A method, system and storage medium for intelligent identification of lithologic traps

Abstract

The present invention provides a lithologic trap intelligent identification method, system and storage medium, including: S1. Acquire seismic data and synthetic records; S2. Perform lithologic inversion to obtain inversion results; S3. Perform lithologic analysis and set sand and mudstone thresholds; S4. Convert the three-dimensional data volume obtained by inversion into a sandstone distribution data volume; S5. Convert fault interpretation and seismic horizons into depth domain data according to the time-depth relationship; S6. Calculate the distribution range of sand bodies that meet the trap conditions according to the trap identification algorithm; S7. Obtain the slope and sand body offset distance fitting formula according to the geophysical two-dimensional forward modeling; S8. Based on the slope and sand body offset distance fitting formula, correct the sand body boundary in the sandstone data volume. The present invention uses high-precision lithologic inversion results as digital drive to achieve pinch-out offset correction in the updip direction of the reservoir sand body and automatic identification of the trap.

Description

Technical Field

[0001] The present invention relates to the technical field, and in particular to a lithologic trap intelligent identification method, system and storage medium. Background Art

[0002] As exploration becomes more mature, finding stratigraphic-lithologic oil and gas reservoirs has gradually become a new direction of exploration. Therefore, the current exploration problem is that there is a large error between the distribution of the actual pinch-out line (overlap line) of the stratigraphic-lithologic trap and the seismic data, and there is a possibility of errors or large errors in the artificial division of traps. Summary of the invention

[0003] The present invention provides a lithologic trap intelligent identification method, system and storage medium, which use high-precision lithologic inversion results as digital drive to achieve pinch-out offset correction in the updip direction of reservoir sand bodies and automatic identification of traps. The method has high applicability, can flexibly set the identification resolution, can be applied to actual work areas and efficiently complete the trap prediction work.

[0004] A first aspect of the embodiments of this specification discloses a lithologic trap intelligent identification method, comprising:

[0005] S1. Interpret the seismic exploration and logging data, calibrate the total records, interpret the seismic horizons, and obtain seismic data and synthetic records;

[0006] S2. Based on the seismic data and synthetic records, high-precision lithology inversion is performed to obtain lithology inversion results;

[0007] S3. Perform lithology analysis based on the lithology inversion results and set sand and mudstone thresholds;

[0008] S4. Based on the sand and mudstone threshold values, the lithology inversion results of the three-dimensional data volume are converted into a sandstone distribution data volume;

[0009] S5. converting the fault data and seismic layer data in the seismic data into depth domain data according to the time-depth relationship;

[0010] S6. Based on the depth domain data, the distribution range of the sand body that meets the trap conditions is calculated according to the trap identification algorithm;

[0011] S7. Based on the geophysical two-dimensional forward modeling, obtain the fitting formula of slope and trap sand body offset distance;

[0012] S8. Based on the slope and the trap sand body offset distance fitting formula, the sand body boundary in the sandstone distribution data volume is corrected.

[0013] In some embodiments of this specification, in S6, the data establishment method flow required by the trap identification algorithm is as follows:

[0014] Unify the top surface structural diagrams of the formation into inLine, xLine, and Z formats;

[0015] Read the interpreted fault polygon data, and convert the storage format of the fault polygon data from the horizontal coordinate, vertical coordinate, and identification code format to the inLine, xLine, and identification code format;

[0016] The formats of formation sand-to-ground ratios calculated by geophysical lithology inversion are unified into inLine, xLine, and Value formats;

[0017] The data after the above conversion is read in and a model is built inside the computer.

[0018] In some embodiments of this specification, in S6, the maze pathfinding step of the trap identification algorithm includes:

[0019] S61. discretize the lithology results of the target layer according to the actual resolution requirement, and calculate the data of the lithology trap;

[0020] S62. Perform pathfinding calculation on the sampling point (x, y) pointed by the pointer, and determine whether the pointer can be moved in the four directions of up, right, down, and left. If it can be moved and the direction of movement is elevation, move one step clockwise, and refresh the pointer position to (x+1, y) or (x-1, y) or (x, y+1) or (x, y-1), and jump to S63; otherwise, jump to S65;

[0021] S63. If the position after moving is not the "exit", add the moving direction to the end of the "direction list", mark the current position as "inaccessible", and jump to S62; otherwise jump to S64.

