Transformed sand body microfacies depicting method under geological process constraint

Abstract

The invention discloses a modified sand body microfacies depicting method under geological process constraint. The method comprises the following steps: acquiring data; interpreting the seismic horizon of the target stratum of the target work area; extracting a single conventional seismic attribute and an RGB (Red, Green and Blue) slice of the target layer strip type sand body form; depicting sedimentary subfacies of land frame strip molding sand; lithology quantitative interpretation is carried out on existing wells of the target work area; target seismic attributes of the target work area are extracted, seismic attribute optimization and fusion are carried out, and favorable sand body thickness distribution of the whole area is fitted; recovering the ancient landform of the target layer of the target work area; and in combination with geological mode understanding, a multi-attribute fusion map, a logging lithology quantitative interpretation result, a favorable sand body thickness prediction distribution map and an ancient landform restoration map, finely depicting the land frame strip type sand body sedimentary microfacies to obtain a land frame strip type sand body sedimentary microfacies map. The land frame strip sand body sedimentary microfacies precision can be improved, and the subsequent lithologic oil and gas reservoir exploration and development progress can be boosted.

Description

Technical Field

[0001] The present invention relates to the technical field of oil exploration and development, and particularly relates to a method for depicting the microfacies of reworked sand bodies under the constraint of geological processes. Background Art

[0002] With the continuous deepening of oil and gas exploration in China, structural oil and gas reservoirs are becoming fewer and fewer, and most of them have entered the high water cut stage. Lithologic oil and gas reservoirs have gradually become important exploration targets for increasing oil and gas reserves and production in China. Lithologic traps can be divided into 4 subcategories according to different formation mechanisms, namely updip pinch-out traps of reservoir rocks, lenticular traps of reservoir rocks, bioherm traps, and diagenetic and epigenetic lithologic traps. On the vast shallow continental shelf, there are often strip-shaped sand bodies modified by various types of hydrodynamic forces such as waves or tides, also known as shelf sand ridges. These sand bodies are usually dozens or hundreds of kilometers away from the coastline, mostly extending along the parallel or nearly parallel direction of the shoreline, and some sand bodies radiate and spread in the direction perpendicular to the shoreline. Such marine strip-shaped thin-layer sand bodies are often wrapped by shelf mudstones to form lenticular lithologic traps, which are matched and communicated with the underlying hydrocarbon source rocks through faults or sand body networks, and have superior oil and gas accumulation conditions. They are important targets for lithologic exploration and development in basins such as the Pearl River Mouth Basin and the Xihu Sag in China.

[0003] The sedimentary microfacies of shelf sand bodies include sand ridges and sand sheets. Among them, the shelf sand ridge sand bodies have relatively large thickness, coarser grain size and better sorting and rounding. They are the highest-quality reservoir types in shelf sand bodies and are also important "sweet spot" sand bodies concerned by oil and gas exploration. However, during the formation process of such shallow continental shelf sand bodies, they have been continuously modified by hydrodynamic forces of different types and intensities for a long time, and overall have characteristics such as thin sand body thickness and rapid lateral facies change, resulting in rapid lateral changes in lithology and physical properties and extremely large differences in oil (gas) content, which greatly increases the exploration and evaluation difficulty coefficient of such lithologic traps.

[0004] With the development of seismic exploration technology, the depiction of sedimentary microfacies of strip-shaped thin-layer sand bodies based on 3D seismic data has been widely used. However, the reservoir thickness of shallow continental shelf sand bodies is generally less than 1 / 8 - 1 / 16 of the seismic wavelength, exceeding the prediction limit of sand bodies by geophysical inversion, and it is difficult to predict the plane distribution of reservoir types and thickness. In addition, due to the scarcity of oil and gas wells in the middle and deep layers, previous studies mainly focused on well-seismic joint sedimentary geomorphic depiction based on the attributes of a single stratigraphic slice, which only reflected the macroscopic distribution outline and sedimentary evolution law of strip-shaped sand bodies, but lacked the depiction of internal sedimentary microfacies differences and reservoir thickness change details, seriously restricting the exploration accuracy of lithologic traps in strip-shaped sand bodies. Summary of the Invention

[0005] In view of the above problems, the present invention aims to provide a method for depicting the microfacies of reworked sand bodies under the constraint of geological processes.

