A multi-level constrained thin-layer single sand body well-seismic joint characterization method

Abstract

The application discloses a multi-level constraint thin-layer single sand body well-seismic joint characterization method, which comprises the following steps: performing lithology prediction analysis on all wells; establishing a high-precision sequence stratigraphic framework, performing stratigraphic division and correlation on the well, and completing seismic horizon tracking and interpretation; performing lithology identification and division on the target layer of the coring well; establishing a sedimentary microfacies identification chart of the working area; converting the phase of the seismic data body; completing extraction of conventional seismic attributes; completing RGB wide-narrow frequency band frequency division attribute fusion extraction; restraining the sedimentary microfacies by using surrounding rock influence removal, profile sedimentary microfacies analysis and river sedimentation mode; checking the sedimentary microfacies; and finally obtaining high-precision thin-layer single sand body level plane sedimentary microfacies. On the basis of the original method, the high-precision thin-layer single sand body level plane sedimentary microfacies under multi-level constraint is characterized by using well-seismic record, river channel parameter, oil-gas-water relationship, source-landform parameter analysis and other methods for checking.

Description

Technical Field

[0001] This invention relates to a method for joint well-seismic characterization of thin-layer single sand bodies with multi-level constraints, belonging to the field of petroleum exploration and development technology. Background Technology

[0002] With the continuous exploration and development of offshore oil fields, water cut is constantly rising, reservoir heterogeneity is increasing, oil-water relationships are becoming more complex and contradictory, and the superposition relationship between sand bodies is chaotic. The sedimentary microfacies characterized by conventional geological methods are difficult to meet the needs of commercial exploration and development. Therefore, high-precision identification and fine characterization of fluvial single sand body sedimentary microfacies is an important foundation for realizing fine exploration and development of offshore oil fields, and it is also the foundation for offshore oil fields to continuously meet the needs of commercial development.

[0003] Currently, the characterization of fluvial sedimentary microfacies mainly involves the following steps: (1) comparing and dividing conventional strata and synthesizing seismic records. (2) analyzing single-well and interconnected-well facies. (3) extracting and analyzing conventional seismic attributes and RGB attributes. (4) determining channel types and parameters under the guidance of sedimentary models. (5) characterizing sedimentary subfacies in the study area. However, this method is only particularly effective for large-scale exploration, but its effect is very weak in fine-scale exploration and development of oil fields, making it difficult to perform fine characterization.

[0004] With the continuous development of technology, under the theoretical guidance of seismic sedimentology, stratigraphic slicing technology, seismic attribute extraction, and RGB frequency division and fusion technology can be used to characterize sedimentary microfacies. However, due to the limited seismic resolution and quality, the superposition of channel sand bodies from different periods is not clearly shown in the seismic attributes, making it difficult to characterize sedimentary microfacies at the single sand body level, which seriously restricts the fine exploration and development of oil fields. Summary of the Invention

[0005] To address the aforementioned problems, this invention aims to overcome the shortcomings of existing technologies by proposing a multi-level constrained well-seismic joint characterization method for thin-layer single sand bodies.

[0006] The technical solution provided by this invention to solve the above-mentioned technical problems is: a method for joint well-seismic characterization of thin-layer single sand bodies with multi-level constraints, comprising the following steps: Step S10: With logging data available, standardize some logging curves with good lithological differentiation capabilities. Based on the results of thin section identification and grain size identification of the core wells, determine the rock type at different depths of the core wells. Then, compare and analyze the curve values ​​at that depth after standardization of the logging curves to establish the relationship between the lithology of the work area and the logging curves. Perform cross-processing on the logging curves and develop a scheme for directional lithological identification based on logging curve values. Use the scheme to predict the lithology of all wells in the target work area. Step S20: Conduct high-precision sub-layer division and comparison in the work area, and at the same time perform well-seismic calibration, complete the tracking and interpretation of seismic sub-layers, and establish a high-precision sequence stratigraphic framework for the entire target work area. Step S30: Conduct lithofacies type analysis and sedimentary microfacies interpretation of the target stratum of the core well, then conduct rock grain size characteristic analysis to distinguish the lithological assemblages of different subfacies, and conduct bedding structure analysis on the lithological column of the core well. Based on the above analysis, obtain the characteristics of sedimentary microfacies of a single well, and make a map to identify the sedimentary microfacies of the work area. Step S40: Calculate the vertical resolution based on the dominant frequency and propagation velocity of the seismic data volume, and then compare the calculated vertical resolution with the sand body thickness in the work area. If the vertical resolution is found to be greater than the sand body thickness, the seismic data volume is converted from 0° phase to -90° phase. Step S50: Perform conventional seismic attribute extraction and analysis on the target layer, and simultaneously extract and analyze RGB, HSL, and HSV frequency-division fusion attributes; Step S60: Study river deposition patterns to determine river types; Step S70: Study the river parameters to determine the river's curvature, width, and height; Step S80: Conduct a combined well-seismic analysis of the target work area to determine the phases of the river channels in the well-controlled area and the characteristics of sedimentary microfacies in the non-well-controlled area. Based on steps S90, S70 and S80, the location and phase of the thin-layer narrow strip channel are identified, and the narrow-band RGB frequency division and fusion attributes of the target work area are extracted and analyzed to further characterize the sedimentary microfacies of the work area. Step S100: Analyze the impact of surrounding rock on the target layer; Step S110: Conduct sedimentary microfacies analysis of the work area to obtain the sedimentary microfacies of individual wells and interconnected wells in the work area; Step S120: Perform source-geomorphological path analysis on the target layer, and extract the thickness of fine sand and silt above all well points according to a scheme for directional identification of lithology based on well logging curve values, and analyze it to predict the characterization of channel sedimentary microfacies and the distribution of oil and gas-bearing areas. Step S130: Under the guidance of the basic oil, gas and water relationship model, analyze the oil, gas and water relationship of well points in the work area; Step S140: Delineate sedimentary microfacies under multiple constraints to obtain a high-precision thin-layer single sand body level sedimentary microfacies fine characterization plate under multi-level constraints.

