A method for characterizing the microfacies of modified sand bodies under geological process constraints

Abstract

This invention discloses a method for characterizing the microfacies of modified sand bodies under geological process constraints, comprising the following steps: acquiring data; interpreting the seismic horizons of the target layer in the target work area; extracting individual conventional seismic attributes and RGB slices of the morphology of the banded sand bodies in the target layer; characterizing the sedimentary subfacies of shelf banded sand bodies; performing quantitative lithological interpretation of existing wells in the target work area; extracting the target seismic attributes of the target work area, and performing seismic attribute optimization and fusion to fit the favorable sand body thickness distribution of the entire area; reconstructing the paleogeography of the target layer in the target work area; and finely characterizing the sedimentary microfacies of shelf banded sand bodies under the constraints of combined geological model understanding, multi-attribute fusion maps, well logging lithological quantitative interpretation results, favorable sand body thickness prediction distribution maps, and paleogeographic reconstruction maps, thereby obtaining a sedimentary microfacies map of shelf banded sand bodies. This invention can improve the accuracy of sedimentary microfacies of shelf banded sand bodies and promote the subsequent exploration and development of lithological oil and gas reservoirs.

Description

Technical Field

[0001] This invention relates to the field of petroleum exploration and development technology, and in particular to a method for characterizing the microfacies of modified sand bodies under geological process constraints. Background Technology

[0002] As oil and gas exploration in my country deepens, structural oil and gas reservoirs are becoming increasingly scarce, and most are entering a high water-cut stage. Lithological oil and gas reservoirs are gradually becoming important exploration targets for increasing oil and gas reserves and production in my country. Lithological traps can be classified into four subcategories based on their formation mechanisms: reservoir rock updip pinch-out traps, reservoir rock lens traps, bioherm traps, and post-diagenetic lithological traps. On the vast shallow marine shelf, there are often strip-shaped sand bodies, also known as shelf sand ridges, formed by various hydrodynamic processes such as waves and tides. These sand bodies are usually tens or hundreds of kilometers from the coastline, mostly extending parallel or nearly parallel to the coastline, with some radiating outwards perpendicular to the coastline. These marine strip-shaped thin-layered sand bodies are often encased by shelf mudstone to form lens-shaped lithological traps, connected to the underlying source rocks through faults or sand body networks. They possess excellent conditions for oil and gas accumulation and are important targets for lithological exploration and development in basins such as the Pearl River Estuary Basin and the Xihu Depression in my country.

[0003] Shelf sandstone sedimentary microfacies include sand ridges and sand sheets. Among them, shelf sand ridges are relatively thick, coarse-grained, and well-sorted and rounded, making them the highest-quality reservoir type among shelf sandstones and an important "sweet spot" for oil and gas exploration. However, these shallow-sea shelf sandstones have been subjected to various types and intensities of hydrodynamic alteration during their formation, resulting in characteristics such as thin sandstone thickness and rapid lateral facies transitions. This leads to rapid lateral variations in lithology and physical properties, and significant differences in oil (gas) content, greatly increasing the difficulty of exploration and evaluation of this type of lithological trap.

[0004] With the development of seismic exploration technology, the characterization of sedimentary microfacies of banded thin sand bodies based on 3D seismic data has been widely applied. However, the reservoir thickness of shallow marine shelf sand bodies is generally less than 1 / 8 to 1 / 16 of the seismic wavelength, exceeding the prediction limit of geophysical inversion of sand bodies, making it difficult to predict the reservoir type and thickness distribution in planar terms. In addition, due to the scarcity of medium-deep oil and gas wells, previous studies mainly relied on well-seismic combined sedimentary geomorphological characterization based on single stratigraphic slice attributes, reflecting only the macroscopic distribution outline and sedimentary evolution of banded sand bodies, but lacking detailed characterization of internal sedimentary microfacies differences and reservoir thickness variations, which seriously restricts the accuracy of lithological trap exploration of banded sand bodies. Summary of the Invention

[0005] To address the aforementioned problems, this invention aims to provide a method for characterizing the microfacies of modified sand bodies under the constraints of geological processes.

