Quantitative sensitive rock physical parameter optimization and lithology classification method and system, electronic equipment and storage medium

Abstract

The invention provides a quantitative sensitive rock physical parameter optimization and lithology classification method and system, electronic equipment and a storage medium. The method comprises the following steps: determining a rock physical analysis target layer section, and removing an abnormal value of logging data of the target layer section; lithology classification indexes are set for the logging data of the target layer section after the abnormal values are removed; performing normalization processing on the different types of logging data; grouping the normalized logging data and calculating a lithologic overlapping coefficient; selecting an optimal lithology distinguishing data pair according to the lithology overlapping coefficient; performing optimal classification on the lithology by using the optimal lithology distinguishing data, wherein the trend line with the minimum misjudgment rate is the optimal classification line; a real optimal lithology classification formula is obtained through reverse normalization; and carrying out lithology classification on the seismic data volume by using the optimal lithology classification formula. According to the method, the optimization of the physical parameters of the sensitive rock is realized, and the purposes of eliminating man-made interference and realizing the optimal classification of lithology are achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of petrophysical analysis methods for seismic data in the field of oil exploration, and particularly relates to a method, system, electronic device and storage medium for quantitatively optimizing sensitive petrophysical parameters and lithology classification. Background Art

[0002] Currently, petrophysical analysis studies the behavior and physical and chemical properties of reservoir rocks in different environments such as on the surface and underground. It includes both the characteristics of reservoir rocks themselves and their interactions with oil, gas, and water. It is a comprehensive discipline mainly used to describe the physical properties of porous geological bodies, the interactions between the pore surfaces of rocks in different fluid domains, and the basic principles of pore size distribution within the pore medium. Petrophysical analysis involves theoretical knowledge of well logging, geophysical prospecting, and reservoir engineering, and is an important technical means for understanding reservoirs in the oil and gas exploration and development industry. As the reservoir targets for exploration and development gradually shift from conventional reservoirs to complex clastic reservoirs, marine and continental carbonate reservoirs, unconventional reservoirs such as tight oil and gas and shale oil and gas, the role of petrophysical analysis as an important technology has become increasingly prominent.

[0003] In actual seismic interpretation production, petrophysical parameter analysis is commonly used in lithology identification, pore fluid identification, and reservoir quantitative prediction. In seismic exploration reservoir prediction, lithology prediction is one of the contents of reservoir prediction. Within a seismic sequence, there are different sedimentary facies belts. Due to different sedimentary environments, there will be significant differences in lithological parameters, including the composition of rocks, the size and shape of particles, the degree of cementation, porosity, the fluid composition and saturation in pores, temperature, pressure, sedimentary thickness, etc. Changes in lithology cause changes in elastic parameters, including elastic modulus, density, velocity, Poisson's ratio, absorption characteristics, etc.

[0004] The determination of lithology plays an important role in the physical property prediction of reservoirs. Traditional seismic lithology classification is mainly achieved through prestack elastic parameter inversion. Based on the sensitivity analysis of reservoir petrophysical parameters, sensitive parameters are optimized, and multi-dimensional crossplot analysis is used to exclude mudstone and tight sandstone. It plays an important role in studying the changes in rock porosity and pore fluid properties, quantitatively predicting the thickness of effective reservoirs, porosity distribution, and identifying the fluid-bearing properties of reservoirs. However, for the problem of lithology classification, it is often difficult to select a set of elastic parameters that are sensitive to different lithology categories; at the same time, during the crossplot analysis process, the accuracy of lithology classification will be affected by human subjective factors.

[0005] The above technical problems need to be solved urgently. Summary of the Invention

[0006] In order to solve the above technical problems, the present invention proposes a technical solution of a quantitative sensitive rock physical parameter optimization and lithology classification method, system, electronic equipment and storage medium to solve the above technical problems.

[0007] The first aspect of the present invention discloses a method for quantitative sensitive rock physical parameter optimization and lithology classification, the method comprising:

[0008] Step S1, determining the target layer section for rock physical analysis, and removing abnormal values ​​of logging data of the target layer section;

[0009] Step S2, setting lithology classification indexes for the logging data of the target layer after removing outliers;

[0010] Step S3, normalizing different types of logging data;

[0011] Step S4, grouping the normalized logging data to calculate the lithology overlap coefficient;

[0012] Step S5, selecting the optimal lithology distinction data pair according to the lithology overlap coefficient;

[0013] Step S6, using the optimal lithology differentiation data to optimally classify the lithology, the trend line with the smallest misjudgment rate is the optimal classification line;

[0014] Step S7, obtaining the true optimal lithology classification formula through inverse normalization;

[0015] Step S8: Use the optimal lithology classification formula to classify the lithology of the seismic data volume.

