based on seismic data and the location information of each well in the target area.

[0068] The steps for determining the number of valid samples are as follows: Based on seismic data and the location information of each well in the target area, determine the first correlation index of each well with a different number of surrounding wells. The first correlation index is used to indicate the similarity between the seismic waveform and the well logging waveform. Based on the first correlation index of each well with a different number of surrounding wells, determine the number of valid samples.

[0069] In implementation, the location information of each well can be its latitude and longitude, or its coordinates in the corresponding plane coordinate system of the target area. The seismic data includes seismic waveforms at various locations within the target area. For each well within the target area, the seismic waveform corresponding to its location can be determined based on its location information, and the seismic waveforms of other wells surrounding it can also be determined. For each well, the similarity between its seismic waveforms and those of one, two, three, and N surrounding wells is determined. Then, based on the similarity, a first correlation index is obtained between each well and different numbers of surrounding wells. For example... Figure 4 As shown, Figure 4 This is a schematic diagram showing the first correlation index between each well in the target area and a different number of surrounding wells. Based on... Figure 4 This allows us to determine the upward trend of the first correlation index for each well as the number of surrounding wells increases. A higher first correlation index indicates lower similarity between wells. Based on the upward trend of the first correlation index for each well, we can determine the target number of wells where the upward trend of the first correlation index for each well begins to level off. The maximum value of the target number for each well can be determined as the effective sample size, or the average value of the target number for each well can be determined as the effective sample size.

[0070] The steps for determining the optimal cutoff frequency are as follows: Based on seismic data and the location information of each well in the target area, determine the second correlation index between the seismic waveform and the logging waveform corresponding to the location of each well at different frequencies; based on the second correlation index corresponding to the location of each well at different frequencies, determine the optimal cutoff frequency.

[0071] In practice, seismic data can include seismic waveforms at different locations within the target area at different seismic frequencies. Then, the similarity between the corresponding seismic waveforms and well logging waveforms of each well at different seismic frequencies (i.e., the second correlation index) is calculated. For example... Figure 5 As shown, Figure 5This diagram illustrates the second correlation index of different wells within the target area at different seismic frequencies. Based on the upward or downward trend of the second correlation index for each well, the target frequency at which the second correlation index for each well begins to level off can be determined. The maximum value of the target frequency for each well can be determined as the optimal cutoff frequency, or the average value of the target frequency for each well can be determined as the optimal cutoff frequency.

[0072] Step 104: Based on the effective number of samples, the optimal cutoff frequency, gamma data, and seismic data, perform inversion to obtain the first inversion prediction model.

[0073] The first inversion prediction model is used to represent the lithology at various locations underground in the target area.

[0074] In implementation, after obtaining the effective sample size and optimal cutoff frequency, the effective sample size, optimal cutoff frequency, gamma data corresponding to the target area, and seismic data can be input into the inversion algorithm for inversion calculation. For example, the inversion algorithm can be a Markov chain-Monte Carlo stochastic simulation algorithm. The specific inversion processing is existing technology and will not be described in detail here. The inversion prediction model obtained after the inversion can be called the first inversion prediction model or the gamma inversion body. The first inversion prediction model includes gamma values ​​corresponding to various spatial locations underground in the target area. These gamma values ​​can be used to represent the lithology of the corresponding locations.

[0075] It should be noted that before inputting the gamma data into the inversion algorithm, the gamma data can be processed into pseudo-acoustic waves to obtain the wave impedance corresponding to the gamma data, and then the inversion calculation can be performed using the wave impedance corresponding to the gamma data.

[0076] Step 105: Filter the first inversion prediction model based on the gamma threshold to obtain the second inversion prediction model after filtering.

[0077] In step 101, the gamma threshold for distinguishing sandstone and mudstone has been determined. Therefore, the first inversion prediction model can be filtered based on the gamma threshold, that is, gamma values ​​greater than the gamma threshold can be filtered out, for example, gamma values ​​greater than the gamma threshold can be set to 0. This results in a filtered second inversion prediction model, in which the gamma value corresponding to mudstone is 0.

[0078] Step 106: Based on the effective number of samples, the optimal cutoff frequency, resistivity data, seismic data, and the second inversion prediction model, perform inversion to obtain the third inversion prediction model.

[0079] The third inversion prediction model is used to represent the hydrocarbon potential at various locations beneath the target area. After obtaining the filtered second inversion prediction model, it can be inverted again. Since the second inversion prediction model is obtained by filtering the gamma-ray inversion body based on a gamma threshold (i.e., removing mudstone from the gamma-ray inversion body), inverting the filtered gamma-ray inversion body again avoids the influence of mudstone on the inversion, further improving the accuracy of hydrocarbon reservoir prediction.

