A method and system for high-resolution division of a seismic sequence of a clastic rock reservoir
By processing seismic data through cumulative prediction error and atomic matching pursuit technology, the long- and short-term cycle sequence interfaces of clastic reservoirs are identified and divided, which solves the accuracy and efficiency problems of sequence division in clastic reservoirs and achieves high-precision reservoir prediction.
Patent Information
- Application Number
- CN202311300913.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-10-09
- Publication Date
- 2025-10-10
- Estimated Expiration
- 2043-10-09
AI Technical Summary
Existing technologies make it difficult to achieve high-resolution sequence division in clastic reservoirs, and the "one-hole view" of logging data leads to inaccurate lateral distribution, affecting the accuracy and efficiency of seismic reservoir prediction.
The cumulative prediction error method and atomic matching pursuit technology are used to process seismic data to identify long- and short-term cycle sequence interfaces, and high-resolution seismic sequence division is performed through wellside seismic traces. A high-precision spatial seismic sequence framework is constructed in combination with well logging sequence interfaces.
It improves the precision and accuracy of clastic reservoir prediction, achieves unified horizontal and vertical resolution of the sequence, and provides an important basis for comprehensive seismic interpretation and high-precision reservoir prediction.
Smart Images
Figure CN119805565B_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of oil and gas exploration, and in particular to a method and system for high-resolution seismic sequence division of clastic rock reservoirs. Background Art
[0002] As the focus of oil and gas exploration and development gradually shifts to tight, low-permeability, and deep formations, the target scale is shrinking, making the search for favorable targets more difficult. Detailed reservoir prediction within the systems tract framework can more accurately predict hidden lithologic targets within the systems tract. A high-resolution sequence framework is the foundation for conducting seismic inversion in areas without well control and for quantitative reservoir prediction using deterministic sedimentary modeling. Therefore, the accuracy of sequence division and interpretation directly impacts the effectiveness and accuracy of comprehensive seismic data interpretation and seismic reservoir prediction. Therefore, creating a high-precision isochronous sequence stratigraphic framework is particularly important.
[0003] In the actual process of sequence delineation, manual delineation based on well logging lithologic interpretation accounts for a large proportion. However, this method is inefficient, highly subjective, and difficult to guarantee accuracy. Consequently, some researchers have proposed the Integrated Prediction Error Filter Analysis (INPEFA) method for stratigraphic boundary delineation and isochronous stratigraphic correlation. This method processes and analyzes well logging curves to extract spectral trend lines reflecting stratigraphic superposition and cyclical structure. It then performs isochronous correlation based on inflection points and changing trends, laying the foundation for accurate stratigraphic delineation and true isochronous correlation. However, this method suffers from the "one-dimensional" nature of well logging data and cannot provide a precise lateral distribution of the sequence. Summary of the Invention
[0004] In order to solve the problems existing in the prior art, the present invention provides a method and system for high-resolution seismic sequence delineation of clastic reservoirs. By using the cumulative prediction error method and the atomic matching pursuit method, seismic data are specially processed to obtain a high-resolution seismic sequence framework, which provides strong support for improving the precision and accuracy of clastic reservoir prediction.
[0005] To achieve the above-mentioned object, the present invention provides the following technical solution: a method for high-resolution seismic sequence division of clastic reservoirs, comprising the following specific steps:
[0006] S1 obtains the sensitive logging curve of the well closest to the seismic trace, processes the sensitive logging curve using the cumulative prediction error method to obtain the cumulative prediction error curve, and performs generalized S transformation on the sensitive logging curve to obtain the time-frequency distribution characteristics;
[0007] S2 uses the cumulative prediction error curve and time-frequency distribution characteristics to identify the long-term and short-term cyclic sequence interfaces and obtains the long-term and short-term cyclic sequence interfaces;
[0008] S3 calibrates the long- and short-term cycle sequence interfaces onto the seismic traces near the well, performs seismic sequence division on the seismic traces near the well, and uses atomic matching pursuit technology to perform well-seismic time-frequency matching analysis in the time-frequency domain to identify seismic implicit sequence interfaces. After all seismic traces are calculated, a high-resolution seismic sequence framework for the clastic reservoir is obtained.
[0009] Furthermore, in S1, the sensitive logging curve is a logging curve that has a good reflection of the stratigraphic superposition relationship and cyclic structure.