[0016] According to the method of the first aspect of the present invention, in step S2, the method of setting lithology classification indexes for the well logging data of the target layer after removing outliers includes:

[0017] In order to find the optimal logging data type to distinguish lithology for intersection analysis, we first select multiple types of logging data and name them c1, c2, c3, c4, c5…cn respectively; we select the logging gamma value as the lithology classification index, and the gamma value greater than M is lithology 1, and the gamma value less than M is lithology 2.

[0018] According to the method of the first aspect of the present invention, in step S3, the method for normalizing different types of logging data includes:

[0019] Assume that there are n types of logging data involved in the selection of sensitive parameters. Different types of logging data are denoted as c1, c2, c3, c4, c5…cn. The normalized result of data ci is denoted as ai. The formula for ai is:

[0020]

[0021] Among them, i is 1, 2, 3, 4... n, max(ci) is the maximum value of the logging data of type i, and min(ci) is the minimum value of the logging data of type i.

[0022] According to the method of the first aspect of the present invention, in step S4, the method for calculating the lithology overlap coefficient by grouping the normalized logging data includes:

[0023] The normalized logging data a1, a2, a3, a4, a5... an are divided into pairs two by two for all permutations and combinations, forming a total of N pairs of data, where Calculate the lithology overlap coefficient of each pair of data.

[0024] According to the method of the first aspect of the present invention, in step S5, the method for selecting the optimal lithology discrimination data pair according to the lithology overlap coefficient includes:

[0025] Sort the lithology overlap coefficients of the N pairs of data pairs from small to large, and the data pairs are denoted as P1, P2... P N ; if there is a data pair with a lithology overlap coefficient of zero, take this data pair as the optimal lithology discrimination data pair P opt ; if there is no data pair with a lithology overlap coefficient of zero, take the data pair with the smallest lithology overlap coefficient as the optimal lithology discrimination data pair P opt ; take the optimal lithology discrimination data pair P opt The corresponding two types of logging data are named P o1 , P o2 .

[0026] According to the method of the first aspect of the present invention, in step S7, the method for obtaining the true optimal lithology classification formula through denormalization includes:

[0027] Denormalize the two types of data in the optimal data pair and substitute them into the optimal boundary formula to obtain the true optimal lithology classification formula; the denormalization formula according to formula (1) is

[0028] ci = ai[max(ci) - min(ci)] + min(ci) (26)

[0029] The two types of logging data for obtaining the optimal lithology classification formula are the most sensitive lithology classification parameters for the target interval.

[0030] According to the method of the first aspect of the present invention, in step S8, the method for lithology classification of seismic data volume using the optimal lithology classification formula includes:

[0031] The most sensitive lithology classification parameter data volume is obtained by using the seismic inversion method for the three-dimensional seismic data volume, and the optimal lithology classification formula is substituted into the sensitive parameter data volume to obtain the lithology data volume in three-dimensional space.

[0032] The second aspect of the present invention discloses a quantitative sensitive rock physical parameter optimization and lithology classification system, the system comprising:

[0033] The first processing module is configured to determine a target layer section for petrophysical analysis and remove abnormal values ​​of well logging data of the target layer section;

[0034] The second processing module is configured to set a lithology classification index for the well logging data of the target layer after removing the outliers;

[0035] A third processing module is configured to perform normalization processing on the different types of logging data;

[0036] A fourth processing module is configured to group the normalized logging data and calculate the lithology overlap coefficient;

[0037] A fifth processing module is configured to select an optimal lithology differentiation data pair according to the lithology overlap coefficient;

[0038] The sixth processing module is configured to use the optimal lithology differentiation data to optimally classify the lithology, and the trend line with the smallest misjudgment rate is the optimal classification line;

[0039] The seventh processing module is configured to obtain a true optimal lithology classification formula through inverse normalization;

[0040] The eighth processing module is configured to perform lithology classification on the seismic data volume using the optimal lithology classification formula.

[0041] The third aspect of the present invention discloses an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps in any one of the quantitative sensitive rock physical parameter optimization and lithology classification methods in the first aspect of the present disclosure are implemented.

[0042] The fourth aspect of the present invention discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the quantitative sensitive rock physical parameter optimization and lithology classification methods in the first aspect of the present disclosure.

[0043] It can be seen that the solution proposed by the present invention, based on well logging data, proposes a method for quantitatively optimizing sensitive rock physical parameters and lithology classification. First, the lithology overlap coefficient is calculated by grouping well logging data. Then, the optimal lithology discrimination data pair is selected according to the lithology overlap coefficient. Third, the optimal lithology classification is performed on the lithology using the optimal lithology discrimination data pair to obtain the optimal classification line. At the same time, the misjudgment rate can be used to verify the classification accuracy of the optimal classification line. Finally, the seismic data volume is classified by the optimal lithology classification formula.