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

[0007] A method for depicting the microfacies of a transformed sand body under the constraint of geological processes, comprising the following steps:

[0008] S1: Obtain the basic data of the target work area, where the basic data includes geological background data, seismic data, logging data, and core / thin section data;

[0009] S2: Interpret the seismic horizons of the target layer in the target work area according to the basic data to obtain the seismic interpretation horizons of the target layer;

[0010] S3: Based on the seismic data and the seismic interpretation horizons of the target layer, extract a single conventional seismic attribute and an RGB slice of the strip-shaped sand body morphology of the target layer;

[0011] S4: Combine the single conventional seismic attribute and the RGB slice to depict the sedimentary subfacies of the shelf strip-shaped sand, and obtain a sedimentary subfacies map of the shelf strip-shaped sand;

[0012] S5: Calibrate the lithology logging curves using the core / thin section data, establish a quantitative lithology interpretation standard, and perform quantitative lithology interpretation on the 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: According to the quantitative lithology interpretation results, extract the thickness values of the favorable lithology of the existing wells in the target work area at the target layer of each well, and fit the thickness distribution of the favorable sand bodies in the whole area to obtain a predicted distribution map of the favorable sand body thickness;

[0015] S8: Based on the seismic data and the seismic interpretation horizons of the target layer, use the imprint method to restore the paleogeomorphology of the target layer in the target work area to obtain a paleogeomorphology restoration map;

[0016] S9: On the basis of the sedimentary subfacies map of the shelf strip-shaped sand, jointly and finely depict the sedimentary microfacies of the shelf strip-shaped sand body under the constraints of geological model understanding, multi-attribute fusion map, quantitative lithology interpretation results, predicted distribution map of favorable sand body thickness, and paleogeomorphology restoration map to obtain a sedimentary microfacies map of the shelf strip-shaped sand body.

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

[0018] Preferably, in step S4, the sedimentary subfacies map of the shelf strip-shaped sand is obtained by the following sub-steps: First, outline the sand body boundaries with a strip-shaped distribution pattern in the whole area based on the RGB slice, then adjust the local strip patterns that are not clear with reference to the conventional seismic attributes, and finally obtain the sedimentary subfacies map of the shelf strip-shaped sand.

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

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

[0021] S52: Calibrate the standardized lithology logging curves according to core / slab data;

[0022] S53: Plot and intersect all the logged data points with calibrated lithology, find out the types of logging curves that can distinguish lithology, and establish a quantitative lithology interpretation standard;

[0023] S54: Conduct quantitative lithology interpretation on the existing wells in the target work area according to the quantitative lithology interpretation standard.

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

[0025] S61: Extract various conventional seismic attributes of the target layer according to the seismic data and the seismic interpretation horizons of the target layer;

[0026] S62: Analyze the correlation between the extracted various conventional seismic attributes and the favorable lithology of the quantitative interpretation, and select multiple seismic attributes according to the correlation analysis results;

[0027] S63: Conduct 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 the favorable lithology of the existing wells in the target work area at the target layer of each well according to the quantitative lithology interpretation results;

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

[0031] S73: Predict the thickness of the favorable rock-sand body of the target layer according to the prediction model between the seismic attributes and the sand thickness, and draw a predicted distribution map of the favorable sand body thickness.

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

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

[0034] S82: Calculate the thickness between the target layer and the draping 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 de-compacted lithologic residual thickness;

[0036] S84: Accumulate the de-compacted lithologic residual thicknesses of each layer to restore the paleogeomorphology of the target layer, and obtain the paleogeomorphology restoration map.

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

[0038] S91: Constrain the shelf strip-shaped sand body sedimentary subfacies map based on the previous understanding of the geological model of the shelf strip-shaped sand body;

[0039] S92: According to the understanding of the planar shape of the strip sand, combined with the multi-attribute fusion map, finely depict the boundary of the sedimentary microfacies of the sand body;

[0040] S93: Finely depict the thickness of the sedimentary microfacies of the sand body according to the favorable sand body thickness prediction distribution map;

[0041] S94: Finely depict the planar sedimentary range of the sedimentary microfacies of the sand body according to the paleogeomorphology restoration map, and finally form the sedimentary microfacies map of the shelf strip-shaped sand body under the constraint of multiple elements.

