Rapid prediction methods, devices, equipment and media for turbidite reservoir distribution

Abstract

This invention discloses a method, apparatus, equipment, and medium for rapid prediction of turbidite reservoir distribution. The method includes: acquiring exploration data of a target area containing turbidites; establishing a third-order sequence stratigraphic framework for the target layer in the target area based on the exploration data; dividing high-frequency sequences based on the third-order sequence stratigraphic framework and logging data from key wells in the target area; determining the base level based on the divided high-frequency sequences; determining and comparing the base level variation parameters of each segment of the high-frequency sequences; predicting the favorable vertical distribution segments of turbidites in the target area based on the comparison results; and characterizing the planar distribution range of the favorable vertical distribution segments of turbidites. Using this invention, favorable vertically developed segments of turbidite reservoirs can be accurately and rapidly predicted, and their planar distribution can be characterized.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration and prediction technology, and in particular to a method, apparatus, equipment and medium for rapid prediction of turbidite reservoir distribution. Background Technology

[0002] In recent years, research on high-precision sequence stratigraphy and base level changes has received widespread attention. High-precision sequence stratigraphy, also known as high-frequency sequence stratigraphy, mainly refers to fourth-order sequences below the third order, fifth-order (and even sixth-order) sequences, and systems tracts. Base level change is a direct reflection of spatial variations and changes relative to sea (lake) level, with high-frequency base level changes primarily controlled by paleoclimate changes (such as Milankovitch cycles). Turbidites, as an important reservoir type in deep-water oil and gas reservoirs, are typically associated with parameters such as the duration of the lowstand systems tract during the base level decline, the magnitude of the initial base level decline in the lowstand systems tract, and the rate of base level rise in the later stages of the lowstand systems tract.

[0003] Currently, methods for identifying high-frequency sequence stratigraphy and determining base-level changes mainly include the high-precision sequence stratigraphy base-level cycle theory, sandstone-mudstone content analysis, well logging wavelet analysis, and Fisher's diagram method, all of which have seen continuous and rapid development. However, how to further quantitatively characterize base-level changes based on high-frequency sequence stratigraphy and base-level determination to quickly predict the distribution of favorable turbidite reservoirs remains a challenging problem in related disciplines and has received relatively little research. Therefore, a rapid prediction scheme for turbidite reservoir distribution is urgently needed. Summary of the Invention

[0004] The technical problem to be solved by this invention is that there is a lack of a rapid prediction method for the distribution of favorable reservoirs in turbidites based on quantitative characterization of changes in the reference surface.

[0005] To address the aforementioned technical problems, this invention provides a method, apparatus, equipment, and medium for rapid prediction of turbidite reservoir distribution based on quantitative characterization of baseline changes.

[0006] A rapid prediction method for turbidite reservoir distribution includes:

[0007] Obtain exploration data of the target area containing turbidites, and establish a third-order sequence stratigraphic framework of the target layer in the target area based on the exploration data;

[0008] Based on the three-level sequence stratigraphic framework and the logging data of key wells in the target area, high-frequency sequences are delineated;

[0009] Determining the reference surface based on the high-frequency sequence of the partition;

[0010] Determine and compare the reference surface variation parameters of each segment of the high-frequency sequence, and predict the favorable vertical distribution segments of turbidites in the target area based on the comparison results;

[0011] Describe the planar distribution range of the turbidite in the vertically advantageous strata.

[0012] Optionally, the step of dividing high-frequency sequences based on the third-order sequence stratigraphic framework and data from key wells in the target area includes:

[0013] High-frequency sequences were delineated under the third-order sequence stratigraphic framework to obtain qualitative delineation results;

[0014] Signal processing is performed on the logging data of key wells in the target area, and high-frequency sequence stratigraphy is divided based on the signal processing results to obtain quantitative division results;

[0015] By comparing the qualitative and quantitative classification results, the high-frequency sequence was finally classified.

[0016] Optionally, after obtaining qualitative partitioning results by dividing high-frequency sequences under the third-order sequence stratigraphic framework, and before finally dividing the high-frequency sequences by comparing the qualitative partitioning results with the quantitative partitioning results, the method further includes:

[0017] Identify the third-order sequence within the third-order sequence stratigraphic framework or the sedimentary types developed within the high-frequency sequence;

[0018] If the sedimentary type is the target rock, then the step of comparing the qualitative and quantitative classification results to finally classify the high-frequency sequence is performed.

[0019] Optionally, the reference surface for determining the high-frequency sequence based on partitioning includes:

[0020] Determine the current thickness of each cycle in the high-frequency sequence;

[0021] The current thickness of each cycle is decompacted and corrected to obtain the original thickness of each cycle;

[0022] Calculate the average value of the original thickness of each cycle and the offset of the original thickness of each cycle from the average value;

[0023] Fisher diagrams are used to determine the reference surface based on the offsets corresponding to each cycle.

[0024] Optionally, the decompaction correction of the current thickness of each cycle to obtain the original thickness of each cycle includes:

[0025]

[0026] Where φ represents formation porosity, x represents burial depth, and φo denoted as surface porosity, C as compaction coefficient, Vs as matrix volume, Xo as original thickness, X2 and X1 as current bottom depth and current top depth, respectively, and X2-X1 as current thickness.

[0027] Optionally, the reference surface change parameters include the duration of the low-level system domain, the magnitude of the reference surface decrease in the first period, and the rate of increase of the reference surface in the second period, wherein the first period is prior to the second period;

[0028] The prediction of favorable vertical distribution intervals of turbidites in the target area based on the comparison results includes:

[0029] The layers in which the duration of the low-lying systems domain is greater than a preset time, the magnitude of the datum level drop in the first period is greater than a preset magnitude, and the rate of datum level rise in the second period is less than a preset rate are selected as favorable layers for the vertical distribution of turbidites in the target area.

[0030] Optionally, the high-frequency sequence includes a fourth-level sequence and a fifth-level sequence; before determining and comparing the reference surface variation parameters of each segment of the high-frequency sequence, the method further includes:

[0031] The cycle times of the fourth-level and fifth-level sequences were quantized to 0.4 Ma and 0.1 Ma, respectively.

[0032] The duration of the low-level system domain of each segment of the high-frequency sequence is determined based on the cycle time limit.

[0033] A rapid prediction device for turbidite reservoir distribution includes:

[0034] The stratigraphic framework establishment module is used to acquire exploration data of a target area containing turbidites and establish a third-order sequence stratigraphic framework of the target layer in the target area based on the exploration data.

[0035] The high-frequency sequence division module is used to divide high-frequency sequences based on the three-level sequence stratigraphic framework and well logging data from key wells in the target area.

[0036] The reference surface determination module is used to determine the reference surface based on the partitioned high-frequency sequence.

[0037] The favorable segment prediction module is used to determine and compare the reference surface variation parameters of each segment of the high-frequency sequence, and predict the favorable segments of the vertical distribution of turbidites in the target area based on the comparison results.

[0038] The planar distribution characterization module is used to characterize the planar distribution range of the turbidite in the vertically distributed favorable strata.

