A sandstone thickness contour map drawing method and intelligent drawing device

Abstract

The present invention belongs to the field of seismic data processing and analysis, and specifically relates to a sandstone thickness contour map drawing method and intelligent drawing device. The sandstone thickness contour map drawing method of the present invention analyzes seismic waveforms and well logging waveforms to determine whether adjacent wells in the same sedimentary environment have similar lithology. Based on this, pseudo-wells are set based on the results of waveform clustering analysis to correct the initial sandstone thickness contour map obtained by identifying the natural potential curve and gamma curve. Ultimately, a sandstone thickness contour map that objectively displays geological laws is obtained, effectively improving the accuracy of the drawing and providing a reliable basis for the next step of well deployment in the oil field.

Description

Technical Field

[0001] The present invention belongs to the field of seismic data processing and analysis, and in particular relates to a sandstone thickness contour map drawing method and an intelligent drawing device. Background Art

[0002] The exploration and development of oil and gas fields requires the use of sandstone thickness contour maps to analyze geological laws. However, as exploration and development continue to deepen, well locations are continuously deployed, and new insights into the geological laws of oil fields continue to emerge, resulting in the continuous updating of sandstone thickness contour maps.

[0003] In the past, the drawing of sandstone thickness contour maps was mainly completed by geologists alone. For well-dense areas, the drawing effect was good, but for well-free areas, the drawn maps were affected by subjective factors and could not objectively show the geological laws.

[0004] Chinese invention patent publication number CN110501743B discloses a method for compiling sandstone thickness contour maps constrained by reservoir architecture. The method comprises the following steps: Step 1: Reservoir architecture unit division, including: 1) reservoir architecture interface prediction; 2) reservoir architecture interface classification; and 3) reservoir architecture interface combination; Step 2: Compilation of sandstone thickness contour maps within reservoir architecture units, including: 1) data preparation; 2) sandstone thickness interpolation for wellpoints constrained by seismic attributes; and Step 3: Sandstone thickness contour map generation, including: 1) analysis of the development stages of adjacent architecture units; and 2) overlay of sandstone thickness contour maps. This prior art uses architecture units as mapping units and incorporates information such as structural relationships within the subsurface reservoir to reflect the true distribution of sandstone thickness. However, the classification and combination of architecture interfaces, as well as the analysis of the development stages of adjacent architecture units, are still significantly influenced by the experience and subjective factors of geologists, and the accuracy of sandstone thickness contour maps remains to be further improved. Summary of the Invention

[0005] The first object of the present invention is to provide a method for drawing a sandstone thickness contour map to solve the problem that the drawing accuracy of the existing sandstone thickness contour map drawing method needs to be further improved.

[0006] The second purpose of the present invention is to provide an intelligent drawing device for sandstone thickness contour map, which can realize the full process automation and intelligence of drawing sandstone thickness contour map, effectively improving work efficiency and drawing accuracy.

[0007] In order to achieve the above objectives, the technical solution adopted by the present invention is:

[0008] A method for drawing a sandstone thickness contour map comprises the following steps:

[0009] (1) Through well-seismic calibration, the drilled target layer is calibrated in the 3D seismic data;

[0010] (2) Select two adjacent drilled wells in the study area and analyze the seismic amplitude correlation of the target layer sections of the two adjacent drilled wells;

[0011] (3) intercepting an acoustic wave curve within the target layer range and analyzing the correlation between the acoustic wave curves of the two adjacent drilled wells;

[0012] (4) When the analysis results of the seismic amplitude correlation in step (2) and the acoustic wave curve correlation in step (3) are both highly correlated, proceed to the next step; otherwise, return to step (2) and reselect the drilled well;

[0013] (5) for the two adjacent drilled wells, identifying the top and bottom depths of the sandstone according to the natural potential curve of the target layer, and using the gamma curve to identify the effective thickness of the target layer at the corresponding top and bottom depths, and drawing an initial sandstone thickness contour map based on the effective thickness;

[0014] (6) Extract seismic attributes based on the layers of seismic tracing, select sensitive seismic attributes, and overlay them with the initial sandstone thickness contour map. Then perform waveform cluster analysis and set pseudo wells based on the waveform cluster analysis results.

[0015] (7) Correcting the initial sandstone thickness contour map according to the pseudo well in step (6) to obtain a final sandstone thickness contour map.

[0016] The inherent mechanism of the sandstone thickness contour map drawing method of the present invention is that in a study area with the same provenance, similar sedimentary characteristics often have similar lithologic combinations, and similar lithologic combinations often have similar seismic waveform characteristics. Therefore, the similarity of the waveforms can be used to improve the accuracy of drawing the sandstone thickness contour map.

