Reservoir quality factor calculation method and device, equipment and storage medium

Abstract

The embodiment of the invention discloses a reservoir quality factor calculation method and device, equipment and a storage medium, and the method comprises the steps: building a seismic convolution model of a target reservoir according to a seismic wavelet, a stratum reflection coefficient, a propagation attenuation function and a seismic recording function of the target reservoir, and enabling the propagation attenuation function to comprise a quality factor variable; based on the numerical range of the quality factor variable and the seismic convolution model, determining a synthetic seismic record corresponding to the seismic record function, and determining an initial quality factor field of the target reservoir according to a matching result of the synthetic seismic record and an actual seismic record of the target reservoir; and constructing a target function including a target quality factor variable based on the seismic amplitude spectrum of the target reservoir, determining a constraint function of the target quality factor variable based on the initial quality factor field and the constraint coefficient, and determining a target quality factor field of the target reservoir based on the target function and the constraint function. And the precision of solving the reservoir quality factor field by using the ground seismic data is improved.

Description

Technical Field

[0001] The present invention relates to the field of seismic exploration technology, and in particular to a calculation method, device, equipment and storage medium for a reservoir quality factor. Background Art

[0002] The non-perfectly elastic nature of underground rock formations causes seismic waves to attenuate as they propagate, resulting in reduced amplitude and frequency. To quantitatively characterize the attenuation effect of a formation, the quality factor (Q) is often used. This factor is defined as the ratio of the energy stored to the energy dissipated per unit period of the seismic wave. Generally speaking, the smaller the Q value, the greater the energy loss per unit period of the seismic wave propagation, indicating a stronger attenuation effect.

[0003] The quality factor is an inherent property of the formation, varying depending on the rock's lithology, microscopic composition, diagenetic environment, degree of compaction, and state. For reservoirs, the quality factor is also related to factors such as porosity, saturation, fluid type, and the degree of fracture development. Research has shown that when a reservoir contains unsaturated fluid or has a high degree of porosity and fractures, the formation exhibits an abnormally low Q. Therefore, studying seismic wave attenuation characteristics and identifying areas of anomalous quality factors are of great significance for reservoir research and oil and gas exploration and development.

[0004] There are three main methods for solving quality factors in seismic exploration: time domain, frequency domain, and time-frequency domain. Time domain methods primarily utilize the energy attenuation characteristics represented by the waveform or envelope of seismic waves. Representative methods include wavelet simulation and rise time methods. These methods are relatively intuitive, but are easily affected by geometric diffusion, reflection, and transmission losses, making it difficult to ensure the accuracy of quality factor solutions. Frequency domain methods are mainly based on the changing characteristics of the amplitude spectrum, main frequency, or bandwidth of the seismic propagation signal. Representative methods include spectral ratio method and centroid frequency method. These methods can effectively avoid the influence of non-stratum factors in time domain methods, but are easily affected by interference effects and signal truncation, and some methods are sensitive to noise. Time-frequency domain methods combine frequency domain methods with time-frequency analysis, utilizing the high temporal resolution of the time-frequency spectrum to reduce interference and truncation effects, improve the accuracy of the amplitude spectrum solution, and thus improve the quality factor calculation effect. Common time-frequency analysis methods include short-time Fourier transform, wavelet transform, and S transform.

[0005] The main types of seismic data used in quality factor solutions are VSP (vertical seismic profile) data and surface seismic data. VSP data are single-way wave data, allowing direct downlink waves to be directly extracted for quality factor solutions. Because these data typically have a relatively high signal-to-noise ratio and resolution, and the downlink direct wave waveform is relatively complete and rarely affected by truncation, they fully conform to the seismic wave propagation model required for quality factor solutions, resulting in high solution accuracy. However, due to the relatively high exploration costs and limited detection range of VSP data, such data are relatively scarce in practice, and their coverage of the stratigraphic space is limited. Given the limitations of VSP data, quality factor solutions have gradually been extended to surface seismic data. Because surface seismic data can cover the stratigraphic space, the stratigraphic space Q field can be established. Especially when facing a reservoir, the reservoir Q field can be obtained, enabling the characterization of reservoir attenuation characteristics and providing support for oil and gas prediction. However, because surface seismic data are two-way wave data, their signal-to-noise ratio and resolution are lower than those of VSP data, and they are easily affected by stratigraphic thickness. It is often difficult to obtain complete seismic wavelet waveforms, making the Q field solution difficult to guarantee.

[0006] Therefore, how to improve the accuracy of solving the reservoir Q field using surface seismic data is a technical problem that needs to be solved urgently. Summary of the Invention

[0007] The present invention provides a method, device, equipment and storage medium for calculating reservoir quality factor, so as to improve the accuracy of solving reservoir Q field using surface seismic data.

