Statistical-based quantitative prediction method for beach facies carbonate reservoirs

Abstract

The application discloses a kind of based on statistics' beach facies carbonate reservoir quantitative prediction method, comprising:1: identifying reservoir development favorable facies zone;2: establish reservoir seismic response mode;3: with waveform classification, qualitative identification is carried out in the low-resistance reservoir development area and high-resistance reservoir development area in reservoir development area;4: respectively obtaining the development thickness of low-resistance reservoir development area and high-resistance reservoir development area;5: respectively obtaining the reservoir parameter of low-resistance reservoir development area and high-resistance reservoir development area;6: according to reservoir parameter and development thickness, average porosity is obtained to realize quantitative prediction to beach facies carbonate reservoir.The beach facies carbonate reservoir of the application is equivalent to a thicker impedance body as a whole, and the whole reservoir is identified by the relative impedance change, as long as the impedance body and the surrounding rock have impedance difference, it can be identified by the waveform change of path integral data, and the technical problem that the existing complex beach facies carbonate reservoir cannot realize accurate reservoir prediction is solved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of reservoir identification of seismic exploration, and particularly relates to a quantitative prediction method for beach facies carbonate reservoirs based on statistics. BACKGROUND

[0002] With the deepening of oilfield exploration and development, and the increasing difficulty, due to the complexity of carbonate rock diagenesis, the beach body has a strong local clouding degree, or a more developed local karst or fracture, resulting in complex reservoir lithology, physical property, and strong heterogeneity. It is necessary to accurately identify the reservoir development degree, so as to avoid risks and further tap the potential of oilfield exploration and development.

[0003] As known, the beach facies carbonate reservoir is a complex carbonate reservoir formed after a set of relatively stable and thin beach facies strata are developed in the carbonate rock deposition background and are reformed by one or more factors such as dissolution and hydrothermal fluid. Dolomite and limestone are mixed in the reservoir, and the dissolution cave, pore and fracture jointly constitute a complex reservoir space, and the reservoir physical property varies greatly in different regions. The reservoir prediction is very difficult because the reservoir is relatively thin, the prestack and poststack reservoir response characteristics are not obvious, and the sidelobe effect of the lithological interface of the overlying and underlying strata also affects the reservoir identification. Moreover, the high resistance and low resistance are mixed in the longitudinal direction of the reservoir, and the lateral heterogeneity is strong, so the seismic identification is very difficult. Therefore, the beach facies carbonate reservoir is a typical complex carbonate reservoir which is difficult to identify.

[0004] The current reservoir quantitative prediction mainly adopts various inversion techniques, specifically including:

[0005] Phase-controlled inversion, which adds a phase control to the conventional inversion, adds a constraint condition to further improve the prediction accuracy. However, this method is relatively good for single-cause reservoirs, but still has limitations for complex beach facies carbonate reservoirs;

[0006] Waveform indication inversion, which fully utilizes the waveform variation law of seismic data, but the specific inversion effect depends on the well calibration. For some areas with less drilling and complex geological conditions, the application effect is also limited;

[0007] Geological statistics inversion, which fully utilizes various elastic parameters and geological information. However, this method requires higher requirements for new control parameters, and needs as many accurate geological parameters as possible. However, some parameters are difficult to obtain, and in the actual production process, it is often difficult to obtain enough and accurate data, which still leads to the failure to accurately predict the beach facies carbonate reservoir. At the same time, this method has a large amount of calculation and slow operation speed, resulting in low prediction efficiency. SUMMARY

[0008] The present application aims at overcoming the above-mentioned problems existing in the prior art, and provides a statistical-based quantitative prediction method for beach facies carbonate reservoirs.

[0009] To achieve the above object, the technical scheme adopted by the present application is as follows:

[0010] A statistical-based quantitative prediction method for beach facies carbonate reservoirs, characterized in that it comprises the following steps:

[0011] Step 1: depicting a pre-deposition palaeogeomorphology map based on the bin integration data of the study area, and identifying a favorable facies belt for reservoir development according to the pre-deposition palaeogeomorphology map;

[0012] Step 2: establishing a reservoir seismic response mode on the bin integration data of the study area through well-seismic fine calibration;

[0013] Step 3: classifying waveforms of the favorable facies belt for reservoir development according to the reservoir seismic response mode, and qualitatively identifying a low-resistance reservoir development zone and a high-resistance reservoir development zone in the reservoir development zone;

[0014] Step 4: respectively obtaining the development thicknesses of the low-resistance reservoir development zone and the high-resistance reservoir development zone according to statistical rules;

[0015] Step 5: respectively obtaining reservoir parameters of the low-resistance reservoir development zone and the high-resistance reservoir development zone according to statistical rules;

[0016] Step 6: obtaining average porosity according to the reservoir parameters and the development thicknesses to realize quantitative prediction of the beach facies carbonate reservoirs.

