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

Abstract

The invention belongs to the technical field of well drilling support, and provides a reservoir prediction method, device, equipment and medium based on multi-dimensional seismic attributes, and the method comprises the steps: calculating the wave impedance and seismic synthesis record of each single well through the seismic data and logging data of a to-be-analyzed region; in each one-dimensional seismic attribute of the seismic synthesis record, screening out a preferred seismic attribute sensitive to the reservoir thickness; constructing a three-dimensional initial wave impedance model of the to-be-analyzed area according to the seismic data of the to-be-analyzed area, the optimized seismic attributes and the wave impedance of each single well, and converting the three-dimensional initial wave impedance model into an initial reflection coefficient model; based on the initial reflection coefficient model and the seismic data of the to-be-analyzed area, correcting the three-dimensional initial wave impedance model through iteration, and performing multi-dimensional seismic attribute fitting inversion to obtain a three-dimensional optimal wave impedance model; and determining the wave impedance range of the reservoir of the to-be-analyzed region by using the three-dimensional optimal wave impedance model. According to the invention, the prediction precision of the ultra-deep and thin reservoir is improved.

Description

TECHNICAL FIELD

[0001] The present disclosure belongs to the technical field of well drilling support, and particularly relates to a reservoir prediction method and device based on multi-dimensional seismic attributes, equipment and medium. BACKGROUND

[0002] With the deepening of oil and gas reservoir development, more and more deep and complex oil and gas fields are developed. In order to develop the oil and gas fields efficiently, a platform multi-lateral horizontal well three-dimensional deployment method is usually used. The key to the success of horizontal well drilling lies in the geological engineering integrated scheme design, especially the accuracy of horizontal well target point deployment. The accuracy of horizontal well target point deployment is directly related to whether the horizontal well can successfully land and the frequency of horizontal section borehole trajectory adjustment. The successful deployment of the target point requires accurate reservoir prediction to finely carve the three-dimensional spatial distribution of the reservoir. Since the seismic resolution of the ultra-deep reservoir is generally low, especially in the case of thin reservoir, the reservoir inversion obtained by the conventional seismic reservoir prediction technology has low precision, and the error range may exceed the reservoir thickness. Therefore, how to effectively predict the ultra-deep thin reservoir is a big problem currently faced.

[0003] The commonly used method for predicting the ultra-deep thin reservoir is the geological statistics inversion method. For example, the thin reservoir prediction method and device disclosed in patent application publication No. CN112711067A uses logging data and seismic data as hard data constraints to perform while-drilling geological statistics simulation inversion, so as to improve the resolution and reliability of the ultra-deep thin reservoir inversion result.

[0004] However, the geological statistics inversion method is based on a random simulation algorithm, and the result obtained by the method has randomness, which leads to a large error in the interwell reservoir prediction and makes it difficult to improve the prediction accuracy. Therefore, how to improve the prediction accuracy of the ultra-deep thin reservoir is a problem to be solved in the field. SUMMARY

[0005] To solve the above problems, the present disclosure provides a reservoir prediction method and device based on multi-dimensional seismic attributes, an electronic device and a storage medium, which aims to improve the prediction accuracy of the ultra-deep thin reservoir.

[0006] To achieve the above purpose, the present disclosure mainly provides the following technical solutions:

[0007] In a first aspect, the present disclosure provides a reservoir prediction method based on multi-dimensional seismic attributes, comprising:

[0008] Seismic data of a to-be-analyzed area and logging data of each single well in the to-be-analyzed area are used to calculate the wave impedance and seismic synthetic record of each single well one by one;

[0009] At least one preferred seismic attribute sensitive to reservoir thickness is selected from the one-dimensional seismic attributes of the seismic synthetic record.

[0010] constructing a three-dimensional initial wave impedance model of the region to be analyzed according to the seismic data of the region to be analyzed, the preferred seismic attribute, and the wave impedance of each single well, and converting the three-dimensional initial wave impedance model into an initial reflection coefficient model;

[0011] performing multi-dimensional seismic attribute fitting inversion by iteratively correcting the three-dimensional initial wave impedance model based on the initial reflection coefficient model and the seismic data of the region to be analyzed, to obtain a three-dimensional optimal wave impedance model;

[0012] determining the wave impedance range of the reservoir of the region to be analyzed by using the three-dimensional optimal wave impedance model.

