Method and device for predicting deepwater turbidite based on amplitude four-order moment function

Abstract

The invention provides a method and a device for predicting deepwater turbidite based on an amplitude four-order moment function. The method comprises the following steps: determining a top layer position and a bottom layer position of a target layer section in a work area by adopting a well seismic calibration method when the earthquake quality of the work area meets requirements through earthquake quality analysis; calculating a non-Gaussian distribution characteristic parameter data volume of amplitude probability distribution of the target interval according to the seismic data of the target interval; extracting a non-Gaussian distribution characteristic parameter plane distribution diagram of the amplitude probability distribution of the target layer section; and according to the deposition law of the deepwater turbidite and the non-Gaussian distribution characteristic parameter plane distribution diagram of the amplitude probability distribution of the target interval, predicting the turbidite enrichment development area. According to the method, the spatial distribution characteristics of the deepwater turbidite and the connectivity in the turbidite are predicted by applying the non-Gaussian distribution characteristic parameters of the amplitude probability distribution function, the turbidite enrichment development area is accurately predicted, and the deepwater turbidite oil reservoir drilling success rate is increased.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration and development of deep-water turbidite reservoirs, and particularly to a method and device for predicting deep-water turbidite based on the fourth-order moment function of amplitude. Background Art

[0002] As an important research field in deep-water oil and gas exploration and development, deep-water turbidite has always been a hot spot in the world's oil and gas exploration and development. Such oil and gas reservoirs have large reserves, good reservoir physical properties, but strong reservoir heterogeneity, great difficulty in identifying turbidite, and difficult to finely describe the internal connectivity of turbidite bodies, which restricts the exploration and development of deep-water turbidite reservoirs.

[0003] It is well known that conventional reflection seismic signals conform to the generalized Gaussian distribution function, and the signals of the generalized Gaussian distribution are a kind of probability density distribution characteristics close to the Gaussian distribution and having a symmetric form. In the case of underground homogeneous layered media, the actual reflection seismic signal can be regarded as a probability density signal of a zero-mean and close-to-symmetric generalized Gaussian distribution. The short-range multiple waves in the deep water layer of the seismic signal, the strong heterogeneity of the formation, the shielding fault edge of the overlying salt rock mass, and the seismic scattering at the turbidite boundary are extremely likely to cause the non-Gaussian characteristics of the seismic exploration signal, posing challenges to the conventional seismic method for identifying turbidite. Summary of the Invention

[0004] To solve the technical problems of strong reservoir heterogeneity of deep-water turbidite, great difficulty in identifying turbidite, and difficult to finely describe the internal connectivity of turbidite bodies, the present invention proposes a method and device for predicting deep-water turbidite based on the fourth-order moment function of amplitude.

[0005] In a first aspect, an embodiment of the present invention provides a method for predicting deep-water turbidite based on the fourth-order moment function of amplitude, including:

[0006] Obtaining seismic data of the work area;

[0007] Performing seismic quality analysis on the work area according to the seismic data of the work area;

[0008] If, after seismic quality analysis, the seismic quality of the work area meets the requirements, the top layer position and the bottom layer position of the target layer section in the work area are determined by using the well-seismic calibration method;

[0009] Calculating a non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target layer section according to the seismic data of the target layer section;

[0010] Extracting a non-Gaussian distribution characteristic parameter plane distribution map of the amplitude probability distribution of the target layer section according to the non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target layer section;

[0011] Predict the turbidite-rich development area according to the sedimentation law of deep-water turbidites and the plane distribution map of the non-Gaussian distribution characteristic parameters of the amplitude probability distribution of the target interval.

[0012] In some implementation manners, calculating the non-Gaussian distribution characteristic parameter volume of the amplitude probability distribution of the target interval based on the seismic data of the target interval includes:

[0013] Calculate the amplitude moving average volume of the target interval according to the seismic data of the target interval;

[0014] Calculate the amplitude moving variance volume of the target interval according to the amplitude moving average volume of the target interval;

[0015] Calculate the amplitude moving fourth-order central moment function volume of the target interval according to the amplitude moving average volume of the target interval;

[0016] Calculate the non-Gaussian distribution characteristic parameter volume of the amplitude probability distribution of the target interval according to the amplitude moving variance volume of the target interval and the amplitude moving fourth-order central moment function volume of the target interval.

[0017] In some implementation manners, the seismic quality analysis of the work area according to the seismic data of the work area includes:

[0018] Evaluate the resolution and signal-to-noise ratio of the stacked seismic data of the target interval according to the seismic data of the work area to determine the geological sedimentation characteristics of deep-water turbidites and analyze the feasibility of predicting deep-water turbidites from the seismic data of the work area.

[0019] In some implementation manners, the amplitude moving average volume of the target interval is calculated by using the following calculation formula:

[0020]

[0021] In the formula, represents the amplitude moving average volume of the target interval, and A i represents the amplitude value of the i-th sampling point of a seismic data trace, where i = 1,..., N.

[0022] In some implementation manners, the amplitude moving variance volume of the target interval is calculated by using the following calculation formula:

[0023]

[0024] In the formula, δ i represents the amplitude moving variance volume of the target interval.