[0006] The technical solution of the present invention is as follows:

[0007] A method for characterizing the microfacies of modified sand bodies under geological process constraints includes the following steps:

[0008] S1: Obtain basic data for the target work area, including geological background data, seismic data, well logging data, and core / thin section data;

[0009] S2: Based on the aforementioned basic data, interpret the seismic horizon of the target layer in the target work area to obtain the seismic interpretation horizon of the target layer;

[0010] S3: Based on the seismic data and the seismic interpretation horizon of the target layer, extract individual conventional seismic attributes and RGB slices of the strip-shaped sand body morphology of the target layer;

[0011] S4: Combining the aforementioned single conventional seismic attributes and RGB slices to characterize the sedimentary subfacies of shelf banded sands, a sedimentary subfacies map of shelf banded sands is obtained;

[0012] S5: Use the core / thin section data to calibrate the lithological logging curves, establish lithological quantitative interpretation standards, and perform lithological quantitative interpretation on existing wells in the target work area;

[0013] S6: Extract the target seismic attributes of the target work area, and perform seismic attribute optimization and fusion to obtain a multi-attribute fusion map;

[0014] S7: Based on the quantitative interpretation results of lithology, extract the thickness values ​​of favorable lithology in the target layer of each well in the existing wells in the target work area, and fit the thickness distribution of favorable sand bodies in the whole area to obtain the predicted distribution map of favorable sand body thickness.

[0015] S8: Based on the seismic data and the seismic interpretation horizon of the target layer, the paleogeography of the target layer in the target work area is restored using the imprinting method to obtain a paleogeographic restoration map;

[0016] S9: Based on the existing shelf-banded sandstone sedimentary subfacies map, and combined with geological model understanding, multi-attribute fusion map, lithological quantitative interpretation results, favorable sand body thickness prediction distribution map, and paleogeomorphological restoration map, the shelf-banded sandstone sedimentary microfacies is finely characterized to obtain the shelf-banded sandstone sedimentary microfacies map.

[0017] Preferably, in step S3, the conventional seismic attribute is the root mean square amplitude attribute, and the RGB slice is a new fused attribute map generated by fusing the three frequency division attributes of RGB.

[0018] Preferably, in step S4, the subfacies map of shelf banded sand deposits is obtained by the following sub-steps: first, the boundaries of sand bodies with banded distribution patterns in the whole area are delineated based on RGB slices; then, for local banded patterns that are unclear, they are adjusted by referring to conventional seismic attributes; finally, the subfacies map of shelf banded sand deposits is obtained.

[0019] Preferably, step S5 specifically includes the following sub-steps:

[0020] S51: Standardize lithology logging curves using the mean-variance method;

[0021] S52: Calibrate the standardized lithology logging curves based on core / thin section data;

[0022] S53: Interact all logging data points that have been calibrated for lithology to identify logging curve types that can distinguish lithology and establish quantitative interpretation standards for lithology;

[0023] S54: Based on the aforementioned lithological quantitative interpretation standard, perform lithological quantitative interpretation on the existing wells in the target work area.

[0024] Preferably, step S6 specifically includes the following sub-steps:

[0025] S61: Based on the seismic data and the seismic interpretation horizon of the target layer, extract various conventional seismic attributes of the target layer;

[0026] S62: Analyze the correlation between the extracted conventional seismic attributes and the quantitatively interpreted favorable lithologies, and select multiple seismic attributes based on the correlation analysis results;

[0027] S63: Perform multi-attribute fusion on the selected multiple seismic attributes to obtain a multi-attribute fusion map.

[0028] Preferably, step S7 specifically includes the following sub-steps:

[0029] S71: Extract the thickness values ​​of favorable lithology in the target layer of each well in the target work area based on the quantitative interpretation results of lithology;

[0030] S72: Perform correlation analysis between the thickness value of the target layer and various conventional seismic attributes of the extracted target layer, and determine the prediction model between seismic attributes and sand thickness;

[0031] S73: Based on the prediction model between seismic attributes and sand thickness, predict the thickness of favorable sand bodies in the target layer and draw a distribution map of the predicted thickness of favorable sand bodies.

[0032] Preferably, step S8 specifically includes the following sub-steps:

[0033] S81: Select the draped marker layer above the target layer;

[0034] S82: Calculate the thickness between the target layer and the draped marker layer to obtain the residual thickness;

[0035] S83: Calculate the compaction coefficient based on the porosity conversion model, and perform compaction correction on the residual thickness according to the compaction coefficient to obtain the lithological residual thickness after decompaction;

[0036] S84: The paleogeography of the target layer is restored by summing the residual thicknesses of each lithology after decompaction, and a paleogeography restoration map is obtained.

[0037] Preferably, step S9 specifically includes the following sub-steps:

[0038] S91: Constrain the subfacies diagram of shelf-banded sand bodies based on previous understanding of geological models of shelf-banded sand bodies;

[0039] S92: Based on the understanding of the planar morphology of the striped sand, the boundaries of the sedimentary microfacies of the sand body are finely depicted by combining the multi-attribute fusion diagram;

[0040] S93: Finely characterize the thickness of sand body sedimentary microfacies based on the predicted distribution map of favorable sand body thickness;

[0041] S94: Based on the paleogeographic reconstruction map, the planar depositional range of the sand body sedimentary microfacies is finely depicted, and finally, the shelf strip-type sand body sedimentary microfacies map under multi-factor constraints is formed.

