A method for predicting shear wave velocity in complex lithology

Abstract

The present invention discloses a method for predicting the shear wave velocity of complex lithologic formations, which belongs to the field of geophysical exploration technology. Based on basic data that is easily obtained in the study area, the present invention rationally uses core data and logging methods to perform quality control and correction on lithology, and further processes the corrected lithologic data according to the logging interpretation of oil and gas conclusions to construct lithologic data containing fluid information. Then, considering the differences in the vertical sedimentary environment, sedimentary cycle, lithology and physical properties of the formation, the shear wave velocity and longitudinal wave velocity relationship of each type of lithology containing fluid information is established for each reservoir unit, and an array container is constructed to predict the shear wave velocity of complex lithologic formations. The shear wave velocity prediction method provided by the present invention can more accurately predict the shear wave velocity of complex lithologic formations, effectively solving the problem of shear wave velocity prediction of complex lithologic formations. The shear wave velocity prediction method can be further applied to prestack inversion, AVO analysis, fluid prediction, geomechanical parameter calculation and other fields.

Description

Technical Field

[0001] The invention belongs to the field of geophysical exploration and relates to a shear wave velocity prediction method, in particular to a shear wave velocity prediction method for complex lithology. Background Art

[0002] Shear wave velocity is essential for rock physics analysis, prestack inversion, AVO analysis, fluid prediction, and geomechanical parameter prediction. However, due to the high cost of shear wave logging and the economics of logging in actual production, only a few wells in oilfield exploration typically include shear wave data. Therefore, calculating shear wave data using known logging data has become an urgent problem.

[0003] Currently, there are two main types of shear wave velocity prediction methods: one is the empirical formula method, that is, predecessors have obtained empirical formulas for shear wave velocity and compressional wave velocity or other parameters based on various rock physics experimental data or P-wave, shear wave, density and other information of measured data in certain areas. When the P-wave velocity or other parameters of a well are known, the shear wave data of the well is obtained through the empirical formula.

[0004] The other type is to predict shear wave velocity based on rock physics modeling. Since rock properties are primarily influenced by factors such as mineral composition and volume fraction, arrangement, porosity, pore geometry, and fluid distribution, assumptions about pore morphology are made while considering these factors to establish a rock physics model. Data such as the mineral composition, mineral volume percentage, porosity, fluid saturation, and P-wave velocity of a particular well are then input into the rock physics model to predict the shear wave velocity of that well. Currently, in practical applications, in addition to using the empirical formulas summarized by predecessors in the first type of method, there are also examples of shear wave predictions in sandstone and mudstone formations based on measured P- and S-wave data in the study area. However, this is primarily done for the entire study area or based on planar sedimentary facies zoning, establishing P- and S-wave velocity relationships for the entire target layer according to mud content.

[0005] The above two types of shear wave velocity prediction methods have the following problems:

[0006] (1) The empirical formulas summarized by predecessors are mainly based on various types of rock physics experimental data or measured data in certain areas. At present, they are mainly empirical formulas for sandstone and mudstone and water-saturated carbonate rocks. There is no shear wave prediction method suitable for igneous rocks, metamorphic rocks, and carbonate rocks saturated with actual fluids with complex lithology. In addition, the vertical sedimentary environment, lithology, and pore structure of sandstone and mudstone formations vary little, but the lithology of igneous rocks, metamorphic rocks, etc. is more complex and diverse, with large variations in vertical generation environment, lithology, mineral composition, pore type, and pore structure. The overall predicted shear wave velocity correlation coefficient is low, and the prediction accuracy is low. Therefore, the existing empirical formulas and the current shear wave prediction methods based on measured P- and S-wave data of sandstone and mudstone formations cannot effectively predict the shear wave velocity of complex lithology formations such as igneous rocks.

[0007] (2) Currently, there are only rock physics models for sandstone, mudstone, shale, and carbonate rocks. Due to the complex mineral composition and lithology, strong heterogeneity, and diverse pore types of igneous rocks and metamorphic rocks, there has been no available model. In addition, the use of rock physics models to predict shear waves requires data such as ESC element logging. Only after obtaining parameters such as the composition of each mineral in the rock and the volume percentage of each mineral composition can shear wave prediction be carried out. However, the use of ESC element determination in well logging faces problems such as long measurement cycles, high testing costs, large equipment investment, and the need for professional testing personnel. Therefore, in actual production, there is generally little element logging data in the study area or only a few wells have element logging. Given the complex lithology of igneous rocks, carbonate rocks, and metamorphic rocks, it is difficult to obtain data such as mineral composition and mineral volume percentage for wells without element logging data. Therefore, it is impossible to effectively predict the shear wave velocity of complex lithological formations through rock physics modeling.

[0008] Therefore, there is an urgent need for a method with easy-to-obtain input data and high prediction accuracy to effectively predict the shear wave velocity of complex lithologic formations such as igneous rocks, carbonate rocks and metamorphic rocks. Summary of the Invention

[0009] The technical problem to be solved by the present invention is to provide a shear wave velocity prediction method suitable for complex lithologic formations such as igneous rocks, carbonate rocks and metamorphic rocks based on lithologic, well logging and geological data. The input data of this shear wave velocity prediction method is easy to obtain and the prediction accuracy is high, which can effectively solve the problem of shear wave velocity prediction in complex lithologic formations.

[0010] In order to achieve the above object, the technical solution adopted by the present invention is:

[0011] A method for predicting shear wave velocity in complex lithologic formations, comprising the following steps performed in sequence:

[0012] S1. Collect and organize information

[0013] Collect and organize well logging data, geological data, mud logging data and / or well logging interpretation data in the study area, obtain core data, reservoir unit data, well logging curve data and well logging interpretation oil and gas conclusion data in the study area, and obtain preliminary lithologic data;

[0014] S2. Quality Control and Correction of Lithologic Data

[0015] The initially acquired lithologic data are quality controlled and corrected using core data and logging methods to obtain corrected lithologic data;

[0016] S3. Constructing lithologic data containing fluid information

[0017] Based on the fluid information in the well logging interpretation oil and gas conclusion, the well logging interpretation oil and gas conclusion is standardized to obtain a standardized well logging interpretation oil and gas conclusion, which is further combined with the corrected lithologic data to construct lithologic data containing fluid information.