[0007] A further technical solution is that the specific steps of step S20 are as follows: Step S21: Based on the actual geological background of the work area, select appropriate sequence stratigraphy theories and methods, and based on well logging data and lithology prediction results, conduct sedimentary cycle comparison and analysis based on the lithology prediction results to establish a high-precision sequence stratigraphic framework. Step S22: Based on the wellbore division results, synthesize seismic records and perform well-seismic calibration of seismic horizons. Sequence boundaries often exhibit seismic facies changes and typical termination relationships, which can also serve as identification markers for sequence boundaries and floodplain boundaries. Sequence boundaries often exhibit medium-amplitude, poorly continuous seismic response characteristics, with sandstone developed above and below the boundary, primarily medium- to fine-grained sandstone. The sandstone content is higher below the boundary and lower above. Floodplain boundaries, on the other hand, often correspond to a stable set of mudstone of varying thickness. The seismic horizons at these boundaries generally show medium to strong amplitude and good continuity. Seismic horizons are interpreted on the seismic framework profile, and then interpreted and traced throughout the region, ultimately establishing a high-frequency sequence stratigraphic framework for the entire area.

[0008] A further technical solution is that the specific steps of step S50 are as follows: Step S51: Extract the maximum, minimum and root mean square amplitude attributes from the seismic data. Referring to the main frequency and bandwidth of the seismic body in the work area, finally select 30 Hz, 60 Hz and 90 Hz as low, medium and high frequency bands, and make frequency-division attribute fusion slices. Step S52: Based on the different characteristics of different amplitude attributes, select the favorable amplitude attributes. Then, based on the different frequency division attributes and color fusion effects, select the optimal frequency division method according to planar, vertical, and color resolution. Among these methods, short-time Fourier transform, Ricker wavelet frequency division, and Morlet wavelet frequency division are compared. Short-time Fourier transform uses a fixed time window, which creates a contradiction in time and frequency domain resolution, resulting in poor prediction performance for thin-layer sand bodies. Continuous wavelet transform can provide different resolutions in different frequency bands based on scale changes and migrations, effectively improving the resolution of thin-layer characterization. Frequency division attribute fusion includes color fusion methods such as RGB, HSV, and HSL. Step S53: Based on the results of the well lithology prediction, analyze the sandstone thickness characteristics of each single well in the target layer, identify the thickness of the sand body, analyze the sand body of the target layer, select the entire bandwidth range when performing RGB frequency division, and select a wide bandwidth to extract the RGB attributes of the frequency division. Based on the extracted attribute map, characterize the sedimentary microfacies.

[0009] A further technical solution is that the specific steps of step S100 are as follows: Step S101: Based on the established high-precision stratigraphic framework for the entire area, conduct stratigraphic slice analysis; Step S102: Based on the conventional attribute analysis of the target layer, select interferometric slices and, based on the well-seismic joint judgment of the river channel, identify the area affected by the surrounding rock and select non-interference points. Step S103: Calculate the interference coefficient of the surrounding rock influence and perform superimposed slice calculation to finally obtain the attribute map after removing the influence of the upper and lower surrounding rocks.

