Well control grid-based seismic data classification evaluation method and system

Abstract

The invention discloses a well control grid-based seismic data classification evaluation method and system, and the method comprises the steps: screening seismic data, analyzing the speed in a research region, building a speed field according with the geological condition, recognizing the fault scale of a target layer, and analyzing the seismic problem according to the fault scale and the speed field. And obtaining a classification result. The well and seismic data are closely integrated through fine well-seismic calibration, the low resolution of the seismic is constrained by the high resolution of the well, the concept of the horizon well-seismic offset and the fault well-seismic offset is introduced, the seismic data classification evaluation standard based on the well control grid is provided, and on the premise of geosteering, the seismic data classification evaluation standard is provided. Quantitative evaluation based on the well control grid is carried out on the quality of the seismic data according to the theoretical resolution of the seismic data, the limitation of conventional seismic data evaluation is broken through, and the method has more realistic significance for defining the rolling potential of a region.

Description

Technical Field

[0001] The present invention relates to the technical field of oil exploration and development, and in particular to an evaluation method and system for seismic data classification based on a well control grid. Background Art

[0002] Traditional seismic data quality evaluation systems typically use fuzzy statistics to comprehensively evaluate the seismic phases of standard axes and local non-standard layers. This approach primarily focuses on the signal-to-noise ratio of reflection events, the layer characteristics of reflection axes, and seismic migration imaging, establishing a three-level evaluation standard: reliable, relatively reliable, and unreliable. As the challenges of discovering oil and gas field exploration and development potential and the difficulty of detailed reservoir description increase, the purpose of seismic data evaluation varies, and evaluation principles are incomplete. This disconnect between evaluation results and the discovery of rolling potential targets hinders the effectiveness of rolling exploration.

[0003] Moreover, there is a common problem of low accuracy in fault and layer interpretation when conducting detailed interpretation based on existing seismic data, which is mainly manifested as: the breakpoints of the seismic data are not clear enough; the breakpoint positions of the seismic profile do not match the breakpoints on the well; the reflection characteristics of the target layer are unclear and have strong multi-solution characteristics; and the seismic layer does not match the stratification on the well.

[0004] Unclear fault breakpoints and unclear reflection layer characteristics will lead to multiple solutions in seismic interpretation, which will directly affect the existence of the trap. Inconsistency between the fault breakpoints and the reflection characteristics of the target layer and the well information will cause a large difference between the high point position controlled by the fault in the time domain and the high point position in the actual depth domain, affecting the reliability of the trap. Summary of the Invention

[0005] In response to the above-mentioned defects, the technical problem solved by the present invention is to provide a seismic data classification and evaluation method based on a well-controlled grid, which quantitatively evaluates the quality of seismic data based on the well-controlled grid according to the theoretical resolution of the seismic data, breaking the limitations of conventional seismic data evaluation and having more practical significance for clarifying the regional rolling potential.

[0006] A first aspect of the present invention provides an evaluation method for seismic data classification based on a well control grid, the method comprising: screening seismic data, analyzing the velocity within the study area, establishing a velocity field that conforms to the geological conditions, identifying the fault scale of the target layer, analyzing the seismic problem based on the fault scale and velocity field, and obtaining a classification result.

[0007] According to one embodiment of the present invention, the velocity analysis within the study area and the establishment of a velocity field that conforms to the geological conditions include: determining seismic records related to the main frequency of the seismic data, comparing the seismic records with data superimposed on the well bypass, and processing the data to obtain time-depth data, and obtaining a well-controlled velocity model and velocity field based on the time-depth data.

[0008] According to one embodiment of the present invention, the seismic records are synthesized by Ricker wavelets.

[0009] According to one embodiment of the present invention, the comparing the seismic record with the superimposed data of the well bypass includes: comparing the seismic synthetic record with the superimposed data of the well bypass by adjusting the wavelet frequency.

[0010] According to one embodiment of the present invention, processing the data to obtain time-well depth relationship data includes: obtaining key well time-depth relationship data by up and down time shift and local stretching and compression methods.