[0022] S64. If the position after the movement is the "exit", determine whether the pointer points to the end of the sampling point set. If the pointer points to the end of the sampling point set, save the "trap result set" and the closure identification algorithm ends; otherwise, point the pointer to the next uncalculated sampling point and refresh the value of (x, y) pointed by the pointer, and jump to S62;

[0023] S65. If the pointer can move in the four directions of up, right, down and left, but the elevation rise in the moving direction cannot be achieved at the same time, then take a step back according to the direction value saved at the end of the "direction list", the "direction list" discards the end value, and jumps to S62.

[0024] In some embodiments of this specification, S61 includes:

[0025] S611. Number each sandstone sampling point according to the discrete result;

[0026] S612. Set a pointer to point to the first digit of the sandstone sampling point number, and set the coordinates pointed to by the pointer to (x, y);

[0027] S613. Set a dynamic linked list type array for storing directions;

[0028] S614. Set a two-dimensional array, the size of which is the same as the discretized lithology result, to store the trap identification result.

[0029] In some embodiments of the present specification, the fitting formula for the slope and the sand body offset distance is: ; x represents the angle, and y is the distance that the sand body pinch-out point should be offset at the x angle.

[0030] A second aspect of the embodiments of this specification discloses a lithologic trap intelligent identification system, which is characterized by being used to implement any one of the lithologic trap intelligent identification methods described above;

[0031] The lithologic trap intelligent identification system comprises:

[0032] The interpretation and synthesis module is used to interpret the seismic exploration and logging data, calibrate the total records, interpret the seismic horizons, and obtain seismic data and synthetic records;

[0033] An inversion module, used for performing high-precision lithology inversion based on the seismic data and synthetic records to obtain lithology inversion results;

[0034] An analysis module, used for performing lithology analysis based on the lithology inversion result and setting sandstone and mudstone threshold values;

[0035] A first conversion module is used to convert the lithology inversion result of the three-dimensional data body into a sandstone distribution data body based on the sandstone and mudstone threshold values;

[0036] A second conversion module is used to convert the fault data and seismic layer data in the seismic data into depth domain data according to the time-depth relationship;

[0037] A trap identification algorithm module is used to calculate the distribution range of sand bodies that meet the trap conditions based on the depth domain data and the trap identification algorithm;

[0038] The formula fitting module is used to obtain the fitting formula of slope and trap sand body offset distance based on geophysical two-dimensional forward modeling;

[0039] The correction module is used to correct the sand body boundary in the sandstone distribution data body based on the slope and the trap sand body offset distance fitting formula.

[0040] A third aspect of the embodiments of this specification discloses a computer-readable storage medium, wherein the medium stores computer instructions. When a computer reads the computer instructions, the computer executes the lithologic trap intelligent identification method as described in any one of the above.

[0041] The embodiments of this specification can at least achieve the following beneficial effects:

[0042] The present invention uses high-precision lithology inversion results as digital drive to achieve pinch-out offset correction in the updip direction of reservoir sand bodies and automatic identification of traps. It has high applicability, can flexibly set the resolution size, can be applied to actual work areas and efficiently complete the trap prediction work.

[0043] Based on the three-dimensional high-precision lithologic inversion data results, the present invention subdivides the lithologic inversion results into user-specified size areas. The intelligent algorithm follows the "automatic discrimination constraint of oil and gas upward obstruction conditions" to determine whether the area has blocking conditions. After traversing all subdivided areas, the algorithm automatically outputs the oil and gas trap range, and finally corrects the actual pinch-out line of the lithologic trap, thereby realizing automatic identification of the trap and being able to correct the pinch-out offset in the updip direction of the reservoir sand body. The present invention has a wide range of applicability and can be applied to trap prediction under complex structural conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0045] Figure 1 It is a schematic diagram of the flow of the intelligent identification method of lithologic traps involved in the present invention.

[0046] Figure 2 It is a schematic diagram of maze path finding involved in the present invention.

[0047] Figure 3 It is a schematic diagram of the initial model of intelligent identification of closure involved in the present invention.

[0048] Figure 4 The figure is a flow chart of the logic of the intelligent trap identification algorithm involved in the present invention.

[0049] Figure 5 It is a schematic diagram of the improvement effect of the intelligent trap identification algorithm involved in the present invention.

[0050] Figure 6 It is a schematic diagram of the differential sealing effect of caprocks by the intelligent trap identification algorithm involved in the present invention.

[0051] Figure 7 It is a schematic diagram of the seismic response characteristics of sand body pinch-out under different slopes involved in the present invention.

[0052] Figure 8 It is a schematic diagram of the trend of the sand body pinch-out offset distance involved in the present invention.

[0053] Fig. 9 It is a schematic diagram of intelligent identification of closures and boundary correction of a certain sequence in the second section of the Dongying Formation in a certain area involved in the present invention.