[0008] According to one aspect of the present invention, a method for calculating a reservoir quality factor is provided, comprising:

[0009] Establishing a seismic convolution model of the target reservoir according to the seismic wavelet, formation reflection coefficient, propagation attenuation function and seismic record function of the target reservoir, wherein the propagation attenuation function includes a quality factor variable;

[0010] Determining a synthetic seismic record corresponding to the seismic record function based on the numerical range of the quality factor variable and the seismic convolution model, and determining an initial quality factor field of the target reservoir based on a matching result between the synthetic seismic record and an actual seismic record of the target reservoir;

[0011] An objective function including a target quality factor variable is constructed based on the seismic amplitude spectrum of the target reservoir, and a constraint function of the target quality factor variable is determined based on the initial quality factor field and a constraint coefficient. A target quality factor field of the target reservoir is determined based on the objective function and the constraint function.

[0012] According to another aspect of the present invention, there is provided a device for calculating a reservoir quality factor, comprising:

[0013] a seismic convolution model building module, configured to build a seismic convolution model of the target reservoir based on the seismic wavelet, formation reflection coefficient, propagation attenuation function, and seismic recording function of the target reservoir, wherein the propagation attenuation function includes a quality factor variable;

[0014] an initial quality factor field determination module, configured to determine a synthetic seismic record corresponding to the seismic record function based on a numerical range of the quality factor variable and the seismic convolution model, and determine an initial quality factor field of the target reservoir based on a matching result between the synthetic seismic record and an actual seismic record of the target reservoir;

[0015] a target quality factor field determination module, configured to construct an objective function including a target quality factor variable based on the seismic amplitude spectrum of the target reservoir, determine a constraint function for the target quality factor variable based on the initial quality factor field and a constraint coefficient, and determine the target quality factor field of the target reservoir based on the objective function and the constraint function.

[0016] According to another aspect of the present invention, an electronic device is provided, comprising:

[0017] at least one processor;

[0018] and a memory communicatively coupled to the at least one processor;

[0019] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the reservoir quality factor calculation method described in any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and wherein the computer instructions are configured to enable a processor to implement the method for calculating the reservoir quality factor according to any embodiment of the present invention when executed.

[0021] The technical solution of the embodiment of the present invention establishes a seismic convolution model of the target reservoir using its seismic wavelet, formation reflection coefficient, propagation attenuation function, and seismic record function, wherein the propagation attenuation function includes a quality factor variable. Then, based on the numerical range of the quality factor variable and the seismic convolution model, a synthetic seismic record corresponding to the seismic record function is determined. The initial quality factor field of the target reservoir is determined based on the matching results between the synthetic seismic record and the actual seismic record of the target reservoir. Finally, an objective function including the target quality factor variable is constructed based on the seismic amplitude spectrum of the target reservoir. Based on the initial quality factor field and constraint coefficients, a constraint function for the target quality factor variable is determined. The target quality factor field of the target reservoir is determined based on the objective function and constraint function. This improves the accuracy of resolving the reservoir quality factor field using surface seismic data. This solves the existing problem of low signal-to-noise ratio and resolution of surface seismic data, which is easily affected by formation thickness, making it difficult to obtain a complete seismic wavelet waveform, resulting in difficulty in ensuring the accuracy of the quality factor field solution. This improves the accuracy of resolving the reservoir quality factor field using surface seismic data.

[0022] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 A flow chart of a method for calculating a reservoir quality factor provided by an embodiment of the present invention;

[0025] Figure 2 A flow chart of a method for calculating a reservoir quality factor provided in yet another embodiment of the present invention;

[0026] Figure 3 A schematic structural diagram of a reservoir quality factor calculation device provided by an embodiment of the present invention;

[0027] Figure 4 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] Figure 1 This is a flow chart of a method for calculating a reservoir quality factor provided by an embodiment of the present invention. This embodiment is applicable to the case where a reservoir quality factor is calculated using surface seismic data. The method can be executed by a reservoir quality factor calculation device, which can be implemented in the form of hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0031] S110 , establishing a seismic convolution model of the target reservoir according to the seismic wavelet, formation reflection coefficient, propagation attenuation function and seismic recording function of the target reservoir.

[0032] The target reservoir refers to the reservoir whose quality factor field needs to be determined; the seismic wavelet of the target reservoir refers to the frequency-domain seismic wavelet corresponding to the target reservoir; the formation reflection coefficient refers to the reflection of a portion of the energy of a seismic wave propagating through the target reservoir and encountering an interface between different media, while another portion of the energy is refracted. The reflected seismic wave is called a reflected wave, and the ratio of its amplitude to the amplitude of the incident wave is called the formation reflection coefficient; the propagation attenuation function can be understood as a function representing the propagation attenuation of the seismic wave in the target reservoir; the seismic record function refers to the time-domain seismic record; and the seismic convolution model can be understood as a time-domain non-stationary seismic convolution model of the target reservoir.