[0011] According to one embodiment of the present invention, obtaining a well control velocity model and a velocity field based on the time-depth relationship data includes:

[0012] The well-controlled velocity model is obtained by interpolation, and the velocity field is obtained by fusing the well-controlled velocity model with the seismic average velocity model.

[0013] According to one embodiment of the present invention, the seismic problem is analyzed according to the fault scale and velocity field to obtain a classification result, including: determining the seismic data evaluation parameters according to the fault scale and velocity field, including: whether the section is clear, whether the layer is clear, the fault well-seismic consistency and the layer well-seismic consistency.

[0014] The seismic data is analyzed according to the seismic data evaluation parameters to obtain classification results, including:

[0015] If the fault or layer is not clear, the first type of evaluation result of the seismic data is obtained.

[0016] If the faults or horizons are clear, and the fault and horizon coincidence degrees are both at the first threshold, the second type of evaluation result of the seismic data is obtained.

[0017] If one of the fault and stratigraphic consistency does not reach the first threshold, but reaches the second threshold, the third type of seismic data evaluation result is obtained.

[0018] All other situations are the fourth type of evaluation results of seismic data.

[0019] A second aspect of the present invention provides a classification and evaluation system for seismic data based on a well control grid, comprising:

[0020] Screening module, used to screen seismic data;

[0021] The first analysis module is used to analyze the velocity in the study area and establish a velocity field that conforms to the geological conditions;

[0022] The second analysis module is used to identify the fault scale according to the velocity field, analyze the seismic data according to the fault scale, and obtain a classification result.

[0023] The third aspect of the present invention provides an intelligent device, including a transmitter, a receiver, a memory and a processor; the memory is used to store computer instructions; the processor is used to run the computer instructions stored in the memory to implement the above classification and evaluation method of seismic data based on the well control grid.

[0024] A fourth aspect of the present invention provides a storage medium comprising: a readable storage medium and computer instructions, wherein the computer instructions are stored in the readable storage medium; the computer instructions are used to implement the above classification and evaluation method of seismic data based on the well control grid.

[0025] The beneficial effects provided by the present invention are as follows: through fine well-seismic calibration, wells and seismic data are closely integrated, the high resolution of wells is used to constrain the low resolution of seismic data, the concepts of well-seismic offset distance of horizon and well-seismic offset distance of fault are introduced, and a seismic data classification and evaluation standard based on the well-controlled grid is proposed. Under the premise of geological guidance, the quality of seismic data is quantitatively evaluated based on the well-controlled grid according to the theoretical resolution of seismic data, breaking the limitations of conventional seismic data evaluation and having more practical significance for clarifying the rolling potential of a region. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0027] Figure 1 A flow chart of a classification and evaluation method for seismic data based on a well control grid disclosed in an embodiment of the present invention;

[0028] Figure 2 A standard process for qualitative evaluation of seismic data of a classification evaluation method of seismic data based on a well control grid disclosed in an embodiment of the present invention;

[0029] Figure 3 A schematic diagram of a quantitative evaluation standard for regional seismic data established according to actual conditions for the classification and evaluation method for seismic data based on the well control grid disclosed in an embodiment of the present invention;

[0030] Figure 4 This is a schematic diagram of the evaluation results of seismic data in the SN area according to the classification evaluation method of seismic data based on the well control grid disclosed in an embodiment of the present invention.

[0031] The above drawings illustrate specific embodiments of the present disclosure, which will be described in more detail below. These drawings and textual descriptions are not intended to limit the scope of the present disclosure in any way, but rather to illustrate the concepts of the present disclosure to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0032] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.

[0033] Based on the optimization of seismic data, the present invention establishes well control statistical standards, subdivides the factors affecting seismic evaluation, clarifies key parameters, establishes classification evaluation standards, and ultimately clarifies the rolling potential of the region. The specific implementation method is as follows:

[0034] In the actual SN block research, since there are a lot of seismic data available in each block, it is necessary to first select the data that is more conducive to seismic interpretation.

