A quantitative prediction method for thin interbedded sandy beach reservoirs

Abstract

The present invention discloses a quantitative prediction method for thin interbedded sand-clast beach reservoirs. The method is based on post-stack seismic data volume data, and the method includes the following steps: analysis of sedimentary lithology combination characteristics of sand-clast beach phase reservoirs; well logging lithology identification, reservoir logging porosity interpretation; drilling synthetic record production, reservoir reflection feature identification; forward model analysis of the relationship between reservoir seismic response characteristics and reservoir thickness; optimal porosity threshold value, so that the statistical high-quality reservoir thickness has a good correlation with the reservoir top amplitude of seismic data; and plane prediction of high-quality reservoir thickness based on the correlation. The present invention can quickly and effectively complete the prediction of the thickness of thin sand body high-quality reservoirs. The method is used to predict the thickness of high-quality reservoirs, and the prediction results are consistent with the actual results. The multiple wells implemented have obtained high-yield industrial gas flow, laying a good foundation for the efficient development of thin beach phase gas reservoirs.

Description

Technical Field

[0001] The invention relates to the technical field of oil and natural gas development, and in particular to a quantitative prediction method for thin interbedded sandy beach reservoirs. Background Art

[0002] The application of the basic theory of seismic reservoir prediction began in the early 20th century. After decades of development, reservoir prediction technology has gradually matured and played an important role in the prediction of reservoir lithology, physical properties and oil content. At present, seismic reservoir prediction has become the leading technology in oil and gas exploration and development, and is constantly developing and improving in practical applications.

[0003] Seismic reservoir prediction mainly predicts reservoir distribution range and reservoir characteristics by analyzing changes in seismic wave parameters such as velocity, amplitude, phase, frequency, and waveform. Spatial changes in lithology thickness, reservoir physical properties, etc., cause corresponding changes in seismic reflection wave velocity, density, wave impedance, amplitude, phase, frequency, waveform, etc. These changes are the main basis for current reservoir seismic prediction.

[0004] As my country's oil and gas exploration deepens, the difficulty of exploration continues to increase. The reservoir classification standard is based on the carbonate reservoir classification standard: Class I reservoir porosity ≥ 12%, Class II reservoir porosity 6-12%, Class III reservoir porosity 2%-6%. Nowadays, most oil and gas exploration targets are Class III thin reservoirs. How to determine the distribution and thickness of thin reservoirs has become a difficulty in current seismic reservoir prediction.

[0005] The dolomite gas reservoir in the sand bank is buried at a depth of more than 4,000 meters. Drilling shows that the thickness of the dolomite reservoir is 5-23 meters, with an average thickness of about 15 meters. The reservoir is thin interlayered, with a single layer thickness of 2-5.5 meters. It is mainly composed of Class III gas layers and has strong heterogeneity. The main frequency of seismic data is about 27Hz, and the minimum resolution of conventional seismic is about 28m. The reservoir thickness is less than the limit resolution, and thin reservoirs are difficult to identify and predict.

[0006] A variety of methods have been tried to predict deep marine thin reservoirs, such as using pre-stack seismic data, post-stack sparse pulse inversion, high-resolution processing, and multi-attribute comprehensive prediction. However, the workload of pre-stack prediction is huge, the calculation time is long, and the prediction results may produce false images due to the influence of data quality. The resolution of post-stack sparse pulse inversion is low, and it is often difficult to strictly distinguish between high-quality reservoirs and surrounding rocks, making it difficult to directly predict high-quality reservoirs. In addition, for thin reservoir data, the effect of improving resolution processing is often poor, and it is easy to produce false images if the resolution is too high. At the same time, multi-attribute prediction is to extract various attributes from the target layer, through neural network learning and training and pattern recognition, and then through optimization and selection of seismic attributes, reservoir thickness prediction and output, the workload is relatively large, and the thin layer attributes may have an impact. In short, for the prediction of deep marine thin reservoirs, the current conventional methods have large calculations, complex processes, and difficult predictions, which are the main problems in the current prediction methods. Summary of the invention

[0007] The purpose of the present invention is to overcome the problem in the prior art that the high-quality dolomite reservoir is thin and the conventional seismic method is not easy to accurately predict the thickness of the high-quality reservoir, and to provide a quantitative prediction method for thin interbedded dolomite reservoirs.