[0025] In some implementations, the amplitude sliding fourth-order central moment function data volume of the target interval is calculated using the following calculation formula:

[0026]

[0027] In the formula, E i represents the amplitude sliding fourth-order central moment function data volume of the target interval.

[0028] In some implementations, the non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target interval is calculated using the following calculation formula:

[0029] β i = E i / δ i4 (4)

[0030] In the formula, β i represents the non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target interval.

[0031] In a second aspect, an apparatus for predicting deep-water turbidite rocks based on the fourth-order moment function of amplitude provided by an embodiment of the present invention includes:

[0032] An acquisition module, configured to acquire seismic data of a work area;

[0033] A quality analysis module, configured to perform seismic quality analysis on the work area according to the seismic data of the work area;

[0034] A well-seismic calibration module, configured to, if the seismic quality of the work area meets the requirements after seismic quality analysis, use the well-seismic calibration method to determine the top position and the bottom position of the target interval in the work area;

[0035] A calculation module, configured to calculate the non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target interval according to the seismic data of the target interval;

[0036] An extraction module, configured to extract the non-Gaussian distribution characteristic parameter plane distribution map of the amplitude probability distribution of the target interval according to the non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target interval;

[0037] A prediction module, configured to predict the turbidite rock enrichment and development area according to the sedimentation law of deep-water turbidite rocks and the non-Gaussian distribution characteristic parameter plane distribution map of the amplitude probability distribution of the target interval.

[0038] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by at least one processor, the method described in the first aspect is implemented.

[0039] In a fourth aspect, an embodiment of the present invention provides an electronic device, including a memory and at least one processor. A computer program is stored on the memory, and when the computer program is executed by the at least one processor, the method described in the first aspect is implemented.

[0040] One or more embodiments of the present invention at least bring the following beneficial effects:

[0041] By applying the non-Gaussian distribution characteristic parameters of the amplitude probability distribution function, the present invention predicts the spatial distribution characteristics of deep-water turbidites, thereby realizing the detection and identification of the lithology change characteristics of underground formations. At the same time, it can also analyze the connectivity inside the turbidites, so as to highlight the detailed information inside the underground turbidites and improve the drilling success rate of deep-water turbidite reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as limiting the scope.

[0043] Figure 1 It is a flowchart of a method for predicting deep-water turbidites based on the fourth-order moment function of amplitude provided by an embodiment of the present invention;

[0044] Figure 2 It is a planar distribution characteristic diagram of deep-water turbidites identified by the fourth-order moment function of amplitude of deep-water turbidite A provided by an embodiment of the present invention;

[0045] Figure 3 It is the planar distribution characteristics of deep-water turbidites identified by the second-order moment function of amplitude of deep-water turbidite A provided by an embodiment of the present invention;

[0046] Figure 4 It is a schematic diagram of a classic sedimentation pattern of the Lower Tertiary turbidity system in the deep water of West Africa provided by an embodiment of the present invention;

[0047] Figure 5 It is a schematic diagram of the quality analysis result of seismic data of deep-water turbidite reservoir B provided by an embodiment of the present invention;

[0048] Figure 6 It is a seismic amplitude profile calibrated by lithology through a known well of deep-water turbidite reservoir B provided by an embodiment of the present invention;

[0049] Figure 7 It is a non-Gaussian characteristic parameter profile of the fourth-order central moment of amplitude through a known well of deep-water turbidite reservoir B provided by an embodiment of the present invention;

[0050] Figure 8It is the planar distribution feature of deep-water turbidite rocks identified by the non-Gaussian characteristic parameters of the fourth-order moment function of amplitude for the target interval of deep-water turbidite reservoir B provided by the embodiments of the present invention;

[0051] Figure 9 It is a schematic diagram of the planar distribution feature of deep-water turbidite rocks identified by the second-order moment function of amplitude for the target interval of deep-water turbidite reservoir B provided by the embodiments of the present invention;

[0052] Figure 10 It is a structural block diagram of a device for predicting deep-water turbidite rocks based on the fourth-order moment function of amplitude provided by the embodiments of the present invention. Specific embodiments

[0053] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Usually, the components of the embodiments of the present invention described and shown in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed present invention, but only represents the selected embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0054] As is well known, conventional reflection seismic signals conform to the generalized Gaussian distribution function, and the signals of the generalized Gaussian distribution are a kind of probability density distribution feature close to the Gaussian distribution and having a symmetric form. In the case of underground homogeneous layered media, the actual reflection seismic signals can be regarded as a kind of probability density signal with zero mean and close to symmetric generalized Gaussian distribution. The short-range multiple waves in the deep water layer of seismic signals, the strong heterogeneity of the formation, the shielding of the overlying salt rock mass, the fault edges and the seismic scattering of the turbidite rock boundaries are extremely likely to cause the non-Gaussian characteristics of seismic exploration signals, posing challenges to the conventional seismic identification methods for turbidite rocks.

