Reservoir prediction method and device based on multi-dimensional seismic attributes, equipment and medium

Abstract

This disclosure belongs to the field of drilling support technology and provides a method, device, equipment, and medium for reservoir prediction based on multi-dimensional seismic attributes. The method includes: calculating the wave impedance and synthetic seismic records of each individual well using seismic data and well logging data of the area to be analyzed; selecting preferred seismic attributes sensitive to reservoir thickness from the one-dimensional seismic attributes of the synthetic seismic records; constructing a three-dimensional initial wave impedance model of the area to be analyzed based on the seismic data, preferred seismic attributes, and wave impedance of each individual well, and converting the three-dimensional initial wave impedance model into an initial reflection coefficient model; performing multi-dimensional seismic attribute fitting and inversion based on the initial reflection coefficient model and the seismic data of the area to be analyzed by iteratively correcting the three-dimensional initial wave impedance model to obtain a three-dimensional optimal wave impedance model; and determining the wave impedance range of the reservoir in the area to be analyzed using the three-dimensional optimal wave impedance model. This disclosure improves the prediction accuracy of ultra-deep, thin reservoirs.

Description

Technical Field

[0001] This disclosure belongs to the field of drilling support technology, and in particular relates to a reservoir prediction method, device, equipment and medium based on multi-dimensional seismic attributes. Background Technology

[0002] As oil and gas reservoir development deepens, more and more deep and complex oil and gas fields are being developed. To efficiently develop these fields, a multi-branch horizontal well deployment strategy is typically employed. The key to successful horizontal well drilling lies in the integrated geological and engineering design, particularly the accuracy of target placement. The accuracy of target placement directly affects the successful landing of the horizontal well and the frequency of wellbore trajectory adjustments in the horizontal section. Successful target placement requires accurate reservoir prediction to precisely sculpt the three-dimensional spatial distribution of the reservoir. Since ultra-deep reservoirs generally have low seismic resolution, especially in the case of thin reservoirs, conventional seismic reservoir prediction techniques yield reservoir inversions with low accuracy; their error range may exceed the reservoir thickness. Therefore, effectively predicting ultra-deep thin reservoirs remains a major challenge.

[0003] Currently, the commonly used method for predicting ultra-deep thin reservoirs is the geostatistical inversion method, such as the thin reservoir prediction method and device with patent application publication number CN112711067A. This method uses well logging data and seismic data as hard data constraints to perform geostatistical simulation inversion while drilling, thereby improving the resolution and reliability of the ultra-deep thin reservoir inversion results.

[0004] However, geostatistical inversion methods, based on stochastic simulation algorithms, produce results with a high degree of randomness, leading to significant inter-well reservoir prediction errors and hindering the improvement of prediction accuracy. Therefore, improving the prediction accuracy of ultra-deep, thin reservoirs is a pressing issue that needs to be addressed in this field. Summary of the Invention

[0005] To address the aforementioned issues, this disclosure provides a reservoir prediction method, apparatus, electronic device, and storage medium based on multi-dimensional seismic attributes, with the aim of improving the prediction accuracy of ultra-deep and thin reservoirs.

[0006] To achieve the above objectives, this disclosure mainly provides the following technical solutions:

[0007] Firstly, this disclosure provides a reservoir prediction method based on multi-dimensional seismic attributes, including:

[0008] Using the seismic data of the area to be analyzed and the well logging data of each well in the area to be analyzed, the wave impedance and seismic composite record of each well are calculated one by one.

[0009] Among the one-dimensional seismic attributes of the aforementioned seismic composite record, at least one preferred seismic attribute that is sensitive to reservoir thickness is selected.

[0010] Based on the seismic data of the area to be analyzed, the preferred seismic attributes, and the wave impedance of each well, a three-dimensional initial wave impedance model of the area to be analyzed is constructed, and the three-dimensional initial wave impedance model is converted into an initial reflection coefficient model.

[0011] Based on the initial reflection coefficient model and the seismic data of the area to be analyzed, the three-dimensional initial wave impedance model is iteratively corrected, and multi-dimensional seismic attribute fitting and inversion are performed to obtain the three-dimensional optimal wave impedance model.

[0012] The impedance range of the reservoir in the region to be analyzed is determined using the three-dimensional optimal impedance model.

[0013] Secondly, this disclosure provides a reservoir prediction device based on multi-dimensional seismic attributes, the device comprising:

[0014] The calculation unit is used to calculate the wave impedance and seismic composite record of each well in the area to be analyzed, using the seismic data of the area to be analyzed and the well logging data of each well in the area to be analyzed.

[0015] A screening unit is used to screen out at least one preferred seismic attribute that is sensitive to reservoir thickness from each one-dimensional seismic attribute of the seismic synthetic record.

[0016] The construction unit is used to construct a three-dimensional initial wave impedance model of the area to be analyzed based on the seismic data of the area to be analyzed, the preferred seismic attributes, and the wave impedance of each single well, and to convert the three-dimensional initial wave impedance model into an initial reflection coefficient model.

[0017] An iterative unit is used to perform multi-dimensional seismic attribute fitting and inversion based on the initial reflection coefficient model and the seismic data of the area to be analyzed, by iteratively correcting the three-dimensional initial wave impedance model, so as to obtain the three-dimensional optimal wave impedance model.

[0018] The determining unit is used to determine the impedance range of the reservoir in the region to be analyzed using the three-dimensional optimal impedance model.

[0019] On the other hand, this disclosure also provides a storage medium for storing a computer program, wherein the computer program, when running, controls the device where the storage medium is located to execute the method described in the first aspect.

[0020] On the other hand, this disclosure also provides an electronic device, the device including at least one processor, and at least one memory and bus connected to the processor; wherein the processor and the memory communicate with each other through the bus; the processor is used to call program instructions in the memory to execute the method as described in the first aspect above.

[0021] On the other hand, this disclosure also provides a computer program product comprising a computer program that, when executed by a processor, implements the method of the first aspect described above.