[0010] A further technical solution is that the calculation formula in step S103 is:

[0011] In the formula: w 0 represents the interference coefficient; b 0 represents the interference amplitude of the target layer; a 0 represents the amplitude of the interference layer; c k To overlay slices; b k The amplitude of the target layer; a k The amplitude of the interference layer.

[0012] A further technical solution is that the specific steps of step S110 are as follows: based on the different responses of different types of sedimentary microfacies in well logging, seismic data and lithology, the well logging curve characteristics, lithological characteristics and seismic reflection characteristics of sedimentary microfacies are identified, and the sedimentary microfacies of single wells and interconnected wells in the work area are obtained.

[0013] A further technical solution is that the specific steps of step S120 are as follows: under the guidance of the sedimentation model, the source-geomorphic path analysis is performed on the target layer, and the top surface structure map and paleogeomorphic map of each sub-layer are made; then, the thickness of fine sand and silt above all well points is extracted and analyzed to obtain the constraint conditions; finally, based on the constraint conditions, the microfacies of channel sediments are characterized and the distribution of oil and gas-bearing areas is predicted.

[0014] A further technical solution is that the specific steps of step S130 are as follows: under the guidance of the basic oil, gas and water relationship model, the oil, gas and water relationship of a single well in the target layer is analyzed to obtain the oil, gas and water relationship interpreted by the well and the oil, gas and water relationship on the connecting wells, and then the direction and swing amplitude of the river channel are analyzed in conjunction with the top surface structural map and paleogeographic map.

[0015] The beneficial effects of this invention are as follows: Based on the original method, this invention uses methods such as removing the influence of surrounding rocks, profile sedimentary microfacies, and river sedimentary models for constraint, and uses methods such as well seismic records, channel parameters, oil-gas-water relationships, and provenance-geomorphological parameter analysis for verification, and finally delineates high-precision thin-layer single sand body level planar sedimentary microfacies under multi-level constraints. Attached Figure Description

[0016] Figure 1 A logging facies-sedimentary microfacies identification chart; Figure 2 The minimum amplitude RGB frequency division fusion attribute map (full frequency band); Figure 3 RGB frequency division and fusion attribute map for minimum amplitude (narrow band) Figure 4 This is a diagram showing the intersection of river width and curvature. Figure 5 A finely detailed plate depicting the microfacies of thin-layered single sand bodies; Figure 6 This is a flowchart of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] like Figure 6 As shown, the present invention provides a method for joint well-seismic characterization of thin-layer single sand bodies with multi-level constraints, comprising the following steps: Step S10: Obtain previous research data, seismic data, well logging data, core data, etc. for the target work area, and investigate the sedimentary background and modern sedimentary model data of the target work area; Step S20: Perform lithological prediction on all wells in the target work area; Step S21: With logging data available, standardize some logging curves that have good lithological differentiation capabilities. Curve standardization correction ensures that logging curves from different wells at the same time have similar frequency distributions or follow a certain spatial trend, based on the principle that "formations of the same time and phase have similar logging responses." Logging curves such as GR, SP, DEN, and VSH are selected and standardized.

[0019] Step S22: Perform wellbore core calibration, match the standardized logging curves with the core data, and establish the relationship between the lithology of the work area and the logging curves. Based on the results of rock thin section identification and grain size thin section identification of the core well, the rock type at different depths of the core well is determined. Then, based on the curve value at that depth after standardization of the logging curve, the two are compared and analyzed to establish the relationship between the lithology of the work area and the logging curve.

[0020] Step S23: Intersect the logging curves to find the logging curves that can clearly distinguish the lithology, and formulate a scheme for directional lithology determination based on logging curve values. Use the logging curves of other wells and the scheme to predict the lithology of all wells in the work area. Different logging curves are intersected pairwise. Based on the effect shown in the intersecting plot, logging curves that can clearly distinguish different lithologies in different logging curve values ​​are identified. Then, based on the interval values ​​of different logging curves corresponding to the lithology of this logging curve, a scheme for the relationship between the lithology of the target layer in the work area and the interval values ​​of logging curves is formulated. Under the guidance of this scheme, artificial intelligence algorithms such as SVM, RF, KNN, and XGBoost are used to predict the lithology of this logging curve value for all wells in the target layer of the work area. Finally, the lithology of each well in the target layer of the work area is obtained.

[0021] Step S30: Establish a high-precision sequence stratigraphic framework for the entire target work area; Step S31: Based on the relevant knowledge of sequence stratigraphy, according to the logging curve characteristics of the wells in the work area, the lithological sedimentary cycle characteristics above the wells, and the lithological prediction results, conduct sedimentary cycle comparison and analysis based on the lithological prediction results, select floodplains and sequence interfaces of different sedimentary cycle levels, and establish a high-precision sequence stratigraphic framework for single wells and interconnected wells in the target work area. In this embodiment, previous researchers have established a three-level, quasi-sequence group, and quasi-sequence level sequence stratigraphic framework for the target work area based on data, but its accuracy is no longer sufficient for actual exploration and development.