[0044] In summary, the solution proposed by the present invention realizes the optimization of sensitive rock physical parameters, achieves the purpose of excluding human interference, and realizes the optimal lithology classification. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0046] Figure 1 FIG. is a flowchart of a method for quantitatively optimizing sensitive rock physical parameters and lithology classification according to an embodiment of the present invention;

[0047] Figure 2 FIG. is a flowchart of the operation of a method for quantitatively optimizing sensitive rock physical parameters and lithology classification according to an embodiment of the present invention;

[0048] Figure 3 FIG. is a schematic diagram of the lithology overlap coefficient according to an embodiment of the present invention;

[0049] Figure 4 FIG. is a flowchart for obtaining the optimal lithology classification trend line when the lithology overlap coefficient is zero according to an embodiment of the present invention;

[0050] Figure 5 FIG. is a flowchart for obtaining the optimal lithology classification trend line when the lithology overlap coefficient is not zero according to an embodiment of the present invention;

[0051] Figure 6 FIG. is a diagram of different types of well logging data according to an embodiment of the present invention;

[0052] Figure 7 FIG. is a diagram of the lithology classification result of seismic data according to an embodiment of the present invention;

[0053] Figure 8 FIG. is a structural diagram of a system for quantitatively optimizing sensitive rock physical parameters and lithology classification according to an embodiment of the present invention;

[0054] Figure 9 The figure is a structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] The purpose of the present invention is to solve the problems that it is difficult to find sensitive rock physical parameters when using logging data for rock physical analysis to distinguish lithology, and that the intersection analysis is affected by subjective factors, so that the calculated lithology classification formula is often not the optimal result. The present invention adopts a quantitative sensitive rock physical parameter optimization and lithology classification method, realizes the quantitative analysis and optimization of sensitive rock physical parameters, and achieves the purpose of eliminating human interference and realizing automatic optimal classification of lithology.

[0057] The present invention proposes a quantitative sensitive rock physical parameter optimization and lithology classification method based on logging rock physics analysis. First, the lithology classification index is clarified, and then the normalized logging data is grouped, and the lithology overlap coefficient of each group is calculated. Then, the optimal lithology distinction data pair is selected according to the lithology overlap coefficient, and the optimal lithology classification line is obtained by using the data pair. The classification line is denormalized to obtain the true lithology classification formula, and finally the true optimal lithology classification formula is used to perform lithology classification on the three-dimensional seismic data to obtain the lithology classification three-dimensional data body in the work area. The invention realizes the quantitative analysis and optimization of sensitive rock physical parameters and the automatic acquisition of lithology classification formula.

[0058] See also Figure 1 and Figure 2 The first aspect of the present invention discloses a method for quantitative sensitive rock physical parameter optimization and lithology classification, the method comprising:

[0059] Step S1, determining the target layer section for rock physical analysis, and removing abnormal values ​​of logging data of the target layer section;

[0060] Specifically, assuming that the goal of seismic interpretation is to classify the lithology of the target layer in the 3D seismic block, after determining the target layer, it is necessary to first perform rock physics analysis on the logging data to find the quantitative relationship between the logging parameters and different lithologies, laying the foundation for the lithology differentiation of the 3D seismic data. Before performing rock physics analysis on the logging data, it is necessary to remove the outliers in the logging data. The outlier removal method is relatively mature and will not be introduced in detail here.

[0061] Step S2: Set lithology classification indicators for the logging data of the target interval after outlier rejection.

[0062] In step S2, cross-plotting is carried out for different types of logging data. Before making the cross-plot, it is necessary to specify the types of logging data for each coordinate axis. The currently common method is to subjectively select the types of logging data for the coordinate axes, which is highly subjective, and the selected data types may not necessarily be the most sensitive ones for lithology discrimination. Therefore, the method for setting lithology classification indicators for the logging data of the target interval after outlier rejection includes:

[0063] For cross-analysis to find the optimal logging data types for lithology discrimination, first select multiple types of logging data, named c1, c2, c3, c4, c5... cn respectively; select the logging gamma value as the lithology classification indicator, where the gamma value greater than M is lithology 1 and the gamma value less than M is lithology 2.

[0064] Step S3: Normalize different types of logging data.

[0065] In step S3, since the dimensions of different types of logging data are inconsistent and the indicated geological meanings are also different, it has a certain impact on subsequent analysis. Therefore, normalization is required. For Figure 6 The method for normalizing different types of logging data shown includes:

[0066] Assume that there are a total of n types of logging data participating in the sensitive parameter optimization. Denote different types of logging data as c1, c2, c3, c4, c5... cn, and denote the normalization result of data ci as ai. Then the formula for ai is:

[0067]

[0068] where i is 1, 2, 3, 4... n, max(ci) is the maximum value of the i-type logging data, and min(ci) is the minimum value of the i-type logging data.

[0069] Step S4: Group the normalized logging data and calculate the lithology overlap coefficient.

