A method for detecting the oil-bearing properties of thin clastic rock reservoirs

Abstract

This invention relates to a method for detecting the oil-bearing potential of thin clastic reservoirs, belonging to the field of petroleum exploration and development technology. The invention first performs fine calibration of the target layer using synthetic records, extracting its wave impedance properties. Then, it uses drilled well data and 3D seismic data covering the reservoir distribution area of ​​the target region to extract the integrated energy properties of that frequency band, and extracts the formation attenuation slope properties. Next, it performs cross-plot analysis on the obtained wave impedance properties, frequency band integrated energy properties, and formation attenuation slope properties to determine the oil-bearing range of the thin clastic reservoir. This invention comprehensively considers the influence of wave impedance properties, frequency band integrated energy properties, and formation attenuation slope properties on the oil-bearing potential of thin clastic reservoirs, improving the detection accuracy of oil-bearing potential in thin clastic reservoirs. The detection results show a high degree of agreement with actual drilled wells, laying the foundation for efficient exploration and development of thin clastic reservoir oil reservoirs.

Description

Technical Field

[0001] This invention relates to a method for detecting the oil-bearing properties of thin clastic rock reservoirs, belonging to the field of petroleum exploration and development technology. Background Technology

[0002] Clastic reservoirs are the main type of oil and gas reservoir in the old oil and gas producing areas of eastern my country. Large-scale oil and gas reservoirs controlled by this type of reservoir have been discovered in basins such as Nanxiang. However, with the deepening of exploration and development, the oil and gas reservoirs in these clastic reservoirs are becoming increasingly complex and hidden, gradually increasing the difficulty of rolling exploration. Most of the old basins in eastern China have undergone complex tectonic and sedimentary evolution, resulting in complex oil and gas accumulation processes and unclear reservoir distribution patterns. This leads to situations where wells deployed primarily for reservoir evaluation encounter reservoirs but fail to find oil, ultimately resulting in drilling failures. In the current challenging environment of high costs and low oil prices, higher requirements are placed on reservoir oil and gas potential detection. The reliability of the prediction results directly determines the drilling success rate and the economic benefits for oil and gas production units.

[0003] The concept of oil and gas detection has been around for nearly 30 years. During this period, detection methods such as "bright spot" technology, AVO inversion, wave impedance inversion, and pre-stack elastic parameter inversion have been widely used and have achieved good application results in the detection of gas content in specific clastic reservoirs. However, due to different geological objectives and different practical problems to be solved, there are also some differences in the ideas and technical routes of oil and gas detection.

[0004] Taking the S oilfield in the eastern old area as an example, the current rolling exploration of clastic reservoirs faces challenges such as low dominant frequency of seismic data, thin reservoir thickness, and the inability of well logging petrophysical parameters to reflect the true formation response. At the same time, due to factors such as interference from multiples in near-offset gathers and low resolution of far-offset gathers, conventional oil and gas detection methods are not well adapted to the detection of oil-bearing properties in thin clastic reservoirs, and the reliability of the detection results is not high, which seriously restricts the efficient rolling exploration and development of thin clastic reservoirs. Summary of the Invention

[0005] The purpose of this invention is to provide a method for detecting the oil content of thin clastic rock reservoirs, in order to solve the problems of low detection accuracy and poor reliability in the current detection of oil content in thin clastic rock reservoirs.

[0006] To address the aforementioned technical problems, this invention provides a method for detecting the oil-bearing capacity of thin clastic rock reservoirs. This method includes the following steps:

[0007] 1) Obtain 3D seismic data for the study area and preprocess it;

[0008] 2) Perform fine calibration of the synthetic record for the target layer, determine the seismic reflection phase axis and time window corresponding to the clastic rocks, perform seismic interpretation, and perform wave impedance inversion to extract the wave impedance properties of the target layer.

[0009] 3) Based on the stratigraphic position and time window obtained in step 2), extract the waveform classification attributes, characterize the range of each waveform category, and determine the reservoir distribution range of the target area.

[0010] 4) Using the drilled well data, 3D seismic data and obtained stratigraphic time windows of the reservoir distribution range in the target area, determine the frequency bands with a certain degree of distinction between the integrated energy of oil-bearing and non-oil-bearing layers, and extract the integrated energy attributes of the frequency bands in the target area.

