Method for predicting reservoir parameters based on seismic attributes

Abstract

The embodiment of the invention provides a method for predicting reservoir parameters based on seismic attributes, and belongs to the technical field of geophysics and oil and gas field exploration, and the method comprises the steps: carrying out the correlation analysis of the reservoir parameters of a known drilling well and / or a first seismic attribute of a target stratum, carrying out the seismic attribute optimization of the first seismic attribute, and carrying out the optimization of the seismic attribute; generating a second seismic attribute; according to the second seismic attributes corresponding to the plurality of seismic sampling points on the seismic waveform of the target stratum, establishing vectors corresponding to the plurality of seismic sampling points of the target stratum; inputting vectors corresponding to a plurality of seismic sampling points on the seismic waveform of the target stratum into a prediction model for classification, and determining a category corresponding to the seismic waveform of the target stratum; and determining reservoir parameters of the target stratum based on the category corresponding to the seismic waveform of the target stratum.

Description

Technical Field

[0001] This invention relates to the fields of geophysics and oil and gas field exploration technology, and specifically to a method, equipment, and computer product for predicting reservoir parameters based on seismic attributes. Background Technology

[0002] Current technologies typically employ drilling wells to obtain core samples and test oil and gas reservoirs to confirm their existence. This process mainly includes three stages: preliminary exploration, initial exploration, and detailed exploration. The preliminary exploration stage determines whether the target area contains oil and gas flows; the initial exploration stage confirms the reservoir size; and the detailed exploration stage further determines the reservoir reserves, obtaining most of the necessary data for subsequent reservoir development. Drilling wells, however, usually requires developing a large number of wells in the target area, resulting in high construction costs and low efficiency. Summary of the Invention

[0003] The purpose of this invention is to provide a method, device, and computer product for predicting reservoir parameters based on seismic attributes. This method can solve or at least partially solve the above-mentioned defects of the prior art.

[0004] To achieve the above objectives, a first aspect of the present invention provides a method for predicting reservoir parameters based on seismic attributes, the method comprising:

[0005] Correlation analysis is performed on the reservoir parameters and / or the first seismic attribute of the target layer of the known well, and the first seismic attribute is optimized to generate the second seismic attribute;

[0006] Based on the second seismic attribute corresponding to multiple seismic sampling points on the seismic waveform of the target layer, a vector corresponding to multiple seismic sampling points of the target layer is established.

[0007] The vectors corresponding to multiple seismic sampling points on the seismic waveform of the target layer are input into the prediction model for classification to determine the category of the seismic waveform of the target layer; and

[0008] Based on the category corresponding to the seismic waveform of the target layer, the reservoir parameters of the target layer are determined.

[0009] Optionally, the step of performing correlation analysis on the reservoir parameters and / or the first seismic attributes of the known well and / or the target layer, and performing seismic attribute optimization on the first seismic attributes to generate the second seismic attribute includes:

[0010] When the number of wells drilled in the work area is greater than or equal to a preset threshold, the correlation between the first seismic attributes is analyzed, and based on the correlation between the first seismic attributes, the first seismic attributes are filtered to generate a second seismic attribute.

[0011] When the number of wells drilled in the work area is less than a preset threshold, the correlation between the first seismic attribute and the reservoir parameter is analyzed, and based on the correlation between the first seismic attribute and the reservoir parameter, the first seismic attribute is screened, and the screened first seismic attribute is combined to generate a second seismic attribute.

[0012] Optionally, when the number of wells drilled in the work area is greater than or equal to a preset threshold, analyzing the correlation between the first seismic attributes and filtering the first seismic attributes based on the correlation between the first seismic attributes to generate a second seismic attribute includes:

[0013] The correlation coefficient between any two first earthquake attributes is calculated in the following manner: the two first earthquake attributes include first earthquake attribute S1 and first earthquake attribute S2;

[0014] Calculate the covariance and standard deviation of the sample data corresponding to the first seismic attribute S1 and the first seismic attribute S2, respectively;

[0015] Based on the covariance and standard deviation of the sample data corresponding to the first earthquake attribute S1 and the first earthquake attribute S2, the correlation coefficient between the first earthquake attribute S1 and the first earthquake attribute S2 is determined.