[0013] In a second aspect, the present disclosure provides a reservoir prediction device based on multi-dimensional seismic attribute, which comprises:

[0014] a calculation unit configured to calculate the wave impedance and the seismic synthetic record of each single well one by one by using the seismic data of the region to be analyzed and the logging data of each single well in the region to be analyzed;

[0015] a screening unit configured to screen at least one preferred seismic attribute sensitive to reservoir thickness from each one-dimensional seismic attribute of the seismic synthetic record;

[0016] a construction unit configured to construct a three-dimensional initial wave impedance model of the region to be analyzed according to the seismic data of the region to be analyzed, the preferred seismic attribute, and the wave impedance of each single well, and convert the three-dimensional initial wave impedance model into an initial reflection coefficient model;

[0017] an iteration unit configured to perform multi-dimensional seismic attribute fitting inversion by iteratively correcting the three-dimensional initial wave impedance model based on the initial reflection coefficient model and the seismic data of the region to be analyzed, to obtain a three-dimensional optimal wave impedance model;

[0018] a determination unit configured to determine the wave impedance range of the reservoir of the region to be analyzed by using the three-dimensional optimal wave impedance model.

[0019] In another aspect, the present disclosure further provides a storage medium for storing a computer program, wherein the computer program controls a device where the storage medium is located to execute the method of the first aspect when the computer program is running.

[0020] In another aspect, the present disclosure further provides an electronic device, which comprises at least one processor, at least one memory connected with the processor through a bus, wherein the processor, the memory, and the bus complete mutual communication; the processor is configured to call program instructions in the memory to execute the method of the first aspect.

[0021] In another aspect, the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the method of the first aspect described above.

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

[0023] The present disclosure utilizes seismic data of a region to be analyzed and logging data of a single well in the region to be analyzed to generate a seismic synthetic record of the single well, screens a plurality of preferred seismic attributes sensitive to reservoir thickness from all one-dimensional seismic attributes of the seismic synthetic record, fits a correlation between the preferred seismic attribute volume and wave impedance, and utilizes the correlation to perform weighted averaging on each preferred seismic attribute to generate an attribute constraint volume; utilizes logging wave impedance data as a hard constraint and the attribute constraint volume as a modeling trend constraint volume to adopt a sequential indicator stochastic modeling algorithm to establish a three-dimensional initial wave impedance model, and convert the three-dimensional initial wave impedance model into an initial reflection coefficient model, obtain a seismic synthetic record of the region to be analyzed through convolution of the initial reflection coefficient model and a seismic wavelet, iterate the three-dimensional initial wave impedance model, compare differences between preferred seismic attributes of the seismic synthetic record of the region to be analyzed and the region to be analyzed and differences between the seismic synthetic record and a seismic pure wave, until the differences are small enough, i.e., multi-dimensional seismic attribute fitting inversion is performed to obtain a three-dimensional optimal wave impedance model. The three-dimensional optimal wave impedance model is utilized to determine a wave impedance range of the region to be analyzed. The present disclosure utilizes multi-dimensional seismic attributes to realize accurate prediction of an ultra-deep and thin reservoir by iterating a mode of the three-dimensional wave impedance model.

[0024] Other features and advantages of the present disclosure will be set forth in the description that follows, and in part will be apparent from the description, or can be learned by practice of the present disclosure. The purposes and other advantages of the present disclosure will be realized and attained by the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, hereinafter, a brief introduction will be given to the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the present disclosure, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

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

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

[0029] Figure 4 A relationship diagram of single-well one-dimensional attributes 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 structural 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 An electronic device structural diagram according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0034] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be described clearly and completely below with reference to the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are some but not all of the embodiments of the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present disclosure.

