A Micro-log Q-value Estimation Method Based on Trend Fitting and Energy Matching

Abstract

The present invention relates to the field of seismic data processing for oil and gas exploration, and particularly to a micro-log Q-value estimation method based on trend fitting and energy matching. The method includes the following steps: calculating the amplitude spectrum of the first arrival wavelet of the micro-log data; calculating the energy of the first arrival wavelet of the micro-log data; distinguishing the lithological and physical property characteristics of each formation; correcting the energy anomaly shots caused by different coupling degrees of the excitation points for the formations with the same lithological and physical property characteristics; scanning the optimal layer Q-value from the perspective of energy matching. The method of the present invention improves the Q-value estimation accuracy of actual micro-log data, solves the problem that the inconsistency of wavelets has a serious impact on Q estimation, has a good Q estimation effect on actual micro-log data, and is conducive to near-surface absorption attenuation investigation and compensation.

Description

Technical Field

[0001] The present invention relates to the field of seismic data processing for oil and gas exploration, and particularly to a micro-log Q value estimation method based on trend fitting and energy matching. Background Art

[0002] The actual earth medium has an absorption and attenuation effect on seismic waves, which is characterized by the Q value. The absorption and attenuation effect near the surface is particularly serious. Estimating the formation Q value of the near surface using micro-log data and compensating for the absorption and attenuation are important attempts in high-resolution seismic exploration in recent years.

[0003] Chinese Patent Application CN112415601A discloses a method for determining the surface quality factor Q value. The method includes establishing a surface horizontal layered geological model based on the micro-log data of the target area; correcting the formation velocity of the low-velocity layer in the surface horizontal layered geological model to obtain a corrected surface horizontal layered geological model; forward modeling the direct wave record using the ray tracing method on the corrected surface horizontal layered geological model; identifying the direct wave position on the micro-log acquisition record according to the forward modeled direct wave record, and removing the seismic traces with non-direct first arrivals on the micro-log acquisition record; using frequency-wavenumber domain filtering to remove interference waves to obtain a filtered micro-log record; and determining the surface quality factor Q value of the target area according to the filtered micro-log record. This method reduces the influence of interference waves on the calculation accuracy of the Q value by removing the seismic traces with non-direct first arrivals and using frequency-wavenumber domain filtering to remove interference waves, thereby improving the accuracy of the surface quality factor Q value.

[0004] Chinese Patent Application CN102109617A discloses a method for measuring the Q value of the near-surface formation using the double-well micro-log technology. This method uses double-well micro-log data, selects the bottom and surface seismic records of the excitation point A located in the high-velocity layer and the excitation point B at the bottom interface of the low-velocity layer, intercepts the first arrival part of the record, performs Fourier transform to obtain the peak frequency of each record; then uses the peak frequency shift method to estimate the Q value formula, establishes a system of equations for the change of the peak frequency of the seismic signal and solves it to obtain Q1 and Q2; finally, for the Q1 and Q2 values obtained at all double-well micro-log points in the work area, perform horizontal interpolation and extrapolation according to the layered results interpreted by the single-well micro-log to obtain the near-surface Q1 and Q2 values of the whole area.

[0005] There are many micro-log Q value estimation methods. The mainstream methods such as spectral ratio and centroid frequency shift all estimate the Q value by using the amplitude attenuation relationship of one or more frequencies or a frequency band range in the first arrival wavelet of the micro-log data. These methods require the micro-log data to meet the assumption conditions and Q attenuation theoretical models. For example, as the excitation well depth increases, the main frequency of the first arrival wavelet decreases; compared with low frequencies, the high frequencies attenuate more, and the energy attenuation amount shows a linear relationship with the frequency, etc.

[0006] With the continuous deepening of research, the inventors found that in actual micro-logging data, due to problems such as inconsistent excitation lithology and inconsistent coupling, the excitation wavelets are not consistent, resulting in data characteristics that do not meet the assumptions. For example, as the excitation well depth increases, the amplitude spectrum of the first arrival wavelet enhances, the main frequency increases, and the low-frequency attenuation is higher than the high-frequency attenuation. This causes the results obtained by traditional micro-logging Q estimation methods to be either negative or have a large deviation, with low credibility and unable to meet the requirements of high-resolution seismic exploration. Summary of the Invention

[0007] The main object of the present invention is to provide a micro-logging Q value estimation method based on trend fitting and energy matching. This estimation method improves the Q value estimation accuracy of actual micro-logging data, well solves the problem that wavelet inconsistency seriously affects Q estimation, has a good Q estimation effect on actual micro-logging data, and is conducive to near-surface absorption attenuation investigation and compensation.