[0016] Based on the correlation coefficient between any two of the first earthquake attributes, the first earthquake attributes are filtered to generate the second earthquake attributes.

[0017] Optionally, when the number of wells drilled in the work area is less than a preset threshold, analyzing the correlation between the first seismic attribute and the reservoir parameters, and filtering the first seismic attribute based on the correlation between the first seismic attribute and the reservoir parameters, includes:

[0018] The correlation coefficient between the first seismic attribute S1 and the reservoir parameter A1 is calculated in the following manner;

[0019] Sort the sample data corresponding to the first seismic attribute S1 and the reservoir parameter A1 in ascending order respectively; calculate the rank of the sample data corresponding to the first seismic attribute S1 and the reservoir parameter A1 respectively.

[0020] Based on the rank of the sample data corresponding to the first seismic attribute S1 and the reservoir parameter A1, the correlation coefficient between the first seismic attribute S1 and the reservoir parameter A1 is determined.

[0021] Based on the correlation coefficient between the first seismic attribute and the reservoir parameters, the first seismic attribute is filtered to generate a second seismic attribute.

[0022] Optionally, the step of combining the selected first seismic attributes to generate the second seismic attribute includes:

[0023] Based on the selected first earthquake attribute, generate multiple sets of first earthquake attribute collections, each set of first earthquake attribute collections including at least one selected first earthquake attribute.

[0024] For any of the first earthquake attribute sets Q1, calculate the comprehensive evaluation score of the information medium corresponding to the first earthquake attribute set Q1. The evaluation score of the information medium corresponding to the first earthquake attribute set Q1 is determined by calculating the single evaluation score of the information medium corresponding to all the first earthquake attributes in the first earthquake attribute set Q1.

[0025] The single evaluation score of the information medium corresponding to any first seismic attribute e1 in the first seismic attribute set Q1 is determined according to the following method:

[0026] Calculate the correlation coefficients between the first earthquake attribute e1 and the remaining first earthquake attributes in the first earthquake attribute set Q1 respectively; and determine the single evaluation score of the information medium corresponding to e1 based on the correlation coefficients between the first earthquake attribute e1 and the remaining first earthquake attributes in the first earthquake attribute set Q1, as well as the correlation coefficients between the first earthquake attributes e1 and e1.

[0027] Based on the comprehensive evaluation score of information media from multiple sets of first earthquake attributes, earthquake attributes are combined to generate second earthquake attributes.

[0028] Optionally, the first seismic attribute of the target layer is determined based on seismic data through a comprehensive structural geological interpretation of the target layer;

[0029] The reservoir parameters of the known wells were determined based on seismic data through well logging formation comparison and well logging curve interpretation.

[0030] Optionally, the reservoir parameters of the known well include: lithology, reservoir thickness, hydrocarbon content, and porosity.

[0031] Optionally, the step of inputting the vectors corresponding to multiple seismic sampling points on the seismic waveform of the target layer into the prediction model for classification, and determining the category corresponding to the seismic waveform of the target layer, includes:

[0032] The lithology corresponding to the seismic waveform of the target layer is determined by calculating the similarity between the seismic waveform of the target layer and the seismic waveforms corresponding to different preset lithologies.

[0033] Optionally, determining the lithology corresponding to the seismic waveform of the target layer by calculating the similarity between the seismic waveform of the target layer and the seismic waveforms corresponding to different preset lithologies includes:

[0034] The lithology corresponding to the seismic waveform of the target layer is determined by calculating the distance between the vectors corresponding to multiple seismic sampling points on the seismic waveform of the target layer and the vectors corresponding to the seismic sampling points of the seismic waveforms of different preset lithologies.

[0035] Optionally, the multiple seismic sampling points on the seismic waveform of the target layer are determined according to a time window;

[0036] As the time interval corresponding to the time window increases, the number of peaks and troughs of the seismic waveform contained in the time window also increases.

[0037] As the time interval corresponding to the time window decreases, the number of peaks and troughs of the seismic waveform contained in the time window also decreases.

[0038] Optionally, the first seismic attribute includes: time-derived attributes, amplitude and frequency-derived attributes, and mixed attributes.

[0039] Optionally, the prediction model is an unsupervised classification model.

[0040] Optionally, the unsupervised classification model is a clustering model.