[0022] Compared with the prior art, this disclosure has the following advantages:

[0023] This disclosure utilizes seismic data of the area to be analyzed and well logging data of individual wells within the area to generate a composite seismic record for each well. From all one-dimensional seismic attributes in the composite seismic record, several preferred seismic attributes sensitive to reservoir thickness are selected. The correlation between the preferred seismic attribute volume and wave impedance is fitted, and this correlation is used to perform a weighted average of each preferred seismic attribute to generate an attribute constraint volume. Using well logging wave impedance data as hard constraints and the attribute constraint volume as a modeling trend constraint volume, a sequential indicator stochastic modeling algorithm is employed to establish a three-dimensional initial wave impedance model, which is then converted into an initial reflection coefficient model. The composite seismic record of the area to be analyzed is obtained through the convolution of the initial reflection coefficient model and seismic wavelet. The three-dimensional initial wave impedance model is iteratively analyzed, comparing the differences between the preferred seismic attributes of the composite seismic record and the preferred seismic attributes of the pure seismic waves in the analysis area, as well as the differences between the composite seismic record and the pure seismic waves, until the differences are sufficiently small. Then, multi-dimensional seismic attribute fitting and inversion are performed to obtain the optimal three-dimensional wave impedance model. The wave impedance range of the reservoir in the area to be analyzed is determined using the optimal three-dimensional wave impedance model. This disclosure utilizes multi-dimensional seismic attributes and an iterative three-dimensional wave impedance model to achieve accurate prediction of ultra-deep, thin reservoirs.

[0024] Other features and advantages of this disclosure will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the disclosure. The objects and other advantages of this disclosure may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A flowchart illustrating a reservoir prediction method based on multi-dimensional seismic attributes according to an embodiment of the present disclosure is shown.

[0027] Figure 2A flowchart illustrating another reservoir prediction method based on multi-dimensional seismic attributes according to an embodiment of the present disclosure is shown.

[0028] Figure 3 A flowchart illustrating another reservoir prediction method based on multi-dimensional seismic attributes according to an embodiment of the present disclosure is shown.

[0029] Figure 4 A graph showing the relationship between one-dimensional properties of a single well and reservoir thickness according to an embodiment of the present disclosure is shown.

[0030] Figure 5 A platform well location deployment diagram according to an embodiment of the present disclosure is shown;

[0031] Figure 6 A single-well prediction result diagram according to an embodiment of the present disclosure is shown;

[0032] Figure 7 A schematic diagram of a reservoir prediction device based on multi-dimensional seismic attributes according to an embodiment of the present disclosure is shown.

[0033] Figure 8 A schematic diagram of an electronic device structure according to an embodiment of the present disclosure is shown. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0035] In the process of deploying multi-branch horizontal wells in a platform, target points need to be deployed. Furthermore, in areas with complex reservoir structures, control points for horizontal sections need to be added based on micro-structures and reservoir development characteristics. The wellbore trajectory is obtained by connecting the target points and control points in series, thus achieving the three-dimensional deployment of horizontal wells across the platform's branches. Addressing a series of issues such as inaccurate reservoir prediction under complex formation conditions in ultra-deep and thin reservoirs, large errors in the deployment of horizontal well target and control points, and the inability of wellbore trajectories to simultaneously consider both geological and engineering "sweet spots," this disclosure provides a reservoir prediction method based on multi-dimensional seismic attributes. This method aims to improve the prediction accuracy of ultra-deep and thin reservoirs, reduce frequent trajectory adjustments during drilling, thereby increasing reservoir drilling efficiency and reducing drilling operation risks.

[0036] like Figure 1 As shown, the reservoir prediction method based on multi-dimensional seismic attributes in this disclosure includes:

[0037] 101. Using the seismic data of the area to be analyzed and the well logging data of each well in the area to be analyzed, calculate the wave impedance and seismic composite record of each well one by one.

[0038] Here, wave impedance is the product of formation density and acoustic velocity. Seismic synthetic records are seismic waveform records generated by convolving the reflection coefficients calculated from well logging data with the seismic wavelet. Seismic synthetic records are used to establish connections between well logging data and seismic data, enabling well-seismic calibration and time-depth conversion.

[0039] In this step, for each well within the analysis area, seismic wavelets can be extracted from the seismic data of the analysis area. The wave impedance of each well is then calculated using well logging data to obtain a wave impedance curve. Based on the wave impedance curve of each well, a reflection coefficient sequence is calculated. The seismic composite record for that well is obtained by convolving this reflection coefficient sequence with the seismic wavelet of that well. It can also be compared with the seismic trace near the well. If the seismic waveform in the composite record is inconsistent with the seismic waveform near the well, the time position of the composite record is adjusted to obtain the time-depth relationship of the well, converting the oil and gas well from the depth domain to the time domain. This facilitates comparison with seismic data and also yields a more accurate composite record. When adjusting the time position of the composite record, the seismic waveform can be shifted overall or stretched / compressed locally to match the peaks or troughs of the composite record with those of the actual seismic trace.

[0040] 102. Among the one-dimensional seismic attributes in the seismic composite record, select at least one preferred seismic attribute that is sensitive to reservoir thickness.

[0041] The seismic composite records for each well are one-dimensional seismic composite records. One-dimensional seismic attributes include first-order derivative attributes, trace integral attributes, frequency division attributes, amplitude envelope attributes, amplitude-weighted phase attributes, and root-mean-square attributes. The preferred seismic attributes are one-dimensional seismic attributes that are sensitive to reservoir thickness.

[0042] In this step, all one-dimensional seismic attributes can be extracted from the seismic composite record of a single well. A one-dimensional geological model of the single well is then constructed. Based on this model, forward modeling of the one-dimensional reservoir parameter model is performed on all the one-dimensional seismic attributes to screen out those sensitive to reservoir thickness, resulting in several preferred seismic attributes. Reservoir parameters include reservoir thickness, density, and porosity. When identifying reservoirs in different strata, porosity or density data can be used for identification; for example, strata with high porosity and low density can be identified as reservoirs.

[0043] 103. Based on the seismic data of the area to be analyzed, the preferred seismic attributes, and the wave impedance of each well, construct a three-dimensional initial wave impedance model of the area to be analyzed, and convert the three-dimensional initial wave impedance model into an initial reflection coefficient model.