[0080] In implementation, the effective sample size, optimal cutoff frequency, resistivity data corresponding to the target area, seismic data, and the second inversion prediction model can be input into the inversion algorithm for inversion calculation. The inversion prediction model obtained after inversion can be called the third inversion prediction model or resistivity inversion body. The third inversion prediction model includes the resistivity corresponding to various spatial locations underground in the target area, which can be used to represent the hydrocarbon content at the corresponding locations.

[0081] Step 107: Filter the third inversion prediction model based on the resistivity threshold to obtain the fourth inversion prediction model after filtering.

[0082] The fourth inversion prediction model is used to represent the underground location of each oil and gas reservoir corresponding to the target area.

[0083] In implementation, the second inversion prediction model includes the resistivity corresponding to various spatial locations in the target area, which represents the oil and gas content of those locations. In step 102, a resistivity threshold for distinguishing oil and gas reservoirs from non-oil and gas reservoirs has been determined. In step 106, a resistivity inversion model is obtained by re-inverting the filtered gamma inversion model. Therefore, the resistivity inversion model can be filtered based on the resistivity threshold, i.e., resistivity values ​​below the threshold are removed, for example, by setting them to 0. This results in the filtered fourth inversion prediction model. The fourth inversion prediction model obtained after this filtering process only includes resistivity values ​​greater than the resistivity threshold. Thus, the spatial region formed by the spatial locations corresponding to each resistivity value in the fourth inversion prediction model represents the underground distribution area of ​​each oil and gas reservoir in the target area.

[0084] In this embodiment, an inversion model representing lithology is obtained by using effective sample size, optimal cutoff frequency, gamma data, and seismic data. The first inversion prediction model is then filtered out based on a determined gamma threshold to obtain a second inversion prediction model. This second model is then inverted to obtain a third inversion prediction model that uses resistivity to represent the underground locations of various oil and gas reservoirs in the target area. Finally, the third inversion prediction model is filtered out based on a resistivity threshold to obtain a fourth inversion prediction model representing the underground distribution of various oil and gas reservoirs in the target area. It is evident that this application incorporates effective sample size, optimal cutoff frequency, and seismic data as references during the inversion process, and filters out the obtained inversion prediction models based on gamma and resistivity thresholds respectively. The inversion prediction model representing the underground locations of various oil and gas reservoirs in the target area is then determined based on the filtered inversion prediction model, which improves the accuracy of predicting the underground distribution of oil and gas reservoirs.

[0085] All of the above-mentioned optional technical solutions can be combined in any way to form optional embodiments of this disclosure, and will not be described in detail here.

[0086] Figure 6 This application provides an embodiment of a device for predicting oil and gas reservoirs. This device can be the terminal in the above embodiments, see [link to previous document]. Figure 6 The device includes:

[0087] The determination module 610 is used to determine, based on the lithological data and gamma data included in the logging data of each well in the target area, a gamma threshold for distinguishing sandstone and mudstone; based on the resistivity data included in the logging data and the gamma data, a resistivity threshold for distinguishing oil and gas reservoirs from other layers; acquire seismic data corresponding to the target area, and based on the seismic data and the location information of each well in the target area, determine the effective number of samples and the optimal cutoff frequency for inversion;

[0088] The inversion module 620 is used to perform inversion based on the effective number of samples, the optimal cutoff frequency, the gamma data and the seismic data to obtain a first inversion prediction model, wherein the first inversion prediction model is used to represent the lithology at various locations underground in the target area;

[0089] Processing module 630 is used to filter the first inversion prediction model based on the gamma threshold to obtain a second inversion prediction model after filtering.

[0090] The inversion module 620 is used to perform inversion based on the effective number of samples, the optimal cutoff frequency, the resistivity data, the seismic data, and the second inversion prediction model to obtain a third inversion prediction model, wherein the third inversion prediction model is used to represent the oil and gas content at various locations underground in the target area.

[0091] The processing module 630 is used to filter the third inversion prediction model based on the resistivity threshold to obtain a fourth inversion prediction model after filtering, wherein the fourth inversion prediction model is used to represent the underground location of each oil and gas reservoir corresponding to the target area.