[0054] Fig.10 It is a schematic diagram of the prediction of lithologic traps of a certain sequence of the Dongying Formation in a certain area involved in the present invention. DETAILED DESCRIPTION

[0055] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the embodiments of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0056] The disclosure below provides many different embodiments or examples to implement different structures of the embodiments of the present invention. In order to simplify the disclosure of the embodiments of the present invention, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the embodiments of the present invention. In addition, the embodiments of the present invention can repeat reference numbers and / or reference letters in different examples, and this repetition is for the purpose of simplification and clarity, which itself does not indicate the relationship between the various embodiments and / or settings discussed.

[0057] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0058] like Figure 1 As shown, the first aspect of the embodiment of this specification discloses a lithologic trap intelligent identification method, comprising:

[0059] S1. Interpret the seismic exploration and logging data, calibrate the total records, interpret the seismic horizons, and obtain seismic data and synthetic records;

[0060] S2. Based on the seismic data and synthetic records, high-precision lithology inversion is performed to obtain lithology inversion results;

[0061] S3. Perform lithology analysis based on the lithology inversion results and set sand and mudstone thresholds;

[0062] S4. Based on the sand and mudstone threshold values, the lithology inversion results of the three-dimensional data body are converted into a sandstone distribution data body; (i.e., the calculation is performed using the high-precision sand body inversion results)

[0063] S5. converting the fault data and seismic layer data in the seismic data into depth domain data according to the time-depth relationship;

[0064] S6. Based on the depth domain data, the distribution range of the sand body that meets the trap conditions is calculated according to the trap identification algorithm;

[0065] S7. Based on the geophysical two-dimensional forward modeling, obtain the fitting formula of slope and trap sand body offset distance;

[0066] S8. Based on the slope and the trap sand body offset distance fitting formula, the sand body boundary in the sandstone distribution data volume is corrected.

[0067] In some embodiments of the present specification, in S6, the data establishment method flow required by the trap identification algorithm is as follows: (the required data include: lithology inversion results, fault data and seismic horizon data obtained by seismic interpretation, synthetic record calibration, and then the time-depth relationship is obtained by using the synthetic record calibration, and the fault data and seismic horizon data are converted from time data to depth data.)

[0068] Unify the top surface structural diagrams of the formation into inLine, xLine, and Z formats;

[0069] Read the interpreted fault polygon data, and convert the storage format of the fault polygon data from the horizontal coordinate, vertical coordinate, and identification code format to the inLine, xLine, and identification code format;

[0070] The formats of formation sand-to-ground ratios calculated by geophysical lithology inversion are unified into inLine, xLine, and Value formats;

[0071] The data after the above conversion is read in and a model is built inside the computer.

[0072] In some embodiments of this specification, in S6, the maze pathfinding step of the trap identification algorithm includes:

[0073] S61. discretize the lithology results of the target layer according to the actual resolution requirement, and calculate the data of the lithology trap;

[0074] S62. Perform pathfinding calculation on the sampling point (x, y) pointed by the pointer, and determine whether the pointer can be moved in the four directions of up, right, down, and left. If it can be moved and the direction of movement is elevation, move one step clockwise, and refresh the pointer position to (x+1, y) or (x-1, y) or (x, y+1) or (x, y-1), and jump to S63; otherwise, jump to S65;

[0075] S63. If the position after moving is not the "exit", add the moving direction to the end of the "direction list", mark the current position as "inaccessible", and jump to S62; otherwise jump to S64.

[0076] S64. If the position after the movement is the "exit", determine whether the pointer points to the end of the sampling point set. If the pointer points to the end of the sampling point set, save the "trap result set" and the closure identification algorithm ends; otherwise, point the pointer to the next uncalculated sampling point and refresh the value of (x, y) pointed by the pointer, and jump to S62;

[0077] S65. If the pointer can move in the four directions of up, right, down and left, but the elevation rise in the moving direction cannot be achieved at the same time, then take a step back according to the direction value saved at the end of the "direction list", the "direction list" discards the end value, and jumps to S62.

[0078] In some embodiments of this specification, S61 includes:

[0079] S611. Number each sandstone sampling point according to the discrete result;

[0080] S612. Set a pointer to point to the first digit of the sandstone sampling point number, and set the coordinates pointed to by the pointer to (x, y);

[0081] S613. Set a dynamic linked list type array for storing directions;

[0082] S614. Set a two-dimensional array, the size of which is the same as the discretized lithology result, to store the trap identification result.