[0039] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program performing the following steps:

[0040] Obtain exploration data of the target area containing turbidites, and establish a third-order sequence stratigraphic framework of the target layer in the target area based on the exploration data;

[0041] Based on the three-level sequence stratigraphic framework and the logging data of key wells in the target area, high-frequency sequences are delineated;

[0042] Determining the reference surface based on the high-frequency sequence of the partition;

[0043] Determine and compare the reference surface variation parameters of each segment of the high-frequency sequence, and predict the favorable vertical distribution segments of turbidites in the target area based on the comparison results;

[0044] Describe the planar distribution range of the turbidite in the vertically advantageous strata.

[0045] A computer-readable storage medium having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0046] Obtain exploration data of the target area containing turbidites, and establish a third-order sequence stratigraphic framework of the target layer in the target area based on the exploration data;

[0047] Based on the three-level sequence stratigraphic framework and the logging data of key wells in the target area, high-frequency sequences are delineated;

[0048] Determining the reference surface based on the high-frequency sequence of the partition;

[0049] Determine and compare the reference surface variation parameters of each segment of the high-frequency sequence, and predict the favorable vertical distribution segments of turbidites in the target area based on the comparison results;

[0050] Describe the planar distribution range of the turbidite in the vertically advantageous strata.

[0051] Compared with the prior art, one or more embodiments of the above solutions may have the following advantages or beneficial effects:

[0052] By establishing a three-level sequence stratigraphic framework for the target layer, dividing high-frequency sequences, and defining the base level, the base level variation parameters of each segment of the high-frequency sequence are used to quantitatively characterize the base level variation. The base level variation parameters of each segment are compared, and the favorable vertical distribution segments of turbidites in the target area are predicted based on the comparison results. The planar distribution range of turbidites in the favorable vertical distribution segments is also characterized. In this way, based on the objective establishment of high-frequency sequences, the base level variation can be quantitatively characterized, and the favorable vertical development segments of turbidite reservoirs can be accurately and quickly predicted, and the planar distribution of the favorable vertical distribution segments can be characterized. Attached Figure Description

[0053] The scope of this disclosure can be better understood by reading the following detailed description of exemplary embodiments in conjunction with the accompanying drawings. The accompanying drawings are:

[0054] Figure 1 This is a flowchart illustrating a rapid prediction method for turbidite reservoir distribution in one embodiment;

[0055] Figure 2 This is a structural block diagram of a rapid prediction device for turbidite reservoir distribution in one embodiment;

[0056] Figure 3 This is a flowchart of a rapid prediction method for turbidite reservoir distribution in one embodiment;

[0057] Figure 4 This is a high-precision seismic profile across work zones in one embodiment;

[0058] Figure 5 This is a high-frequency sequence partitioning diagram in one embodiment;

[0059] Figure 6 This represents the planar distribution range of the favorable turbidite strata SQ1-3 in one embodiment. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the present invention clearer, the implementation method of the present invention will be described in detail below with reference to the accompanying drawings and embodiments, so that the process of how the present invention uses technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly.

[0061] In existing technologies, methods for identifying high-frequency sequences and determining base level changes mainly include the high-precision sequence stratigraphy base level cycle theory, sandstone-mudstone content analysis and calculation, well logging wavelet analysis, and Fisher diagram methods, which have achieved continuous and rapid development. Turbidites, as an important reservoir type in deep-water oil and gas reservoirs, are a hot topic in exploration research. The traditional or general method for predicting turbidite reservoir distribution is to divide into third-order and fourth-order sequences (fourth-order sequences are subdivided within third-order sequences), and then characterize the planar distribution range of each fourth-order sequence (or third-order sequence, depending on the research scale or precision), and then select the best from among them. However, how to further quantitatively characterize base level changes based on high-frequency sequence division and base level determination to quickly predict the distribution of favorable turbidite reservoirs is a difficult problem faced by related disciplines and has received relatively little research. Based on this, the present invention provides a rapid prediction scheme for turbidite reservoir distribution. Compared with traditional methods, the present invention is based on quantitative characterization of baseline changes. It first achieves rapid and accurate prediction of favorable vertical distribution segments of turbidite, and then delineates the planar distribution range of turbidite in favorable segments. It does not require delineation of every segment, thus enabling rapid prediction of turbidite reservoir distribution.

[0062] Example 1

[0063] This invention provides a rapid prediction method for the distribution of turbidite reservoirs, such as... Figure 1 As shown, the method includes:

[0064] S110: Obtain exploration data for the target area containing turbidites, and establish a third-order sequence stratigraphic framework for the target layer in the target area based on the exploration data.

[0065] The exploration data may include seismic data, drilling data, etc. The target area refers to the area to be studied, which contains turbidites. For example, if the target area is a deepwater exploration block in a passive continental margin basin, high-precision cross-block seismic data, combined with drilling data calibration, can be used to track and identify regional angular unconformities, local angular unconformities and their corresponding conformable surfaces, and establish a three-order sequence stratigraphic framework for the target layer.

[0066] S120: Based on the three-level sequence stratigraphic framework and logging data from key wells in the target area, high-frequency sequences are delineated.

[0067] High-frequency sequence can include fourth-level sequence and fifth-level sequence.

[0068] S130: Determine the reference surface based on the high-frequency sequence of partitions.

[0069] S140: Determine and compare the reference surface variation parameters of each segment of the high-frequency sequence, and predict the favorable segments for the vertical distribution of turbidites in the target area based on the comparison results.

[0070] The reference surface variation parameters of each segment of the high-frequency sequence are determined, and the reference surface variation parameters are used to quantitatively characterize the reference surface variation.

[0071] S150: Describes the planar distribution range of turbidites in the vertically favorable strata.

[0072] Specifically, the RMS (Root mean square) amplitude properties and drilling calibration can be used to characterize the planar distribution range of turbidites in vertically favorable strata, which can be used to locate target rocks laterally and explore various controlling factors (source supply, slope, and paleoclimate change, etc.).

[0073] The aforementioned rapid prediction method for turbidite reservoir distribution establishes a three-level sequence stratigraphic framework for the target layer, divides high-frequency sequences, and defines the base level. It uses the base level variation parameters of each segment of the high-frequency sequence to quantitatively characterize the base level variation. By comparing the base level variation parameters of each segment and predicting the favorable vertical distribution segments of turbidites in the target area based on the comparison results, it also delineates the planar distribution range of turbidites in the favorable vertical distribution segments. In this way, based on the objective establishment of high-frequency sequences, it quantitatively characterizes the base level variation, accurately and quickly predicts the favorable vertical development segments of turbidite reservoirs, and delineates the planar distribution of the favorable vertical distribution segments.

[0074] Preferably, step S120 includes steps (a1) to (a3).

[0075] Step (a1): Divide high-frequency sequences under the third-order sequence stratigraphic framework to obtain qualitative division results.

[0076] Within the third-order sequence stratigraphic framework, fourth-order and fifth-order sequences are divided based on classical geological theory, resulting in qualitatively classified fourth-order and fifth-order sequences.