[0018] S4. Processing of known well data to establish the relationship between shear wave velocity and longitudinal wave velocity in known wells

[0019] Based on the reservoir unit data, well logging curve data, and lithologic data containing fluid information, shear wave data, compressional wave data, and lithologic data containing fluid information in known wells are processed at reservoir unit levels of different vertical scales, thereby establishing a relationship between shear wave velocity and compressional wave velocity for lithologic properties of different intervals and different fluid information at reservoir unit levels of different scales in the known wells, and constructing an array container containing the reservoir unit, lithologic properties containing fluid information, and the relationship between shear wave velocity and compressional wave velocity;

[0020] S5. Predicting the shear wave velocity of unknown wells

[0021] The P-wave data in the unknown well are depth-matched according to the corresponding scale reservoir unit level and lithologic data containing fluid information in step S4. After obtaining the reservoir unit and lithologic data containing fluid information at the depth of each sampling point in the P-wave data of the unknown well, the data are matched with the array container described in step S4 to obtain the relationship between the P-wave velocity at different depths of the unknown well and the P-wave and S-wave velocity of the corresponding reservoir unit and the corresponding lithologic data containing fluid information. The S-wave velocity at different depths of different sampling points in the unknown well is calculated, thereby obtaining the S-wave velocity data at all depths of the unknown well. The above steps are then processed in sequence for other wells to predict the S-wave velocity data of other unknown wells.

[0022] As a first limitation of the shear wave velocity prediction method of the present invention, in step S1,

[0023] The well logging curve data includes natural gamma, P-wave acoustic time difference, P-wave velocity, resistivity and density curves, and also includes at least one known well with known shear wave data and P-wave data;

[0024] The reservoir units of different scales are stratigraphic units that characterize different vertical scales, including at least two levels: oil layer groups and sand layer groups, and may also include three levels: oil layer groups, sand layer groups and small layers, or four levels: oil layer groups, sand layer groups, small layers and single sand bodies.

[0025] As a second limitation of the shear wave velocity prediction method of the invention, in step S2, the logging method is based on the logging data and / or logging interpretation data and logging curve data, and obtains the logging lithology identification analysis and logging intersection diagram through summarization, analysis and drawing.

[0026] As a third limitation of the shear wave velocity prediction method of the present invention, in step S3,

[0027] Well logging interpretation of oil and gas conclusions includes oil layer, poor oil layer, oil-water layer, oil-water layer, water layer, coal layer, dry layer, tight layer, gas layer, poor gas layer, gas-water layer, gas-water layer, suspected oil layer and suspected gas layer. Well logging interpretation of oil and gas conclusion data consists of four columns: well name, top depth, bottom depth and oil and gas conclusion.

[0028] The fluid information in the oil and gas conclusion of the well logging interpretation described in this application includes three categories: oil-bearing, gas-bearing and oil-and-gas-free;

[0029] The standardization of the well logging interpretation oil and gas conclusions includes classifying the well logging interpretation oil and gas conclusion data into three categories: oil-containing, gas-containing, and oil-free. That is, the well logging interpretation oil and gas conclusion data are classified into one category, the well logging interpretation oil and gas conclusion data are classified into another category, and the well logging interpretation oil and gas conclusion data are classified into another category.

[0030] The four categories of oil layers, poor oil layers, oil-water layers, and suspicious oil layers are grouped into one category as oil-bearing;

[0031] The four categories of gas layers, poor gas layers, gas-water layers, and suspicious gas layers are grouped into one category as gas-bearing;

[0032] The four categories of dry, tight, water, and coal seams are grouped together as containing no oil or gas;

[0033] Since the oil and gas conclusion of well logging interpretation only interprets reservoirs and does not interpret non-reservoir layers, non-reservoir layers are also classified as containing no oil and gas;

[0034] The combining of the standardized logging interpretation oil and gas conclusions with the corrected lithologic data includes matching the standardized oil and gas interpretation conclusion data with the corrected lithologic data obtained in step S2, that is, matching the top and bottom depths in the corrected lithologic data with the top and bottom depth values ​​in the logging interpretation oil and gas conclusions, so that the corrected lithologic data can indicate fluid information: if the depth contains oil, then "contains oil" is added before the lithologic name corresponding to the depth; if the depth contains gas, then "contains gas" is added before the lithologic name corresponding to the depth; if the depth does not contain oil and gas, then the lithologic name can be directly used; thereby constructing lithologic data containing oil and gas information.

[0035] As a fourth limitation to the shear wave velocity prediction method of the present invention, in step S4,

[0036] The reservoir units of different scales may include two levels: oil layer group and sand layer group; three levels: oil layer group, sand layer group and small layer; or four levels: oil layer group, sand layer group, small layer and single sand body. The reservoir unit data consists of four columns: well name, layer name, layer top depth and layer bottom depth.

[0037] The processing of the shear wave data, the compressional wave data and the lithologic data containing fluid information in the known wells includes sorting the shear wave data and the compressional wave data in the known wells for the reservoir units at the corresponding scale levels in accordance with the vertical sedimentary environment, lithologic changes and heterogeneity of the study area, and then grouping the compressional and shear wave velocity data of the lithologic data containing fluid information in the reservoir units at the corresponding scale levels into one category, and establishing a relationship between the shear wave velocity and the compressional wave velocity of the lithologic data of the different reservoir units and different fluid information in the reservoir units at the corresponding scale levels through least squares fitting;

[0038] The relationship between the shear wave velocity and the longitudinal wave velocity is shown in Formula 1:

[0039] V sij =a ij V pij +b ij 1

[0040] Where V sij is the shear wave velocity corresponding to the lithology of the jth fluid information in the i-th unit, V pij is the P-wave velocity corresponding to the j-th fluid lithology in the i-th unit, a ij 、b ij It is the coefficient of the P-wave and S-wave velocity relationship corresponding to the lithology of the j-th fluid information in the i-th unit.

