A method for fine description of mound and beach reservoir-permeable body under sequence stratigraphic framework
By combining geological models with seismic prediction methods, utilizing geological model constraints and high-resolution vertical data from wells, and employing 3D sculpting technology to finely depict carbonate rock mounds and shoals, the problem of insufficient prediction accuracy for thin interbedded layers under deep burial conditions is solved, enabling fine reservoir characterization and guidance for exploration and development.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SOUTHWEST PETROLEUM UNIV
- Filing Date
- 2023-09-20
- Publication Date
- 2026-06-19
Smart Images
Figure CN117270048B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of petroleum exploration and development, and in particular to a method for fine characterization of hill and shoal reservoirs within a sequence stratigraphic framework. Background Technology
[0002] Carbonate rock formations, primarily composed of algae and fungi, are formed through physical processes of sedimentation (mud deposition) and chemical processes of diagenesis. Clastic particles formed by wave-induced fracturing of the microbial mounds are deposited around them, collectively forming mound-shaped elevations known as mound-shoal complexes (or simply mound-shoal bodies). These structures have been found from the Precambrian to the Quaternary periods. As important carriers of new oil and gas resources, mound-shoal bodies have demonstrated enormous exploration and development potential in global oil and gas exploration practices, becoming a current research hotspot in carbonate reservoir oil and gas exploration.
[0003] Currently, carbonate hill-shoal reservoirs are constrained by their unique sedimentary characteristics and reservoir formation mechanisms, resulting in extremely high homogeneity in both vertical and horizontal directions, and extremely well-developed thin interbedded layers in the vertical direction. At the same time, under deep burial conditions, the resolution of existing seismic data cannot meet the requirements for thin reservoir prediction, and is also constrained by the lack of corresponding geological sedimentary models. Current seismic prediction methods have strong ambiguity and insufficient prediction accuracy, which is far from meeting the needs of oilfield exploration and development. Summary of the Invention
[0004] To overcome the above technical problems, the purpose of this invention is to propose a method for fine characterization of hill-shoal reservoirs within a sequence stratigraphic framework. By fully combining geological models with seismic prediction methods, the constraints of geological models are used to reduce the ambiguity of seismic prediction methods. At the same time, the continuity of seismic data and the high vertical resolution of well data are combined to improve the prediction accuracy of thin interbedded hill-shoal reservoirs. Finally, three-dimensional sculpting technology is used to achieve fine spatial characterization of hill-shoal reservoirs.
[0005] This invention provides the following technical solution:
[0006] A method for fine characterization of facies-controlled hill-shoal reservoirs within a sequence stratigraphic framework includes the following steps:
[0007] S1: Data collection and work area establishment: Collect relevant data in the study area, including regional survey data, field outcrop data, core thin section data, well completion data, porosity and permeability data, test data, and three-dimensional post-stack seismic data. At the same time, establish a geological-seismic interpretation work area, classify and organize the data and import it into the geological-seismic interpretation work area.
[0008] S2. Single-well sequence stratigraphy, fine well-seismic calibration, construction of a well-seismic isochronous stratigraphic framework for the whole area, fine tracking and interpretation of the isochronous seismic horizons of the target layer, and clarification of the longitudinal and transverse distribution patterns of sequence stratigraphy in the study area.
[0009] S3: Based on the regional sedimentary background, identify the sedimentary facies zone types in the study area and the corresponding microfacies types; combine the field outcrops, cores, thin sections and well logs in the study area to establish identification templates for various sedimentary microfacies, establish the correspondence between lithology and lithofacies (sedimentary microfacies), draw a comprehensive sedimentary columnar section of a single well and a comparative profile of sedimentary facies across wells; determine the longitudinal and transverse distribution patterns of sedimentary microfacies in the area.
[0010] S4: Statistically analyze the formation thickness and favorable microfacies thickness of all target drilling intervals in the study area. The favorable microfacies are the hill and shoal thickness. Calculate the ratio of hill and shoal thickness to formation thickness in a single well, i.e., the hill-shoal ratio. Establish the quantitative relationship between the hill-shoal ratio and the microfacies in a single well to determine the sedimentary microfacies type of each target drilling interval.