[0022] In this embodiment, multi-level floodplains were selected for establishing a high-precision sequence stratigraphic framework. Floodplains are relatively easy to identify and interpret in drilling and seismic data. In drilling, they typically appear as a thick layer of mudstone, with a GR curve showing a high value greater than 90 API, and opposite depositional cycles above and below the interface. In seismic data, they appear as peaks with strong reflections and strong continuity.

[0023] In this embodiment, based on the high GR value corresponding to the interface and the principles of sub-layer correlation, the following methods are used: 1. Well-seismic integration (good matching between well logging stratigraphic framework and seismic stratigraphic framework); 2. Isochronous correlation (finding floodplain mud); 3. Hierarchical control (grading according to the relationship between sequence stratigraphic correlation and sedimentary cycles); 4. Full-area closure (achieving three-dimensional closure when constructing a connected well framework profile); 5. Model guidance (generally using the "equielevation correlation method" in fluvial sediments, and dividing single-layer corresponding to single-stage channel sand bodies under the control of floodplain mudstone). Multiple sixth-order floodplains are identified within a quasi-sequence, and a comprehensive columnar section of a single well is completed. A connected well framework profile is constructed in the work area in a grid pattern, and high-frequency sequence stratigraphic division and correlation are completed on the connected wells to establish a high-frequency sequence stratigraphic framework.

[0024] Step S32: Based on the well-ground division results, synthesize seismic records, perform well-seismic calibration of seismic horizons, interpret seismic horizons on the seismic skeleton profile, and then interpret and track horizons throughout the entire area to establish a high-precision sequence stratigraphic framework for the target work area. This invention can be used for well-seismic calibration of seismic horizons, and can also be used as a marker for identifying sequence boundaries and floodplain boundaries, as well as for recognizing changes in seismic facies and typical termination relationships at the interfaces.

[0025] Sequence boundaries often exhibit seismic response characteristics of medium amplitude and poor continuity. Sandstone is developed above and below the boundary, with the lithology mainly being medium to fine sandstone. The sandstone content is higher below the boundary and lower above it. In contrast, flood boundaries often correspond to a stable set of mudstone with varying thickness. The seismic phase axes at the boundary generally show medium to strong amplitude and good continuity.

[0026] Step S40: Identify and classify the single-well facies of the core wells in the target work area; We conducted logging curve characteristic analysis, rock grain size characteristic analysis, and rock facies type analysis of the target layer of the core well. Finally, we established a single-well facies type identification chart and sedimentary microfacies identification chart for the work area. according to Figure 1 The lithofacies types and sedimentary microfacies of the meandering river in Minghua Town were interpreted as follows: massive matrix-supported lithofacies, massive gravelly fine sandstone lithofacies, cross-bedding fine sandstone lithofacies, parallel-bedding fine sandstone lithofacies, cross-bedding siltstone lithofacies, horizontally bedding siltstone / silty mudstone lithofacies, massive mudstone lithofacies, and horizontally bedding mudstone lithofacies. Based on the analysis of the core sections from the core wells, the GR and SP curves of point sandbars were found to be of medium amplitude with slightly toothed bell shape; the GR curve of natural dikes was of low amplitude with finger shape, and the SP curve was of low amplitude with tooth shape; the GR curve of breach spurs was of medium amplitude with toothed funnel shape, and the SP curve was of medium amplitude with funnel shape; the GR and SP curves of floodplain and overflow sand were of low amplitude with tooth shape; the GR and SP curves of abandoned channels were of low amplitude with slightly toothed bell shape; and the GR and SP curves of sediments retained at the bottom of the riverbed were of high amplitude with funnel shape. Rock grain size analysis was conducted to distinguish lithological assemblages of different subfacies (e.g., conglomerate, medium sandstone, fine sandstone, siltstone, argillaceous siltstone, silty mudstone, mudstone, etc.). Bedding analysis of the core well lithological columns revealed that the bedding structure of stagnant sediments and point-bar sediments at the riverbed bottom was dominated by large-scale trough cross-bedding; the bedding structure of natural dikes and breach fans was dominated by horizontal and parallel bedding; the bedding structure of floodplains and overflow sands was dominated by horizontal, parallel, and massive bedding; and the bedding structure of abandoned river channels was dominated by horizontal and massive structures. Based on the above analysis, the characteristics of single-well sedimentary microfacies were determined, and a map was created to identify the sedimentary microfacies of the work area.