[0017] In Step 1, the specific depiction method of the pre-deposition palaeogeomorphology map is as follows: first, bin integration calculation is performed according to the conventional seismic data of the study area to obtain bin integration data; then, the interface between the high-speed stratum above the target layer and the argillaceous stratum below is identified on the bin integration data to depict the thickness of the argillaceous stratum, i.e. to obtain the pre-deposition palaeogeomorphology map of the study area.

[0018] In Step 1, the conventional seismic data refer to pre-stack time migration seismic data.

[0019] In step 2, the established reservoir seismic response mode is that low-resistivity reservoirs show a double-peak reflection response mode on the gather data, high-resistivity reservoirs show a strong multiple reflection response mode on the gather data, and no reservoirs show a single-axis strong peak reflection response mode.

[0020] In step 3, the waveform classification refers to using the gather data, taking the top and bottom positions of the target layer as the upper and lower boundaries of the time window, using the unsupervised waveform clustering analysis method based on the adaptive neural network, dividing the similar waveforms of the seismic data in the favorable facies belt of the reservoir development into a class, extracting the typical waveform of each class, comparing the typical waveform with the corresponding response waveform in different reservoir response modes, identifying the reservoir type corresponding to each typical waveform, and finally identifying the high-resistivity reservoir development area, the low-resistivity reservoir development area and the no-reservoir area.

[0021] In step 4, the development thickness is calculated by:

[0022] S1: On the gather data, track the maximum amplitude position of the high-speed stratum overlying the reservoir development area as a reference layer, and calculate the time thickness between the reference layer and the top interface of the argillaceous stratum;

[0023] S2: Intersectively analyze the actual reservoir thickness of all drilled wells in the study area with the time thickness, and statistically obtain the linear relationship between the actual reservoir thickness and the time thickness of different types of reservoirs;

[0024] S3: Obtain the calculation formula of the development thickness according to the statistical linear relationship;

[0025] S4: Calculate the development thickness of the low-resistivity reservoir development area and the high-resistivity reservoir development area according to the formula obtained in S3.

[0026] In step S3, the calculation formula of the development thickness of the low-resistivity reservoir development area is:

[0027] H 发 =f 低阻 (h)

[0028] The calculation formula of the development thickness of the high-resistivity reservoir development area is:

[0029] H 发 =f 高阻 (h)

[0030] In the formula, h is the time thickness, and H 发 is the development thickness.

[0031] In step 5, the reservoir parameter is calculated by:

[0032] S11: Using the trace integral data, the maximum amplitude value in a 10ms window upward from the top boundary of the argillaceous formation is extracted by seismic attribute analysis;

[0033] S22: The actual reservoir parameter of the drilled well is calculated according to the actual reservoir thickness and the average porosity of the drilled well;

[0034] S33: The actual reservoir parameter of the drilled well is intersected and analyzed with the extracted maximum amplitude value, the linear relationship between the actual reservoir parameter and the maximum amplitude value is counted, and the calculation formula of the reservoir parameter in the study area is obtained according to the counted linear relationship;

[0035] S44: The reservoir parameters of the low-resistance reservoir development area and the high-resistance reservoir development area are respectively calculated according to the formula obtained in S33.

[0036] In step S33, the linear relationship between the actual reservoir parameter of the drilled well and the extracted maximum amplitude value is:

[0037] When the time thickness of the underlying argillaceous formation of the reservoir is less than a set value,

[0038] a 实 =g 高地 (x);

[0039] When the time thickness of the underlying argillaceous formation of the reservoir is greater than a set value,

[0040] a 实 =g 斜坡 (x);

[0041] The calculation formula of the reservoir parameter in the study area is obtained according to the counted linear relationship:

[0042] When the time thickness of the underlying argillaceous formation of the reservoir is less than a set value,

[0043] a 研究区 =g 高地 (x)

[0044] When the time thickness of the underlying argillaceous formation of the reservoir is greater than a set value,

[0045] a 研究区 =g 斜坡 (x)

[0046] In the formula, a 实 is the actual reservoir parameter of the drilled well, a 研究区 is the reservoir parameter of the low-resistance reservoir development area and the high-resistance reservoir development area to be calculated in the study area, and x is the maximum peak amplitude value below the top boundary of the target layer.

[0047] In step 6, the calculation method of the average porosity is:

[0048] Por=a研究区 / H 发

[0049] In the formula, Por is the average porosity of the reservoir.