[0041] A second aspect of the present invention provides an apparatus for predicting reservoir parameters, the apparatus including a processor, a memory, and a program stored in the memory and executable on the processor, wherein the processor executes the program to run the method for predicting reservoir parameters based on seismic attributes.

[0042] A third aspect of the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the method for predicting reservoir parameters based on seismic attributes.

[0043] The method provided in this invention statistically analyzes the reservoir parameter information of known wells in the target work area, performs correlation analysis on the reservoir parameters of the known wells and / or the seismic attributes of the target layer, optimizes the seismic attributes, classifies the target layer based on the optimized seismic attributes, and finally predicts the reservoir parameters based on the classification results. This method can effectively extrapolate the lithological information of some known wells in the target work area to the entire work area, thereby avoiding the development of a large number of drilling wells in the target work area, reducing construction costs and improving operational efficiency.

[0044] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0045] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0046] Figure 1 This is a flowchart illustrating the method for predicting reservoir parameters based on seismic attributes provided in an embodiment of the present invention.

[0047] Figure 2 This is a graph showing the results of the correlation analysis between the first seismic attributes provided in this embodiment of the invention;

[0048] Figure 3 This is a graph showing the correlation analysis results between the first seismic attribute and reservoir parameters provided in this embodiment of the invention;

[0049] Figure 4 This is a vector diagram corresponding to multiple seismic sampling points provided in an embodiment of the present invention;

[0050] Figure 5 This is a partial enlarged view of the seismic waveform provided in an embodiment of the present invention;

[0051] Figure 6 These are seismic waveform diagrams corresponding to different lithologies provided in the embodiments of the present invention;

[0052] Figure 7 This is a cross-sectional view showing the result of predicting reservoir parameters of the target layer using the method provided in the embodiments of the present invention;

[0053] Figure 8 This is a plan view showing the result of predicting reservoir parameters of the target layer using the method provided in the embodiments of the present invention. Detailed Implementation

[0054] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0055] Figure 1 This is a flowchart illustrating the method for predicting reservoir parameters based on seismic attributes provided in an embodiment of the present invention; as shown below. Figure 1 As shown, the method includes:

[0056] Step S101: Perform correlation analysis on the reservoir parameters of the known well and / or the first seismic attribute of the target layer, and perform seismic attribute optimization on the first seismic attribute to generate the second seismic attribute.

[0057] Specifically, the target layer generally refers to the predetermined layer to be drilled in a drilling mission, according to different types of drilling design tasks, and the target layer is usually an oil and gas reservoir. Seismic attributes refer to the geometric, dynamic, and kinematic characteristics of seismic waves derived from pre-stack or post-stack seismic data.

[0058] Furthermore, the first seismic attribute includes: time-derived attributes, amplitude and frequency-derived attributes, and mixed attributes. Specifically, time-derived attributes are helpful in interpreting structural details; amplitude and frequency-derived attributes are used to resolve stratigraphic and reservoir characteristics; amplitude is the most robust and valuable attribute; frequency attributes are more helpful in revealing stratigraphic details; and mixed attributes include both amplitude and frequency factors.

[0059] Known reservoir parameters for drilling include: lithology, reservoir thickness, hydrocarbon content, and porosity.

[0060] In some embodiments, the first seismic attribute is extracted from the seismic data corresponding to the inter-layer and along-layer relationships of the target layer.

[0061] In some embodiments, the step of performing correlation analysis on the reservoir parameters and / or the first seismic attributes of the known well and / or the target layer, and performing seismic attribute optimization on the first seismic attributes to generate a second seismic attribute includes:

[0062] When the number of wells drilled in the work area is greater than or equal to a preset threshold, the correlation between the first seismic attributes is analyzed, and based on the correlation between the first seismic attributes, the first seismic attributes are filtered to generate a second seismic attribute.

[0063] Furthermore, drilling in a work area refers to independent wells that have already been drilled within that work area. Correlation is a non-deterministic relationship, and the correlation coefficient is a measure of the degree of linear correlation between variables. The correlation between variables can be calculated using the Pearson correlation coefficient, Spearman correlation coefficient, and Kendall correlation coefficient.