[0011] 5) Using the seismic data obtained in step 1) and the stratigraphic and time window information obtained in step 2), extract the stratigraphic attenuation slope attribute of the target area;

[0012] 6) Perform cross-intersection analysis on the obtained wave impedance properties, frequency band integral energy properties and formation attenuation slope properties to determine the oil-bearing range of the clastic thin reservoir.

[0013] Furthermore, step 5) employs a two-stage well control attribute cascade cross-section. First, a first-stage cross-section is performed using the wave impedance attribute and the frequency band integral energy attribute to delineate the attribute value range of the oil well. Then, within the delineated oil well attribute value range, a second-stage cross-section is performed using the wave impedance attribute and the formation attenuation slope attribute to determine the oil-bearing range of the clastic thin reservoir.

[0014] Furthermore, the wave impedance property is an instantaneous amplitude property.

[0015] Furthermore, the preprocessing in step 1) includes filtering and frequency extension processing.

[0016] Furthermore, the filtering method employed is tilt-guided filtering.

[0017] Furthermore, the aforementioned frequency extension processing employs compact wavelet transform to decompose the signal into time-frequency signals of different scales, and uses wavelet spectral extrema for logarithmic domain inversion to achieve signal projection mapping on different frequency bases, thereby realizing the frequency band extension of the seismic signal.

[0018] Further, step 2) involves establishing a stratigraphic framework model with the upper and lower layers of the target layer as the top and bottom, performing waveform indicator impedance inversion, obtaining an impedance inversion body, loading the impedance inversion body data according to three-dimensional seismic data, and extracting the impedance attributes between the upper and lower layers of the target layer based on the impedance inversion body in the manner of instantaneous amplitude attributes.

[0019] The beneficial effects of this invention are as follows: First, by conducting fine calibration of the synthetic record of the target layer, the wave impedance properties of the target layer are extracted. Then, using drilled well data and 3D seismic data of the reservoir distribution range in the target area, the integrated energy properties of the frequency band in the target area are extracted, and the formation attenuation slope properties of the target area are also extracted. Next, cross-analysis is performed on the obtained wave impedance properties, frequency band integrated energy properties, and formation attenuation slope properties to determine the oil-bearing range of thin clastic reservoirs. This invention comprehensively considers the influence of wave impedance properties, frequency band integrated energy properties, and formation attenuation slope properties on the oil-bearing properties of thin clastic reservoirs, improving the detection accuracy of oil-bearing properties in thin clastic reservoirs. The detection results show a high degree of agreement with actual drilling, laying the foundation for the efficient exploration and development of thin clastic reservoir oil reservoirs. Attached Figure Description

[0020] Figure 1 This is a flowchart of the method for detecting the oil content of thin clastic rock reservoirs according to the present invention;

[0021] Figure 2 This is a schematic diagram comparing three-dimensional seismic data before (top) and after (bottom) filtering and frequency extension processing in an embodiment of the present invention;

[0022] Figure 3a This is a schematic diagram of the correlation of synthesized records before filtering and frequency extension processing in an embodiment of the present invention;

[0023] Figure 3b This is a schematic diagram illustrating the correlation of synthesized records after filtering and frequency extension processing in an embodiment of the present invention;

[0024] Figure 4 This is a schematic diagram of the top and bottom lines of the H1 sublayer based on the waveform indicator impedance inversion result in an embodiment of the present invention;

[0025] Figure 5 This is a schematic diagram of the impedance properties of the H1 sublayer in this embodiment of the invention;

[0026] Figure 6 This is a schematic diagram of the difference analysis of H1 small layer point spectrum features in an embodiment of the present invention;

[0027] Figure 7 This is a schematic diagram of the integrated energy properties of the H1 sublayer in the 60-80Hz frequency band in an embodiment of the present invention;

[0028] Figure 8 This is a schematic diagram of the attenuation slope attribute of the H1 sub-layer in this embodiment of the invention;

[0029] Figure 9a This is a schematic diagram of the first-order intersection analysis of instantaneous amplitude and frequency band integral energy in an embodiment of the present invention;

[0030] Figure 9b This is a schematic diagram of the second-order cross-section analysis of instantaneous amplitude and formation attenuation slope in an embodiment of the present invention;