[0050] By adopting the technical scheme, the application has the beneficial technical effects:

[0051] The method is specifically used for quantitative prediction of the beach facies carbonate reservoir, and specifically comprises six steps, wherein,

[0052] Step 1 adopts the trace integral data to depict the pre-deposition paleogeomorphology map and identify the favorable facies belt of the reservoir development, and since the trace integral data is sensitive to the change of the lithology, the lithology interface that is difficult to identify on the conventional seismic data can be more accurately depicted, and the accuracy of the paleogeomorphology depiction is improved. This step realizes a basic facies-controlled identification of the reservoir development area through the paleogeomorphology depiction, and provides an accurate basis for the subsequent quantitative prediction.

[0053] Step 2 establishes the reservoir seismic response mode through well-seismic fine calibration. It should be noted that the difficulty in identifying the complex carbonate reservoir lies in that, firstly, the reservoir is internally mixed by multiple layers of high-resistance and low-resistance reservoirs, and it is difficult to form an effective reservoir response on the conventional seismic data, so that the conventional technology cannot accurately predict the reservoir; meanwhile, the reservoir changes rapidly in the horizontal direction, and the lithology and physical property are greatly different between different wells, and various inversion methods for prediction can only realize the prediction of part of the reservoir. The method equivalently integrates the complex reservoir as a relatively thick impedance body, and identifies the complex reservoir as a whole through the relative impedance change, and the high-resistance or low-resistance reservoir can be macroscopically identified through the waveform change of the trace integral data as long as the impedance body and the surrounding rock have impedance difference, so that the difficulty in identifying the reservoir can be effectively reduced through the establishment of the trace integral data reservoir response mode.

[0054] Step 3 further identifies the reservoir more accurately through waveform classification on the basis of the reservoir prediction of the favorable facies belt in step 1 and the reservoir response mode established in step 2, and the reservoir type (high resistance or low resistance) is divided, which provides a basis for the subsequent quantitative prediction.

[0055] Further, this step adopts the waveform clustering analysis method based on the unsupervised adaptive neural network, which can avoid the interference of human factors, ensure that each type of waveform has good similarity, and the typical waveform is more representative, so that the comparison can ensure that the waveforms of this type conform to the reservoir response mode.

[0056] Step 4 uses statistical rules to obtain the development thickness of low-resistance reservoir development area and high-resistance reservoir development area respectively. Due to the fact that the high resistance and low resistance are often mixed in the longitudinal direction of the reservoir, which is very complex, it is difficult to obtain the thickness of the reservoir. Therefore, the complex reservoir is equivalent to an impedance body, and the maximum envelope surface thickness of the internal impedance change of the purpose layer is extracted from the integral data to obtain the correlation formula of different types of reservoirs by intersecting the reservoir thickness of the known drilling, which is beneficial to realize the accurate calculation of the thickness change of the reservoir.

[0057] Step 5 introduces reservoir parameters according to statistical rules. The difficulty of complex reservoir prediction lies in the fact that the lithology and physical property change greatly, and it is difficult to establish an effective relationship between the geophysical parameters of the reservoir and the physical property of the reservoir. This step introduces geological information, and according to the difference of the reservoir genesis under different landforms, the reservoir is artificially divided into two categories according to the change of the landform, so as to realize the hook between the reservoir parameters and the seismic reflection, thereby realizing the accurate prediction of the reservoir.

[0058] In summary, the present application provides a practical and effective solution to the problem of predicting complex carbonate reservoirs in beach facies. The integral data in the prediction method is simple, fast and easy to obtain, and has the advantages of waveform indication and phase-controlled reservoir prediction, and can fully utilize the seismic data to fully integrate the geological information into the reservoir prediction to realize the accurate prediction of the reservoir, thereby effectively solving the technical problem that the existing complex beach facies carbonate reservoir cannot realize the accurate prediction of the reservoir. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 The flow chart of the present application;

[0060] Figure 2 It is a schematic diagram of the top boundary of the purpose layer of the conventional seismic well profile;

[0061] Figure 3 It is a schematic diagram of the top boundary of the purpose layer of the integral data well profile;

[0062] Figure 4 It is a depicted pre-sedimentation paleogeomorphology map;

[0063] Figure 5 It is a plan view of the qualitatively identified reservoir development area;

[0064] Figure 6 It is a plan view of the time thickness between the reference layer and the top boundary of the argillaceous stratum;

[0065] Figure 7 It is a plan view of the internal maximum wave peak amplitude attribute (amplitude x) of the integral. DETAILED DESCRIPTION

[0066] Example 1

[0067] The embodiment provides a statistical-based quantitative prediction method for beach facies carbonate reservoirs, as shown in the following steps. Figure 1 The embodiment provides a statistical-based quantitative prediction method for beach facies carbonate reservoirs, as shown in the following steps.