[0055] The high-order statistic signal processing method extracts the characteristic parameters caused by the deviation from Gaussianity from the high-order statistics of non-Gaussian signals to describe the detailed information of various signals, especially the information that cannot be extracted from the first- and second-order statistics. The high-order statistic method is not only an important supplement to the random signal processing method based on the correlation function or power spectrum, but also can provide new tools for many signal processing problems that cannot be solved by the second-order statistic method. Therefore, the non-Gaussian distribution function characteristics based on the fourth-order central moment function of seismic amplitude provide a new tool for identifying deep-water turbidite rocks in reflection seismic signals. In view of the non-Gaussianity and low signal-to-noise ratio characteristics of the reflection seismic signals of deep-water turbidite rock reservoirs, the present invention proposes a method for predicting deep-water turbidite rocks based on the fourth-order moment function of amplitude, which overcomes the requirement that the application of the root mean square function of seismic amplitude to predict deep-water turbidite rocks must conform to the Gaussian distribution of reflection seismic signals, improves the effectiveness of predicting and identifying deep-water turbidite rocks by reflection seismic, detects and identifies the lithological change characteristics of underground strata, is conducive to the connectivity analysis inside turbidite rocks, can highlight the detailed information inside underground turbidite rocks, and improves the success rate of drilling deep-water turbidite rock reservoirs.

[0056] Embodiment 1

[0057] This embodiment provides a method for predicting deep-water turbidite rocks based on the fourth-order moment function of amplitude, as Figure 1 shown, including:

[0058] Step S1, obtaining the seismic data of the work area.

[0059] Step S2, performing seismic quality analysis on the work area according to the seismic data of the work area. Thereby determining the geological sedimentary characteristics of deep-water turbidite rocks and analyzing the feasibility of predicting deep-water turbidite rocks with the seismic data of the work area.

[0060] In some implementation manners, step S2 includes:

[0061] According to the seismic data of the work area, evaluating the resolution and signal-to-noise ratio of the stacked seismic data of the target interval to determine the geological sedimentary characteristics of deep-water turbidite rocks and analyzing the feasibility of predicting deep-water turbidite rocks with the seismic data of the work area.

[0062] Step S3, if the seismic quality of the work area meets the requirements after seismic quality analysis, using the well-seismic calibration method to determine the top and bottom horizons of the target interval in the work area.

[0063] Step S4, calculating the non-Gaussian distribution characteristic parameter volume of the amplitude probability distribution of the target interval according to the seismic data of the target interval.

[0064] In some implementation manners, step S4 includes the following steps:

[0065] Step S41: Calculate the amplitude moving average data volume of the target interval based on the seismic data of the target interval. The amplitude moving average data volume of the target interval is calculated using the following formula:

[0066]

[0067] In the formula, represents the amplitude moving average data volume of the target interval, and A i represents the amplitude value of the i-th sampling point of a seismic trace, where i = 1,..., N.

[0068] Step S42: Calculate the amplitude moving variance data volume of the target interval based on the amplitude moving average data volume of the target interval. The amplitude moving variance data volume of the target interval is calculated using the following formula:

[0069]

[0070] In the formula, δ i represents the amplitude moving variance data volume of the target interval.

[0071] Step S43: Calculate the amplitude moving fourth-order central moment function data volume of the target interval based on the amplitude moving average data volume of the target interval. The amplitude moving fourth-order central moment function data volume of the target interval is calculated using the following formula:

[0072]

[0073] In the formula, E i represents the amplitude moving fourth-order central moment function data volume of the target interval.

[0074] Step S44: Calculate the non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target interval based on the amplitude moving variance data volume and the amplitude moving fourth-order central moment function data volume of the target interval. The non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target interval is calculated using the following formula:

[0075] β i = E i / δ i4 (4)

[0076] In the formula, β i represents the non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target interval.

[0077] Step S5: Extract the non-Gaussian distribution characteristic parameter plane distribution map of the amplitude probability distribution of the target interval based on the non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target interval.

[0078] Step S6: Predict the enrichment and development area of turbidite rocks based on the sedimentation law of deep-water turbidite rocks and the plane distribution map of the non-Gaussian distribution characteristic parameters of the amplitude probability distribution of the target interval. Preferably, deep-water turbidite rocks are taken as the drilling targets.

[0079] A method for predicting deep-water turbidite rocks based on the fourth-order moment function of amplitude provided in this embodiment is applicable to the prediction of deep-water turbidite rocks under the conditions of non-Gaussian distribution of reflected seismic signals or low signal-to-noise ratio. By using the non-Gaussian distribution characteristic parameters of the amplitude probability distribution function, the spatial distribution characteristics of deep-water turbidite rocks are predicted, the lithology change characteristics of underground strata are detected and identified, and at the same time, the connectivity inside the turbidite rocks can be analyzed, the detailed information inside the underground turbidite rocks can be highlighted, the enrichment and development area of turbidite rocks can be determined, and the drilling success rate of deep-water turbidite rock reservoirs can be improved.

