Predeposit sand body dessert prediction method based on sequence framework iteration constraint seismic inversion

Abstract

The invention discloses a pre-deposit sand body dessert prediction method based on sequence framework iteration constraint seismic inversion. The method comprises the following steps: dividing a single well sequence stratum; comparing well connection sequence and establishing a sequence stratigraphic fine division scheme; performing three-level sequence interface seismic interpretation and tracking, and establishing a three-level sequence seismic interpretation framework; screening identification sensitive parameters of the previous sand body dessert reservoir; establishing a three-level sequence seismic interpretation framework constraint post-stack seismic inversion and system domain seismic interpretation framework; establishing a system domain seismic interpretation framework constraint post-stack seismic inversion and four-level sequence seismic interpretation framework; establishing a four-level sequence seismic interpretation framework to constrain post-stack seismic inversion and a five-level sequence seismic interpretation framework; carrying out five-level sequence seismic interpretation framework constraint post-stack seismic inversion; carrying out five-level sequence seismic interpretation framework constraint pre-stack seismic inversion; and finely depicting the desserts of the pre-deposited sand body reservoir and the like. According to the method, high-resolution prediction of the pre-deposited sand body under the low seismic resolution is realized, and the reservoir depicting precision of the pre-deposited sand body is improved.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas technology, specifically relating to a method for predicting sweet spots of pre-sedimentary sand bodies based on sequence lattice iterative constrained seismic inversion. Background Technology

[0002] Forelandic sand bodies are an important reservoir type in sedimentary basins, typically composed of well-sorted, moderately sized sandstone with high porosity and permeability. They are widely developed in sedimentary environments such as delta fronts, fan deltas, and submarine fans. Due to their unique sedimentary characteristics and excellent reservoir properties, forelandic sand bodies are often the main sites of oil and gas enrichment, exhibiting high single-well production and long stable production periods, resulting in significant economic benefits for exploration and development. Detailed characterization of the spatial distribution of sweet spot reservoirs in forelandic sand bodies can reveal the distribution patterns of oil and gas reservoirs, effectively reducing exploration risks and improving the development efficiency of oil and gas fields.

[0003] However, prograde sand bodies are typically characterized by thin thickness, rapid lateral variation, and multiple overlapping phases, and their spatial distribution is complex due to the influence of sedimentary environment and hydrodynamic conditions. Furthermore, the limited resolution of seismic data, especially under deep or complex geological conditions, often masks the seismic response characteristics of prograde sand bodies, increasing the difficulty of identification and prediction.

[0004] Current seismic prediction methods for proterostratigraphic sand bodies mainly include seismic attribute analysis, sequence stratigraphy, and seismic inversion. However, these methods have their own limitations in practical applications. Seismic attribute analysis (such as amplitude and frequency) can reflect the distribution characteristics of sand bodies, but it heavily relies on the resolution of seismic data and struggles to distinguish between multi-stage superimposed proterostratigraphic sand bodies. While sequence stratigraphy can help delineate sequences and systems tracts, the interpretation and tracing of seismic sequence boundaries are also limited by the resolution of seismic data, making it difficult to construct high-precision sequence frameworks. Traditional seismic inversion methods typically lack full utilization of high-precision sequence framework constraints, resulting in low resolution inversion results and difficulty in effectively characterizing the spatial distribution and superposition relationships of proterostratigraphic sand bodies. Summary of the Invention

[0005] This invention is proposed to solve the problems existing in the prior art, and its purpose is to provide a method for predicting sweet spots of pre-sedimentary sand bodies based on sequence lattice iterative constraint seismic inversion.

[0006] This invention is achieved through the following technical solution: A method for predicting sweet spots of pre-sedimentary sand bodies based on sequence lattice iterative constrained seismic inversion includes the following steps: S1. Sequence stratigraphy of a single well: Specifically, the following steps are included: S11. Based on the vertical variation of lithology and mud content, identify third-order sequence boundaries in the study area; The vertical variation patterns of the lithology and mud content were determined based on well logging and core data analysis.