[0042] The beneficial effects of the present invention are:

[0043] The present invention takes into account hydrodynamic conditions, sea-level rise and fall cycles, geomorphic highs and lows, distance from the provenance, and relative internal development positions, and can more accurately depict the microfacies of shelf strip-shaped sand bodies under the constraint of geological processes, providing technical support for the subsequent exploration and development of lithologic oil and gas reservoirs. Description of the Drawings

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0045] Figure 1 It is a flow chart of the method for depicting the modified sand body microfacies under the constraint of geological processes of the present invention;

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

[0047] Figure 3 It is a schematic diagram of the RGB slice in a specific embodiment;

[0048] Figure 4Schematic diagram of sedimentary subfacies characterized by root mean square amplitude and RGB slices in a specific embodiment;

[0049] Figure 5 Schematic diagram of well logging curve standardization in a specific embodiment;

[0050] Figure 6 Schematic diagram of the intersection of different well logging curves in a specific embodiment;

[0051] Figure 7 Schematic diagram of the quantitative discrimination result of lithology interpretation in a specific embodiment;

[0052] Figure 8 Multi-attribute fusion map in a specific embodiment;

[0053] Figure 9 Sandstone thickness prediction map in a specific embodiment;

[0054] Figure 10 Principle diagram of paleogeomorphology restoration by impression method;

[0055] Figure 11 Paleogeomorphology restoration map in a specific embodiment;

[0056] Figure 12 Sedimentary microfacies map of shelf strip-shaped sand bodies in a specific embodiment. Detailed implementation manners

[0057] The present invention will be further described below in conjunction with the accompanying drawings and embodiments. It should be noted that, without conflict, the embodiments in the present application and the technical features in the embodiments can be combined with each other. It should be pointed out that unless otherwise specified, all the technologies and scientific terms used in the present application have the same meanings as those commonly understood by those of ordinary skill in the technical field to which the present application belongs. The terms "including" or "comprising" and the like used in the present invention disclosure mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0058] As Figure 1 shown, the present invention provides a method for characterizing the microfacies of transformed sand bodies under the constraint of geological processes, including the following steps:

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

[0060] In a specific embodiment, taking the Pearl River Formation sedimentary area in Huizhou Sag as an example, the target work area has been affected by the north-east to south-west tidal action and the south-west alongshore current for a long time; the tidal current is mainly bidirectional, and the planar shape of the sand body is more inclined to a symmetric strip shape, while the alongshore current is mainly unidirectional, and the sand body is prone to form a shape with convergence in the north and divergence in the south on the plane; the lithology is mainly fine sandstone, siltstone, muddy siltstone and silty mudstone; in the high part of the structure, the hydrodynamic stress transformation effect is stronger, and it is easy to form high-quality reservoirs with better sorting and roundness.

[0061] S2: Interpret the seismic horizons of the target layer in the target work area according to the basic data to obtain the seismic interpretation horizons of the target layer.

[0062] In a specific embodiment, when performing seismic horizon interpretation, follow the principles of "isochronous correlation, hierarchical control and model guidance", adopt the well-seismic combination method, interact between plane and profile, and achieve the closed interpretation of the sand body horizons.

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

[0064] The shelf strip-shaped sand body is often thinner, with thick mudstone at the top and bottom. The conventional seismic attributes and RGB slices can reflect the morphological characteristics of the shelf strip-shaped sand body.

[0065] In a specific embodiment, the conventional seismic attribute is the root mean square amplitude attribute, and the extraction result is as Figure 2 shown, where the strong amplitude (biased towards the red color scale) represents coarse-grained sediments; the medium-strength amplitude (biased towards the green color scale) represents transitional lithology; the weak amplitude (biased towards the purple or blue color scale) represents mud-rich sediments. It can be clearly seen from Figure 2 the planar distribution pattern of the strip-shaped, north-east to south-west sand body.

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

[0067] In a specific embodiment, when making the RGB slice, first perform frequency division processing on the original seismic data of the work area, preferably select three frequency division bodies with different frequencies for RGB fusion display, and by adjusting the brightness of the red, green and blue colors, highlight the contour of the shelf strip-shaped sand body characterized by the RGB fusion attribute.

[0068] The original seismic data of the target work area has a dominant frequency of about 31 Hz, a frequency bandwidth of 5 - 170 Hz, and the sand body thickness is concentrated approximately between 2 - 18 m. Sand bodies with a thickness of more than 15 m in the target work area are defined as thick sands, those with a thickness of 10 - 15 m are defined as medium-thick sands, and those with a thickness of less than 10 m are defined as thin sands. At intervals of 5 Hz and 10 Hz respectively, the relationship between seismic event axes and sand body responses is analyzed. Finally, 16 Hz, 35 Hz, and 54 Hz frequency-divided fusion is selected, and the amplitudes at different frequencies correspond to red, green, and blue respectively, representing thick-layer, medium-thick-layer, and thin-layer sand bodies. The results are as Figure 3 shown. From Figure 3 it can be seen that the frequency-divided RGB slices can clearly reflect the banded sand body morphology in bright white, among which the bright white bands in the southwestern and midwestern regions are relatively clear.