[0068] Step 1: Based on the bin integration data of the research area, a pre-deposition palaeogeomorphology map is drawn, and a favorable facies belt for reservoir development is identified according to the pre-deposition palaeogeomorphology map.

[0069] The development of the beach facies carbonate reservoir is controlled by the development degree of the beach body, and the pre-deposition palaeogeomorphology of a high area is a favorable area for the development of the beach body, and the thinner the thickness of the argillaceous stratum at the bottom of the target layer is, the higher the pre-deposition palaeogeomorphology is. Therefore, the specific drawing method of the pre-deposition palaeogeomorphology map is as follows:

[0070] First, conventional seismic data of the research area are obtained, mainly pre-deposition time migration seismic data of the research area. Then, bin integration data are obtained by performing bin integration calculation according to the obtained conventional seismic data. Then, the interface between the high-speed stratum at the upper part of the target layer and the argillaceous stratum at the lower part can be clearly identified on the bin integration data, and the thickness of the argillaceous stratum is drawn, that is, the pre-deposition palaeogeomorphology map of the research area can be accurately obtained.

[0071] Step 2: On the bin integration data of the research area, a reservoir seismic response mode is established through well-seismic fine calibration.

[0072] The reservoir thickness is relatively thin, the lithology and physical property of the reservoir are complex, and the internal high resistance and low resistance are mixed, so the reservoir response on the conventional seismic data is not obvious, and various geophysical parameters have a higher degree of differentiation for the reservoir and non-reservoir, so the effect of the pre-deposition and post-deposition inversion methods can only identify part of the reservoir. The bin integration data represent relative impedance, and the identification is more intuitive, so the high-resistance and low-resistance reservoirs have a certain response on the bin integration data, which can be used as a basis for qualitative identification of the reservoir, and the method is simple and effective.

[0073] Based on this, the method divides the reservoir into two categories according to the different reservoir impedance of the target layer, that is, a low-resistance reservoir with a relatively thick low-resistance reservoir section that can be clearly identified, and a high-resistance reservoir with no obvious impedance reduction compared with the surrounding rock, and the impedance of part of the section is higher than that of the surrounding rock. Therefore, the established reservoir seismic response mode is as follows: the low-resistance reservoir shows a double-peak reflection response mode on the bin integration data, the high-resistance reservoir shows a strong multiple reflection response mode on the bin integration data, and the non-reservoir shows a single-axle strong peak reflection response mode.

[0074] Step 3: According to the reservoir seismic response mode, the waveform of the favorable facies belt for reservoir development is classified, and the low-resistance reservoir development area and the high-resistance reservoir development area in the reservoir development area are qualitatively identified.

[0075] The waveform classification is classified by using trace integration data, taking the top and bottom layer positions of the target layer as the upper and lower boundaries of the time window, using unsupervised waveform clustering analysis method based on adaptive neural network, dividing the similar waveforms of the seismic data in the favorable facies belt of reservoir development into a class, extracting the typical waveform of each class, comparing the typical waveform with the corresponding response waveform in different reservoir response modes, identifying the reservoir type corresponding to each typical waveform, and finally identifying the high-resistivity reservoir development area, low-resistivity reservoir development area and non-reservoir area.

[0076] Step 4: The development thicknesses of the low-resistivity reservoir development area and the high-resistivity reservoir development area are respectively obtained according to statistical rules.

[0077] The development thicknesses of the low-resistivity reservoir development area and the high-resistivity reservoir development area are respectively obtained according to statistical rules.

[0078] S1: The maximum amplitude position of the high-speed stratum of the overlying stratum of the reservoir development area (the upper axis in the biaxial or complex wave reflection) is tracked on the trace integration data as a reference layer, and the time thickness between the reference layer and the top interface of the argillaceous stratum is obtained.

[0079] S2: The actual reservoir thicknesses of all drilled wells in the study area are respectively intersected with the time thickness, and according to the statistics, a linear relationship between the actual reservoir thickness and the time thickness of different types of reservoirs can be obtained, as follows:

[0080] H 实 =f 低阻 (h)

[0081] H 实 =f 高阻 (h)

[0082] In the formula, h is the time thickness, H 实 is the actual reservoir thickness of the drilled well.

[0083] S3: According to the linear relationship obtained in step S2, the calculation formula of the development thickness can be obtained, which is as follows:

[0084] The calculation formula of the development thickness of the low-resistivity reservoir development area is:

[0085] H 发 =f 低阻 (h)

[0086] The calculation formula of the development thickness of the high-resistivity reservoir development area is:

[0087] H 发 =f 高阻 (h)

[0088] In the formula, h is the time thickness, H 发 is the development thickness.