[0008] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0009] A quantitative prediction method for thin interbedded sandy beach reservoirs comprises the following steps:

[0010] S1. Analyze the sedimentary lithology combination characteristics of the sand-clast facies reservoir section of the explored oil and gas wells in the target area to clarify the lithology combination characteristics of the reservoir section;

[0011] S2. Based on the full-well logging data in the target area, four-property relationship analysis is carried out, a multi-mineral model is established and the porosity interpretation of the target layer is carried out; the (residual) dolomite has good physical properties, and the reservoir classification standard is in accordance with the carbonate reservoir classification standard (Class I reservoir porosity ≥12%, Class II reservoir porosity 6-12%, Class III reservoir porosity 2%-6%).

[0012] S3, synthetic record production, clear reflection characteristics of target layer; the reflection characteristics identification refers to the strength, continuity, deformation characteristics, etc. of seismic reflection; synthetic record is a bridge between drilling and logging and seismic. The first step in reservoir prediction is to produce synthetic records, clarify the time position and reflection characteristics corresponding to the standard layer, and the time position and reflection characteristics corresponding to the reservoir; the reservoir development section is thin in thickness, and the reflection with the adjacent thin lithology interface is not obvious, so it can only be characterized as an interlayer section as a whole;

[0013] S4. Analyze the relationship between reservoir seismic response characteristics and reservoir thickness through forward modeling; according to the actual lithology development of the formation, use software to establish the corresponding formation model, input parameters such as density and velocity of each layer, calculate and output the corresponding seismic reflection profile, and analyze the relationship between reservoir seismic attributes such as amplitude, impedance, frequency and reservoir thickness;

[0014] S5. Determine the porosity threshold value to form a correlation between the reservoir thickness and the reservoir top amplitude of the seismic data, and correct the reservoir thickness according to the variation law between the reservoir thickness and the reservoir top amplitude of the seismic data;

[0015] S6. Based on the correlation in step S5, a planar prediction of the thickness of the high-quality reservoir is performed.

[0016] The present invention is based on the lithologic combination characteristics of thin interlayers of explored oil and gas wells, and then further obtains synthetic records through full-well logging data; then, the relationship between reservoir thickness and seismic top amplitude is established through a forward model, and the porosity boundary is determined, so that a correlation is formed between the reservoir thickness and the reservoir top amplitude of the seismic data; the reservoir thickness is corrected according to the variation law between the reservoir thickness and the reservoir top amplitude of the seismic data; thereby, the screening, calculation and determination process of the thickness of the thin interlayer sand-chip beach reservoir is optimized, and the calculation amount of the thickness of the thin interlayer sand-chip beach reservoir is greatly reduced, which not only makes the prediction of the thickness of the thin interlayer sand-chip beach reservoir easier, but also can obtain a more accurate quantitative prediction of the thickness of the thin reservoir at the position to be measured.

[0017] Preferably, in step S2, the four-property relationship analysis includes reservoir lithology, physical properties, oil and gas content and electrical properties of logging curves, and the relationship between the three properties of reservoir lithology, physical properties and oil and gas content and the electrical properties of logging curves is analyzed to establish a logging interpretation model, and carry out lithology identification and porosity interpretation.

[0018] Preferably, the logging curve parameters include gamma curve, sonic curve, neutron curve and density curve. Use natural gamma, sonic, neutron and density curves to establish a multi-mineral logging interpretation model of mineral composition (calcite, dolomite, anhydrite, clay minerals) and porosity (gas and water), and carry out lithology identification and porosity interpretation. The reservoir classification standard is in accordance with the carbonate reservoir classification standard (Class I reservoir porosity ≥ 12%, Class II reservoir porosity 6-12%, Class III reservoir porosity 2%-6%).

[0019] Preferably, in step S3, the synthetic record is finely produced to clarify the seismic reflection positions and corresponding reflection characteristics of various lithologic segments of the target layer, and accurately determine the position of the reservoir in the seismic reflection profile. The reservoir development segment is thin in thickness, and the reflection with the adjacent thin lithologic interface is not obvious, so it can only be characterized as an interlayer segment as a whole. On the seismic reflection profile, the reservoir development segment is located as a whole at the lower edge of the seismic wave crest reflection and the upper edge of the wave trough.

[0020] Preferably, a forward model of target layer thickness change is established based on the thickness of various lithologies in the target layer during actual drilling. In the forward model, the thickness of other rock layers is kept unchanged, and the thickness of the sand-chip reservoir is gradually increased from undeveloped. The law of change of the peak amplitude energy at the top of the sand-chip reservoir is obtained, that is, the change in the strength of the reservoir reflection amplitude energy as the reservoir thickness changes is determined from the forward modeling result diagram.