[0017] The sandstone thickness contour map drawing method of the present invention determines whether adjacent wells have similar lithology under the same sedimentary environment by analyzing seismic waveforms and logging curve waveforms. On this basis, pseudo wells are set through waveform clustering analysis results to correct the initial sandstone thickness contour map obtained by natural potential curve and gamma curve identification, and finally a sandstone thickness contour map that can objectively show geological laws is obtained, which effectively improves the accuracy of the drawing and provides a reliable basis for the next well site deployment in the oil field.

[0018] In step (1), the drilled target layer can be accurately calibrated in the three-dimensional seismic data through high-precision well-seismic calibration.

[0019] In step (2), two adjacent drilled wells in the study area are selected. In the top and bottom intervals of the target layer, the seismic amplitude values ​​of the drilled target layer can be statistically calculated according to proportional sampling points. Based on the statistical results, the seismic amplitude values ​​of the target layer of the two adjacent wells are corrected to the same depth, and a depth-amplitude curve is generated. Then, the correlation of the amplitude curves of the same target layer of the two adjacent wells is analyzed. In this step, the correlation coefficient can be calculated using the CORREL function, and the threshold value of the correlation in the study area can be inferred based on the correlation coefficient of the sample wells.

[0020] Preferably, in step (2), the analysis of the seismic amplitude correlation includes: correcting the seismic amplitude values ​​of the target layer sections of two adjacent drilled wells to the same depth, making a depth-seismic amplitude curve graph, and then analyzing the correlation of the depth-seismic amplitude curves of the same target layer section of the two adjacent drilled wells.

[0021] In step (3), the acoustic wave curves are filtered, and then the acoustic wave curves of the target layer of two adjacent wells are corrected to the same depth starting point. A depth-acoustic wave curve graph is then generated. The correlation of the acoustic wave curves of the same target layer of the two adjacent wells is then analyzed. In this step, the correlation coefficient can be calculated using the CORREL function, and the correlation threshold of the study area can be inferred based on the correlation coefficient of the sample wells.

[0022] Preferably, in step (3), the analysis of the correlation of the acoustic wave curves includes: correcting the acoustic wave curves of the two adjacent drilled target layer segments to the same depth, and making a depth-acoustic wave curve graph, and then analyzing the correlation of the acoustic wave curves of the two adjacent drilled target layer segments.

[0023] In step (4), when the amplitude correlation and the acoustic correlation are low, it is considered that there may be lithologic changes in the target layers of the two adjacent wells, and it is necessary to return to step (2) and reselect the drilled wells. Generally speaking, the amplitude correlation and acoustic correlation of two adjacent wells in the same phase belt are consistent (the threshold value of the external correlation may be different). If the amplitude correlation and the acoustic correlation are high and low respectively, there may be problems with the logging data or seismic processing. These two situations rarely occur. If they occur, the relevant data can be deleted.

[0024] In step (5), the natural potential curve and gamma curve of the target layer section of the drilled well can be counted, and then the effective thickness of the sand layer can be efficiently identified based on the identification characteristics of the corresponding curves. In this step, the natural potential curves of the corresponding depth range of the target layer of the two wells that can be judged as the same layer are counted respectively, and the corresponding sand layer is identified based on the natural potential curve within this section. The top and bottom depths of the sandstone identified by the natural potential curve are then used as the identification interval range, and then the gamma curve with higher sensitivity is used to identify the effective thickness of the target layer within the said interval range. Since GR sensitivity is very high, on the well logging chart, high GR is mudstone and low GR is sandstone. The effective thickness of the target layer can be identified by the well logging chart, or by drawing the mudstone baseline. The value lower than the mudstone value is sandstone.

[0025] In step (6), seismic attributes are extracted based on the layers of the seismic trace, and sensitive attributes are selected. Based on the sensitive attributes and the initial sandstone thickness contour map, sampling points are selected on the plane to statistically analyze the similarity of seismic waveforms and perform waveform cluster analysis.

[0026] Preferably, in step (6), the seismic attribute is selected from root mean square amplitude, instantaneous phase, sweet spot, instantaneous acceleration or instantaneous phase cosine.

[0027] Preferably, in step (6), the pseudo-well is set at the boundary between sandstone and mudstone. Further preferably, in step (7), the correction is a Kriging interpolation correction. The pseudo-well is assigned a value of 0, and then gridding is performed using Kriging interpolation to obtain a new contour line with a value of 0; the contour line with a value of 0 is spliced ​​with the initial sandstone thickness contour line to obtain a final sandstone thickness contour map.