[0070] In step S4, the method for grouping the normalized logging data and calculating the lithology overlap coefficient includes:

[0071] Pairwise group the normalized logging data a1, a2, a3, a4, a5... an for all permutations and combinations, forming a total of N pairs of data, where Calculate the lithology overlap coefficient for each pair of data. Combining Figure 3 , the specific calculation process is as follows:

[0072] ① Calculate the range of values of the logging data a1, a2, a3, a4, a5…an in Lithology 1, and name them a1 max , a1 min , a2 max , a2 min …an max , an min . Calculate the range of values of the logging data a1, a2, a3, a4, a5…an in Lithology 2, and name them a1’ max , a1’ min , a2’ max , a2’ min …an’ max , an’ min .

[0073] ② Assume that the logging data types corresponding to Data Pair 1 are a1, a2. Calculate the area of the a1, a2 data pair in Lithology 1 as

[0074] Area1 = abs[(a1 max - a1 min ) × (a2 max - a2 min )] (2)

[0075] The area of the a1, a2 data pair in Lithology 2 is

[0076] Area2 = abs[(a1' max - a1' min ) × (a2' max - a2' min )] (3)

[0077] ③ Calculate whether Lithology 1 and Lithology 2 overlap and the overlapping range in Data Pair 1. Lithology 1 and Lithology 2 are divided into two cases: overlapping and non - overlapping. First, judge the non - overlapping case. If the judgment condition a1’ min ≥a1 max , or a1 min ≥a1’ max appears, then Lithology 1 and Lithology 2 do not overlap in the a1 data, which is set as meeting Judgment Condition 1. If the judgment condition a2’ min ≥a2 max , or a2 min ≥a2’ max appears, then Lithology 1 and Lithology 2 do not overlap in the a2 data, which is set as Judgment Condition 2. If both Judgment Condition 1 and Judgment Condition 2 are met, then the overlapping area range of Lithology 1 and Lithology 2 in Data Pair 1 is 0, that is, they do not overlap. Let the overlapping range of Lithology 1 and Lithology 2 in a1 be L1, then L1 = 0. Let the overlapping range of Lithology 1 and Lithology 2 in a2 be L2, then L2 = 0.

[0078] If the above two judgment conditions cannot be satisfied simultaneously, it indicates that there is an overlapping situation, and it is necessary to calculate the overlapping area range. If Judgment Condition 1 is not satisfied, it means that Lithology 1 and Lithology 2 have an overlapping situation in the a1 data. If a1 min <a1’ min <a1 max and a1’ max >a1 max , then the overlapping range

[0079] L1 = a1 max - a1’ min (4)

[0080] If a1’ min <a1 min <a1’ max , and a1’ max <a1 max , then the overlapping range

[0081] L1 = a1’ max - a1 min (5)

[0082] If the above two situations are not satisfied, in the a1 data, there is a situation where Lithology 2 completely includes Lithology 1, or Lithology 1 completely includes Lithology 2, making it impossible to distinguish the two lithologies in the a1 data. L1 is set to an invalid value, L1 = Nan.

[0083] Similarly, if Judgment Condition 2 is not satisfied, it means that Lithology 1 and Lithology 2 have an overlapping situation in the a2 data. If a2 min <a2’ min <a2 max and a2’ max >a2 max , then the overlapping range

[0084] L2 = a2 max - a2’ min (6)

[0085] If a2’ min <a2 min <a2’ max , and a2’ max <a2 max , then the overlapping range

[0086] L2 = a2’ max - a2 min (7)

[0087] If the above two cases are not satisfied, in the a2 data, there is a situation where Lithology 2 completely includes Lithology 1, or Lithology 1 completely includes Lithology 2, making it impossible to distinguish the two lithologies in the a2 data. L2 is set to an invalid value, L2 = Nan.

[0088] When L1 and L2 have valid values, calculate the overlapping area

[0089] Area L = L1 × L2 (8)

[0090] ④ Calculate the lithology overlap coefficient of Data Pair 1. Let the area included in the value range of data a1 be Area1, and the area included in the value range of data a2 be Area2. Let the smaller value of Area1 and Area2 be Area min , if Area1 ≥ Area2, then Area min = Area2, otherwise Area min = Area1. The lithology overlap coefficient of Data Pair 1

[0091]

[0092] ⑤ Select Data Pairs 2 to N, repeat Processes ② to ④, and calculate the lithology overlap coefficients of different data pairs Litho2... Litho N .