[0089] S4: The formula obtained according to S3 can be used to separately calculate the development thickness of the low-resistance reservoir development area and the high-resistance reservoir development area.

[0090] Step 5: According to statistical rules, the reservoir parameters of the low-resistance reservoir development area and the high-resistance reservoir development area are separately calculated.

[0091] The method for calculating the reservoir parameters is as follows:

[0092] S11: Using the trace integral data, the maximum amplitude value in a 10ms time window upward from the top boundary of the argillaceous formation is extracted through seismic attribute analysis.

[0093] S22: The actual reservoir parameter of the drilled well is calculated according to the product of the actual reservoir thickness and the average porosity of the drilled well.

[0094] S33: The actual reservoir parameter of the drilled well is cross-analyzed with the extracted maximum amplitude value, and according to statistics, it can be concluded that there is a linear relationship between the actual reservoir parameter and the maximum amplitude value as follows:

[0095] When the time thickness of the underlying argillaceous formation of the reservoir is less than a set value,

[0096] a 实 =g 高地 (x);

[0097] When the time thickness of the underlying argillaceous formation of the reservoir is greater than a set value,

[0098] a 实 =g 斜坡 (x).

[0099] In the formula, a 实 is the actual reservoir parameter of the drilled well, and x is the maximum peak amplitude value below the top boundary of the target layer.

[0100] Then, according to the statistical linear relationship, the calculation formula of the reservoir parameter of the study area can be obtained, which is as follows:

[0101] When the time thickness of the underlying argillaceous formation of the reservoir is less than a set value,

[0102] a 研究区 =g 高地 (x)

[0103] When the time thickness of the underlying argillaceous formation of the reservoir is greater than a set value,

[0104] a 研究区 =g 斜坡 (x)

[0105] In the formula, a 研究区 is the reservoir parameter of the low-resistance reservoir development area and the high-resistance reservoir development area to be calculated in the study area.

[0106] S44: The formula obtained according to S33 can be used to obtain the reservoir parameters of the low-resistance reservoir development area and the high-resistance reservoir development area respectively.

[0107] It should be noted that the key of the quantitative description of the reservoir is to associate the reservoir parameters with the geophysical parameters. The trace integration can respond to both the high-resistance reservoir and the low-resistance reservoir, and the fundamental reason is that the reservoir and the surrounding rock have impedance difference, which causes the change of the trace integration. Although the reservoir is very complex and it is difficult to obtain a unified rule, the reservoir can be simplified and regarded as a group impedance body as a whole, and therefore the reservoir parameter a (the product of the average porosity of the reservoir and the thickness of the reservoir) is introduced, which contains the factors affecting the change of the trace integration data, i.e. the thickness of the reservoir and the reservoir property (porosity). It can also be found through the statistics of the actual drilling that the reservoir parameter is directly related to the amplitude of the trace integration. However, due to the complex carbonate reservoir, the reservoir is not subject to the same rule after multiple modifications, but after the introduction of the geomorphology control factor, the reservoir can be identified as multiple rules, the reservoir in the ancient geomorphology highland conforms to an amplitude function, and the reservoir in the ancient geomorphology slope conforms to another amplitude function, and the quantitative description of the reservoir in the research area can be realized by calculating the two regions respectively. This method can be popularized in other complex reservoir areas, and under the constraint of the known reservoir control factor, the complex reservoir which does not conform to a unified geophysical rule can be analyzed.

[0108] Step 6: The average porosity is obtained according to the reservoir parameter and the development thickness to realize the quantitative prediction of the beach carbonate reservoir.

[0109] The calculation method of the average porosity is as follows:

[0110] Por=a 研究区 / H 发

[0111] In the formula, Por is the average porosity of the reservoir.

[0112] In summary, the present application is equivalent to equivalent the whole complex beach carbonate reservoir to a relatively thick impedance body, and the whole complex reservoir is identified through the relative impedance change, and the macroscopic identification can be realized through the waveform change of the trace integration data as long as the impedance body has impedance difference with the surrounding rock. In the prediction method of the present application, the trace integration data are simple, fast and easy to obtain, and the advantages of waveform indication and phase-controlled reservoir prediction are combined, the geological information can be fully integrated into the reservoir prediction on the basis of fully utilizing the seismic data to realize the accurate prediction of the reservoir, and therefore the technical problem that the existing complex beach carbonate reservoir cannot realize the accurate prediction of the reservoir is effectively solved.