[0027] Step S50: Perform phase transformation on the seismic data volume; Step S51: Calculate the vertical resolution that can be identified based on the dominant frequency and propagation velocity of the seismic data volume; The original seismic data for the work area has a dominant frequency of approximately 60 Hz and an average velocity of approximately 2400 m / s for the target layer; the vertical resolution is approximately 11 m.

[0028] Step S52: Based on the calculated vertical resolution, compare it with the sand body thickness in the work area. If the vertical resolution is found to be greater than the sand body thickness, convert the seismic data volume from 0° phase to -90° phase. Based on the thickness of the target sand body in the wellbore, the thickness of a single sand body is approximately 5 m, which is less than the vertical resolution of the seismic data. Therefore, the original seismic data volume is converted from 0° phase to -90° phase to ensure a better correspondence between the single sand body in the work area and the seismic phase axis. A comparison of the 0° phase and -90° phase seismic profiles in the embodiment shows a significant improvement in the relationship between the sand body and the negative amplitude on the -90° phase-converted seismic profile.

[0029] Step S60: Perform conventional seismic attribute extraction and analysis on the target layer, and simultaneously extract and analyze RGB, HSL, and HSV frequency-division fusion attributes; Step S61: Extract the maximum, minimum and root mean square amplitude attributes based on the available seismic data, and select the corresponding low-frequency, mid-frequency and high-frequency data volumes to create frequency-division attribute fusion slices. The target layer was subjected to conventional seismic attribute extraction. Based on the dominant frequency and bandwidth of the seismic body in the work area, 30Hz, 60Hz, and 90Hz were finally selected as low, medium, and high frequency bands for the production of frequency-division attribute fusion slices.

[0030] Step S62: When extracting and analyzing conventional seismic attributes, select the favorable amplitude attributes based on the different characteristics of different amplitude attributes, and select the optimal frequency division method based on the different frequency division attributes and different color fusion effects, according to the plane, vertical and color resolution. Since the wave troughs on the seismic profile match the sand body well after the seismic body is phase-shifted from 0° to -90°, the minimum amplitude attribute can better reflect the planar distribution of the sand body in the work area, which is a favorable amplitude attribute.

[0031] Comparing frequency division methods such as Short-Time Fourier Transform (SFT), Ricker wavelet frequency division, and Morlet wavelet frequency division, the SFT uses a fixed time window, resulting in a contradiction between time and frequency domain resolution, leading to poor prediction results for thin seismic sand bodies. Continuous wavelet transform, on the other hand, can provide different resolutions in different frequency bands based on scale changes and migrations, effectively improving the resolution of thin-layer characterization. Frequency division attribute fusion includes color fusion methods such as RGB, HSV, and HSL. 1. RGB color fusion mode: The river is clearly displayed, and more details can be identified; 2. HSV color blending mode: The river is displayed relatively clearly, but the details of the river are somewhat blurry; 3. HSL color blending mode: The colors are too bright. It can be well identified for thicker river sand bodies, but the detail display effect of thinner sand bodies is poor. Therefore, the RGB color blending mode is more effective for thin sand bodies.

[0032] Step S63: Based on the characteristics of the sand body thickness, and selecting a wide frequency band for frequency division RGB attribute extraction, the sedimentary microfacies are characterized according to the extracted attribute map. Based on the results of the well lithology prediction, the sandstone thickness characteristics of each single well in the target layer are analyzed to identify the thickness of the sand body. The sand body of the target layer is analyzed, and the entire bandwidth is selected when performing RGB frequency division. For example, the seismic body bandwidth in this work area is 30Hz to 90Hz. Such a frequency division scheme can highlight the target layer sand body, thereby constraining the characterization of sedimentary microfacies.

[0033] Step S70: Determine the river type based on river sedimentary model data; Based on the analysis of seismic attribute slices of the target layer, RGB frequency-division attribute fusion slices, lithology of wells in the work area, and lithological prediction results, it was found that the RGB frequency-division attribute slices and conventional seismic attribute slices have a high degree of agreement. According to the channel patterns shown by the amplitude within the attribute maps, and referring to existing channel pattern charts, similar channel patterns were identified and their patterns were determined. Furthermore, for channels in well-controlled areas, the lithology from bottom to top generally conforms to the patterns on the charts, thus ultimately determining the river depositional model.

[0034] Step S80: Determine the river's curvature, width, and height based on the river parameters; Step S90: Conduct a combined well-seismic analysis of the target work area to determine the phases of the river channels in the well-controlled area and the characteristics of sedimentary microfacies in the non-well-controlled area. Based on the high-precision sequence stratigraphic framework established in step S30, the well seismic records are analyzed to identify areas without well control but where microfacies can be identified on the seismic attribute map. These locations are then located on the seismic profile for analysis to verify the accuracy of the areas without well control on the seismic attribute map and to determine the period of the river channel.