[0035] In the process of stereoscopic deployment of platform multi-lateral horizontal wells, target points need to be deployed, and in complex reservoir structure positions, horizontal segment control points also need to be added according to micro-facies structure and reservoir development characteristics, and wellbore trajectories are obtained through the connection of target points and control points, so as to realize the stereoscopic deployment of platform multi-lateral horizontal wells. In view of the problems such as inaccurate reservoir prediction, large deployment error of horizontal well target points and control points, and the fact that wellbore trajectories cannot take into account the geological engineering “double sweet spot” under the complex formation conditions of ultra-deep and thin reservoirs, the present embodiment provides a reservoir prediction method based on multi-dimensional seismic attributes to improve the prediction accuracy of ultra-deep and thin reservoirs, reduce the frequent adjustment of trajectories in the drilling process, thereby improving the reservoir drilling rate and reducing the drilling operation risk.

[0036] As shown in Figure 1 The reservoir prediction method based on multi-dimensional seismic attributes according to the present embodiment 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 first constructed using geological data, such as geological structure horizon, well layering, etc. In the process of constructing the stratigraphic framework model, the structure at the well point can be corrected using the well layering. After the stratigraphic framework model is constructed, a three-dimensional initial wave impedance model of the region to be analyzed can be constructed based on the stratigraphic framework model. Specifically, an attribute constraint volume is established using the seismic data and the preferred seismic attributes of the region to be analyzed, the wave impedance model at the well point is generated using the wave impedance of each single well as a hard constraint, the wave impedance between wells is generated based on the stratigraphic framework model and the attribute constraint volume as a lateral constraint condition using an interpolation method, and a sequential indicator simulation algorithm can be used as the interpolation algorithm, thereby constructing a three-dimensional initial wave impedance model of the region to be analyzed. After the three-dimensional initial wave impedance model is constructed, it needs to be converted into a corresponding initial reflection coefficient model, so as to iteratively update the three-dimensional initial wave impedance model using the initial reflection coefficient model.

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

[0046] In this step, the seismic synthetic record of the region to be analyzed can be obtained by convolving the seismic wavelet in the seismic data of the region to be analyzed and the initial reflection coefficient model. The seismic synthetic record is a three-dimensional seismic synthetic record. The preferred seismic attributes of the region to be analyzed are extracted from the seismic synthetic record of the region to be analyzed, and a target function is established using the seismic synthetic record of the region to be analyzed, the preferred seismic attributes of the region to be analyzed, and the seismic data and the preferred seismic attributes of the region to be analyzed. The value of the target function is calculated, and the value of the target function is compared with a preset threshold value. If the value of the target function is greater than the preset threshold value, the three-dimensional initial wave impedance model is modified to obtain a new three-dimensional wave impedance model, and a new reflection coefficient model is obtained by converting the new three-dimensional wave impedance model, so as to compare the new value of the target function with the preset threshold value again using the new reflection coefficient model. The three-dimensional wave impedance model obtained when the value of the target function is less than the preset threshold value is the three-dimensional optimal wave impedance model, which is the final multi-dimensional attribute fitting inversion result and is a relative wave impedance value. The preset threshold value of the embodiment of the present disclosure can be set according to the reservoir thickness of the actual region, and is usually in the range of 0.05-0.1.

[0047] 105、Using 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, the three-dimensional optimal wave impedance model can be calibrated by using a single-well one-dimensional geological model, which can be a vertical combination of reservoirs with varying thicknesses. Specifically, the display color scale in the three-dimensional optimal wave impedance model can be modified through a human-computer interaction mode to find the relative wave impedance range corresponding to the reservoir, i.e., to obtain the reservoir inversion result. After the relative wave impedance range corresponding to the reservoir is determined, a horizontal well target point can be deployed within the wave impedance range corresponding to the reservoir, and a well trajectory can be designed to reduce the frequency of adjusting the well trajectory during drilling.