[0069] S4: Combining the above-mentioned single conventional seismic attribute and the RGB slices to depict the sedimentary subfacies of the shelf banded sand, and obtaining the sedimentary subfacies map of the shelf banded sand.

[0070] When depicting the sand body contour, the principle of "mainly based on RGB, supplemented by single attribute (RMS)" is followed. In a specific embodiment, the sedimentary subfacies map of the shelf banded sand is obtained through the following sub-steps: First, based on the RGB slices, outline the boundaries of the sand bodies with a banded distribution pattern in the whole area, then adjust the local unclear banded patterns with reference to the conventional seismic attributes, and finally obtain the sedimentary subfacies map of the shelf banded sand. The results are as Figure 4 shown.

[0071] S5: Using the above-mentioned core / slab data to calibrate the lithologic logging curves, establishing a quantitative lithologic interpretation standard, and conducting quantitative lithologic interpretation on the existing wells in the target work area.

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

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

[0074] In a specific embodiment, the lithologic logging curves include natural gamma logging (GR), density logging (DEN), neutron logging (CNL), and induction logging (CON). The changing trend of the natural gamma logging curve after standardization is as Figure 5 shown.

[0075] When conducting the standardization process, the principle of mean-variance is as follows: First, select the correct logging curve values of the standard layer in a standard well, denoted as: X 1 , X 2 , …, X N . The values of the corresponding logging curves of the standard layer in the well to be standardized are denoted as: Y 1 , Y 2, …, Y N 。

[0076] Assume that the data after standardizing the Y series data is Z 1 , Z 2 , …, Z N , and there is a linear relationship between them, which is expressed as Z = (a)Y + (b). Then when the means and variances of X and Z are the same, the coefficients a and b are obtained.

[0077] When the means of the two are the same, E(X) = E(Z), and the variances are the same, E(X) = E(Z)(X) = V(Z), according to the principles of mathematical statistics, there are:

[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] Among them, the means and variances of the X and Y series data can be obtained, and substituting them into the linear formula can perform full-well standardization processing.

[0083] Select Well HZ27-3-4DSa as the standard well, and a set of stable and developed mudstone layers below the strip-shaped sand body layer as the standard layer, and standardize the lithologic logging curves by the mean-variance method.

[0084] S52: Calibrate the standardized lithologic logging curves according to core / slab data;

[0085] The lithologic identification reports of cores and slabs are generally identified based on the depth points of a certain well. The logging curve values corresponding to the lithology at a certain depth point on the well are statistically analyzed to complete the calibration of lithology and logging data. The lithologies of the strip-shaped sand bodies in the southwestern region of the Huizhou Sag are mainly fine sandstone, pure siltstone, and muddy siltstone, among which the favorable reservoir lithologies are fine sandstone and pure siltstone.

[0086] S53: Plot and intersect all the logged data points with calibrated lithology, find out the types of logging curves that can distinguish lithology, and establish a quantitative interpretation standard for lithology;

[0087] Different logging curves have different abilities to distinguish different lithologies. According to the principle of plotting the results calibrated by different logging curves for the same lithology, the standardized natural gamma logging, density logging, neutron logging, and induction logging are plotted and intersected with each other to find out the logging curves with stronger lithology discrimination ability. The results are as Figure 6As shown, the lithology discrimination ability of the natural gamma logging curve is finally determined to be relatively strong.

[0088] S54: According to the above lithology quantitative interpretation standard, conduct lithology quantitative interpretation on the existing wells in the target work area.

