Quantitative analysis technology of source-channel-sink ternary coupled lakebed fan rich sand

Abstract

The invention discloses a source-channel-sink ternary coupled lakebed fan sand-rich quantitative analysis technology. The technology comprises the following steps: (1) analyzing lakebed fan sand-rich control factors under the guidance of a geological mode; (2) calculation and preliminary selection of modeling factors; (3) quantitatively optimizing a modeling factor based on a geographic detector; and (4) weighted stacking is carried out to establish a lakebed fan sand-rich evaluation model. According to the method, control factors of the lakebed fan sand content are analyzed based on a geological mode; quantitatively calculating influence factors, and preliminarily screening factors having large correlation with the sand content; based on a geographic detector model, analyzing a factor detection result, an interaction detection result and an ecological result of the selected factors, and determining final modeling parameters; a factor detection result and an AHP model are subjected to coupling analysis, and a lakebed fan sand-rich quantitative analysis model is established. According to the method, the lakebed fan sand-rich degree quantitative analysis is completed for the first time, and an important reference is provided for more perfect sand-rich analysis in geological research.

Description

Technical Field

[0001] The invention provides a "source-channel-sink" ternary coupled lake bottom fan rich sand quantitative analysis technology, belonging to the technical fields of sedimentology, data processing and model building. Background Art

[0002] In recent years, as oil and gas exploration has gradually moved deeper, the exploration value of sublacustrine fan reservoirs has become more prominent. Sand-rich reservoirs have good porosity and permeability, and can provide channels for the migration of oil and gas. Therefore, sand-richness is an important aspect of sublacustrine fan research and is of great significance in oil and gas reservoir exploration.

[0003] With the progress of geology and geotechnical science, many research methods have been used for sand-rich research. Common research methods include sedimentological analysis, petrological analysis, rock physical testing, geophysical exploration, etc. However, conventional analysis has the following problems: First, data dependence. The qualitative analysis of sand-richness depends on well data, such as thin sections, analytical tests, cores, etc. However, for oil fields, especially offshore oil fields, there are fewer coring wells, and the research on reservoir sand-richness is limited; second, the work is complicated. Most oil fields with few wells use seismic exploration methods to establish forward or inversion models, extract the reflection wave characteristics of sand bodies, and infer the thickness and depth of sand bodies. This method requires a lot of manual identification processes, and the work is complicated and the accuracy is difficult to guarantee.

[0004] With the continuous development of technology, sand-richness analysis has entered the semi-quantitative-quantitative stage. Numerical simulation and modeling technology are also applied. By establishing three-dimensional sedimentary models, pore structure models, etc., the sand-richness and permeability distribution of sand bodies can be predicted. However, such quantitative analysis models also require a large amount of data support, and their prediction accuracy is affected by the amount of data. At present, there are few studies on the sand-richness of lake bottom fans, and most of them are still in the qualitative-semi-quantitative stage, which is difficult to meet the exploration needs. Based on the above reasons, this patent proposes a "source-channel-sink" ternary coupled lake bottom fan sand-rich quantitative analysis technology.

[0005] This technology firstly analyzes the reasons for the rich sand content of the lake bottom fan based on geological data, that is, summarizes all factors that may affect the rich sand content of the lake bottom fan as comprehensively as possible, and then establishes a linear relationship between the sand content and the above factors, and preliminarily selects the modeling factors. The "geographic detector" software is used for comprehensive analysis, and the modeling factors are further optimized. The factor detection results provided by it are combined with the AHP model to weight each factor and establish a quantitative evaluation model for sand richness. Summary of the invention

[0006] The purpose of the present invention is to provide a "source-channel-sink" three-element coupled lake fan sand-rich quantitative analysis technology, aiming to solve the problem of quantitative analysis of lake fan sand-richness.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] The present invention provides a "source-channel-sink" three-element coupled quantitative analysis technology for lake bottom fan rich sand, which includes the following steps:

[0009] Step 1: Analysis of controlling factors of sand-rich lake fan under the guidance of geological model;

[0010] Step 2, calculation and preliminary selection of modeling factors;

[0011] Step 3: quantitatively select the optimal modeling factors based on the “geographic detector”;

[0012] Step 4, weighted superposition to establish a lake bottom fan rich sand evaluation model;

[0013] According to the above scheme, step 1 specifically includes the following sub-steps:

[0014] Step 1.1: Source supply analysis;

[0015] Step 1.2: Sediment transport process;

[0016] Step 1.3: Accommodate space analysis.