[0007] S12. Within each third-order sequence identified in step S11, based on the stratigraphic stacking pattern and the relative rise and fall of the base level, the location of the maximum floodplain and the location of the water transgression-regression cycle are identified first. Using the location of the maximum floodplain and the location of the water transgression-regression cycle as the interface, each third-order sequence is divided into the lowstand systems tract (LST), the transgressive systems tract (TST), and the highstand systems tract (HST). The stratigraphic stacking pattern and the relative rise and fall of the reference surface are based on existing data.

[0008] S13. Within each system domain divided in step S12, based on the secondary high-frequency cycle variation characteristics, fourth-order and fifth-order sequences are divided to establish a single-well sequence stratigraphic framework, providing a basis for well-to-well sequence correlation.

[0009] S2. Compare well sequence stratigraphy and establish a fine sequence stratigraphic division scheme: Conventional seismic sequence stratigraphy of the study area was performed on multiple adjacent wells and compared with the results of single-well sequence stratigraphy determined in step S1 to ensure the consistency of the sequence stratigraphy; the characteristics of water-continuation-regression stratigraphic depositional patterns were summarized, the spatial distribution characteristics and superposition relationships of pre-depositional sand bodies in each stage were clarified, and a fine sequence stratigraphic division scheme for the study area was established. The water-congression-regression stratigraphic sedimentary pattern characteristics are the stratigraphic sedimentary pattern characteristics of the sequence where the water-congression-regression cycle change location is identified in step S12. S3. Establish a three-level sequence seismic interpretation framework: Based on the well sequence stratigraphic division results using seismic data and the single-well sequence stratigraphic division results obtained in step S1, a third-order sequence boundary seismic interpretation and tracing is performed to establish a third-order sequence seismic interpretation framework, providing sequence framework constraints for subsequent preliminary seismic inversion. S4. Utilize well logging, core data, and other data to conduct petrophysical characteristic analysis of sweet spot reservoirs in foreland sand bodies, screen sensitive parameters for identifying sweet spot reservoirs in foreland sand bodies, and provide target parameters for seismic inversion; The physical characteristics of the sweet spot reservoir rocks include lithology and porosity; The selection of sensitive parameters should be determined based on detailed petrophysical analysis, as different study areas vary in geology, sedimentary background, and depth. S5. Establishing a three-level sequence seismic interpretation framework to constrain post-stack seismic inversion and systems tract seismic interpretation framework: Using the third-order sequence seismic interpretation framework traced in step S4 as a constraint, the first round of post-stack seismic inversion was performed to obtain a preliminary post-stack seismic inversion body to reflect the macroscopic distribution characteristics of the pre-sedimentary sand bodies. However, the resolution is low and it is difficult to identify thin sand bodies. It can provide an initial model for the second round of post-stack inversion. According to the well-connected system tract division scheme, the system tract interface is traced on the first round of post-stack seismic inversion body to establish a system tract seismic interpretation framework. S6. Establishing a system-domain seismic interpretation framework constrained by post-stack seismic inversion and a fourth-order sequence seismic interpretation framework: Using the post-stack seismic inversion body obtained from the first round of post-stack seismic inversion in step S5 as the initial model, and the seismic interpretation framework of the system tracts traced in step S5 as constraints, the vertical resolution and lateral continuity of the inversion results are controlled to conduct a second round of post-stack seismic inversion, further improving the prediction accuracy of forelandite bodies, obtaining the second round of post-stack seismic inversion body, clarifying the distribution pattern