[0064] In some embodiments, the correlation coefficient between any two first seismic attributes is calculated in the following manner: the any two first seismic attributes include first seismic attribute S1 and first seismic attribute S2;

[0065] Calculate the covariance and standard deviation of the sample data corresponding to the first seismic attribute S1 and the first seismic attribute S2, respectively;

[0066] Based on the covariance and standard deviation of the sample data corresponding to the first earthquake attribute S1 and the first earthquake attribute S2, the correlation coefficient between the first earthquake attribute S1 and the first earthquake attribute S2 is determined.

[0067] Based on the correlation coefficient between any two of the first earthquake attributes, the first earthquake attributes are filtered to generate the second earthquake attributes.

[0068] Specifically, taking the Pearson correlation coefficient calculation method as an example, this paper explains in detail how to calculate the correlation coefficient between any two of the first seismic attributes.

[0069] The formula for calculating the Pearson correlation coefficient is:

[0070]

[0071] Where con(X,Y) represents the covariance of variables X and Y; σX and σY represent the standard deviations of X and Y; and r represents the correlation coefficient between variables X and Y. The Pearson correlation coefficient ranges from -1 to 1. When r>0, it indicates a positive correlation; when r<0, it indicates a negative correlation; and when r=0, it indicates no correlation.

[0072] Ten different first earthquake attributes were selected and labeled. The labels for the ten different first earthquake attributes are 122, 125, 123, 128, 126, 133, 138, 139, 143 and 145.

[0073] The correlation coefficient between any two of the 10 different first earthquake attributes can be calculated using the Pearson correlation coefficient formula. It should be noted that any two first earthquake attributes can be the same, that is, the correlation coefficient between 122 and 122 can be calculated.

[0074] Figure 2 This is a graph showing the correlation analysis results between the first seismic attributes provided in this embodiment of the invention; the graph shows the correlation coefficients between 10 different first seismic attributes calculated using the Pearson correlation coefficient formula.

[0075] Reference Figure 2 Correlation analysis shows that the correlation coefficient between the two earthquake attributes within the red dashed circle is greater than 0.9, and one of them needs to be removed. Further correlation analysis indicates that the correlation coefficients for 128 and 145, 128 and 139, 139 and 145, and 122 and 123 are greater than 0.9. Therefore, the seven different first earthquake attributes (122, 125, 128, 126, 133, 138, and 143) are retained for subsequent prediction. These seven different first earthquake attributes are the second earthquake attributes.

[0076] When the number of wells drilled in the work area is less than a preset threshold, the correlation between the first seismic attribute and the reservoir parameters (reservoir thickness, porosity, etc.) is analyzed, and based on the correlation between the first seismic attribute and the reservoir parameters, the first seismic attribute is screened, and the screened first seismic attribute is combined to generate a second seismic attribute.

[0077] It should be noted that when the number of independent wells already drilled in the work area is small, the sample requirements for statistical analysis cannot be met. The probability of producing spurious correlations by simply using correlation analysis methods is very high. Applying a combination of data correlation and information optimization methods for attribute selection can improve the accuracy of the calculation results.

[0078] Specifically, the correlation coefficient between the first seismic attribute S1 and the reservoir parameter A1 is calculated in the following manner;

[0079] Sort the sample data corresponding to the first seismic attribute S1 and the reservoir parameter A1 in ascending order respectively; calculate the rank of the sample data corresponding to the first seismic attribute S1 and the reservoir parameter A1 respectively.

[0080] Based on the rank of the sample data corresponding to the first seismic attribute S1 and the reservoir parameter A1, the correlation coefficient between the first seismic attribute S1 and the reservoir parameter A1 is determined.

[0081] Based on the correlation coefficient between the first seismic attribute and the reservoir parameters, the first seismic attribute is filtered to generate a second seismic attribute.

[0082] Furthermore, taking the Spearman correlation coefficient calculation method as an example, we will explain in detail how to calculate the correlation coefficient between seismic attributes and reservoir parameters.

[0083] When using the Spearman correlation coefficient method to calculate the correlation coefficient between variables X and Y, it is necessary to sort the sample data corresponding to variables X and Y in either orthogonal or in reverse order.

[0084] Spearman correlation coefficient R s The calculation formula is:

[0085]

[0086] Among them, R Xi R represents the rank of the sample data corresponding to variable X; Yi This indicates the rank of the sample data corresponding to variable Y; It represents the average of the ranks of the sample data corresponding to variable X; This represents the average of the ranks of the sample data corresponding to variable Y.

