Tight sandstone reservoir distribution prediction method based on two-dimensional earthquake, terminal equipment and storage medium

Abstract

The invention relates to the technical field of seismic reservoir inversion prediction, in particular to a compact sandstone reservoir distribution prediction method based on two-dimensional earthquakes, terminal equipment and a storage medium, and aims to solve the problem of poor seismic waveform consistency at different survey line intersections of two-dimensional seismic inversion through survey line intersection trace pseudo-well constraint inversion, perform survey line consistency adjustment and improve the seismic waveform distribution prediction accuracy. And establishing a target function by using the inversion result at the intersection point of the measuring lines, solving the equation by adopting a simulated annealing method to obtain a parameter value, and finally adjusting the inversion result of each measuring line to obtain a final target equation. According to the method, the problem of poor seismic waveform consistency of a two-dimensional earthquake at the intersection point of different measuring lines can be improved, the inversion precision is improved, the distribution characteristics of the sandstone reservoir are accurately described, and technical support is provided for subsequent utilization and development of oil and gas resources.

Description

A method, terminal equipment, and storage medium for predicting the distribution of tight sandstone reservoirs based on 2D seismic data. Technical Field

[0001] This invention relates to the field of seismic reservoir inversion and prediction technology, and is a method, terminal equipment and storage medium for predicting the distribution of tight sandstone reservoirs based on two-dimensional seismic data. Background Technology

[0002] Tight sandstone reservoirs, as an important unconventional oil and gas resource, possess enormous potential reserves that can effectively compensate for the declining conventional oil and gas reserves caused by exploration and development. In my country, tight oil and gas are widely distributed and geologically abundant, with large amounts of tight sandstone reservoirs developed in regions such as the Ordos Basin, Sichuan Basin, Songliao Basin, Bohai Bay Basin, and Junggar Basin. Therefore, exploring predictive methods for the distribution of tight sandstone reservoirs is of great significance to my country's energy development.

[0003] With the development of horizontal drilling and fracturing technologies, tight sandstone reservoirs have become important oil and gas resources. However, due to their scattered sand body distribution, poor continuity, unclear spatial distribution characteristics, and thin reservoir thickness, reservoir prediction faces significant challenges. Predicting tight sandstone reservoirs and exploring the distribution patterns of high-quality reservoirs plays a crucial role in the effective exploration and development of these reservoirs.

[0004] Currently, commonly used prediction methods in the industry include pre-stack waveform indicator inversion methods, phase-controlled seismic inversion, geostatistical inversion methods, and nonlinear inversion algorithms controlled by phase models, which elevate the single inversion problem to a joint inversion problem. However, these prediction methods originate from macroscopic seismic facies exploration and implement sequence stratigraphic interpretation, without considering lithological correlation and seismic attributes. Summary of the Invention

[0005] This invention provides a method, terminal equipment, and storage medium for predicting the distribution of tight sandstone reservoirs based on two-dimensional seismic data. It overcomes the shortcomings of the prior art and can effectively solve the problem that existing prediction methods do not consider lithological correlation and seismic attributes.

[0006] One of the technical solutions of this invention is achieved through the following measures: a method for predicting the distribution of tight sandstone reservoirs based on two-dimensional seismic logging, comprising the following steps: finding the nearest projection point of the wells in the work area; performing well-seismic calibration on each well; performing well-constrained inversion; performing pseudo-well-constrained inversion at intersection points; adjusting the consistency of logging lines; obtaining the sandstone-mudstone differentiation criteria based on logging curves and logging interpretation results; obtaining the sandstone distribution on the logging lines based on the sandstone-mudstone differentiation criteria; calculating the variation functions in the x and y directions according to the sandstone distribution on the two-dimensional logging lines; obtaining the sandstone distribution between logging lines; and performing an inverse transformation on the x-coordinate.

[0007] The following are further optimizations and / or improvements to one of the above-mentioned technical solutions:

[0008] The above-mentioned intersection point pseudo-well constraint inversion may include the following steps: using a two-dimensional survey line that passes through many wells, perform phase-controlled intelligent waveform chaotic inversion under the constraint of the wells to obtain preliminary inversion results; using the preliminary inversion results of the survey line, extract the inversion results of intersection points with other survey lines as pseudo-wells, and constrain other survey lines together with other wells for inversion; compare the consistency of inversion results at different survey line intersection points, and if the difference is large, use the new inversion results as pseudo-wells and invert again.