of forelandite bodies in different system tracts, and providing an initial model for the third round of post-stack inversion; and according to the well-connected four-level sequence stratigraphy scheme, the fourth-level sequence boundary is traced on the second round of post-stack seismic inversion body to establish a fourth-level sequence seismic interpretation framework; The vertical resolution of the inversion results is controlled by using the system domain interpretation framework as a hard constraint and strictly limiting the inversion process to each fourth-order sequence or system domain unit. This isolates the interference of information from adjacent layers, focuses on the thin layer response within the unit, and integrates high-frequency information from well logging to compensate for the missing frequency bands in seismic data. This effectively improves the characterization of single sand body thickness and the ability to distinguish thin interlayers. The control of the lateral continuity of the inversion results is specifically achieved by using the geological model established by the interpretation framework as a spatial constraint to guide the inversion algorithm to follow the established sedimentary structure and stratigraphic contact relationship in the lateral direction. Through methods such as co-simulation or co-kriging, the inversion attributes are made to change smoothly and reasonably within the same system tract or genetic unit, thereby effectively suppressing isolated anomaly noise, enhancing the continuity of the same phase axis, and making the outline of the pre-sedimentary sand bodies with the same sedimentary background clearer and the spatial distribution more geologically regular. S7. Establishing a fourth-order sequence seismic interpretation framework to constrain post-stack seismic inversion and a fifth-order sequence seismic interpretation framework: Using the second round of post-stack seismic inversion obtained in step S6 as the initial model, and the fourth-order sequence seismic interpretation framework traced in step S6 as a constraint, the vertical resolution and lateral continuity of the inversion results are further controlled to perform the third round of post-stack seismic inversion, further refine the predicted results of the forelandite bodies, obtain the third round of post-stack seismic inversion, characterize the forelandite body features within the fourth-order sequence, and provide an initial model for the fourth round of post-stack inversion. According to the well-connected five-order sequence division scheme, the fifth-order sequence interface is traced on the third round of post-stack seismic inversion to establish the fifth-order sequence seismic interpretation framework. S8. Five-level sequence seismic interpretation framework-constrained post-stack seismic inversion: Using the third-round post-stack seismic inversion body obtained in step S7 as the initial model, and the fifth-order sequence seismic interpretation framework tracked in step S7 as the constraint, a fourth-round post-stack seismic inversion is performed to obtain the fourth-round post-stack seismic inversion body, thereby achieving high-precision prediction of the pre-depositional sand bodies within the fifth-order sequence. S9, Five-level sequence seismic interpretation framework-constrained pre-stack seismic inversion: Using the fifth-order sequence seismic interpretation framework tracked in step S7 as a constraint, high-resolution pre-stack seismic inversion is performed to obtain a high-precision pre-stack seismic inversion volume, which inverts richer reservoir information. S10, detailed characterization of the sweet spot of the pre-aggregate sand body reservoir: Through the iterative constraints of the sequence framework and seismic inversion in steps S3, S5~S9, high-resolution post-stack inversion is carried out to improve the resolution of the post-stack and pre-stack seismic inversion results, obtain a high-precision post-stack inversion volume, and identify sensitive parameters of the pre-sedimentary sand body sweet spot reservoir selected in step S4. By combining the post-stack and pre-stack inversion results, the spatial distribution, stacking relationship, and thickness variation characteristics of the pre-sedimentary sand body are identified, and its physical property parameters are predicted, thereby achieving a fine characterization of the sweet spot of the pre-sedimentary sand body reservoir.