[0080] Taking the deep-water turbidite rock A identified from certain actual seismic data as an example, applying the method for predicting deep-water turbidite rocks based on the fourth-order moment function of amplitude provided in this embodiment, the feasibility analysis of the seismic data in this area is completed, and the calculations of the amplitude moving average data volume, the amplitude moving variance data volume, the amplitude moving fourth-order central moment function data volume, and the non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution are carried out, and the plane distribution map of the non-Gaussian distribution characteristic parameters of the amplitude probability distribution of the target interval in this area is extracted to determine the enrichment and development area of turbidite rocks.

[0081] Figure 2 It is the planar distribution characteristics (dark color) of deep-water turbidite rock A identified by the fourth-order moment function of amplitude. Figure 3 It is the planar distribution characteristics (dark color) of deep-water turbidite rock A identified by the second-order moment function of amplitude. Through the comparative analysis of the two figures, it shows that the distribution characteristics of deep-water turbidite rocks identified by the fourth-order moment function of amplitude are much clearer than those identified by the second-order moment function of amplitude in the right half of the area, which conforms to the actual north-south regional sedimentation law of deep-water turbidite rocks, providing reliable information for preferentially selecting the drilling target PF well. Based on the comprehensive research results, one well position PF is deployed, and 35.73 meters of effective oil-bearing sand layers are drilled through. Subsequently, it is confirmed to be a commercial production well, with good economic benefits, which verifies the effectiveness of this method.

[0082] Taking a deep-water turbidite rock reservoir B in the sea as an example, this deep-water turbidite rock reservoir B is a turtle-back anticline structure that dips in all directions (with a salt core). The salt movement and tension during the Miocene and Oligocene epochs have led to a high degree of development of reservoir faults and complex cutting relationships, making it a complex fault-block reservoir. The water depth is between 1300 meters and 1800 meters. The main target is the deep-water turbidite rock reservoir in the Oligocene. The sedimentary type of the reservoir is the sedimentation of the deep-sea channel turbidite system in the Tertiary, such as Figure 4In the classical sedimentation model of the deep-water Tertiary turbidity current system in West Africa shown, turbidites are distributed in the form of channel sands and lobes. Such reservoirs have strong heterogeneity, with developed internal shale interlayers, and the spatial distribution law is not well understood. Due to the influence of deep water and salt bodies, the accuracy of seismic identification of turbidites is not high, which restricts the exploration and development of deep-water turbidite reservoirs.

[0083] Carry out relevant analysis according to the process of the method for predicting deep-water turbidites using the fourth-order moment function of amplitude provided in this embodiment.

[0084] For the seismic data of the target interval in this area, carry out seismic quality analysis using the short-time Fourier transform. As Figure 5 shown, according to the spectral analysis, the main frequency of the seismic data increases by 28 Hz, and the effective spectral width broadens by 68 Hz. According to the quality of the seismic quality, relevant research can be carried out on the project.

[0085] Carry out well-seismic calibration on the target interval in this area to determine the top and bottom horizons of the target interval. The seismic amplitude profile calibrated by the lithology of the known well is as Figure 6 shown. The non-Gaussian characteristic parameter profile of the fourth-order central moment of amplitude through the known well is as Figure 7 shown. From Figure 7 it can be seen that the lithological characteristics of deep-water turbidites and the connectivity of sand bodies are very clear.

[0086] Calculate the data volume of the non-Gaussian distribution characteristic parameters of the amplitude probability distribution of the target interval, and extract the plane distribution map of the non-Gaussian distribution characteristic parameters of the amplitude probability distribution of the target interval. The planar distribution characteristics (dark color) of deep-water turbidites identified by the non-Gaussian characteristic parameters of the fourth-order moment function of amplitude in the target interval are as Figure 8 shown. At the same time, we also extracted the planar distribution characteristics of deep-water turbidites identified by the second-order moment function of amplitude in the target interval, as Figure 9 shown. Figure 8 The comparative analysis with Figure 9 shows that the planar distribution characteristics of deep-water turbidites identified by the non-Gaussian characteristic parameters of the fourth-order moment function of amplitude are clearer and more obvious, and can better study the spatial distribution characteristics of turbidites. The turbidites are distributed in the southeast-northwest direction, and the provenance is in the southeast direction. Based on the comprehensive research results of deep-water turbidite reservoirs, one PF well position was deployed, and 35.73 meters of effective oil-bearing sand layers were drilled through. Subsequently, it was confirmed to be a commercial production well, generating good economic benefits.

[0087] Embodiment 2

[0088] This embodiment provides a device for predicting deep-water turbidites based on the fourth-order moment function of amplitude. As Figure 10 shown, it includes an acquisition module 21, a quality analysis module 22, a well-seismic calibration module 23, a calculation module 24, an extraction module 25, and a prediction module 26.

[0089] The acquisition module 21 is used to acquire seismic data of the work area.

[0090] The quality analysis module 22 is used to perform seismic quality analysis on the work area according to the seismic data of the work area.

[0091] The well-seismic calibration module 23 is used to, if the seismic quality of the work area meets the requirements after seismic quality analysis, determine the top and bottom horizons of the target interval in the work area by using the well-seismic calibration method.