[0093] Step S5: Select the optimal lithology discrimination data pair according to the lithology overlap coefficient;

[0094] In Step S5, the method of selecting the optimal lithology discrimination data pair according to the lithology overlap coefficient includes:

[0095] Sort the lithology overlap coefficients of the N data pairs from small to large. The data pairs are denoted as P1, P2... P N ; if there is a data pair with a lithology overlap coefficient of zero, take this data pair as the optimal lithology discrimination data pair P opt ; if there is no data pair with a lithology overlap coefficient of zero, take the data pair with the smallest lithology overlap coefficient as the optimal lithology discrimination data pair P opt ; name the two types of logging data corresponding to the optimal lithology discrimination data pair P opt as P o1 , P o2 .

[0096] Step S6: Use the optimal lithology discrimination data pair to perform optimal classification of lithologies. The trend line with the minimum misjudgment rate is the optimal classification line; combined with Figure 4 and Figure 5 , the specific process is as follows

[0097] ① Use data Po1 , P o2 , perform elliptical fitting on the lithology 1 data, and the elliptical fitting formula is:

[0098] A1x 2 + B1xy + C1y 2 + D1x + E1y + 1 = 0 (10)

[0099] Use the least squares method to solve for the five elliptical parameters A1, B1, C1, D1, and E1.

[0100] ② Similarly, perform elliptical fitting on the lithology 2 data. The elliptical fitting parameters for the lithology 2 data are A2, B2, C2, D2, and E2, and the formula for the fitted ellipse of lithology 2 is:

[0101] A2x 2 + B2xy + C2y 2 + D2x + E2y + 1 = 0 (11)

[0102] ③ Calculate the center points of the two ellipses and the angles of the major axes of the ellipses. The center points of the ellipses are named (x1, y1) and (x2, y2) respectively, and the angles of the major axes of the ellipses are named φ1 and φ2. The calculation formulas for the center points of the ellipses and the angles of the major axes are:

[0103]

[0104]

[0105]

[0106] In the above formula, i is 1 or 2, representing the data corresponding to lithology 1 and lithology 2 respectively.

[0107] ④ If the lithology overlap coefficient is zero, then calculate the points where the line connecting the center points of the two ellipses intersects the two ellipses respectively, denoted as (x j1 , y j1 )(x j2 , y j2 ), calculate the position of the midpoint of the two intersection points, denoted as (x mid , y mid ), calculate the average value of the angles of the major axes of the two ellipses, denoted as φ mid , then:

[0108]

[0109]

[0110]

[0111] Convert the angle φ mid to the slope kmid , through the midpoint (x mid , y mid ) and the slope k mid obtain the formula for the optimal classification line

[0112] y = k mid (x - x mid ) + y mid (18)

[0113] After completing this process, directly proceed to step S7.

[0114] ⑤ If the lithology overlap coefficient is not zero, then connect the coordinates of the centers of the two ellipses, and divide this line segment into w equal parts. Then the coordinates of each division point are:

[0115]

[0116]

[0117] where, x min is the smaller value of x1 and x2, x max is the larger value of x1 and x2, x min is the smaller value of y1 and y2, y max is the larger value of y1 and y2, i takes values 1, 2, 3... w - 1, x i , y i respectively refer to the x and y coordinates of the i-th division point.

[0118] At the same time, divide the range of the major axis angles of the two ellipses into w equal parts. Then each division angle is

[0119]

[0120] where, φ min is the smaller value of φ1 and φ2, φ max is the larger value of φ1 and φ2, i takes values 1, 2, 3... w - 1.

[0121] Convert each angle φ i into the slope k i .

[0122] Note: The value of w can be set by oneself, and the value is within 5 - 30. If the set value is less than 5, there are too few dividing lines and it is difficult to find the optimal dividing line. If the set value is greater than 30, there are too many dividing lines, which is time-consuming and laborious without obvious improvement in accuracy and affects the work progress. Therefore, during the work process, the interpreter can set an appropriate value according to the actual situation.

[0123] ⑥ Combine w - 1 segmentation trend lines using w - 1 segmentation point coordinates and their corresponding slopes. The formula for the i-th segmentation trend line is:

[0124] y = k i (x - x i ) + y i (22)

[0125] where i takes values 1, 2, 3... w - 1.

[0126] ⑦ Calculate the lithology misclassification rates of the two types of lithologies according to each segmentation trend line, that is, the accuracy of distinguishing lithology 1 and lithology 2 under different segmentation trend lines. For the segmentation trend line i, the number of logging data points misjudged as lithology 1 is s1, and the number of logging data points correctly classified as lithology 1 is q1. Then the misclassification rate E i1 is

[0127]

[0128] Suppose the number of logging data points misjudged as lithology 2 is s2, and the number of logging data points correctly classified as lithology 2 is q2. Then the misclassification rate E i2 is

[0129]

[0130] Then the overall misclassification rate E1 of the segmentation trend line classification is

[0131]

[0132] It can be seen from the above formula that the lower the misclassification rate, the better the classification effect.

[0133] ⑧ Select the segmentation trend line with the lowest misclassification rate as the optimal lithology boundary line.