[0010] Furthermore, in S1, the cumulative prediction error method is used to process the logging curve to obtain the cumulative prediction error curve. The specific steps are as follows:
[0011] 1) Sensitive logging curve x(t) Perform maximum entropy spectrum estimation to obtain the maximum entropy spectrum and the maximum entropy spectral coefficient ;
[0012] 2) Using the maximum entropy spectrum coefficient Perform curve prediction to obtain discretized predicted logging curve :
[0013]
[0014] Where, n is the sampling point number, n > M , For the n The predicted value of the curve at each sampling point;
[0015] 3) Estimating the cumulative prediction error of the logging data to obtain the cumulative prediction error curve :
[0016]
[0017] Where, For the i The original logging curve value at each sampling point, For the i The predicted logging curve value at each sampling point.
[0018] Furthermore, in S2, the local extreme value of high-frequency energy on the time-frequency distribution diagram corresponding to the inflection point of the cumulative prediction error curve is the sequence interface.
[0019] Furthermore, in S3, the atomic matching pursuit technique was used to perform a step-by-step refined well-seismic time-frequency matching analysis in the time-frequency domain, with the third-order sequence interface and the long-term cyclic sequence interface as constraints.
[0020] Further, in S3, each seismic trace selects the nearest well, carries out S1 and S2, obtains long-term and short-term cycle sequence interface, and then carries out S3 to identify the seismic implicit sequence interface under the well-seismic time-frequency matching analysis.
[0021] Further, in S3, the specific steps are as follows:
[0022] 1) the time-frequency distribution of the seismic trace beside the well is calculated by using the atomic matching tracking technology, and a seismic time-frequency distribution map is obtained;
[0023] 2) the seismic time-frequency distribution map is divided into multiple regions in the time direction by using the long-term cycle sequence interface;
[0024] 3) two frequency separation lines are picked up in the frequency direction, and the seismic time-frequency distribution map is divided into low frequency, medium frequency and high frequency three parts;
[0025] 4) the sequence of the seismic trace beside the well is identified by using the amplitude of the low, medium and high frequency of the seismic time-frequency distribution map, and the seismic implicit sequence interface is obtained.
[0026] Further, in S3, the picking method of the two frequency separation lines is as follows: a separation line is drawn from low frequency and high frequency respectively, and moved to the center, and the amplitude average A in each grid is calculated. When the variance of each grid reaches the maximum, the frequency separation line is determined:
[0027] .
[0028] Further, in S3, the amplitude average of low frequency, medium frequency and high frequency is calculated, the amplitude average is converted into the numerical value of the color RGB three primary colors, the amplitude average of low frequency corresponds to the R color value, the amplitude average of medium frequency corresponds to the G color value, and the amplitude average of high frequency corresponds to the B color value.
[0029] The application also provides a clastic rock reservoir seismic sequence high-resolution division system, comprising:
[0030] The time-frequency distribution feature acquisition module is used for acquiring the sensitive well logging curve of the well closest to the seismic trace, processing the sensitive well logging curve to obtain a cumulative prediction error curve by using a cumulative prediction error method, and carrying out a generalized S transform on the sensitive well logging curve to obtain a time-frequency distribution feature.
[0031] The sequence interface identification module is used for identifying the long-term and short-term cycle sequence interface by using the cumulative prediction error curve and the time-frequency distribution feature, and obtaining the long-term and short-term cycle sequence interface.
[0032] The seismic sequence framework construction module is used to calibrate the long- and short-term cycle sequence interfaces onto the near-well seismic traces, perform seismic sequence division based on the near-well seismic traces, and use atomic matching pursuit technology to perform well-seismic time-frequency matching analysis in the time-frequency domain to identify seismic implicit sequence interfaces. Once all seismic traces are calculated, a high-resolution seismic sequence framework for clastic reservoirs is obtained.
[0033] Compared with the prior art, the present invention has at least the following beneficial effects:
[0034] The present invention proposes a high-resolution seismic sequence division method for clastic reservoirs. The INPEFA curve is calculated from sensitive well logging curves using the cumulative prediction error method, and the long- and short-term cyclic sequence interfaces are identified in combination with the generalized S transform. Then, the atomic matching pursuit technology is used to perform near-well seismic trace sequence division. The well logging sequence interface is picked up using the INPEFA curve. Based on the vertical high-resolution sequence division of the well logging data, the seismic data is interpreted with high resolution to obtain a high-precision spatial seismic sequence framework, thereby improving the rate and quality of sequence division. The well logging sequence division is mapped to the seismic data, highlighting the characteristics of the seismic implicit sequence interface, and unifying the horizontal and vertical resolution of the sequence. This provides an important basis for the combination of geological and ground feature data, provides a good basic support for comprehensive seismic interpretation and high-precision reservoir prediction, and provides technical support for clastic oil and gas exploration. BRIEF DESCRIPTION OF THE DRAWINGS
[0035] Figure 1 This is a flow chart of the high-resolution seismic sequence delineation method for clastic reservoirs.