[0008] To achieve the above object, the present invention adopts the following technical solutions:

[0009] The present invention provides a micro-logging Q value estimation method based on trend fitting and energy matching, which includes the following steps: calculating the amplitude spectrum of the first arrival wavelet of micro-logging data; calculating the energy of the first arrival wavelet of micro-logging data; distinguishing the lithology and physical property characteristics of each formation; correcting the energy abnormal shots caused by different coupling degrees of excitation points for the formations with the same lithology and physical property characteristics; scanning the best layer Q value from the perspective of energy matching.

[0010] Further, the single-scale Gabor transform method is used to calculate the amplitude spectrum of the first arrival wavelet of micro-logging data.

[0011] Further, according to the amplitude spectrum, calculate the energy of the first arrival wavelet of micro-logging data. The calculation formula for the energy of the first arrival wavelet of micro-logging data is as follows:

[0012]

[0013] Wherein, P i is the energy of the first arrival wavelet of the i-th shot in the micro-logging data, f is the frequency, and W i (f) is the amplitude spectrum of the first arrival wavelet of the i-th shot.

[0014] Further, distinguish the formation lithology and physical properties according to the variation relationship of the calculated energy of the first arrival wavelet of micro-logging data with depth.

[0015] Furthermore, in the actual data, where there is a large jump in the energy of the first arrival wavelet with depth, it is judged to be caused by different excitation lithologies, and thus different excitation lithologies can be distinguished; where there is no large jump, it is judged to be the same lithology; and a small jump is judged to be caused by different coupling degrees of excitation points.

[0016] Furthermore, for formations with the same lithological and physical property characteristics, a trend fitting is performed on the curve of energy varying with depth to correct the energy anomaly shots caused by different degrees of coupling at the excitation points.

[0017] Furthermore, the trend fitting adopts the method of polynomial fitting.

[0018] Furthermore, the corrected energy satisfies:

[0019]

[0020] where P i+1 is the first arrival wavelet energy of the (i + 1)-th shot, f is the frequency, t i is the first arrival time of the i-th shot, W i (f) is the amplitude spectrum of the first arrival wavelet of the i-th shot, and Q i,i+1 is the formation Q value between the i-th shot and the (i + 1)-th shot.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] The method of the present invention improves the accuracy of Q value estimation for actual micro-logging data, well solves the problem that the influence of wavelet inconsistency on Q estimation is serious, has a good effect on Q estimation of actual micro-logging data, and is beneficial to near-surface absorption attenuation investigation and compensation. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The attached drawings forming a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments and descriptions thereof of the present invention are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0024] Figure 1 is a schematic flow chart of the micro-logging Q value estimation method based on trend fitting and energy matching according to Embodiment 1 of the present invention;

[0025] Figure 2 is a schematic diagram of the micro-logging acquisition system according to Embodiment 1 of the present invention;

[0026] Figure 3 is a set of typical micro-logging data according to Embodiment 1 of the present invention;

[0027] Figure 4 is the time-frequency spectrum of the micro-logging single trace record and its Gabor transform according to Embodiment 1 of the present invention: A is the micro-logging single trace record; B is the single-scale Gabor spectrum; C is the normalized single-scale Gabor spectrum;

[0028] Figure 5Schematic diagram of the amplitude spectrum of the first arrival wavelet that does not conform to logic in the micro-logging data described in Embodiment 1 of the present invention: A is the amplitude spectrum of the first and second shots. As the depth of the shot point increases, the frequency spectrum of the first arrival wave is enhanced; B is the amplitude spectrum of the 18th and 19th shots. As the depth of the shot point increases, the main frequency of the first arrival wave spectrum increases; C is the amplitude spectrum of the 25th and 26th shots. As the depth of the shot point increases, the low-frequency attenuation is stronger than the high-frequency attenuation.

[0029] Figure 6 Schematic diagram of using the energy of the first arrival wavelet to distinguish formation lithology and the energy abnormal shot existing therein described in Embodiment 1 of the present invention: a is the relationship curve of the energy of the first arrival wavelet and the depth of the shot point; b is a partial enlarged view of a;

[0030] Figure 7 Schematic diagram of trend fitting of the energy curve and correcting the energy abnormal shot described in Embodiment 1 of the present invention;

[0031] Figure 8 Comparison chart of Q estimation values obtained by using the traditional method and the method of the present invention for the actual micro-logging data described in Embodiment 1 of the present invention: A is the Q estimation value chart obtained by the traditional method; B is the Q estimation value chart obtained by the method described in Embodiment 1. Detailed implementation mode

[0032] It should be noted that the following detailed description is exemplary and is intended to provide further illustration of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0033] It should be noted that the terms used herein are only for describing specific implementation modes and are not intended to limit the exemplary implementation modes according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular forms are also intended to include the plural forms. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, and / or combinations thereof.