[0083] In some embodiments of the present specification, the fitting formula for the slope and the sand body offset distance is: ; x represents the angle, and y is the distance that the sand body pinch-out point should be offset at the x angle.

[0084] A second aspect of the embodiments of this specification discloses a lithologic trap intelligent identification system, which is characterized by being used to implement any one of the lithologic trap intelligent identification methods described above;

[0085] The lithologic trap intelligent identification system comprises:

[0086] The interpretation and synthesis module is used to interpret the seismic exploration and logging data, calibrate the total records, interpret the seismic horizons, and obtain seismic data and synthetic records;

[0087] An inversion module, used for performing high-precision lithology inversion based on the seismic data and synthetic records to obtain lithology inversion results;

[0088] An analysis module, used for performing lithology analysis based on the lithology inversion result and setting sandstone and mudstone threshold values;

[0089] A first conversion module is used to convert the lithology inversion result of the three-dimensional data body into a sandstone distribution data body based on the sandstone and mudstone threshold values;

[0090] A second conversion module is used to convert the fault data and seismic layer data in the seismic data into depth domain data according to the time-depth relationship;

[0091] A trap identification algorithm module is used to calculate the distribution range of sand bodies that meet the trap conditions based on the depth domain data and the trap identification algorithm;

[0092] The formula fitting module is used to obtain the fitting formula of slope and trap sand body offset distance based on geophysical two-dimensional forward modeling;

[0093] The correction module is used to correct the sand body boundary in the sandstone distribution data body based on the slope and the trap sand body offset distance fitting formula.

[0094] A third aspect of the embodiments of this specification discloses a computer-readable storage medium, wherein the medium stores computer instructions. When a computer reads the computer instructions, the computer executes the lithologic trap intelligent identification method as described in any one of the above.

[0095] 1 Intelligent identification and tracking of closures

[0096] The identification of closures is actually a highly repetitive mechanical work. After the required data is calculated, it is hoped that there will be a fully automatic program for identifying closures, which can automatically identify closures and output the final results with a small number of custom input parameters.

[0097] 1.1 Intelligent Identification of Traps Theory

[0098] First, the process of automatically picking up closures can be regarded as a set of "maze starting points" that need to find an exit. Starting from the "maze starting point", all possible paths are tried, and a "maze solution" process to find an exit can be regarded as a closure identification algorithm. However, the closure identification algorithm is more complicated than the "maze solution". The process of solving this "maze exit" is not only affected by the height of the top structure, but also must take into account the blocking of faults. Finally, the sandstone sampling points that cannot find the path to the exit after all attempts can be classified as closures. The sampling points that find the channel and can reach the exit are marked as non-traps (at this time, oil and gas can escape). After traversing all sandstone sampling points, the process of intelligent automatic identification of closures is completed.

[0099] Maze pathfinding methods Figure 2As shown in the figure, the entire maze is completely randomly generated, with a size of 30×30. The figure shows the entrance, exit, obstacles (impassable), and passable areas. The path to the "maze exit" is represented by continuous arrows. The exit conditions for calculating a single sampling point are the following two, and you can exit if one of them is met:

[0100] 1. Traverse the entire maze area but cannot find the exit.

[0101] 2. Find the exit and complete the maze.

[0102] The closure identification algorithm and the maze algorithm are very similar in nature. The following will develop an intelligent closure identification algorithm based on the maze pathfinding algorithm and make appropriate modifications and corresponding optimizations.

[0103] 1.2 Basic Implementation of Intelligent Trap Identification Algorithm

[0104] The intelligent trap identification algorithm is calculated according to the idea of ​​"automatic identification and constraint of oil and gas upward obstruction conditions", but how to fully evaluate and verify the trap identification algorithm is a difficult problem. If real work area data is used, there are disadvantages such as a small number of traps and limited trap structural styles, and there is a probability that the verification is insufficient. In order to avoid this situation, a computer is used to randomly generate a large number of traps to evaluate and verify the intelligent trap identification algorithm. The specific method is: a computer random number is used to generate a large amount of stratigraphic data, fault data, and lithology data. Then the random data is sorted into a specified size and the same data storage format for calculation. After a saturation test involving all types of traps, the intelligent trap identification method is finally proved to be accurate.

[0105] 1. Data establishment method flow for intelligent identification of closures:

[0106] 1. The top surface structure map of the stratum (such as Figure 3 a) Unified to inLine, xLine, Z, format ( Figure 3 b is the contour line of the top surface structure map).