[0049] The embodiment of the present disclosure generates a seismic synthetic record of a single well by using seismic data of a region to be analyzed and logging data of a single well in the region to be analyzed, screens a plurality of preferred seismic attributes sensitive to reservoir thickness from all one-dimensional seismic attributes of the seismic synthetic record, establishes a correlation between each attribute and wave impedance, and performs weighted calculation on each attribute volume to obtain an attribute constraint volume. The three-dimensional initial wave impedance model is constructed by using the attribute constraint volume and well data constraints, and is converted into an initial reflection coefficient model. The seismic synthetic record of the region to be analyzed is obtained by convolving the initial reflection coefficient model and a seismic wavelet of the region to be analyzed. The three-dimensional initial wave impedance model is iterated, and the difference between the preferred seismic attributes of the seismic synthetic record of the region to be analyzed and the preferred seismic attributes of the actual seismic data of the region to be analyzed and the difference between the seismic synthetic record and the pure seismic data are compared until the difference is small enough, i.e., multi-dimensional seismic attribute fitting inversion is performed to obtain a three-dimensional optimal wave impedance model. The wave impedance range of the reservoir in the region to be analyzed is determined by using the three-dimensional optimal wave impedance model. The embodiment of the present disclosure realizes accurate prediction of an ultra-thin reservoir by using multi-dimensional seismic attributes and iterating the three-dimensional wave impedance model.

[0050] To more specifically describe the reservoir prediction method based on multi-dimensional seismic attributes proposed by the present disclosure, another embodiment of the reservoir prediction method based on multi-dimensional seismic attributes is proposed. The specific implementation steps of the embodiment of the present disclosure are as shown in Figure 2

[0051] 201. The wave impedance and the seismic synthetic record of each single well are calculated one by one by using the seismic data of the region to be analyzed and the logging data of each single well in the region to be analyzed.

[0052] In an implementable manner, the specific steps of determining the wave impedance and the seismic synthetic record of the single well include steps one to four:

[0053] Step one, the logging data of the target single well is obtained, and the logging data includes the acoustic travel time and the formation density.

[0054] Step two, the wave impedance of the target single well is calculated according to the acoustic travel time and the formation density, and the reflection coefficient of the target single well is calculated by using the wave impedance.

[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 velocity 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] The standard well is used to represent the longitudinal distribution pattern of the reservoir in the region to be analyzed.

[0068] After the seismic synthetic records of each single well in the region to be analyzed are obtained, all one-dimensional seismic attributes of each single well are extracted, such as first derivative attribute, trace integration attribute, frequency division attribute, amplitude envelope attribute, amplitude weighted phase attribute, and root mean square attribute. According to the actual situation of the region to be analyzed, a well having good correlation between each one-dimensional seismic attribute and reservoir thickness is found, and the well is used as the standard well in the region to be analyzed.

[0069] 203. The one-dimensional seismic attributes of the standard well are obtained, and the sensitivity of each one-dimensional seismic attribute to the reservoir parameter is analyzed to obtain at least one preferred seismic attribute sensitive to the reservoir thickness.

[0070] The preferred seismic attribute is a one-dimensional seismic attribute sensitive to the reservoir in the region to be analyzed.

[0071] In this step, the one-dimensional seismic attributes of the standard well can be normalized, and the sensitivity of each normalized one-dimensional seismic attribute to the reservoir thickness is compared. The one-dimensional seismic attribute having a sensitivity greater than a sensitivity threshold can be used as the preferred seismic attribute. Generally, there are multiple preferred seismic attributes.

[0072] 204. The attribute constraint volume is determined according to the seismic data of the region to be analyzed and the preferred seismic attribute.

[0073] The attribute constraint volume is used as a modeling trend constraint to construct a three-dimensional wave impedance model.

[0074] In an implementable manner, the specific manner of determining the attribute constraint volume includes steps one to three:

[0075] Step one, the correlation coefficient of each preferred seismic attribute and wave impedance is determined according to the wave impedance of the standard well and the preferred seismic attribute.

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

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

[0078] Step two, data processing of the seismic data of the area to be analyzed to obtain the seismic pure wave data of the area to be analyzed, and extracting the preferred seismic attribute of the seismic pure wave data.

[0079] The seismic pure wave data is obtained by seismic data processing, such as deconvolution and denoising. The seismic pure wave data only retains the reflection coefficient information of the underground formation and eliminates the influence of seismic wavelet and surface interference, and is closer to the true reflection interface characteristics of the underground.

[0080] Step three, according to the correlation coefficient, weighted average of the preferred seismic attribute of the seismic pure wave data to obtain the attribute constraint body.

[0081] The attribute constraint body is a comprehensive data body that integrates multi-dimensional seismic attribute related information, and its value reflects the constraint trend after the weighted superposition of different preferred seismic attributes.