[0044] In this step, a stratigraphic framework model can be constructed first using geological data, such as geological structural horizons and well layers. During the construction of the stratigraphic framework model, the structure at well points can be corrected using well layers. After the stratigraphic framework model is completed, a three-dimensional initial acoustic impedance model of the area to be analyzed can be constructed based on this model. Specifically, an attribute constraint body is established using seismic data and optimized seismic attributes of the area to be analyzed. The acoustic impedance of each individual well is used as a hard constraint to generate the acoustic impedance model at each well point. The acoustic impedance between wells is generated using interpolation based on the stratigraphic framework model and the attribute constraint body as lateral constraints. The interpolation algorithm can employ a sequentially indicated stochastic simulation algorithm, thereby constructing the three-dimensional initial acoustic impedance model of the area to be analyzed. After constructing the three-dimensional initial acoustic impedance model, it needs to be converted into a corresponding initial reflection coefficient model so that the three-dimensional initial acoustic impedance model can be iteratively updated using this initial reflection coefficient model.

[0045] 104. Based on the initial reflection coefficient model and seismic data of the area to be analyzed, the three-dimensional initial wave impedance model is iteratively corrected, and multi-dimensional seismic attribute fitting and inversion are performed to obtain the optimal three-dimensional wave impedance model.

[0046] In this step, a three-dimensional (3D) seismic composite record of the region to be analyzed is obtained by convolution of the seismic wavelet from the seismic data of the region to be analyzed with an initial reflection coefficient model. Preferred seismic attributes of the region to be analyzed are extracted from this composite record. An objective function is established using the composite record, the preferred seismic attributes, and the seismic data and their preferred attributes. The value of the objective function is calculated and compared with a preset threshold value. If the value is greater than the preset threshold value, the initial 3D wave impedance model is modified to obtain a new 3D wave impedance model. A new reflection coefficient model is then obtained through this new model. The new reflection coefficient model is used to compare the new objective function value with the preset threshold value again. This process is iteratively updated until the objective function value is less than the preset threshold value. The 3D wave impedance model obtained when the value is less than the preset threshold value is the optimal 3D wave impedance model, which is the final multidimensional attribute fitting and inversion result, representing a relative wave impedance value. The preset threshold value in this embodiment can be set according to the actual reservoir thickness of the region, typically ranging from 0.05 to 0.1.

[0047] 105. Use the three-dimensional optimal wave impedance model to determine the wave impedance range of the reservoir in the region to be analyzed.

[0048] In this step, a three-dimensional optimal acoustic impedance model can be calibrated using a single-well one-dimensional geological model. This one-dimensional geological model can represent the vertical combination of reservoir thickness variations. Specifically, through a human-computer interactive mode, the display color scale in the three-dimensional optimal acoustic impedance model can be modified to find the relative acoustic impedance range corresponding to the reservoir, thus obtaining the reservoir inversion result. After determining the relative acoustic impedance range corresponding to the reservoir, horizontal well targets can be deployed within the corresponding acoustic impedance range, and the wellbore trajectory can be designed to reduce the frequency of well trajectory adjustments during drilling.

[0049] This embodiment utilizes seismic data of the area to be analyzed and well logging data of individual wells within that area to generate a composite seismic record for each well. From all one-dimensional seismic attributes in the composite seismic record, several preferred seismic attributes sensitive to reservoir thickness are selected, and the correlation between each attribute and wave impedance is established. Weighted calculations of each attribute volume yield an attribute constraint volume. This attribute constraint volume and well data constraints are used to construct a three-dimensional initial wave impedance model, which is then converted into an initial reflection coefficient model. The composite seismic record of the area to be analyzed is obtained by convolving the initial reflection coefficient model with the seismic wavelet of the area to be analyzed. The three-dimensional initial wave impedance model is iterated, and the differences between the preferred seismic attributes of the composite seismic record and the preferred seismic attributes of the actual seismic data in the analysis area, as well as the differences between the composite seismic record and the pure seismic wave data, are compared until the differences are sufficiently small. Then, multi-dimensional seismic attribute fitting and inversion are performed to obtain the optimal three-dimensional wave impedance model. The wave impedance range of the reservoir in the area to be analyzed is determined using the optimal three-dimensional wave impedance model. This embodiment utilizes multi-dimensional seismic attributes and an iterative three-dimensional wave impedance model to achieve accurate prediction of ultra-deep, thin reservoirs.

[0050] To illustrate in more detail the reservoir prediction method based on multi-dimensional seismic attributes proposed in this disclosure, this disclosure also presents an embodiment of another reservoir prediction method based on multi-dimensional seismic attributes. The specific implementation steps of this embodiment are as follows: Figure 2 As shown, it includes:

[0051] 201. Using the seismic data of the area to be analyzed and the well logging data of each well in the area to be analyzed, calculate the wave impedance and seismic composite record of each well one by one.

[0052] In one feasible approach, the specific steps for determining the wave impedance and seismic composite record of a single well include steps one through four:

[0053] Step 1: Obtain logging data for the target well, including sonic transit time and formation density.

[0054] Step 2: Calculate the wave impedance of the target well based on the acoustic transit time and formation density, and use the wave impedance to calculate the reflection coefficient of the target well.

[0055] Among them, formation density can be the formation density curve data for the entire well section. Sonic transit time is the sonic transit time curve for the entire well section. Reflection coefficient is the reflection coefficient sequence for the entire well section.

[0056] In this step, the acoustic transit time curve of the entire target well section can be integrated to obtain the acoustic velocity of each formation in the target well. The acoustic velocity and formation density are used to calculate the wave impedance of the target well, and the wave impedance of the target well is converted into the reflection coefficient of the target well.

[0057] The formula for wave impedance is:

[0058] Z = ρ × v

[0059] In the formula, Z is the wave impedance, g / (cm) 2 ·s), ρ is the formation density, g / cm³ 3 v is the speed of sound, in m / s.

[0060] The formula for the reflection coefficient is:

[0061]

[0062] In the formula, R is the reflection coefficient, Z2 is the wave impedance of the stratum below the reflection interface, and Z1 is the wave impedance of the stratum above the reflection interface.

[0063] Step 3: Extract the seismic wavelet of the target well from the seismic data of the area to be analyzed.

[0064] Step 4: Obtain the seismic composite record of the target single well by convolution using the seismic wavelet and reflection coefficient.