[0021] According to the actual drilling strata and the development of sand-chip beach reservoirs, a simplified stratigraphic geological model was established. The geological parameters related to different lithologies: rock thickness, longitudinal wave velocity, density and impedance were used to increase the thickness of the sand-chip beach reservoir from no development to 16 m. The inversion results show that the peak amplitude energy at the top of the reservoir gradually weakens from strong to weak as the reservoir thickness increases (the "dark spot" reflection feature).

[0022] Preferably, in step S5, the porosity limit value X is determined between 2% and 6% according to the energy variation law of the peak amplitude at the top of the sandy beach reservoir, and the conventional Class III reservoir is further divided into Class III-1 reservoir and Class III-2 reservoir within the porosity limit value range. The porosity range of the Class III-1 reservoir is X-6%, and the porosity range of the Class III-2 reservoir is 2-X%.

[0023] Preferably, the specific method for determining the porosity threshold is:

[0024] S51, taking the peak layer at the top of the reservoir as the target layer, taking the top and bottom layers of the reservoir as the time window to extract the root mean square amplitude attribute, and calculating the magnitude of the seismic amplitude attribute value near the single well;

[0025] S52, according to the reservoir porosity values ​​of each well interpreted by well logging, the accumulated thickness values ​​of the reservoirs of the single well are respectively calculated according to different value ranges; the thickness values ​​include the thickness values ​​of Class I + Class II reservoirs, the thickness values ​​of Class I + Class II + Class III reservoirs, and the thickness values ​​of Class I + Class II + Class III reservoirs with a porosity between 2% and 6%; the thickness values ​​are regressed and analyzed with the seismic amplitude attribute values ​​near the single well;

[0026] S53. Screen a porosity cutoff value in the range of 2%-6% so that the reservoir cumulative thickness value greater than or equal to the porosity cutoff value has a good negative correlation with the seismic amplitude attribute value.

[0027] Preferably, the thickness plane map of high-quality reservoirs is predicted based on the prediction of the root mean square amplitude attribute of the reservoir top. Based on the correlation formula, the thickness plane map of high-quality reservoirs is predicted based on the prediction of the root mean square amplitude attribute of the reservoir top, and the prediction results are compared and analyzed with the actual drilling situation, and the consistency is high.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] 1. The present invention establishes the relationship between reservoir thickness and seismic top amplitude through the forward model, determines the porosity threshold, forms a correlation between reservoir thickness and seismic data reservoir top amplitude, and corrects the reservoir thickness according to the variation law between reservoir thickness and seismic data reservoir top amplitude; it can quickly and effectively complete the prediction of high-quality reservoir thickness in Class III thin interbedded sandy beaches, greatly reducing the difficulty of exploration and saving drilling costs.

[0030] 2. The reservoir development predicted by the present invention is highly consistent with the actual completion test results, which improves drilling efficiency, improves the accuracy of reservoir prediction, meets the needs of reservoir evaluation, and provides a fast and effective technical means for the exploration and development of oil and gas reservoirs. It has a high prediction consistency rate, a short research cycle, and low cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a schematic diagram of a flowchart of the technical solution of the present invention;

[0032] Figure 2 This is a comparative diagram of well-connected sediments in a certain area of ​​the present invention;

[0033] Figure 3 A well logging interpretation result diagram of a certain area of ​​the present invention;

[0034] Figure 4 It is a well-connected cross-sectional diagram of reservoir seismic response characteristics of the present invention;

[0035] Figure 5 This is a forward model analysis diagram of a sandy beach reservoir in a certain area of ​​the present invention;

[0036] Figure 6 This is the root mean square amplitude attribute distribution diagram of the dolomite reservoir section of the present invention;

[0037] Figure 7 This is a correlation analysis diagram of the high-quality reservoir thickness and the reservoir top amplitude of the seismic data of the present invention;

[0038] Figure 8 This is the plane diagram for predicting the thickness of high-quality reservoirs according to the present invention. DETAILED DESCRIPTION

[0039] The present invention is further described in detail below in conjunction with test examples and specific implementation methods. However, this should not be understood as the scope of the above subject matter of the present invention being limited to the following embodiments, and all technologies realized based on the content of the present invention belong to the scope of the present invention.