[0011] S5. Well-seismic calibration: Combined with seismic forward modeling, establish the mapping relationship between single-well seismic facies and sedimentary microfacies. Under the constraint of isochronous seismic stratigraphic framework, use neural network clustering method to obtain the planar distribution characteristics of seismic waveforms in the target layer of the study area, i.e., the planar distribution of seismic facies in the target layer. Based on the seismic facies-sedimentary microfacies mapping relationship mentioned above, use planar seismic facies to draw the planar distribution map of sedimentary facies in the study area.
[0012] S6: Well logging curve quality analysis, standardize well logging curves within the region to improve the consistency of curves across multiple wells, and perform cross-analysis of well lithology and physical properties in the study area based on the standardized well logging curves to select the sensitive parameter curves for reservoir identification;
[0013] S7: Well-seismic calibration and seismic wavelet extraction. Combining the stratigraphic characteristics of the target layer in the study area, the seismic horizon of the target layer is used for constraint and control. Based on the single-well calibration results, a low-frequency impedance model is established, and deterministic inversion is carried out to obtain the P-wave impedance volume.
[0014] S8: Statistically analyze single-well reservoir data, calculate the reservoir variation function, combine the sedimentary microfacies distribution map characterized in step S5, draw a planar scale map based on the reservoir proportion of the target layer statistically based on the well point statistics, and use this as a control condition to carry out high-resolution geostatistical inversion to obtain the reservoir probability volume.
[0015] S9: Based on the reservoir probability volume results obtained from the geostatistical inversion in step S8, extract the time thickness of the target layer reservoir. Then, according to the porosity curve of the wells in the area, statistically analyze the correlation between the well point reservoir thickness and the inverted time thickness, fit the proportional formula, and convert it into a depth domain reservoir thickness planar map.
[0016] S10: By using the obtained reservoir probability volume and P-wave impedance volume through co-simulation, and combining the pairwise relationship between P-wave impedance and porosity in geostatistical parameters, the reservoir porosity volume is calculated based on the two, and the average porosity plane distribution map of the target segment is extracted based on the porosity volume.
[0017] S11: Comprehensive analysis is conducted to obtain the energy storage coefficient of the target layer by calculating the product of the predicted porosity and thickness of the reservoir, which further characterizes the changes in reservoir storage performance in the study area. At the same time, seismic interpretation software is used to perform three-dimensional perspective on the reservoir probability volume, and the spatial distribution characteristics of the hill and shoal reservoir morphology are sculpted. The detailed characterization of the hill and shoal reservoir in the study area is completed.
[0018] According to some implementation methods, in S2, the single-well sequence division needs to combine lithology, logging cycles, carbon isotopes, and seismic stratigraphic characteristics to establish a standard sequence division template; the fine well-seismic calibration needs to determine the reflection characteristics of each sequence interface, determine the seismic tracking interpretation scheme of each interface in the area, and based on the scheme, build a well-connected grid profile, and then gradually densify the interpretation according to the seismic grid density of 64→32→16→8→4, and finally complete the fine interpretation of each sequence interface.
[0019] According to some implementation methods, in S4, when calculating the thickness of the target layer and the thickness of the hills and shoals, well inclination needs to be considered. First, the measurement depth is converted into vertical depth before the statistics are performed. Calculating the thickness of the hills and shoals means calculating the thickness of the lithological section corresponding to the hills and shoals.
[0020] According to some implementation methods, in step S4, based on regional geological understanding, the range of mound ratio values corresponding to each sedimentary microfacies type is determined through comprehensive analysis of single-well lithology, lithofacies, and mound ratio. The identification criteria for single-well sedimentary microfacies are quantified. The quantitative relationship between mound ratio and single-well facies is based on A as a threshold. The value of A ranges from 0 to 1. When the mound ratio of a single well is greater than A, it is a favorable microfacies. The specific value of A needs to be determined in conjunction with the actual sedimentary background.