[0087] It should be noted that for sample data corresponding to a variable, if its elements are sorted from smallest to largest, then for non-repeating elements, the sorted position is its rank; for repeating elements, the average of the positions of these equal elements is their rank.

[0088] Spearman correlation coefficient (R²) s R is between -1 and 1. s >0 indicates a positive correlation, R0 s <0 indicates a negative correlation. R s absolute value (|R) s The larger the |), the stronger the correlation between variables.

[0089] Figure 3 This is a correlation analysis result diagram between the first seismic attribute and reservoir parameters provided in this embodiment of the invention; the result diagram is obtained by applying the Spearman correlation coefficient (R²). s The 12 first seismic attributes calculated are: 1-number of troughs, 2-average signal-to-noise ratio, 3-effective bandwidth, 4-number of crests, 5-reflection intensity, 6-arc length, 13-number of zero crossovers, 15-reflection intensity slope, 16-average energy, 17-energy half-decay, 18-ratio of positive and negative sampling points, 19-energy half-decay slope and the correlation coefficient between P1-reservoir thickness in the reservoir parameters.

[0090] Reference Figure 3 Correlation analysis shows that the seismic attribute codes 16-average energy and 17-energy half-decay time are highly correlated with the well reservoir parameter-reservoir thickness (correlation coefficient greater than 0.7). Therefore, these two seismic attributes, 16-average energy and 17-energy half-decay time, are preferred to predict the reservoir thickness in the reservoir parameter.

[0091] Furthermore, the step of combining the selected first seismic attributes to generate the second seismic attribute includes:

[0092] Based on the selected first earthquake attributes, multiple sets of first earthquake attributes are generated, each set including at least one selected first earthquake attribute. It should be noted that multiple sets of first earthquake attributes can be generated using permutations and combinations, and each set may include one, more, or all of the selected first earthquake attributes.

[0093] For any of the first earthquake attribute sets Q1, calculate the comprehensive evaluation score of the information medium corresponding to the first earthquake attribute set Q1. The evaluation score of the information medium corresponding to the first earthquake attribute set Q1 is determined by calculating the single evaluation score of the information medium corresponding to all the first earthquake attributes in the first earthquake attribute set Q1.

[0094] The single evaluation score of the information medium corresponding to any first seismic attribute e1 in the first seismic attribute set Q1 is determined according to the following method:

[0095] Calculate the correlation coefficients between the first earthquake attribute e1 and the remaining first earthquake attributes in the first earthquake attribute set Q1 respectively; and determine the single evaluation score of the information medium corresponding to e1 based on the correlation coefficients between the first earthquake attribute e1 and the remaining first earthquake attributes in the first earthquake attribute set Q1, as well as the correlation coefficients between the first earthquake attributes e1 and e1.

[0096] Based on the comprehensive evaluation score of information media from multiple sets of first earthquake attributes, earthquake attributes are combined to generate second earthquake attributes.

[0097] Specifically, the Helwig optimization selection method can be used to select the optimal first set of seismic attributes.

[0098] The formula for Helwig's optimal selection method is as follows:

[0099] H m =Max(H1,H2,H3,…,H) k )

[0100] Among them, H k Q represents the first set of earthquake attributes. k The corresponding comprehensive evaluation score for information media, Max(H1,H2,H3,…,H k This represents the set of first seismic attributes with the highest comprehensive evaluation score for the information medium.

[0101] H k It is calculated using the following formula:

[0102]

[0103] Among them, h j Q represents the first set of earthquake attributes. k any of the first seismic attributes e k The corresponding single evaluation score for the information medium; m represents the first set of earthquake attributes Q. k The number of first earthquake attributes in the data.

[0104] h jIt is calculated using the following formula:

[0105]

[0106] Where, r jj Q represents the first set of earthquake attributes. k The first seismic attribute e j and the first earthquake attribute e j The correlation coefficient, r, between the corresponding sample data ji Q represents the first set of earthquake attributes. k The first seismic attribute e j and the first earthquake attribute e i The correlation coefficient between the corresponding sample data; m represents the first set of earthquake attributes Q. k The number of first earthquake attributes in the data, where i ≠ j.