[0040] Example 1

[0041] The present invention provides a quantitative prediction method for thin interbedded sandy beach reservoirs. Figure 1 As shown, the specific steps include:

[0042] Step 101: Analyze the sedimentary lithology combination characteristics of the sand-clast facies reservoir section of the explored oil and gas wells in the target area to clarify the lithology combination characteristics of the target layer;

[0043] Step 102: Based on the full-well logging data in the target area, four-property relationship analysis is carried out to establish a multi-mineral model and interpret the porosity of the target layer;

[0044] Step 103: Synthetic record production to clarify the time position and reflection characteristics corresponding to the target layer;

[0045] Step 104: Analyze the relationship between reservoir seismic response characteristics and reservoir thickness by establishing a forward model;

[0046] Step 105: Determine the porosity threshold value, so that a correlation is formed between the reservoir thickness and the reservoir top amplitude of the seismic data, and correct the reservoir thickness according to the variation law between the reservoir thickness and the reservoir top amplitude of the seismic data;

[0047] Step 106: Based on the correlation in step S5, a planar prediction of the high-quality reservoir thickness is performed.

[0048] Specifically, in step 101, the sedimentary lithology combination characteristics of the reservoir development section in a certain area are analyzed, and it is clarified that the reservoir is characterized by "early beach and late gypsum" in the vertical direction, and can be generally divided into two parts, the upper part is the gypsum layer (thickness 40-50m), and the lower part is dolomite (thickness about 30m). Through a microscope, a systematic thin-section lithology identification is carried out on the reservoir development section, and it is clarified that the reservoir lithology is dolomite, mainly (residual) clastic dolomite and powder crystal dolomite. The reservoir mainly develops intra-platform beach, platform flat, lagoon, and inter-beach subfacies, which can be further divided into clastic beach, inter-beach, dolomite flat, gypsum flat, gypsum dolomite flat, gypsum lagoon, mud lagoon (lagoon) and other microfacies. Among them, the favorable sedimentary microfacies is the clastic beach, which is mainly developed in the lower part vertically, such as Figure 2 As shown, the single layer thickness of the reservoir is 1-5m.

[0049] In step 102, based on the standardization of the drilling and logging data of all wells in the area, the four-property relationship analysis is carried out, such as Figure 3As shown. Using natural gamma, sonic waves, neutrons, and density curves, a multi-mineral logging interpretation model of mineral composition (calcite, dolomite, anhydrite, clay minerals) and porosity (gas and water) was established to carry out lithology identification and porosity interpretation. Through quality control analysis, the multi-mineral model evaluation method can meet the accuracy requirements. The interpreted porosity and lithology are consistent with the actual drilling core analysis results, and the reconstructed theoretical logging curves and actual logging records overlap well, which can meet the accuracy requirements. According to the carbonate reservoir classification standard, it is evaluated that the reservoirs in this area are mainly Class III, with a reservoir thickness of 5.6-19m, and an average thickness of about 13m.

[0050] In step 103, the synthetic record production module of the seismic software is used to carry out well seismic calibration for all the wells in the area. The reflection coefficient is calculated by using the acoustic wave and density logging curve, and then the reflection coefficient is convolved with the zero-phase wavelet extracted from the seismic trace beside the well to obtain the initial synthetic seismic record, which is then matched with the seismic trace beside the well and the time-depth relationship is finely adjusted so that the synthetic record and the seismic trace beside the well reach the maximum correlation position to obtain the final synthetic seismic record. Through the fine production of synthetic records, the corresponding position of the reservoir in the well in the seismic reflection profile can be accurately determined, and the seismic reflection characteristics corresponding to the reservoir can be comprehensively analyzed. Figure 4 As shown in the well-linked seismic profile, it can be seen that the reservoir development section in the work area is located as a whole at the lower edge of the seismic wave peak reflection and the upper edge of the wave trough.

[0051] The peak reflection of the upper reservoir and the trough reflection of the lower reservoir have relatively stable waveform characteristics, and the phase axis is continuous, which can be compared and tracked in the whole area. Using the interpretation module of the seismic software, the well-connected seismic profile is first interpreted to build an interpretation framework for the target layer (i.e., the peak of the upper dolomite reservoir and the trough of the lower reservoir), and then on this basis, the entire seismic interpretation work of the target layer in the target work area is completed.