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

[0083] 205, obtaining the geological data of the area to be analyzed, and creating a stratigraphic framework model according to the geological data.

[0084] The geological data includes geological structure map, fault, well layering, etc.

[0085] After creating the stratigraphic framework model according to the geological data, the strata in the stratigraphic framework model can be vertically split. By vertically splitting the strata into smaller thickness, the model grid can be adapted to the vertical scale of thin layers, avoiding the smoothing or neglect of reservoir characteristics due to over-thick layering. The thickness is usually 1 / 2 of the seismic sampling interval.

[0086] 206, based on the stratigraphic framework model, taking the wave impedance of each single well as a hard constraint and the attribute constraint body as a trend constraint, using a sequential indicator stochastic modeling algorithm to construct a three-dimensional wave impedance model of the area to be analyzed, and converting the three-dimensional wave impedance model into a reflection coefficient model.

[0087] In this step, the wave impedance of each single well is taken as a hard constraint, the attribute constraint body is taken as a trend constraint, the sequential indicator stochastic modeling algorithm is used to interpolate and extrapolate along the geologic structure horizon in the stratum framework model, the three-dimensional initial wave impedance model of the region to be analyzed is constructed, and the three-dimensional initial wave impedance model is converted into an initial reflection coefficient model by using the above reflection coefficient formula. In the subsequent iteration process, the three-dimensional initial wave impedance model is iterated into a new three-dimensional wave impedance model, and the initial reflection coefficient model is correspondingly iterated into a new reflection coefficient model.

[0088] 207、In the seismic data of the region to be analyzed, a seismic wavelet of the region to be analyzed is extracted; and according to the seismic wavelet of the region to be analyzed and the initial reflection coefficient model, a seismic synthetic record of the region to be analyzed is obtained by convolution.

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

[0090] 208、According to the seismic synthetic record of the region to be analyzed and the preferred seismic attribute thereof, and the seismic pure wave data of the region to be analyzed and the preferred seismic attribute thereof, a target function is constructed, and a value of the target function is calculated;

[0091] In order to quantify the matching degree of the preferred seismic attribute of the seismic synthetic record and the preferred seismic attribute of the actual seismic data and the matching of the seismic synthetic record and the actual seismic pure wave, the three-dimensional wave impedance model is iterated to make the inversion result fit the actual seismic data as much as possible, so as to improve the reservoir prediction accuracy, and a target function needs to be constructed. The deviation of the inversion model and the actual data is measured by calculating the sum of squares of the difference between the actual and the synthetic preferred seismic attributes. The smaller the deviation is, the closer the three-dimensional wave impedance model is to the true stratum characteristics.

[0092] The formula of the target function is:

[0093]

[0094] In the formula, F(m) represents the target function, S represents the seismic pure wave data, Gm represents the seismic synthetic record, S attri1 , S attri2 , S attri3 represents the preferred seismic attribute of the pure wave seismic, (Gm) attri1 , (Gm) attri2 , (Gm) attri3 represents the preferred seismic attribute of the seismic synthetic record.

[0095] 209、Judge whether the value of the target function is greater than a preset threshold value.

[0096] In this step, according to the target function and the preset threshold value, target reservoir multidimensional seismic attribute inversion is performed, whether the value of the target function is greater than the preset threshold value is judged, if yes, the step 206 is executed to iteratively correct the three-dimensional wave impedance model, and if no, the step 210 is executed.

[0097] 210, determining that the current three-dimensional wave impedance model is a three-dimensional optimal wave impedance model, calibrating the three-dimensional optimal wave impedance model by using the one-dimensional geological model of the standard well to determine the wave impedance range of the reservoir in the region to be analyzed.

[0098] The embodiment of the present disclosure effectively broadens the seismic frequency band, improves the resolution of seismic prediction, reduces the multi-solution of seismic inversion results, solves the problem of low reservoir prediction accuracy and large target design error leading to frequent adjustment of well trajectory, effectively controls the smoothness of well trajectory, improves the reservoir drilling rate, and reduces the drilling operation risk. The embodiment of the present disclosure can be applied to reservoir prediction of various strata and various oil and gas reservoirs, and can be compatible with multi-source data structure.