[0113] Example 2

[0114] The embodiment selects a set of limestone as the target layer in a certain research area to verify the method described in embodiment 1. Figure 2 is a schematic diagram of the target layer top boundary flattened by the conventional seismic well tie profile in the research area. As can be seen from the diagram, the target layer in the research area forms a strong reflection with the overlying shale formation, the bottom of the target layer is argillaceous limestone formation, and forms a wave peak reflection with the underlying limestone formation. The reservoir develops in the upper high-speed limestone formation inside the target layer. The reflection inside the target layer has no obvious correlation with the reservoir development, so it is difficult to identify the reservoir response through conventional seismic in the research area.

[0115] Step 1: Based on the trace integration data of the research area, a pre-deposition paleogeomorphology map is drawn. Among them,

[0116] Figure 3 is a schematic diagram of the target layer top boundary flattened by the trace integration data well tie profile. Since the trace integration data is sensitive to the change of lithology, the lithological interface can be accurately identified through the trace integration data.

[0117] Figure 4 is the drawn pre-deposition paleogeomorphology map. As can be seen from the diagram, the time thickness of the argillaceous formation greater than 30ms is a paleogeomorphology depression, the time thickness between 25-30ms is a slope, and the time thickness less than 25ms is a paleogeomorphology high.

[0118] Step 2: On the trace integration data of the research area, the reservoir seismic response mode is established through well-to-seismic fine calibration. Specifically, from Figure 3 it can be seen that the low-resistance reservoir shows a double-axle feature on the trace integration data, the high-resistance reservoir shows a strong multiple wave feature on the trace integration, and the single-axle strong wave peak reflection feature shows no reservoir.

[0119] Step 3: According to the reservoir seismic response mode, the wave shape of the favorable facies belt for reservoir development is classified. The change of wave shape can distinguish the type of reservoir. The paleogeomorphology high and slope are the favorable facies belt for reservoir development (the time thickness of argillaceous formation is less than 30ms), and the reservoir development area is obtained. Figure 5 is a plan view of the qualitatively identified reservoir development area. The double-wave peak reflection is a low-resistance reservoir development area, the strong multiple wave reflection is a high-resistance reservoir development area, and the single-axle strong wave peak reflection is no reservoir, which specifically includes an underdeveloped reservoir area and a non-developed reservoir area.

[0120] Step 4: According to the statistical law, the development thickness of the low-resistance reservoir development area and the high-resistance reservoir development area is obtained respectively.

[0121] The reservoir thickness in the research area is controlled by the development degree of the beach, the sedimentary thickness increases with the development of the reservoir, but the total thickness of the high-velocity strata cannot reflect the change of the sedimentary thickness because the top interface of the target layer is eroded, and the development interval of the reservoir is located at the bottom of the high-velocity strata and is not affected by the erosion, and the high-resistance and low-resistance reservoirs have impedance differences with the overlying surrounding rock as a whole.

[0122] Therefore, the maximum amplitude position (the upper axis in the biaxial and complex wave reflection) of the high-velocity strata of the overlying strata of the reservoir development area on the trace integration data is tracked as a reference layer, and the time thickness between the reference layer and the top interface of the argillaceous strata is calculated, Figure 6 The planar graph of the time thickness between the reference layer and the top interface of the argillaceous strata is shown in the figure. The reference layer can be regarded as the stratum center of the high-velocity surrounding rock overlying the Maokou Formation reservoir, and the thickness relationship between the bottom interface of the high-velocity strata and the top interface of the argillaceous strata can approximately reflect the change of the sedimentation of the reservoir interval. However, because the internal impedance of different types of reservoirs is different, the response on the trace integration is also changed, resulting in the change of the position relationship represented by the reference layer, and therefore the thickness relationship needs to be calculated according to the reservoir type. According to the waveform classification results, the rules of the two types of reservoirs are respectively obtained:

[0123] The calculation formula of the development thickness of the low-resistance reservoir development area is:

[0124] H 发 =f 低阻 (h)

[0125] The calculation formula of the development thickness of the high-resistance reservoir development area is:

[0126] H 发 =f 高阻 (h)

[0127] In the formula, h is the time thickness, H 发 is the development thickness.

[0128] Further, the development thickness of a plurality of wells is calculated and the error is verified in the embodiment, as shown in Table 1 below:

[0129] Table 1: Calculation of the thickness and error statistics

[0130]

[0131] Because the number of drilled wells is limited, the statistical rules of the low-impedance reservoir are verified by 3 wells, and the error between the calculation result and the reservoir thickness interpreted by the well logging is less than 1.1 m. The statistical wells of the high-impedance reservoir are 4 wells, and the error between the calculation result and the thickness interpreted by the well logging is less than 1.2 m. Therefore, it can be known that the development thickness of the low-resistance reservoir development area and the high-resistance reservoir development area can be accurately calculated by the method.