[0065] This embodiment of the disclosure can integrate the sonic transit time curve of a target well to obtain the sonic velocity of each formation in the target well. Using the sonic velocity, a depth-time conversion is performed on the target well in the depth domain to obtain the time of each formation above the well, thus obtaining the time-depth pair of the target well, i.e., the time-depth relationship. By adjusting the time of each formation above the well, the time position of the formation in the single well can be adjusted so that the seismic synthetic record of the target well matches the actual seismic waveform. That is, the peaks of the seismic synthetic record are aligned with the peaks and troughs of the actual seismic waveform, at which point the adjusted time-depth relationship can be obtained. This time-depth relationship is used to calibrate the geological meaning corresponding to the seismic phase. It should be noted that this embodiment of the disclosure can calculate the wave impedance, reflection coefficient, and seismic synthetic record of each well in the area to be analyzed, one by one, as the target well.

[0066] 202. For each single well, extract each one-dimensional seismic attribute from the seismic synthetic record, and select the single well with the best correlation between each one-dimensional seismic attribute and reservoir thickness to obtain the standard well in the analysis area.

[0067] Among them, standard wells are used to characterize the vertical distribution pattern of reservoirs in the area to be analyzed.

[0068] In this embodiment, after completing well seismic calibration for all individual wells within the analysis area, i.e., obtaining the seismic composite record for each individual well, all one-dimensional seismic attributes are extracted from the seismic composite record of each individual well within the analysis area. These one-dimensional seismic attributes include, for example, first derivative attributes, trace integral attributes, frequency division attributes, amplitude envelope attributes, amplitude-weighted phase attributes, and root mean square attributes. Based on the actual conditions of the analysis area, wells exhibiting a good correlation between various one-dimensional seismic attributes and reservoir thickness can be identified and used as standard wells within the analysis area.

[0069] 203. Obtain the one-dimensional seismic attributes of each standard well, and analyze the sensitivity of each one-dimensional seismic attribute to reservoir parameters to obtain at least one preferred seismic attribute that is sensitive to reservoir thickness.

[0070] Among them, the preferred seismic attribute is the one-dimensional seismic attribute that is sensitive to the reservoir in the area to be analyzed.

[0071] In this step, the one-dimensional seismic attributes of the standard wells are normalized, and the sensitivity of each normalized one-dimensional seismic attribute to reservoir thickness is compared. The one-dimensional seismic attribute with a sensitivity greater than the sensitivity threshold is selected as the preferred seismic attribute. Typically, there are multiple preferred seismic attributes.

[0072] 204. Based on the seismic data of the area to be analyzed and the preferred seismic attributes, determine the attribute constraint body.

[0073] In this embodiment, the attribute constraint body is used as a modeling trend constraint to construct a three-dimensional wave impedance model.

[0074] In one feasible approach, determining the specific method for the attribute constraint body includes steps one through three:

[0075] Step 1: Based on the wave impedance of the standard well and the preferred seismic attributes, determine the correlation coefficient between each preferred seismic attribute and the wave impedance.

[0076] The wave impedance of the standard well can be the wave impedance of each formation in the standard well, that is, the wave impedance at each depth point of each layer in the standard well. The correlation coefficient between the preferred seismic attribute and the wave impedance is used to determine the contribution of the corresponding preferred seismic attribute to the wave impedance inversion.

[0077] In this step, the relationship between the target layer wave impedance and the preferred seismic attribute can be fitted using a standard well. For example, since each layer depth point of the standard well has a wave impedance value, and each preferred seismic attribute also has a preferred seismic attribute value at each layer depth point, a linear or nonlinear relationship between the preferred seismic attribute value and the wave impedance value can be established for each preferred seismic attribute. For example, the relationship between the two is Y = ax + b, where a is the correlation coefficient between the corresponding preferred seismic attribute and the wave impedance.

[0078] Step 2: Process the seismic data of the area to be analyzed to obtain the pure wave seismic data of the area to be analyzed, and extract the preferred seismic attributes from the pure wave seismic data.

[0079] Among them, pure seismic wave data is obtained through seismic data processing, such as deconvolution and denoising. Pure seismic wave data retains only the reflection coefficient information of the underground strata, eliminating the influence of seismic wavelets and surface interference, and is closer to the actual reflection interface characteristics of the underground.

[0080] Step 3: Based on the correlation coefficient, perform a weighted average of the preferred seismic attributes of the pure seismic wave data to obtain the attribute constraint body.

[0081] Among them, the attribute constraint volume is a comprehensive data volume that integrates multi-dimensional seismic attribute information. Its value reflects the constraint trend after different preferred seismic attributes are superimposed according to weights.

[0082] In this step, the correlation coefficient is used as the weight of each preferred seismic attribute, and a weighted average is performed on the preferred seismic attributes of the pure seismic data to obtain the attribute constraint volume. The preferred seismic attribute with the larger the correlation coefficient has, the greater its proportion in the weighted average, and the more significant its contribution to the attribute constraint volume.

[0083] 205. Obtain geological data of the area to be analyzed, and create a stratigraphic framework model based on the geological data.

[0084] The geological data includes geological structure maps, fault data, well stratification data, and other data.

[0085] In this step, after creating a stratigraphic framework model based on geological data, the strata in the stratigraphic framework model can be vertically subdivided. By vertically subdividing the strata into smaller thicknesses, the model mesh can be adapted to the vertical scale of thinner layers, avoiding the smoothing or neglect of reservoir features due to excessively coarse layering. This thickness is typically half of the seismic sampling interval.

[0086] 206. Based on the formation framework model, the wave impedance of each single well is used as a hard constraint, and the attribute constraint volume is used as a trend constraint. The sequential indicator stochastic modeling algorithm is used to construct a three-dimensional wave impedance model of the area to be analyzed, and the three-dimensional wave impedance model is converted into a reflection coefficient model.

[0087] In this step, the acoustic impedance of each individual well is used as a hard constraint, and the attribute constraint volume is used as a trend constraint. A sequential indicator stochastic modeling algorithm is employed to interpolate and extrapolate along the geological structural strata in the stratigraphic framework model, constructing a three-dimensional initial acoustic impedance model for the area to be analyzed. Using the aforementioned reflection coefficient formula, the three-dimensional initial acoustic impedance model is converted into an initial reflection coefficient model. In subsequent iterations, the three-dimensional initial acoustic impedance model is iterated into a new three-dimensional acoustic impedance model, and correspondingly, the initial reflection coefficient model is iterated into a new reflection coefficient model.