[0042] The beneficial effects of this invention are:

[0043] This invention takes into account hydrodynamics, sea-level rise and fall cycles, topographic elevation, distance from the source, and relative internal development, enabling it to more accurately characterize the microfacies of shelf-type sand bodies under the constraints of geological processes, and providing technical support for subsequent lithologic oil and gas reservoir exploration and development. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a schematic flowchart of the method for characterizing the microfacies of modified sand bodies under geological process constraints according to the present invention.

[0046] Figure 2 This is a schematic diagram of the root mean square amplitude properties in a specific embodiment;

[0047] Figure 3 This is a schematic diagram of RGB slicing in a specific embodiment;

[0048] Figure 4This is a schematic diagram of sedimentary subphases depicted based on root mean square amplitude and RGB slices in a specific embodiment;

[0049] Figure 5 This is a schematic diagram of the standardized logging curves in a specific embodiment;

[0050] Figure 6 This is a schematic diagram of the intersection of different logging curves in a specific embodiment;

[0051] Figure 7 This is a schematic diagram of the quantitative discrimination results of lithological interpretation in a specific embodiment;

[0052] Figure 8 This is a multi-attribute fusion graph in a specific embodiment;

[0053] Figure 9 Here is a sandstone thickness prediction diagram in a specific embodiment;

[0054] Figure 10 A schematic diagram illustrating the principle of using the impression method to reconstruct ancient landforms;

[0055] Figure 11 This is a reconstruction map of ancient landforms in a specific embodiment;

[0056] Figure 12 This is a microfacies diagram of shelf-striped sand bodies in a specific embodiment. Detailed Implementation

[0057] The present invention will be further described below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and technical features described in this application can be combined with each other. It should also be pointed out that, unless otherwise indicated, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terms "comprising" or "including" and similar words used in this invention refer to elements or objects preceding the word that encompass the elements or objects listed following the word and their equivalents, without excluding other elements or objects.

[0058] like Figure 1 As shown, this invention provides a method for characterizing the microfacies of modified sand bodies under geological process constraints, comprising the following steps:

[0059] S1: Obtain basic data for the target work area, including geological background data, seismic data, well logging data, and core / thin section data.

[0060] In a specific embodiment, taking the Zhujiang Formation sedimentary area in the Huizhou Depression as an example, this target work area has long been affected by northeast-southwest tidal forces and southwest-southwest coastal currents. The tidal currents are mainly bidirectional, and the sand bodies tend to have a symmetrical strip-like planar morphology. The coastal currents, on the other hand, are mainly unidirectional, and the sand bodies tend to form a shape that converges in the north and diverges in the south. The lithology is mainly fine sandstone, siltstone, argillaceous siltstone, and silty mudstone. In the structural high parts, the hydrodynamic stress modification is more intense, which makes it easier to form high-quality reservoirs with better sorting and rounding.

[0061] S2: Based on the aforementioned basic data, interpret the seismic horizons of the target layer in the target work area to obtain the seismic interpretation horizons of the target layer.

[0062] In one specific embodiment, when interpreting seismic horizons, the principle of "isochronous correlation, hierarchical control and model guidance" is followed, and a combined well-seismic approach is adopted, with interaction between planar and cross-sectional views, to achieve closed interpretation of sand body horizons.

[0063] S3: Based on the seismic data and the seismic interpretation horizon of the target layer, extract individual conventional seismic attributes of the strip-shaped sand body morphology of the target layer and RGB slices.

[0064] Shelf-banded sand bodies are often thin, with thick mudstone layers at the top and bottom. Conventional seismic properties and RGB slices can reflect the morphological characteristics of shelf-banded sand bodies.

[0065] In one specific embodiment, the conventional seismic attribute is the root mean square amplitude attribute, and the extraction result is as follows: Figure 2 As shown, strong amplitude (reddish color mark) represents coarse-grained sediments; medium amplitude (greenish color mark) represents transitional lithology; and weak amplitude (purple or blue color mark) represents argillaceous sediments. Figure 2 The sand bodies are clearly visible in strip-shaped, northeast-southwest orientation.

[0066] In one specific embodiment, the RGB slice is a new fused attribute map generated by fusing the three frequency division attributes of RGB (based on the principle of three primary colors, R represents red, G represents green, and B represents blue).

[0067] In one specific embodiment, when creating RGB slices, the original seismic data of the work area is first divided into frequencies, and three frequency dividers with different frequencies are selected for RGB fusion display. By adjusting the brightness of the red, green and blue colors, the outline of the shelf strip sand body characterized by the RGB fusion attribute is highlighted.