[0017] In step 1.1, the material supply analysis includes factors such as material supply intensity, material supply direction and material transportation distance. The sublacustrine fan is mainly a sedimentary body formed by the accumulation of clastic sediments on the lake bottom at the front of the continental lake basin delta that has not been completely consolidated. It is triggered by certain factors and the sediments transported by gravity flow deposition are deposited on the lake bottom. Clarifying the correspondence between the sublacustrine fan and the material source is the prerequisite for analyzing the sand-richness of the sublacustrine fan. The correspondence between the sublacustrine fan and the material source can be identified by using the heavy mineral combination and the central trend statistics of the thickness of the sublacustrine fan. First, the heavy mineral components are used to determine the mineral components of the parent rock and the material source direction; then, the thickness change trend of the sublacustrine fan is analyzed according to the well location, and the material source direction of the sublacustrine fan can be further determined. For sublacustrine fans with the same material supply, when the material supply intensity and material supply direction are consistent, the transportation distance at different locations should be considered. The longer the transportation distance, the more debris is lost.

[0018] In the step 1.2, the sediment transport process is analyzed, and the influence of slope on the lake bottom fan is mainly reflected in the particle size, sedimentation rate, sedimentation mode and morphological characteristics of the lake bottom fan. Places with larger slopes usually have coarser sediments and steeper lake bottom fan morphology; while places with smaller slopes deposit finer particles, forming a wider and gentler lake bottom fan structure. The slope, slope direction, the angle between the center of the lake bottom fan and the vertical line of the source area boundary, and the distance to the high slope value area are considered here for analysis;

[0019] In step 1.3, the accommodative space is analyzed, and the accommodative space affects the morphology and scale of the sublacustrine fan. A larger accommodative space promotes the accumulation of sediments and is easy to form a significant and well-developed sublacustrine fan; on the contrary, it will limit the accumulation of sediments, and the distribution of sublacustrine fans will be more limited and the morphology will be relatively flat. The accommodative space is limited by many factors, including climate conditions, lake level changes, fault activity, topography and water depth. Because the climate and lake level changes of the sublacustrine fans formed in the same period are basically the same, in this step, the fault (distance to the fault) and paleo-water depth and paleo-geomorphological conditions are mainly considered.

[0020] According to the above scheme, step 2 specifically includes the following sub-steps:

[0021] Step 2.1: Calculation of modeling factors;

[0022] Step 2.2: Preliminary screening of modeling factors;

[0023] In step 2.1, the control factors selected in step 1 (Table 1) are summarized and calculations are performed.

[0024] Table 1 Controlling factors affecting the development of sublacustrine fans

[0025]

[0026]

[0027] ① Source transport distance: Count the distance from the existing lake bottom fan wells to the delta front boundary. Import the well coordinate data and source area boundary data into ArcGIS, and use spatial analysis-distance-Euclidean distance to count the distance from each lake bottom fan well to the source area;

[0028] ② Slope: First, according to the seismic layer data, the “imprint method” is used to restore the ancient landform. The landform data is imported into ArcGIS, and the slope data can be obtained by using spatial analysis-surface analysis-slope.

[0029] ③ The angle between the slope direction and the vertical line from the center of the lake bottom fan to the boundary of the provenance area: First, restore the ancient slope direction, import the geomorphic data into ArcGIS, and use spatial analysis-surface analysis-slope direction to obtain the slope direction data; then extract the direction of the vertical line from each lake bottom fan well site to the boundary of the provenance, and then obtain the angle between the vertical line and the slope direction of the point.

[0030] ④ Distance to the high-value slope area: The method is the same as the source transportation distance, and it is calculated using ArcGIS's spatial analysis-distance-Euclidean distance tool.

[0031] ⑤ Distance to the fault: Conduct seismic profile observation, identify the location and distribution of the fault, and use ArcGIS Euclidean distance calculation.

[0032] ⑥ Paleo-water depth: Paleo-water depth restoration is first based on the "TOC" theory to restore the water depth of existing wells. For locations without wells, the "mirror method" is used to restore the depth in combination with paleo-geomorphological data. The formula is as follows (according to Wei Yaqiong et al., 2017):

[0033]

[0034] Where: H is the ancient water depth, TOC r is the residual organic carbon content, K′ c is the organic carbon recovery coefficient, which can be determined based on the vitrinite reflectance and kerogen type.