[0021] According to some implementation methods, in S5, the geological model in the forward modeling needs to be designed based on actual drilling data. Among them, the rock physical parameters include: the thickness of the target layer, the sonic velocity, the rock density, and the development scale, thickness, velocity and density of various microfacies. At the same time, the microfacies types, development locations and scales involved in the model need to be consistent with the actual drilling conditions.
[0022] According to some implementation methods, in S5, the mapping relationship between seismic facies and sedimentary facies in a single well is established by comparing the theoretical profile of forward modeling with the actual profile of passing through the well, analyzing the seismic response characteristics of different sedimentary microfacies, statistically analyzing the sedimentary microfacies and their corresponding seismic facies at each well point, and establishing the mapping relationship between the two.
[0023] According to some implementation methods, in step S6, the logging curve standardization adopts a logging curve standardization method with phase-controlled high-frequency range constraints.
[0024] In the above embodiments, based on the constraints of sedimentary facies zones, the method transforms the traditional standardization operation corresponding to the "main peak" into the standardization operation corresponding to the "high frequency range", which allows for the standardization anomalies caused by minor geomorphic differences within the same large facies zone, thereby improving the accuracy and fault tolerance of standardization.
[0025] According to some implementation methods, in S7, the seismic wavelet extraction is selected from the following two methods: the first is to solve the problem using the least squares method based on the well-side seismic records of existing well logging data; the second is to extract the wavelet using the autocorrelation statistical method of multichannel records based on actual seismic data.
[0026] In the above embodiments, the first method for seismic wavelet extraction uses the least squares method to solve for the wavelet from existing well-logging data and adjacent seismic records. However, this method is affected by both seismic noise and well-logging errors, leading to distortions in the wavelet amplitude and phase spectrum. Therefore, the method itself is very sensitive to changes in seismic noise and estimation window length, resulting in poor stability of the wavelet estimation effect. The second method uses actual seismic data and extracts the wavelet using a multichannel autocorrelation statistical method. The synthetic record produced by this method has the same frequency band as the actual seismic record, a good correspondence with the wave impedance of the actual seismic record, and stable application results. Therefore, the latter extraction method is recommended.
[0027] According to some implementation methods, in S7, the deterministic inversion adopts a sparse pulse inversion method faithful to seismic data. The inversion results need to be verified by well point data. If the lateral variation trend of the inversion results is consistent with the well point impedance results or the error is small, the inversion results are considered to have good vertical and lateral predictive power and can proceed to the next step. If the inversion results have a large error with the well point data, the inversion parameters need to be adjusted and the inversion needs to be performed again until the results are consistent or the error is small.
[0028] According to some implementation methods, in S8, the high-resolution geostatistical inversion results also require well point data as quality control, the target layer reservoir thickness of the well point is statistically analyzed, and the predicted reservoir thickness value at the well point of the statistical inversion results is extracted. If the seismic inversion results are consistent with the reservoir thickness of the verification well or the error is less than 10%, the statistical inversion results are considered to meet the prediction requirements.
[0029] Compared with the prior art, the present invention has the following beneficial effects:
[0030] (1) The method for fine characterization of phase-controlled hill and shoal reservoirs provided by the present invention is an innovative and comprehensive application of existing technologies. By fully combining geological models and seismic prediction methods, the constraints of geological models are used to reduce the ambiguity of seismic prediction methods. At the same time, the combination of seismic data continuity and vertical high resolution of well data improves the prediction accuracy of thin interbedded hill and shoal reservoirs. Finally, the three-dimensional carving technology is used to realize the fine spatial characterization of hill and shoal reservoirs, which can better guide the exploration and development of hill and shoal reservoirs.
[0031] (2) The fine characterization method provided by the present invention uses a variety of high-resolution inversion methods to obtain reservoir porosity volume and reservoir probability volume. The two are organically combined to obtain the energy storage coefficient of the target layer, which can further characterize the changes in reservoir storage performance in the study area and guide production. Attached Figure Description
[0032] Figure 1 This is a flowchart of a method for fine characterization of phase-controlled hill-shoal reservoirs and permeable bodies within a sequence stratigraphic framework, provided by an embodiment of the present invention.