[0041] As the first limitation of the fourth limitation of the shear wave velocity prediction method described in the present invention, in step S4, the array container also includes the coefficient of the relationship between shear wave velocity and compressional wave velocity, and constructing an array container including reservoir units, lithologies containing fluid information, the relationship between shear wave velocity and compressional wave velocity, and the coefficient of the relationship between shear wave velocity and compressional wave velocity refers to constructing an array container with the number of rows being the number of lithologies containing fluid information under a certain scale reservoir unit and the number of columns being 5, and from the first column to the fifth column, respectively include the reservoir unit, the lithology containing fluid information under the reservoir unit, the relationship between shear wave velocity and compressional wave velocity corresponding to the lithology containing fluid information under the reservoir unit, the corresponding coefficient a of the relationship between shear wave velocity and compressional wave velocity, and the corresponding coefficient b of the relationship between shear wave velocity and compressional wave velocity.

[0042] As a second limitation of the fourth limitation of the shear wave velocity prediction method of the present invention, in step S5, the unknown well is a well for which the P-wave data is known but the S-wave data has not been measured.

[0043] As the first limitation of the fourth limitation of the shear wave velocity prediction method of the invention or a further limitation of the first limitation, in step S5, after obtaining the P-wave velocity at different depths of the unknown well and the P-wave and S-wave velocity relationship equations of the corresponding reservoir units and the corresponding lithology containing fluid information, it also includes obtaining the P-wave and S-wave velocity relationship equation coefficients of the corresponding lithology containing fluid information.

[0044] Due to the adoption of the above technical solution, the present invention has the following technical advancements compared with the prior art:

[0045] 1. The present invention uses easily collected basic data, such as well logging, mud logging, logging interpretation, and geological data, combined with fluid information, to predict shear wave velocity in complex lithologic formations by reservoir unit and lithology. Compared with methods based on rock physics modeling, this shear wave velocity prediction method does not require measurement of indicators such as mineral composition, mineral volume percentage, porosity, and fluid saturation in the study area. The input data of the prediction method provided by the present invention is basic data of the study area, which is easy to obtain. This method is more cost-effective while meeting prediction accuracy.

[0046] ② The shear wave velocity prediction method provided by the present invention is applicable to study areas with complex lithologic formations including igneous rocks, carbonate rocks, metamorphic rocks, etc., where there is one or more wells with known shear wave data in the study area or adjacent areas, and other wells have not undergone shear wave logging. It has a wide range of applications;

[0047] ③ Taking into account the lithologic deviations caused by complex geological conditions such as volcanic rocks, differences in the experience of logging or logging interpretation personnel, and drilling engineering, the present invention corrects the lithologic data based on core data and logging methods including logging lithologic identification analysis and logging crossplots, and establishes P-wave and S-wave velocity relationships based on known wells in the study area. This makes the established P-wave and S-wave velocity relationships for various lithologies more reasonable, and further makes the final predicted S-wave velocity closer to the true value;

[0048] ④ When rocks contain oil and gas, their P- and S-wave velocities will change. This method considers both fluid information and lithology, introduces the oil and gas conclusions from well logging interpretation and combines them with lithology to construct lithology data containing fluid information, thereby improving the accuracy of S-wave prediction.

[0049] ⑤ The present invention considers the differences in vertical sedimentary environment, sedimentary cycle, and lithology, establishes the P-wave and S-wave velocity relationship between each reservoir unit of a known well and the lithology with different fluid information according to a certain scale level of reservoir units, constructs an array container, and realizes the calculation of S-wave velocity of unknown wells by reservoir unit and lithology with different fluid information, thereby improving the accuracy of S-wave prediction results;

[0050] In summary, the shear wave velocity prediction method for complex lithologic formations provided by the present invention is based on well logging data, mud logging, logging interpretation data and geological data that are easily obtained in the study area. By rationally using core data and logging methods to correct lithologic data, and matching the logging interpretation oil and gas conclusions with the corrected lithologic data, lithologic data containing fluid information is constructed. At the same time, considering the differences in vertical sedimentary environments, sedimentary cycles and lithologies, the P-wave and S-wave data are processed according to reservoir units and lithologies containing fluid information, and then P-wave and S-wave velocity relationships are established for lithologies with different reservoir units and different fluid information, and then the S-wave velocity of unknown wells is predicted. This method can more accurately predict the S-wave velocity of complex lithologic formations.

[0051] The present invention provides a method for predicting shear wave velocity in complex lithologic formations, which can be applied to the prediction of shear wave velocity in complex lithologic formations, and further applied to the fields of prestack inversion, AVO analysis, fluid prediction, and geomechanical parameter prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.