[0077] Step (a2): Perform signal processing on the logging data of key wells in the target area, and divide high-frequency sequences based on the signal processing results to obtain quantitative division results.

[0078] Specifically, signal processing can be signal denoising and wavelet transform. For example, natural gamma (energy spectrum logging) data from key wells can be denoised. After denoising, wavelets such as Meyer (function formula is equation ①) can be selected for one-dimensional discrete wavelet transform. Based on the wavelet transform results, high-frequency sequences can be divided to obtain quantitatively identifiable fourth-level and fifth-level sequences.

[0079]

[0080] Step (a3): Compare the qualitative and quantitative partitioning results to finally partition the high-frequency sequence.

[0081] By comparing the quantitative and qualitative partitioning results, the final high-frequency sequence partitioning scheme is determined, and the high-frequency sequence is finally partitioned. Specifically, after the final high-frequency sequence partitioning, the present top and bottom depths of each cycle in the high-frequency sequence can be objectively determined. By comparing the qualitative and quantitative high-frequency sequence partitioning, the final high-frequency sequence partitioning can be established, thereby improving the accuracy of high-frequency sequence partitioning.

[0082] Preferably, after step (a1) and before step (a3), the method further includes: identifying the sedimentary type developed within the third-order sequence or high-frequency sequence of the third-order sequence stratigraphic framework; if the sedimentary type is the target rock, then step (a3) ​​is performed.

[0083] The target rock refers to the type of rock to be analyzed, such as turbidite sandstone. Specifically, it can be the identification of the main sedimentary unit types and systems within the sequence stratigraphy. For example, by comprehensively utilizing well logging data, core data, and seismic data, the main sedimentary unit types and systems within the sequence stratigraphy can be identified to determine whether the target rock is present. By first identifying the sedimentary type and confirming that it is the target rock, subsequent operations can be performed, avoiding ineffective processing.

[0084] Preferably, step S130 may include steps (b1) to (b4).

[0085] Step (b1): Determine the current thickness of each cycle in the high-frequency sequence.

[0086] Specifically, after objectively determining the current top depth and current bottom depth of each cycle in the final high-frequency sequence, the current thickness of the cycle can be obtained by calculating the difference between the current bottom depth and the current top depth.

[0087] Step (b2): Perform decompaction correction on the current thickness of each cycle to obtain the original thickness of each cycle.

[0088] Specifically, the current thickness of each cycle in the fifth-order sequence can be decompacted and corrected to obtain the original thickness of each cycle in the fifth-order sequence.

[0089] Step (b3): ​​Calculate the average value of the original thickness of each cycle and the offset between the original thickness of each cycle and the average value.

[0090] Step (b4): Based on the offset corresponding to each cycle, perform Fisher diagram to determine the reference plane.

[0091] The offset corresponding to each cycle is the offset between the original thickness and the average value of each cycle. Specifically, Fisher diagrams are constructed using the offset of each cycle in the five-level sequence and the cycle number as the vertical and horizontal axes to determine the reference surface.

[0092] By compacting and correcting the current thickness, and then performing Fisher diagrams based on the corrected original thickness, the reference surface can be determined more accurately.

[0093] Preferably, step (b2) includes:

[0094]

[0095] Where φ represents formation porosity, x represents burial depth, and φ o denoted as surface porosity, C as compaction coefficient, Vs as matrix volume, Xo as original thickness, X2 and X1 as current bottom depth and current top depth, respectively, and X2-X1 as current thickness.

[0096] Based on the functional relationship between formation porosity and burial depth and the matrix volume conservation equation, the above set of equations ② is used to perform back-stripping and iterative solution of Xo to achieve decompaction correction with high accuracy.

[0097] Preferably, the reference level variation parameters include the duration of the low-lying systems domain, the magnitude of the reference level decline in the first period, and the rate of reference level rise in the second period, with the first period preceding the second period. For example, the magnitude of the reference level decline in the first period is the magnitude of the reference level decline in the earlier period, and the rate of reference level rise in the second period is the rate of reference level rise in the later period. Specifically, step S140 may involve determining and comparing the reference level variation parameters of each segment of the fourth-order sequence, and predicting favorable segments for vertical distribution based on the comparison results.

[0098] Deepwater channel systems / turbidites, as an important reservoir type in deepwater oil and gas reservoirs, are typically associated with the lowstand systems tract (LST) during the base level decline. Parameters such as the duration (t), initial base level decline (h), and subsequent base level rise rate (v) of the LST comprehensively influence the development scale, grain size, and physical properties of deepwater turbidites. Quantitatively characterizing base level changes using the duration (t), initial base level decline (h), and subsequent base level rise rate (v) of the LST allows for accurate and rapid prediction of favorable vertical distribution intervals in turbidites.

[0099] In step S140, the favorable vertical distribution of turbidites in the target area is predicted based on the comparison results, including: selecting the segments in which the duration of the lowstand systems tract is greater than a preset time, the base level drop in the first period is greater than a preset range, and the base level rise rate in the second period is less than a preset rate, as favorable vertical distribution segments of turbidites in the target area.

[0100] Specifically, the optimal segment can be selected from the segments where the duration of the low-level systems tract is greater than the preset time, the magnitude of the base level drop in the first period is greater than the preset magnitude, and the rate of base level rise in the second period is less than the preset rate. For example, the segment with the longest duration, the largest magnitude of the base level drop in the first period, and the smallest rate of base level rise in the second period can be selected as the favorable segment for the vertical distribution of turbidites in the target area.

[0101] Preferably, the high-frequency sequence includes a fourth-order sequence and a fifth-order sequence. Before step S140, the method further includes: quantizing the cycle time limits of the fourth-order sequence and the fifth-order sequence to 0.4 Ma and 0.1 Ma, respectively; and determining the duration of the low-level system domains of each segment of the high-frequency sequence based on the cycle time limits.

[0102] Specifically, the response relationship of high-frequency sequences to Mie cycles is revealed, and the cycle durations of fourth-order and fifth-order sequences are quantified as 0.4 Ma (long-period eccentricity) and 0.1 Ma (short-period eccentricity), respectively. Specifically, the duration of the low-side system domains in each segment of the fourth-order sequence can be determined based on the cycle duration of the fourth-order sequence.

[0103] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0104] Example 2

[0105] This invention provides a rapid prediction device for turbidite reservoir distribution, such as... Figure 2 As shown, the device includes:

[0106] The stratigraphic framework establishment module 210 is used to obtain exploration data of the target area containing turbidites and establish a third-order sequence stratigraphic framework of the target layer in the target area based on the exploration data.

[0107] The exploration data may include seismic data, drilling data, etc. The target area refers to the area to be studied, which contains turbidites. For example, if the target area is a deepwater exploration block in a passive continental margin basin, high-precision cross-block seismic data, combined with drilling data calibration, can be used to track and identify regional angular unconformities, local angular unconformities and their corresponding conformable surfaces, and establish a three-order sequence stratigraphic framework for the target layer.

[0108] The high-frequency sequence delineation module 220 is used to delineate high-frequency sequences based on the third-order sequence stratigraphic framework and logging data from key wells in the target area. The high-frequency sequences can include fourth-order and fifth-order sequences.