[0036] Figure 2 It is the cumulative prediction error curve and the atomic matching pursuit time-frequency distribution diagram.
[0037] Figure 3 It is a high-resolution seismic sequence framework map. DETAILED DESCRIPTION
[0038] The specific embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawings. The illustrative examples and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.
[0039] like Figure 1 As shown, the present invention provides a high-resolution (fourth to fifth order) seismic sequence division method for clastic reservoirs based on cumulative prediction error, and the specific steps are as follows:
[0040] Step 1: Select sensitive logging curves that are sensitive to the stratigraphic superposition relationship and cyclic structure, such as natural gamma ray (GR) and natural potential (SP);
[0041] Step 2: Use the cumulative prediction error method to process the sensitive logging curve to obtain the cumulative prediction error curve, as follows:
[0042] First, use formula (1) to calculate the sensitive logging curve x(t) Perform maximum entropy spectrum estimation to obtain the maximum entropy spectrum and the maximum entropy spectral coefficient :
[0043] (1)
[0044] Where, is the maximum entropy spectrum, is the frequency, is the coefficient, For time, j is the imaginary part, M is the order of the entropy spectrum.
[0045] Secondly, the maximum entropy spectrum coefficient Substitute into formula (2) and carry out curve prediction to obtain the discretized prediction logging curve :
[0046] (2)
[0047] Where, n is the sampling point number, n > M , For the n The predicted value of the curve at each sampling point.
[0048] Next, the cumulative prediction error of the logging data is estimated to obtain the cumulative prediction error curve , which can reflect the changing characteristics of the sedimentary cycle trend:
[0049] (3)
[0050] Where, For the i The original logging curve value at each sampling point, For the i The predicted logging curve value at each sampling point.
[0051] Step 3: Perform generalized S-transformation on the sensitive logging curve to extract the time-frequency characteristics and obtain the time-frequency distribution characteristics of the sensitive logging curve at the wellbore;
[0052] Step 4: Compare the cumulative prediction error curve with the time-frequency distribution characteristics. If the inflection point of the cumulative prediction error curve corresponds to the local extreme value of the high-frequency energy of the time-frequency distribution characteristics, it is the sequence interface, completing the identification and comparative analysis of the long-term and short-term cycle sequence interfaces;
[0053] Step 5: Calibrate the long- and short-term cyclic sequence interfaces interpreted from the sensitive well logging curves onto the near-well seismic traces to perform seismic sequence delineation (generally, the long-term cycles delineated on the well logging curves correspond to the third-order seismic sequences, and the short-term cycles correspond to the fourth- and fifth-order seismic sequences). Using the third-order sequence interface and the long-term cyclic sequence interface as constraints, atomic matching pursuit technology is used to perform a step-by-step well-seismic time-frequency matching analysis in the time-frequency domain.
[0054] Atom matching pursuit is a signal processing technique commonly used for time-frequency analysis of various signals. The idea behind the matching pursuit algorithm is to project the original signal onto a series of time-frequency atoms, representing the original signal as a linear combination of these time-frequency atoms. These time-frequency atoms are then used to accurately represent the original signal. The formula is as follows:
[0055] (4)
[0056] Where, Wavelet atoms The Wigner distribution function, is the atomic coefficient, For time, is the frequency, is the order;
[0057] Firstly, the time-frequency distribution of the seismic trace near the well is calculated using the atomic matching pursuit technique to obtain the time-frequency distribution map.
[0058] Secondly, the long-term cyclic sequence interface picked up in step 4 is used to divide the time-frequency distribution map into multiple regions in the time direction.
[0059] Then, two frequency separation lines are picked up in the frequency direction to divide the time-frequency distribution diagram into three parts: low frequency, medium frequency, and high frequency. At this time, the time-frequency distribution diagram becomes a grid with multiple rows and three columns.
[0060] The method of picking up two frequency separation lines is: draw a separation line from the low frequency and high frequency, move it toward the center, calculate the amplitude average value A in each grid, and when the variance of each grid is When the maximum is reached, the frequency separation line is determined.
[0061] (5)
[0062] Where, for A The average value of N is the number of sample points.