[0053] Figure 1 This is a technical flow chart of a method for predicting shear wave velocity in complex lithologic formations according to an embodiment of the present invention;

[0054] Figure 2 This is a characteristic diagram of the igneous rock logging curve of the W1 well of the 4th sand layer group of the Yingcheng Formation in the embodiment of the present invention;

[0055] Figure 3 This is a cross-plot of the Yingcheng Formation cycle 4 logging curves in an embodiment of the present invention;

[0056] Figure 4 This is a lithologic comparison diagram before and after correction of Cycle 4 of the Yingcheng Formation in Well W1 of the Changshen 40 well area in the embodiment of the present invention;

[0057] Figure 5 The intersection diagram and relationship of shear wave velocity and compressional wave velocity for six types of lithology in the known well 4 of the Yingcheng Formation cycle in the embodiment of the present invention are shown;

[0058] Figure 6 The intersection diagram and relationship of shear wave velocity and compressional wave velocity of two types of lithology in the known well of Cycle 3 of the Yingcheng Formation in the embodiment of the present invention are shown;

[0059] Figure 7 The intersection diagram and relationship of shear wave velocity and compressional wave velocity for eight types of lithology in the known well of Cycle 2 of the Yingcheng Formation in the embodiment of the present invention are shown;

[0060] Figure 8 The intersection diagram and relationship of shear wave velocity and compressional wave velocity of three types of lithology in the known well of Cycle 1 of the Yingcheng Formation in the embodiment of the present invention are shown;

[0061] Figure 9 This is a comparison chart of the predicted shear wave velocity and the measured shear wave velocity of the Jingyingcheng Formation in the embodiment of the present invention;

[0062] Figure 10 A bar graph showing the relative error between the predicted and measured shear wave velocities of the Jingyingcheng Formation in the embodiment of the present invention;

[0063] Figure 11 In order to compare the S-wave velocity prediction method, the S-wave velocity and P-wave velocity intersection diagram and relationship of the four types of lithology in the known well of Cycle 4 of the Yingcheng Formation are used;

[0064] Figure 12 This is a bar graph of the relative errors between the predicted and measured shear wave velocities at some sampling points in Cycle 4 of the Yingcheng Formation using two shear wave prediction methods in an embodiment of the present invention. DETAILED DESCRIPTION

[0065] The present invention will be further described in detail below by means of specific embodiments in conjunction with the accompanying drawings. It should be understood that the embodiments described are preferred examples of the present invention and are only used to explain the present invention and are not intended to limit the present invention.

[0066] Example

[0067] This embodiment takes the igneous rock formation research area as an example to provide a shear wave velocity prediction method for complex lithologic formations. The technical flow chart of the shear wave velocity prediction method is as follows: Figure 1 As shown, it specifically includes the following steps performed in sequence:

[0068] S1. Collect and organize information

[0069] Collect well logging data, geological data, mud logging data, and / or logging interpretation data from 18 wells in the Yingcheng Formation (igneous rock formation) in the Changshen 40 well area of ​​the Changling Fault Depression in the Songliao Basin to obtain information on lithology, stratification, and well logging curves in the target area;

[0070] After sorting, it was found that the lithology of each well in the study area was recorded in the mud logging data, and there was no well logging interpretation data or core data; the reservoir unit data had two levels: oil layer group and cycle (sand layer group level);

[0071] It is worth noting that the reservoir unit is a stratigraphic unit that represents different vertical scales. In this embodiment, the reservoir unit includes two levels: oil layer group and sand layer group. However, according to the specific conditions of other study areas, the reservoir unit can also include three levels: oil layer group, sand layer group and small layer, or four levels: oil layer group, sand layer group, small layer and single sand body.

[0072] The target layer in this study area is the Yingcheng Formation (oil layer group level), which is divided into four cycles (sand layer group level) from bottom to top: cycle 1 (xh1), cycle 2 (xh2), cycle 3 (xh3), and cycle 4 (xh4).

[0073] The well logging curve data of the target layer in the study area were obtained, including curves of natural gamma, P-wave acoustic time difference, P-wave velocity, resistivity and density, as well as 4 known wells with known S-wave data and P-wave data.

[0074] S2. Quality Control and Correction of Lithologic Data

[0075] Based on the logging lithology and logging curve data, analyze and summarize the logging curve characteristics of different lithologies, carry out logging lithology identification analysis, and produce logging lithology and logging curve characteristic maps;

[0076] Based on the logging lithology and logging curve data, make logging curve intersection diagrams and analyze the logging curve value ranges of various lithologies;

[0077] The lithologic data of the 18 wells collected in the previous step were quality controlled and corrected based on the above-mentioned logging methods such as the lithologic logging curve characteristic diagram and the logging curve crossplot to obtain the corrected lithologic data;

[0078] Among them, the characteristic diagram of the igneous rock logging curve of the w1 well of the Yingcheng Formation cycle 4 sand layer group is as follows: Figure 2 The range of various lithologic logging parameters for Cycle 4 of the Yingcheng Formation is shown in Table 1. The cross-plot of logging curves for Cycle 4 of the Yingcheng Formation is shown in Table 1. Figure 3 As shown in the figure; the lithologic comparison diagram before and after correction of cycle 4 of Yingcheng Formation in W1 well of Changshen 40 well area is shown in the figure Figure 4 shown.

[0079] Table 1 Range of various lithologic logging parameters for Cycle 4 of the Yingcheng Formation

[0080] Lithology AC (μs / m) GR(API) tuff >200 >84 basalt <195 <84 Dacite <200 >84 volcanic breccia >190 <76

[0081] The detailed table of some lithologic data after quality control and correction for Wells W1 to W4 of the Yingcheng Formation in the Changshen 40 well area is shown in Table 2 below.

[0082] Table 2 Partial lithologic data after quality control and correction of wells W1 to W4 of the Yingcheng Formation in the Changshen 40 well area

[0083]

[0084]

[0085] S3. Constructing lithologic data containing fluid information

[0086] Based on the fluid information in the well logging interpretation oil and gas conclusion, the well logging interpretation oil and gas conclusion is standardized to obtain a standardized well logging interpretation oil and gas conclusion, and the standardized well logging interpretation oil and gas conclusion is combined with the corrected lithologic data, including matching the standardized oil and gas interpretation conclusion data with the corrected lithologic data obtained in step S2, that is, matching the top and bottom depths in the corrected lithologic data with the top and bottom depth values ​​in the well logging interpretation oil and gas conclusion, so that the corrected lithologic data can indicate fluid information: if the depth contains oil, then add "oil-containing" before the lithologic name corresponding to the depth; if the depth contains gas, then add "gas-containing" before the lithologic name corresponding to the depth; if the depth does not contain oil and gas, then directly use the lithologic name; thereby constructing lithologic data containing oil and gas information.