[0033] Figure 2 This is the seismic isochronous stratigraphic framework profile of the entire study area in this embodiment of the invention.
[0034] Figure 3 It is a template for identifying various sedimentary microfacies in the study area in the embodiments of the present invention.
[0035] Figure 4 This is a composite columnar section of sedimentary formations in the Penglai area, specifically Pengtan 1 (left) and Pengtan 101 (right), as described in this embodiment of the invention.
[0036] Figure 5 This is a comparison diagram of interconnected wells in the study area of this invention.
[0037] Figure 6 This is a planar distribution map of sedimentary microfacies in the upper sub-section of Deng 2 in the Penglai area, as described in this embodiment of the invention.
[0038] Figure 7 This is a cross-analysis diagram of lithology and physical properties from multiple wells in an embodiment of the present invention.
[0039] Figure 8 This is the low-frequency impedance model of the well-connected area in the study area in this embodiment of the invention.
[0040] Figure 9 This is a predicted average reservoir thickness from the inversion of the upper sub-section of lamp 2 in this embodiment of the invention.
[0041] Figure 10 This is a root mean square plane diagram of the porosity of the upper sub-segment of lamp two in an embodiment of the present invention.
[0042] Figure 11This is a spatial carving diagram of the upper sub-section of the study area in this embodiment of the invention. Detailed Implementation
[0043] The present invention will now be described in detail with reference to embodiments and accompanying drawings. However, it should be understood that the embodiments and drawings are for illustrative purposes only and do not constitute any limitation on the scope of protection of the present invention. All reasonable modifications and combinations included within the inventive spirit of the present invention fall within the scope of protection of the present invention.
[0044] The present invention will be further described below with reference to the accompanying drawings.
[0045] Example 1
[0046] This embodiment is a detailed characterization study of the hilly and shoal reservoirs and permeable bodies in the upper sub-member of the Deng II segment of the Sinian System in the Penglai-Zhongjiang area of the central and northern Sichuan Basin.
[0047] like Figure 1 As shown, a method for fine characterization of facies-controlled hill-shoal reservoirs within a sequence stratigraphic framework includes the following steps:
[0048] S1. Data collection and work area establishment: Collect relevant data in the study area, including regional survey data, field outcrop data, core thin section data, well completion data, porosity and permeability data, test data, three-dimensional post-stack seismic data, etc. At the same time, establish a geological-seismic interpretation work area, classify and organize the above data and import them into the work area.
[0049] The seismic data in the study area consists of 18 contiguous post-stack seismic blocks, including Penglai-Gaomo-Shehong, and the drilling data includes 13 wells, such as Pengtan 1, Pengtan 101, Pengtan 102, Pengtan 103, Pengtan 104, Pengtan 106, Zhongshen 103, Zhongshen 102, and Pengshen 5.
[0050] S2. Single-well sequence stratigraphy, fine well-seismic calibration, construction of a well-seismic isochronous stratigraphic framework for the whole area, fine tracking and interpretation of the isochronous seismic horizons of the target layer, and clarification of the longitudinal and transverse distribution patterns of sequence stratigraphy in the study area.
[0051] Specifically, firstly, based on regional geological survey data, the stratigraphic profile of the study area was clarified. Using the sequence stratigraphic boundary identification standard, and based on the lithological, electrical, and sedimentary cycle characteristics of each well section in the study area, the Deng 2 Member was subdivided into upper and lower sub-members, corresponding to two third-order sequences (the upper sub-member of Deng 2 corresponds to SQ2, and the lower sub-member of Deng 2 corresponds to SQ1). The boundary between SQ1 and SQ2 is a typical type II sequence boundary; the lower sub-member (SQ1) and the upper sub-member (SQ2) are separated by a set of low-GR, low-resistivity strata and an underlying set of relatively high-GR, high-resistivity strata. Secondly, single-well synthetic seismic records were generated using well logging curves (sonic and density curves) to conduct fine well-seismic calibration, determining the correspondence between each sequence boundary and the seismic reflection phase axis. Specifically, the top boundary of the second segment was calibrated at the mid-intensity wave peak, while the bottom boundary of the second segment and the bottom boundary of the upper sub-segment of the second segment were both calibrated at the strong wave trough. These calibrations are stable and continuously traceable within the study area. Finally, a full-area isochronous seismic stratigraphic framework was constructed. Figure 2 The step-by-step encryption method provides a detailed interpretation of the isochronous seismic horizons of the target layer.