[0010] The beneficial effects of this invention are: This invention provides a method for predicting sweet spots in foreland sand bodies based on iteratively constrained seismic inversion using a sequence framework, thereby improving the prediction accuracy of sweet spot reservoirs in foreland sand bodies. This invention employs a high-precision sequence framework constraint: through single-well sequence division, well-to-well correlation, and seismic interpretation tracing, a high-precision sequence framework from third-order to fifth-order sequences is constructed, providing reliable constraints for inversion; it employs multi-scale iterative inversion: seismic inversion is performed step-by-step from third-order to fifth-order sequences, and the resolution of the inversion results is gradually improved through iterative optimization, achieving a fine characterization of foreland sand bodies; and it integrates multi-source data: combining seismic, well logging, and core data, and comprehensively utilizing post-stack and pre-stack seismic information, the reliability and accuracy of the inversion results are improved.

[0011] This invention organically combines sequence stratigraphy with seismic inversion technology. Through iterative constraints of the sequence framework, it achieves high-resolution prediction of forelandite bodies under low seismic resolution conditions. This not only improves the accuracy of forelandite reservoir characterization but also provides new technical means for oil and gas exploration under complex geological conditions, and has broad application prospects. Attached Figure Description

[0012] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a sequence stratigraphic-sedimentary composite columnar section of a single well (WZ6-A1 well) in Example 1 of this invention; Figure 3This is a sequence diagram comparing wells in the study area in Embodiment 1 of the present invention; Figure 4 This is a diagram illustrating the detailed sequence stratigraphic division scheme for the study area in Embodiment 1 of the present invention; Figure 5 This is a preferred diagram of the sweet spot sensitive parameters of the three-section pre-depositional sand body in the study area of ​​Embodiment 1 of the present invention; Figure 6 This is a diagram showing the intersection of porosity and P-wave impedance of the pre-depositional sandstone reservoir in the study area of ​​Example 1 of this invention. Figure 7 This is a diagram showing the tracing of the third-level sequence interface and the establishment of the third-level sequence grid in the study area of ​​Embodiment 1 of the present invention; Figure 8 This is a diagram showing the post-stack velocity inversion profile and system domain lattice establishment of the study area under the constraint of a three-level sequence lattice in Embodiment 1 of the present invention. Figure 9 This is a diagram showing the seismic inversion profile of post-stack velocity and the establishment of the fourth-order sequence lattice in the study area of ​​Embodiment 1 of the present invention. Figure 10 This is a diagram showing the post-stack velocity seismic inversion profile constrained by the fourth-order sequence lattice and the establishment of the fifth-order sequence lattice in the study area of ​​Embodiment 1 of the present invention. Figure 11 This is a seismic inversion profile of the fifth-order sequence lattice constrained post-stack velocity in the study area of ​​Embodiment 1 of the present invention; Figure 12 This is a five-level sequence lattice-constrained pre-stack Poisson's ratio seismic inversion profile of the study area in Embodiment 1 of the present invention; Figure 13 This is a cross-sectional view of the porosity prediction and sweet spot characterization of the pre-deposited sand body in the study area in Embodiment 1 of the present invention.

[0013] For those skilled in the art, other related figures can be obtained from the above figures without any creative effort. Detailed Implementation

[0014] To enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0015] Example 1 The method described in this application has been applied in the fine characterization study of the sweet spot reservoir of the pre-depositional sand body in the X section of the Liu-3 Member in the western Weinan Depression of the South China Sea. The method has finely characterized the spatial distribution and physical properties of the pre-depositional sand body, providing important technical support for the exploration and development of oil and gas reservoirs.

[0016] The X area of ​​the Weinan Depression in the western South China Sea has great potential for oil and gas resources. However, due to geological factors such as complex faults, unclear sedimentary patterns, unknown reservoir sensitivity petrophysical parameters, and strong reservoir heterogeneity, existing reservoir prediction technologies are not accurate enough in predicting the "sweet spot" reservoirs in this area, making it difficult to predict and characterize the distribution of "sweet spot" reservoirs. This invention proposes a method for predicting sweet spot reservoirs in pre-sedimentary sand bodies based on sequence lattice iterative constraint seismic inversion, which clarifies the development patterns and spatiotemporal distribution of high-quality reservoirs, providing a basis for decision-making in the next step of drilling deployment.