[0092] The calculation module 24 is used to calculate the non-Gaussian distribution characteristic parameter volume of the amplitude probability distribution of the target interval according to the seismic data of the target interval.

[0093] The extraction module 25 is used to extract the non-Gaussian distribution characteristic parameter plane distribution map of the amplitude probability distribution of the target interval according to the non-Gaussian distribution characteristic parameter volume of the amplitude probability distribution of the target interval.

[0094] The prediction module 26 is used to predict the turbidite-rich development area according to the sedimentation law of deep-water turbidite and the non-Gaussian distribution characteristic parameter plane distribution map of the amplitude probability distribution of the target interval. It is preferred to use deep-water turbidite as the drilling target.

[0095] In some implementation manners, the quality analysis module 22 is used to evaluate the resolution and signal-to-noise ratio of the stacked seismic data of the target interval according to the seismic data of the work area, so as to determine the geological sedimentation characteristics of deep-water turbidite and analyze the feasibility of predicting deep-water turbidite in the seismic data of the work area.

[0096] In some implementation manners, the process of calculating the non-Gaussian distribution characteristic parameter volume of the amplitude probability distribution of the target interval in the calculation module 24 includes the following steps:

[0097] Step S41, calculate the amplitude moving average volume of the target interval according to the seismic data of the target interval. The amplitude moving average volume of the target interval is calculated by using the following calculation formula:

[0098]

[0099] In the formula, represents the amplitude moving average volume of the target interval, and A i represents the amplitude value of the i-th sampling point of a seismic trace, where i = 1,..., N.

[0100] Step S42, calculate the amplitude moving variance volume of the target interval according to the amplitude moving average volume of the target interval. The amplitude moving variance volume of the target interval is calculated by using the following calculation formula:

[0101]

[0102] In the formula, δ i represents the amplitude sliding variance data volume of the target interval.

[0103] Step S43: Calculate the amplitude sliding fourth-order central moment function data volume of the target interval according to the amplitude sliding average data volume of the target interval. The amplitude sliding fourth-order central moment function data volume of the target interval is calculated by the following calculation formula:

[0104]

[0105] In the formula, E i represents the amplitude sliding fourth-order central moment function data volume of the target interval.

[0106] Step S44: Calculate the non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target interval according to the amplitude sliding variance data volume of the target interval and the amplitude sliding fourth-order central moment function data volume of the target interval. The non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target interval is calculated by the following calculation formula:

[0107] β i = E i / δ i4 (4)

[0108] In the formula, β i represents the non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target interval.

[0109] The device for predicting deep-water turbidite rocks based on the fourth-order moment function of amplitude provided in this embodiment is applicable to the prediction of deep-water turbidite rocks under the non-Gaussian distribution or low signal-to-noise ratio of reflection seismic signals. By using the non-Gaussian distribution characteristic parameters of the amplitude probability distribution function, it can predict the spatial distribution characteristics of deep-water turbidite rocks, detect and identify the lithology change characteristics of underground formations, and at the same time can analyze the connectivity inside the turbidite rocks, highlight the detailed information inside the underground turbidite rocks, determine the enrichment and development areas of turbidite rocks, and improve the drilling success rate of deep-water turbidite rock reservoirs.

[0110] Embodiment 3

[0111] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by at least one processor, it implements the method for predicting deep-water turbidite rocks based on the fourth-order moment function of amplitude in the foregoing embodiment.

[0112] Among them, the computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic memory, flash memory, magnetic disk or optical disc.

[0113] A method for predicting deep-water turbidite rocks based on the fourth-order moment function of amplitude implemented in this embodiment, as Figure 1 shown, includes:

[0114] Step S1, obtaining seismic data of the work area.

[0115] Step S2, performing seismic quality analysis on the work area according to the seismic data of the work area. Thereby determining the geological sedimentary characteristics of deep-water turbidite rocks and analyzing the feasibility of predicting deep-water turbidite rocks from the seismic data of the work area.

[0116] In some implementation manners, step S2 includes:

[0117] According to the seismic data of the work area, evaluating the resolution and signal-to-noise ratio of the stacked seismic data of the target interval to determine the geological sedimentary characteristics of deep-water turbidite rocks and analyzing the feasibility of predicting deep-water turbidite rocks from the seismic data of the work area.

[0118] Step S3, if the seismic quality of the work area meets the requirements after seismic quality analysis, using the well-seismic calibration method to determine the top and bottom horizons of the target interval in the work area.

[0119] Step S4, calculating the non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target interval according to the seismic data of the target interval.

[0120] In some implementation manners, step S4 includes the following steps:

[0121] Step S41, calculating the amplitude moving average data volume of the target interval according to the seismic data of the target interval. The amplitude moving average data volume of the target interval is calculated by the following calculation formula:

[0122]

[0123] In the formula, represents the amplitude moving average data volume of the target interval, A i represents the amplitude value of the i-th sampling point of a seismic trace, where i = 1,..., N.