[0068] The original seismic data for the target area has a dominant frequency of approximately 31 Hz and a bandwidth of 5–170 Hz. The sand body thickness is concentrated between 2 and 18 m. Sand bodies thicker than 15 m are defined as thick sand, those between 10 and 15 m as medium-thick sand, and those less than 10 m as thin sand. The relationship between the seismic phase axis and the sand body response is analyzed at 5 Hz and 10 Hz intervals. Finally, 16 Hz, 35 Hz, and 54 Hz are selected for frequency division and fusion. Different frequency amplitudes correspond to red, green, and blue, representing thick, medium-thick, and thin sand bodies, respectively. The results are as follows: Figure 3 As shown. From Figure 3 As can be seen, the frequency-divided RGB slices can clearly reflect the bright white striped sand body morphology, with the bright white stripes in the southwest and central-western regions being more distinct.

[0069] S4: Combining the single conventional seismic attributes and RGB slices to characterize the sedimentary subfacies of shelf banded sands, a sedimentary subfacies map of shelf banded sands is obtained.

[0070] When characterizing sand body contours, the principle of "RGB as the primary method and single-attribute (RMS) as a secondary method" is followed. In a specific embodiment, the shelf banded sand sedimentary subfacies map is obtained through the following sub-steps: first, the boundaries of sand bodies with banded distribution patterns across the entire area are delineated based on RGB slices; then, for areas where the banded patterns are unclear, adjustments are made using conventional seismic attributes; finally, the shelf banded sand sedimentary subfacies map is obtained, as shown in the figure. Figure 4 As shown.

[0071] S5: Use the core / thin section data to calibrate the lithological logging curves, establish lithological quantitative interpretation standards, and perform lithological quantitative interpretation on existing wells in the target work area.

[0072] In one specific embodiment, step S5 specifically includes the following sub-steps:

[0073] S51: Standardize lithology logging curves using the mean-variance method;

[0074] In one specific embodiment, the lithological logging curves include natural gamma ray logging (GR), density logging (DEN), neutron logging (CNL), and induction logging (CON), wherein the standardized trend of the natural gamma ray logging curve is as follows: Figure 5 As shown.

[0075] When performing standardization, the principle of mean and variance is as follows: First, select the correct logging curve values ​​of a standard well and standard layer, denoted as: X1, X2, ..., X... N For wells requiring standardization, the values ​​of the same logging curve corresponding to the standard layer are designated as: Y1, Y2, ..., Y N .

[0076] Assume the standardized data of the Y series are Z1, Z2, ..., Z N And they have a linear relationship, denoted by Z = (a)Y + (b). Then, when the mean and variance of X and Z are the same, the coefficients a and b are obtained.

[0077] When both have the same mean E(X) = E(Z) and the same variance E(X) = E(Z) and (X) = V(Z), according to the principles of mathematical statistics, we have:

[0078] E(Z)=aE(Y)+b=E(X) (1)

[0079] V(Z)=a 2 V(Y)=V(X) (2)

[0080] From the above formula, we get:

[0081]

[0082] The mean and variance of the X and Y series data can be obtained, and the entire well can be standardized by substituting them into the linear formula.

[0083] HZ27-3-4DSa well was selected as the standard well, and a set of stable and well-developed mudstone layers adjacent to the strip-shaped sand body was selected as the standard layer. The mean-variance method was used to standardize the lithological logging curves.

[0084] S52: Calibrate the standardized lithology logging curves based on core / thin section data;

[0085] Lithology identification reports for core samples and thin sections are generally conducted at a specific depth point within a well. The logging curve values ​​corresponding to the lithology at that depth point are statistically analyzed to calibrate the lithology against the logging data. In the southwestern part of the Huizhou Depression, the banded sandstone bodies are mainly composed of fine sandstone, pure siltstone, and argillaceous siltstone, with fine sandstone and pure siltstone being the most favorable reservoir lithologies.

[0086] S53: Interact all logging data points that have been calibrated for lithology to identify logging curve types that can distinguish lithology and establish quantitative interpretation standards for lithology;

[0087] Different logging curves have varying abilities to distinguish between different lithologies. Following the principle of calibrating different logging curves for the same lithology and then interpolating the data, standardized natural gamma ray logging, density logging, neutron logging, and induction logging are interpolated to identify the logging curve with the strongest ability to distinguish between lithologies. The results are as follows: Figure 6 As shown, the natural gamma logging curves ultimately demonstrate strong lithological differentiation capabilities.

[0088] S54: Based on the aforementioned lithological quantitative interpretation standard, perform lithological quantitative interpretation on the existing wells in the target work area.