[0107] 2. First, read the interpreted fault polygon data, and convert the fault polygon storage format: horizontal coordinate, vertical coordinate, identification code (the default is 6 for the beginning, 7 for the middle, and 8 for the end) to inLine, xLine, identification code data storage format (such as Figure 3 The polygon formed by the “+” in c).

[0108] 3. The formation sand-to-ground ratio calculated by geophysical lithology inversion is unified into the following formats: inLine, xLine, and Value.

[0109] 4. Read these data and build a model inside the computer. Take this experiment as an example ( Figure 3c), the white area represents sandstone with good physical parameters; the gray area represents mudstone, which can meet the conditions of being a cap rock and lateral plug of the trap; the grid composed of "+" represents the faults in the formation, which are represented by polygons; the black border represents the channel, through which oil and gas can escape. Figure 3 In the figure, a. randomly generated structural map; b. contour line form of figure a with random faults added; c. randomly generated geological model.

[0110] 2. Logical Process of Trap Identification

[0111] The detailed steps of maze pathfinding for trap identification calculation are as follows:

[0112] 1. Discrete the lithology results of the target layer according to the actual resolution requirements, and prepare the data for calculating the lithology trap:

[0113] ① Number each sandstone sampling point according to the discrete results.

[0114] ② Set a pointer to point to the first digit of the sandstone sampling point number, and set the coordinates pointed by the pointer to (x, y).

[0115] ③Set up a dynamic linked list type array to store directions.

[0116] ④ Set up a two-dimensional array with the same size as the discretized lithology results to store the trap identification results.

[0117] 2. Perform pathfinding calculations on the sampling point (x, y) pointed by the pointer. Explore whether the pointer can move in the four directions of up, right, down, and left. If it can move and the direction of movement is elevation, move one step clockwise and refresh the pointer position to (x+1, y) or (x-1, y) or (x, y+1) or (x, y-1), and the logic flow jumps to step 3. Otherwise, jump to step 5.

[0118] 3. If the position after moving is not the "exit", add the moving direction to the end of the "direction list", mark the current position as "inaccessible", and jump to the beginning of step 2. Otherwise, jump to step 4.

[0119] 4. If the position after the move is the "exit", determine whether the pointer points to the end of the sampling point set. If the pointer points to the end of the sampling point set, save the "trap result set" and the intelligent trap identification algorithm ends. Otherwise, point the pointer to the next uncalculated sampling point and refresh the value of (x, y) pointed by the pointer, and the logic flow jumps to the beginning of step 2 to continue.

[0120] 5. If the pointer can move in the up, right, down, and left directions of the position it points to, but the elevation rise in the moving direction cannot be achieved at the same time, then take a step back according to the direction value saved at the end of the "direction linked list", discard the end value of the "direction linked list", and the logic flow jumps to the beginning of step 2 to continue.

[0121] In summary, the logic flow chart of the intelligent trap identification algorithm is as follows: Figure 4 shown.

[0122] The intermediate process of intelligent automatic calculation of traps is as follows Figure 5 The figures a to f in the figure are the real-time model status inside the computer. Starting from the upper left corner, each sampling point is calculated. After the path finding algorithm and the conditions for trap formation, each sampling point is marked as a trap. If the oil and gas at this point meet the conditions for trap formation, it is recorded in the memory and the position of the point is sent to the Figure 5 The structural contour map shown in the figure is used to draw the traps (each dot in the figure indicates that a sampling point is identified as a trap). After all the sand bodies that may become traps are traversed, the model sandstone sampling points in the figure total 8587. There are 15 fault polygons in total. The experiment on the random model shows that the intelligent identification method of closures based on the path-finding algorithm can better fit the faults and identify smaller closures. Figure 5 a. Addressed 1092 times; b. Addressed 2099 times; c. Addressed 2898 times; d. Addressed 2898 times; e. Addressed 5031 times; f. Addressed 6337 times; the isovalue lines in af represent the height of the reservoir top surface structure.

[0123] 3. Calculation of Trap Caprock

[0124] According to the previous experimental results, the intelligent trap identification algorithm is basically realized. The intelligent automatic trap identification algorithm has not yet considered the impact of caprock coverage on traps. In order to make the algorithm have a wider range of application, the caprock is used as an optional input condition. If the caprock factor needs to be considered to identify the trap, the caprock related parameters can be input together with the reservoir. The implementation method is to input the caprock sand-to-soil ratio as the caprock related condition input calculation, and the lithology threshold value for channel activation is input by the user.