[0124] Step S42: Calculate the amplitude moving variance data volume of the target interval according to the amplitude moving average data volume of the target interval. The amplitude moving variance data volume of the target interval is calculated by the following formula:

[0125]

[0126] In the formula, δ i represents the amplitude moving variance data volume of the target interval.

[0127] Step S43: Calculate the amplitude moving fourth-order central moment function data volume of the target interval according to the amplitude moving average data volume of the target interval. The amplitude moving fourth-order central moment function data volume of the target interval is calculated by the following formula:

[0128]

[0129] In the formula, E i represents the amplitude moving fourth-order central moment function data volume of the target interval.

[0130] Step S44: Calculate the non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target interval according to the amplitude moving variance data volume of the target interval and the amplitude moving fourth-order central moment function data volume of the target interval. The non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target interval is calculated by the following formula:

[0131] β i = E i / δ i4 (4)

[0132] In the formula, β i represents the non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target interval.

[0133] Step S5: Extract the non-Gaussian distribution characteristic parameter plane distribution map of the amplitude probability distribution of the target interval according to the non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target interval.

[0134] Step S6: Predict the enriched development area of turbidite rocks based on the sedimentation law of deep-water turbidite rocks and the plane distribution map of the non-Gaussian distribution characteristic parameters of the amplitude probability distribution in the target interval. Preferably, deep-water turbidite rocks are selected as the drilling targets.

[0135] A method for predicting deep-water turbidite rocks based on the fourth-order moment function of amplitude provided in this embodiment is applicable to the prediction of deep-water turbidite rocks under the conditions of non-Gaussian distribution or low signal-to-noise ratio of reflection seismic signals. By using the non-Gaussian distribution characteristic parameters of the amplitude probability distribution function, the spatial distribution characteristics of deep-water turbidite rocks can be predicted, the lithology change characteristics of underground strata can be detected and identified, and at the same time, the connectivity inside the turbidite rocks can be analyzed, the detailed information inside the underground turbidite rocks can be highlighted, the enriched development area of turbidite rocks can be determined, and the drilling success rate of deep-water turbidite rock reservoirs can be improved.

[0136] Embodiment 4

[0137] This embodiment provides an electronic device, including a memory and at least one processor. A computer program is stored on the memory, and when the computer program is executed by the at least one processor, the method of the foregoing embodiment is implemented.

[0138] Among them, the processor can be implemented by an application specific integrated circuit (ASIC for short), a digital signal processor (DSP for short), a digital signal processing device (DSPD for short), a programmable logic device (PLD for short), a field programmable gate array (FPGA for short), a controller, a microcontroller unit (MCU), a microprocessor or other electronic components, and is used to execute a method for predicting deep-water turbidite rocks based on the fourth-order moment function of amplitude in the foregoing embodiment.

[0139] A method for predicting deep-water turbidite rocks based on the fourth-order moment function of amplitude implemented in this embodiment, as Figure 1 shown, includes:

[0140] Step S1: Obtain the seismic data of the work area.

[0141] Step S2: Conduct seismic quality analysis on the work area according to the seismic data of the work area. Thereby, the geological sedimentation characteristics of deep-water turbidite rocks are determined and the feasibility of predicting deep-water turbidite rocks from the seismic data of the work area is analyzed.

[0142] In some implementation manners, step S2 includes:

[0143] Based on the seismic data of the work area, evaluate the resolution and signal-to-noise ratio of the stacked seismic data of the target interval to determine the geological sedimentary characteristics of deep-water turbidites and analyze the feasibility of predicting deep-water turbidites from the seismic data of the work area.

[0144] Step S3, if the seismic quality of the work area meets the requirements after seismic quality analysis, use the well-seismic calibration method to determine the top horizon and bottom horizon of the target interval in the work area.

[0145] Step S4, calculate the non-Gaussian distribution characteristic parameter volume of the amplitude probability distribution of the target interval based on the seismic data of the target interval.

[0146] In some implementation manners, step S4 includes the following steps:

[0147] Step S41, calculate the amplitude moving average volume of the target interval based on the seismic data of the target interval. The amplitude moving average volume of the target interval is calculated using the following calculation formula:

[0148]

[0149] In the formula, represents the amplitude moving average volume of the target interval, and A i represents the amplitude value of the i-th sampling point of a seismic trace, where i = 1,..., N.

[0150] Step S42, calculate the amplitude moving variance volume of the target interval based on the amplitude moving average volume of the target interval. The amplitude moving variance volume of the target interval is calculated using the following calculation formula:

[0151]

[0152] In the formula, δ i represents the amplitude moving variance volume of the target interval.

[0153] Step S43, calculate the amplitude moving fourth-order central moment function volume of the target interval based on the amplitude moving average volume of the target interval. The amplitude moving fourth-order central moment function volume of the target interval is calculated using the following calculation formula:

[0154]

[0155] In the formula, δ i represents the amplitude moving fourth-order central moment function volume of the target interval.

[0156] Step S44: Calculate the non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target interval based on the amplitude sliding variance data volume of the target interval and the amplitude sliding fourth-order central moment function data volume of the target interval. The non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target interval is calculated using the following formula:

[0157] β i = E i / δ i4 (4)

[0158] In the formula, β i represents the non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target interval.