[0009] The above-mentioned adjustment of survey line consistency may include the following steps: Assuming that the two-dimensional survey line has N intersection points, M survey lines, and V x (t) represents the inversion result of a survey line in the x-direction, V y (t) represents the inversion result along a certain survey line in the y direction; V xi (t) and V yi (t) represents the inversion results of the two survey lines x and y passing through the intersection point i at the intersection point i (1≤x, y≤M);

[0010]

[0011]

[0012] Establish the objective function:

[0013]

[0014] Choose a long survey line passing through the middle of the work area, and select its corresponding coefficient k. m =1, b m =0; k is obtained by solving the objective function equation using the simulated annealing method. j and b j (1≤j≤m), adjust the inversion results for each survey line, with the objective function being:

[0015] I m (t)=k m v m (t)+b m (4)

[0016] The distribution of sandstone between survey lines can be obtained using the Kriging interpolation method.

[0017] When obtaining the sandstone distribution between survey lines using the Kriging interpolation method, if γ x ≠γ y Therefore, the x-coordinate needs to be transformed, and the transformation formula is as follows:

[0018]

[0019] In the formula, γ x and γ y These represent the ranges in the x and y directions, respectively.

[0020] Then according to the range γ y Perform Kriging interpolation.

[0021] The inverse transformation of the x-coordinate described above may specifically include the following steps:

[0022] After Kriging interpolation is completed, the x-coordinate is restored using the following formula:

[0023]

[0024] The second technical solution of the present invention is achieved through the following measures: a terminal device, including a memory and a processor, wherein the memory stores a program that can run on the processor, and the processor executes the program to implement the above-mentioned method for predicting the distribution of tight sandstone reservoirs based on two-dimensional seismic data.

[0025] The third technical solution of the present invention is achieved through the following measures: a storage medium storing one or more programs, which can be executed by one or more processors to realize the above-mentioned method for predicting the distribution of tight sandstone reservoirs based on two-dimensional seismic data.

[0026] This invention addresses the problem of poor seismic waveform consistency at different seismic line intersections in 2D seismic inversion by employing pseudo-well constraint inversion at seismic line intersection points. First, seismic line consistency is adjusted. Then, an objective function is established using the inversion results at the seismic line intersection points. The equations are solved using simulated annealing to obtain parameter values. Finally, the inversion results for each seismic line are adjusted to obtain the final objective equation. This invention addresses the difficulties of 2D seismic inversion by utilizing a phase-controlled intelligent waveform chaotic inversion method and designing an inversion process with pseudo-well constraints at intersection points. This invention proposes an effective method for predicting the distribution of tight sandstone reservoirs based on 2D seismic data. It can improve the problem of poor seismic waveform consistency at different seismic line intersection points, increase inversion accuracy, accurately characterize the distribution features of sandstone reservoirs, and provide technical support for the subsequent utilization and development of oil and gas resources. Attached Figure Description

[0027] Figure 1 is a flowchart illustrating the method for predicting the distribution of tight sandstone reservoirs based on two-dimensional seismic data according to an embodiment of the present invention.

[0028] Figure 2 is a schematic diagram of a two-dimensional seismic inversion survey line according to an embodiment of the present invention.

[0029] Figure 3 is a sandstone thickness distribution diagram according to an embodiment of the present invention. Detailed Implementation

[0030] The present invention is not limited to the following embodiments, and the specific implementation can be determined according to the technical solution of the present invention and the actual situation.

[0031] The present invention will be further described below with reference to embodiments:

[0032] Example 1: As shown in Figures 1 to 3, the method for predicting the distribution of tight sandstone reservoirs based on two-dimensional seismic data includes the following steps:

[0033] Step 1: Locate the nearest projection point of the well within the work area;

[0034] Step 2: Perform well vibration calibration on each well;

[0035] Step 3: Perform well-constrained inversion, i.e. phase-controlled intelligent waveform chaotic inversion, to improve the resolution of thin-layer inversion;

[0036] Step 4: Intersection Pseudo-Well Constraint Inversion; Intersection pseudo-well constraint inversion is an inversion method that enhances pseudo-well constraints. In this embodiment, as shown in Figure 2, the specific steps for performing intersection pseudo-well constraint inversion are as follows: First step: Using the two-dimensional survey line ② which passes through many wells, perform phase-controlled intelligent waveform chaotic inversion under the constraint of the wells to obtain preliminary inversion results; Second step: Using the preliminary inversion results of survey line ②, extract the inversion results of intersections with other survey lines as pseudo-wells, and constrain other survey lines together with other wells, such as survey line ⑦; Third step: Compare the consistency of inversion results at the intersections of different survey lines. If the difference is large, use the new inversion results as pseudo-wells and invert again.