[0035] Due to the plane velocity variation, regional velocity analysis is required during well-seismic calibration to select the appropriate velocity for calibration.

[0036] It is necessary to clarify the reflection characteristics of faults and marker layers in each area and use them as the criterion for judging the goodness of fit between well and seismic data. Based on the seismic data analysis, the goodness of fit between well and seismic data between faults and layers is evaluated based on the well control grid.

[0037] like Figure 1 As shown, the method includes: screening seismic data, analyzing the velocity in the study area, establishing a velocity field that conforms to the geological conditions, identifying the fault scale of the target layer, analyzing the seismic problem based on the fault scale and velocity field, and obtaining a classification result.

[0038] Specifically, first, count N sets of seismic data in the study area. First, compare the acquisition years and characteristics of each set of data, and select data with a relatively new age, unified bin, and high and uniform coverage; second, compare the processing time and methods, and select seismic data with mature prestack time (depth) migration technology after 2012; third, compare the processing purposes and requirements, and select processed data that meet the requirements according to the research content. For example, for reservoir prediction, data with improved resolution should be preferred. On this basis, select M (M < N) sets of seismic data; finally, check the main frequency (20 hz - 28 hz), frequency bandwidth (5 - 60 hz), signal-to-noise ratio of each set of seismic data, the reflection characteristics of the target layer, and the characteristics of fault imaging, and select 2 - 3 sets of seismic data with high main frequency, wide frequency bandwidth, high signal-to-noise ratio, reflection characteristics of the target layer that conform to geological laws, and clear fault breakpoints.

[0039] Secondly, through fine well-seismic calibration, analyze the velocity in the study area and establish a velocity field that conforms to the geological situation. First, according to the preliminary analysis of the seismic data in the above steps, select a Ricker wavelet with a main frequency equivalent to that of the seismic data to synthesize a seismic record. By adjusting the wavelet frequency, superimpose and compare the synthesized record with the well-side trace. Through up and down time shifting, local stretching and compression, etc., improve the accuracy of horizon calibration and obtain a relatively accurate well time-depth relationship; secondly, repeat the above steps to obtain the time-depth relationships of key wells in the work area.

[0040] The work area refers to a specific area for geological exploration (i.e., the work area). In oil and gas exploration, the work area is a region within a specific geographical area where drilling, exploration, development, and other work are carried out.

[0041] Key wells refer to some drilling or oil production wells in a work area that are considered key wells. These wells usually have high geological value, exploration value, or economic value, may represent the key geological information of the area, or may provide important data for unexploited resources.

[0042] The time-depth relationship describes the relationship between the propagation time of seismic waves and the depth of underground rock formations. Since seismic waves travel at different speeds in different underground media, there is a relationship between the time of seismic data and the well depth. The time-depth relationship is usually corrected by reflection seismic data and well data to form a time-depth conversion of reflected waves.

[0043] During the exploration process, understanding the time-depth relationship is crucial for converting seismic data into an underground structure model. The propagation time of seismic waves can be converted into depth, which helps geologists understand the underground structure and the distribution of oil and gas layers.

[0044] The analysis of the time-depth relationship of key wells can be used to verify the accuracy of seismic exploration data, perform depth correction, and ensure that the underground geological structure reflected by the seismic data is accurate.

[0045] "Time-depth relationship of key wells in the work area" is a data analysis of key wells in a specific work area, used to study the conversion relationship between time and underground depth, especially for depth correction in seismic exploration.

[0046] An initial well-controlled velocity model is interpolated based on multiple interpreted target layers. Seismic stacking velocities are then converted using the Dix formula to generate an average velocity curve. This well-controlled velocity model is then integrated with the seismic average velocity model and corrected using well stratification to obtain a velocity field that conforms to the geological conditions. It is important to note that velocities vary significantly between the upper and lower walls of a major fault. Wells in the same wall exhibit minimal velocity variation along the strike direction of the fault, but the average velocity increases with depth, highlighting the influence of specific geological bodies (igneous rocks) on velocity.