[0052] In step 104, the relationship between reservoir seismic response characteristics and reservoir thickness is analyzed by forward modeling. According to the development of the strata and sand-shoal reservoirs in the area drilled by actual wells, a simplified stratigraphic geological model is established, such as Figure 5 As shown:

[0053] The thickness of the gypsum layer is 40m, and the relevant geological parameters are P-wave velocity 5900m / s, density 2.966g / cm 3 、Impedance 17500m / s·g / cm 3 ;

[0054] The thickness of the dolomite layer is 40m. The relevant geological parameters are:

[0055] Parameters of mud-microcrystalline dolomite: P-wave velocity 6600m / s, density 2.833g / cm 3 、Impedance 18700m / s·g / cm3 ;

[0056] Sandy beach reservoir: P-wave velocity 6150m / s, density 2.78g / cm 3 、Impedance 17100m / s·g / cm 3 The sandy beach reservoir thickens from undeveloped → 4m → 8m → 16m in sequence. The seismic reflection results generated by the above geological model are calculated using the forward modeling module of the seismic software, as shown in the figure below: Figure 5 shown.

[0057] From the reflection characteristics of the forward model, it can be seen that when the sand-chip beach reservoir is undeveloped or underdeveloped, the seismic reflection mainly shows a strong crest reflection feature; when the sand-chip beach is developed and the thickness gradually increases, the seismic reflection crest energy begins to weaken significantly, the frequency becomes lower, and the greater the thickness, the more obvious the weakening of the amplitude energy, which is a "dark spot" reflection feature.

[0058] In step 105, the porosity threshold is further determined. From the forward model analysis results, it can be seen that the thicker the reservoir is, the weaker the peak amplitude energy of the upper reservoir is, and the two have a negative correlation. However, it is necessary to optimize which part of the reservoir has a greater impact on the seismic amplitude energy and a better correlation. The specific process is:

[0059] First, the peak layer in the upper part of the reservoir is taken as the target layer, and the top and bottom layers of the reservoir are taken as the time window to extract the root mean square amplitude attribute, such as Figure 6 As shown in the figure, the weakest part of the amplitude attribute is displayed in the darkest color, and as the amplitude attribute increases, the color becomes lighter, and the strongest part of the amplitude attribute is displayed in the lightest color. The magnitude of the earthquake amplitude attribute value near a single well is counted.

[0060] Secondly, according to the reservoir porosity values ​​of each well interpreted by logging, the cumulative thickness values ​​of the single well reservoirs are counted according to different value ranges, such as the thickness values ​​of Class I + Class II reservoirs, the thickness values ​​of Class I + Class II + Class III reservoirs, the thickness values ​​of Class I + Class II + Class III reservoirs with porosity greater than or equal to a certain value (between 2% and 6%), etc., and the statistical thickness values ​​and the single well amplitude attribute values ​​previously counted are regressed using the data analysis function of Excel, such as Figure 7 As shown, the horizontal axis is the seismic amplitude attribute value, and the vertical axis is the reservoir thickness value.

[0061] Finally, the porosity cutoff value is optimized so that the cumulative thickness value of the reservoir with a porosity greater than or equal to the cutoff value has a good negative correlation with the seismic amplitude attribute value. Through analysis, the porosity cutoff value is determined to be 4%, and the conventional Class III reservoir is further subdivided into Class III-1 reservoirs (porosity of 4-6%) and Class III-2 reservoirs (porosity of 2-4%). ​​High-quality reservoirs are reservoirs with a porosity greater than or equal to 4%, including Class I, Class II and Class III-1 reservoirs. The thickness of high-quality reservoirs has a good negative correlation with the peak amplitude of the reservoir top in seismic data. The resulting correlation formula is:

[0062] y=-0.0085x+10.771, where x is the seismic amplitude attribute and y is the thickness of the high-quality reservoir

[0063] In step 106, the plane prediction of the high-quality reservoir thickness is completed based on the above correlation. The plane map prediction of the high-quality reservoir thickness in the area is carried out using the relevant calculation function modules of the seismic software, such as Figure 8 It is predicted that the high-quality gas reservoir in this area is 6-11m thick, with an average thickness of about 9m, and the high-quality reservoir is mainly developed in the eastern part of the work area.