[0063] Finally, the amplitudes of the low, medium, and high frequencies in the time-frequency distribution are used to identify the stratigraphic sequences of the well bypasses and obtain the seismic implicit sequence interface. Specifically, the average amplitudes of the low, medium, and high frequencies are calculated and converted into RGB primary color values. The low-frequency amplitude average corresponds to the R color value, the medium-frequency amplitude average corresponds to the G color value, and the high-frequency amplitude average corresponds to the B color value.
[0064] The RGB primary color values are between 0 and 255. The specific setting standard is: the maximum value of the amplitude averages of the three frequency bands corresponds to a color value of 255, the minimum value corresponds to a color value of 0, and the intermediate values are standardized so that the corresponding color values are between 0 and 255. The colors corresponding to the seismic traces near the well are obtained, and different colors are used to represent the stratigraphic characteristics of the seismic records.
[0065] Step 6: For each seismic trace, first select the nearest well and perform steps 2 to 4 to obtain long- and short-term cyclic sequence interfaces. Then proceed to step 5 to identify the seismic implicit sequence interfaces using well-seismic time-frequency matching analysis. Once all seismic traces are calculated, a high-resolution seismic sequence framework for the clastic reservoir can be established.
[0066] Figure 1 This is a flowchart of a high-resolution seismic sequence delineation method for clastic reservoirs. This method primarily utilizes the cumulative prediction error method and atomic matching pursuit technology for high-resolution sequence delineation. First, the cumulative prediction error method is used to calculate INPEFA curves from well logs. This is then combined with the generalized S transform to identify long- and short-term cyclical variations. Next, atomic matching pursuit technology is used to delineate sequences from near-well seismic traces. Finally, the characteristics of implicit seismic sequence interfaces are calculated to obtain a high-resolution seismic sequence framework.
[0067] Figure 2 The cumulative prediction error curve (INPEFA curve) and the atomic matching pursuit time-frequency distribution diagram are used. The well logging sequence interface picked up by the INPEFA curve is mapped onto the seismic data under the well-seismic time-frequency matching analysis to obtain the sequence interface of the seismic record.
[0068] Figure 3 It is a high-resolution seismic sequence framework map, which conducts well-seismic time-frequency matching analysis on seismic records of different cycles and performs high-resolution seismic sequence division under a step-by-step refinement strategy. Figure 3 It is difficult to trace the fourth-order sequence interface on the medium-level seismic profile, but it can be clearly identified on the seismic cycle profile (shown by the ellipse).
Claims
1. A high-resolution seismic sequence division method for clastic reservoirs, characterized by: The specific steps include: S1 obtains the sensitive logging curve of the well closest to the seismic trace, processes the sensitive logging curve using the cumulative prediction error method to obtain the cumulative prediction error curve, and performs generalized S transformation on the sensitive logging curve to obtain the time-frequency distribution characteristics; S2 uses the cumulative prediction error curve and time-frequency distribution characteristics to identify the long-term and short-term cyclic sequence interfaces and obtains the long-term and short-term cyclic sequence interfaces; S3 calibrates the long- and short-term cycle sequence interfaces onto the seismic traces near the wells, performs seismic sequence division on the seismic traces near the wells, and uses the atomic matching pursuit technique to perform well-seismic time-frequency matching analysis in the time-frequency domain to identify the seismic implicit sequence interfaces. After all seismic traces are calculated, a high-resolution seismic sequence framework for the clastic reservoir is obtained. In S3, the specific steps are as follows: 1) Use atomic matching pursuit technology to calculate the time-frequency distribution of seismic traces near the well and obtain the seismic time-frequency distribution map; 2) Using the long-term cyclic sequence interface to divide the earthquake time-frequency distribution map into multiple regions in the time direction; 3) Pick two frequency separation lines in the frequency direction to divide the earthquake time-frequency distribution diagram into three parts: low frequency, medium frequency, and high frequency; 4) Use the amplitude of low, medium and high frequencies in the earthquake time-frequency distribution diagram to identify the stratigraphic sequence of the well bypass and obtain the seismic implicit sequence interface; In S3, the method of picking up the two frequency separation lines is as follows: draw a separation line from the low frequency and high frequency, move it toward the center, calculate the amplitude average value A in each grid, and when the variance of each grid is When the maximum is reached, the frequency separation line is determined by: Where, for A The average value of N is the number of sample points; In S3, the amplitude averages of low frequency, medium frequency, and high frequency are calculated and converted into the values of the three primary colors RGB. The low frequency amplitude average corresponds to the R color value, the medium frequency amplitude average corresponds to the G color value, and the high frequency amplitude average corresponds to the B color value. Different colors are used to represent the stratigraphic characteristics of the seismic record.