[0031] Figure 10 This is a schematic diagram illustrating the results of attribute cascade cross-plot analysis for detecting the oil-bearing properties of thin clastic reservoirs in an embodiment of the present invention. Detailed Implementation

[0032] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0033] This invention focuses on thin clastic reservoirs. It employs dip-guided filtering and basis mapping band extension high-resolution processing on 3D seismic data to improve the signal-to-noise ratio and resolution. Waveform-indicating impedance inversion enhances the resolution of thin clastic reservoirs. Based on this, the top and bottom interfaces of the thin reservoir are drawn linearly to ensure the accuracy of the reservoir's impedance volume properties. Phase bands are delineated through waveform classification. Based on this, well selection is performed to analyze point spectrum differences, clarifying the frequency bands that can distinguish oil wells, water wells, and dry wells using integrated energy, and extracting the corresponding frequency band integrated energy attributes. The fluid-bearing range is delineated by extracting the formation attenuation slope attribute along the thin clastic reservoir. Secondary attribute cascade cross-plot analysis yields the oil-bearing range of oil wells satisfying multiple attribute conditions. By analogy, the possible oil-bearing range of undrilled areas is inferred, thus achieving the purpose of detecting the oil-bearing potential of thin clastic reservoirs. The implementation process of this method is as follows: Figure 1 As shown below, a detailed explanation will follow.

[0034] 1. Obtain 3D seismic data for the study area and preprocess it.

[0035] In this embodiment, after acquiring the three-dimensional seismic data of the study area, preprocessing was performed using filtering and high-resolution processing to avoid interference.

[0036] The filtering process employs dip-guided filtering to improve the signal-to-noise ratio of seismic data. This step involves two key parameters: grid radius and time window length. Since the target object of this step is clastic reservoirs, and the filtering process is primarily for noise reduction, the grid radius cannot be too large to avoid losing seismic details. In this case, the grid radius is set to 3. The time window length is selected using an apparent wavelength of 30 milliseconds to calculate the formation dip angle parameters. These two parameter values ​​are then used to filter the 3D seismic data.

[0037] Based on filtering, this invention also performs high-resolution processing on the basis mapping bandwidth extension of the three-dimensional seismic data volume. It adopts compact wavelet transform to decompose the signal into time-frequency signals of different scales, and uses wavelet spectral extrema to perform logarithmic domain inversion to realize the basis projection mapping of the signal at different frequencies, thereby realizing the bandwidth extension of the seismic signal. Figure 2This is a schematic diagram comparing three-dimensional seismic data before (top) and after (bottom) frequency upscaling. Through this process, the dominant frequency of the existing seismic data was increased from 35Hz to 55Hz. The low cutoff frequency of the processed seismic data remained unchanged. By compensating for the high-frequency information, the bandwidth was effectively expanded. The wave group characteristics were well preserved after frequency upscaling, and the internal information was richer. Figure 3a and Figure 3b The examples show the synthesized seismic records before and after frequency upsetting of well A. Before frequency upsetting, a 35Hz Ricker wavelet was selected for the calibration of the synthesized records, and after frequency upsetting, a 55Hz Ricker wavelet was selected for the calibration of the synthesized records. By comparison, it can be seen that the correlation between the synthesized seismic records before and after frequency upsetting and the seismic traces near the well remains unchanged. This indicates that the frequency-upsetting 3D seismic data can improve the resolution of thin clastic reservoirs while maintaining relative amplitude and fidelity, and has high reliability.

[0038] 2. Perform synthetic record calibration on the target layer to determine the seismic reflection phase axis and time window corresponding to the clastic rocks, and perform seismic interpretation. Perform wave impedance inversion to extract the wave impedance properties of the target layer.

[0039] This embodiment uses layer H1 as the target layer. Declastic rock tests are conducted on layer H1 to identify the wave crest at the calibrated location as the corresponding seismic reflection axis. This wave crest is traced and interpreted using a 1×1 interpretation grid. Simultaneously, relatively stable unidirectional axes adjacent to H1 are selected and traced and interpreted using a 1×1 interpretation grid. A stratigraphic framework model is established with the upper and lower layers as the top and bottom layers to perform waveform indicator impedance inversion. The impedance inversion volume data is loaded into the software as 3D seismic data and displayed according to threshold values, such as... Figure 4 The top and bottom interfaces of H1 are drawn using line drawing. The top line drawing layer (blue dashed line) is named Ht, and the bottom line drawing layer (black solid line) is named Hb. Then, based on the wave impedance inversion volume, the wave impedance properties between the Ht layer and the Hb layer are extracted according to the instantaneous amplitude properties. Figure 5 ).