[0089] Based on the lithological calibration results, a lithological interpretation standard for the southwestern region of the Huizhou Depression was established:

[0090] When the API value of the standardized GR is less than 113, it is interpreted as fine sandstone;

[0091] When the API value of the standardized GR is between 113 and 120, it is interpreted as siltstone;

[0092] When the API value of the standardized GR is between 120 and 130, it is interpreted as argillaceous siltstone;

[0093] When the API value of the standardized GR is between 130 and 143, it is interpreted as silty mudstone;

[0094] The reliability of the quantitative interpretation of lithology was verified using the Xgboost algorithm, and the results are as follows: Figure 7 As shown, the accuracy rate can reach 0.79.

[0095] S6: Extract the target seismic attributes of the target work area, and perform seismic attribute optimization and fusion to obtain a multi-attribute fusion map.

[0096] In a specific embodiment, step S6 specifically includes the following sub-steps:

[0097] S61: Based on the seismic data and the seismic interpretation horizon of the target layer, extract various conventional seismic attributes of the target layer;

[0098] In a specific embodiment, the extracted conventional seismic attributes mainly include 4 categories and 17 types, namely, root mean square amplitude, maximum amplitude, minimum amplitude, and waveform attributes, which are amplitude-related, statistical, signal-related, and waveform-related. The specific attributes are shown in Table 1.

[0099] Table 1. Various conventional earthquake attributes

[0100]

[0101] S62: Analyze the correlation between the extracted conventional seismic attributes and the quantitatively interpreted favorable lithologies, and select multiple seismic attributes based on the correlation analysis results;

[0102] In one specific embodiment, a combination of Pearson correlation analysis and random forest ranking is used to preferentially select seismic attributes that rank highly in relation to lithology, but Pearson correlation ranking takes precedence. It is important to note that if an attribute ranks low in Pearson correlation but high in random forest ranking, the random forest ranking should be considered to prioritize that attribute, thus avoiding errors introduced by a single correlation analysis.

[0103] The formula for calculating the Pearson correlation coefficient is as follows:

[0104]

[0105] In the formula: x represents lithological information; y represents seismic attribute information.

[0106] The strength of the correlation between seismic attribute information and lithological information is determined based on the absolute value of the Pearson correlation coefficient: when Pearson is between 0.8 and 1.0, the correlation is extremely strong; when Pearson is between 0.6 and 0.8, the correlation is strong; when Pearson is between 0.4 and 0.6, the correlation is moderate; when Pearson is between 0.2 and 0.4, the correlation is weak; and when Pearson is between 0.0 and 0.2, the correlation is extremely weak or irrelevant.

[0107] Random forest ranking mainly considers the contribution of each lithological feature to the attribute features, and ranks them by averaging the overall contribution.

[0108] The top three attributes in Pearson correlation ranking are arc length, average negative amplitude, and average energy, while the top three attributes in random forest ranking are arc length, average instantaneous amplitude, and half-energy.

[0109] The top three attributes of the two differ. To reduce analysis errors, a total of five attributes were selected for fusion: arc length, average negative amplitude, average energy, average instantaneous amplitude, and half-energy.

[0110] S63: Perform multi-attribute fusion on the selected multiple seismic attributes to obtain a multi-attribute fusion map.

[0111] Based on the correlation ranking results, arc length, average negative amplitude, average energy, average instantaneous amplitude, and half-energy were selected for multi-attribute fusion to obtain the fused attributes of the target layer. The results are as follows: Figure 8 As shown. Figure 8 In the middle, the distribution characteristics of the banded sand bodies are relatively clear. The purple part represents mudstone deposits, the green part represents siltstone deposits, and the red part represents fine sandstone deposits.

[0112] A generalized additive model is used for multi-attribute fusion. This model can incorporate regression models that fit linear or nonlinear data, allowing for a more flexible approach to data fitting. The calculation formula is shown below:

[0113] y i = β0+f1(x i1 )+f2(x i2 )+…+f n (x in )+e i (5)

[0114] In the formula: f can be a function of any form, for each individual x in Calculate f for the nth variable in the i-th data set, and finally combine the results to make a prediction.

[0115] S7: Based on the quantitative interpretation results of lithology, extract the thickness values ​​of favorable lithology in the target layer of each well in the target work area, and fit the thickness distribution of favorable sand bodies in the whole area to obtain the predicted distribution map of favorable sand body thickness.

[0116] In one specific embodiment, step S7 specifically includes the following sub-steps:

[0117] S71: Extract the thickness values ​​of favorable lithology in the target layer of each well in the target work area based on the quantitative interpretation results of lithology;

[0118] The shelf-type sandstone bodies in the southwestern part of the Huizhou Depression have fine-grained sediments, and the favorable reservoir lithologies are mostly pure siltstone and fine sandstone. The thickness data of siltstone and fine sandstone in existing wells throughout the region can be extracted based on the lithological interpretation results.