[0033] In an embodiment of the present invention, establishing a seismic convolution model of the target reservoir based on the seismic wavelet, formation reflection coefficient, propagation attenuation function and seismic recording function of the target reservoir includes: determining a frequency domain non-stationary seismic convolution model of the target reservoir based on the convolution of the seismic wavelet and the formation reflection coefficient; and performing an inverse Fourier transform on the frequency domain non-stationary seismic convolution model to obtain the seismic convolution model of the target reservoir.

[0034] Among them, the earthquake convolution model is a non-stationary earthquake convolution model in the time domain.

[0035] Specifically, according to Robinson's theory, stationary seismic records under perfectly elastic conditions can be expressed as the convolution of the seismic wavelet and the formation reflection coefficient. However, under viscoelastic conditions, seismic waves attenuate during propagation, exhibiting non-stationary characteristics. Therefore, it is necessary to introduce a propagation attenuation function based on the stationary model to form a non-stationary seismic model, whose frequency domain expression is:

[0036]

[0037] Where f represents frequency, t represents frequency domain time, i represents imaginary unit, S(f) represents frequency domain seismic record, W(f) represents frequency domain seismic wavelet, r(t) represents formation reflection coefficient, and α(t,f) represents propagation attenuation function, which is expressed as follows:

[0038]

[0039] The propagation attenuation function of formula (2) includes the quality factor variable Q; f R The reference frequency is generally the main frequency of the seismic data.

[0040] Performing the Fourier inverse transform on expression (1) yields the time-domain non-stationary earthquake convolution model:

[0041]

[0042] Where s(t) represents the time domain earthquake record and u represents the time quantity.

[0043] S120. Determine a synthetic seismic record corresponding to the seismic record function based on the numerical range of the quality factor variable and the seismic convolution model, and determine an initial quality factor field of the target reservoir based on a matching result between the synthetic seismic record and an actual seismic record of the target reservoir.

[0044] The numerical range of the quality factor variable can be understood as the numerical range of the quality factor in the target reservoir. The data range can be set by the exploration personnel based on historical exploration experience. Some values ​​are selected from the numerical range as the specific values ​​of the quality factor variable, and then substituted into the seismic convolution model to obtain the synthetic seismic record corresponding to the seismic record function. The synthetic seismic record corresponding to the seismic record function refers to s(t) after substituting the specific values ​​of the quality factor variable into formula (3).

[0045] It should also be noted that by substituting different values ​​corresponding to the quality factor variable into the seismic convolution model, different synthetic seismic records s(t) are obtained. The initial quality factor field of the target reservoir is determined based on the matching results between the synthetic seismic records and the actual seismic records of the target reservoir.

[0046] Specifically, for the synthetic seismic record s(t), which can also be called the synthetic seismic trace s(t), the wavelet term W(f) in expression (3) can be obtained by statistical extraction using seismic data, and the reflection coefficient term r can be obtained from the velocity and density curves of the well logging data. Therefore, only Q in the propagation attenuation function α is unknown. Since Q is in the exponential term, it is difficult to solve directly. The synthetic seismic trace s(t) can be compared with the wellside seismic trace s * The best match of (t) is taken as the goal and solved by segmented scanning and gradual refinement.

[0047] On the basis of the above embodiment, the method of determining the synthetic seismic record corresponding to the seismic record function based on the numerical range of the quality factor variable and the seismic convolution model includes: dividing the numerical range according to preset numerical intervals to obtain a plurality of quality factor values ​​to be used, and substituting the quality factor values ​​to be used into the seismic convolution model to obtain the synthetic seismic record to be used corresponding to the seismic record function.

[0048] The preset numerical interval refers to the interval that divides the numerical range of the quality factor variable, the quality factor values ​​to be used refer to the specific values ​​of the multiple quality factor variables obtained after dividing the data range, and the synthetic seismic record to be used essentially refers to the synthetic seismic record s(t) obtained by substituting the quality factor values ​​to be used into the seismic convolution model. If there are multiple quality factor values ​​to be used, there will also be multiple synthetic earthquakes to be used.

[0049] On the basis of the above scheme, the initial quality factor field of the target reservoir is determined based on the matching results of the synthetic seismic record and the actual seismic record of the target reservoir, including: calculating the correlation coefficient between the synthetic seismic record to be used and the actual seismic record, and using the correlation coefficient as the matching result; determining a target synthetic seismic record from a plurality of synthetic seismic records to be used based on the matching result, and determining the initial quality factor field based on the quality factor to be used corresponding to the target synthetic seismic record.