[0064] At present, the exploration level is constantly deepening, and the difficulty of exploration is constantly increasing. Most reservoirs are Class III thin reservoirs. The exploration of such reservoirs is difficult and the investment cost is high. The quantitative prediction method for thin reservoirs in this application optimizes the screening, calculation and determination process of the thickness of thin interlayer sand-chip beach reservoirs, which greatly reduces the amount of calculation of the thickness of thin interlayer sand-chip beach reservoirs. It can not only make the prediction of the thickness of thin interlayer sand-chip beach reservoirs easier, but also obtain more accurate quantitative prediction data of the thickness of thin reservoirs at the test location. Through comprehensive evaluation and analysis, two development horizontal wells were deployed in the eastern part where high-quality reservoirs are developed. The actual drilling confirmed the reservoir development and the test obtained high-yield industrial gas flow. This also further confirms the superiority of this method.

[0065] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A quantitative prediction method for thin interbedded sandy beach reservoirs, characterized in that: The following steps are involved: S1. Analyze the sedimentary lithology combination characteristics of the sand-clast facies reservoir section of the explored oil and gas wells in the target area to clarify the lithology combination characteristics of the reservoir section; S2. Based on the full-well logging data in the target area, four-property relationship analysis is carried out, a multi-mineral model is established and the porosity interpretation of the reservoir section is performed; S3, synthetic record production, clarifying the time position and reflection characteristics corresponding to the reservoir segment; S4. Analyze the relationship between reservoir seismic response characteristics and reservoir thickness by establishing a forward model; establish a forward model of reservoir thickness change according to the thickness range of the reservoir section, in which the thickness of other rock layers is kept unchanged, and the thickness of the sand-chip reservoir is gradually increased from undeveloped to obtain a peak amplitude energy change diagram of the sand-chip reservoir top; S5. Determine the porosity threshold value to form a correlation between the reservoir thickness and the reservoir top amplitude of the seismic data, and correct the reservoir thickness according to the variation law between the reservoir thickness and the reservoir top amplitude of the seismic data; In the step S5, the porosity threshold value X is determined between 2% and 6% according to the peak amplitude energy variation diagram of the sandy beach phase reservoir top, wherein the value range of X is 2%-6%; the conventional Class III reservoir is further divided into Class III-1 reservoir and Class III-2 reservoir within the porosity threshold range, wherein the porosity range of Class III-1 reservoir is X-6%, and the porosity range of Class III-2 reservoir is 2%-X; The specific method for determining the porosity threshold in step S5 is: S51, taking the peak layer at the top of the reservoir as the target layer, taking the top and bottom layers of the reservoir as the time window to extract the root mean square amplitude attribute, and calculating the magnitude of the seismic amplitude attribute value near the single well; S52, according to the reservoir porosity values ​​of each well interpreted by well logging, the accumulated thickness values ​​of the reservoirs of the single wells are respectively calculated according to different value ranges; the thickness values ​​include the thickness values ​​of Class I + Class II reservoirs, the thickness values ​​of Class I + Class II + Class III reservoirs, and the thickness values ​​of Class I + Class II + Class III reservoirs with a porosity between 2% and 6%; the thickness values ​​are regressed and analyzed with the seismic amplitude attribute values ​​near the single well; S53, selecting a porosity cutoff value in the range of 2%-6% so that the reservoir cumulative thickness value greater than or equal to the porosity cutoff value has a good negative correlation with the seismic amplitude attribute value; S6. Based on the correlation in step S5, a planar prediction of the thickness of the high-quality reservoir is performed.

2. A quantitative prediction method for thin interbedded sandy beach reservoirs according to claim 1, characterized in that: In step S2, the four-property relationship analysis includes the lithology, physical properties, oil and gas content and electrical properties of the well logging curve of the reservoir section. The relationship analysis is performed between the lithology, physical properties and oil and gas content of the reservoir section and the electrical properties of the well logging curve, a well logging interpretation model is established, and lithology identification and porosity interpretation are carried out.

3. A quantitative prediction method for thin interbedded sandy beach reservoirs according to claim 2, characterized in that: Well logging curve parameters include gamma curve, sonic curve, neutron curve and density curve.

4. A quantitative prediction method for thin interbedded sandy beach reservoirs according to claim 1, characterized in that: In step S3, the synthetic record is produced to clearly identify the seismic reflection positions and corresponding reflection characteristics of various lithology segments of the target layer, and accurately determine the position of the reservoir in the seismic reflection profile.

5. The quantitative prediction method for thin interbedded sandy beach reservoir according to claim 1, characterized in that: The value range of X is 4%-5%.

6. A quantitative prediction method for thin interbedded sandy beach reservoirs according to claim 1, characterized in that: Based on the prediction of the RMS amplitude attribute of the reservoir top, the thickness surface map prediction of high-quality reservoirs is carried out.

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