[0089] Based on the calibration results of the lithology, the lithology interpretation standard for the southwestern region of the Huizhou Sag is established:

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

[0091] When the API value of GR after standardization is in the range of 113 to 120, it is interpreted as siltstone;

[0092] When the API value of GR after standardization is in the range of 120 to 130, it is interpreted as argillaceous siltstone;

[0093] When the API value of GR after standardization is in the range of 130 to 143, it is interpreted as silty mudstone;

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

[0095] S6: Extract the target seismic attributes in the target work area, and conduct 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: According to the above seismic data and the seismic interpretation horizons 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 the root mean square amplitude, maximum amplitude, minimum amplitude, waveform attributes, etc. of the amplitude class, statistical class, signal class and waveform class. The specific attributes are shown in Table 1:

[0099] Table 1 Various conventional seismic attributes

[0100]

[0101] S62: Analyze the correlation between the extracted various conventional seismic attributes and the favorable lithologies of the quantitative interpretation, and select multiple seismic attributes according to the correlation analysis results;

[0102] In a specific embodiment, a combination of Pearson correlation analysis and random forest ranking is adopted to preferably select seismic attributes with a high ranking in terms of correlation with lithology, with Pearson correlation ranking being the main criterion. It should be noted that when the ranking of a certain type of attribute is relatively low in Pearson correlation but relatively high in random forest ranking, the ranking of the random forest needs to be considered to preferably select this attribute to avoid errors caused by single correlation analysis.

[0103] The calculation formula of the Pearson correlation coefficient is as follows:

[0104]

[0105] In the formula: x is lithology information; y is seismic attribute information.

[0106] The correlation strength between the seismic attribute information and the lithology information is judged according to the absolute value of the Pearson correlation coefficient: when pearson is between 0.8 - 1.0, it is extremely strongly correlated; when pearson is between 0.6 - 0.8, it is strongly correlated; when pearson is between 0.4 - 0.6, it is moderately correlated; when pearson is between 0.2 - 0.4, it is weakly correlated; when pearson is between 0.0 - 0.2, it is extremely weakly correlated or uncorrelated.

[0107] The random forest ranking mainly depends on the contribution value of each lithology feature to the attribute feature, and the overall average value is taken to compare the contribution degree for ranking.

[0108] The top 3 attributes in Pearson correlation ranking are arc length, average negative amplitude, and average energy respectively, and the top 3 attributes in random forest ranking are arc length, average instantaneous amplitude, and semi - energy respectively.

[0109] There are differences in the top three attributes of the two rankings. To reduce the analysis error, a total of 5 attributes are preferably selected for fusion: arc length, average negative amplitude, average energy, average instantaneous amplitude, and semi - energy.

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

[0111] According to the correlation ranking results, arc length, average negative amplitude, average energy, average instantaneous amplitude, and semi - energy are selected for multi - attribute fusion, and finally the fusion attributes of the target layer are obtained, as shown in Figure 8 shown. Figure 8 Among them, the distribution characteristics of the banded sand bodies are relatively clear. The part with a purple attribute represents mudstone deposition, the part with a greenish color represents siltstone deposition, and the part with a reddish color represents fine sandstone deposition.

[0112] The generalized additive model is used for multi-attribute fusion. The generalized additive model can incorporate regression models for linear or non-linear fitting together and fit the data in a more flexible way with higher degrees of freedom. The calculation formula is as follows:

[0113] y i = β 0 +f 1 (x i1 )+f 2 (x i2 )+…+f n (x in )+e i (5)

[0114] where: f can be a function in any form. Calculate f for each individual x in (the nth variable of the ith group of data), and finally unify the results for prediction.

[0115] S7: According to the quantitative lithology interpretation results, extract the thickness values of the favorable lithology of the existing wells in the target work area at the target horizons of each well, and fit the thickness distribution of the favorable sand bodies in the whole area to obtain the predicted distribution map of the favorable sand body thickness.

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

[0117] S71: Extract the thickness values of the favorable lithology of the existing wells in the target work area at the target horizons of each well according to the quantitative lithology interpretation results;

[0118] In the southwestern area of the Huizhou Sag, the sediment grain size of the shelf strip-shaped sand bodies is relatively fine, and the favorable reservoir lithologies are mostly pure siltstone and fine sandstone. The thickness data of siltstone and fine sandstone of the existing wells in the whole area can be extracted according to the lithology interpretation results;

[0119] S72: Conduct a correlation analysis on the thickness values of the target horizons and various conventional seismic attributes extracted from the target horizons, and determine the prediction model between the seismic attributes and the sand thickness;

[0120] In a specific embodiment, an artificial intelligence algorithm is used to predict the thickness distribution of favorable sand bodies, which can make the sand thickness prediction method more intelligent and automated, and can provide a more reliable and efficient prediction means. Optionally, the artificial intelligence algorithm adopts any one or more of the random forest regression algorithm (RF), support vector machine (SVM), boosted regression tree (BRT), and artificial neural network (ANN).