[0050] The target synthetic seismic record refers to the synthetic seismic record to be used with the largest correlation coefficient, and the Q value corresponding to the target synthetic seismic record is used as the initial Q value of the entire reservoir segment.

[0051] For example, the value range of the quality factor variable is set to [Qmin, Qmax] and the preset value interval ΔQ, and [Qmin, Qmax] is divided according to ΔQ to obtain multiple Q values, i.e., the quality factor values ​​to be used. Then, a constant Q value scan is performed on the reservoir section as a whole, i.e., the synthetic seismic record (synthetic seismic record to be used) s(t) corresponding to each Q value is obtained using expression (3), and compared with the wellside seismic trace (actual seismic record) s * (t) Calculate the correlation coefficient and take the Q value corresponding to the maximum correlation coefficient as the initial Q value of the entire reservoir segment.

[0052] Based on the above scheme, the determining of the initial quality factor field based on the to-be-used quality factor corresponding to the target synthetic seismic record includes: dividing the target reservoir into a first target reservoir segment and a second target reservoir segment, determining the quality factor value range of the first target reservoir segment and the second target reservoir segment based on the to-be-used quality factor and the disturbance range corresponding to the target synthetic seismic record; using the first target reservoir segment or the second target reservoir segment as a to-be-processed reservoir segment, and matching the synthetic seismic record of the to-be-processed reservoir segment with an actual seismic record within the quality factor value range to obtain the initial quality factor of the to-be-processed reservoir segment, and recording the number of divisions; when the number of divisions is less than a preset number, using the to-be-processed reservoir segment as the target reservoir segment, repeatedly performing the operations of dividing the target reservoir segment and determining the initial quality factor to obtain a plurality of initial quality factors, and constructing the initial quality factor field based on the plurality of initial quality factors.

[0053] It can be understood that in order to more accurately determine the quality factor of the target reservoir segment at different positions, the target reservoir segment is divided according to depth, gradually refined into multiple reservoir segments, and the quality factor value corresponding to each reservoir segment is determined, so that the quality factor distribution of the target reservoir segment in the depth direction can be obtained.

[0054] Specifically, the target reservoir is divided equally into a first target reservoir segment and a second target reservoir segment, that is, into an upper half and a lower half. Then, based on the set perturbation range and the previously calculated initial Q value of the target reservoir segment, a new numerical range is obtained, which serves as the quality factor numerical range for the first and second target reservoir segments. Next, the first or second target reservoir segment is used as the reservoir segment to be processed, and the specific quality factor values ​​within the corresponding quality factor numerical range are substituted into the seismic convolution model to obtain a synthetic seismic record for the reservoir segment to be processed. This is then matched with the actual seismic record, and the quality factor value corresponding to the synthetic seismic record with the maximum correlation coefficient is used as the initial quality factor for the reservoir segment to be processed. Based on this, the initial quality factors of the first and second target reservoir segments are obtained. The first target reservoir segment is then further divided to obtain two corresponding first target reservoir subsegments. The quality factor corresponding to each subsegment is then determined using the same processing method.

[0055] It should be noted that when the number of divisions is less than the preset number, the above-mentioned process of division, matching, and quality factor calculation is repeated, and finally multiple initial quality factors corresponding to the target reservoir segment in the depth direction are obtained, and then the initial quality factor field is constructed based on the multiple initial quality factors.

[0056] For example, the quality factor perturbation range Q is set to d , the entire reservoir section is divided into two parts. For the upper half, a Q scan is performed within the disturbance range of the initial Q value of the entire reservoir section to obtain the correlation coefficient between the synthetic seismic record and the wellbore seismic trace. The Q value corresponding to the maximum value of the correlation coefficient is taken as the Q value of the upper half; the same method is used to solve the lower half.

[0057] Then, the entire reservoir section is divided into four, eight, etc. parts in turn. For each segment, the Q obtained in the previous step is taken as the value center, and Q scans are performed within the disturbance range to obtain the optimal Q value until the synthetic seismic record and the wellbore seismic trace are optimally matched.

[0058] Through the above steps, the quality factor of the reservoir section at each well point can be obtained. On this basis, combined with the distribution trend of the seismic horizon, interpolation methods such as inverse distance weighted method or Kriging method are used for interpolation processing to obtain the initial Q field of the reservoir section, that is, Q M .

[0059] It can be understood that the actual seismic records correspond to the number of well points in the target reservoir on the horizontal plane, that is, the above solution process can obtain multiple initial quality factors corresponding to the target reservoir in the depth direction at a certain well point. However, the initial quality factor field is three-dimensional, so it is necessary to interpolate on the basis of the multiple initial quality factors that have been obtained to obtain a three-dimensional initial quality factor field Q M , and also the quality factor distribution of the target reservoir in the xyz three-dimensional space.