[0017] like Figure 1 As shown, a method for predicting sweet spots in pre-sequenced sand bodies based on sequence lattice iterative constrained seismic inversion includes the following steps: S1. Sequence stratigraphy of a single well: Based on well logging and well logging data, and taking into account the seismic response characteristics of sedimentary interfaces, we conducted a study on the division of stratigraphic cycles and the variation of the base level in a single well. In this embodiment, based on the vertical variation of lithology and mud content, two third-order sequences (third-order sedimentary cycles) were identified in the third section of the flow. The upper sub-member of Liusan has relatively fine lithology and multiple sets of semi-deep lacustrine dark mudstone and shale. The overall sedimentary environment is relatively deep, but the total radioactivity indicated by the gamma curve still shows a characteristic of gradually increasing and then rapidly decreasing from bottom to top. Within a sequence stratigraphy, based on stratigraphic stacking patterns and relative rise and fall of the base level, the location of the maximum floodplain and the water transgression-regression cycles are identified first. Then, based on changes in sedimentary secondary cycles, fourth- and fifth-order cycles are identified. Figure 2 ).

[0018] S2. Compare well sequence stratigraphy and establish a fine sequence stratigraphic division scheme: Combining conventional seismic sequence stratigraphy of the study area with the single-well sequence stratigraphy determined in step S1, well-to-well sequence correlation was carried out. Figure 3 To ensure the consistency of sequence stratigraphy, summarize the characteristics of water-continuum-regression stratigraphic depositional styles, clarify the spatial distribution and superposition relationships of proterolithic bodies in each phase, and establish a detailed sequence stratigraphic division scheme for the study area. Figure 4 ); The spatial distribution characteristics and superposition relationships refer to: combining the conventional seismic sequence stratigraphy of the study area and the single-well sequence stratigraphy determined in step S1, conducting well-to-well sequence correlation to determine the initial (lower precision) morphology, orientation, strike, and scale of the pre-depositional sand bodies (spatial characteristics), as well as the contact relationships, combinations, and arrangements of sand bodies in each phase (superposition relationships); subsequent sequence seismic interpretation frameworks are iteratively constrained based on this, thus obtaining a five-level sequence seismic interpretation framework; In this embodiment, the lower sequence of the Liu-3 stratigraphy exhibits a predominantly progradational sequence structure, while the upper sub-section of the Liu-3 stratigraphy exhibits a predominantly retrogradational sequence structure. The lower sub-section of the Liu-3 stratigraphy can be identified as having three fourth-order sequence units and seven fifth-order sequence units, exhibiting a "two-retrograde and five-progradation" stratigraphic development characteristic. Specifically, in the highstand systems tract of the lower sequence of the Liu-3 stratigraphy, due to base level fluctuations and shoreline migration, five phases of deltaic forelandic strata developed successively, but the number, phases, and thickness of the forelandic strata varied significantly across different wells. In contrast, the upper sequence of the Liu-3 stratigraphy can be identified as having three fourth-order sequence units and four fifth-order sequence units, exhibiting an overall "three-retrograde and one-progradation" stratigraphic development characteristic.

[0019] S3. Establish a three-level sequence seismic interpretation framework: Based on the well sequence stratigraphic division results using seismic data and the single-well sequence stratigraphic division results obtained in step S1, a third-order sequence boundary seismic interpretation and tracing is performed to establish a third-order sequence seismic interpretation framework, providing sequence framework constraints for subsequent preliminary seismic inversion. In this embodiment, due to the low resolution of seismic data in the study area (dominant frequency of approximately 16 Hz), the accuracy of sequence identification based on seismic stratigraphy is limited, making it impossible to conduct high-frequency stratigraphic division and stratigraphic style studies. Only the sequence boundaries of three third-order sequences can be identified: T90, T88, and T86. Figure 7 This establishes a three-level sequence seismic interpretation framework, providing sequence framework constraints for subsequent preliminary seismic inversion.

[0020] S4. Utilize well logging, core data, and other data to conduct petrophysical characteristic analysis of sweet spot reservoirs in foreland sand bodies, screen sensitive parameters for identifying sweet spot reservoirs in foreland sand bodies, and provide target parameters for seismic inversion; The physical characteristics of the sweet spot reservoir rocks include lithology and porosity; In this embodiment, the reservoir identification sensitive parameters include P-wave velocity, Poisson's ratio, shear modulus, and Lamé coefficient; In this embodiment, petrophysical analysis of sweet spot reservoirs in foresand bodies is carried out based on well logging data. P-wave velocity and Poisson's ratio are selected as sensitive parameters for identifying sweet spot reservoirs. Sweet spot reservoirs are characterized by high P-wave velocity and low Poisson's ratio. Figure 5 ).