[0047] On the basis of the above-mentioned well-seismic calibration, the reflection characteristics of the main target layer are clarified, such as the strong peak reflection and weak peak reflection of the three-phase; the influence of the special geological body (igneous rock) on the target layer is confirmed. Due to the high-speed shielding of the special geological body (igneous rock), the upper and lower strata show blank reflection, and the occurrence and reflection characteristics of the target layer are affected; the fault scale (20m) with the minimum fault throw that can be identified by seismic data is clarified. First, the existence of the fault is qualitatively determined based on the misalignment and distortion of the seismic phase axis, the abnormal wave group relationship, and the abnormal change of the waveform. Secondly, combined with geology, the existence of small-scale faults is determined by well comparison. The high-resolution seismic technology and multi-attribute fusion technology are used to comprehensively judge the fault scale with the minimum fault throw that can be identified, which is mainly affected by the frequency and velocity of the seismic data.

[0048] The main problems in current seismic interpretation are analyzed in Figure 2 , it defines four main parameters for seismic data evaluation: whether the section is clear, whether the layer is clear, the degree of fit between the fault and the well, and the degree of fit between the layer and the well. If the section or layer is unclear, the reliability of the interpreted fault and layer is low, and the seismic data is evaluated as poor; if both the section and the layer are clear, and the degree of fit between the fault and the layer and the well is good, the comprehensive evaluation is good; if any one of the fault and layer well fit is poor, the reliability of the seismic interpretation is low, and the seismic data is evaluated as poor; the others are medium. The rolling well distance is generally between 300-400 meters, so the evaluation interval of this plan is set at 400m, dividing the study area into evenly sized grids.

[0049] exist Figure 2On this basis, the qualitative and quantitative judgment of the fit between faults and horizons requires a comprehensive judgment based on the errors in seismic interpretation itself and the impact on the actual well location design. In this study, the well-to-horizontal offset distance (SdH) of the horizon is evaluated based on the theoretical resolution of seismic data, and the fault-to-horizontal offset distance (SdF) is qualitatively evaluated based on the seismic trace spacing (Td). The fault-to-horizontal offset distance (SdF) refers to the horizontal distance between the well breakpoint and the seismic breakpoint at the same depth. The selected faults are the oil-controlling faults of each rolling development unit. When analyzing the fit between faults and wells, the trace spacing (Td) is first determined. When SdF≤1Td, the error is within a reasonable range and the seismic reliability is judged to be good; when 1Td<SdF≤3Td, the seismic reliability is judged to be medium; and when SdF>3Td, the seismic reliability is judged to be poor.

[0050] The well-to-seismic offset distance (SdH) refers to the longitudinal time difference between the well layer and the seismic phase axis at the same point on the plane. The selected layer is the marker layer with strong seismic reflection. When analyzing the well-to-seismic consistency of the layer, first calculate the apparent time period T based on the apparent main frequency f of the target layer of the seismic data using the formula T=1 / f. Theoretically, the minimum thickness of the sand body that can be distinguished by seismic data is λ / 8, corresponding to a time domain thickness of T / 8; when the thickness is greater than λ / 4, the sand body can be completely separated, corresponding to a time domain thickness of T / 4. In order to meet the requirements of sand body identification as much as possible, the study believes that when SdH≤T / 8, the seismic reliability is judged to be good; when T / 8<SdH≤T / 4, the seismic reliability is judged to be medium; when SdH>T / 4, the seismic interpretation reliability is judged to be poor. See for details. Figure 3 .

[0051] Using the quantitative evaluation criteria of seismic data in step 5 ( Figure 3 ), calculate the apparent dominant frequency and apparent period of the target layer of seismic data, and through fine well-seismic comparison, use the formula v = λ / T = λf, where v is the average velocity read from the acoustic time difference curve, and λ is the apparent wavelength. λ can be obtained, and then the theoretical maximum vertical resolution of seismic data can be calculated, as shown below: Figure 4 The results of earthquake data evaluation.