[0040] 3. Determine the reservoir boundary range.

[0041] Based on the explanation of the H1 layer in step 2, the waveform classification attributes along the layer are extracted. This step mainly includes two key parameters: the number of classification categories and the time window. Since the study area of ​​the embodiment mainly develops four microfacies types: underwater distributary channels, mouth bars, interchannels, and sheet sands, the waveform classification categories are set to 4 categories. The time window is selected as 6ms above and below the H1 sublayer, with a total of 12ms containing a complete apparent peak. The waveform classification attributes are extracted using this time window. The attributes are displayed in four different color blocks according to the classification category. The same color represents similar waveform features and also represents the same microfacies type. By depicting the boundaries of each color block, the distribution range of each microfacies reservoir can be qualitatively characterized.

[0042] 4. Determine the integral energy characteristics of oil wells using data from known wells.

[0043] Data on known wells passing through the target layer was acquired. In this embodiment, there are 16 known wells passing through the H1 sub-layer. The production and interpretation results of the wells within each waveform category range in step 3, and the H1 sub-layer of each well, were statistically analyzed. The 16 wells were divided into three categories: oil wells (red), water wells (blue), and dry wells (black). Using the 3D seismic data obtained in step 1 and the H1 sub-layer and time window information obtained in steps 2 and 3, a point spectrum feature difference analysis was conducted on the wells within the range. Figure 6 As shown, the integrated energy of oil-bearing, water-bearing, and dry layers is well distinguishable within the 60-80Hz frequency band. Oil wells exhibit strong integrated energy characteristics. Based on these analysis results, the integrated energy attributes within the 60-80Hz frequency band are extracted. Figure 7 ).

[0044] 5. Determine the formation attenuation slope attribute.

[0045] Using the seismic data obtained in step 1, the stratigraphic horizons obtained in step 2, and the time window information obtained in step 3, the stratigraphic attenuation slope attribute along the stratigraphic horizon is extracted. This step includes two key parameters: the stratigraphic horizon and the time window. In this step, the stratigraphic horizon H1 is selected, and the time window is selected to be 6ms above and below the minor stratigraphic horizon H1, for a total of 12ms. The extracted data is as follows: Figure 8 The formation attenuation slope attribute shown in the figure exhibits high-frequency attenuation when the reservoir contains fluids. The formation attenuation slope is relatively large, but the distinction between oil and water layers is not obvious. Therefore, it is necessary to carry out cascade cross-plot analysis involving multiple reservoir attributes to extract the oil-bearing information.

[0046] 6. Conduct oil content testing.

[0047] Using the frequency band integrated energy attribute obtained from the statistical well in step 4, the wave impedance attribute (instantaneous amplitude) of the H1 sublayer obtained in step 2, and the formation attenuation slope attribute obtained in step 5, a secondary well-controlled attribute cascade cross-plot was performed, as shown below. Figure 9a and Figure 9b As shown. First, a first-order intersection is performed between the instantaneous amplitude and the frequency band integral energy to delineate the attribute value range of the oil well (e.g., Figure 9a (The brown area in the middle), such as Figure 9a As shown; then, a second-order cross-section is performed to show the influence of this range on the instantaneous amplitude and the formation attenuation slope, as follows. Figure 9b As shown, the attribute value range of the oil well is further delineated within the brown area of ​​the secondary intersection (e.g., Figure 9b (The purple area in the image) thus yields the oil-bearing range of an oil well that satisfies various attribute conditions, such as... Figure 10The purple area represents the potential oil-bearing range for wells B3, B6, B9, and B14, all of which are oil wells. To verify the reliability of this method, further investigation is needed. Figure 10 Well B17 was deployed within the purple area enclosed by the black dashed line shown. This well successfully encountered industrial oil flow in the H1 sub-layer, resulting in a new discovery of large-scale reserves. The application of this method achieved the goal of detecting the oil-bearing properties of thin clastic reservoirs.