[0088] 207. Extract the seismic wavelet of the region to be analyzed from the seismic data of the region to be analyzed; and obtain the seismic synthetic record of the region to be analyzed by convolution based on the seismic wavelet and the initial reflection coefficient model of the region to be analyzed.

[0089] The seismic synthetic record of the area to be analyzed is a three-dimensional seismic synthetic record.

[0090] 208. Based on the seismic synthetic records and their preferred seismic attributes of the area to be analyzed, as well as the seismic pure wave data and their preferred seismic attributes of the area to be analyzed, construct the objective function and calculate the objective function value;

[0091] In this embodiment, to quantify the matching degree between the preferred seismic attributes of the seismic synthetic record and the preferred seismic attributes of the actual seismic data, as well as the matching between the synthetic record and the actual pure wave, an objective function needs to be constructed by iterating a three-dimensional acoustic impedance model to maximize the fit between the inversion results and the actual seismic data, thereby improving the reservoir prediction accuracy. The deviation between the inversion model and the actual data is measured by calculating the sum of squares of the differences between the actual and synthetic preferred seismic attributes. The smaller the deviation, the closer the three-dimensional acoustic impedance model is to the true stratigraphic characteristics.

[0092] The formula for the objective function is:

[0093]

[0094] In the formula, F(m) represents the objective function, S represents the seismic pure wave data, Gm represents the seismic synthetic record, and S attri1 S attri2 S attri3 The preferred seismic attribute for a pure wave earthquake is represented by (Gm). attri1 (Gm) attri2 (Gm) attri3 This indicates the preferred seismic attributes of the composite seismic record.

[0095] 209. Determine whether the value of the objective function is greater than the preset threshold value.

[0096] In this step, based on the objective function and the preset threshold value, the multi-dimensional seismic attributes of the target reservoir are inverted. It is determined whether the value of the objective function is greater than the preset threshold value. If so, step 206 is executed to iteratively correct the three-dimensional wave impedance model. If not, step 210 is executed.

[0097] 210. Determine that the current three-dimensional impedance model is the optimal three-dimensional impedance model. Use the one-dimensional geological model of a standard well to calibrate the optimal three-dimensional impedance model in order to determine the impedance range of the reservoir in the area to be analyzed.

[0098] This disclosure innovatively utilizes multiple one-dimensional seismic attributes (i.e., multi-dimensional seismic attributes) from standard wells to fit the relationship between attributes and reservoir thickness, and employs multi-attribute optimized combination to constrain seismic impedance inversion. This disclosure effectively broadens the seismic frequency band, improves seismic prediction resolution while reducing the ambiguity of seismic inversion results, and solves the problems of low reservoir prediction accuracy and frequent well trajectory adjustments caused by large target point design errors. It effectively controls wellbore trajectory smoothness, improves reservoir encounter rate, and reduces drilling risks. This disclosure is adaptable to reservoir prediction in various formations and oil and gas reservoirs, and is compatible with multi-source data structures.

[0099] Verification results from multiple thin reservoirs in western China show that, considering drilling engineering, horizontal well targets deployed based on the multi-attribute fitting inversion results of this disclosure, using multi-control point design of the wellbore trajectory, achieve a high reservoir penetration rate and require fewer than 2 / 300m of trajectory adjustments, reducing the complexity of drilling operations and achieving the goal of speeding up and improving efficiency.

[0100] To further illustrate the reservoir prediction method based on multi-dimensional seismic attributes proposed in this disclosure, this disclosure presents another embodiment of the reservoir prediction method based on multi-dimensional seismic attributes. The specific implementation steps of this embodiment are as follows: Figure 3 As shown, it includes:

[0101] Synthetic well-seismic calibration: Seismic data and well logging data for the area to be analyzed are acquired. Using these data, a synthetic seismic record for each well within the analyzed area is obtained, thus achieving well-seismic calibration. Well logging data refers to both well logging and well logging data.

[0102] Framework modeling: Constructing a stratigraphic framework model based on geological data.

[0103] Standard well selection: Based on the logging data of each individual well, the well that best represents the vertical distribution of the reservoir in the area to be analyzed is selected as the standard well.

[0104] One-dimensional geological model of standard well: Using logging data from standard wells, a one-dimensional geological model is constructed to characterize the formation thickness of the standard well.

[0105] One-dimensional reservoir parameter model forward modeling: Based on the standard well one-dimensional geological model, a vertical one-dimensional reservoir parameter model is constructed, with reservoir parameters such as thickness, velocity, and density. The seismic response corresponding to this model is simulated through seismic forward modeling algorithms, such as convolution to obtain synthetic seismic records.

[0106] One-dimensional reservoir sensitive attribute optimization: For the synthetic seismic record obtained by forward modeling, one-dimensional geological attributes are extracted, and seismic attributes sensitive to changes in reservoir parameters are screened through correlation analysis to obtain several preferred seismic attributes.

[0107] Reconstructing the wave impedance model: Based on the stratigraphic framework model, the three-dimensional wave impedance model of the area to be analyzed is reconstructed using optimized seismic attributes, seismic data, and well logging data, and then converted into a reflection coefficient model.

[0108] 3D seismic forward modeling: 3D synthetic seismic records are obtained by convolving the seismic wavelet of the region to be analyzed with a 3D reflection coefficient model.

[0109] Multidimensional attribute optimization analysis: Extract the preferred seismic attributes from the actual seismic data and the three-dimensional synthetic seismic data of the area to be analyzed to obtain the optimized combination of seismic attributes.

[0110] Establish the objective function: Based on the actual seismic data and the 3D synthetic seismic data, as well as their respective preferred seismic attributes, establish the objective function. Determine whether the value of the objective function is optimal. If it is optimal, output the result; otherwise, return to the reconstructed wave impedance model step.

[0111] This embodiment of the disclosure selects standard wells within the area to be analyzed to obtain preferred seismic attributes that are sensitive to reservoir thickness. Based on each preferred seismic attribute, the reservoir thickness inversion data volume is finally obtained. This embodiment of the disclosure achieves accurate prediction of ultra-deep thin reservoirs through multi-dimensional seismic attribute fitting and inversion.