[0021] Based on the identification (qualitative) of sweet spot reservoirs, quantitative evaluation of sweet spot reservoirs can be carried out. That is, by utilizing the fitting correlation between P-wave impedance and porosity, the porosity of the reservoir can be calculated using P-wave impedance parameters, thus achieving the purpose of quantitative evaluation. In this embodiment: Within sweet spot reservoirs, the intersection of reservoir porosity and P-wave impedance reveals that P-wave impedance is a sensitive parameter for quantitative prediction of reservoir porosity. Favorable reservoirs are categorized into Class I reservoirs (porosity > 12%), Class II reservoirs (8% < porosity ≤ 12%), and non-porosity reservoirs (0% < porosity ≤ 8%). The criteria and geophysical prediction classification scheme for effective reservoirs in the three flow sections are also defined. Figure 6 ).

[0022] S5. Establishing a three-level sequence seismic interpretation framework to constrain post-stack seismic inversion and systems tract seismic interpretation framework: Using the third-order sequence seismic interpretation framework traced in step S4 as a constraint, the first round of post-stack seismic inversion was performed to obtain a preliminary post-stack seismic inversion body to reflect the macroscopic distribution characteristics of progradational sand bodies. However, the resolution is low, making it difficult to identify thin-layered sand bodies. This can provide an initial model for the second round of post-stack inversion. Based on the well-connected systems tract division scheme and the first round of post-stack seismic inversion body, the maximum lacustrine flooding surface (MFS) was identified and traced within the third-order sequence. Above the MFS interface, high-stand progradational bodies are developed, while below the MFS is a low-stand + transgressive tract, reflecting the layered stratigraphic structure characteristics of filling and filling-in genesis. A systems tract seismic interpretation framework was established. Figure 8 ); S6. Establishing a system-domain seismic interpretation framework constrained by post-stack seismic inversion and a fourth-order sequence seismic interpretation framework: Using the post-stack seismic inversion body obtained from the first round of post-stack seismic inversion in step S5 as the initial model, and the seismic interpretation framework of the system tracts traced in step S5 as constraints, the vertical resolution and lateral continuity of the inversion results are controlled to conduct a second round of post-stack seismic inversion, further improving the prediction accuracy of forelandite bodies and obtaining the second round of post-stack seismic inversion body. This clarifies the distribution patterns of forelandite bodies within different system tracts and provides an initial model for the third round of post-stack inversion. Furthermore, based on the well-connected fourth-order sequence stratigraphy scheme, the fourth-order sequence boundary is traced and interpreted in the lower sequence high-beam domain of the third section of the flow path in the second round of post-stack seismic inversion body, establishing a fourth-order sequence seismic interpretation framework. Figure 9 ); S7. Establishing a fourth-order sequence seismic interpretation framework to constrain post-stack seismic inversion and a fifth-order sequence seismic interpretation framework: Using the second-round post-stack seismic inversion body obtained in step S6 as the initial model, and constrained by the fourth-order sequence seismic interpretation framework traced in step S6, the vertical resolution and lateral continuity of the inversion results are further controlled to perform a third-round post-stack seismic inversion. This further refines the predicted results of progradational sand bodies, resulting in a third-round post-stack seismic inversion body that characterizes the progradational sand body features within the fourth-order sequence. Simultaneously, it provides an initial model for the fourth-round post-stack inversion. Based on the well-connected five-order sequence division scheme and the third-round post-stack seismic inversion body, the fifth-order sequence boundary is traced within the fourth-order sequence. Within the high-beam region, based on the progradational relationships of sandstone layers, at least five progradational phases are identified, thereby establishing a high-frequency fifth-order sequence seismic interpretation framework. Figure 10 ); S8. Five-level sequence seismic interpretation framework-constrained post-stack seismic inversion: Using the third-round post-stack seismic inversion volume obtained in step S7 as the initial model, and the high-frequency fifth-order sequence seismic interpretation framework tracked in step S7 as constraints, a fourth-round post-stack seismic inversion is performed to obtain the fourth-round post-stack seismic inversion volume. This significantly improves the vertical resolution and lateral continuity of the seismic inversion, enabling high-precision prediction of pre-seismic bodies within the fifth-order sequence. Figure 11 ); S9, Five-level sequence seismic interpretation framework-constrained pre-stack seismic inversion: Using the high-frequency five-level sequence seismic interpretation framework tracked in step S7 as a constraint, high-resolution pre-stack seismic inversion was performed to obtain a high-precision pre-stack seismic Poisson's ratio inversion volume. Figure 12 ), which inversely yields richer reservoir information; S10, detailed characterization of the sweet spot of the pre-aggregate sand body reservoir: Constrained by establishing a high-frequency, five-level sequence seismic interpretation framework, high-resolution post-stack P-wave impedance inversion was conducted to obtain a high-precision P-wave impedance inversion body. Based on petrophysical analysis of sweet spot reservoirs in foreseminated sandstone bodies, sweet spot reservoirs were qualitatively identified by combining post-stack P-wave velocity inversion results and pre-stack Poisson's ratio inversion results. Furthermore, within sweet spot reservoirs, porosity was quantitatively predicted based on the fitting relationship between P-wave impedance and porosity, thereby achieving a detailed characterization of sweet spots in foreseminated sandstone bodies. Figure 13 ).