[0121] In a specific embodiment, the sand body prediction results are obtained through the above four artificial intelligence algorithms, and a correlation analysis is carried out with the actual drilling, and the artificial intelligence algorithms with high correlation and their sand body prediction results are selected. In this embodiment, after calculating through four artificial intelligence algorithm models, the model with a higher correlation with the actual well is RF, and the correlation reaches 0.82. That is, the random forest regression algorithm model is selected as the prediction model between seismic attributes and sand thickness.

[0122] S73: Predict the thickness of the favorable lithologic sand body of the target layer according to the prediction model between seismic attributes and sand thickness, and draw a prediction distribution map of the favorable sand body thickness.

[0123] In a specific embodiment, the drawing result of the prediction distribution map of the favorable sand body thickness is as Figure 9 shown. From Figure 9 it can be clearly seen the planar morphological characteristics of the sand body showing strip-shaped distribution, which is similar to the strip-shaped sand body distribution characteristics reflected by single attribute and fused attribute. In the prediction distribution map, the color being more purple represents a thinner sand body, and the color being more red represents a thicker sand body.

[0124] S8: Based on the seismic data and the seismic interpretation horizons of the target layer, the paleogeomorphology of the target layer in the target work area is restored by the impression method to obtain a paleogeomorphology restoration map.

[0125] As Figure 10 shown, the impression method for restoring paleogeomorphology is based on the principle of filling and leveling. The interface when the formation to be restored ends erosion and begins deposition is regarded as an isochronous interface. Through the mirror image relationship between the residual formation thickness and the paleogeomorphology, the relative paleogeomorphology characteristics before deposition are semi-quantitatively reflected.

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

[0127] S81: Select the overlying covering marker bed above the target layer;

[0128] The marker bed needs to meet the following conditions: 1. It is an isochronous interface that can be traced within the whole area; 2. It is preferably as close as possible to the weathered crust surface; 3. The seismic reflection event amplitude is strong, the continuity is good, and it is easy to identify. Usually, the sequence boundary and the maximum flooding surface are preferably selected as the overlying covering marker bed. In a specific embodiment, the first maximum flooding surface just covering the carbonate platform is selected as the marker bed.

[0129] S82: Calculate the thickness between the target layer and the overlying covering marker bed 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 de-compacted lithologic residual thickness;

[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; ф 0 represents the original sedimentary porosity;

[0134] The compaction coefficients of different lithologies are different. The values of the original sedimentary porosity and the current porosity of different lithologies in the work area can be obtained according to data or research, and are defaulted to known data.

[0135] S84: Accumulate the residual thicknesses of each lithology after decompaction to restore the paleogeomorphology of the target layer, and obtain the paleogeomorphology restoration map.

[0136] In a specific embodiment, the impression method is used to restore the paleogeomorphology of the target layer in the target work area, and the obtained paleogeomorphology restoration map is as Figure 11 shown.

[0137] S9: Based on the above-mentioned shelf strip-shaped sand deposition subfacies map, combine the understanding of geological models, multi-attribute fusion maps, quantitative lithology interpretation results, favorable sand body thickness prediction distribution maps, and paleogeomorphology restoration maps to finely depict the sedimentary microfacies of the shelf strip-shaped sand body, and obtain the sedimentary microfacies map of the shelf strip-shaped sand body.

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

[0139] S91: Constrain the shelf strip-shaped sand deposition subfacies map according to the previous understanding of the geological model of the shelf strip-shaped sand body;

[0140] In a specific embodiment, according to the previous research status of the shelf strip-shaped sand body, the summarized geological model of the shelf strip-shaped sand body includes:

[0141] (1) Morphological constraint 1: In the shallow sea shelf environment, shelf sand bodies usually develop in the middle to outer shelf areas dozens to hundreds of kilometers away from the coast. The genetic mechanism of the sand bodies is complex, and they are all modified by hydrodynamic forces such as waves and tides in the southwest direction; their distribution directions are mostly parallel to the shoreline, and their shapes are mainly strip-shaped or radial, showing a northeast-southwest distribution characteristic.

[0142] (2) Morphological constraint 2: In the southwestern region of the Huizhou Sag, when the sea level drops, the energy of the southwest waves and alongshore currents increases, and the strip sand bodies show an asymmetric shape with a narrow northeast and a wide southwest; when the sea level rises, the tidal force dominates, and the strip sand bodies show a symmetric shape with equal width in the east and west.