[0060] S130. Construct an objective function including a target quality factor variable based on the seismic amplitude spectrum of the target reservoir, determine a constraint function of the target quality factor variable based on the initial quality factor field and a constraint coefficient, and determine a target quality factor field for the target reservoir based on the objective function and the constraint function.

[0061] The target quality factor variable can be understood as a variable representing the interlayer quality factor between any two reservoir segments in the target reservoir. The objective function refers to a function of the seismic amplitude spectrum, interlayer propagation time interval, and interlayer quality factor. The constraint function is a function used to constrain the target quality factor variable using the initial quality factor field. The target quality factor field is the new quality factor field determined based on the aforementioned objective and constraint functions.

[0062] It is understood that the objective function can be used to obtain the interlayer quality factor value, i.e., the value of the target quality factor variable, using only the seismic amplitude spectrum of the target reservoir. However, the value of the target quality factor variable must satisfy the constraints of the initial quality factor field obtained in step S120.

[0063] In an embodiment of the present invention, constructing an objective function including a target quality factor variable based on the seismic amplitude spectrum of the target reservoir includes: determining the seismic amplitude spectra of any two adjacent reservoir segments in the target reservoir based on surface seismic data of the target reservoir, and constructing objective functions corresponding to the two adjacent reservoir segments based on the seismic amplitude spectra and propagation time intervals. Determining a target quality factor field for the target reservoir based on the objective function and the constraint function includes: calculating a target quality factor value between the two adjacent reservoir segments in the target reservoir based on the objective function and the constraint function; and constructing a target quality factor field for the target reservoir based on the target quality factor value.

[0064] For ground seismic data, assuming that the seismic amplitude spectra of the k-1th layer and the kth layer are S k-1 (f) and S k (f), the propagation time interval and inter-layer quality factor between the two are t k and Q k , then

[0065]

[0066] Among them, C k Represents a coefficient term that is unrelated to seismic wave attenuation. In order to solve the interlayer quality factor, the target equation is usually established:

[0067]

[0068] Among them F min and F max Represent the minimum and maximum values ​​of the earthquake effective frequency band respectively. Since C in expression (5) k Unknown, you can solve C for the target equation G k Taking the partial derivative of , and setting the result to zero, we get

[0069]

[0070] Since expression (6) is relatively complex, it is simplified here. Usually, the signal-to-noise ratio at the main frequency of earthquake is the highest, so the value at the main frequency can be used to replace expression (6), and let

[0071]

[0072] Then expression (6) becomes

[0073]

[0074] Substituting expression (8) into the target equation (5), we get

[0075]

[0076] Expression (9) is the basic equation (i.e., the objective function) for solving the quality factor using only seismic data. On this basis, it is necessary to introduce the initial quality factor field Q obtained by viscoelastic well-seismic matching: M As a constraint:

[0077]

[0078] Where J is the constraint coefficient, Indicates that between the k-1th layer and the kth layer, the initial quality factor field Q is used M The relationship between the two is:

[0079]

[0080] Where N represents the number of sampling points between the k-1th layer and the kth layer, Q(i)M represents the Q of the i-th sampling point in the layer M The number of sampling points can be understood as the initial quality factor field QM The number of initial quality factors included in the k-1th layer and the kth layer.

[0081] Combining expressions (9) and (10), we can obtain the target equation for solving the reservoir quality factor by combining well and seismic analysis:

[0082]

[0083] Where λ is the constraint coefficient, which is used to adjust the weight of the constraint term in the objective equation.

[0084] In equation (12), only the target quality factor variable Q k Unknown, you can obtain each Q through Q scanning k The target value F(Q k ), F(Q k ) corresponds to the global minimum value of Q k The value is the quality factor between the k-1th layer and the kth layer. k Assign different values ​​and then find the target value F(Q k ), F(Q k ) corresponds to the global minimum value of Q k The value of is used as the quality factor between the k-1th layer and the kth layer. Then, the quality factor is obtained for any k-1th layer and kth layer in this way, and is used as the target quality factor. All target quality factors constitute the target quality factor field of the target reservoir.