[0035] ⑦ Paleomorphology: Paleomorphology is the basis for restoring other parameters. Here, the “imprint method” of seismic data is used for restoration.

[0036] In step 2.2, the modeling factors are preliminarily screened. The sedimentary environment in each region is different, and the effective influencing factors on the sand-richness of the lake bottom fan are also different. Based on statistical analysis, the main influencing factors can be effectively identified. The sand-richness of the lake bottom fan can be expressed by the sand content. A linear relationship between the lake bottom fan sand content and the above parameters is established for the existing well locations, and the influencing factors with a low linear relationship are removed.

[0037] According to the above scheme, step 3 specifically includes the following sub-steps:

[0038] Step 3.1: Select factors based on the “geographic detector” model;

[0039] Step 3.2: Obtain weights based on factor detection results and AHP model;

[0040] In step 3.1, the factors are selected based on the "geographic detector" model. The factors and sand content selected in step 2 are used to create a control factor database, and the independent variables are reclassified using the natural break method; the geographic detector software integrated into Excel is opened, the reclassified factor database is imported, the data is read, the X and Y variables are set, and the geographic detector is run to finally obtain the result data of the four detectors, which are presented in four tables: interaction detection results (Interaction_detector), ecological detection results (Ecological_detector), factor detection results (Factor_detector), and risk area detection results (Risk_detector). Factor detection analysis is used to find factors that explain the spatial differentiation of the sand-rich degree of the lake bottom fan; interaction detection result analysis is used to detect whether the two influencing factors will increase or weaken the explanatory power of the sand-rich degree of the lake bottom fan when they act together, and the modeling factors are constrained accordingly; ecological detection results are used to further analyze whether the dependent variable has the same effect on the sand-rich degree of the lake bottom fan. Based on the above three detection results, the modeling factors are selected.

[0041] In the step 3.2, based on the factor detection results and the AHP model to obtain the weights, a judgment matrix is ​​first constructed. The factor detection results in the "Geographic Detector" software, expert scoring, and pairwise comparisons between the influencing parameters are combined to determine the appropriate scale. The 1-5 point scaling method (or 1-9 point scaling method, see Table 2) can be used to establish the judgment matrix and obtain the model superposition weights; after calculating the final weights, a consistency test is performed, mainly including the consistency index CI and the consistency ratio CR. If the two tests pass, the normalized eigenvector is the weight vector; if not, the judgment matrix calculation is reconstructed.

[0042] Table 21-9 Meaning of the scale method

[0043]

[0044] According to the above scheme, step 4 specifically includes the following sub-steps:

[0045] Step 4.1: Data preprocessing;

[0046] Step 4.2: Construct a quantitative analysis model for the rich sand of the lake bottom fan;

[0047] In step 4.1, data preprocessing. After the model factor screening, a model factor database is established, and each data is inversely and normalized to eliminate the influence of the dimension and convert the data range into 0-1. For extremely large indicators, the larger the data value, the better the sand-rich degree of the dependent variable lake bottom fan, and only normalization is required; for extremely small indicators, the larger the data value, the worse the sand-rich degree of the dependent variable lake bottom fan, and inverse processing is required.

[0048] The indicator normalization processing formula is as follows:

[0049] Y=(XX min ) / (X max -X min )

[0050] Among them, X is the original data, X min is the minimum value of the original data, X max is the maximum value of the original data.

[0051] The indicator reverse processing formula is as follows:

[0052] Y=(X max -X) / (X max -X min )

[0053] Among them, X is the original data, X min is the minimum value of the original data, X max is the maximum value of the original data.

[0054] In step 4.2, a quantitative analysis model of sand-rich lake fan is constructed. After the model weight is determined and the data is processed, it is imported into ArcGIS, and the map algebra-raster calculator tool is used to overlay multiple layers and visualize the results, so that the sand-rich situation of the lake fan outside the well position can be obtained, and the sand-rich degree of each point of the lake fan can be finely analyzed through the "multi-value extraction to point" tool.