[0125] The experimental results are as follows Figure 6 As shown, Figure 6 a is the default calculation result of complete cover blocking. Figure 6 b is that there are oil and gas escape channels in the caprock. The range of the escape channels is as follows: Figure 6 The rectangular range formed by the “x” in the middle of b shows that no more closures are identified in the escapeable area. Figure 6 The areas enclosed by the numbered dotted circles in a and b show obvious changes. The enclosed areas in the dotted circles No. 1 and 2 change greatly. Figure 6The oblique "8" shaped enclosure range of a is Figure 6 The only remaining small trap at the structural high point in b and a small fault-blocked trap; the dotted circle No. 3 shows that the perimeter of the trap is shrinking simultaneously; the dotted circle No. 4 is Figure 6 a shows an overall large trap that becomes Figure 6 b shows the split into two small traps, which shows that the calculation result of the trap with the participation of the caprock is completely correct. Figure 6 In the figure, a. The top surface is well sealed and b. The top surface is not completely sealed. The isovalue lines in Figures a and b represent the height of the reservoir top surface structure, with light colors representing high and dark colors representing low.

[0126] 1.3 Pinch-out offset distance in the up-dip direction of reservoir sand bodies corrected by seismic forward modeling

[0127] For seismic exploration, the vertical resolution of seismic exploration is always restricted by a quarter wavelength. When the stratum overlaps the slope at a certain angle, there is often a large gap between the actual pinch-out point of the stratum and the pinch-out point identified on the seismic. In order to quantitatively analyze the relationship between the stratum angle and the actual pinch-out point and the pinch-out point of seismic imaging, a set of seismic forward simulation experiments is designed. The data of the actual work area is used as much as possible. The geological modeling is consistent with the measured data of the well logging. The two-dimensional phase shift method (Yang Xiaojiang, 2013) is used to simulate seismic waves. The forward geological model and its forward migration imaging are shown in Figure 2. Figure 7 As shown, Figure 7 The a-1 in the figure shows the geological lithology model. Based on the lithology and the actual work area acoustic time difference (AC) logging data, the lithology velocity distribution range of the southern section of the Liaoxi uplift is inferred, and the velocity profile is calculated and provided to the two-dimensional forward simulation. The forward simulation algorithm uses the phase shift method wave equation forward modeling and depth migration imaging, so the seismic forward simulation profiles in this simulation are all depth domain models. The simulation results are shown in Figure 7 As shown in a-2, the geological model angle is 18°. By comparing with the original geological model, it can be found that the difference between the actual pinch-out point and the pinch-out point of the seismic data is about 45m; Figure 7 aSimilar, Figure 7 In b~f, the upper figure shows the lithology model, and the lithology is expressed as the percentage of sandstone; the lower figure is the geophysical forward simulation result of the geological model shown in the upper figure. The difference (m) between the sandstone inclination angle (°) and the distance between the seismic pinch-out point and the actual pinch-out point of the sand body model is shown in Table 1-1. Figure 7 In the figure, a-1 is the 18° lithology model; a-2 is the forward modeling result of the model in Figure a-1; b-1 is the 14° lithology model; b-2 is the forward modeling result of the model in Figure b-1; c-1 is the 10° lithology model; c-2 is the forward modeling result of the model in Figure c-1; d-1 is the 7° lithology model; d-2 is the forward modeling result of the model in Figure d-1; e-1 is the 4° lithology model; e-2 is the forward modeling result of the model in Figure e-1; f-1 is the 3° lithology model; f-2 is the forward modeling result of the model in Figure f-1.

[0128] According to the summary of the experimental data in Table 1-1, it is found that the power function can fit the data well. The data in Table 1-1 are fitted according to the power function. Figure 8 The dashed line is the fitting line, and the power function formula of the dashed line is as follows: Figure 8 As shown in the upper right corner, the mathematical expression of the function is:

[0129] y=991.38x -1.067 #(1)

[0130] Table 1-1 Angle and offset correspondence table

[0131] Angle (°) 18 14 10 7 4 3 Offset distance (m) 45 60 90 110 255 290

[0132] The independent variable x represents the angle. By substituting the custom angle, the distance y that the pinch-out point of the sand body should be offset at this angle can be calculated, with R2=0.975, which is a very good fit.

[0133] Since the modeling of this geological model is consistent with the sedimentary pattern of the region, the velocity field data of the forward simulation are all derived from the well logging data, which can restore the actual lithological characteristics and seismic characteristics of the region relatively well. Therefore, the distance that needs to be offset for calculating the pinch-out point at different slopes can be used according to formula (1) to correct the boundary range of the reservoir sand body that pinches out in the updip direction.