[0087] Among them, the oil and gas conclusions of well logging interpretation include oil layer, poor oil layer, oil-water layer, oil-water layer, water layer, coal layer, dry layer, tight layer, gas layer, poor gas layer, gas-water layer, gas-water layer, suspicious oil layer and suspicious gas layer. The oil and gas conclusion data of well logging interpretation consists of four columns: well name, top depth, bottom depth and oil and gas conclusion.

[0088] The fluid information in the oil and gas conclusion of the well logging interpretation described in this application includes three categories: oil-bearing, gas-bearing and oil-and-gas-free;

[0089] The standardization of the well logging interpretation oil and gas conclusions includes classifying the well logging interpretation oil and gas conclusion data into three categories: oil-containing, gas-containing, and oil-free. That is, the well logging interpretation oil and gas conclusion data are classified into one category, the well logging interpretation oil and gas conclusion data are classified into another category, and the well logging interpretation oil and gas conclusion data are classified into another category. Specifically:

[0090] The four categories of oil layers, poor oil layers, oil-water layers, and suspicious oil layers are grouped into one category as oil-bearing;

[0091] The four categories of gas layers, poor gas layers, gas-water layers, and suspicious gas layers are grouped into one category as gas-bearing;

[0092] The four categories of dry, tight, water, and coal seams are grouped together as containing no oil or gas;

[0093] Since the oil and gas conclusions of well logging interpretation only interpret reservoirs and do not interpret non-reservoir layers, non-reservoir layers are also classified as those that do not contain oil and gas.

[0094] The lithologic data table containing fluid information for wells W1 to W4 of the Yingcheng Formation in the Changshen 40 well area is shown in Table 3 below.

[0095] Table 3 Lithologic data of some fluid information in wells W1 to W4 of the Yingcheng Formation in the Changshen 40 well area

[0096]

[0097]

[0098] S4. Processing of known well data to establish the relationship between shear wave velocity and longitudinal wave velocity in known wells

[0099] Based on the reservoir unit data, well logging curve data and lithologic data containing fluid information, a reservoir unit of a certain scale level is determined according to the vertical sedimentary environment, lithologic changes and heterogeneity of the study area. For the reservoir unit of this scale level, such as the sand layer group level (e.g., divided into sand layer group I, sand layer group II, ...), the depth corresponding to the shear wave velocity or longitudinal wave velocity at each sampling point of each well is compared with the top and bottom depths in the sand layer group level data table of the well to obtain the reservoir unit information corresponding to the depth, such as sand layer group I, sand layer group II, ..., etc. Then, the depth value is matched with the top and bottom depths of the lithologic data containing fluid information obtained in step S3 to obtain the lithologic data containing fluid information corresponding to the depth, such as gas-bearing lithology 1, lithology 2, etc., and the process is repeated in sequence to obtain the reservoir units, lithologic data containing fluid information, P-wave velocity, and S-wave velocity data at all depths of all known wells. Finally, the P-wave and S-wave velocity data are sorted by reservoir unit and lithologic data containing fluid information, and the P-wave and S-wave velocity data of the same unit and lithologic data containing fluid information are grouped together.

[0100] In this embodiment, the reservoir unit includes two levels: oil layer group and sand layer group. Taking the Yingcheng Formation as an example, the oil layer group is divided into four cycles (sand layer group level) from bottom to top, namely cycle 1 (xh1), cycle 2 (xh2), cycle 3 (xh3), and cycle 4 (xh4). The reservoir unit data consists of four columns of data: well name, layer name, layer top depth, and layer bottom depth. The lithology data containing fluid information consists of four columns of data: well name, lithology top depth, lithology bottom depth, and lithology name. Lithologies include tuff, basalt, volcanic breccia, and dacite.

[0101] Among them, the detailed data table of some stratification of wells w1 to w4 at the level of the Yingcheng Formation sand layer in the Changshen 40 well area is shown in Table 4 below;

[0102] The measured P-wave velocity and S-wave velocity data of some wells W1 to W4 of the Yingcheng Formation in the Changshen 40 well area are detailed in Table 5 below;

[0103] The statistical table of measured P-wave velocity and S-wave velocity data of wells W1 to W4 after processing according to reservoir units and lithology containing fluid information is shown in Table 6 below.

[0104] Table 4. Data of some stratifications of wells w1 to w4 at the level of the Yingcheng Formation sand layer in the Changshen 40 well area

[0105]

[0106]

[0107] Table 5 Table of measured P-wave velocity and S-wave velocity data of some wells W1 to W4 of the Yingcheng Formation in the Changshen 40 well area

[0108]

[0109]

[0110] Table 6 Statistics of measured P-wave velocity and S-wave velocity data of wells W1 to W4 after sand layer group and lithology processing containing fluid information

[0111]

[0112]

[0113] processing the shear wave data and compressional wave data of known wells in the logging curve data according to the reservoir units and the lithology containing fluid information, including matching the corresponding depth values ​​of the shear wave velocity and compressional wave velocity curves with the top and bottom depths of the reservoir units at a certain scale level and the top and bottom depth values ​​of the lithology data containing fluid information at that scale level, obtaining the reservoir units, lithology containing fluid information, compressional wave velocity, and shear wave velocity data at each depth, and so on, obtaining the reservoir units, lithology containing fluid information, compressional wave velocity, and shear wave velocity data at all depths of all known wells, sorting the compressional wave velocity data and shear wave velocity data according to the reservoir units and lithology containing fluid information, classifying the compressional and shear wave velocity data of the same unit and lithology with the same fluid information into one category, establishing the shear wave velocity and compressional wave velocity relationship equations of different units and lithologies with different fluid information by least squares fitting, and obtaining the coefficients of the corresponding compressional and shear wave velocity relationship equations;

[0114] The relationship between the shear wave velocity and the longitudinal wave velocity is shown in Formula 1:

[0115] V sij =a ij V pij +b ij 1

[0116] Where V sij is the shear wave velocity corresponding to the jth fluid lithology in the i-th unit, V pij is the P-wave velocity corresponding to the j-th fluid lithology in the i-th unit, a ij 、b ij is the coefficient of the P-wave and S-wave velocity relationship corresponding to the j-th fluid lithology in the i-th unit;

[0117] For example, at the sand layer group level, there are sand layer group I, sand layer group II, etc. If the known wells in sand layer group I include n types of lithology categories, such as lithology 1 containing fluid information, lithology 2 containing fluid information, etc., lithology n containing fluid information, the following relationship is obtained by performing least squares fitting on the P-wave and S-wave velocities of various lithologies. The specific relationship is shown in Equations 2 to 6:

[0118] Lithology of the first sand layer group containing fluid information 1: Vs 11 =a 11 Vp 11 +b 11 2 Lithology of the first sand layer group containing fluid information 2: Vs 12 =a 12 Vp 12 +b 12 3 Lithology of the first sand layer group containing fluid information n: Vs 1n =a 1n Vp 1n +b 1n4 Lithology of the second sand layer group containing fluid information 1: Vs 21 =a 21 Vp 21 +b 21 5 Lithology of the mth sand layer group containing fluid information n: Vs mn =a mn Vp mn +b mn In formula 6, V s11 、V s12 、V s1n are the shear wave velocities corresponding to lithology 1, lithology 2, and lithology n containing fluid information in the first sand group; Vs 21 The shear wave velocity corresponding to lithology 1 containing fluid information in the second sand group; Vs nn The shear wave velocity corresponding to the lithology n containing fluid information in the mth sand group; V p11 、V p12 、V p1n are the longitudinal wave velocities corresponding to lithology 1, lithology 2, and lithology n containing fluid information in the first sand group; Vp 21 P-wave velocity corresponding to lithology 1 containing fluid information in the second sand group; Vp mn The P-wave velocity corresponding to the lithology n containing fluid information in the mth sand group; a 11 、b 11 、a 12 、b 12 、a 1n 、b 1n are the coefficients of the P-wave and S-wave velocity relationship corresponding to lithology 1, lithology 2, and lithology n containing fluid information in the first sand group; a 21 、b 21 are the coefficients of the P-wave and S-wave velocity relationship corresponding to the lithology 1 containing fluid information in the second sand group; a mn 、b mn are the coefficients of the P-wave and S-wave velocity relationship corresponding to the lithology n containing fluid information in the mth sand group.

[0119] Constructing an array container containing reservoir units, lithologies containing fluid information under the reservoir units, corresponding shear wave velocity and compressional wave velocity relationship expressions, and corresponding coefficients of the shear wave velocity and compressional wave velocity relationship expressions, refers to constructing an array container with the number of rows equal to the number of lithologies containing fluid information under reservoir units of a certain scale and the number of columns being 5, wherein the first to fifth columns respectively include reservoir units, lithologies containing fluid information, shear wave velocity and compressional wave velocity relationship expressions, coefficient a of the shear wave velocity and compressional wave velocity relationship expression, and coefficient b of the shear wave velocity and compressional wave velocity relationship expression.

[0120] In this embodiment, wells w1 to w3 are regarded as known wells for establishing the relationship between the P- and S-wave velocities, and well w4 is treated as an unknown well. Well w4 is also a verification well.

[0121] From the P-wave and S-wave velocity data of various igneous rocks at the sand layer group level (cycle) in the previous step, obtain the P-wave and S-wave velocities of various igneous rocks at the sand layer group level in three known wells (wells w1 to w3);

[0122] According to geological data, the Changshen 40 well area is divided into four cycles from bottom to top. Cycle 4 contains four types of lithologies: tuff, dacite, basalt, and volcanic breccia. Tuff and volcanic breccia contain gas reservoirs. Least squares fitting was performed on the P- and S-wave data of the six types of lithologies containing fluid information in Cycle 4. The P- and S-wave velocity relationship equations for these six types of lithologies containing fluid information in Cycle 4 were obtained. The specific relationship equations are shown in Equations 7 to 12.

[0123] Tuff: y=0.610515x-349.524 7

[0124] Dacite: y = 0.672857x - 496.11 8

[0125] Basalt: y = 0.611306x - 278.264 9

[0126] Volcanic breccia: y = 0.601545x - 244.962 10

[0127] Gas-bearing volcanic breccia: y = 0.57483x - 144.385 11

[0128] Gas tuff: y=0.550877x-104.429 12

[0129] By analogy, the relationship between the P-wave and S-wave velocities of various lithologies in Cycle 3, Cycle 2, and Cycle 1 can be calculated. Figure 5 、 Figure 6 、 Figure 7 、 Figure 8 The intersection diagrams and relationship equations of shear wave velocity and compressional wave velocity for the lithology containing fluid information of various igneous rocks in known wells of Cycle 4, Cycle 3, Cycle 2, and Cycle 1 of the Yingcheng Formation are shown respectively.

[0130] An array container with 19 rows and 5 columns is constructed, including the sand layer group level, the lithology of the fluid-containing information of each cycle at the sand layer group level, the corresponding shear wave velocity and compressional wave velocity relationship, the coefficient a corresponding to the shear wave velocity and compressional wave velocity relationship, and the coefficient b corresponding to the shear wave velocity and compressional wave velocity relationship. Part of the data of the array container is as follows:

[0131]

[0132] S5. Predicting the shear wave velocity of unknown wells

[0133] Performing depth matching on the P-wave data in the unknown well according to the corresponding scale reservoir unit level and the lithologic data containing fluid information in step S4 to obtain the reservoir unit and lithologic data containing fluid information at the depth of each sampling point of the P-wave data of the unknown well. Then, based on the reservoir unit and lithologic data containing fluid at the depth of each sampling point of the P-wave velocity, the P-wave and S-wave velocity relationship formulas and coefficients of the corresponding reservoir unit and the corresponding lithologic data containing fluid information are obtained from the array container in step S4. Then, based on the P-wave velocity at each depth of the unknown well and the P-wave and S-wave velocity relationship formulas and coefficients of the corresponding reservoir unit and the corresponding lithologic data containing fluid information, the S-wave velocity at each sampling point of the unknown well is calculated, thereby obtaining the S-wave velocity data at all depths of the unknown well.