[0023] The applicant declares that the above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention fall within the protection and disclosure scope of the present invention.

Claims

1. A method for predicting sweet spots in pre-sequenced sand bodies based on sequence lattice iterative constrained seismic inversion, characterized in that: Includes the following steps: S1. Sequence stratigraphy of a single well: S2. Compare well sequence stratigraphy and establish a fine sequence stratigraphic division scheme: S3. Based on the well sequence stratigraphic division results using seismic data and the single-well sequence stratigraphic division results obtained in step S1, perform third-order sequence boundary seismic interpretation and tracing, and establish a third-order sequence seismic interpretation framework. S4. Conduct petrographic analysis of pre-sedimentary sweet spot reservoirs and screen sensitive parameters for identifying pre-sedimentary sweet spot reservoirs. S5. Establish a three-level sequence seismic interpretation framework to constrain post-stack seismic inversion and systems tract seismic interpretation framework; S6. Establish a system domain seismic interpretation framework constrained by post-stack seismic inversion and a fourth-order sequence seismic interpretation framework; S7. Establish a fourth-order sequence seismic interpretation framework to constrain post-stack seismic inversion and a fifth-order sequence seismic interpretation framework; S8, Level 5 sequence seismic interpretation lattice-constrained post-stack seismic inversion; S9, Level 5 sequence seismic interpretation lattice-constrained pre-stack seismic inversion; S10, the sweet spot of the pre-aggregate sand body reservoir is finely depicted.

2. The method for predicting sweet spots of pre-sedimentary sand bodies based on sequence lattice iterative constrained seismic inversion according to claim 1, characterized in that: Step S1 specifically includes the following steps: S11. Based on the vertical variation of lithology and mud content, identify third-order sequence boundaries in the study area; S12. Within each third-order sequence identified in step S11, based on the stratigraphic stacking pattern and the relative rise and fall of the reference surface, identify the location of the maximum floodplain and the location of the water transgression-regression cycle. Using the location of the maximum floodplain and the location of the water transgression-regression cycle as the interface, divide each third-order sequence into a lowstand systems tract, a transgressive systems tract, and a highstand systems tract. S13. Within each system domain divided in step S12, based on the secondary high-frequency cycle variation characteristics, fourth-order and fifth-order sequences are divided to establish a single-well sequence stratigraphic framework.