[0037] Step 5: Survey Line Consistency Adjustment; The specific steps involved in survey line consistency adjustment are as follows: Assume there are N intersection points and M survey lines in the two-dimensional survey line, V... x (t) represents the inversion result of a survey line in the x-direction, V y (t) represents the inversion result along a certain survey line in the y direction; V xi (t) and V yi (t) represents the inversion results of the two survey lines x and y passing through the intersection point i at the intersection point i (1≤x, y≤M);

[0038]

[0039]

[0040] Theoretically V x (t) and V y(t) are equal, but in reality, we need to minimize the error. Therefore, we establish the objective function:

[0041]

[0042] Choose a long survey line passing through the middle of the work area, for example, m, and its corresponding coefficient k. m =1, b m =0; k is obtained by solving the objective function equation using the simulated annealing method. j and b j (1≤j≤m), adjust the inversion results for each survey line, with the objective function being:

[0043] I m (t)=k m v m (t)+b m (4)

[0044] Step 6: Based on the logging curves and logging interpretation results, obtain the criteria for distinguishing sandstone and mudstone;

[0045] Step 7: Based on the sandstone-mudstone differentiation criteria, obtain the sandstone distribution along the survey line;

[0046] Step 8: Based on the sandstone distribution along the two-dimensional survey line, calculate the variation functions in the x and y directions respectively. In this embodiment, the ranges are γ... x and γ y ;

[0047] Step Nine: Obtain the sandstone distribution between survey lines; in this embodiment, the sandstone distribution between survey lines is obtained using Kriging interpolation. When obtaining the sandstone distribution between survey lines using Kriging interpolation, if γ x ≠γ y Therefore, the x-coordinate needs to be transformed, and the transformation formula is:

[0048]

[0049] In the formula, γ x and γ y These represent the ranges in the x and y directions, respectively.

[0050] Then according to the range γ y Perform Kriging interpolation.

[0051] Step 10: Perform an inverse transformation on the x-coordinate. The inverse transformation of the x-coordinate includes the following steps:

[0052] After Kriging interpolation is completed, the x-coordinate is restored using the following formula:

[0053]

[0054] In this embodiment, as shown in Figure 1, the present invention mainly improves the problem of poor seismic waveform consistency at the intersection points of different seismic lines in 2D seismic inversion by using pseudo-well constraint inversion at the intersection points of seismic lines. First, seismic line consistency adjustment is performed. An objective function is established using the inversion results at the intersection points of the seismic lines. Simulated annealing is used to solve the equation to obtain parameter values. Finally, the inversion results of each seismic line are adjusted to obtain the final objective equation. As shown in Figure 2, using the 2D seismic line ② which passes through many wells, phase-controlled intelligent waveform chaotic inversion is performed under well constraints. Using the preliminary inversion results of seismic line ②, the inversion results at intersection points with other seismic lines are extracted as pseudo-wells and used to constrain the inversion of other seismic lines together with these other wells. As shown in Figure 3, in the sandstone thickness distribution map, based on the sand body characterization of the 2D seismic lines, the sand body thickness distribution map within the study area is obtained.

[0055] This 2D seismic-based method for predicting the distribution of tight sandstone reservoirs improves the inconsistency of seismic waveforms at different seismic line intersections by employing pseudo-well constraint inversion at seismic line intersection points. First, seismic line consistency is adjusted. Then, an objective function is established using the inversion results at the seismic line intersection points. The equations are solved using simulated annealing to obtain parameter values. Finally, the inversion results for each seismic line are adjusted to obtain the final objective equation. Addressing the challenges of 2D seismic inversion, a phase-controlled intelligent waveform chaotic inversion method is used, and a pseudo-well constraint inversion process at intersection points is designed. This 2D seismic-based method for predicting the distribution of tight sandstone reservoirs can improve the inconsistency of seismic waveforms at different seismic line intersection points, enhance inversion accuracy, accurately characterize the distribution features of sandstone reservoirs, and provide technical support for subsequent oil and gas resource utilization and development.

[0056] Example 2: This example provides a terminal device, which includes a memory, a processor, a communication interface, and a communication bus. The memory stores a program that can run on the processor. When the processor executes the program, it implements the tight sandstone reservoir distribution prediction method based on two-dimensional seismic data in the above example.

[0057] The processor can be a central processing unit, or it can be other general-purpose processors, digital signal processors, application-specific integrated circuits, field-programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0058] The memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and units, such as the program units corresponding to those in the above-described method embodiments of the present invention. The processor executes various functional applications and data processing by running the non-transitory software programs, instructions, and modules stored in the memory, thereby realizing the method for predicting the distribution of tight sandstone reservoirs based on two-dimensional seismic data in the above embodiments.