[0134] The above steps are then processed for other wells in turn to predict the shear wave velocity data of other wells with unknown shear wave data.

[0135] The depth values ​​corresponding to the P-wave velocity curve of the unknown well (well W4) in the study area are compared with the top and bottom depths of the reservoir unit at the corresponding scale level and the top and bottom depths of the lithologic data containing fluid information in step S4 to obtain the reservoir unit, lithologic data containing fluid information, and P-wave velocity data at each depth, as specifically shown in the data section of well W4 in Tables 2 to 5. Then, the P-wave and S-wave velocity relationship equations and coefficients corresponding to the reservoir unit and lithologic data containing fluid information at each depth of the P-wave velocity of the unknown well are obtained from the array container of 19 rows and 5 columns in step S4. Then, the S-wave velocity of the unknown well is calculated based on the P-wave velocity at each depth of the well and the P-wave and S-wave velocity relationship equations and coefficients corresponding to the reservoir unit and lithologic data containing fluid information, thereby obtaining the S-wave velocity data at different depths of the unknown well.

[0136] The statistical table of measured and predicted shear wave velocity data for some of the W4 wells in the Yingcheng Formation in the Changshen 40 well area is shown in Table 7 below.

[0137] Table 7 Statistics of measured and predicted shear wave velocities of some parts of Well W4 of Yingcheng Formation in Changshen 40 well area

[0138]

[0139]

[0140] In this embodiment, a known well (w4) that is not involved in the establishment of the P-wave and S-wave velocity relationship in step S4 is used as a verification well. The predicted S-wave velocity of the well is calculated according to the S-wave velocity prediction method provided by the present invention. The predicted S-wave velocity of the verification well is further compared with the measured S-wave velocity. Figure 9 To verify the comparison between the predicted and measured shear wave velocities of the Jingyingcheng Formation, Figure 9Where Vp is the measured longitudinal wave velocity, Vs is the measured shear wave velocity, and Vs_cal is the shear wave velocity predicted by the prediction method of the present invention. Figure 9 It can be seen from the figure that the trends of the predicted shear wave velocity and the measured shear wave velocity are basically consistent and the values ​​are close.

[0141] This embodiment also calculates the relative error between the predicted shear wave velocity and the measured shear wave velocity of the verification well Yingcheng formation. The relative error bar graph is shown in the figure below. Figure 10 As shown. Figure 10 It can be seen that the relative error between the predicted shear wave velocity and the measured shear wave velocity of the verification well is within 0.08, mainly concentrated in the range of 0.04, indicating that the overall error is small and basically normally distributed, further demonstrating that the shear wave velocity prediction method provided by the present invention based on lithology based on reservoir units and fluid content information can achieve effective prediction of shear wave velocity in complex lithologic formations.

[0142] In this example, the shear wave velocity prediction method based on reservoir units and lithology without considering fluid information and the shear wave velocity prediction method for complex lithology formations proposed by the present invention are compared with the measured shear wave velocity to evaluate the prediction accuracy of the two shear wave velocity prediction methods. The specific methods are as follows:

[0143] (1) Establishing a comparative shear wave velocity prediction method

[0144] A shear wave velocity prediction method based on reservoir units and lithology (without considering fluid information) is established as a comparative shear wave velocity prediction method. The process of establishing the comparative shear wave velocity prediction method is basically the same as the process of establishing the shear wave velocity prediction method for complex lithology formations mentioned above. The only difference is that:

[0145] A. The establishment of the comparative shear wave velocity prediction method does not include step S3. Constructing lithologic data containing fluid information;

[0146] B. When establishing a comparative shear wave velocity prediction method, in step S4, the shear wave data, compressional wave data, and corrected lithologic data of a known well are processed to establish a relationship between shear wave velocity and compressional wave velocity for different intervals and lithologies at different reservoir unit levels of the known well;

[0147] Among them, by comparing the shear wave velocity prediction method, the shear wave velocity and compression wave velocity intersection diagram and relationship of the four types of lithology in the known well of Yingcheng Formation Cycle 4 are as follows: Figure 11 shown.

[0148] (2) Comparison of the relative errors between the predicted S-wave velocities and the measured S-wave velocities by the two S-wave velocity prediction methods

[0149] A number of sampling points of the Yingcheng Formation of the verification well (w4) were randomly selected, and the shear wave velocity prediction method and the comparative shear wave velocity prediction method of the complex lithologic formation proposed in the present invention were respectively applied to predict the shear wave velocity of the sampling points. The data were recorded and summarized, and a relative error comparison diagram of the shear wave velocity predicted by the two prediction methods and the measured shear wave velocity was drawn. Among them, 25 sampling points in the "volcanic breccia" and "tuff" of the Yingcheng Formation cycle 4 of the randomly selected verification well (w4) were compared, and the relative error comparison diagram of the shear wave velocity obtained by the two shear wave velocity prediction methods and the measured shear wave velocity was drawn as shown in FIG. Figure 12 shown.

[0150] Depend on Figure 12 As can be seen, the relative error for Series 1 is significantly higher than that for Series 2. Specifically, the relative error between the predicted S-wave velocity using the "comparative S-wave velocity prediction method" and the measured S-wave velocity is significantly higher than the relative error between the predicted S-wave velocity and the measured S-wave velocity using the "S-wave velocity prediction method for complex lithologic formations" proposed in this invention. This further demonstrates that the S-wave velocity prediction method for complex lithologic formations proposed in this invention significantly improves prediction accuracy compared to S-wave velocity prediction methods based solely on reservoir units and lithology without considering fluid information.