[0035] Based on steps S100, S80 and S90, the location and phase of the thin-layer narrow strip channel are identified, and the narrow-band RGB frequency division and fusion attributes of the target work area are extracted and analyzed to further characterize the sedimentary microfacies of the work area. The results obtained from conventional RGB attribute extraction cannot accurately represent the distribution of narrow bands on a plane. Therefore, after determining the river channel's period through a combination of well and seismic analysis, we identify river channels with weaker amplitudes on the seismic profile. These river channels are narrow band channels. During RGB frequency division, we select the higher frequency range across the entire bandwidth. For example, if the seismic body bandwidth in this work area is 30Hz to 90Hz, then a high-frequency and relatively narrow bandwidth, such as 60Hz to 90Hz, can be selected. Such a frequency division scheme can highlight thin sand bodies, thereby constraining the characterization of sedimentary microfacies at the single sand body level.

[0036] Step S110: Analyze the target layer to remove the influence of surrounding rock and obtain the attribute map after removing the influence of upper and lower surrounding rock. Step S111: Based on the established high-precision stratigraphic framework for the entire region, conduct stratigraphic slice analysis; Traditionally, it was believed that identifying thin layers required increasing resolution to meet specific resolution requirements. However, due to the limited bandwidth of seismic signals, even using frequency extension methods to improve resolution still falls short of the requirements for thin layer identification. Based on a high-precision stratigraphic framework, slice analysis of each individual layer revealed that a channel appeared simultaneously in both the upper and lower layers of a sublayer. This made it impossible to determine the channel's sequence, although the sequence could be discerned from the seismic profile. However, due to the interference of seismic waves, the thin interbedded reservoir was severely disturbed by the surrounding rock, making it impossible to determine the specific sequence of the channel.

[0037] Step S112: Based on the conventional attribute analysis of the target layer, select interferometric slices and, based on the well-seismic joint judgment, identify the river channel and select non-interference points in the area affected by the surrounding rock. In the selection of non-interference points, the well-seismic joint method is used to determine the river channel period. On the seismic attribute slices, the slices in which the same river channel appears on different slices are found. These slices are the slices affected by the surrounding rock. Next, the specific location of the river channel is found in the seismic profile, the river channel period is determined, and on the different slices in which the river channel actually exists, a certain point of the river channel is selected as the non-interference point.

[0038] Step S113: Calculate the interference coefficient of the surrounding rock influence and perform superimposed slice calculation to finally obtain the attribute map after removing the influence of the upper and lower surrounding rocks. To remove interference from the surrounding rock, this embodiment employs a "stacked slicing algorithm." By calculating the interference coefficient of each interference layer, the product of the interference layer's amplitude and the interference coefficient w0 is subtracted from the amplitude of the target layer (the ratio of the target layer's interference amplitude to the amplitude of the upper interference layer: w0 = b0 / a0). This removes the interference effect of the interference layer on the target layer. The stacked slice is defined (with B as the target layer): c k =b k-w0a k Then c k A slice is a new slice for the target layer.

[0039] Step S120: Conduct sedimentary microfacies analysis of the work area to obtain the sedimentary microfacies of individual wells and interconnected wells in the work area; Step S121: Based on the different responses of different types of sedimentary microfacies in well logging, seismic analysis, and lithology, clarify the well logging curve characteristics, lithological characteristics, and seismic reflection characteristics of sedimentary microfacies, and finally obtain the sedimentary microfacies of single wells and interconnected wells in the work area; Based on the single-well facies type of the core well established in step S40 and the sedimentary microfacies identification map of the work area, the sedimentary microfacies characteristics of all single wells in the work area and the well-connected skeleton profile of the work area established in step S30 are determined.

[0040] Step S130: Perform source-geomorphic path analysis on the target layer, and then extract, analyze and verify the thickness of the sand body in the work area. A provenance-geomorphic path analysis was conducted on the target layer, and top structural maps and paleogeomorphic maps of each sublayer were created. It was found that the central and northern parts of the work area had higher elevations. Since rivers always flow from high to low, the direction of the river can be determined, further confirming that the provenance direction is from northwest to southeast. Based on a scheme for directional lithology determination using well logging curve values ​​established in step S23, the thicknesses of fine sand and silt and above at all well points were extracted and analyzed. Fine sand and above generally indicate microfacies such as point dams, natural embankments, and crevasse spurs. The thicknesses of silt and above and fine sand and above were compared. If the thicknesses of silt and above at well points differed significantly from those of fine sand and above, it indicated that the sand body at that well point was predominantly siltstone, possibly overflow sand from riverbanks during floods or abandoned river channels (such as oxbow lakes). Based on the above constraints, the microfacies of river channel sediments were characterized and the distribution of oil and gas-bearing areas was predicted.