[0119] S72: Perform correlation analysis between the thickness value of the target layer and various conventional seismic attributes of the extracted target layer, and determine the prediction model between seismic attributes and sand thickness;

[0120] In one specific embodiment, an artificial intelligence algorithm is used to predict the favorable sand body thickness distribution, which makes the sand thickness prediction method more intelligent and automated, and provides a more reliable and efficient prediction means. Optionally, the artificial intelligence algorithm adopts any one or more of the following: Random Forest Regression (RF), Support Vector Machine (SVM), Augmented Regression Tree (BRT), and Artificial Neural Network (ANN).

[0121] In one specific embodiment, sand body prediction results obtained through the above four artificial intelligence algorithms are compared with actual drilling data, and the artificial intelligence algorithm with high correlation and its sand body prediction results are selected. In this embodiment, after calculation by the four artificial intelligence algorithm models, the model with high correlation with actual drilling is RF, with a correlation of 0.82. That is, the Random Forest Regression algorithm model is selected as the prediction model between seismic attributes and sand thickness.

[0122] S73: Based on the prediction model between seismic attributes and sand thickness, predict the thickness of favorable sand bodies in the target layer and draw a distribution map of the predicted thickness of favorable sand bodies.

[0123] In a specific embodiment, the result of drawing the favorable sand body thickness prediction distribution map is as follows: Figure 9 As shown. From Figure 9The planar morphological characteristics of the striped sand bodies are clearly visible, similar to the striped sand body distribution characteristics reflected by single attributes and fused attributes. In the predicted distribution map, purple indicates that the sand body is thinner, and red indicates that the sand body is thicker.

[0124] S8: Based on the seismic data and the seismic interpretation horizon of the target layer, the paleogeography of the target layer in the target work area is restored using the imprinting method to obtain a paleogeographic restoration map.

[0125] like Figure 10 As shown, the imprint method for restoring paleogeography is based on the principle of filling and supplementing. The interface at which the strata to be restored end erosion and begin to receive sediment is regarded as the isochronous interface. By the mirror relationship between the thickness of the residual strata and the paleogeography, the relative paleogeographic features before deposition can be reflected semi-quantitatively.

[0126] In one specific embodiment, step S8 specifically includes the following sub-steps:

[0127] S81: Select the draped marker layer above the target layer;

[0128] The marker layer needs to meet the following conditions: 1. It should be an isochronous interface that can be traced throughout the entire area; 2. The closer it is to the weathering crust, the better; 3. The seismic reflection phase axis should have strong amplitude, good continuity, and be easy to identify. Sequence boundaries and the maximum floodplain are usually preferred as the drapery marker layers. In a specific embodiment, the first maximum floodplain that has just submerged the carbonate platform is selected as the marker layer.

[0129] S82: Calculate the thickness between the target layer and the draped marker layer to obtain the residual thickness;

[0130] S83: Calculate the compaction coefficient based on the porosity conversion model, and perform compaction correction on the residual thickness according to the compaction coefficient to obtain the lithological residual thickness after decompaction;

[0131] The porosity conversion model is as follows:

[0132] Λ=(1-ф) / (1-ф0) (6)

[0133] In the formula: Λ represents the compaction coefficient; ф represents the current porosity; ф0 represents the original sedimentary porosity;

[0134] Different lithologies have different compaction coefficients. The original sedimentary porosity values ​​and current porosity values ​​of different lithologies in the work area can be obtained from data or surveys, and are assumed to be known data.

[0135] S84: The paleogeography of the target layer is restored by summing the residual thicknesses of each lithology after decompaction, and a paleogeography restoration map is obtained.

[0136] In a specific embodiment, the paleomorphology of the target layer in the target work area is restored using the impression method, and the resulting paleomorphology restoration map is shown below. Figure 11 As shown.

[0137] S9: Based on the existing shelf-banded sandstone sedimentary subfacies map, and combined with geological model understanding, multi-attribute fusion map, lithological quantitative interpretation results, favorable sand body thickness prediction distribution map, and paleogeomorphological restoration map, the shelf-banded sandstone sedimentary microfacies is finely characterized to obtain the shelf-banded sandstone sedimentary microfacies map.

[0138] In one specific embodiment, step S9 specifically includes the following sub-steps:

[0139] S91: Constrain the subfacies diagram of shelf-banded sand bodies based on previous understanding of geological models of shelf-banded sand bodies;

[0140] In one specific embodiment, based on previous research on shelf-banded sand bodies, the geological models of shelf-banded sand bodies are summarized as follows:

[0141] (1) Morphological constraints 1: In shallow marine shelf environments, shelf sand bodies usually develop in the middle to outer shelf areas tens to hundreds of kilometers away from the coast. The formation mechanism of sand bodies is complex and they are all modified by hydrodynamic forces such as waves and tides in the southwest direction. Their distribution direction is mostly parallel to the coastline, and their morphology is mainly strip-shaped or radial, showing a northeast-southwest distribution characteristic.