[0028] An intelligent device for drawing a sandstone thickness contour map comprises a processor and a memory. The processor executes a computer program stored in the memory to implement the method for drawing the sandstone thickness contour map.

[0029] The intelligent drawing device for sandstone thickness contour map of the present invention realizes the full process automation and intelligence of objectively drawing sandstone thickness contour map based on new geological understanding, effectively improving work efficiency and drawing accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A flow chart of a method for generating a sandstone thickness contour map according to the present invention;

[0031] Figure 2 This is a seismic amplitude correlation analysis diagram of two adjacent wells in the sandstone thickness contour map drawing method of the present invention;

[0032] Figure 3This is a correlation analysis diagram of acoustic wave curves of two adjacent wells in the method for drawing a sandstone thickness contour map of the present invention;

[0033] Figure 4 This is an overlay of the waveform cluster analysis and the initial sandstone thickness contour map in the present invention. DETAILED DESCRIPTION

[0034] The implementation process of the present invention is described in detail below with reference to specific embodiments.

[0035] Example 1

[0036] The method for drawing the sandstone thickness contour map of this embodiment has a workflow diagram as shown in FIG. Figure 1 As shown, the following steps are included:

[0037] (1) Through high-precision well-seismic calibration, the target layers of the adjacent wells W1 and W2 are accurately calibrated in the 3D seismic data.

[0038] (2) In the seismic profile, the seismic amplitude values ​​of the target layer sections of Well W1 and Well W2 are counted at every 20ms interval.

[0039] (3) In Excel, unify the starting points of the same target layer of the two wells to the same depth value. At this time, the amplitude values ​​of the two wells are comparable, and then make a depth-amplitude curve (such as Figure 2 ), it can be seen that the seismic amplitudes of the target layers of Wells W1 and W2 are well correlated. At this time, judging from the seismic perspective, the sand bodies of the target layers of these two wells are consistent.

[0040] In other cases, a threshold is tested based on the selected sample wells. Values ​​above the threshold indicate high correlation, while values ​​below the threshold indicate low correlation. This is quantified using the CORREL function. In practice, simply plotting a depth-amplitude curve will clearly determine the correlation.

[0041] (4) According to the range of the target layer, the acoustic wave curve is intercepted and filtered, such as Figure 3 As shown in Figure 2, after filtering, the acoustic wave curve changes from completely random to gradually determined from high frequency to low frequency. It can be seen that the acoustic wave correlation between the target layers of wells W1 and W2 is very good. Combined with (3), it can be judged that the sand bodies of the target layers of these two wells are consistent.

[0042] (5) The top and bottom depths of the sandstones identified by the natural potential curves of the target layer sections of Wells W1 and W2 are counted respectively, and the effective thicknesses of the target layers corresponding to Wells W1 and W2 are identified using the gamma curve at the corresponding top and bottom depths.

[0043] (6) Based on the effective thickness described in step (5), a contour map of the initial sandstone thickness is drawn.

[0044] (7) According to the layers of earthquake tracing, multiple attributes such as root mean square amplitude, instantaneous phase, sweet spot, instantaneous acceleration, and instantaneous phase cosine are extracted respectively. After comparison, the instantaneous phase cosine attribute is used as the sensitive earthquake attribute.

[0045] The instantaneous phase cosine attribute map is superimposed with the initial sandstone thickness contour map, and sampling points are selected in the superimposed map to analyze the similarity of the seismic waveforms and perform waveform clustering analysis to form a waveform clustering attribute plane map (such as Figure 4 ).

[0046] (8) Set pseudo-wells at the color boundaries of the waveform cluster attribute plane map as correction points for the initial sandstone thickness contour map. Figure 4 In the figure, red represents sandstone and blue represents mudstone. A pseudo-well is selected at the sandstone-mudstone boundary. There can be many pseudo-wells; the two wells in the figure are just examples. The pseudo-well is assigned a value of 0 (this gives it a coordinate and a value of 0). Then, the data is gridded using kriging interpolation to obtain a new contour line with a value of 0.

[0047] (9) The contour line with a value of 0 in step (8) is spliced ​​with the initial sandstone thickness contour line to obtain a final sandstone thickness contour map.