[0055] Based on the "geographic detector" model and combined with core, logging and seismic data, we went a step further on the basis of previous qualitative analysis of the sand-rich degree of sublacustrine fans. We selected seven factors, including source transportation distance, slope, slope direction and the angle between the vertical line from the center of the sublacustrine fan to the boundary of the source area, distance to the high-value slope area, distance to the fault, ancient water depth and ancient landform, and combined them with their linear relationship with sand content, interaction detection results, factor detection results, and ecological detection results for screening. We established a qualitative analysis model for the sand-rich nature of sublacustrine fans, which solved the problem of quantitative analysis of the sand-rich nature of sublacustrine fans and provided guidance for further improving the research on sand-rich nature and the exploration and development of sublacustrine fan oil and gas reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0057] Figure 1 It is a flow chart of the method provided by the present invention;

[0058] Figure 2 It is the sedimentary phase distribution diagram of the Luda 10-X block provided in Example 1 of the present invention;

[0059] Figure 3 The paleogeomorphic map of the third section of the eastern Liaodong Bay provided in Example 1 of the present invention;

[0060] Figure 4 The paleo-bathymetric map of the third section of the eastern section of Liaodong Bay provided in Example 1 of the present invention;

[0061] Figure 5 The ancient slope and slope aspect map of the third section of the eastern section of Liaodong Bay provided in Example 1 of the present invention;

[0062] Figure 6 is a linear relationship diagram between various influencing factors and sand content provided in Example 1 of the present invention;

[0063] Figure 7 Schematic diagram of the sand-richness of the lake bottom fan provided by Example 1 of the present invention; DETAILED DESCRIPTION

[0064] In order to enable those skilled in the art to more clearly understand the technical solution of the present invention, the technical solution of the present invention will be described in detail below in conjunction with specific embodiments.

[0065] The braided river delta front and sublacustrine fan are well developed in the eastern third section of the Luda 10-X block in Liaodong Bay. We used the above-mentioned "a ternary coupling "source-channel-sink" sublacustrine fan sand-rich quantitative analysis technology" to complete the first qualitative analysis of the sublacustrine fan sand-richness in the Luda 10-X block in the Liaodong Bay Depression.

[0066] like Figure 1 As shown in the figure, a "source-channel-sink" three-element coupled quantitative analysis technology for lake bottom fan rich sand includes the following steps:

[0067] 1. Analysis of controlling factors of sand-rich lake fan under the guidance of geological model of Luda 10-X block;

[0068] Stratigraphic sequence studies have shown that the provenance of the Luda 10-X block includes the Liaoxi Uplift and the western Yanshan Fold Belt. In the early stage of the East Third Section, part of the Liaoxi Uplift was exposed above the water surface, which could provide a certain source of material; in the middle stage of the East Third Section, most of the Liaoxi Uplift was submerged underwater, and the Yanshan Fold Belt was the main source area, forming large-scale braided river delta deposits to provide the source of the lake bottom fan. Through zircon dating, during the deposition of the East Third Section, the exposed parent rock in the Liaoxi Uplift was mainly Mesozoic igneous rock, and the Yanshan Fold Belt exposed a large area of ​​Archean metamorphic rocks. The detrital zircon dating of the lake bottom fan in the East Third Section showed that its age was mainly 2500Ma, with a small amount distributed at 200Ma, indicating that the zircon in the clastic rocks in the study area is mainly Archean detrital zircon, and the Yanshan Fold Belt is the main source supply area. After determining the source direction, combined with existing sedimentary studies, it was found that the lake bottom fan in this area is concentrated ( Figure 2 ), the sources of materials are the same, so the sand richness is mainly affected by the transportation distance.

[0069] The topographic slope can provide favorable conditions for sediment sliding and landslide, directly controlling the main development position of gravity flow unloading, and further controlling the distribution morphology of the lake bottom fan and the overall sand-rich degree. According to previous studies (Niu Chengmin et al., 2024), the development of lake bottom fans in this area is affected by the slope. The No. 1 lake bottom fan is relatively concentrated, while the sliding and landslide body of the No. 2 lake bottom fan is deposited at the foot of the slope, showing a tongue-shaped body extending toward the lake ( Figure 2 ). In addition, the slope direction affects the direction of sediment collapse, and the angle between the slope direction and the vertical line from the center of the sublacustrine fan to the boundary of the provenance area (i.e., the provenance direction) affects the degree of sediment collapse. Therefore, restoring the paleo-slope, the distance to the high-slope area, and the paleo-slope direction of the Luda 10-X block is of great significance for quantitative analysis of the sand-rich nature of the sublacustrine fan.