2. The high-resolution seismic sequence division method for clastic reservoirs according to claim 1, characterized in that: In S1, the sensitive logging curve is a logging curve that has a good reflection of the stratigraphic superposition relationship and cyclic structure.
3. The high-resolution seismic sequence division method for clastic reservoirs according to claim 1, characterized in that: In S1, the cumulative prediction error method is used to process the logging curve to obtain the cumulative prediction error curve. The specific steps are as follows: 1) Sensitive logging curve x(t) Perform maximum entropy spectrum estimation to obtain the maximum entropy spectrum and the maximum entropy spectral coefficient ; 2) Using the maximum entropy spectrum coefficient Perform curve prediction to obtain discretized predicted logging curve : Where, n is the sampling point number, n > M , For the n The predicted value of the curve at each sampling point; 3) Estimating the cumulative prediction error of the logging data to obtain the cumulative prediction error curve : Where, For the i The original logging curve value at each sampling point, For the i The predicted logging curve value at each sampling point.
4. The high-resolution seismic sequence division method for clastic reservoirs according to claim 1, characterized in that: In S2, the inflection point of the cumulative prediction error curve corresponds to the local extreme value of high-frequency energy on the time-frequency distribution diagram, which is the sequence interface.
5. The high-resolution seismic sequence division method for clastic reservoirs according to claim 1, characterized in that: In S3, the atomic matching pursuit technique was used to perform a step-by-step refined well-seismic time-frequency matching analysis in the time-frequency domain, with the third-order sequence interface and the long-term cyclic sequence interface as constraints.
6. The high-resolution seismic sequence division method for clastic reservoirs according to claim 1, characterized in that: In S3, the nearest well to each seismic trace is selected to perform S1 and S2 to obtain the long- and short-term cyclic sequence interfaces, and then S3 is performed to identify the seismic implicit sequence interfaces under the well-seismic time-frequency matching analysis.
7. A high-resolution seismic sequence division system for clastic reservoirs, characterized by: include: The time-frequency distribution feature acquisition module is used to obtain the sensitive logging curve of the well closest to the seismic trace, process the sensitive logging curve using the cumulative prediction error method to obtain the cumulative prediction error curve, and perform generalized S transformation on the sensitive logging curve to obtain the time-frequency distribution feature; Sequence interface identification module is used to identify long-term and short-term cyclic sequence interfaces using the cumulative prediction error curve and time-frequency distribution characteristics to obtain long-term and short-term cyclic sequence interfaces; The seismic sequence framework construction module is used to calibrate the long- and short-term cycle sequence interfaces onto the seismic traces near the wells, perform seismic sequence division on the seismic traces near the wells, and use the atomic matching pursuit technique to perform well-seismic time-frequency matching analysis in the time-frequency domain to identify the seismic implicit sequence interfaces. After all seismic traces are calculated, a high-resolution seismic sequence framework for clastic reservoirs is obtained. In the seismic sequence framework construction module, the specific steps are as follows: 1) Use atomic matching pursuit technology to calculate the time-frequency distribution of seismic traces near the well and obtain the seismic time-frequency distribution map; 2) Using the long-term cyclic sequence interface to divide the earthquake time-frequency distribution map into multiple regions in the time direction; 3) Pick two frequency separation lines in the frequency direction to divide the earthquake time-frequency distribution diagram into three parts: low frequency, medium frequency, and high frequency; 4) Use the amplitude of low, medium and high frequencies in the earthquake time-frequency distribution diagram to identify the stratigraphic sequence of the well bypass and obtain the seismic implicit sequence interface; In the seismic sequence framework construction module, the method of picking two frequency separation lines is: draw a separation line from the low frequency and high frequency respectively, move it toward the center, calculate the amplitude average value A in each grid, and when the variance of each grid is When the maximum is reached, the frequency separation line is determined by: Where, for A The average value of N is the number of sample points; In the seismic sequence framework construction module, the amplitude averages of low frequency, medium frequency and high frequency are calculated and converted into the values of the three primary colors RGB. The low frequency amplitude average corresponds to the R color value, the medium frequency amplitude average corresponds to the G color value, and the high frequency amplitude average corresponds to the B color value. Different colors are used to represent the sequence characteristics of the seismic record.