[0099] The verification results of multiple thin reservoirs in western China show that the horizontal well target points deployed according to the multi-attribute fitting inversion results of the embodiment of the present disclosure have high reservoir drilling rate and trajectory adjustment frequency <2 / 300m under the condition of considering drilling engineering, and the complexity of drilling operation is reduced, and the speed and efficiency are improved.

[0100] In order to further illustrate the reservoir prediction method based on multidimensional seismic attribute proposed by the present disclosure, the present disclosure further proposes another embodiment of a reservoir prediction method based on multidimensional seismic attribute, and the specific implementation steps of the embodiment of the present disclosure are as shown in Figure 3 The embodiment of the present disclosure comprises the following steps:

[0101] Synthetic well-seismic calibration: obtain seismic data and well logging data of the region to be analyzed, and obtain seismic synthetic records of each single well in the region to be analyzed by using the seismic data and well logging data, and realize well-seismic calibration. The well logging data is well logging and logging data.

[0102] Framework modeling: constructing a stratum framework model according to geological data.

[0103] Standard well optimization: selecting a single well that can best represent the longitudinal distribution of the reservoir in the region to be analyzed as a standard well according to the well logging data of each single well in all single wells.

[0104] Standard well one-dimensional geological model: constructing a one-dimensional geological model for representing the stratum thickness of the standard well by using the well logging data of the standard well.

[0105] One-dimensional reservoir parameter model forward: based on the one-dimensional geological model of the standard well, a vertical one-dimensional reservoir parameter model is constructed, and the reservoir parameters such as thickness, velocity, density, etc. are simulated by a seismic forward algorithm to simulate the corresponding seismic response of the model, such as convolution to obtain a synthetic seismogram.

[0106] One-dimensional reservoir sensitive attribute optimization: the synthetic seismogram obtained by forward is extracted, and the seismic attributes sensitive to the change of the reservoir parameter are selected by correlation analysis, and a plurality of optimized seismic attributes are obtained.

[0107] Reconstruction of wave impedance model: based on the stratigraphic framework model, the three-dimensional wave impedance model of the region to be analyzed is reconstructed by using the optimized seismic attribute, seismic data and logging data, and is converted into a reflection coefficient model.

[0108] Three-dimensional seismic forward: the three-dimensional synthetic seismogram is obtained by convolution of the seismic wavelet of the region to be analyzed and the three-dimensional reflection coefficient model.

[0109] Multi-dimensional attribute optimization analysis: the actual seismic data and the three-dimensional synthetic seismic data of the region to be analyzed are extracted, and the optimized seismic attribute combination is obtained.

[0110] Establishment of objective function: according to the actual seismic data and the three-dimensional synthetic seismic data, and the respective optimized seismic attributes, the objective function is established, and whether the value of the objective function is optimal is judged, if the result is optimal, if not, the step of reconstructing the wave impedance model is returned.

[0111] The embodiment of the present disclosure selects the standard well in the region to be analyzed, obtains the optimized seismic attribute sensitive to the reservoir thickness, and finally obtains the reservoir thickness inversion data volume based on each optimized seismic attribute. The embodiment of the present disclosure realizes the accurate prediction of the ultra-thin reservoir by multi-dimensional seismic attribute fitting inversion.

[0112] The following is described taking an oil and gas clastic rock reservoir in northwest China as an example. The block is a lake basin delta deposit, the reservoir is mainly underwater distributary channel sand, the sand bodies are vertically superimposed, the interlayer is developed, the single sand body thickness is thin, and the evaluation is about 3-5m thick; the sand body extends horizontally, the phase changes seriously, the reservoir prediction accuracy is low, and the target point deployment is difficult, resulting in frequent trajectory adjustment during horizontal well drilling, low reservoir drilling rate, and high drilling risk. In order to effectively solve the problem of horizontal well reservoir drilling rate, reservoir prediction is the key, therefore, the embodiment of the present disclosure is used for reservoir prediction, which can finely carve the three-dimensional space distribution range of the sand body, and can take into account the geological engineering "double sweet spot" in advance when deploying the horizontal well, so as to achieve the purpose of optimizing and fast drilling and improving quality and efficiency.