[0092] Optionally, the determining module 610 is used for:

[0093] Based on the lithological data and gamma values ​​included in the logging data of each well in the target area, the content values ​​of sandstone and mudstone under different gamma values ​​were determined.

[0094] Based on the content values ​​corresponding to sandstone and mudstone under the different gamma values, a gamma threshold for distinguishing sandstone and mudstone is determined.

[0095] Optionally, the determining module 610 is used for:

[0096] Based on the resistivity data corresponding to oil and gas reservoirs and other layers in the well logging data and the gamma data, a resistivity threshold for distinguishing oil and gas reservoirs from other layers is determined.

[0097] Optionally, the determining module 610 is used for:

[0098] Based on the earthquake data and the location information of each well in the target area, a first correlation index is determined for each well and a different number of surrounding wells;

[0099] The effective sample size is determined based on the first correlation index between each well and a different number of surrounding wells.

[0100] Based on the earthquake data and the location information of each well in the target area, a second correlation index between the earthquake waveform and the well logging waveform corresponding to the location of each well at different earthquake frequencies is determined.

[0101] The optimal cutoff frequency is determined based on the second correlation index corresponding to the location of each well at different seismic frequencies.

[0102] It should be noted that the apparatus for predicting oil and gas reservoirs provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the equipment can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus for predicting oil and gas reservoirs provided in the above embodiments and the method embodiments for predicting oil and gas reservoirs belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be repeated here.

[0103] Figure 7 This illustration shows a structural block diagram of a computer device 700 provided in an exemplary embodiment of this application. The computer device 700 can be a terminal as described in the above embodiments, or 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 computer device 700 may also be referred to as a user device, portable terminal, laptop terminal, desktop terminal, or other names.

[0104] Typically, computer device 700 includes a processor 701 and a memory 702.

[0105] Processor 701 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 701 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 701 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 701 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 701 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0106] The memory 702 may include one or more computer-readable storage media, which may be non-transitory. The memory 702 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 702 are used to store at least one instruction, which is executed by the processor 701 to implement the method for predicting oil and gas reservoirs provided in the method embodiments of this application.

[0107] In some embodiments, the computer device 700 may optionally include a peripheral device interface 703 and at least one peripheral device. The processor 701, memory 702, and peripheral device interface 703 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 703 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of the following: a radio frequency circuit 704, a display screen 705, a camera assembly 706, an audio circuit 707, a positioning assembly 708, and a power supply 709.

[0108] Peripheral device interface 703 can be used to connect at least one I / O (Input / Output) related peripheral device to processor 701 and memory 702. In some embodiments, processor 701, memory 702 and peripheral device interface 703 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 701, memory 702 and peripheral device interface 703 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0109] The radio frequency (RF) circuit 704 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 704 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 704 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. Optionally, the RF circuit 704 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 704 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 704 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0110] Display screen 705 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 705 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 701 for processing. In this case, display screen 705 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, display screen 705 may be a single screen, disposed on the front panel of computer device 700; in other embodiments, display screen 705 may be at least two screens, disposed on different surfaces of computer device 700 or in a folded design; in still other embodiments, display screen 705 may be a flexible display screen, disposed on a curved or folded surface of computer device 700. Furthermore, display screen 705 may be configured as a non-rectangular, irregular shape, i.e., a non-rectangular screen. Display screen 705 may be made of materials such as LCD (Liquid Crystal Display) or OLED (Organic Light-Emitting Diode).

[0111] The camera assembly 706 is used to acquire images or videos. Optionally, the camera assembly 706 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 706 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.

[0112] The audio circuit 707 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 701 for processing, or input to the radio frequency circuit 704 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each located in a different part of the computer device 700. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert electrical signals from the processor 701 or the radio frequency circuit 704 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 707 may also include a headphone jack.

[0113] The positioning component 708 is used to locate the current geographical location of the computer device 700 in order to enable navigation or LBS (Location Based Service). The positioning component 708 can be a positioning component based on the US GPS (Global Positioning System), China's BeiDou system, or Russia's Galileo system.

[0114] Power supply 709 is used to supply power to the various components in computer device 700. Power supply 709 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 709 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.

[0115] In some embodiments, the computer device 700 further includes one or more sensors 710. The one or more sensors 710 include, but are not limited to: an accelerometer 711, a gyroscope 712, a pressure sensor 713, a fingerprint sensor 714, an optical sensor 715, and a proximity sensor 716.