[0134] Step S7: Obtain the true optimal lithology classification formula through denormalization;

[0135] In step S7, the method of obtaining the true optimal lithology classification formula through denormalization includes:

[0136] Denormalize the two types of data in the optimal data pair and substitute them into the optimal boundary line formula to obtain the true optimal lithology classification formula; The denormalization formula according to formula (1) is

[0137] ci = ai[max(ci) - min(ci)] + min(ci) (26)

[0138] The two logging data for obtaining the optimal lithology classification formula are the most sensitive lithology classification parameters for the target interval.

[0139] Step S8: Use the optimal lithology classification formula to classify the lithology of the seismic data volume.

[0140] In step S8, the method of using the optimal lithology classification formula to classify the lithology of the seismic data volume includes:

[0141] The most sensitive lithology classification parameter data volume is obtained by using the seismic inversion method for the three-dimensional seismic data volume, and the optimal lithology classification formula is substituted into the sensitive parameter data volume to obtain the lithology data volume in three-dimensional space. Figure 7 shown.

[0142] In summary, the scheme proposed in the present invention first calculates the lithology overlap coefficient by grouping well logging data, then selects the optimal lithology distinction data pair according to the lithology overlap coefficient, and thirdly uses the optimal lithology distinction data to optimally classify the lithology and obtain the optimal classification line. At the same time, the classification accuracy of the optimal classification line can be verified by the misjudgment rate. Finally, the optimal lithology classification formula is used to classify the seismic data body. The present invention realizes the optimization of sensitive rock physical parameters, eliminates human interference, and achieves the purpose of optimal lithology classification.

[0143] Furthermore, in order to illustrate the feasibility and practicality of the content of the present invention, and to make the purpose, technical solution and advantages of the present invention clearer, the optimal classification standard of sandstone and mudstone is found by performing sensitive rock physics analysis on the logging data of a well in a certain work area, and the standard is applied to the three-dimensional seismic data to realize the classification of sandstone and mudstone in the three-dimensional space of the work area. The present invention is further described in detail below.

[0144] The logging data of a well in a certain work area is used to optimize the sandstone and mudstone lithology sensitive parameters and obtain the optimal lithology boundary line. The target formation of this test is the Xujiahe Formation. The test is carried out according to the invention process, and the test process is as follows:

[0145] 1. Perform quality control on different types of logging curves in the logging data to check for outliers. If there are outliers, remove them to ensure that all values ​​are within a reasonable range and to ensure the effectiveness of subsequent analysis.

[0146] 2. Use the gamma curve to distinguish sandstone and mudstone. For the Xujiahe Formation in this work area, mudstone is defined as having a gamma value greater than 85, and sandstone is defined as having a gamma value less than 85. Since the gamma value is not easy to obtain from seismic data, it is necessary to use rock parameters that are easy to obtain from seismic data, such as P-wave velocity, P-wave impedance, S-wave velocity, S-wave impedance, density, etc., to find the two parameters that are most sensitive to lithology.

[0147] 3. The rock physics curves in the logging curves are normalized using formula (1) to normalize all the curves involved in the calculation to the same dimension.

[0148] 4. Group different logging curves into groups of two each to generate multiple data pairs, and calculate the lithology overlap coefficients of different data pairs according to step S4.

[0149] 5. Sort the lithology overlap coefficients from small to large. There is no data pair with an overlap coefficient of 0, so the pair with the smallest overlap coefficient is selected as the most sensitive data pair.

[0150] 6. Calculate the fitting ellipse, ellipse center point, and ellipse major axis inclination of the two well logging curve data in this data pair, select w=20, and calculate the coordinates of the 19 segmentation points between the two ellipse center points and the angles of the 19 segmentation inclinations. Convert the angles into slopes to obtain 19 different segmentation trend line formulas.

[0151] The lithology misjudgment coefficient of each segmentation line was calculated, and the segmentation trend line with the smallest coefficient was selected as the optimal lithology classification line for the sandstone and mudstone of the Xujiahe Formation in the area.

[0152] 7. Use formula (26) to denormalize the optimal lithology classification line formula to obtain the true lithology classification formula.

[0153] 8. Based on the inversion results of seismic data and the optimal lithology classification formula, the three-dimensional data volume of the sandstone and mudstone classification of the Xujiahe Formation in this block was calculated.

[0154] The feasibility and practicability of the content of the present invention are verified through the above embodiments.