[0142] (2) Morphological constraint 2: In the southwestern part of Huizhou Depression, when the sea level drops, the energy of the waves and coastal currents in the southwest direction increases, and the strip sand bodies exhibit an asymmetrical shape that is narrow in the northeast and wide in the southwest; when the sea level rises, the tidal forces dominate, and the strip sand bodies exhibit a symmetrical shape with equal width in the east and west.

[0143] (3) Thickness constraint 1: The distance from the source material has a significant impact on the distribution pattern of shelf-striped sand bodies. In the southwestern part of the Huizhou Depression, the ancient Pearl River Delta continuously transported sandy material to the shelf area. The closer to the coastline, the greater the thickness of the sand body and the coarser the grain size.

[0144] (4) Thickness constraint 2: Topographic relief has a significant controlling effect on the location of sand body deposition. Existing studies have shown that in the southwestern part of the Huizhou Depression, shelf sand bodies tend to deposit in high-lying areas (where the alteration force is strong), while in low-lying areas the hydrodynamic alteration is weak. This difference results in sand bodies with greater deposition thickness and coarser grain size in high-lying areas.

[0145] (5) Thickness constraint 3: The sedimentary microfacies of shelf-banded sand bodies can be divided into two types according to different thicknesses: shelf ridges and shelf mats. Shelf ridges develop in the core of shelf-banded sand bodies, are relatively thick (greater than 17m), and have coarser sediment grains (mostly fine sand deposits); shelf mats develop in the outer edge of shelf-banded sand bodies, are relatively thin (mostly in the range of 12-17m), and have finer sediment grains (mostly silt and muddy silt deposits).

[0146] S92: Based on the understanding of the planar morphology of the striped sand, the boundaries of the sedimentary microfacies of the sand body are finely depicted by combining the multi-attribute fusion diagram;

[0147] The fusion attribute reflects the distribution trend of lithology. On the fusion attribute map, light blue to dark red represent sandy sediments, while purple represents argillaceous sediments. Based on the understanding of the planar morphology of banded sands, the boundaries of sand bodies are finely delineated using the fusion attribute map.

[0148] S93: Finely characterize the thickness of sand body sedimentary microfacies based on the predicted distribution map of favorable sand body thickness;

[0149] The thickness of the sand body corresponds to the fusion properties. Sand sheets and ridges are finely characterized according to the principle that the sand sheet thickness is relatively thin and the sand ridge thickness is relatively thick. Based on actual drilling calibration and previous regional knowledge, sand thickness of 3-12m is classified as sand sheet microfacies, and thickness greater than 12m is classified as sand ridge microfacies. Sand ridges are further divided into two categories: the main body of the sand ridge (>17m) and the side edge of the sand ridge (12-17m).

[0150] S94: Based on the paleogeographic reconstruction map, the planar depositional range of the sand body sedimentary microfacies is finely depicted, and finally, the shelf strip-type sand body sedimentary microfacies map under multi-factor constraints is formed.

[0151] Based on previous understanding of the location of banded sand deposits: coarse-grained materials are deposited in the higher parts and fine-grained materials are deposited in the lower parts. The planar depositional range of sand sheets and sand ridges can be finely depicted by referring to paleogeomorphological and fusion attribute maps and predicted sand thickness maps.

[0152] Sand sheets are mainly distributed in the paleogeographic region ranging from 50 to 130 ms, while sand ridges are distributed at higher geomorphic locations, ranging from 50 to 100 ms. Paleogeographic constraints were used to further enhance the accuracy of sedimentary microfacies plane location and boundary delineation, ultimately forming a shelf-banded sand body sedimentary microfacies map under multi-factor constraints. The results are as follows: Figure 12 As shown.

[0153] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations 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 alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for characterizing the microfacies of modified sand bodies under the constraints of geological processes, characterized in that, Includes the following steps: S1: Obtain basic data for the target work area, including geological background data, seismic data, well logging data, and core / thin section data; S2: Based on the aforementioned basic data, interpret the seismic horizon of the target layer in the target work area to obtain the seismic interpretation horizon of the target layer; S3: Based on the seismic data and the seismic interpretation horizon of the target layer, extract individual conventional seismic attributes and RGB slices of the strip-shaped sand body morphology of the target layer; S4: Combining the aforementioned single conventional seismic attributes and RGB slices to characterize the sedimentary subfacies of shelf banded sands, a sedimentary subfacies map of shelf banded sands is obtained; S5: Use the core / thin section data to calibrate the lithological logging curves, establish lithological quantitative interpretation standards, and perform lithological quantitative interpretation on existing wells in the target work area; S6: Extract the target seismic attributes of the target work area, and perform seismic attribute optimization and fusion to obtain a multi-attribute fusion map; S7: Based on the quantitative interpretation results of lithology, extract the thickness values ​​of favorable lithology in the target layer of each well in the existing wells in the target work area, and fit the thickness distribution of favorable sand bodies in the whole area to obtain the predicted distribution map of favorable sand body thickness. S8: Based on the seismic data and the seismic interpretation horizon of the target layer, the paleogeography of the target layer in the target work area is restored using the imprinting method to obtain a paleogeographic restoration map; S9: Based on the existing shelf-banded sandstone sedimentary subfacies map, and combined with geological model understanding, multi-attribute fusion map, lithological quantitative interpretation results, favorable sand body thickness prediction distribution map, and paleogeomorphological restoration map, the shelf-banded sandstone sedimentary microfacies is finely characterized to obtain the shelf-banded sandstone sedimentary microfacies map.