[0070] Micro-paleomorphology, paleo-water depth and faults control the size of the accommodation space, and the development scale and morphology of the sublacustrine fan are limited by the accommodation space. A large accommodation space is conducive to the unloading of the sublacustrine fan sand body and has good sand-richness. As for the water depth, the slow water flow and stable sedimentary environment in the deep water area are conducive to the long-term accumulation of sediments (especially sandstone with larger grain size), thereby promoting the gradual formation of sand-rich sublacustrine fans. The space provided by paleomorphology and faults for the development of sublacustrine fans is mainly reflected in the water flow path, flow velocity, sediment source and other aspects. In the Luda 10-X block, affected by the micro-paleomorphology, the migration of debris in the No. 1 sublacustrine fan was blocked and developed more concentratedly, while the No. 2 sublacustrine fan spread toward the lake along the fault zone.

[0071] 2. Calculation and preliminary selection of modeling factors;

[0072] According to the calculation method of modeling factors in step 2.1, the above parameters were calculated. Figure 3 ) and paleo-water depth ( Figure 4 ) restored the entire Liaodong Bay area and circled the range of Luda 10-X and the distribution of the lake fan in the figure, while the slope and aspect were calculated locally for the Luda 10-X block ( Figure 5 The specific results are shown in Table 3.

[0073] Table 3 Calculation results of each factor

[0074]

[0075] The sand content of the existing lake bottom fan wells in the study area was statistically analyzed and the results are shown in Table 4.

[0076] Table 4 Statistical results of sand content in lake bottom fan wells

[0077]

[0078] The linear relationship between sand content and the above parameters is established, and the results are shown in Figure 3 It can be found that the sand-richness of the lake submergence fan in the Luda 10-X block has a good linear relationship with the distance to the provenance area, the slope, the angle between the slope direction and the vertical line from the lake submergence fan center to the boundary of the provenance area, the paleo-water depth and the paleo-geomorphology, and the above factors are preliminarily selected for subsequent analysis.

[0079] 3. Quantitatively select modeling factors based on the “geographic detector”;

[0080] When using the geographic detector model for analysis, the original data is first placed in ArcGIS, the data is reclassified using the natural breaks method, and then copied into Excel for factor analysis.

[0081] (1) Analysis of factor detection results

[0082] The calculation results of q values ​​of all factors are shown in Table 5. The q value represents the influence of the factor on the spatial differentiation of sand-richness of the sublacustrine fan. It can be seen that the slope, the angle between the vertical line from the center of the sublacustrine fan to the boundary of the provenance area, the paleo-water depth and the paleo-geomorphology have a great influence on the sand-richness of the sublacustrine fan.

[0083] Table 5 Factor detection results

[0084]

[0085] (2) Analysis of interaction detection results

[0086] The results of interaction detection are shown in Table 6, focusing on the analysis of four factors: slope, angle between the center of the lake fan and the vertical line of the provenance area boundary, paleo-water depth and paleo-geomorphology. It can be seen that among the above four factors, two of them affect each other, and their effect on the sand-rich degree of the lake fan is stronger than when a factor exists alone.

[0087] Table 6 Interaction detection results

[0088]

[0089]

[0090] (3) Analysis of ecological detection results

[0091] The results of ecological exploration are shown in Table 7, focusing on the analysis of four factors: slope, angle between the center of the lake fan and the vertical line of the provenance area boundary, paleo-water depth, and paleo-geomorphology. It can be seen that among the above four factors, the influence of paleo-water depth on the sand-richness of the lake fan is significantly different from that of other factors. Therefore, water depth data is a must for modeling factors.

[0092] Table 7 Ecological factor detection results

[0093]

[0094] Based on the above analysis results, the paleo-geomorphic factors have a lower impact on the spatial differentiation of sand-richness of the lake fan than other factors, and their impact on the sand-richness of the lake fan is not significantly different from the two factors of slope and the angle between the center of the lake fan and the vertical line of the provenance area boundary, so they are removed in the final modeling. Finally, the three factors of slope, the angle between the center of the lake fan and the vertical line of the provenance area boundary and the paleo-water depth are selected for modeling, and the sand-richness of the lake fan is qualitatively analyzed.