[0109] The reference surface determination module 230 is used to determine the reference surface based on the partitioned high-frequency sequence.

[0110] The favorable stratigraphic segment prediction module 240 is used to determine and compare the base level variation parameters of each segment of the high-frequency sequence, and predict the favorable vertical distribution segments of turbidites in the target area based on the comparison results. Specifically, it determines the base level variation parameters of each segment of the high-frequency sequence, and then uses the base level variation parameters to quantitatively characterize the base level variation.

[0111] The planar distribution characterization module 250 is used to characterize the planar distribution range of turbidites in the vertically distributed favorable strata.

[0112] Specifically, the RMS (root mean square) amplitude properties and drilling calibration can be used to characterize the planar distribution range of turbidites in the vertically favorable strata, thereby obtaining the planar distribution range, which can be used to locate target rocks laterally and explore various controlling factors (source supply, slope, and paleoclimate change, etc.).

[0113] The aforementioned rapid prediction device for turbidite reservoir distribution establishes a three-level sequence stratigraphic framework for the target layer, divides high-frequency sequences, and defines the base level. It uses the base level variation parameters of each segment of the high-frequency sequence to quantitatively characterize the base level variation, compares the base level variation parameters of each segment, and predicts the favorable vertical distribution segments of turbidites in the target area based on the comparison results. It also delineates the planar distribution range of turbidites in the favorable vertical distribution segments. In this way, based on the objective establishment of high-frequency sequences, it can quantitatively characterize the base level variation, accurately and quickly predict the favorable vertical development segments of turbidite reservoirs, and delineate the planar distribution of the favorable vertical distribution segments.

[0114] Preferably, the high-frequency sequence delineation module 220 is used to: delineate high-frequency sequences under a third-order sequence stratigraphic framework to obtain qualitative delineation results; perform signal processing on logging data of key wells in the target area, delineate high-frequency sequences based on the signal processing results to obtain quantitative delineation results; and compare the qualitative delineation results with the quantitative delineation results to finally delineate the high-frequency sequences.

[0115] Specifically, within the third-order sequence stratigraphic framework, fourth-order and fifth-order sequences are divided based on classical geological theory, resulting in qualitatively classified fourth-order and fifth-order sequences.

[0116] Specifically, signal processing can be signal denoising and wavelet transform. For example, natural gamma (energy spectrum logging) data from key wells can be denoised. After denoising, wavelets such as Meyer (function formula is equation ①) can be selected for one-dimensional discrete wavelet transform. Based on the wavelet transform results, high-frequency sequences can be divided to obtain quantitatively identifiable fourth-level and fifth-level sequences.

[0117] By comparing the quantitative and qualitative partitioning results, the final high-frequency sequence partitioning scheme is determined, and the high-frequency sequence is finally partitioned. Specifically, after the final high-frequency sequence partitioning, the present top and bottom depths of each cycle in the high-frequency sequence can be objectively determined. By comparing the qualitative and quantitative high-frequency sequence partitioning, the final high-frequency sequence partitioning can be established, thereby improving the accuracy of high-frequency sequence partitioning.

[0118] Preferably, after obtaining the qualitative classification results and before comparing the qualitative and quantitative classification results, the high-frequency sequence division module 220 is also used to: identify the sedimentary types developed within the third-order sequence or high-frequency sequence of the third-order sequence stratigraphic framework; if the sedimentary type is the target rock, the qualitative and quantitative classification results are compared to finally classify the high-frequency sequence.

[0119] The target rock refers to the type of rock to be analyzed, such as turbidite sandstone. Specifically, it can be the identification of the main sedimentary unit types and systems within the sequence stratigraphy. For example, by comprehensively utilizing well logging data, core data, and seismic data, the main sedimentary unit types and systems within the sequence stratigraphy can be identified to determine whether the target rock is present. By first identifying the sedimentary type and confirming that it is the target rock, subsequent operations can be performed, avoiding ineffective processing.

[0120] Preferably, the reference plane determination module 230 is used to: determine the current thickness of each cycle in the high-frequency sequence; perform decompaction correction on the current thickness of each cycle to obtain the original thickness of each cycle; calculate the average value of the original thickness of each cycle and the offset between the original thickness of each cycle and the average value; and perform Fisher diagram based on the offset corresponding to each cycle to determine the reference plane.

[0121] Specifically, after objectively determining the current top and bottom depths of each cycle in the final high-frequency sequence, the current thickness of the cycle can be obtained by calculating the difference between the current bottom and top depths. Alternatively, the current thickness of each cycle in the fifth-order sequence can be decompacted to obtain the original thickness of each cycle. Furthermore, a Fisher diagram can be constructed using the offset and cycle number of each cycle in the fifth-order sequence as the x and y axes to determine the reference surface. By decompacting the current thickness and then constructing a Fisher diagram based on the corrected original thickness, the reference surface can be determined more accurately.

[0122] Preferably, the reference plane determination module 230 obtains the original thickness using the following set of equations:

[0123]

[0124] Where φ represents formation porosity, x represents burial depth, and φ o denoted as surface porosity, C as compaction coefficient, Vs as matrix volume, Xo as original thickness, X2 and X1 as current bottom depth and current top depth, respectively, and X2-X1 as current thickness.

[0125] Based on the functional relationship between formation porosity and burial depth and the matrix volume conservation equation, the above set of equations ② is used to perform back-stripping and iterative solution of Xo to achieve decompaction correction with high accuracy.

[0126] Preferably, the reference level change parameters include the duration of the low-lying systems domain, the magnitude of the reference level decline in the first period, and the rate of reference level rise in the second period, with the first period preceding the second period. For example, the magnitude of the reference level decline in the first period is the magnitude of the reference level decline in the earlier period, and the rate of reference level rise in the second period is the rate of reference level rise in the later period. Specifically, the favorable segment prediction module 240 can determine and compare the reference level change parameters of each segment of the fourth-order sequence, and predict the vertically distributed favorable segments based on the comparison results.

[0127] Deepwater channel systems / turbidites, as an important reservoir type in deepwater oil and gas reservoirs, are typically associated with the lowstand systems tract (LST) during the base level decline. Parameters such as the duration (t), initial base level decline (h), and subsequent base level rise rate (v) of the LST comprehensively influence the development scale, grain size, and physical properties of deepwater turbidites. Quantitatively characterizing base level changes using the duration (t), initial base level decline (h), and subsequent base level rise rate (v) of the LST allows for accurate and rapid prediction of favorable vertical distribution intervals in turbidites.

[0128] The favorable stratigraphic segment prediction module 240 selects stratigraphic segments in which the duration of the low-lying systems tract is greater than a preset time, the base level drop in the first period is greater than a preset range, and the base level rise rate in the second period is less than a preset rate, as favorable stratigraphic segments for the vertical distribution of turbidites in the target area.