[0059] The memory may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor, etc. Furthermore, the memory may include high-speed random access memory and non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. The memory may optionally include memory remotely located relative to the processor, which can be connected to the processor via a network. One or more programs are stored in the memory and, when executed by the processor, perform the tight sandstone reservoir distribution prediction method based on two-dimensional seismic data as described in the above embodiments.

[0060] Example 3: This example provides a storage medium for computer-readable storage. The storage medium stores one or more programs, which can be executed by one or more processors to implement the tight sandstone reservoir distribution prediction method based on two-dimensional seismic data as described in the above examples.

[0061] The storage medium can be an internal storage unit of the terminal device, such as the hard drive or memory of the terminal device. Alternatively, the storage medium can be an external storage device of the terminal device, such as a plug-in hard drive, smart memory card, secure digital card, or flash memory card installed on the terminal device.

[0062] The above technical features constitute the embodiments of the present invention, which have strong adaptability and implementation effect. Unnecessary technical features can be added or removed according to actual needs to meet the needs of different situations.

Claims

1. A method for predicting the distribution of tight sandstone reservoirs based on two-dimensional seismic data, characterized in that... Includes the following steps: Find the nearest projection point of the wells within the work area; perform well seismic calibration for each well; perform well-constrained inversion; Intersection pseudo-well constraint inversion; logging line consistency adjustment; obtaining sandstone and mudstone differentiation criteria based on logging curves and logging interpretation results; obtaining sandstone distribution on the logging line based on the sandstone and mudstone differentiation criteria; calculating the variation functions in the x and y directions according to the sandstone distribution on the two-dimensional logging line; obtaining the sandstone distribution between logging lines; performing inverse transformation on the x coordinate.

2. The method for predicting the distribution of tight sandstone reservoirs based on two-dimensional seismic data according to claim 1, characterized in that... When performing pseudo-well constraint inversion at intersection points, the specific steps include: using two-dimensional survey lines that pass through many wells, performing phase-controlled intelligent waveform chaotic inversion under the constraint of the wells to obtain preliminary inversion results; using the preliminary inversion results of the survey lines, extracting the inversion results of intersection points with other survey lines as pseudo-wells, and constraining other survey lines together with other wells for inversion; comparing the consistency of inversion results at intersection points of different survey lines, if the difference is large, using the new inversion results as pseudo-wells, and re-inverting.

3. The method for predicting the distribution of tight sandstone reservoirs based on two-dimensional seismic data according to claim 1 or 2, characterized in that... When adjusting the consistency of survey lines, the specific steps include: Assuming there are N intersection points and M survey lines in a two-dimensional survey line, V x (t) represents the inversion result of a survey line in the x-direction, V y (t) represents the inversion result along a certain survey line in the y direction; V xi (t) and V yi (t) represents the inversion results of the two survey lines x and y passing through the intersection point i at the intersection point i (1≤x, y≤M); Establish the objective function: Choose a long survey line passing through the middle of the work area, and select its corresponding coefficient k. m =1, b m =0; k is obtained by solving the objective function equation using the simulated annealing method. j and b j (1≤j≤m), adjust the inversion results for each survey line, with the objective function being: I m (t)=k m v m (t)+b m (4).

4. The method for predicting the distribution of tight sandstone reservoirs based on two-dimensional seismic data according to claim 1 or 2, characterized in that... The distribution of sandstone between survey lines was obtained using the Kriging interpolation method.

5. The method for predicting the distribution of tight sandstone reservoirs based on two-dimensional seismic data according to claim 4, characterized in that... When using Kriging interpolation to obtain the sandstone distribution between survey lines, if γ x ≠γ y Therefore, the x-coordinate needs to be transformed, and the transformation formula is: In the formula, γ x and γ y The ranges in the x and y directions are respectively; then, according to the range γ... y Perform Kriging interpolation.

6. The method for predicting the distribution of tight sandstone reservoirs based on two-dimensional seismic data according to claim 1, 2, or 5, characterized in that... The inverse transformation of the x-coordinate includes the following steps: After Kriging interpolation, the x-coordinate is restored using the following formula:

7. A terminal device, comprising a memory and a processor, wherein the memory stores a program executable on the processor, characterized in that, When the processor executes the program, it implements the tight sandstone reservoir distribution prediction method based on two-dimensional seismic data as described in any one of claims 1 to 6.

8. A storage medium, characterized in that, The storage medium stores one or more programs, which can be executed by one or more processors to implement the method for predicting the distribution of tight sandstone reservoirs based on two-dimensional seismic data as described in any one of claims 1 to 6.

Images