[0034] In order to enable those skilled in the art to more clearly understand the technical solution of the present invention, the technical solution of the present invention will be described in detail below in conjunction with specific embodiments.

[0035] Embodiment 1

[0036] As Figure 1 shown, the micro-logging Q value estimation method based on trend fitting and energy matching includes:

[0037] Step 1: Calculate the amplitude spectrum of the first arrival wavelet of the micro-logging data;

[0038] Among them, as a relatively preferred embodiment of the present invention, a single-scale Gabor transform is selected to calculate the amplitude spectrum of the first arrival wavelet. The process of calculating the amplitude spectrum of the first arrival wavelet using the single-scale Gabor transform can be specifically referred to the following formula (1):

[0039]

[0040] where g a (t) is a Gaussian function, called the window function, a is the width of the window, and the parameter b is used to translate the window. Add a sliding window g a (t - b) to the single-channel signal d(t) of the micro-logging data, and then perform a Fourier transform to obtain the single-scale Gabor transform result G(a, b, f) of the signal d(t). The amplitude spectrum W i (f) of the first arrival wavelet of the micro-logging data can be extracted from G(a, b, f), and i represents the trace number or shot number.

[0041] Step 2: Calculate the energy of the first arrival wavelet of the micro-logging data according to the amplitude spectrum;

[0042] On the basis of completing Step 1, calculate the energy according to the amplitude spectrum of the first arrival wavelet. The energy satisfies the following formula (2):

[0043]

[0044] where P i is the energy of the first arrival wavelet of the i-th shot in the micro-logging data, f is the frequency, and W i (f) is the amplitude spectrum of the first arrival wavelet of the i-th shot.

[0045] Step 3: Distinguish different lithologies and physical properties of strata according to the relationship between the energy of the first arrival wavelet and depth;

[0046] On the basis of completing Step 2, distinguish the lithology of the strata where the shot point is located according to the relationship between the energy of the first arrival wavelet and depth. According to the acquisition system of the micro-logging, as the depth increases, the energy of the first arrival wavelet should be monotonically decreasing. In the actual data, where there is a large jump in the energy of the first arrival wavelet with depth, it is judged to be caused by different excitation lithologies. Thus, different excitation lithologies can be distinguished. Where there is no large jump, it is judged to be the same lithology, and a small jump is judged to be caused by different coupling degrees of the excitation points.

[0047] Step 4: Correct the energy abnormal shots caused by different coupling degrees of excitation points through trend fitting in strata with the same lithology and physical properties;

[0048] On the basis of completing Step 3, further perform trend fitting on the small energy jump points in the strata with the same excitation lithology to correct the energy anomaly caused by different coupling degrees of excitation points. Preferably, polynomial fitting is used for trend fitting correction.

[0049] Step 5: Scan the optimal layer Q value from the perspective of energy matching.

[0050] On the basis of completing Step 4, further determine the optimal layer Q value by scanning from the perspective of energy matching. Specifically, the optimal layer Q value satisfies the following formula (3):

[0051]

[0052] where P i+1 is the first arrival wavelet energy of the (i + 1)-th shot, f is the frequency, t i is the first arrival time of the i-th shot, W i (f) is the amplitude spectrum of the first arrival wavelet of the i-th shot, and Q i，i+1 is the formation Q value between the i-th shot and the (i + 1)-th shot.

[0053] Thus, the Q value of the actual micro-logging data can be estimated.

[0054] Next, the estimation method provided in this embodiment will be specifically described in combination with typical actual micro-logging data. As Figures 2 - 8 shown, where Figure 2 is a schematic diagram of the micro-logging acquisition system. Shots are fired from shallow to deep in the well, and received at the wellhead. The first arrival wavelet of each shot passes through the formation above the shot point and will be absorbed and attenuated; Figure 3 is a set of typical micro-logging data. Each trace of data corresponds to one shot. As the trace number increases, the shot point gets deeper, the first arrival time gets longer, and only the first arrival information is used for Q estimation; Figure 4 is the time-frequency spectrum of the single-trace record of the micro-logging and its single-scale Gabor transform. The spectral resolution is the same at different frequencies, and it can better extract the amplitude spectrum of the first arrival wavelet; Figure 5 is a schematic diagram of the amplitude spectrum of the first arrival wavelet that does not conform to logic in a set of micro-logging data. As the shot point gets deeper in the micro-logging data, the propagation time and distance of the first arrival wavelet increase, and the absorption attenuation becomes more serious. Theoretically, the main frequency should decrease, the spectrum should become weaker, and the high-frequency attenuation amount should be higher than the low-frequency attenuation amount. However, there are a large number of phenomena that do not conform to theory and logic in this actual micro-logging data, and good results cannot be obtained using traditional methods based on this data spectrum, and even negative Q values may appear.