[0116] Accelerometer 711 can detect the magnitude of acceleration along the three coordinate axes of a coordinate system established by computer device 700. For example, accelerometer 711 can be used to detect the components of gravitational acceleration along the three coordinate axes. Processor 701 can control display screen 705 to display the user interface in either a landscape or portrait view based on the gravitational acceleration signal acquired by accelerometer 711. Accelerometer 711 can also be used for games or for acquiring user motion data.

[0117] The gyroscope sensor 712 can detect the orientation and rotation angle of the computer device 700. The gyroscope sensor 712, in conjunction with the accelerometer sensor 711, can collect 3D motion data from the user on the computer device 700. Based on the data collected by the gyroscope sensor 712, the processor 701 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.

[0118] The pressure sensor 713 can be disposed on the side bezel of the computer device 700 and / or on the lower layer of the display screen 705. When the pressure sensor 713 is disposed on the side bezel of the computer device 700, it can detect the user's grip signal on the computer device 700, and the processor 701 can perform left / right hand recognition or quick operation based on the grip signal collected by the pressure sensor 713. When the pressure sensor 713 is disposed on the lower layer of the display screen 705, the processor 701 can control the operable controls on the UI interface based on the user's pressure operation on the display screen 705. The operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0119] The fingerprint sensor 714 is used to collect a user's fingerprint. The processor 701 identifies the user based on the fingerprint collected by the fingerprint sensor 714, or vice versa. When the user's identity is verified as trusted, the processor 701 authorizes the user to perform relevant sensitive operations, including unlocking the screen, viewing encrypted information, downloading software, making payments, and changing settings. The fingerprint sensor 714 can be located on the front, back, or side of the computer device 700. When the computer device 700 has physical buttons or a manufacturer's logo, the fingerprint sensor 714 can be integrated with the physical buttons or the manufacturer's logo.

[0120] An optical sensor 715 is used to collect ambient light intensity. In one embodiment, the processor 701 can control the display brightness of the display screen 705 based on the ambient light intensity collected by the optical sensor 715. Specifically, when the ambient light intensity is high, the display brightness of the display screen 705 is increased; when the ambient light intensity is low, the display brightness of the display screen 705 is decreased. In another embodiment, the processor 701 can also dynamically adjust the shooting parameters of the camera assembly 706 based on the ambient light intensity collected by the optical sensor 715.

[0121] A proximity sensor 716, also known as a distance sensor, is typically mounted on the front panel of a computer device 700. The proximity sensor 716 is used to detect the distance between the user and the front of the computer device 700. In one embodiment, when the proximity sensor 716 detects that the distance between the user and the front of the computer device 700 is gradually decreasing, the processor 701 controls the display screen 705 to switch from a screen-on state to a screen-off state; when the proximity sensor 716 detects that the distance between the user and the front of the computer device 700 is gradually increasing, the processor 701 controls the display screen 705 to switch from a screen-off state to a screen-on state.

[0122] Those skilled in the art will understand that Figure 7 The structure shown does not constitute a limitation on the computer device 700, and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0123] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory including instructions that can be executed by a processor in a terminal to complete the method for predicting oil and gas reservoirs in the above embodiments. This computer-readable storage medium may be non-transitory. For example, the computer-readable storage medium may be ROM (Read-Only Memory), RAM (Random Access Memory), magnetic tape, floppy disk, and optical data storage devices, etc.

[0124] 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.

[0125] The above description is only a preferred embodiment 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 method for predicting oil and gas reservoirs, characterized in that, The method includes: Based on the lithological and gamma data included in the logging data of each well in the target area, a gamma threshold for distinguishing sandstone and mudstone is determined. Based on the resistivity data and gamma data included in the well logging data, a resistivity threshold for distinguishing oil and gas reservoirs from other layers is determined. Obtain seismic data corresponding to the target area, and determine the effective number of samples and the optimal cutoff frequency for inversion based on the seismic data and the location information of each well in the target area; Based on the effective number of samples, the optimal cutoff frequency, the gamma data, and the seismic data, an inversion is performed to obtain a first inversion prediction model, wherein the first inversion prediction model is used to represent the lithology at various locations underground in the target area; Based on the gamma threshold, the first inversion prediction model is filtered out to obtain the second inversion prediction model after filtering. Based on the effective number of samples, the optimal cutoff frequency, the resistivity data, the seismic data, and the second inversion prediction model, an inversion is performed to obtain a third inversion prediction model, wherein the third inversion prediction model is used to represent the oil and gas content at various locations underground in the target area. The third inversion prediction model is filtered out based on the resistivity threshold to obtain a fourth inversion prediction model after filtering out the resistivity threshold. The fourth inversion prediction model is used to represent the underground location of each oil and gas reservoir corresponding to the target area.