[0132] Step 5: The reservoir development in the study area is described by reservoir parameters. Among them Figure 7 is a plan of the internal maximum peak amplitude attribute (amplitude x) of the trace integral, and it is found by statistics of drilled wells that the higher the reservoir development degree, the greater the maximum peak amplitude value (the lower axis of the double-axis trace integral data, the complex wave amplitude) of the trace integral data 30-40 ms below the top boundary of the target layer (as shown in Figure 7 ). The wells located in the paleogeomorphological high (the underlying argillaceous strata time thickness is less than 25 ms), the reservoir in this area is located in the high part of the sedimentary landform, the beach body development degree is high, the clouding degree is high, the porosity reservoir contributes more, and the reservoir parameter and the amplitude value meet the functional relationship:

[0133] When the underlying argillaceous strata time thickness of the reservoir is less than the set value,

[0134] a 研究区 =g 高地 (x)

[0135] while the wells located in the slope area around the high (the argillaceous strata thickness is greater than 25 ms and less than 30 ms), the karstification contributes more to the reservoir formation, and the reservoir parameter and the amplitude value meet another functional relationship:

[0136] When the underlying argillaceous strata time thickness of the reservoir is greater than the set value,

[0137] a 研究区 =g 斜坡 (x)

[0138] In the formula, a 研究区 is the reservoir parameter of the low-resistance reservoir development area and the high-resistance reservoir development area to be obtained in the study area, and x is the maximum peak amplitude value 30-40 ms below the top boundary of the target layer.

[0139] Further, the reservoir parameters of a plurality of wells are calculated and error verification is performed in the embodiment, as shown in the following table 2:

[0140] Table 2: coefficient calculation results and error statistics table

[0141]

[0142] Due to the limited drilled wells, 3 wells are counted in the high position, 3 wells are verified, the error between the calculation results and the calculation parameters of the well interpretation results is less than 7.9; 3 wells are counted in the slope position, and there is no verification well, the error between the calculation results and the calculation parameters of the well interpretation is less than 0.42. It can be known that the method can accurately calculate the reservoir parameters of the low-resistance reservoir development area and the high-resistance reservoir development area.

[0143] Step 6: The reservoir parameters and development thickness are obtained, and the average porosity can be obtained according to the calculation. The specific is shown in the following table 3:

[0144] Table 3 porosity calculation results and error statistics table

[0145]

[0146] From the above table, the carbonate reservoir in the study area is located at more than 5,000 meters underground, the error of the seismic prediction reservoir thickness is less than 1.2m, the error between the average porosity calculation result of the reservoir in the study area and the average porosity interpreted by the well is less than 0.33%, the precision fully meets the requirements of the quantitative prediction of the seismic reservoir, and with the increase of the drilling, the understanding of the rule is more clear, and the prediction result will be further improved.

[0147] The above is only a specific embodiment of the present application, any feature disclosed in the specification can be replaced by other equivalent or similar purpose replacement features unless specifically described, and all features disclosed or all steps in the method or process can be combined in any way except for mutually exclusive features and / or steps.