[0112] The following description uses an oil and gas clastic reservoir in Northwest China as an example. This block is a lacustrine delta deposit, with the reservoir mainly composed of underwater distributary sands. Vertically, the sand bodies are stacked, with numerous interlayers and thin individual sand bodies, estimated to be approximately 3-5 meters thick. Laterally, the sand body extensions exhibit severe facies changes, resulting in low reservoir prediction accuracy and difficulty in target deployment. This leads to frequent trajectory adjustments during horizontal well drilling, low reservoir encounter rates, and high drilling risks. To effectively solve the problem of low reservoir encounter rates in horizontal wells, reservoir prediction is crucial. Therefore, this disclosure adopts an embodiment for reservoir prediction, which can precisely depict the three-dimensional spatial distribution range of sand bodies. When deploying horizontal wells, it can proactively consider both geological and engineering "sweet spots," thereby achieving efficient and high-quality drilling.

[0113] Well-seismic calibration analysis: One-dimensional seismic composite records of a single well are obtained by convolving the seismic wavelet with the well logging reflection coefficient. Based on this, the position of the seismic composite records is adjusted to obtain the time-depth relationship of a single well.

[0114] Single-well multi-dimensional attribute optimization: Using the one-dimensional synthetic seismic record calculated from a single well, all one-dimensional seismic attributes are extracted to obtain seismic attributes in each dimension. The relationship between these seismic attributes and reservoir thickness is then analyzed to determine several preferred one-dimensional seismic attributes. For example... Figure 4 As shown, the second derivative property is most sensitive to reservoir thickness. During the inversion process, the three-dimensional wave impedance model can be modified according to the sensitivity of seismic properties.

[0115] Establish the relationship between attributes and impedance: Utilize the correlation between the preferred seismic attributes extracted from standard wells and wave impedance.

[0116] Create attribute constraint volume: Extract the three-dimensional preferred seismic attribute volume corresponding to the one-dimensional preferred seismic attribute of a single well using pure seismic wave data, and weight each three-dimensional preferred seismic attribute volume using the relationship between each preferred seismic attribute and wave impedance to obtain the attribute constraint volume.

[0117] Reconstructing the wave impedance model: Based on the geological structure and well layers, a stratigraphic framework model is established. Then, using the wave impedance of a single well in the area to be analyzed, i.e., the wave impedance curve, interpolation and extrapolation are performed along the geological structure. The attribute constraint volume is used as the volume constraint condition to obtain the three-dimensional wave impedance model and convert it into a reflection coefficient model.

[0118] 3D seismic forward modeling: The seismic synthetic record data volume is obtained by convolving the seismic wavelet in the region to be analyzed with the 3D reflection coefficient model, i.e., the 3D seismic synthetic record.

[0119] Seismic attribute extraction: Selected seismic attributes are extracted from the pure wave data and synthetic seismic data volumes of the area to be analyzed, respectively, to obtain the multi-dimensional seismic attribute volumes of the two, which are used for subsequent iterative optimization of the objective function.

[0120] Establish the objective function: Based on the pure wave seismic data and synthetic seismic data volume of the area to be analyzed, as well as their multi-dimensional seismic attribute volumes, construct the objective function and calculate its value. According to the actual situation of the area to be analyzed, set the threshold value of the work area to 0.07. Iteratively optimize the three-dimensional wave impedance model until the objective function value is less than the threshold value. The obtained wave impedance model is the final inversion result.

[0121] Inversion result calibration: Using a one-dimensional thickness model of a standard well, the wave impedance model is calibrated in a human-computer interactive mode to obtain the wave impedance value range corresponding to the reservoir. This wave impedance range is the relative wave impedance value.

[0122] Target and control point deployment: Within the reservoir impedance range, horizontal well target points and horizontal section control points are deployed according to geological engineering requirements, thereby reducing wellbore trajectory adjustments and improving reservoir drilling rate. Figure 5 The horizontal wells deployed based on the seismic inversion results are shown in white. The white area represents the sand body distribution area. A total of 11 wells were deployed. In the northern half, the physical properties are relatively poor, and there is a sand body pinch-out phenomenon in the middle of the horizontal wells. Five wells were deployed, and the average reservoir drilling rate is expected to be 85%. In the southern half, the physical properties are relatively good, but there is also the possibility of phase change in the horizontal section of the reservoir. Six wells were deployed, and the average reservoir encounter rate is expected to be 90%. Figure 6 This image shows the drilling results of one of the wells, with the darker areas representing the reservoir. Pre-drilling predictions indicated mudstone would be encountered in the central part of the reservoir. Six control points were added during the target point design, with a designed horizontal section length of 1700m and a designed encounter rate of 82%. During actual drilling, based on seismic inversion results, the drilling trajectory was proactively predicted and designed. The final actual reservoir encounter rate was 81.5%, achieving a design compliance rate of 99.3%. Furthermore, by adding control points based on seismic inversion results before drilling, only three major trajectory adjustments and five minor adjustments were made during the drilling process, shortening the drilling cycle by 1.5 days and significantly reducing drilling risks. Post-drilling open-hole logging showed that the reservoir location interpreted from the horizontal section largely matched the high-quality reservoir location identified by seismic inversion. Subsequent fracturing and production commencement were successful, with the well achieving a production rate of 525,000 cubic meters per day, far exceeding design expectations and achieving the reservoir design target, thus contributing to the block's production goals.

[0123] Based on the above method, this disclosure provides a reservoir prediction device based on multi-dimensional seismic attributes, which is used to improve the prediction accuracy of ultra-deep thin reservoirs. The embodiments of this device correspond to the foregoing method embodiments. For ease of reading, this embodiment will not repeat the details of the foregoing method embodiments one by one, but it should be clear that the device in this embodiment can implement all the contents of the foregoing method embodiments. Specifically, as follows... Figure 7 As shown, the device includes:

[0124] The calculation unit 31 is used to calculate the wave impedance and seismic composite record of each well in the area to be analyzed by using the seismic data of the area to be analyzed and the well logging data of each well in the area to be analyzed.

[0125] The screening unit 32 is used to screen out at least one preferred seismic attribute that is sensitive to reservoir thickness from each one-dimensional seismic attribute of the seismic synthetic record.