[0155] The second aspect of the present invention discloses a quantitative sensitive rock physical parameter optimization and lithology classification system. Figure 8 : is a structural diagram of a quantitative sensitive rock physical parameter optimization and lithology classification system according to an embodiment of the present invention; Figure 8 As shown, the system 100 includes:

[0156] The first processing module 101 is configured to determine a target layer section for petrophysical analysis and remove abnormal values ​​of logging data of the target layer section;

[0157] The second processing module 102 is configured to set a lithology classification index for the well logging data of the target layer after removing the outliers;

[0158] The third processing module 103 is configured to perform normalization processing on the different types of logging data;

[0159] The fourth processing module 104 is configured to group the normalized logging data and calculate the lithology overlap coefficient;

[0160] The fifth processing module 105 is configured to select an optimal lithology discrimination data pair according to the lithology overlap coefficient;

[0161] The sixth processing module 106 is configured to perform optimal classification of the lithology using the optimal lithology discrimination data pair, and the trend line with the minimum misjudgment rate is the optimal classification line;

[0162] The seventh processing module 107 is configured to obtain a true optimal lithology classification formula through anti-normalization;

[0163] The eighth processing module 108 is configured to perform lithology classification on the seismic data volume using the optimal lithology classification formula.

[0164] For the system according to the second aspect of the present invention, the second processing module 102 is specifically configured to perform crossplot analysis to find the optimal logging data type for lithology discrimination. First, select multiple types of logging data, named c1, c2, c3, c4, c5... cn respectively; select the logging gamma value as the lithology classification index, where the gamma value greater than M is lithology 1, and the gamma value less than M is lithology 2.

[0165] For the system according to the second aspect of the present invention, the method for the third processing module 103 to perform normalization processing on different types of logging data includes:

[0166] Assume that there are a total of n types of logging data participating in the sensitive parameter optimization. Denote different types of logging data as c1, c2, c3, c4, c5... cn, and denote the normalization result of data ci as ai. Then the formula for ai is:

[0167]

[0168] where i is 1, 2, 3, 4... n, max(ci) is the maximum value of the i-type logging data, and min(ci) is the minimum value of the i-type logging data.

[0169] For the system according to the second aspect of the present invention, the fourth processing module 104 is specifically configured to divide the normalized logging data a1, a2, a3, a4, a5... an into pairs two by two, perform all permutations and combinations, and a total of N groups of data pairs are formed, where Calculate the lithology overlap coefficient of each group of data pairs.

[0170] For the system according to the second aspect of the present invention, the fifth processing module 105 is specifically configured to sort the lithology overlap coefficients of the N pairs of data pairs from small to large, and the data pairs are denoted as P1, P2... P N ; if there is a data pair with a lithology overlap coefficient of zero, then take this data pair as the optimal lithology discrimination data pair P opt; If there is no data pair with a lithology overlap coefficient of zero, then select the data pair with the smallest lithology overlap coefficient as the optimal lithology discrimination data pair P opt1 ; Name the two types of logging data corresponding to the optimal lithology discrimination data pair P opt as P o1 , P o2 .

[0171] According to the system of the second aspect of the present invention, the seventh processing module 107 is specifically configured to perform denormalization on the two types of data in the optimal data pair, substitute them into the optimal boundary formula, and obtain the true optimal lithology classification formula; the denormalization formula according to formula (1) is

[0172] ci = ai[max(ci) - min(ci)] + min(ci) (26)

[0173] The two types of logging data for obtaining the optimal lithology classification formula are the most sensitive lithology classification parameters for the target interval.

[0174] According to the system of the second aspect of the present invention, the eighth processing module 108 is specifically configured to obtain the most sensitive lithology classification parameter data volume from the three-dimensional seismic data volume through seismic inversion, and substitute the optimal lithology classification formula into the sensitive parameter data volume to obtain the lithology data volume in three-dimensional space.

[0175] The third aspect of the present invention discloses an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, it implements the steps in any one of the quantitative sensitive rock physics parameter optimization and lithology classification methods of the first aspect disclosed in the present invention.

[0176] Figure 9 is a structural diagram of an electronic device according to an embodiment of the present invention. As Figure 9 shown, the electronic device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be implemented through WIFI, a carrier network, near field communication (NFC), or other technologies. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen. The input device of the electronic device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the electronic device, or an external keyboard, a touchpad, or a mouse, etc.

[0177] Those skilled in the art can understand that Figure 9 the structure shown in Figure 9 is only the structure diagram of the part related to the technical solution of the present disclosure, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0178] A fourth aspect of the present invention discloses a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps in a method for optimizing quantitative sensitive rock physical parameters and lithology classification according to any one of the first aspects disclosed in the present invention are implemented.

[0179] Please note that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification. The above embodiments only represent several implementation manners of the present application, and their descriptions are relatively specific and detailed, but they should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

[0180] The above are the preferred implementation manners of the present invention. It should be noted that for those of ordinary skill in the technical field, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for quantitatively optimizing sensitive rock physical parameters and lithology classification, characterized in that, The method includes: Step S1: Determine the target interval for petrophysical analysis, and eliminate the outliers in the logging data of the target interval; Step S2: Set lithology classification indexes for the logging data of the target interval after eliminating the outliers; Step S3: Normalize the logging data of different types; Step S4: Group the normalized logging data and calculate the lithology overlap coefficient; Step S5: Select the optimal lithology discrimination data pair according to the lithology overlap coefficient; Step S6: Use the optimal lithology discrimination data pair to perform optimal lithology classification, and the trend line with the minimum misjudgment rate is the optimal classification line; Step S7: Obtain the true optimal lithology classification formula through anti-normalization; Step S8: Use the optimal lithology classification formula to perform lithology classification on the seismic data volume.