2. The method according to claim 1, characterized in that, The determination of the gamma threshold for distinguishing sandstone and mudstone based on lithological and gamma data from well logging data in the target area includes: Based on the lithological data and gamma values ​​included in the logging data of each well in the target area, the content values ​​of sandstone and mudstone under different gamma values ​​were determined. Based on the content values ​​corresponding to sandstone and mudstone under the different gamma values, a gamma threshold for distinguishing sandstone and mudstone is determined.

3. The method according to claim 1, characterized in that, The determination of a resistivity threshold for distinguishing oil and gas reservoirs from other layers based on the resistivity data and gamma data included in the well logging data includes: Based on the resistivity data corresponding to oil and gas reservoirs and other layers in the well logging data and the gamma data, a resistivity threshold for distinguishing oil and gas reservoirs from other layers is determined.

4. The method according to claim 1, characterized in that, The process of acquiring seismic data corresponding to the target area, and determining the effective number of samples and the optimal cutoff frequency for inversion based on the seismic data and the location information of each well in the target area, includes: Based on the earthquake data and the location information of each well in the target area, a first correlation index is determined between each well and a different number of surrounding wells; The effective sample size is determined based on the first correlation index between each well and a different number of surrounding wells. Based on the earthquake data and the location information of each well in the target area, a second correlation index between the earthquake waveform and the well logging waveform corresponding to the location of each well at different earthquake frequencies is determined. The optimal cutoff frequency is determined based on the second correlation index corresponding to the location of each well at different seismic frequencies.

5. An apparatus for predicting oil and gas reservoirs, characterized in that, The device includes: The determination module is used to determine the gamma threshold for distinguishing sandstone and mudstone based on the lithological and gamma data included in the logging data of each well in the target area; to determine the resistivity threshold for distinguishing oil and gas reservoirs from other layers based on the resistivity data included in the logging data and the gamma data; to acquire the seismic data corresponding to the target area; and to determine the effective number of samples and the optimal cutoff frequency for inversion based on the seismic data and the location information of each well in the target area. The inversion module is used to perform inversion based on the effective number of samples, the optimal cutoff frequency, the gamma data, and the seismic data to obtain a first inversion prediction model, wherein the first inversion prediction model is used to represent the lithology at various locations underground in the target area; The processing module is used to filter the first inversion prediction model based on the gamma threshold to obtain the second inversion prediction model after filtering. The inversion module is used to perform inversion based on the effective number of samples, the optimal cutoff frequency, the resistivity data, the seismic data, and the second inversion prediction model to obtain a third inversion prediction model, wherein the third inversion prediction model is used to represent the oil and gas content at various locations underground in the target area. The processing module is used to filter the third inversion prediction model based on the resistivity threshold to obtain a fourth inversion prediction model after filtering, wherein the fourth inversion prediction model is used to represent the underground location of each oil and gas reservoir corresponding to the target area.

6. The apparatus according to claim 5, characterized in that, The determining module is used for: Based on the lithological data and gamma values ​​included in the logging data of each well in the target area, the content values ​​of sandstone and mudstone under different gamma values ​​were determined. Based on the content values ​​corresponding to sandstone and mudstone under the different gamma values, a gamma threshold for distinguishing sandstone and mudstone is determined.

7. The apparatus according to claim 5, characterized in that, The determining module is used for: Based on the resistivity data corresponding to oil and gas reservoirs and other layers in the well logging data and the gamma data, a resistivity threshold for distinguishing oil and gas reservoirs from other layers is determined.

8. The apparatus according to claim 5, characterized in that, The determining module is used for: Based on the earthquake data and the location information of each well in the target area, a first correlation index is determined between each well and a different number of surrounding wells; The effective sample size is determined based on the first correlation index between each well and a different number of surrounding wells. Based on the earthquake data and the location information of each well in the target area, a second correlation index between the earthquake waveform and the well logging waveform corresponding to the location of each well at different earthquake frequencies is determined. The optimal cutoff frequency is determined based on the second correlation index corresponding to the location of each well at different seismic frequencies.

9. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing at least one instruction, which is loaded and executed by the processor to perform the operations performed by the method for predicting oil and gas reservoirs as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that, The storage medium stores at least one instruction, which is loaded and executed by a processor to perform the operations of the method for predicting oil and gas reservoirs as described in any one of claims 1 to 4.

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