[0143] (3) Thickness Constraint 1: The distance from the provenance has a significant impact on the distribution pattern of shelf strip sand bodies. In the southwestern area of the Huizhou Sag, the ancient Pearl River Delta continuously transported sandy materials to the shelf area. The closer to the shoreline, the greater the development thickness of the sand body and the coarser the grain size.

[0144] (4) Thickness Constraint 2: Geomorphic undulations have a significant controlling effect on the accumulation position of sand bodies. Existing studies have shown that in the southwestern area of the Huizhou Sag, shelf sand bodies tend to accumulate in high geomorphic positions (where the transformation force is strong), while in low positions, the hydrodynamic transformation is weak. This difference results in a larger accumulation thickness and coarser grain size of sand bodies in high geomorphic positions.

[0145] (5) Thickness Constraint 3: The sedimentary microfacies of shelf strip-shaped sand bodies can be divided into two types according to different thicknesses: shelf sand ridges and shelf sand sheets. Shelf sand ridges develop in the core of shelf strip-shaped sand bodies, with a relatively thick thickness (greater than 17m) and coarser sediment grain size (mostly dominated by fine sand deposition); shelf sand sheets develop on the outer edge of shelf strip-shaped sand bodies, with a relatively thin thickness (mostly in the range of 12 - 17m) and finer sediment grain size (mostly dominated by silt and muddy silt deposition).

[0146] S92: According to the understanding of the planar shape of strip sand, combined with the multi-attribute fusion map, finely depict the boundary of the sedimentary microfacies of the sand body;

[0147] The fused attributes reflect the distribution trend of lithology. On the fused attribute map, light blue - dark red are all sandy sediments, and purple is more muddy sediments. According to the understanding of the planar shape of strip sand, combined with the fused attribute map, finely depict the boundary of the sand body.

[0148] S93: According to the favorable sand body thickness prediction distribution map, finely depict the thickness of the sedimentary microfacies of the sand body;

[0149] The thickness of the sand body can echo with the fused attributes. According to the principle that the sand sheet has a thinner thickness and the sand ridge has a thicker thickness, finely depict the sand sheet and the sand ridge. Through calibration with actual drilling and previous regional understanding, a sand thickness of 3 - 12m is the sand sheet microfacies, and greater than 12m is the sand ridge microfacies. Among them, the sand ridge is 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: According to the restored paleogeomorphic map, finely depict the planar sedimentary range of the sedimentary microfacies of the sand body, and finally form the sedimentary microfacies map of the shelf strip-shaped sand body under the constraint of multiple elements.

[0151] According to the previous understanding of the sedimentation position of strip sand: coarse-grained materials are deposited in high positions, and fine-grained materials are deposited in low positions. Referring to the paleogeomorphic map, the fused attribute map, and the predicted sand thickness map, the planar sedimentary range of the sand sheet and the sand ridge can be finely depicted.

[0152] The sand sheets are mainly distributed in the area where the paleogeomorphic numerical range is 50 - 130 ms. The sand ridges are distributed at higher geomorphic positions, with a range of 50 - 100 ms. By further constraining the paleogeomorphology, the accuracy of depicting the planar position and boundary of sedimentary microfacies is increased. Finally, a sedimentary microfacies map of shelf strip-shaped sand bodies under multi-factor constraints is formed, and the result is as shown in Figure 12 shown.

[0153] As mentioned above, it is only a preferred embodiment of the present invention, and there is no any form of limitation to the present invention. Although the present invention has been disclosed above with the preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments by using the disclosed technical content within the scope of the technical solution of the present invention. However, as long as it does not depart from the content of the technical solution of the present invention, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for characterizing microfacies of reformed sand bodies under geological process constraints, characterized in that: The following steps are involved: S1: Obtain basic data of the target work area, including geological background data, seismic data, well logging data, and core / thin section data; S2: interpreting the seismic horizon of the target layer in the target work area according to the basic data to obtain the seismic interpretation horizon of the target layer; S3: extracting a single conventional seismic attribute and an RGB slice of the banded sand body morphology of the target layer based on the seismic data and the seismic interpretation horizon of the target layer; S4: combining the single conventional seismic attribute and the RGB slice to characterize the sedimentary subfacies of the shelf strip sand, and obtaining a sedimentary subfacies map of the shelf strip sand; S5: calibrate the lithology logging curve using the core / thin section data, establish a lithology quantitative interpretation standard, and perform lithology quantitative interpretation on the 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: According to the results of lithology quantitative interpretation, the thickness values ​​of favorable lithology in the target layers of the existing wells in the target work area are extracted, and the thickness distribution of favorable sand bodies in the whole area is fitted 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 paleo-geomorphology of the target layer in the target work area is restored by using the stamp method to obtain a paleo-geomorphology restoration map; S9: On the basis of the above-mentioned shelf-strip-type sand sedimentary subfacies map, the shelf-strip-type sand sedimentary microfacies are finely depicted under the constraints of the combined geological model understanding, multi-attribute fusion map, lithology quantitative interpretation results, favorable sand body thickness prediction distribution map and paleo-geomorphology restoration map to obtain the shelf-strip-type sand sedimentary microfacies map.