[0048] This invention improves the resolution of thin clastic reservoirs by using waveform-indicating impedance inversion. Based on this, it determines the top and bottom interfaces of the thin reservoir, ensuring the accuracy of the impedance volume properties. Then, it classifies phase bands by waveform classification, and conducts point spectrum characteristic difference analysis on selected wells to clarify the frequency band ranges for distinguishing oil wells, water wells, and dry wells using integrated energy, extracting the corresponding frequency band integrated energy attributes. It delineates the fluid-bearing range by extracting the formation attenuation slope attribute along the thin clastic reservoir. Through secondary attribute cascade cross-plot analysis, it obtains the oil-bearing range of oil wells meeting both attribute conditions, and further infers the possible oil-bearing range of undrilled areas, thus achieving the purpose of detecting the oil-bearing potential of thin clastic reservoirs. The detection results of this invention show a high degree of agreement with actual drilled wells. The deployed verification wells encountered industrial oil flows, adding significant recoverable reserves. This provides technical support for the efficient exploration and development of thin clastic reservoir oil reservoirs and has achieved good economic benefits.

Claims

1. A method for detecting the oil-bearing properties of thin clastic rock reservoirs, characterized in that, The detection method includes the following steps: 1) Obtain 3D seismic data for the study area and preprocess it; 2) Perform fine calibration of the synthetic record for the target layer, determine the seismic reflection phase axis and time window corresponding to the clastic rocks, perform seismic interpretation, and perform wave impedance inversion to extract the wave impedance properties of the target layer. 3) Based on the target layer and time window obtained in step 2), extract the waveform classification attributes, characterize the range of each waveform category, and determine the reservoir distribution range of the target area; 4) Using the drilled well data, 3D seismic data and obtained stratigraphic time windows of the reservoir distribution range in the target area, determine the frequency bands with a certain degree of distinction between the integrated energy of oil-bearing and non-oil-bearing layers, and extract the integrated energy attributes of the frequency bands in the target area. 5) Using the seismic data obtained in step 1) and the stratigraphic and temporal window information obtained in step 2), extract the stratigraphic attenuation slope attribute of the target area; 6) Perform cross-intersection analysis on the obtained wave impedance properties, frequency band integral energy properties and formation attenuation slope properties to determine the oil-bearing range of the clastic thin reservoir.

2. The method for detecting the oil-bearing properties of thin clastic reservoirs according to claim 1, characterized in that, Step 6) employs a two-stage well control attribute cascade cross-section. First, a first-stage cross-section is performed using the wave impedance attribute and the frequency band integral energy attribute to delineate the attribute value range of the oil well. Then, within the delineated oil well attribute value range, a second-stage cross-section is performed using the wave impedance attribute and the formation attenuation slope attribute to determine the oil-bearing range of the clastic thin reservoir.

3. The method for detecting the oil-bearing properties of thin clastic rock reservoirs according to claim 1 or 2, characterized in that, The wave impedance property mentioned is an instantaneous amplitude property.

4. The method for detecting the oil-bearing capacity of thin clastic reservoirs according to claim 1, characterized in that, The preprocessing in step 1) includes filtering and frequency extension processing.

5. The method for detecting the oil-bearing properties of thin clastic rock reservoirs according to claim 4, characterized in that, The filtering method described above employs an angle-guided filtering approach.

6. The method for detecting the oil-bearing properties of thin clastic reservoirs according to claim 4, characterized in that, The aforementioned frequency extension processing employs compact wavelet transform to decompose the signal into time-frequency signals of different scales. Logarithmic domain inversion is performed using wavelet spectral extrema to achieve signal projection mapping on different frequency bases, thereby realizing the frequency band extension of the seismic signal.

7. The method for detecting the oil-bearing properties of thin clastic reservoirs according to claim 3, characterized in that, Step 2) involves establishing a stratigraphic framework model with the upper and lower layers of the target layer as the top and bottom, performing waveform indicator impedance inversion, obtaining an impedance inversion body, loading the impedance inversion body data according to three-dimensional seismic data, and extracting the impedance attributes between the upper and lower layers of the target layer based on the impedance inversion body in the form of instantaneous amplitude attributes.

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