[0052] S3. Based on the regional sedimentary background, the sedimentary background of the second member of the Dengying Formation of the Sinian System in the Penglai area is considered to be shallow-water carbonate platform sedimentation, mainly belonging to a microbial mound-shoal complex system developed under a relatively stable sedimentary background. This indicates that the sedimentary microfacies types of the second member of the Dengying Formation in the study area mainly include mound cores, grain shoals (sand shoals), mound flanks, inter-mound-shoal areas, and platforms, among which grain shoals and mound cores are the dominant facies zones for reservoir development. Based on field outcrop, core, thin section, and imaging logging data, interpretation templates for various sedimentary microfacies are established. Figure 3 Based on this template, a comprehensive sedimentary columnar section of all wells within the area was drawn. Figure 4 Simultaneously, a detailed sedimentary micro-comparison was conducted on the sequence stratigraphic profile within the area. Figure 5 From the well profile, it can be seen that the study area mainly develops sandy shoal microfacies, followed by mound microfacies; it can be seen that the sedimentary characteristics of the particle shoal microfacies in the upper sub-section of the Penglai gas area have gradually migrated upward from the Pengshen 5 well area to the Zhongshen 103 well area (Pengtan 1 well area).
[0053] S4. Statistically analyze the formation thickness and favorable microfacies (mound cores and sandy shoals) thickness of all wells in the upper sub-member of the drilling lamp within the study area. Calculate the ratio of mound (shoal) thickness to formation thickness in a single well (referred to as the mound-shoal ratio). Combined with the distribution pattern of sedimentary facies in the zone, establish a quantitative relationship between the mound-shoal ratio and the single-well facies, and determine the corresponding mound-shoal ratio threshold for each sedimentary microfacies type. In this embodiment, a mound-shoal ratio greater than 0.5 indicates mound core and sandy shoal microfacies; a ratio between 0.3 and 0.5 indicates mound wing microfacies; and a ratio less than 0.3 indicates inter-mound microfacies. Furthermore, in this embodiment, to better analyze the planar distribution pattern of sedimentary microfacies, if the mound-shoal ratio is greater than 0.5 and the proportion of sandy shoals exceeds 50%, the single-well microfacies is defined as a grainy shoal microfacies. If the mound-shoal ratio is greater than 0.5 and the proportion of mound cores exceeds 50%, the single-well microfacies is defined as a mound-shoal complex microfacies.
[0054] S5. Based on the sedimentary microfacies research results in step S4, a theoretical geological model of typical sedimentary facies sequences (vertical combinations of various sedimentary microfacies) in the area is established. The petrophysical parameters of this model are obtained from well logging data. Forward modeling is performed using the wave equation method to obtain the theoretical seismic response characteristics of typical sedimentary facies sequences. Based on well-seismic calibration and combined with well-pass seismic profiles, the actual seismic waveform characteristics corresponding to different microfacies types are analyzed, and a seismic facies-sedimentary facies mapping relationship is established. Secondly, under the constraint of the isochronous seismic stratigraphic framework, the planar distribution characteristics of the seismic waveforms of the target segment in the study area are obtained using a neural network clustering method, i.e., the planar distribution of seismic facies in the upper sub-section of Deng 2. Based on the aforementioned seismic facies-sedimentary facies mapping relationship, a planar distribution map of sedimentary facies in the study area is drawn using planar seismic facies. Figure 6 ).