[0159] Step S5: Extract the non-Gaussian distribution characteristic parameter plane distribution map of the amplitude probability distribution of the target interval based on the non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target interval.

[0160] Step S6: Predict the turbidite-rich development area according to the sedimentation law of deep-water turbidites and the non-Gaussian distribution characteristic parameter plane distribution map of the amplitude probability distribution of the target interval. Preferably, deep-water turbidites are used as the drilling target.

[0161] A method for predicting deep-water turbidites based on the fourth-order moment function of amplitude provided in this embodiment is applicable to the prediction of deep-water turbidites under the conditions of non-Gaussian distribution or low signal-to-noise ratio of reflection seismic signals. By using the non-Gaussian distribution characteristic parameters of the amplitude probability distribution function, it can predict the spatial distribution characteristics of deep-water turbidites, detect and identify the lithological change characteristics of underground formations, and at the same time analyze the connectivity inside the turbidites, highlight the detailed information inside the underground turbidites, determine the turbidite-rich development area, and improve the drilling success rate of deep-water turbidite reservoirs.

[0162] Embodiment 5

[0163] This embodiment provides a computer program product, which, when running on a processor, executes the method for predicting deep-water turbidites based on the fourth-order moment function of amplitude in the foregoing embodiment.

[0164] In practical applications, the computer program product can be implemented to run on an electronic device.

[0165] The method for predicting deep-water turbidites based on the fourth-order moment function of amplitude implemented in this embodiment, as Figure 1 shown, includes:

[0166] Step S1: Obtain the seismic data of the work area.

[0167] Step S2: Analyze the seismic quality of the work area based on the seismic data of the work area, so as to determine the geological sedimentary characteristics of deep-water turbidites and analyze the feasibility of predicting deep-water turbidites from the seismic data of the work area.

[0168] In some implementation manners, step S2 includes:

[0169] Based on the seismic data of the work area, evaluate the resolution and signal-to-noise ratio of the stacked seismic data of the target interval, so as to determine the geological sedimentary characteristics of deep-water turbidites and analyze the feasibility of predicting deep-water turbidites from the seismic data of the work area.

[0170] Step S3: If the seismic quality of the work area meets the requirements after the seismic quality analysis, use the well-seismic calibration method to determine the top horizon and bottom horizon of the target interval in the work area.

[0171] Step S4: Calculate the non-Gaussian distribution characteristic parameter volume of the amplitude probability distribution of the target interval according to the seismic data of the target interval.

[0172] In some implementation manners, step S4 includes the following steps:

[0173] Step S41: Calculate the amplitude moving average volume of the target interval according to the seismic data of the target interval. The amplitude moving average volume of the target interval is calculated by the following calculation formula:

[0174]

[0175] In the formula, represents the amplitude moving average volume of the target interval, and A i represents the amplitude value of the i-th sampling point of a seismic trace, where i = 1,..., N.

[0176] Step S42: Calculate the amplitude moving variance volume of the target interval according to the amplitude moving average volume of the target interval. The amplitude moving variance volume of the target interval is calculated by the following calculation formula:

[0177]

[0178] In the formula, δ i represents the amplitude moving variance volume of the target interval.

[0179] Step S43: Calculate the amplitude moving fourth-order central moment function volume of the target interval according to the amplitude moving average volume of the target interval. The amplitude moving fourth-order central moment function volume of the target interval is calculated by the following calculation formula:

[0180]

[0181] In the formula, E i represents the data volume of the amplitude sliding fourth-order central moment function of the target interval.

[0182] Step S44: Calculate the non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target interval according to the amplitude sliding variance data volume of the target interval and the amplitude sliding fourth-order central moment function data volume of the target interval. The non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target interval is calculated by the following calculation formula:

[0183] β i = E i / δ i4 (4)

[0184] In the formula, β i represents the non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target interval.

[0185] Step S5: Extract the non-Gaussian distribution characteristic parameter plane distribution map of the amplitude probability distribution of the target interval according to the non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target interval.

[0186] Step S6: Predict the turbidite-rich development area according to the sedimentation law of deep-water turbidite and the non-Gaussian distribution characteristic parameter plane distribution map of the amplitude probability distribution of the target interval. It is preferred to use deep-water turbidite as the drilling target.

[0187] A method for predicting deep-water turbidite based on the fourth-order moment function of amplitude provided in this embodiment is applicable to the prediction of deep-water turbidite under the conditions of non-Gaussian distribution or low signal-to-noise ratio of reflection seismic signals. By using the non-Gaussian distribution characteristic parameters of the amplitude probability distribution function, it can predict the spatial distribution characteristics of deep-water turbidite, detect and identify the lithology change characteristics of underground strata, and at the same time can analyze the connectivity inside the turbidite, highlight the detailed information inside the underground turbidite, determine the turbidite-rich development area, and improve the drilling success rate of deep-water turbidite reservoirs.

[0188] In several embodiments provided by the embodiments of the present invention, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device and method embodiments described above are only illustrative.