[0048] The sandstone thickness contour map drawn under the control of multiple well areas has a higher accuracy, but the contour lines drawn in the area without well control have no seismic basis. The accuracy of Example 1 lies in the participation of earthquakes. Under the combination of seismic geology, a higher-precision sandstone thickness contour map can be drawn.

[0049] Example 2

[0050] The intelligent drawing device for sandstone thickness contour map of this embodiment includes a processor and a memory. The memory stores a computer program that can be run on the processor. The processor implements the method of the above-mentioned embodiment 1 when executing the computer program.

[0051] In other words, the method described in the above method embodiment should be understood as a process for drawing a sandstone thickness contour map, which can be implemented using computer program instructions. These computer program instructions can be provided to a processor, so that the processor executes these instructions to achieve the functions specified in the above method flow. Based on the operability of the above method of the present invention, new geological insights are achieved, and the entire process of objectively drawing sandstone thickness contour maps is automated and intelligent, effectively improving work efficiency and mapping accuracy.

[0052] The processor referred to in this embodiment is a processing device such as a microprocessor MCU or a programmable logic device FPGA.

[0053] The memory referred to in this embodiment includes physical devices used to store information, typically digitizing the information and then storing it in electrical, magnetic, or optical media. Examples include various types of memory that use electrical energy to store information, such as RAM and ROM; various types of memory that use magnetic energy to store information, such as hard disks, floppy disks, magnetic tapes, magnetic core memory, bubble memory, and USB flash drives; and various types of memory that use optical energy to store information, such as CDs and DVDs. Of course, other types of memory exist, such as quantum memory and graphene memory.

[0054] The device composed of the above-mentioned memory, processor and computer program is implemented by the processor executing corresponding program instructions in the computer. The processor can be equipped with various operating systems, such as Windows operating system, Linux system, Android system, iOS system, etc.

Claims

1. A method for drawing a sandstone thickness contour map, characterized in that: The following steps are involved: (1) Through well-seismic calibration, the drilled target layer is calibrated in the 3D seismic data; (2) Select two adjacent drilled wells in the study area and analyze the seismic amplitude correlation of the target layer sections of the two adjacent drilled wells; (3) intercepting acoustic wave curves within the target layer range and analyzing the correlation between the acoustic wave curves of the two adjacent drilled wells; (4) When the analysis results of the seismic amplitude correlation in step (2) and the acoustic wave curve correlation in step (3) are both highly correlated, proceed to the next step; otherwise, return to step (2) and reselect the drilled well; (5) For the two adjacent drilled wells, identify the top and bottom depths of the sandstone according to the natural potential curve of the target layer, and use the gamma curve to identify the effective thickness of the target layer at the corresponding top and bottom depths, and draw an initial sandstone thickness contour map based on the effective thickness; (6) Extracting seismic attributes based on the layers of seismic tracing, selecting sensitive seismic attributes, and overlaying them with the initial sandstone thickness contour map, then performing waveform clustering analysis, and setting pseudo wells based on the waveform clustering analysis results; setting the pseudo wells at the junction of sandstone and mudstone; and assigning the pseudo wells a value of 0; (7) Correcting the initial sandstone thickness contour map according to the pseudo well in step (6) to obtain a final sandstone thickness contour map.

2. The method for drawing a sandstone thickness contour map according to claim 1, wherein: In step (2), the analysis of the seismic amplitude correlation includes: correcting the seismic amplitude values ​​of the target layer sections of two adjacent wells that have been drilled to the same depth, making a depth-seismic amplitude curve graph, and then analyzing the correlation of the depth-seismic amplitude curves of the same target layer section of the two adjacent wells that have been drilled.

3. The method for drawing a sandstone thickness contour map according to claim 1, wherein: In step (3), the analysis of the correlation of the acoustic wave curves includes: correcting the acoustic wave curves of the two adjacent drilled target layer sections to the same depth, and making a depth-acoustic wave curve graph, and then analyzing the correlation of the acoustic wave curves of the two adjacent drilled target layer sections.

4. The method for drawing a sandstone thickness contour map according to claim 1, wherein: In step (6), the seismic attribute is selected from root mean square amplitude, instantaneous phase, sweet spot, instantaneous acceleration or instantaneous phase cosine.

5. The method for drawing a sandstone thickness contour map according to any one of claims 1 to 4, characterized in that: In step (7), the correction is a Kriging interpolation correction.

6. An intelligent device for drawing a sandstone thickness contour map, characterized in that: The method comprises a processor and a memory, wherein the processor executes a computer program stored in the memory to implement the method for drawing a sandstone thickness contour map according to any one of claims 1 to 5.

Images