[0129] Specifically, the optimal segment can be selected from the segments where the duration of the low-level systems tract is greater than the preset time, the magnitude of the base level drop in the first period is greater than the preset magnitude, and the rate of base level rise in the second period is less than the preset rate. For example, the segment with the longest duration, the largest magnitude of the base level drop in the first period, and the smallest rate of base level rise in the second period can be selected as the favorable segment for the vertical distribution of turbidites in the target area.

[0130] Preferably, the high-frequency sequence includes a fourth-order sequence and a fifth-order sequence. The favorable segment prediction module 240 can also be used to quantize the cycle time limits of the fourth-order sequence and the fifth-order sequence to 0.4 Ma and 0.1 Ma, respectively; and determine the duration of the low-level system domains of each segment of the high-frequency sequence based on the cycle time limits.

[0131] Specifically, the response relationship of high-frequency sequences to Mie cycles is revealed, and the cycle durations of fourth-order and fifth-order sequences are quantified as 0.4 Ma (long-period eccentricity) and 0.1 Ma (short-period eccentricity), respectively. Specifically, the duration of the low-side system domains in each segment of the fourth-order sequence can be determined based on the cycle duration of the fourth-order sequence.

[0132] The modules in the aforementioned rapid prediction device for turbidite reservoir distribution can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module. It should be noted that the module division in this embodiment is illustrative and represents only a logical functional division; in actual implementation, other division methods may be used.

[0133] Example 3

[0134] This invention provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0135] Step 10: Obtain exploration data for the target area containing turbidites, and establish a third-order sequence stratigraphic framework for the target layer in the target area based on the exploration data.

[0136] The exploration data may include seismic data, drilling data, etc. The target area refers to the area to be studied, which contains turbidites. For example, if the target area is a deepwater exploration block in a passive continental margin basin, high-precision cross-block seismic data, combined with drilling data calibration, can be used to track and identify regional angular unconformities, local angular unconformities and their corresponding conformable surfaces, and establish a three-order sequence stratigraphic framework for the target layer.

[0137] Step 20: Based on the three-level sequence stratigraphic framework and logging data from key wells in the target area, high-frequency sequences are delineated.

[0138] High-frequency sequence can include fourth-level sequence and fifth-level sequence.

[0139] Step 30: Determine the reference surface based on the partitioned high-frequency sequence.

[0140] Step 40: Determine and compare the reference surface variation parameters of each segment of the high-frequency sequence, and predict the favorable segments of vertical distribution of turbidites in the target area based on the comparison results.

[0141] The reference surface variation parameters of each segment of the high-frequency sequence are determined, and the reference surface variation parameters are used to quantitatively characterize the reference surface variation.

[0142] Step 50: Describe the planar distribution range of turbidites in the vertically favorable strata.

[0143] Specifically, the RMS (root mean square) amplitude properties and drilling calibration can be used to characterize the planar distribution range of turbidites in the vertically favorable strata, thereby obtaining the planar distribution range, which can be used to locate target rocks laterally and explore various controlling factors (source supply, slope, and paleoclimate change, etc.).

[0144] The aforementioned computer equipment has a memory storing a computer program that establishes a three-level sequence stratigraphic framework for the target layer, divides high-frequency sequences, and defines the base level. It uses the base level variation parameters of each segment of the high-frequency sequence to quantitatively characterize the base level variation, compares the base level variation parameters of each segment, and predicts the favorable vertical distribution segments of turbidites in the target area based on the comparison results, and delineates the planar distribution range of turbidites in the favorable vertical distribution segments. In this way, based on the objective establishment of high-frequency sequences, it can quantitatively characterize the base level variation, accurately and quickly predict the favorable vertical development segments of turbidite reservoirs, and delineate the planar distribution of the favorable vertical distribution segments.

[0145] Preferably, when the processor executes the computer program, step 20 includes steps (a1) to (a3).

[0146] Step (a1): Divide high-frequency sequences under the third-order sequence stratigraphic framework to obtain qualitative division results.

[0147] Within the third-order sequence stratigraphic framework, fourth-order and fifth-order sequences are divided based on classical geological theory, resulting in qualitatively classified fourth-order and fifth-order sequences.

[0148] Step (a2): Perform signal processing on the logging data of key wells in the target area, and divide high-frequency sequences based on the signal processing results to obtain quantitative division results.

[0149] Specifically, signal processing can be signal denoising and wavelet transform. For example, natural gamma (energy spectrum logging) data from key wells can be denoised. After denoising, wavelets such as Meyer (function formula is equation ①) can be selected for one-dimensional discrete wavelet transform. Based on the wavelet transform results, high-frequency sequences can be divided to obtain quantitatively identifiable fourth-level and fifth-level sequences.

[0150]

[0151] Step (a3): Compare the qualitative and quantitative partitioning results to finally partition the high-frequency sequence.

[0152] By comparing the quantitative and qualitative partitioning results, the final high-frequency sequence partitioning scheme is determined, and the high-frequency sequence is finally partitioned. Specifically, after the final high-frequency sequence partitioning, the present top and bottom depths of each cycle in the high-frequency sequence can be objectively determined. By comparing the qualitative and quantitative high-frequency sequence partitioning, the final high-frequency sequence partitioning can be established, thereby improving the accuracy of high-frequency sequence partitioning.

[0153] Preferably, when the processor executes the computer program, after step (a1) and before step (a3), it further includes: identifying the sedimentary type developed within the third-order sequence or high-frequency sequence of the third-order sequence stratigraphic framework; if the sedimentary type is the target rock, then step (a3) ​​is executed.

[0154] The target rock refers to the type of rock to be analyzed, such as turbidite sandstone. Specifically, it can be the identification of the main sedimentary unit types and systems within the sequence stratigraphy. For example, by comprehensively utilizing well logging data, core data, and seismic data, the main sedimentary unit types and systems within the sequence stratigraphy can be identified to determine whether the target rock is present. By first identifying the sedimentary type and confirming that it is the target rock, subsequent operations can be performed, avoiding ineffective processing.

[0155] Preferably, when the processor executes the computer program, step 30 may include steps (b1) to (b4).

[0156] Step (b1): Determine the current thickness of each cycle in the high-frequency sequence.

[0157] Specifically, after objectively determining the current top depth and current bottom depth of each cycle in the final high-frequency sequence, the current thickness of the cycle can be obtained by calculating the difference between the current bottom depth and the current top depth.

[0158] Step (b2): Perform decompaction correction on the current thickness of each cycle to obtain the original thickness of each cycle.

[0159] Specifically, the current thickness of each cycle in the fifth-order sequence can be decompacted and corrected to obtain the original thickness of each cycle in the fifth-order sequence.

[0160] Step (b3): ​​Calculate the average value of the original thickness of each cycle and the offset between the original thickness of each cycle and the average value.

[0161] Step (b4): Based on the offset corresponding to each cycle, perform Fisher diagram to determine the reference plane.

[0162] The offset corresponding to each cycle is the offset between the original thickness and the average value of each cycle. Specifically, Fisher diagrams are constructed using the offset of each cycle in the five-level sequence and the cycle number as the vertical and horizontal axes to determine the reference surface.

[0163] By compacting and correcting the current thickness, and then performing Fisher diagrams based on the corrected original thickness, the reference surface can be determined more accurately.