[0134] 2. Intelligent automatic identification of actual work area data traps

[0135] The lithology data is calculated by the inversion method, and the structural map is obtained by time-depth conversion of the seismic interpretation stratum. The coordinate system is unified (the calculation resolution can be customized. In this example, the resolution is 25m×25m, and the highest resolution is the seismic data resolution. The larger the resolution, the longer the calculation time) and the plane distribution characteristics of the reservoir sand are identified by intelligence. Fig. 9 a), since the data sources of seismic attributes and geophysical inversion results are both seismic data, and the vertical resolution of seismic data is limited by a quarter wavelength, the lithologic traps determined by seismic data and inversion data need to be corrected for the pinch-out line. According to the stratigraphic overlap pinch-out recovery method summarized at the end of the previous section, the algorithm will continue to convert the pinch-out line into stratigraphic structural depth data, and calculate the slope of the stratum from the actual stratigraphic height difference. Finally, according to formula (1), the distance that needs to be extended is calculated, and finally the pinch-out recovery map of the reservoir sand body is completed ( Fig. 9 b). Fig.10 It shows the prediction of lithologic traps in a certain sequence of the Dongying Formation in the southern section of the Liaoxi Uplift. This area is mainly stratigraphically shielded traps. Fig.10 a is a superimposed display of sandstone, mudstone, fault and its overlying layer. The intelligent identification of traps is as follows: Fig.10As shown in (b), it can be seen that the intelligent algorithm is more precise, comprehensive and accurate in identifying closures than manual identification. Fig. 9 a. Sand body identified by the intelligent trap identification algorithm; b. The result of the sand body updip pinch-out offset correction by the intelligent trap identification algorithm. Fig.10 In the figure, a. Superposition display of sand body and cap rock; b. Intelligent identification result of lithologic trap.

[0136] The above-described embodiments are used to illustrate the present invention, not to limit the present invention, so changes in the exemplified values ​​or replacement of equivalent elements should still fall within the scope of the present invention.

[0137] From the above detailed description, it can be understood by those skilled in the art that the present invention can indeed achieve the aforementioned objectives and is in compliance with the provisions of the Patent Law.

[0138] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they are aware of the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. It should be pointed out that any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0139] It should be noted that the above description of the relevant process is only for example and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the process under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0140] The basic concepts have been described above. Obviously, for those of ordinary skill in the art who have read this application, the above invention disclosure is only for example and does not constitute a limitation of this application. Although not explicitly stated here, those of ordinary skill in the art may make various modifications, improvements and amendments to this application. Such modifications, improvements and amendments are suggested in this application, so such modifications, improvements and amendments still belong to the spirit and scope of the exemplary embodiments of this application.

[0141] At the same time, the present application uses specific words to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present application can be appropriately combined.

[0142] In addition, it will be appreciated by those skilled in the art that various aspects of the present application may be illustrated and described by a number of patentable categories or situations, including any new and useful combination of processes, machines, products or substances, or any new and useful improvements thereto. Therefore, various aspects of the present application may be implemented entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may all be referred to as "units", "modules" or "systems". In addition, various aspects of the present application may take the form of a computer program product embodied in one or more computer-readable media, wherein computer-readable program code is contained therein.

[0143] The computer program code required for the operation of each part of the application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., such as C programming language, VisualBasic, Fortran2103, Perl, COBOL2102, PHP, ABAP, such as Python, Ruby and Groovy dynamic programming language or other programming languages. The program code can be run completely on the user's computer, or run on the user's computer as an independent software package, or run partly on the user's computer and partly on the remote computer, or run completely on the remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0144] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this application, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this application. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this application. For example, although the implementation of the various components described above can be embodied in a hardware device, it can also be implemented as a pure software solution, for example, installation on an existing server or mobile device.

[0145] Similarly, it should be noted that in order to simplify the description disclosed in this application and thus help understand one or more embodiments of the invention, in the above description of the embodiments of the application, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this method of the application should not be interpreted as reflecting the intention that the claimed object requires more features than those explicitly stated in each claim. On the contrary, the subject of the invention should have fewer features than the above single embodiment.