[0095] 4. Weighted superposition to establish a lake bottom fan rich sand evaluation model

[0096] (1) Determination of model factor weights

[0097] The AHP model is a common basic evaluation model, but it is subject to the influence of expert scoring and is highly subjective. Therefore, in this method, the factor detection results of the geographic detector are coupled with the AHP model for analysis for the first time, taking into full account the geological experience of experts, data analysis and the targeted sedimentary environment of the Luda 10-X block. After calculation, the weight value of the slope is 0.40, the weight value of the angle between the center of the lake fan and the vertical line of the source area boundary is 0.28, and the weight value of the paleo-water depth is 0.32.

[0098] (2) Model establishment and analysis

[0099] After determining the layer overlay weights, use the spatial analysis-map algebra-raster calculator in ArcGIS to overlay the layers of each factor according to the weights to obtain the final lake bottom fan rich sand layer ( Figure 7 ) and visualize the results to intuitively analyze the sand-richness of each part of the lake fan. The multi-value extraction to point tool can also be used to quantitatively obtain the sand-richness of each point on the lake fan.

[0100] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principles of the present invention shall be equivalent replacement modes and shall be included in the protection scope of the present invention.

Claims

1. A "source-channel-sink" three-element coupling quantitative analysis technology for lake bottom fan rich sand, characterized by Guided by the geological model, a qualitative analysis of the controlling factors of the sand-rich nature of the lake fan was carried out. After establishing a linear relationship between seven factors such as water depth, distance to the source area, and slope and the sand content of the lake fan, the modeling factors were preliminarily screened. The "geographic detector" model was used to analyze the influence of each factor on the sand-rich nature of the lake fan, and the factors were further optimized to establish an evaluation model to solve the problem of quantitative prediction of the sand-rich nature of the lake fan, including the following steps: (1) Analysis of controlling factors of sand-rich lake fans under the guidance of geological models; (2) Calculation and preliminary selection of modeling factors; (3) Quantitative optimization of modeling factors based on "geographic detectors"; (4) Weighted superposition is used to establish a lake bottom fan sand-rich evaluation model.

2. According to claim 1, a "source-channel-sink" three-element coupled lake bottom fan rich sand quantitative analysis technology is characterized by: In step 1, guided by the geological model, a qualitative analysis was conducted on the reasons for the sand-rich nature of the sublacustrine fan, that is, all factors that may affect the sand-rich nature of the sublacustrine fan were summarized as comprehensively as possible. The core of this step is "source-channel-sink". "Source" refers to the strength of the provenance supply, which controls the sedimentary scale, survival time, quantity and type of the sublacustrine fan. The distance from the sublacustrine fan to the provenance area is mainly considered here; "channel" refers to the sediment transportation process, which directly controls the position and scale of the sublacustrine fan. The factors of slope, slope direction, the angle between the vertical line from the center of the sublacustrine fan to the boundary of the provenance area and the distance to the high slope area are selected here; "sink" refers to the accommodating space, which mainly considers the water depth, distance to the fault and geomorphic conditions.

3. The "source-channel-sink" three-element coupled quantitative analysis technology for lake bottom fan rich sand according to claim 1 is characterized in that: In step 2, the relevant factors are calculated and screened. First, the seismic data is used to restore the paleo-geomorphology using the "imprint method", and then the TOC theory and paleo-geomorphological results are used to restore the paleo-water depth. The paleo-geomorphological data are imported into ArcGIS, and the spatial analysis-surface analysis tool is used to calculate the slope and aspect, and the angle between the aspect and the line connecting the center of the lake fan to the center of the provenance area is obtained. Other distance factors are obtained in ArcGIS using spatial analysis-distance-Euclidean distance. A linear relationship between the sand content of the lake fan and the above factors is established, and preliminary modeling factor selection is performed.

4. According to claim 1, a "source-channel-sink" three-element coupled lake bottom fan rich sand quantitative analysis technology is characterized by: In step 3, the modeling factors were quantitatively screened based on the "geographic detector". The statistical linear relationship can analyze the impact of a single factor on the sand-rich lake fan, but ignores the interaction between multiple factors. Here, the "geographic detector" model is used to analyze the factor detection results, interaction detection results, and ecological detection results, respectively, to optimize the modeling factors.

5. The "source-channel-sink" three-element coupled quantitative analysis technology for lake bottom fan rich sand according to claim 1 is characterized in that: In step 4, the factor detection results of step 3 are coupled with the AHP model to weight the preferred modeling factors, and the weighted overlay is used to establish the sand-rich evaluation model of the lake bottom fan. The map algebra-raster calculator tool is used in ArcGIS to overlay multiple layers and visualize the results.

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