[0126] The construction unit 33 is used to construct a three-dimensional initial wave impedance model of the area to be analyzed based on the seismic data of the area to be analyzed, the preferred seismic attributes, and the wave impedance of each single well, and to convert the three-dimensional initial wave impedance model into an initial reflection coefficient model.

[0127] Iteration unit 34 is used to perform multi-dimensional seismic attribute fitting and inversion based on the initial reflection coefficient model and the seismic data of the area to be analyzed by iteratively correcting the three-dimensional initial wave impedance model, so as to obtain the three-dimensional optimal wave impedance model.

[0128] The determination unit 35 is used to determine the wave impedance range of the reservoir in the region to be analyzed using the three-dimensional optimal wave impedance model.

[0129] Furthermore, the filtering unit includes:

[0130] The extraction module is used to extract one-dimensional seismic attributes from the seismic synthetic record for each individual well.

[0131] The screening module is used to select the wells with the best correlation between one-dimensional seismic attributes and reservoir thickness from each well, and obtain the standard wells in the analysis area.

[0132] The analysis module is used to obtain the one-dimensional seismic attributes of the standard wells and analyze the sensitivity of each one-dimensional seismic attribute to reservoir parameters to obtain at least one preferred seismic attribute that is sensitive to reservoir thickness.

[0133] Furthermore, the building unit includes:

[0134] The determination module is used to determine the attribute constraint body based on the seismic data of the area to be analyzed and the preferred seismic attributes;

[0135] A module is created to acquire geological data of the area to be analyzed and to create a stratigraphic framework model based on the geological data.

[0136] The construction module is used to construct a three-dimensional initial wave impedance model of the region to be analyzed based on the formation framework model, taking the wave impedance of each single well as a hard constraint and the attribute constraint volume as a trend constraint, and using a sequential indicator stochastic modeling algorithm, and converting the three-dimensional initial wave impedance model into an initial reflection coefficient model.

[0137] Furthermore, the determining module is specifically used for:

[0138] Based on the wave impedance of the standard well and the preferred seismic attributes, determine the correlation coefficient between each preferred seismic attribute and the wave impedance;

[0139] The seismic data of the region to be analyzed is processed to obtain the pure wave seismic data of the region to be analyzed, and the preferred seismic attributes of the pure wave seismic data are extracted.

[0140] Based on the correlation coefficient, the preferred seismic attributes of the seismic pure wave data are weighted and averaged to obtain the attribute constraint body.

[0141] Furthermore, the determining unit includes:

[0142] The determination module is used to calibrate the three-dimensional optimal wave impedance model using the one-dimensional geological model of the standard well, so as to determine the wave impedance range of the reservoir in the area to be analyzed.

[0143] Furthermore, the iterative unit includes:

[0144] The extraction module is used to extract the seismic wavelet of the region to be analyzed from the seismic data of the region to be analyzed;

[0145] The convolution module is used to obtain the synthetic seismic record of the region to be analyzed by convolution based on the seismic wavelet of the region to be analyzed and the initial reflection coefficient model.

[0146] A construction module is used to construct an objective function based on the seismic synthetic records and their preferred seismic attributes of the region to be analyzed, as well as the seismic pure wave data and their preferred seismic attributes.

[0147] The iterative module is used to perform multi-dimensional seismic attribute fitting and inversion by iteratively correcting the three-dimensional initial wave impedance model using the objective function, so as to obtain the three-dimensional optimal wave impedance model.

[0148] Furthermore, the formula for the objective function is:

[0149]

[0150] In the formula, F(m) represents the objective function, S represents the seismic pure wave data, Gm represents the seismic synthetic record, and S attri1 S attri2 S attri3 The preferred seismic attribute for a pure wave earthquake is represented by (Gm). attri1 (Gm) attri2 (Gm) attri3 This indicates the preferred seismic attributes of the composite seismic record.

[0151] Furthermore, the computing unit includes:

[0152] The acquisition module is used to acquire logging data of a target single well, the logging data including sonic transit time and formation density;

[0153] The calculation module is used to calculate the wave impedance of the target well based on the acoustic transit time and the formation density, and to calculate the reflection coefficient of the target well using the wave impedance.

[0154] The extraction module is used to extract the seismic wavelet of the target single well from the seismic data of the area to be analyzed;

[0155] The convolution module is used to obtain the seismic composite record of the target single well by convolution using the seismic wavelet and the reflection coefficient.

[0156] Furthermore, each of the one-dimensional seismic attributes includes at least the first derivative attribute, trace integral attribute, frequency division attribute, amplitude envelope attribute, amplitude-weighted phase attribute, and root mean square attribute.

[0157] Furthermore, this disclosure also provides a processor for running a program, wherein the program executes the above-described... Figure 1-3 The method described in [the document / article].

[0158] Furthermore, this disclosure also provides a storage medium for storing a computer program, wherein the computer program, when running, controls the device where the storage medium is located to execute the above-described... Figure 1-3 The method described in [the document / article].

[0159] Furthermore, this disclosure also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described... Figure 1-3 The method described in [the document / article].

[0160] Furthermore, embodiments of this disclosure provide an electronic device 4, such as... Figure 8 As shown, the device includes at least one processor 41, at least one memory 42 connected to the processor 41, and a bus 43; wherein the processor 41 and the memory 42 communicate with each other through the bus 43; the processor 41 is used to call program instructions in the memory 42 to execute the above-mentioned reservoir prediction method based on multi-dimensional seismic attributes. The device in this paper can be a server, PC, PAD, mobile phone, etc.

[0161] Although the present disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A reservoir prediction method based on multi-dimensional seismic attributes, characterized in that, The method includes: Using the seismic data of the area to be analyzed and the well logging data of each well in the area to be analyzed, the wave impedance and seismic composite record of each well are calculated one by one. Among the one-dimensional seismic attributes of the aforementioned seismic composite record, at least one preferred seismic attribute that is sensitive to reservoir thickness is selected. Based on the seismic data of the area to be analyzed, the preferred seismic attributes, and the wave impedance of each well, a three-dimensional initial wave impedance model of the area to be analyzed is constructed, and the three-dimensional initial wave impedance model is converted into an initial reflection coefficient model. Based on the initial reflection coefficient model and the seismic data of the area to be analyzed, the three-dimensional initial wave impedance model is iteratively corrected, and multi-dimensional seismic attribute fitting and inversion are performed to obtain the three-dimensional optimal wave impedance model. The impedance range of the reservoir in the region to be analyzed is determined using the three-dimensional optimal impedance model.