3. The method for predicting sweet spots of pre-sedimentary sand bodies based on sequence lattice iterative constrained seismic inversion according to claim 1, characterized in that: Step S2 specifically involves: performing conventional seismic sequence stratigraphy on multiple adjacent wells in the study area, and comparing the results with the single-well sequence stratigraphy determined in step S1 to ensure consistency in sequence stratigraphy; summarizing the characteristics of water-continuation-regression stratigraphic depositional patterns, clarifying the spatial distribution characteristics and superposition relationships of pre-depositional sand bodies in each phase, and establishing a refined sequence stratigraphic division scheme for the study area.

4. The method for predicting sweet spots of pre-sedimentary sand bodies based on sequence lattice iterative constrained seismic inversion according to claim 1, characterized in that: Specifically, step S5 involves: using the third-order sequence seismic interpretation framework traced in step S4 as a constraint, performing the first round of post-stack seismic inversion to obtain a preliminary post-stack seismic inversion body; and tracing the system domain interface on the first round of post-stack seismic inversion body according to the well-connected system domain division scheme to establish a system domain seismic interpretation framework.

5. The method for predicting sweet spots of pre-sedimentary sand bodies based on sequence lattice iterative constrained seismic inversion according to claim 1, characterized in that: Step S6 specifically involves: using the post-stack seismic inversion body obtained from the first round of post-stack seismic inversion in step S5 as the initial model, and using the seismic interpretation framework of the system tract tracked in step S5 as a constraint to control the vertical resolution and horizontal continuity of the inversion results, performing a second round of post-stack seismic inversion to obtain a second round of post-stack seismic inversion body; and, according to the well-connected four-level sequence stratigraphy scheme, tracing the fourth-level sequence boundary on the second round of post-stack seismic inversion body to establish a fourth-level sequence seismic interpretation framework.

6. The method for predicting sweet spots of pre-sedimentary sand bodies based on sequence lattice iterative constrained seismic inversion according to claim 1, characterized in that: Step S7 specifically involves: using the second-round post-stack seismic inversion body obtained in step S6 as the initial model, and using the fourth-order sequence seismic interpretation framework traced in step S6 as a constraint, performing a third-round post-stack seismic inversion to further refine the pre-sedimentary sand body prediction results, obtaining the third-round post-stack seismic inversion body, and tracing the fifth-order sequence interface on the third-round post-stack seismic inversion body according to the well-connected five-order sequence division scheme to establish a fifth-order sequence seismic interpretation framework.

7. The method for predicting sweet spots of pre-sedimentary sand bodies based on sequence lattice iterative constrained seismic inversion according to claim 1, characterized in that: Specifically, step S8 involves using the third-round post-stack seismic inversion body obtained in step S7 as the initial model, and using the fifth-order sequence seismic interpretation framework tracked in step S7 as a constraint, to perform a fourth-round post-stack seismic inversion and obtain the fourth-round post-stack seismic inversion body.

8. The method for predicting sweet spots of pre-sedimentary sand bodies based on sequence lattice iterative constrained seismic inversion according to claim 1, characterized in that: Specifically, step S9 involves performing a fifth round of pre-stack seismic inversion using the five-level sequence seismic interpretation framework traced in step S7 as a constraint, to obtain a high-precision pre-stack seismic inversion model.

9. The method for predicting sweet spots of pre-sedimentary sand bodies based on sequence lattice iterative constrained seismic inversion according to claim 1, characterized in that: Step S10 specifically involves: Through the iterative constraints of the sequence framework and seismic inversion in steps S3, S5~S9, high-resolution post-stack inversion is carried out to obtain a high-precision post-stack inversion volume. Based on the sensitive parameters of the pre-sedimentary sand body sweet spot reservoir selected in step S4, and by combining the post-stack and pre-stack inversion results, the spatial distribution, stacking relationship, and thickness variation characteristics of the pre-sedimentary sand body are identified, and its physical property parameters are predicted, thereby achieving a fine characterization of the sweet spot of the pre-sedimentary sand body reservoir.

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