[0189] It should be noted that in this text, the terms "first", "second", etc. in the description, claims and above-mentioned drawings of this application are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. The term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, article or device including the said element.

[0190] Although the disclosed embodiments of the present invention are as above, the above content is only an embodiment adopted for the convenience of understanding the present invention and is not used to limit the present invention. Any person skilled in the art within the technical field to which the present invention pertains may make any modifications and changes in the form of implementation and details without departing from the spirit and scope disclosed by the present invention. However, the scope of patent protection of the present invention shall still be subject to the scope defined by the appended claims.

Claims

1. A method for predicting deep-water turbidite based on the fourth-order moment function of amplitude, characterized in that, Including: Obtaining seismic data of the work area; Conducting seismic quality analysis on the work area according to the seismic data of the work area; If the seismic quality of the work area meets the requirements after seismic quality analysis, using the well-seismic calibration method to determine the top and bottom positions of the target interval in the work area; Calculating the non-Gaussian distribution characteristic parameter volume of the amplitude probability distribution of the target interval according to the seismic data of the target interval; Extracting the non-Gaussian distribution characteristic parameter plane map of the amplitude probability distribution of the target interval according to the non-Gaussian distribution characteristic parameter volume of the amplitude probability distribution of the target interval; Predicting the turbidite-rich development area according to the sedimentation law of deep-water turbidite and the non-Gaussian distribution characteristic parameter plane map of the amplitude probability distribution of the target interval.

2. The method for predicting deep-water turbidite based on the fourth-order moment function of amplitude according to claim 1, wherein The calculating the non-Gaussian distribution characteristic parameter volume of the amplitude probability distribution of the target interval according to the seismic data of the target interval includes: Calculating the amplitude moving average volume of the target interval according to the seismic data of the target interval; Calculating the amplitude moving variance volume of the target interval according to the amplitude moving average volume of the target interval; Calculating the amplitude moving fourth-order central moment function volume of the target interval according to the amplitude moving average volume of the target interval; Calculating the non-Gaussian distribution characteristic parameter volume of the amplitude probability distribution of the target interval according to the amplitude moving variance volume and the amplitude moving fourth-order central moment function volume of the target interval.

3. The method for predicting deep-water turbidite based on the fourth-order moment function of amplitude according to claim 1, wherein The conducting seismic quality analysis on the work area according to the seismic data of the work area includes: Evaluating the resolution and signal-to-noise ratio of the stacked seismic data of the target interval according to the seismic data of the work area to determine the geological sedimentation characteristics of deep-water turbidite and analyzing the feasibility of predicting deep-water turbidite from the seismic data of the work area.

4. The method for predicting deep-water turbidite based on the fourth-order moment function of amplitude according to claim 2, wherein The amplitude moving average volume of the target interval is calculated by using the following calculation formula: In the formula, represents the amplitude moving average data volume of the target interval, A i represents the amplitude value of the i-th sampling point of a seismic trace, where i = 1,..., N.

5. The method for predicting deep-water turbidite based on the fourth-order moment function of amplitude according to claim 4, wherein The amplitude moving variance volume of the target interval is calculated by using the following calculation formula: where δ i represents the amplitude sliding variance data volume of the target interval.

6. The method for predicting deep-water turbidite based on the fourth-order moment function of amplitude according to claim 5, characterized in that The amplitude moving fourth-order central moment function volume of the target interval is calculated by using the following calculation formula: where, E i represents the amplitude-sliding fourth-order central moment function data volume of the target interval.

7. The method for predicting deep-water turbidite based on the fourth-order moment function of amplitude according to claim 5, wherein The non-Gaussian distribution characteristic parameter volume of the amplitude probability distribution of the target interval is calculated by using the following calculation formula: β i = E i / δ i4 In Equation (4), β i represents the non-Gaussian distribution characteristic parameter data volume of the amplitude probability distribution of the target interval.

8. An apparatus for predicting deep-water turbidite based on the fourth-order moment function of amplitude, characterized in that, Including: An acquisition module for obtaining seismic data of the work area; A quality analysis module for conducting seismic quality analysis on the work area according to the seismic data of the work area; A well-seismic calibration module for, if the seismic quality of the work area meets the requirements after seismic quality analysis, using the well-seismic calibration method to determine the top and bottom positions of the target interval in the work area; A calculation module for calculating the non-Gaussian distribution characteristic parameter volume of the amplitude probability distribution of the target interval according to the seismic data of the target interval; An extraction module for extracting the non-Gaussian distribution characteristic parameter plane map of the amplitude probability distribution of the target interval according to the non-Gaussian distribution characteristic parameter volume of the amplitude probability distribution of the target interval; A prediction module, configured to predict the enriched development area of turbidite rocks according to the sedimentation law of deep-water turbidite rocks and the plane distribution map of non-Gaussian distribution characteristic parameters of the amplitude probability distribution of the target interval.

9. An electronic device, characterized in that, It includes a memory and at least one processor, and a computer program is stored on the memory. When the computer program is executed by the at least one processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium. When the computer program is executed by at least one processor, the method according to any one of claims 1 to 7 is implemented.

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