[0113] Well-to-seismic calibration analysis: The one-dimensional synthetic seismogram of a single well is obtained by the convolution of the seismic wavelet and the well reflection coefficient. The time-depth relationship of the single well is obtained by adjusting the position of the synthetic seismogram.

[0114] Single-well multi-dimensional attribute optimization: The one-dimensional synthetic seismogram of a single well is used to extract all one-dimensional seismic attributes, obtain seismic attributes in each dimension, and analyze the relationship between each dimension seismic attribute and reservoir thickness to determine multiple one-dimensional optimal seismic attributes. Figure 4 As shown in the figure, the second derivative attribute is most sensitive to reservoir thickness. In the inversion process, the three-dimensional wave impedance model can be modified according to the sensitivity of the seismic attribute.

[0115] Establishing the relationship between the attribute and impedance: The correlation between the optimal seismic attribute extracted from the standard well and the wave impedance is used.

[0116] Creating an attribute constraint body: The three-dimensional optimal seismic attribute volume corresponding to the single-well one-dimensional optimal seismic attribute is extracted from the seismic pure wave data, and the three-dimensional optimal seismic attribute volume is weighted using the mutual relationship between the optimal seismic attribute and the wave impedance to obtain the attribute constraint body.

[0117] Reconstructing the wave impedance model: The stratigraphic framework model is established according to the geological structure horizon and well stratification, and then the wave impedance of the single well in the area to be analyzed, i.e., the wave impedance curve, is used to interpolate and extrapolate along the geological structure horizon, and the attribute constraint body is used as the volume constraint condition; the three-dimensional wave impedance model is obtained and converted into a reflection coefficient model.

[0118] Three-dimensional seismic forward modeling: The seismic synthetic record data volume, i.e., the three-dimensional seismic synthetic record, is obtained by the convolution of the seismic wavelet and the three-dimensional reflection coefficient model in the area to be analyzed.

[0119] Extracting seismic attributes: The optimal seismic attributes in the seismic pure wave data and the synthetic seismic data volume of the area to be analyzed are extracted to obtain the multi-dimensional seismic attribute volumes of the two, which are used for subsequent target function iterative optimization.

[0120] Establishing the target function: The target function is constructed according to the seismic pure wave data and the synthetic seismic data volume of the area to be analyzed, as well as the multi-dimensional seismic attribute volumes of the two, and the target function value is calculated. According to the actual situation of the area to be analyzed, the threshold value of the work area is set to 0.07, and the three-dimensional wave impedance model is iteratively optimized until the target function value is less than the threshold value, and the obtained wave impedance model is the final inversion result.

[0121] Inversion result calibration: The one-dimensional thickness model of the standard well is used to scale the wave impedance model by using the man-machine interaction mode to obtain the wave impedance value range corresponding to the reservoir. The wave impedance range is the relative wave impedance value.

[0122] Target and control point deployment: within the numerical range of reservoir wave impedance, according to the requirements of geological engineering, the target point and the control point of the horizontal section are deployed, thereby reducing the adjustment of the well trajectory and improving the reservoir drilling rate. Figure 5 For the platform horizontal well deployed according to the seismic inversion result, the white area is the sand body distribution area, and a total of 11 wells are deployed. In the northern half branch, the physical property is poor, and there is a sand body pinch-out phenomenon in the middle of the horizontal well, and 5 wells are deployed, and the average reservoir drilling rate is expected to be 85%; in the southern half branch, the physical property is good, but the reservoir in the horizontal section may also have a phase change, and 6 wells are deployed, and the average reservoir drilling rate is expected to be 90%. Figure 6 For the drilling result of one of the wells, the dark area in the figure is the reservoir. Before drilling, it is predicted that the middle of the reservoir will drill through mudstone, and when the target point is designed, 6 control points are added in the middle, the length of the horizontal section is designed to be 1700m, and the drilling rate is designed to be 82%. In the actual drilling process, the trajectory to be drilled is predicted and designed in advance according to the seismic inversion result, and finally the reservoir drilling rate is 81.5%, and the design coincidence rate is 99.3%. At the same time, according to the seismic inversion result, control points are added before drilling. During the drilling process of the well, only 3 major trajectory adjustments are made, and 5 fine adjustments are made. The drilling cycle is shortened by 1.5 days, greatly reducing the drilling engineering risk. After drilling, open hole logging is carried out, and the reservoir position interpreted by the horizontal section logging is basically consistent with the high-quality reservoir position interpreted by the seismic inversion. In the later stage, the fracturing operation is put into production, and the yield of this well is 525,000 cubic meters per day, which is much higher than the design expectation, reaching the goal of reservoir design and helping the block to reach the yield standard.