[0055] S6. Well logging curve quality analysis: Well logging curves are standardized within the region to improve the consistency of curves across multiple wells. Based on the standardized well logging curves, cross-analysis of well lithology and physical properties in the study area is performed to select the sensitive parameter curves for reservoir identification. In this embodiment, sensitive parameter analysis is performed on all wells in the study area. Figure 7 It was found that porosity and acoustic transit time have a good correlation, and this parameter is selected as the sensitive parameter for subsequent inversion process.
[0056] S7. Select wells with complete acoustic and density data for detailed well-seismic calibration, producing synthetic seismic records for 13 wells in the study area, establishing the time-depth relationship between seismic and well logging data; within the target layer, wells with high correlation in the synthetic records are selected for multi-well composite wavelet extraction. Multi-well wavelet comparison shows that different single-well wavelets have similar morphologies and consistent phases within the effective seismic frequency band; combined with the stratigraphic characteristics of the upper sub-member of Deng 2, the seismic interpretation horizon of the study area is used for lateral constraints. Based on the calibration results of the 13 wells, the high-resolution strata information of the well logging data is extended vertically to the entire study area, and the natural neighbor interpolation algorithm is used for lateral interpolation and extrapolation to establish a three-dimensional low-frequency impedance model for the study area. Figure 8 Finally, deterministic inversion is carried out using the sparse pulse inversion method to obtain the longitudinal wave impedance volume.
[0057] S8. Statistically analyze single-well reservoir data, calculate the reservoir variation function, combine the sedimentary microfacies distribution map of the upper Deng 2 sub-member depicted in step S5, and draw the reservoir ratio plane distribution map of the upper Deng 2 sub-member based on the reservoir ratio (the ratio of reservoir thickness to actual formation thickness) of the upper Deng 2 sub-member according to well point statistics. Use this as a constraint to carry out high-resolution geostatistical inversion and obtain the reservoir probability volume.
[0058] S9. Based on the reservoir probability volume results obtained from the geostatistical inversion in step S8, the predicted time thickness of the upper sub-member of the Deng 2 formation in the study area is extracted. Subsequently, according to the porosity curves of wells in the area, the correlation between wellpoint reservoir thickness and inverted time thickness is statistically analyzed, a proportional formula is fitted, and it is converted into a depth-domain reservoir thickness planar map. Figure 9 );
[0059] S10. Through co-simulation, utilizing the obtained lithological body and P-wave impedance volume, and combining the pairwise relationship between P-wave impedance and porosity in geostatistical parameters, the reservoir porosity volume is calculated based on both, and the average porosity planar distribution map of the upper sub-section of the second lamp is extracted based on this porosity volume. Figure 10 );
[0060] S11. Comprehensive analysis: By calculating the product of predicted reservoir porosity and thickness, the energy storage coefficient of the upper sub-section of Deng 2 is obtained to further characterize the changes in reservoir performance of Deng 2 section reservoir in the study area; simultaneously, seismic interpretation software is used to perform three-dimensional perspective analysis of the reservoir probability volume, carving out the spatial distribution characteristics of the hill-shoal reservoir morphology. Figure 11 ); Comprehensive and detailed characterization of the hilly and shoal reservoirs in the study area was completed.
[0061] Further explanation is provided regarding the application of detailed characterization methods for hill-shoal reservoirs within sequence stratigraphic frameworks in other locations:
[0062] 1. Regarding the application of this method in other areas, if the resolution of seismic data is low, frequency division can be used to obtain high-frequency and low-frequency seismic data to assist in the interpretation of sequence stratigraphy and stratigraphic tracking.
[0063] 2. For the application of this method in other places, when characterizing seismic phases, the attributes used are not limited to waveform clustering attributes. The attribute algorithm can be optimized according to the actual situation and extraction results, such as amplitude attributes, frequency attributes, and trace integral attributes, etc.