2. The quantitative optimization method for sensitive rock physical parameters and lithology classification method according to claim 1, characterized in that In the step S2, the method for setting lithology classification indexes for the logging data of the target interval after eliminating the outliers includes: Perform crossplot analysis to find the optimal logging data type for distinguishing lithology. First, select multiple types of logging data, named c1, c2, c3, c4, c5... cn respectively; select the logging gamma value as the lithology classification index. When the gamma value is greater than M, it is lithology 1, and when the gamma value is less than M, it is lithology 2.

3. A method for quantitatively optimizing sensitive rock physical parameters and lithology classification according to claim 2, characterized in that, In the step S3, the method for normalizing the logging data of different types includes: Assume that there are n types of logging data participating in the sensitive parameter optimization. Denote the different types of logging data as c1, c2, c3, c4, c5... cn, and denote the normalization result of data ci as ai. Then the formula for ai is: where i is 1, 2, 3, 4... n, max(ci) is the maximum value of the logging data of type i, and min(ci) is the minimum value of the logging data of type i.

4. A method for quantitatively optimizing sensitive rock physical parameters and lithology classification according to claim 3, characterized in that, In the step S4, the method for grouping the normalized logging data and calculating the lithology overlap coefficient includes: The normalized logging data a1, a2, a3, a4, a5…an are grouped in pairs to perform all permutations and combinations, forming a total of N pairs of data, where calculate the lithology overlap coefficient of each pair of data.

5. A method for quantitatively optimizing sensitive rock physical parameters and lithology classification according to claim 4, characterized in that In the step S5, the method for selecting the optimal lithology discrimination data pair according to the lithology overlap coefficient includes: Sort the lithology overlap coefficients of the N pairs of the data pairs from small to large. The data pairs are denoted as P1, P2... P N ; if there is a data pair with a lithology overlap coefficient of zero, take this data pair as the optimal lithology discrimination data pair P opt ; if there is no data pair with a lithology overlap coefficient of zero, take the data pair with the smallest lithology overlap coefficient as the optimal lithology discrimination data pair P opt ; name the two types of logging data corresponding to the optimal lithology discrimination data pair P opt as P o1 , P o2 .

6. A method for quantitatively optimizing sensitive rock physical parameters and lithology classification according to claim 4, characterized in that In the step S7, the method for obtaining the true optimal lithology classification formula through anti-normalization includes: Perform anti-normalization on the two types of data in the optimal data pair and substitute them into the optimal boundary formula to obtain the true optimal lithology classification formula; the anti-normalization formula according to formula (1) is ci = ai[max(ci) - min(ci)] + min(ci) (26) The two types of logging data for obtaining the optimal lithology classification formula are the most sensitive lithology classification parameters for this target interval.

7. A method for quantitatively optimizing sensitive rock physical parameters and lithology classification according to claim 1, characterized in that, In the step S8, the method for using the optimal lithology classification formula to perform lithology classification on the seismic data volume includes: Obtain the most sensitive lithology classification parameter data volume from the three-dimensional seismic data volume through seismic inversion, and substitute the optimal lithology classification formula into the sensitive parameter data volume to obtain the lithology data volume in three-dimensional space.

8. A system for quantitatively optimizing sensitive rock physical parameters and lithology classification, characterized in that, The system includes: The first processing module is configured to determine the target interval for petrophysical analysis and eliminate the outliers in the logging data of the target interval; A second processing module, configured to set a lithology classification index for the logging data of the target interval after outliers are removed; A third processing module, configured to perform normalization processing on different types of the logging data; A fourth processing module, configured to calculate a lithology overlap coefficient by grouping the normalized logging data; A fifth processing module, configured to select an optimal lithology discrimination data pair according to the lithology overlap coefficient; A sixth processing module, configured to perform optimal classification of lithology using the optimal lithology discrimination data pair, and the trend line with the minimum misjudgment rate is the optimal classification line; A seventh processing module, configured to obtain a true optimal lithology classification formula through inverse normalization; An eighth processing module, configured to perform lithology classification on a seismic data volume using the optimal lithology classification formula.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps in a method for optimizing quantitative sensitive rock physical parameters and classifying lithology according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps in a method for optimizing quantitative sensitive rock physical parameters and classifying lithology according to any one of claims 1 to 7 are implemented.

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