[0085] The technical solution of the embodiment of the present invention establishes a seismic convolution model of the target reservoir using its seismic wavelet, formation reflection coefficient, propagation attenuation function, and seismic record function, wherein the propagation attenuation function includes a quality factor variable. Then, based on the numerical range of the quality factor variable and the seismic convolution model, a synthetic seismic record corresponding to the seismic record function is determined. The initial quality factor field of the target reservoir is determined based on the matching results between the synthetic seismic record and the actual seismic record of the target reservoir. Finally, an objective function including the target quality factor variable is constructed based on the seismic amplitude spectrum of the target reservoir. Based on the initial quality factor field and constraint coefficients, a constraint function for the target quality factor variable is determined. The target quality factor field of the target reservoir is determined based on the objective function and constraint function. This improves the accuracy of resolving the reservoir quality factor field using surface seismic data. This solves the existing problem of low signal-to-noise ratio and resolution of surface seismic data, which is easily affected by formation thickness, making it difficult to obtain a complete seismic wavelet waveform, resulting in difficulty in ensuring the accuracy of the quality factor field solution. This improves the accuracy of resolving the reservoir quality factor field using surface seismic data.

[0086] Figure 2This is a flowchart of a method for calculating a reservoir quality factor provided by another embodiment of the present invention. This embodiment is a preferred embodiment of the above embodiment. Its specific implementation method can refer to the technical solution of this embodiment. Among them, the technical terms that are the same or corresponding to the above embodiment are not repeated here. Figure 2 As shown, the method includes:

[0087] Based on actual data, the implementation process of well-seismic joint quality factor calculation is as follows:

[0088] ① Obtain seismic intercept P data. First, preprocess the seismic CRP gathers, including denoising, frequency boosting, and leveling, to improve the quality of the CRP gathers. On this basis, extract the AVO intercept P based on the amplitude variation characteristics of the gathers. Compared with conventional seismic post-stack data, the intercept P data is more representative of the seismic self-excitation and self-receiving records at zero offset, so the intercept P data is used as the seismic data basis for solving the quality factor;

[0089] ② Obtain the initial Q field of the reservoir by viscoelastic well-seismic matching. First, pre-process the logging data, including wild value processing, environmental correction, consistency correction, etc., to improve the quality of the logging curve. On this basis, the intercept P data is viscoelasticly matched with the logging synthetic record. Based on expression (3), the reservoir Q value at the well point is obtained by segmented scanning and gradual refinement. Then, the logging interpolation field Q is established through inter-well interpolation processing. M ;

[0090] ③ Spectral analysis of intercept P data. The time-frequency spectrum of intercept P data is analyzed. Common methods include short-time Fourier transform and wavelet transform to obtain the time-frequency amplitude spectrum, and select the high signal-to-noise ratio band to determine the effective bandwidth [F min ,F max ];

[0091] ④ Solving the quality factor of well-seismic joint analysis. The seismic data are processed in layers, and the interlayer Q value is obtained by Q scanning based on equation (12) in each layer.

[0092] The technical solution of the embodiment of the present invention first obtains intercept P data representing zero offset based on the seismic CRP gather. Then, viscoelastic well-seismic matching is used to obtain the reservoir Q value at the well point, and interpolation processing is performed to form an initial Q field. Finally, using the interpolated Q field as a constraint, the well-seismic joint quality factor is solved based on the time-frequency spectrum of the intercept P data to obtain the reservoir Q field. When solving the quality factor in seismic exploration, conventional methods only use a single data source, which may make it difficult to establish the quality factor field or ensure the accuracy of the solution. The technical solution of the embodiment of the present invention utilizes multi-source data, integrates seismic and well logging information, and jointly solves the reservoir quality factor to obtain a highly accurate quality factor field, providing support for reservoir prediction or oil and gas detection. The quality factor is one of the important properties of the reservoir. Quality factor anomalies are often related to the degree of pore development or the type of fluid contained in the reservoir. The technical solution of the embodiment of the present invention can obtain a highly accurate quality factor field, which is of great significance for fine seismic exploration.

[0093] Figure 3 A schematic diagram of a reservoir quality factor calculation device provided by an embodiment of the present invention. Figure 3 As shown, the device includes:

[0094] A seismic convolution model building module 310 is configured to build a seismic convolution model of the target reservoir based on the seismic wavelet, formation reflection coefficient, propagation attenuation function, and seismic record function of the target reservoir, wherein the propagation attenuation function includes a quality factor variable;

[0095] an initial quality factor field determination module 320 for determining a synthetic seismic record corresponding to the seismic record function based on the numerical range of the quality factor variable and the seismic convolution model, and determining an initial quality factor field of the target reservoir based on a matching result between the synthetic seismic record and an actual seismic record of the target reservoir;

[0096] The target quality factor field determination module 330 is configured to construct an objective function including a target quality factor variable based on the seismic amplitude spectrum of the target reservoir, determine a constraint function for the target quality factor variable based on the initial quality factor field and a constraint coefficient, and determine the target quality factor field for the target reservoir based on the objective function and the constraint function.