[0064] 3. When statistically analyzing the reservoir-to-land ratio distribution map of the study area, if the number of wells in the area is small or the location distribution is uneven, dummy wells can be established, that is, artificially controlled well points. Based on the distribution law of sedimentary facies, an artificially assigned reservoir-to-land ratio value is given to make the well location distribution in the study area more uniform, the well point control area is reasonably distributed, and the entire area can be controlled.
[0065] The above embodiments are merely preferred embodiments of the present invention, and the scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.
Claims
1. A method for fine characterization of facies-controlled hill-shoal reservoirs within a sequence stratigraphic framework, comprising the following steps: S1: Data collection and work area establishment: Collect relevant data in the study area, including regional survey data, field outcrop data, core thin section data, well completion data, porosity and permeability data, test data, and three-dimensional post-stack seismic data. At the same time, establish a geological-seismic interpretation work area, classify and organize the data and import it into the geological-seismic interpretation work area. S2: Single-well sequence stratigraphy, fine well-seismic calibration, construction of a well-seismic isochronous stratigraphic framework for the whole area, fine tracking and interpretation of the isochronous seismic horizons of the target layer, and clarification of the longitudinal and transverse distribution patterns of sequence stratigraphy in the study area; S3: Based on the regional sedimentary background, clarify the sedimentary facies zone types in the study area and identify the corresponding microfacies types; combine field outcrops, cores, thin sections and well logging in the study area to establish identification templates for various sedimentary microfacies, establish lithology-lithological correspondence, draw single-well sedimentary composite columnar sections and well-to-well sedimentary comparative profiles; determine the longitudinal and transverse distribution patterns of sedimentary microfacies in the area; S4: Statistically analyze the formation thickness and favorable microfacies thickness of all target drilling intervals in the study area. The favorable microfacies are the thickness of the hills and shoals. Calculate the ratio of the hill and shoal thickness to the formation thickness of a single well, i.e., the hill-shoal ratio. Establish the quantitative relationship between the hill-shoal ratio and the microfacies of a single well to determine the sedimentary microfacies type of each target drilling interval. S5: Well-seismic calibration, combined with seismic forward modeling, establishes the mapping relationship between single-well seismic facies and sedimentary microfacies. Under the constraint of isochronous seismic stratigraphic framework, the planar distribution characteristics of seismic waveforms in the target layer of the study area are obtained by using neural network clustering method, that is, the planar distribution of seismic facies in the target layer. Based on the seismic facies-sedimentary microfacies mapping relationship mentioned above, the planar distribution map of sedimentary facies in the study area is drawn using planar seismic facies. S6: Well logging curve quality analysis, standardize well logging curves within the region to improve the consistency of curves across multiple wells, and perform cross-analysis of well lithology and physical properties in the study area based on the standardized well logging curves to select the sensitive parameter curves for reservoir identification; S7: Well-seismic calibration and seismic wavelet extraction. Combining the stratigraphic characteristics of the target layer in the study area, the seismic horizon of the target layer is used for constraint and control. Based on the single-well calibration results, a low-frequency impedance model is established, and deterministic inversion is carried out to obtain the P-wave impedance volume. S8: Statistically analyze single-well reservoir data, calculate the reservoir variation function, combine the sedimentary microfacies distribution map characterized in step S5, draw a planar scale map based on the reservoir proportion of the target layer statistically based on the well point statistics, and use this as a control condition to carry out high-resolution geostatistical inversion to obtain the reservoir probability volume. S9: Based on the reservoir probability volume results obtained from the geostatistical inversion in step S8, extract the time thickness of the target layer reservoir. Then, according to the porosity curve of the wells in the area, statistically analyze the correlation between the well point reservoir thickness and the inverted time thickness, fit the proportional formula, and convert it into a depth domain reservoir thickness planar map. S10: By using the obtained reservoir probability volume and P-wave impedance volume through co-simulation, and combining the pairwise relationship between P-wave impedance and porosity in geostatistical parameters, the reservoir porosity volume is calculated based on the two, and the average porosity plane distribution map of the target segment is extracted based on the porosity volume. S11: Comprehensive analysis is conducted to obtain the energy storage coefficient of the target layer by calculating the product of the predicted porosity and thickness of the reservoir, which further characterizes the changes in reservoir storage performance in the study area. At the same time, seismic interpretation software is used to perform three-dimensional perspective on the reservoir probability volume, and the spatial distribution characteristics of the hill and shoal reservoir morphology are sculpted. The detailed characterization of the hill and shoal reservoir in the study area is completed.