[0164] Preferably, when the processor executes the computer program, step (b2) includes:

[0165]

[0166] Where φ represents formation porosity, x represents burial depth, and φ o denoted as surface porosity, C as compaction coefficient, Vs as matrix volume, Xo as original thickness, X2 and X1 as current bottom depth and current top depth, respectively, and X2-X1 as current thickness.

[0167] Based on the functional relationship between formation porosity and burial depth and the matrix volume conservation equation, the above set of equations ② is used to perform back-stripping and iterative solution of Xo to achieve decompaction correction with high accuracy.

[0168] Preferably, the reference level change parameters include the duration of the low-level systems domain, the magnitude of the reference level decrease in the first period, and the rate of reference level rise in the second period, with the first period preceding the second period. For example, the magnitude of the reference level decrease in the first period is the magnitude of the reference level decrease in the earlier period, and the rate of reference level rise in the second period is the rate of reference level rise in the later period. Specifically, when the processor executes the computer program, step 40 may involve determining and comparing the reference level change parameters of each segment of the fourth-level sequence, and predicting the vertically distributed favorable segments based on the comparison results.

[0169] Deep-water turbidites, as an important reservoir type in deep-water oil and gas reservoirs, are typically associated with the lowstand systems tract (LST) during the base level decline. Parameters such as the duration (t), initial base level decline (h), and subsequent base level rise rate (v) of the LST comprehensively influence the development scale, grain size, and physical properties of deep-water turbidites. Quantitatively characterizing base level changes using the duration (t), initial base level decline (h), and subsequent base level rise rate (v) of the LST allows for accurate and rapid prediction of favorable vertical distribution intervals in turbidites.

[0170] When the processor executes the computer program, in step 40, the favorable vertical distribution of turbidites in the target area is predicted based on the comparison results, including: selecting the segments in which the duration of the lowstand systems tract is greater than a preset time, the magnitude of the base level drop in the first period is greater than a preset magnitude, and the rate of the base level rise in the second period is less than a preset rate, as favorable vertical distribution segments of turbidites in the target area.

[0171] Specifically, the optimal segment can be selected from the segments where the duration of the low-level systems tract is greater than the preset time, the magnitude of the base level drop in the first period is greater than the preset magnitude, and the rate of base level rise in the second period is less than the preset rate. For example, the segment with the longest duration, the largest magnitude of the base level drop in the first period, and the smallest rate of base level rise in the second period can be selected as the favorable segment for the vertical distribution of turbidites in the target area.

[0172] Preferably, the high-frequency sequence includes a fourth-level sequence and a fifth-level sequence. When the processor executes the computer program, before step 40, the process further includes: quantizing the cycle time limits of the fourth-level sequence and the fifth-level sequence to 0.4 Ma and 0.1 Ma, respectively; and determining the duration of the low-order system domains of each segment of the high-frequency sequence based on the cycle time limits.

[0173] Specifically, the response relationship of high-frequency sequences to Mie cycles is revealed, and the cycle durations of fourth-order and fifth-order sequences are quantified as 0.4 Ma (long-period eccentricity) and 0.1 Ma (short-period eccentricity), respectively. Specifically, the duration of the low-side system domains in each segment of the fourth-order sequence can be determined based on the cycle duration of the fourth-order sequence.

[0174] Example 4

[0175] This invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, performs the following steps:

[0176] Step 10: Obtain exploration data for the target area containing turbidites, and establish a third-order sequence stratigraphic framework for the target layer in the target area based on the exploration data.

[0177] The exploration data may include seismic data, drilling data, etc. The target area refers to the area to be studied, which contains turbidites. For example, if the target area is a deepwater exploration block in a passive continental margin basin, high-precision cross-block seismic data, combined with drilling data calibration, can be used to track and identify regional angular unconformities, local angular unconformities and their corresponding conformable surfaces, and establish a three-order sequence stratigraphic framework for the target layer.

[0178] Step 20: Based on the three-level sequence stratigraphic framework and logging data from key wells in the target area, high-frequency sequences are delineated.

[0179] High-frequency sequence can include fourth-level sequence and fifth-level sequence.

[0180] Step 30: Determine the reference surface based on the partitioned high-frequency sequence.

[0181] Step 40: Determine and compare the reference surface variation parameters of each segment of the high-frequency sequence, and predict the favorable segments of vertical distribution of turbidites in the target area based on the comparison results.

[0182] The reference surface variation parameters of each segment of the high-frequency sequence are determined, and the reference surface variation parameters are used to quantitatively characterize the reference surface variation.

[0183] Step 50: Describe the planar distribution range of turbidites in the vertically favorable strata.

[0184] Specifically, the RMS (root mean square) amplitude properties and drilling calibration can be used to characterize the planar distribution range of turbidites in the vertically favorable strata, thereby obtaining the planar distribution range, which can be used to locate target rocks laterally and explore various controlling factors (source supply, slope, and paleoclimate change, etc.).

[0185] The aforementioned computer-readable storage medium stores a computer program that establishes a three-level sequence stratigraphic framework for the target layer, divides high-frequency sequences, and defines the base level. It then uses the base level variation parameters of each segment of the high-frequency sequence to quantitatively characterize the base level variation, compares the base level variation parameters of each segment, and predicts the favorable vertical distribution segments of turbidites in the target area based on the comparison results. Finally, it delineates the planar distribution range of turbidites within these favorable vertical distribution segments. Thus, based on the objective establishment of high-frequency sequences, it quantitatively characterizes the base level variation, accurately and quickly predicts favorable vertical development segments of turbidite reservoirs, and delineates the planar distribution of these favorable vertical distribution segments.

[0186] Preferably, when the computer program is executed by the processor, step 20 includes steps (a1) to (a3).

[0187] Step (a1): Divide high-frequency sequences under the third-order sequence stratigraphic framework to obtain qualitative division results.

[0188] Within the third-order sequence stratigraphic framework, fourth-order and fifth-order sequences are divided based on classical geological theory, resulting in qualitatively classified fourth-order and fifth-order sequences.

[0189] Step (a2): Perform signal processing on the logging data of key wells in the target area, and divide high-frequency sequences based on the signal processing results to obtain quantitative division results.

[0190] Specifically, signal processing can be signal denoising and wavelet transform. For example, natural gamma (energy spectrum logging) data from key wells can be denoised. After denoising, wavelets such as Meyer (function formula is equation ①) can be selected for one-dimensional discrete wavelet transform. Based on the wavelet transform results, high-frequency sequences can be divided to obtain quantitatively identifiable fourth-level and fifth-level sequences.

[0191]

[0192] Step (a3): Compare the qualitative and quantitative partitioning results to finally partition the high-frequency sequence.

[0193] By comparing the quantitative and qualitative partitioning results, the final high-frequency sequence partitioning scheme is determined, and the high-frequency sequence is finally partitioned. Specifically, after the final high-frequency sequence partitioning, the present top and bottom depths of each cycle in the high-frequency sequence can be objectively determined. By comparing the qualitative and quantitative high-frequency sequence partitioning, the final high-frequency sequence partitioning can be established, thereby improving the accuracy of high-frequency sequence partitioning.