[0151] The shear wave velocity prediction method for complex lithologic formations proposed in the present invention can be further applied to prestack inversion, AVO analysis, fluid prediction, geomechanical parameter prediction and other fields.

[0152] The above description is merely an optional embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present disclosure shall be included in the scope of protection of the present disclosure.

Claims

1. A method for predicting shear wave velocity in complex lithologic formations, characterized in that: The shear wave velocity prediction method includes the following steps performed in sequence: S1. Collect and organize information Collect and organize well logging data, geological data, mud logging data and / or well logging interpretation data in the study area, obtain core data, reservoir unit data, well logging curve data and well logging interpretation oil and gas conclusion data in the study area, and obtain preliminary lithologic data; S2. Quality Control and Correction of Lithologic Data The initially acquired lithologic data are quality controlled and corrected using core data and logging methods to obtain corrected lithologic data; S3. Constructing lithologic data containing fluid information Based on the fluid information in the well logging interpretation oil and gas conclusion, the well logging interpretation oil and gas conclusion is standardized to obtain a standardized well logging interpretation oil and gas conclusion, and the standardized well logging interpretation oil and gas conclusion is further combined with the corrected lithologic data to construct lithologic data containing fluid information; S4. Processing of known well data, establishing the relationship between shear wave velocity and longitudinal wave velocity in the known well, and then constructing an array container Based on the reservoir unit data, well logging curve data, and lithologic data containing fluid information, shear wave data, compressional wave data, and lithologic data containing fluid information in known wells are processed at reservoir unit levels of different vertical scales, thereby establishing a relationship between shear wave velocity and compressional wave velocity for lithologic properties of different intervals and different fluid information at reservoir unit levels of different scales in the known wells, and constructing an array container containing the reservoir unit, lithologic properties containing fluid information, and the relationship between shear wave velocity and compressional wave velocity; S5. Predicting the shear wave velocity of unknown wells Performing depth matching on the P-wave data in the unknown well according to the corresponding scale reservoir unit level and the lithology data containing fluid information in step S4, obtaining the reservoir unit and lithology data containing fluid information at the depth of each sampling point in the P-wave data of the unknown well, and then matching them with the array container described in step S4, obtaining the relationship between the P-wave velocity at different depths of the unknown well and the P-wave and S-wave velocity of the corresponding reservoir unit and the corresponding lithology containing fluid information, and calculating the S-wave velocity at the depth of each sampling point in the unknown well; Wherein, in step S4, the reservoir unit includes at least two levels: oil layer group and sand layer group; The processing of the shear wave data, the longitudinal wave data, and the lithologic data containing fluid information in the known wells includes determining reservoir units of different scale levels based on the vertical sedimentary environment, lithologic changes, and heterogeneity of the study area, sorting the shear wave data and the longitudinal wave data in the known wells for the reservoir units of the corresponding scale levels in combination with the lithologic data containing fluid information, thereby classifying the longitudinal and shear wave velocity data of the lithologic data of the same reservoir unit and the same fluid information into one category, and establishing a relationship between the shear wave velocity and the longitudinal wave velocity of the lithologic data of different reservoir units and different fluid information by least squares fitting; The relationship between the shear wave velocity and the longitudinal wave velocity is shown in Formula 1: V sij =a ij V pij +b ij 1 Where V sij is the shear wave velocity corresponding to the jth lithology containing fluid information in the i-th unit, V pij is the P-wave velocity corresponding to the jth lithology containing fluid information in the i-th unit, a ij 、b ij It is the coefficient of the P-wave and S-wave velocity relationship corresponding to the lithology of the j-th fluid information in the i-th unit.

2. The method for predicting shear wave velocity in complex lithologic formations according to claim 1, characterized in that: In step S1, The well logging curve data includes natural gamma, P-wave acoustic time difference, P-wave velocity, resistivity and density curves, and also includes at least one known well with known shear wave data and P-wave data; The reservoir unit includes at least two levels: oil layer group and sand layer group.

3. The method for predicting shear wave velocity in complex lithologic formations according to claim 1, characterized in that: In step S2, the well logging method is to obtain well logging lithology identification analysis and well logging cross plot based on well logging data and / or well logging interpretation data and well logging curve data through summarization, analysis and plotting.

4. The method for predicting shear wave velocity in complex lithologic formations according to claim 1, characterized in that: In step S3, the fluid information in the oil and gas conclusion of the well logging interpretation includes three categories: oil-containing, gas-containing, and no oil and gas; The standardization of the oil and gas conclusions of well logging interpretation includes classifying the oil and gas conclusion data of well logging interpretation into three categories: oil-bearing, gas-bearing and oil-free; The combining of the standardized logging interpretation oil and gas conclusions with the corrected lithologic data includes matching the standardized oil and gas interpretation conclusion data with the corrected lithologic data obtained in step S2, so that the corrected lithologic data can indicate fluid information, thereby constructing lithologic data containing oil and gas information.

5. The method for predicting shear wave velocity in complex lithologic formations according to any one of claims 1 to 4, characterized in that: In step S4, the array container also includes coefficients of the relationship between shear wave velocity and longitudinal wave velocity.

6. The method for predicting shear wave velocity in complex lithologic formations according to any one of claims 1 to 4, characterized in that: In step S5, the unknown well is a well for which the longitudinal wave data is known but the shear wave data has not been measured.

7. The method for predicting shear wave velocity in complex lithologic formations according to claim 6, characterized in that: In step S5, after obtaining the P-wave velocity at different depths of the unknown well and the P-wave and S-wave velocity relationship equations of the corresponding reservoir units and the corresponding lithology containing fluid information, it also includes obtaining the P-wave and S-wave velocity relationship equation coefficients of the corresponding lithology containing fluid information.

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