2. The method for fine characterization of facies-controlled mound-shoal reservoirs and permeability bodies within the sequence stratigraphic framework according to claim 1, characterized in that: In S2, single-well sequence division requires the establishment of a standard sequence division template by combining lithology, logging cycles, carbon isotopes, and seismic stratigraphy characteristics. Well-seismic fine calibration requires determining the reflection characteristics of each sequence interface, determining the seismic tracking interpretation scheme for each interface in the area, and constructing a well-connected grid profile based on the scheme. Then, the interpretation is gradually densified according to the seismic grid density of 64→32→16→8→4, and finally, the fine interpretation of each sequence interface is completed.
3. The method according to claim 1, characterized in that: In S4, when calculating the thickness of the target layer and the thickness of the hills and shoals, well inclination needs to be considered. First, the measured depth is converted into vertical depth before the statistics are performed. Calculating the thickness of the hills and shoals means calculating the thickness of the lithological section corresponding to the hills and shoals.
4. The method according to claim 1, characterized in that: In S4, based on regional geological understanding, the range of mound ratio values corresponding to each sedimentary microfacies type is determined through comprehensive analysis of single-well lithology, lithofacies, and mound ratio. The identification criteria for single-well sedimentary microfacies are quantified. The quantitative relationship between mound ratio and single-well facies is based on A as the threshold. The value of A ranges from 0 to 1. When the mound ratio of a single well is greater than A, it is a favorable microfacies. The specific value of A needs to be determined in conjunction with the actual sedimentary background.
5. The method according to claim 1, wherein the method is characterized by: In S5, the geological model in the forward modeling needs to be designed based on actual drilling data. Among them, the rock physical parameters include: the formation thickness of the target section, the sonic velocity, the rock density, and the development scale, thickness, velocity and density of various microfacies. At the same time, the microfacies types, development locations and scales involved in the model need to be consistent with the actual drilling conditions.
6. The method according to claim 1, wherein the method is characterized by: In S5, the mapping relationship between seismic facies and sedimentary facies in a single well is established by comparing the theoretical profile of forward modeling with the actual profile of passing through the well, analyzing the seismic response characteristics of different sedimentary microfacies, statistically analyzing the sedimentary microfacies and their corresponding seismic facies at each well point, and establishing the mapping relationship between the two.
7. The method according to claim 1, wherein the method is characterized by: In S6, the logging curve standardization adopts the logging curve standardization method with phase-controlled high-frequency range constraints.
8. The method according to claim 1, characterized in that: In S7, the seismic wavelet extraction is selected from the following two methods: the first is to solve it using the least squares method based on the well-side seismic records of existing well logging data; the second is to extract it using the autocorrelation statistical method of multichannel records based on actual seismic data.
9. The method according to claim 1, wherein the method is characterized by: In S7, the deterministic inversion adopts the sparse pulse inversion method faithful to the seismic data. The inversion results need to be verified by well point data. If the lateral variation trend of the inversion results is consistent with the well point impedance results or the error is small, the inversion results are considered to have good vertical and lateral predictive power and can proceed to the next step. If the inversion results have a large error with the well point data, the inversion parameters need to be adjusted and the inversion needs to be performed again until the results are consistent or the error is small.
10. The method according to claim 1, wherein the method is characterized by: In S8, the high-resolution geostatistical inversion results also require well point data as quality control. The target layer reservoir thickness of the well point is statistically analyzed, and the predicted reservoir thickness value at the well point of the statistical inversion results is extracted. If the seismic inversion results are consistent with the reservoir thickness of the verification well or the error is less than 10%, the statistical inversion results are considered to meet the prediction requirements.