Claims

1. A lithologic trap intelligent identification method, characterized in that: include: S1. Interpret the seismic exploration and logging data, calibrate the total records, interpret the seismic horizons, and obtain seismic data and synthetic records; S2. Based on the seismic data and synthetic records, high-precision lithology inversion is performed to obtain lithology inversion results; S3. Perform lithology analysis based on the lithology inversion results and set sand and mudstone thresholds; S4. Based on the sand and mudstone threshold values, the lithology inversion results of the three-dimensional data volume are converted into a sandstone distribution data volume; S5. converting the fault data and seismic layer data in the seismic data into depth domain data according to the time-depth relationship; S6. Based on the depth domain data, the distribution range of the sand body that meets the trap conditions is calculated according to the trap identification algorithm; S7. Based on the geophysical two-dimensional forward modeling, obtain the fitting formula of slope and trap sand body offset distance; S8. Based on the slope and the trap sand body offset distance fitting formula, the sand body boundary in the sandstone distribution data volume is corrected.

2. The lithologic trap intelligent identification method according to claim 1, characterized in that: In S6, the method for establishing the data required by the trap identification algorithm is as follows: Unify the top surface structural diagrams of the formation into inLine, xLine, and Z formats; Reading the fault polygon data in the interpreted seismic data, and converting the storage format of the fault polygon data from the horizontal coordinate, vertical coordinate, and identification code format to the inLine, xLine, and identification code format; The formats of formation sand-to-ground ratios calculated by geophysical lithology inversion are unified into inLine, xLine, and Value formats; The data after the above conversion is read in and a model is built inside the computer.

3. The lithologic trap intelligent identification method according to claim 1, characterized in that: In S6, the maze pathfinding step of the trap identification algorithm includes: S61. discretize the lithology results of the target layer according to the actual resolution requirement, and calculate the data of the lithology trap; S62. Perform pathfinding calculation on the sampling point (x, y) pointed by the pointer, and determine whether the pointer can be moved in the four directions of up, right, down, and left. If it can be moved and the direction of movement is elevation, move one step clockwise, and refresh the pointer position to (x+1, y) or (x-1, y) or (x, y+1) or (x, y-1), and jump to S63; otherwise, jump to S65; S63. If the position after moving is not the "exit", add the moving direction to the end of the "direction list", mark the current position as "inaccessible", and jump to S62; otherwise jump to S64; S64. If the position after the movement is the "exit", determine whether the pointer points to the end of the sampling point set. If the pointer points to the end of the sampling point set, save the "trap result set" and the closure identification algorithm ends; otherwise, point the pointer to the next uncalculated sampling point and refresh the value of (x, y) pointed by the pointer, and jump to S62; S65. If the pointer can move in the four directions of up, right, down and left, but the elevation rise in the moving direction cannot be achieved at the same time, then move back one step according to the direction value saved at the end of the "direction list", the "direction list" discards the end value, and jumps to S62.

4. The lithologic trap intelligent identification method according to claim 3, characterized in that: S61 includes: S611. Number each sandstone sampling point according to the discrete result; S612. Set a pointer to point to the first digit of the sandstone sampling point number, and set the coordinates pointed to by the pointer to (x, y); S613. Set a dynamic linked list type array for storing directions; S614. Set a two-dimensional array, the size of which is the same as the discretized lithology result, to store the trap identification result.

5. The lithologic trap intelligent identification method according to claim 1, characterized in that: The fitting formula of the slope and the sand body offset distance is: y = 991.38x -1.067 ; x represents the angle, and y is the distance that the sand body pinch-out point should be offset at the x angle.

6. A lithologic trap intelligent identification system, characterized in that: Used to implement the lithologic trap intelligent identification method described in any one of claims 1 to 5; The lithologic trap intelligent identification system comprises: The interpretation and synthesis module is used to interpret the seismic exploration and logging data, calibrate the total records, interpret the seismic horizons, and obtain seismic data and synthetic records; An inversion module, used for performing high-precision lithology inversion based on the seismic data and synthetic records to obtain lithology inversion results; An analysis module, used for performing lithology analysis based on the lithology inversion result and setting sandstone and mudstone threshold values; A first conversion module is used to convert the lithology inversion result of the three-dimensional data body into a sandstone distribution data body based on the sandstone and mudstone threshold values; A second conversion module is used to convert the fault data and seismic layer data in the seismic data into depth domain data according to the time-depth relationship; A trap identification algorithm module is used to calculate the distribution range of sand bodies that meet the trap conditions based on the depth domain data and the trap identification algorithm; The formula fitting module is used to obtain the fitting formula of slope and trap sand body offset distance based on geophysical two-dimensional forward modeling; The correction module is used to correct the sand body boundary in the sandstone distribution data body based on the slope and the trap sand body offset distance fitting formula.

7. A computer-readable storage medium, characterized in that: The medium stores computer instructions. When a computer reads the computer instructions, the computer executes the lithologic trap intelligent identification method according to any one of claims 1 to 5.

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