[0097] The technical solution of the embodiment of the present invention establishes a seismic convolution model of the target reservoir using its seismic wavelet, formation reflection coefficient, propagation attenuation function, and seismic record function, wherein the propagation attenuation function includes a quality factor variable. Then, based on the numerical range of the quality factor variable and the seismic convolution model, a synthetic seismic record corresponding to the seismic record function is determined. The initial quality factor field of the target reservoir is determined based on the matching results between the synthetic seismic record and the actual seismic record of the target reservoir. Finally, an objective function including the target quality factor variable is constructed based on the seismic amplitude spectrum of the target reservoir. Based on the initial quality factor field and constraint coefficients, a constraint function for the target quality factor variable is determined. The target quality factor field of the target reservoir is determined based on the objective function and constraint function. This improves the accuracy of resolving the reservoir quality factor field using surface seismic data. This solves the existing problem of low signal-to-noise ratio and resolution of surface seismic data, which is easily affected by formation thickness, making it difficult to obtain a complete seismic wavelet waveform, resulting in difficulty in ensuring the accuracy of the quality factor field solution. This improves the accuracy of resolving the reservoir quality factor field using surface seismic data.

[0098] Optionally, the seismic convolution model building module 310 includes:

[0099] a stationary seismic convolution model determination module, which determines a frequency-domain non-stationary seismic convolution model of the target reservoir according to the convolution of the seismic wavelet and the formation reflection coefficient;

[0100] The non-stationary seismic convolution determination module is used to perform inverse Fourier transform on the frequency domain non-stationary seismic convolution model to obtain a seismic convolution model of the target reservoir, wherein the seismic convolution model is a time domain non-stationary seismic convolution model.

[0101] Optionally, the initial quality factor field determination module 320 includes:

[0102] The module for determining the synthetic seismic record to be used is used to divide the numerical range according to preset numerical intervals to obtain multiple quality factor values ​​to be used, and substitute the quality factor values ​​to be used into the seismic convolution model to obtain the synthetic seismic record to be used corresponding to the seismic record function.

[0103] Optionally, the initial quality factor field determining module 320 includes:

[0104] a matching calculation module, configured to calculate a correlation coefficient between the synthetic seismic record to be used and the actual seismic record, and use the correlation coefficient as the matching result;

[0105] The quality factor field determination module is configured to determine a target synthetic seismic record from the plurality of synthetic seismic records to be used based on the matching result, and determine the initial quality factor field based on the quality factor to be used corresponding to the target synthetic seismic record.

[0106] Optionally, the quality factor field determination module includes:

[0107] a numerical range determining unit, configured to divide the target reservoir into a first target reservoir segment and a second target reservoir segment, and determine the numerical ranges of the quality factors of the first target reservoir segment and the second target reservoir segment based on the to-be-used quality factors and the disturbance range corresponding to the target synthetic seismic records;

[0108] an initial quality factor calculation unit, configured to take the first target reservoir segment or the second target reservoir segment as a reservoir segment to be processed, and match the synthetic seismic records of the reservoir segment to be processed with actual seismic records within the quality factor value range to obtain an initial quality factor of the reservoir segment to be processed, and record the number of divisions;

[0109] An initial quality factor field establishing unit is configured to, when the number of divisions is less than a preset number, use the reservoir segment to be processed as the target reservoir segment, repeatedly perform the operations of dividing the target reservoir segment and determining the initial quality factor, obtain a plurality of the initial quality factors, and construct the initial quality factor field based on the plurality of the initial quality factors.

[0110] Optionally, the target quality factor field determination module 330 includes:

[0111] The objective function determination module is used to determine the seismic amplitude spectrum of any two adjacent reservoir segments in the target reservoir based on the surface seismic data of the target reservoir, and to construct the objective function corresponding to the two adjacent reservoir segments based on the seismic amplitude spectrum and the propagation time interval.

[0112] Optionally, the target quality factor field determination module 330 includes:

[0113] A target quality factor field calculation module is used to calculate the target quality factor value between two adjacent reservoir segments in the target reservoir based on the target function and the constraint function; and to construct the target quality factor field of the target reservoir based on the target quality factor value.

[0114] The reservoir quality factor calculation device provided in the embodiment of the present invention can execute the reservoir quality factor calculation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0115] Figure 4A schematic diagram of the structure of an electronic device provided for an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0116] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0117] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0118] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for calculating a reservoir quality factor.

[0119] In some embodiments, a method for calculating a reservoir quality factor can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps for calculating a reservoir quality factor described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute a method for calculating a reservoir quality factor in any other suitable manner (e.g., via firmware).