2. The method for characterizing the microfacies of a reformed sand body under geological process constraints according to claim 1, characterized in that: In step S3, the conventional seismic attribute is a 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 a reformed sand body under geological process constraints according to claim 1, characterized in that: In step S4, the shelf strip sand deposition subfacies map is obtained by the following sub-steps: firstly outlining the boundaries of the sand bodies with strip-like distribution in the whole area based on RGB slices, and then adjusting the unclear local strip morphology with reference to conventional seismic attributes, and finally obtaining the shelf strip sand deposition subfacies map.

4. The method for characterizing the microfacies of a reformed sand body under geological process constraints according to claim 1, characterized in that: Step S5 specifically includes the following sub-steps: S51: Standardize the lithology logging curve using the mean-variance method; S52: Calibrate the standardized lithology logging curve based on core / thin section data; S53: All the logging data points calibrated with lithology are intersected and projected to find the logging curve type that can distinguish lithology, and establish the lithology quantitative interpretation standard; S54: According to the lithology quantitative interpretation standard, perform lithology quantitative interpretation on the existing wells in the target work area.

5. The method for characterizing the microfacies of a reformed sand body under geological process constraints according to claim 1, characterized in that: Step S6 specifically includes the following sub-steps: S61: extracting multiple conventional seismic attributes of the target layer according to the seismic data and the seismic interpretation horizon of the target layer; S62: analyzing the correlation between the extracted multiple conventional seismic attributes and the quantitatively interpreted favorable lithology, and selecting 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 graph.

6. The method for characterizing the microfacies of a reformed sand body under geological process constraints according to claim 5, characterized in that: Step S7 specifically includes the following sub-steps: S71: extracting the thickness of the target layer of each well of the favorable lithology of the existing wells in the target work area according to the lithology quantitative interpretation results; S72: performing correlation analysis on the thickness value of the target layer and the extracted multiple conventional seismic attributes of the target layer, and determining a prediction model between the seismic attribute and the sand thickness; S73: Predicting the thickness of favorable rock sand bodies of the target layer according to the prediction model between seismic attributes and sand thickness, and drawing a distribution map of predicted thickness of favorable sand bodies.

7. The method for characterizing the microfacies of a reformed sand body under geological process constraints according to claim 1, characterized in that: Step S8 specifically includes the following sub-steps: S81: Select the covering marker layer above the target layer; S82: Calculate the thickness between the target layer and the coating marker layer to obtain the residual thickness; S83: Calculating a compaction coefficient based on a porosity conversion model, and performing compaction correction on the residual thickness according to the compaction coefficient to obtain a lithology residual thickness after decompaction; S84: The residual thickness of each lithology after decompaction is accumulated to restore the paleo-geomorphology of the target layer, and obtain a paleo-geomorphology restoration map.

8. The method for characterizing microfacies of reformed sand bodies under geological process constraints according to any one of claims 1 to 7, characterized in that: Step S9 specifically includes the following sub-steps: S91: Constrain the sedimentary subfacies diagram of shelf-strip sand bodies based on previous understanding of the geological model of shelf-strip sand bodies; S92: Based on the understanding of the plane morphology of strip sand, the boundaries of sand body sedimentary microfacies are finely depicted in combination with multi-attribute fusion maps; S93: Finely characterize the thickness of sand body sedimentary microfacies based on the predicted distribution map of favorable sand body thickness; S94: The planar deposition range of the sand body sedimentary microfacies is finely depicted according to the paleo-geomorphological restoration map, and finally the shelf strip-type sand body sedimentary microfacies map is formed under the constraints of multiple factors.

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