[0194] Preferably, when the computer program is executed by the processor, after step (a1) and before step (a3), it further includes: identifying the sedimentary type developed within the third-order sequence or high-frequency sequence of the third-order sequence stratigraphic framework; if the sedimentary type is the target rock, then step (a3) ​​is executed.

[0195] The target rock refers to the type of rock to be analyzed, such as turbidite sandstone. Specifically, it can be the identification of the main sedimentary unit types and systems within the sequence stratigraphy. For example, by comprehensively utilizing well logging data, core data, and seismic data, the main sedimentary unit types and systems within the sequence stratigraphy can be identified to determine whether the target rock is present. By first identifying the sedimentary type and confirming that it is the target rock, subsequent operations can be performed, avoiding ineffective processing.

[0196] Preferably, when the computer program is executed by the processor, step 30 may include steps (b1) to (b4).

[0197] Step (b1): Determine the current thickness of each cycle in the high-frequency sequence.

[0198] Specifically, after objectively determining the current top depth and current bottom depth of each cycle in the final high-frequency sequence, the current thickness of the cycle can be obtained by calculating the difference between the current bottom depth and the current top depth.

[0199] Step (b2): Perform decompaction correction on the current thickness of each cycle to obtain the original thickness of each cycle.

[0200] Specifically, the current thickness of each cycle in the fifth-order sequence can be decompacted and corrected to obtain the original thickness of each cycle in the fifth-order sequence.

[0201] Step (b3): ​​Calculate the average value of the original thickness of each cycle and the offset between the original thickness of each cycle and the average value.

[0202] Step (b4): Based on the offset corresponding to each cycle, perform Fisher diagram to determine the reference plane.

[0203] The offset corresponding to each cycle is the offset between the original thickness and the average value of each cycle. Specifically, Fisher diagrams are constructed using the offset of each cycle in the five-level sequence and the cycle number as the vertical and horizontal axes to determine the reference surface.

[0204] By compacting and correcting the current thickness, and then performing Fisher diagrams based on the corrected original thickness, the reference surface can be determined more accurately.

[0205] Preferably, when the computer program is executed by the processor, step (b2) includes:

[0206]

[0207] Where φ represents formation porosity, x represents burial depth, and φ o denoted as surface porosity, C as compaction coefficient, Vs as matrix volume, Xo as original thickness, X2 and X1 as current bottom depth and current top depth, respectively, and X2-X1 as current thickness.

[0208] Based on the functional relationship between formation porosity and burial depth and the matrix volume conservation equation, the above set of equations ② is used to perform back-stripping and iterative solution of Xo to achieve decompaction correction with high accuracy.

[0209] Preferably, the reference level change parameters include the duration of the low-level systems domain, the magnitude of the reference level decline in the first period, and the rate of reference level rise in the second period, with the first period preceding the second period. For example, the magnitude of the reference level decline in the first period is the magnitude of the reference level decline in the earlier period, and the rate of reference level rise in the second period is the rate of reference level rise in the later period. Specifically, when the computer program is executed by the processor, step 40 may involve determining and comparing the reference level change parameters of each segment of the fourth-order sequence, and predicting the vertically distributed favorable segments based on the comparison results.

[0210] Deepwater channel systems / turbidites, as an important reservoir type in deepwater oil and gas reservoirs, are typically associated with the lowstand systems tract (LST) during the base level decline. Parameters such as the duration (t), initial base level decline (h), and subsequent base level rise rate (v) of the LST comprehensively influence the development scale, grain size, and physical properties of deepwater turbidites. Quantitatively characterizing base level changes using the duration (t), initial base level decline (h), and subsequent base level rise rate (v) of the LST allows for accurate and rapid prediction of favorable vertical distribution intervals in turbidites.

[0211] When the computer program is executed by the processor, in step 40, the favorable vertical distribution of turbidites in the target area is predicted based on the comparison results, including: selecting the segments in which the duration of the lowstand systems tract is greater than a preset time, the magnitude of the base level drop in the first period is greater than a preset magnitude, and the rate of the base level rise in the second period is less than a preset rate, as favorable vertical distribution segments of turbidites in the target area.

[0212] Specifically, the optimal segment can be selected from the segments where the duration of the low-level systems tract is greater than the preset time, the magnitude of the base level drop in the first period is greater than the preset magnitude, and the rate of base level rise in the second period is less than the preset rate. For example, the segment with the longest duration, the largest magnitude of the base level drop in the first period, and the smallest rate of base level rise in the second period can be selected as the favorable segment for the vertical distribution of turbidites in the target area.

[0213] Preferably, the high-frequency sequence includes a fourth-level sequence and a fifth-level sequence. When the computer program is executed by the processor, before step 40, the program further includes: quantizing the cycle time limits of the fourth-level sequence and the fifth-level sequence to 0.4 Ma and 0.1 Ma, respectively; and determining the duration of the low-order system domains of each segment of the high-frequency sequence based on the cycle time limits.

[0214] Specifically, the response relationship of high-frequency sequences to Mie cycles is revealed, and the cycle durations of fourth-order and fifth-order sequences are quantified as 0.4 Ma (long-period eccentricity) and 0.1 Ma (short-period eccentricity), respectively. Specifically, the duration of the low-side system domains in each segment of the fourth-order sequence can be determined based on the cycle duration of the fourth-order sequence.

[0215] Example 5

[0216] The following example focuses on the Miocene strata in Block A of the Lower Congo-Congo Fan Basin in West Africa, characterized by typical marine turbidite sandstones, and provides an anatomical analysis. Figure 3 This embodiment demonstrates the main process for objectively identifying high-frequency sequences, quantitatively characterizing base level changes, and accurately and rapidly predicting the distribution of turbidite reservoirs.

[0217] 1. Utilizing high-precision, cross-regional 3D seismic profiles, major unconformities / local unconformities and their corresponding conformable surfaces (SB1-SB3) were identified and traced, dividing the Middle Miocene into two third-order sequences, SQ1-SQ2. Figure 4 As shown.

[0218] 2. Within the third-level sequence, qualitatively divide it into fourth and fifth-level high-frequency sequences, referring to... Figure 5 .

[0219] 3. Summarize the main lithofacies assemblage characteristics, identify the main sedimentary systems and their facies types, and refer to... Figure 4 and Figure 5 .

[0220] 4. Utilize key single wells (W1, well location see...) Figure 6 The natural GR curve of the signal was used for signal denoising. Then, Meyer wavelets were selected to perform one-dimensional discrete wavelet transform on the signal (order and maximum level were both set to 11), resulting in 11 one-dimensional discrete wavelet curves (d1-d11). The d11, d9, and d7 curves were selected to quantitatively identify the fourth and fifth level high-frequency sequences (see...). Figure 5 ).

[0221] 5. Comparing the results of steps 2 and 4, the high-frequency sequence division results are established, namely, 2 third-level sequences, 9 fourth-level sequences, and 36 fifth-level sequences are identified. Furthermore, the current top and bottom depths of the 36 fifth-level cycles are determined (see...). Figure 5 ).