[0120] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0121] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0122] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0123] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0124] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0125] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0126] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0127] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for calculating a reservoir quality factor, characterized in that: include: Establishing a seismic convolution model of the target reservoir according to the seismic wavelet, formation reflection coefficient, propagation attenuation function and seismic record function of the target reservoir, wherein the propagation attenuation function includes a quality factor variable; Determining a synthetic seismic record corresponding to the seismic record function based on the numerical range of the quality factor variable and the seismic convolution model, and determining an initial quality factor field of the target reservoir based on a matching result between the synthetic seismic record and an actual seismic record of the target reservoir; An objective function including a target quality factor variable is constructed based on the seismic amplitude spectrum of the target reservoir, and a constraint function of the target quality factor variable is determined based on the initial quality factor field and a constraint coefficient. A target quality factor field of the target reservoir is determined based on the objective function and the constraint function.

2. The method according to claim 1, characterized in that The method of establishing a seismic convolution model of the target reservoir according to the seismic wavelet, formation reflection coefficient, propagation attenuation function and seismic recording function of the target reservoir comprises: Determining a frequency-domain non-stationary seismic convolution model of the target reservoir based on the convolution of the seismic wavelet and the formation reflection coefficient; Performing an inverse Fourier transform on the frequency-domain non-stationary seismic convolution model to obtain a seismic convolution model of the target reservoir, wherein the seismic convolution model is a time-domain non-stationary seismic convolution model.

3. The method according to claim 1, characterized in that The step of determining a synthetic seismic record corresponding to the seismic record function based on the numerical range of the quality factor variable and the seismic convolution model includes: The numerical range is divided according to preset numerical intervals to obtain a plurality of quality factor values ​​to be used, and the quality factor values ​​to be used are substituted into the seismic convolution model to obtain the synthetic seismic record to be used corresponding to the seismic record function.

4. The method according to claim 3, characterized in that The determining of the initial quality factor field of the target reservoir according to the matching result between the synthetic seismic record and the actual seismic record of the target reservoir comprises: Calculating a correlation coefficient between the synthetic seismic record to be used and the actual seismic record, and using the correlation coefficient as the matching result; A target synthetic seismic record is determined from the plurality of synthetic seismic records to be used based on the matching result, and the initial quality factor field is determined based on the quality factor to be used corresponding to the target synthetic seismic record.

5. The method according to claim 4, characterized in that The determining the initial quality factor field based on the quality factor to be used corresponding to the target synthetic seismic record includes: Dividing the target reservoir into a first target reservoir segment and a second target reservoir segment, and determining quality factor value ranges of the first target reservoir segment and the second target reservoir segment based on the to-be-used quality factor and disturbance range corresponding to the target synthetic seismic record; Taking the first target reservoir segment or the second target reservoir segment as a reservoir segment to be processed, and matching the synthetic seismic record of the reservoir segment to be processed with the actual seismic record within the quality factor value range, obtaining an initial quality factor of the reservoir segment to be processed, and recording the number of divisions; When the number of divisions is less than a preset number, the reservoir segment to be processed is used as the target reservoir segment, and the operations of dividing the target reservoir segment and determining the initial quality factor are repeatedly performed to obtain a plurality of the initial quality factors, and the initial quality factor field is constructed based on the plurality of the initial quality factors.

6. The method according to claim 1, wherein The constructing of an objective function including a target quality factor variable based on the seismic amplitude spectrum of the target reservoir comprises: Based on the surface seismic data of the target reservoir, the seismic amplitude spectra of any two adjacent reservoir segments in the target reservoir are determined, and the target functions corresponding to the two adjacent reservoir segments are constructed based on the seismic amplitude spectra and the propagation time interval.

7. The method according to claim 6, characterized in that Determining a target quality factor field of the target reservoir based on the objective function and the constraint function includes: Calculating a target quality factor value between two adjacent reservoir segments in a target reservoir based on the objective function and the constraint function; A target quality factor field for the target reservoir is constructed based on the target quality factor value.

8. A device for calculating reservoir quality factor, characterized in that: include: a seismic convolution model building module, configured to build a seismic convolution model of the target reservoir based on the seismic wavelet, formation reflection coefficient, propagation attenuation function, and seismic recording function of the target reservoir, wherein the propagation attenuation function includes a quality factor variable; an initial quality factor field determination module, configured to determine a synthetic seismic record corresponding to the seismic record function based on a numerical range of the quality factor variable and the seismic convolution model, and determine an initial quality factor field of the target reservoir based on a matching result between the synthetic seismic record and an actual seismic record of the target reservoir; a target quality factor field determination module, configured to construct an objective function including a target quality factor variable based on the seismic amplitude spectrum of the target reservoir, determine a constraint function for the target quality factor variable based on the initial quality factor field and a constraint coefficient, and determine the target quality factor field of the target reservoir based on the objective function and the constraint function.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to perform the reservoir quality factor calculation method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for calculating the reservoir quality factor according to any one of claims 1 to 7 when executed.

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