[0055] Figure 6 is a schematic diagram of using the first arrival wavelet energy to distinguish formation lithology and the energy abnormal shot among them. The method of the present invention calculates the energy using the amplitude spectrum of the first arrival wavelet and draws the relationship curve between the first arrival wavelet energy and the shot point depth. Theoretically, as the shot point depth increases, the absorption attenuation becomes more serious and the energy becomes weaker, and the curve should be monotonically decreasing. Thus, the jump points in the actual curve are judged as energy abnormal shots. Larger jumps are caused by the excitation lithology, and thus the formation lithology can be distinguished. Smaller jumps are caused by different excitation coupling degrees.

[0056] Figure 7Schematic diagram for trend fitting of energy curve and correcting energy anomaly guns. For trend fitting of energy curve, smaller jump points in the same lithology are corrected, and formation Q value is scanned and determined according to the principle of energy matching in different lithology strata.

[0057] Figure 8 Comparison chart of Q estimated values obtained by traditional method and the method of the present invention for actual micro-logging data. The above results show that: the traditional method is unstable and prone to obtaining negative Q values. The method for estimating micro-logging Q value provided by the present invention can obtain more accurate and reasonable results, which is more conducive to near-surface investigation and absorption attenuation compensation.

[0058] The traditional method refers to the conventional spectral ratio method. The initial arrival wavelet amplitude spectrum is obtained by using single-scale Gabor transform. The specific steps are as follows: sorting adjacent two-channel data; respectively applying single-scale Gabor transform to obtain time-frequency spectrum; extracting the initial arrival wavelet amplitude spectrum in the time-frequency spectrum; applying the spectral ratio method to the initial arrival wavelet amplitude spectra of adjacent two channels to obtain the Q value between adjacent two channels.

[0059] The above embodiments are the preferred embodiments of the present invention, but the embodiments of the present invention are not limited by the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principle of the present invention shall be equivalent replacement methods and are all included in the protection scope of the present invention.

Claims

1. Micro-log Q-value estimation method based on trend fitting and energy matching, Characterized in that, Comprising the following steps: Calculating the amplitude spectrum of the first arrival wavelet of micro-log data; Calculating the energy of the first arrival wavelet of micro-log data; distinguishing the lithology and physical property characteristics of each formation; for formations with the same lithology and physical property characteristics, correcting the energy abnormal shots caused by different coupling degrees of excitation points; scanning the optimal layer Q-value from the perspective of energy matching; Distinguishing the lithology and physical properties of formations according to the variation relationship of the energy of the first arrival wavelet of micro-log data with depth; In the actual data, where there is a large jump in the energy of the first arrival wavelet with depth, it is judged to be caused by different lithologies of excitation, and thus different excitation lithologies can be distinguished; where there is no large jump, it is judged to be the same lithology; a small jump is judged to be caused by different coupling degrees of excitation points; For formations with the same lithology and physical property characteristics, performing trend fitting on the curve of energy variation with depth to correct the energy abnormal shots caused by different coupling degrees of excitation points; The trend fitting adopts the method of polynomial fitting; The corrected energy satisfies: Among them, P i+1 is the first arrival wavelet energy of the (i + 1)-th shot, f is the frequency, and t i is the first arrival time of the i-th shot, W i (f) is the amplitude spectrum of the first arrival wavelet of the i-th shot, and Q i,i+1 is the formation Q value between the i-th shot and the (i + 1)-th shot.

2. The method according to claim 1, Characterized in that, The single-scale Gabor transform method is used to calculate the amplitude spectrum of the first arrival wavelet of micro-log data.

3. The method according to claim 1, Characterized in that, Calculating the energy of the first arrival wavelet of micro-log data according to the amplitude spectrum, and the calculation formula of the energy of the first arrival wavelet of micro-log data is as follows: where P i is the first arrival wavelet energy of the i-th shot in the micro-logging data, f is the frequency, and W i (f) is the first arrival wavelet amplitude spectrum of the i-th shot.

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