[0222] 6. Explore the response relationship between high-frequency cycles and Milankovitch cycles. Quantify the time limits of fourth and fifth order cycles with a ratio of 1:4 into long-period cycles (0.4 Ma) and short-period cycles (0.1 Ma) of Earth's eccentricity.

[0223] 7. The deposition thickness of each "meter-level cycle" is corrected for decompaction using the equation set ② mentioned above.

[0224] 8. Using the average thickness cumulative offset and cycle number as the x and y axes, construct a Fisher diagram to determine the datum plane variation (refer to...). Figure 5 ).

[0225] 9. The duration (t) of the low-lying systems tract, the initial base level drop (h), and the subsequent base level rise rate (v, which is negatively correlated with the slope k of the fitted linear equation of the base level rise) were selected to quantitatively characterize the base level changes, and the favorable vertical distribution interval of the turbidite sandstone was quickly and accurately predicted to be SQ1-3 (see...). Figure 5The SQ1-3 turbidite sandstone has the longest LST duration (t1-3) (approximately 0.2 Ma, significantly greater than other cycles), a larger initial base level drop (h1-3) (slightly less than h1-4), and a smaller later base level rise rate (the slope of the fitted linear equation k1-3 is approximately 1, greater than k1-4). Exploration practice has confirmed that the SQ1-3 turbidite sandstone is a high-quality reservoir.

[0226] 10. Using RMS (Root Mean Square) amplitude property slices and drilling calibration, the planar distribution range of turbidite in the favorable SQ1-3 interval was delineated, such as... Figure 6 Furthermore, it was clarified that the lateral distribution of the sand body was mainly controlled by changes in paleogeographic slope (caused by salt structures and faults).

[0227] The rapid prediction method for turbidite reservoir distribution provided by this invention can compensate for the current deficiencies in research on high-frequency sequence stratigraphy identification, base level change determination, and accurate and rapid prediction of turbidite reservoir distribution. It provides a research method for quantitative characterization of base level changes and rapid prediction of turbidite reservoir distribution. Based on the objective establishment of a high-frequency sequence stratigraphic framework, it quantitatively characterizes base level changes, especially by comprehensively evaluating parameters such as the duration (t) of the lowstand systems tract, the initial base level drop (h), and the later base level rise rate (v). This allows for more accurate and rapid prediction of favorable vertical development intervals in turbidite sandstone reservoirs and characterization of their planar distribution. Furthermore, it explores the controlling effects of paleoclimate change, high-frequency sea-level oscillations, and paleogeographic slope on the development and distribution of favorable turbidite reservoirs, thus better serving deepwater oil and gas exploration prediction.

[0228] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0229] While the embodiments disclosed in this invention are as described above, the content is merely for the purpose of facilitating understanding of the invention and is not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope disclosed herein; however, the scope of protection of this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A rapid prediction method for turbidite reservoir distribution, characterized in that, include: Obtain exploration data of the target area containing turbidites, and establish a third-order sequence stratigraphic framework of the target layer in the target area based on the exploration data; Based on the three-level sequence stratigraphic framework and the logging data of key wells in the target area, high-frequency sequences are delineated; Determining the reference surface based on the high-frequency sequence of the partition; The reference level variation parameters of each segment of the high-frequency sequence are determined and compared. Based on the comparison results, the favorable segments for the vertical distribution of turbidites in the target area are predicted. The reference level variation parameters include the duration of the lowstand systems tract, the magnitude of the reference level drop in the first period, and the rate of reference level rise in the second period. The first period is located before the second period. Describe the planar distribution range of the turbidite in the vertically advantageous strata.

2. The method according to claim 1, characterized in that, The high-frequency sequence stratigraphy, based on the third-order sequence stratigraphic framework and data from key wells in the target area, includes: High-frequency sequences were delineated under the third-order sequence stratigraphic framework to obtain qualitative delineation results; Signal processing is performed on the logging data of key wells in the target area, and high-frequency sequence stratigraphy is divided based on the signal processing results to obtain quantitative division results; By comparing the qualitative and quantitative classification results, the high-frequency sequence was finally classified.

3. The method according to claim 2, characterized in that, After obtaining qualitative classification results by dividing high-frequency sequences under the third-order sequence stratigraphic framework, and before finally dividing the high-frequency sequences by comparing the qualitative classification results with the quantitative classification results, the process further includes: Identify the third-order sequence within the third-order sequence stratigraphic framework or the sedimentary types developed within the high-frequency sequence; If the sedimentary type is the target rock, then the step of comparing the qualitative and quantitative classification results to finally classify the high-frequency sequence is performed.

4. The method according to claim 1, characterized in that, The high-frequency sequence determination reference surface based on partitioning includes: Determine the current thickness of each cycle in the high-frequency sequence; The current thickness of each cycle is decompacted and corrected to obtain the original thickness of each cycle; Calculate the average value of the original thickness of each cycle and the offset of the original thickness of each cycle from the average value; Fisher diagrams are used to determine the reference surface based on the offsets corresponding to each cycle.

5. The method according to claim 1, characterized in that, The reference surface change parameters include the duration of the low-level system domain, the magnitude of the reference surface decrease in the first period, and the rate of increase of the reference surface in the second period, wherein the first period is prior to the second period. The prediction of favorable vertical distribution intervals of turbidites in the target area based on the comparison results includes: The layers in which the duration of the low-lying systems domain is greater than a preset time, the magnitude of the datum level drop in the first period is greater than a preset magnitude, and the rate of datum level rise in the second period is less than a preset rate are selected as favorable layers for the vertical distribution of turbidites in the target area.

6. The method according to claim 5, characterized in that, The high-frequency sequence includes a fourth-level sequence and a fifth-level sequence; before determining and comparing the reference surface variation parameters of each segment of the high-frequency sequence, the process also includes: The cycle times of the fourth-order and fifth-order sequences were quantized to 0.4 Ma and 0.1 Ma, respectively. The duration of the low-level system domain of each segment of the high-frequency sequence is determined based on the cycle time limit.

7. A rapid prediction device for turbidite reservoir distribution, characterized in that, include: The stratigraphic framework establishment module is used to acquire exploration data of a target area containing turbidites and establish a third-order sequence stratigraphic framework of the target layer in the target area based on the exploration data. The high-frequency sequence division module is used to divide high-frequency sequences based on the three-level sequence stratigraphic framework and well logging data from key wells in the target area. The reference surface determination module is used to determine the reference surface based on the partitioned high-frequency sequence. The favorable stratigraphic segment prediction module is used to determine and compare the reference level variation parameters of each stratigraphic segment of the high-frequency sequence, and predict the favorable stratigraphic segments of the vertical distribution of turbidites in the target area based on the comparison results; the reference level variation parameters include the duration of the lowstand systems tract, the magnitude of the reference level drop in the first period, and the rate of reference level rise in the second period; wherein, the first period is located before the second period. The planar distribution characterization module is used to characterize the planar distribution range of the turbidite in the vertically distributed favorable strata.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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