[0052] The beneficial effects provided by the present invention are as follows: This scheme tightly integrates wells and seismic data through fine well-seismic calibration, uses the high resolution of wells to constrain the low resolution of seismic data, introduces the concepts of well-seismic offset distances of horizons and fault well-seismic offset distances, and proposes a seismic data classification and evaluation standard based on the well-controlled grid. Under the premise of geological guidance, the quality of seismic data is quantitatively evaluated based on the well-controlled grid according to the theoretical resolution of the seismic data, breaking the limitations of conventional seismic data evaluation and having more practical significance for clarifying the rolling potential of a region.

[0053] Obviously, the above specific implementation cases are merely examples for illustrating the application of the present method, and are not intended to limit the implementation methods. A person skilled in the art can make other variations and modifications based on the above description to study other related issues. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

[0054] Those skilled in the art will understand that all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk, etc. Various media that can store program codes.

[0055] The embodiments of electronic devices and the like described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the embodiments. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0056] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, rather than to limit them. Although the embodiments of the present invention have been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

[0058] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the following claims.

[0059] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for evaluating seismic data classification based on well control grid, characterized in that: The method comprises: Screen seismic data, analyze the velocity in the study area, and establish a velocity field that conforms to the geological conditions. The fault scale of the target layer is identified, and the seismic problem is analyzed according to the fault scale and velocity field to obtain a classification result.

2. The method according to claim 1, characterized in that The velocity analysis in the study area and the establishment of a velocity field that conforms to the geological conditions include: Determine the seismic records related to the main frequency of the seismic data, compare the seismic records with the data superimposed on the well bypass, and process the data to obtain time-depth data, and obtain the well control velocity model and velocity field based on the time-depth data.

3. The method according to claim 2, characterized in that The seismic records are synthesized by Ricker wavelets.

4. The method according to claim 3, characterized in that The comparing the seismic record with the superimposed data of the well bypass includes: comparing the seismic synthetic record with the superimposed data of the well bypass by adjusting the wavelet frequency.

5. The method according to claim 2, characterized in that The processing of the data to obtain time-well depth relationship data includes: The time-depth relationship data of key wells are obtained through up-down time shift and local tension-compression methods.

6. The method according to claim 5, characterized in that The method of obtaining a well control velocity model and a velocity field according to the time-depth relationship data includes: The well-controlled velocity model is obtained by interpolation, and the velocity field is obtained by fusing the well-controlled velocity model with the seismic average velocity model.

7. The method according to claim 6, characterized in that The analyzing the earthquake problem according to the fault scale and velocity field to obtain a classification result includes: Determining seismic data evaluation parameters based on the fault scale and velocity field includes: whether the section is clear, whether the layer is clear, the fit between the fault and the well, and the fit between the layer and the well; The seismic data is analyzed according to the seismic data evaluation parameters to obtain classification results, including: If the fault or horizon is not clear, the first type of evaluation result of the seismic data is obtained; If the faults or horizons are clear, and the fault and horizon coincidence degrees are both at the first threshold, the second type of evaluation result of the seismic data is obtained; If one of the fault and stratigraphic coincidence scores does not reach the first threshold, but reaches the second threshold, the third type of seismic data evaluation result is obtained; All other situations are the fourth type of evaluation results of seismic data.

8. A classification and evaluation system for seismic data based on a well control grid, characterized in that: The system comprises: Screening module, used to screen seismic data; The first analysis module is used to analyze the velocity in the study area and establish a velocity field that conforms to the geological conditions; The second analysis module is used to identify the fault scale according to the velocity field, analyze the seismic data according to the fault scale, and obtain a classification result.

9. A smart device, characterized in that: include: transmitter, receiver, memory, and processor; The memory is used to store computer instructions; The processor is used to execute the computer instructions stored in the memory to implement the classification and evaluation method of seismic data based on the well control grid as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: include: a readable storage medium and computer instructions, wherein the computer instructions are stored in the readable storage medium; The computer instructions are used to implement the evaluation method for seismic data classification based on well control grid as described in any one of claims 1 to 7.

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