[0041] Step S140: Under the guidance of the basic oil, gas and water relationship model, analyze the oil, gas and water relationship of well points in the work area, and further constrain the characterization of sedimentary microfacies. The oil, gas and water relationship of the target layer is analyzed by single well. The basic pattern of the oil, gas and water relationship is: "the distribution characteristics of gas, oil and water from top to bottom". Based on the oil, gas and water relationship interpreted by single well, the oil, gas and water relationship of the connecting well, and the top surface structural map and paleogeographic map made in step S130, the direction and swing amplitude of the river channel are analyzed in combination (based on the indication of single well microfacies).

[0042] Step S150: Delineate sedimentary microfacies under various constraints to obtain a high-precision thin-layer single sand body level sedimentary microfacies fine characterization plate under multi-level constraints.

[0043] The above description is not intended to limit the present invention in any way. Although the present invention has been disclosed through the above embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall fall within the scope of the present invention.

Claims

1. A method for combined well-seismic characterization of thin-layer single sand bodies with multi-level constraints, characterized in that, Includes the following steps: Step S10: With logging data available, standardize some logging curves with good lithological differentiation capabilities. Based on the results of thin section identification and grain size identification of the core wells, determine the rock type at different depths of the core wells. Then, compare and analyze the curve values ​​at that depth after standardization of the logging curves to establish the relationship between the lithology of the work area and the logging curves. Perform cross-processing on the logging curves and develop a scheme for directional lithological identification based on logging curve values. Use the scheme to predict the lithology of all wells in the target work area. Step S20: Carry out high-precision sub-layer division and comparison in the work area, and at the same time perform well-seismic calibration, complete the tracking and interpretation of seismic sub-layers, and establish a high-precision sequence stratigraphic framework for the entire target work area. Step S30: Conduct lithofacies type analysis and sedimentary microfacies interpretation of the target stratum of the core well, then conduct rock grain size characteristic analysis to distinguish the lithological assemblages of different subfacies, and conduct bedding structure analysis on the lithological column of the core well. Based on the above analysis, obtain the characteristics of sedimentary microfacies of a single well, and make a map to identify the sedimentary microfacies of the work area. Step S40: Calculate the vertical resolution based on the dominant frequency and propagation velocity of the seismic data volume, and then compare the calculated vertical resolution with the sand body thickness in the work area. If the vertical resolution is found to be greater than the sand body thickness, the seismic data volume is converted from 0° phase to -90° phase. Step S50: Perform conventional seismic attribute extraction and analysis on the target layer, and simultaneously extract and analyze RGB, HSL, and HSV frequency-division fusion attributes; Step S60: Study river deposition patterns to determine river types; Step S70: Study the river parameters to determine the river's curvature, width, and height; Step S80: Conduct a combined well-seismic analysis of the target work area to determine the phases of the river channels in the well-controlled area and the characteristics of sedimentary microfacies in the non-well-controlled area. Based on steps S90, S70 and S80, the location and phase of the thin-layer narrow strip channel are identified, and the narrow-band RGB frequency division and fusion attributes of the target work area are extracted and analyzed to further characterize the sedimentary microfacies of the work area. Step S100: Analyze the impact of surrounding rock on the target layer; Step S110: Conduct sedimentary microfacies analysis of the work area to obtain the sedimentary microfacies of individual wells and interconnected wells in the work area; Step S120: Perform source-geomorphological path analysis on the target layer, and extract the thickness of fine sand and silt above all well points according to a scheme for directional identification of lithology based on well logging curve values, and analyze it to predict the characterization of channel sedimentary microfacies and the distribution of oil and gas-bearing areas. Step S130: Under the guidance of the basic oil, gas and water relationship model, analyze the oil, gas and water relationship of well points in the work area; Step S140: Delineate sedimentary microfacies under multiple constraints to obtain a high-precision thin-layer single sand body level sedimentary microfacies fine characterization plate under multi-level constraints.