2. The method for characterizing the microfacies of modified sand bodies under geological process constraints according to claim 1, characterized in that, In step S3, the conventional earthquake attribute is the root mean square amplitude attribute, and the RGB slice is a new fused attribute map generated by fusing the three frequency division attributes of RGB.

3. The method for characterizing the microfacies of modified sand bodies under geological process constraints according to claim 1, characterized in that, In step S4, the shelf strip-shaped sand sedimentary subfacies map is obtained through the following sub-steps: based on RGB slices, the boundaries of sand bodies with strip-shaped distribution in the whole area are first delineated, and then the local strip morphology is adjusted with reference to conventional seismic attributes, and finally the shelf strip-shaped sand sedimentary subfacies map is obtained.

4. The method for characterizing the microfacies of modified sand bodies under geological process constraints according to claim 1, characterized in that, Step S5 specifically includes the following sub-steps: S51: Standardize lithology logging curves using the mean-variance method; S52: Calibrate the standardized lithology logging curves based on core / thin section data; S53: Interact all logging data points that have been calibrated for lithology to identify logging curve types that can distinguish lithology and establish quantitative interpretation standards for lithology; S54: Based on the aforementioned lithological quantitative interpretation standard, perform lithological quantitative interpretation on the existing wells in the target work area.

5. The method for characterizing the microfacies of modified sand bodies under geological process constraints according to claim 1, characterized in that, Step S6 specifically includes the following sub-steps: S61: Based on the seismic data and the seismic interpretation horizon of the target layer, extract various conventional seismic attributes of the target layer; S62: Analyze the correlation between the extracted conventional seismic attributes and the quantitatively interpreted favorable lithologies, and select multiple seismic attributes based on the correlation analysis results; S63: Perform multi-attribute fusion on the selected multiple seismic attributes to obtain a multi-attribute fusion map.

6. The method for characterizing the microfacies of modified sand bodies under geological process constraints according to claim 5, characterized in that, Step S7 specifically includes the following sub-steps: S71: Extract the thickness values ​​of favorable lithology in the target layer of each well in the target work area based on the quantitative interpretation results of lithology; S72: Perform correlation analysis between the thickness value of the target layer and various conventional seismic attributes of the extracted target layer, and determine the prediction model between seismic attributes and sand thickness; S73: Based on the prediction model between seismic attributes and sand thickness, predict the thickness of favorable sand bodies in the target layer and draw a distribution map of the predicted thickness of favorable sand bodies.

7. The method for characterizing the microfacies of modified sand bodies under geological process constraints according to claim 1, characterized in that, Step S8 specifically includes the following sub-steps: S81: Select the draped marker layer above the target layer; S82: Calculate the thickness between the target layer and the draped marker layer to obtain the residual thickness; S83: Calculate the compaction coefficient based on the porosity conversion model, and perform compaction correction on the residual thickness according to the compaction coefficient to obtain the lithological residual thickness after decompaction; S84: The paleogeography of the target layer is restored by summing the residual thicknesses of each lithology after decompaction, and a paleogeography restoration map is obtained.

8. The method for characterizing the microfacies of modified sand bodies under geological process constraints according to any one of claims 1-7, characterized in that, Step S9 specifically includes the following sub-steps: S91: Constrain the subfacies diagram of shelf-banded sand bodies based on previous understanding of geological models of shelf-banded sand bodies; S92: Based on the understanding of the planar morphology of the striped sand, the boundaries of the sedimentary microfacies of the sand body are finely depicted by combining the multi-attribute fusion diagram; S93: Finely characterize the thickness of sand body sedimentary microfacies based on the predicted distribution map of favorable sand body thickness; S94: Based on the paleogeographic reconstruction map, the planar depositional range of the sand body sedimentary microfacies is finely depicted, and finally, the shelf strip-type sand body sedimentary microfacies map under multi-factor constraints is formed.

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