2. The method according to claim 1, characterized in that, From the one-dimensional seismic attributes of the aforementioned seismic composite records, at least one preferred seismic attribute sensitive to reservoir thickness is selected, including: For each individual well, extract the one-dimensional seismic attributes from the seismic composite record; The best correlation between one-dimensional seismic attributes and reservoir thickness is selected from each individual well to obtain the standard wells in the area to be analyzed. The one-dimensional seismic attributes of each standard well are obtained, and the sensitivity of each one-dimensional seismic attribute to reservoir parameters is analyzed to obtain at least one preferred seismic attribute that is sensitive to reservoir thickness.

3. The method according to claim 2, characterized in that, Based on the seismic data of the area to be analyzed, the preferred seismic attributes, and the wave impedance of each well, a three-dimensional initial wave impedance model of the area to be analyzed is constructed, and the three-dimensional initial wave impedance model is converted into an initial reflection coefficient model, including: Based on the seismic data of the area to be analyzed and the preferred seismic attributes, determine the attribute constraint body; Obtain geological data of the area to be analyzed, and create a stratigraphic framework model based on the geological data; Based on the formation framework model, the wave impedance of each single well is used as a hard constraint, and the attribute constraint volume is used as a trend constraint. A sequential indicator stochastic modeling algorithm is used to construct a three-dimensional initial wave impedance model of the area to be analyzed, and the three-dimensional initial wave impedance model is converted into an initial reflection coefficient model.

4. The method according to claim 3, characterized in that, Based on the seismic data of the area to be analyzed and the preferred seismic attributes, the attribute constraint body is determined, including: Based on the wave impedance of the standard well and the preferred seismic attributes, determine the correlation coefficient between each preferred seismic attribute and the wave impedance; The seismic data of the region to be analyzed is processed to obtain the pure wave seismic data of the region to be analyzed, and the preferred seismic attributes of the pure wave seismic data are extracted. Based on the correlation coefficient, the preferred seismic attributes of the seismic pure wave data are weighted and averaged to obtain the attribute constraint body.

5. The method according to claim 4, characterized in that, Determining the acoustic impedance range of the reservoir in the region to be analyzed using the three-dimensional optimal acoustic impedance model includes: The three-dimensional optimal wave impedance model is calibrated using the one-dimensional geological model of the standard well to determine the wave impedance range of the reservoir in the area to be analyzed.

6. The method according to claim 4, characterized in that, Based on the initial reflection coefficient model and the seismic data of the area to be analyzed, the three-dimensional initial wave impedance model is iteratively corrected, and multi-dimensional seismic attribute fitting and inversion are performed to obtain the optimal three-dimensional wave impedance model, including: Extract the seismic wavelet of the region to be analyzed from the seismic data of the region to be analyzed; Based on the seismic wavelet of the region to be analyzed and the initial reflection coefficient model, the seismic synthetic record of the region to be analyzed is obtained by convolution. Based on the seismic synthetic records and their preferred seismic attributes of the region to be analyzed, and the seismic pure wave data and their preferred seismic attributes, an objective function is constructed; Using the objective function, the three-dimensional initial wave impedance model is iteratively modified, and multi-dimensional seismic attribute fitting and inversion are performed to obtain the three-dimensional optimal wave impedance model.

7. The method according to claim 6, characterized in that, The formula for the objective function is: In the formula, Describe the objective function. Represents seismic pure wave data. Represents a composite earthquake record. , , This represents the preferred seismic attributes of pure wave seismic data. , , This indicates the preferred seismic attributes of the composite seismic record.

8. The method according to claim 1, characterized in that, Using seismic data of the area to be analyzed and well logging data of each well within that area, the wave impedance and seismic composite record of each well are calculated one by one, including: Acquire logging data of the target single well, the logging data including sonic transit time and formation density; Based on the acoustic transit time and the formation density, the wave impedance of the target well is calculated, and the reflection coefficient of the target well is calculated using the wave impedance. Extract the seismic wavelet of the target single well from the seismic data of the area to be analyzed; The seismic composite record of the target single well is obtained by convolution using the seismic wavelet and the reflection coefficient.

9. The method according to any one of claims 1-8, characterized in that, Each of the one-dimensional seismic attributes includes at least the first derivative attribute, trace integral attribute, frequency division attribute, amplitude envelope attribute, amplitude-weighted phase attribute, and root mean square attribute.

10. A reservoir prediction device based on multi-dimensional seismic attributes, characterized in that, The device includes: The calculation unit is used to calculate the wave impedance and seismic composite record of each well in the area to be analyzed, using the seismic data of the area to be analyzed and the well logging data of each well in the area to be analyzed. A screening unit is used to screen out at least one preferred seismic attribute that is sensitive to reservoir thickness from each one-dimensional seismic attribute of the seismic synthetic record. The construction unit is used to construct a three-dimensional initial wave impedance model of the area to be analyzed based on the seismic data of the area to be analyzed, the preferred seismic attributes, and the wave impedance of each single well, and to convert the three-dimensional initial wave impedance model into an initial reflection coefficient model. An iterative unit is used to perform multi-dimensional seismic attribute fitting and inversion based on the initial reflection coefficient model and the seismic data of the area to be analyzed, by iteratively correcting the three-dimensional initial wave impedance model, so as to obtain the three-dimensional optimal wave impedance model. The determining unit is used to determine the impedance range of the reservoir in the region to be analyzed using the three-dimensional optimal impedance model.

11. An electronic device, characterized in that, The device includes at least one processor, at least one memory connected to the processor, and a bus; wherein the processor and the memory communicate with each other through the bus; the processor is used to call program instructions in the memory to execute the method as described in any one of claims 1-9.

12. A computer storage medium, characterized in that, The storage medium is used to store a computer program, wherein the computer program, when running, controls the device where the storage medium is located to execute the method described in any one of claims 1-9.

13. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method according to any one of claims 1-9.

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