2. The method for combined well-seismic characterization of thin-layer single sand bodies with multi-level constraints according to claim 1, characterized in that, The specific steps of step S20 are as follows: Step S21: Based on the actual geological background of the work area, select appropriate sequence stratigraphy theories and methods, and based on well logging data and lithology prediction results, conduct sedimentary cycle comparison and analysis based on the lithology prediction results to establish a high-precision sequence stratigraphic framework. Step S22: Based on the wellbore division results, synthesize seismic records and perform well-seismic calibration of seismic horizons. Sequence boundaries often exhibit seismic facies changes and typical termination relationships, which can also serve as identification markers for sequence boundaries and floodplain boundaries. Sequence boundaries often exhibit medium-amplitude, poorly continuous seismic response characteristics, with sandstone developed above and below the boundary, primarily medium- to fine-grained sandstone. The sandstone content is higher below the boundary and lower above. Floodplain boundaries, on the other hand, often correspond to a stable set of mudstone of varying thickness. The seismic horizons at these boundaries generally show medium to strong amplitude and good continuity. Seismic horizons are interpreted on the seismic framework profile, and then interpreted and traced throughout the region, ultimately establishing a high-frequency sequence stratigraphic framework for the entire area.

3. The method for combined well-seismic characterization of thin-layer single sand bodies with multi-level constraints according to claim 1, characterized in that, The specific steps of step S50 are as follows: Step S51: Extract the maximum, minimum and root mean square amplitude attributes from the seismic data. Referring to the main frequency and bandwidth of the seismic body in the work area, finally select 30 Hz, 60 Hz and 90 Hz as low, medium and high frequency bands, and make frequency-division attribute fusion slices. Step S52: Based on the different characteristics of different amplitude attributes, select the favorable amplitude attributes. Then, based on the different frequency division attributes and color fusion effects, select the optimal frequency division method according to planar, vertical, and color resolutions. Among these, short-time Fourier transform, Ricker wavelet frequency division, and Morlet wavelet frequency division are compared. Short-time Fourier transform uses a fixed time window, which presents a contradiction in time and frequency domain resolution, resulting in poor prediction performance for thin-layer sand bodies. "Continuous wavelet transform" can provide different resolutions in different frequency bands based on scale changes and migrations, effectively improving the resolution of thin-layer characterization. Frequency division attribute fusion includes color fusion methods such as RGB, HSV, and HSL. Step S53: Based on the results of the well lithology prediction, analyze the sandstone thickness characteristics of each single well in the target layer, identify the thickness of the sand body, analyze the sand body of the target layer, select the entire bandwidth range when performing RGB frequency division, and select a wide bandwidth to extract the RGB attributes of the frequency division. Based on the extracted attribute map, characterize the sedimentary microfacies.

4. The method for combined well-seismic characterization of thin-layer single sand bodies with multi-level constraints according to claim 1, characterized in that, The specific steps of step S100 are as follows: Step S101: Based on the established high-precision stratigraphic framework for the entire area, conduct stratigraphic slice analysis; Step S102: Based on the conventional attribute analysis of the target layer, select interferometric slices and, based on the well-seismic joint judgment of the river channel, identify the area affected by the surrounding rock and select non-interference points. Step S103: Calculate the interference coefficient of the surrounding rock influence and perform superimposed slice calculation to finally obtain the attribute map after removing the influence of the upper and lower surrounding rocks.

5. The method for combined well-seismic characterization of thin-layer single sand bodies with multi-level constraints according to claim 4, characterized in that, The calculation formula in step S103 is: In the formula: w 0 represents the interference coefficient; b 0 represents the interference amplitude of the target layer; a 0 represents the amplitude of the interference layer; c k To overlay slices; b k The amplitude of the target layer; a k The amplitude of the interference layer.

6. The method for combined well-seismic characterization of thin-layer single sand bodies with multi-level constraints according to claim 1, characterized in that, The specific steps of step S110 are as follows: based on the different responses of different types of sedimentary microfacies in well logging, seismic data and lithology, the well logging curve characteristics, lithological characteristics and seismic reflection characteristics of sedimentary microfacies are identified, and the sedimentary microfacies of single wells and interconnected wells in the work area are obtained.

7. The method for combined well-seismic characterization of thin-layer single sand bodies with multi-level constraints according to claim 1, characterized in that, The specific steps of step S120 are as follows: under the guidance of the sedimentation model, the source-geomorphic path analysis is performed on the target layer, and the top surface structure map and paleogeomorphic map of each sub-layer are produced; then, the thickness of fine sand and silt above all well points is extracted and analyzed to obtain the constraints; finally, based on the constraints, the microfacies of channel sediments are characterized and the distribution of oil and gas-bearing areas is predicted.

8. The method for combined well-seismic characterization of thin-layer single sand bodies with multi-level constraints according to claim 7, characterized in that, The specific steps of step S130 are as follows: under the guidance of the basic oil, gas and water relationship model, the oil, gas and water relationship of a single well in the target layer is analyzed to obtain the oil, gas and water relationship interpreted by the well and the oil, gas and water relationship on the interconnected wells. Then, the direction and swing amplitude of the river channel are analyzed in conjunction with the top surface structural map and paleogeographic map.

Images