Claims

1. A statistical-based quantitative prediction method for beach carbonate reservoirs, characterized in that, The method comprises the following steps: Step 1: a pre-deposition palaeogeomorphology map is drawn based on the binning data of the study area, and a favorable facies zone of reservoir development is identified according to the pre-deposition palaeogeomorphology map; Step 2: a reservoir seismic response mode is established on the binning data of the study area through well-seismic fine calibration; Step 3: waveform classification is performed on the favorable facies zone of reservoir development according to the reservoir seismic response mode, and a low-resistance reservoir development zone and a high-resistance reservoir development zone in the reservoir development zone are qualitatively identified; Step 4: the development thicknesses of the low-resistance reservoir development zone and the high-resistance reservoir development zone are respectively calculated according to statistical rules; Step 5: reservoir parameters of the low-resistance reservoir development zone and the high-resistance reservoir development zone are respectively calculated according to statistical rules; Step 6: average porosity is calculated according to the reservoir parameters and the development thicknesses to quantitatively predict the beach facies carbonate reservoirs; In step 5, the reservoir parameters are calculated by the following method: S11: the maximum peak amplitude value in a 10ms time window upward from the top boundary of the argillaceous formation is extracted through seismic attribute analysis using the binning data; S22: the actual reservoir parameters of the drilled wells are calculated according to the actual reservoir thicknesses and average porosities of the drilled wells; S33: the actual reservoir parameters of the drilled wells and the extracted maximum peak amplitude values are analyzed, a linear relationship between the actual reservoir parameters of the drilled wells and the extracted maximum peak amplitude values is obtained, and a calculation formula of the reservoir parameters of the study area is obtained according to the statistical linear relationship; S44: the reservoir parameters of the low-resistance reservoir development zone and the high-resistance reservoir development zone are respectively calculated according to the formula obtained in S33; In step S33, the linear relationship between the actual reservoir parameters of the drilled wells and the extracted maximum peak amplitude values is as follows: When the time thickness of the underlying argillaceous formation of the reservoir is less than a set value, a 实1 = g 高地 (x); When the time thickness of the underlying argillaceous formation of the reservoir is greater than the set value, a 实2 = g 斜坡 (x); According to the statistical linear relationship, the calculation formula of the reservoir parameters of the study area is as follows: When the time thickness of the underlying argillaceous formation of the reservoir is less than a set value, a 研究区1 = g 高地 (x) When the time thickness of the underlying argillaceous formation of the reservoir is greater than the set value, a 研究区2 = g 斜坡 (x) wherein a 实1 is the actual reservoir parameter of the drilled well when the time thickness of the underlying shale formation of the reservoir is less than the set value, a 实2 is the actual reservoir parameter of the drilled well when the time thickness of the underlying shale formation of the reservoir is greater than the set value, a 研究区1 is the reservoir parameter of the low-resistivity reservoir development area and the high-resistivity reservoir development area to be determined in the study area when the time thickness of the underlying shale formation of the reservoir is less than the set value, a 研究区2 is the reservoir parameter of the low-resistivity reservoir development area and the high-resistivity reservoir development area to be determined in the study area when the time thickness of the underlying shale formation of the reservoir is greater than the set value, x is the maximum wave crest amplitude value below the top boundary of the target layer.

2. The method according to claim 1, wherein the method is characterized by: In step 1, the specific drawing method of the pre-deposition palaeogeomorphology map is as follows: binning data is obtained by performing binning calculation on the conventional seismic data of the study area; the interface between the high-speed formation above the target layer and the argillaceous formation below is identified on the binning data, and the thickness of the argillaceous formation is drawn to obtain the pre-deposition palaeogeomorphology map of the study area.

3. The statistical-based quantitative prediction method of the beach facies carbonate reservoir according to claim 2, characterized in that: In step 1, the conventional seismic data refers to pre-stack time migration seismic data.

4. The method according to claim 1, wherein the method is characterized by: In step 2, the established reservoir seismic response mode is as follows: the low-resistance reservoir appears as a double-peak reflection response mode on the binning data, the high-resistance reservoir appears as a strong multiple reflection response mode on the binning data, and the no-reservoir appears as a single-axle strong peak reflection response mode.

5. The method according to claim 1, wherein the method is characterized by: In step 3, the waveform classification refers to: using the trace integration data, taking the top and bottom layer positions of the target layer as the upper and lower boundaries of the time window, using the unsupervised waveform clustering analysis method based on adaptive neural network, dividing the similar waveforms of the seismic data in the favorable facies belt of the reservoir development into a class, extracting the typical waveform of each class, comparing the typical waveform with the corresponding response waveform in different reservoir response modes, identifying the reservoir type corresponding to each typical waveform, and finally identifying the high-resistivity reservoir development area, the low-resistivity reservoir development area and the non-reservoir area.

6. The statistical-based quantitative prediction method of tidal carbonate reservoir according to any one of claims 1-5, characterized in that: In step 4, the method for calculating the development thickness is as follows: S1: Track the maximum amplitude position of the overlying high-speed stratum in the reservoir development area on the trace integration data as a reference layer, and calculate the time thickness between the top interface of the argillaceous stratum and the reference layer; S2: Intersect the actual reservoir thickness of all drilled wells in the study area with the time thickness respectively, and statistically obtain the linear relationship between the actual reservoir thickness and the time thickness of different types of reservoirs; S3: Obtain the calculation formula of the development thickness according to the statistical linear relationship; S4: Calculate the development thickness of the low-resistivity reservoir development area and the high-resistivity reservoir development area respectively according to the formula obtained in S3.

7. The statistical-based quantitative prediction method of tidal carbonate reservoirs according to claim 6, characterized in that: In step S3, the calculation formula of the development thickness of the low-resistivity reservoir development area is: H 发1 =f 低阻 (h) The calculation formula of the development thickness of the high-resistivity reservoir development area is: H 发2 =f 高阻 (h) In the formula, h is the time thickness, H 发1 is the development thickness of the low-resistance reservoir development area 发2 is the development thickness of the high-resistance reservoir development area.

Images