[0123] Based on the above method, the disclosure embodiment 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 the device correspond to the foregoing method embodiments, and for the sake of reading, the details of the foregoing method embodiments will not be described one by one, but it should be clear that the device in this embodiment can correspondingly implement all the contents in the foregoing method embodiments. Specifically, as shown in the figure, the device comprises: Figure 7

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

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

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

[0127] iteratively correct the three-dimensional initial wave impedance model based on the initial reflection coefficient model and seismic data of the region to be analyzed, to obtain a three-dimensional optimal wave impedance model through multi-dimensional seismic attribute fitting inversion;

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

[0129] Further, the screening unit comprises:

[0130] The extraction module is configured to extract one-dimensional seismic attributes in a seismic synthetic record for each single well.

[0131] The screening module is configured to screen single wells in which the one-dimensional seismic attributes have the best correlation with the reservoir thickness, to obtain standard wells in the region to be analyzed.

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

[0133] Further, the constructing unit comprises:

[0134] The determining module is configured to determine an attribute constraint body according to the seismic data of the region to be analyzed and the preferred seismic attribute.

[0135] The creating module is configured to obtain geological data of the region to be analyzed, and create a stratigraphic framework model according to the geological data.

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

[0137] Further, the determining module is specifically configured to:

[0138] determine a correlation coefficient between each preferred seismic attribute and the wave impedance according to the wave impedance of the standard wells and the preferred seismic attribute;

[0139] perform data processing on the seismic data of the region to be analyzed, to obtain seismic pure wave data of the region to be analyzed, and extract preferred seismic attributes of the seismic pure wave data;

[0140] perform weighted average on the preferred seismic attributes of the seismic pure wave data according to the correlation coefficient, to obtain an attribute constraint body.

[0141] Further, the determining unit comprises:

[0142] A determining module is configured to calibrate the three-dimensional optimal wave impedance model by using the one-dimensional geological model of the standard well, so as to determine the wave impedance range of the reservoir in the region to be analyzed.

[0143] Further, the iteration unit comprises:

[0144] An extracting module is configured to extract the seismic wavelet of the region to be analyzed from the seismic data of the region to be analyzed.

[0145] A convolution module is configured to convolve the seismic wavelet of the region to be analyzed and the initial reflection coefficient model to obtain the seismic synthetic record of the region to be analyzed.

[0146] A constructing module is configured to construct an objective function according to the seismic synthetic record of the region to be analyzed and the preferred seismic attributes thereof, and the seismic pure wave data and the preferred seismic attributes thereof.

[0147] An iteration module is configured to correct the three-dimensional initial wave impedance model by iteration by using the objective function, so as to perform multi-dimensional seismic attribute fitting inversion, and obtain a three-dimensional optimal wave impedance model.

[0148] Further, the objective function is as follows:

[0149]

[0150] In the formula, F(m) represents the objective function, S represents the seismic pure wave data, Gm represents the seismic synthetic record, S attri1 , S attri2 , S attri3 represents the preferred seismic attributes of the pure wave seismic data, (Gm) attri1 , (Gm) attri2 , (Gm) attri3 represents the preferred seismic attributes of the seismic synthetic record.

[0151] Further, the calculating unit comprises:

[0152] An obtaining module is configured to obtain the logging data of a target single well, wherein the logging data comprises acoustic travel time and formation density.

[0153] A calculating module is configured to calculate the wave impedance of the target single well according to the acoustic travel time and the formation density, and calculate the reflection coefficient of the target single well by using the wave impedance.

[0154] An extracting module is configured to extract the seismic wavelet of the target single well from the seismic data of the region 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... Figures 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... Figures 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... Figures 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 was selected from each individual well to obtain the standard wells in the analysis area. 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 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 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, 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.

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, and at least one memory and a 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 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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