[0107] Furthermore, the first seismic attributes sensitive to reservoir gas content can be determined using the above methods, including: maximum peak amplitude, average peak amplitude, maximum trough amplitude, average trough amplitude, average instantaneous phase, energy half-decay, sum of absolute amplitude values, sum of amplitude values, average energy, sum of energy values, average amplitude, related components, dominant frequency, instantaneous frequency, root mean square amplitude, spectral energy, absorption coefficient, absorption attenuation rate, and their derived attributes.

[0108] The first seismic properties sensitive to reservoir thickness variations include: autocorrelation function bandwidth, maximum time, number of troughs, average signal-to-noise ratio, effective bandwidth, number of crests, reflection intensity, arc length, number of zero crossovers, reflection intensity slope, average energy, energy half-life, ratio of positive to negative sampling points, energy half-life slope, and their derived properties.

[0109] Properties sensitive to changes in reservoir sandstone porosity include: average trough value, average energy, amplitude summation, energy half-life, threshold value, first zero crossover point, time-domain autocorrelation value, autocorrelation, response phase, normalization of amplitude summation and its derived properties.

[0110] It should also be noted that the first seismic attribute of the target layer is determined based on seismic data through a comprehensive structural geological interpretation of the target layer;

[0111] The reservoir parameters of the known wells were determined based on seismic data through well logging formation comparison and well logging curve interpretation.

[0112] Step S102: Based on the second seismic attributes corresponding to multiple seismic sampling points on the seismic waveform of the target layer, establish the vector corresponding to the multiple seismic sampling points of the target layer.

[0113] Figure 4This is a vector diagram corresponding to multiple seismic sampling points provided in an embodiment of the present invention; refer to Figure 4 It can preprocess the values ​​corresponding to the second seismic attribute of multiple seismic sampling points, including correcting and deleting incomplete, inconsistent or duplicate data, and then establish the vector corresponding to multiple seismic sampling points of the target layer based on the preprocessed values.

[0114] Furthermore, multiple seismic sampling points on the seismic waveform of the target layer are determined based on a time window.

[0115] Figure 5 This is a partially enlarged view of the seismic waveform provided in an embodiment of the present invention, with reference to... Figure 5 Points Xi and Xj have the same amplitude and may be classified into the same type, but comparing the morphology of (Xi-1, Xi, Xi+1) and (Xj-1, Xj, Xj+1) completely represents different types of reflection characteristics. Adjusting the time window can change the resolution of the profile corresponding to the seismic attributes, thus effectively reflecting the lithological characteristics of the strata. The time window contains different waveforms, such as... Figure 6 As shown, within a complete waveform time window (approximately 60 milliseconds), different lithologies will produce different seismic waveforms because different lithologies have different velocities. The greater the velocity, the larger the reflection amplitude and the larger the waveform amplitude. Sandstone velocity > silty mudstone velocity > mudstone velocity, and the corresponding waveform amplitudes are as follows: Figure 5 As shown, 2 represents the seismic waveform amplitude of sandstone, >3 represents the seismic waveform amplitude of silty mudstone, and >1 represents the seismic waveform amplitude of mudstone. A time window that is too large or too small will affect the classification results.

[0116] In some embodiments, when the time interval corresponding to the time window increases, the number of peaks and troughs of the seismic waveform contained in the time window increases accordingly; when the time interval corresponding to the time window decreases, the number of peaks and troughs of the seismic waveform contained in the time window decreases accordingly.

[0117] Step S103: Input the vectors corresponding to multiple seismic sampling points on the seismic waveform of the target layer into the prediction model for classification, and determine the category corresponding to the seismic waveform of the target layer.

[0118] In some embodiments, the prediction model is an unsupervised classification model; further, the unsupervised classification model is a clustering model.

[0119] Specifically, the step of inputting the vectors corresponding to multiple seismic sampling points on the seismic waveform of the target layer into the prediction model for classification, and determining the category corresponding to the seismic waveform of the target layer includes:

[0120] The lithology corresponding to the seismic waveform of the target layer is determined by calculating the similarity between the seismic waveform of the target layer and the seismic waveforms corresponding to different preset lithologies.

[0121] Furthermore, since the well logging data corresponding to the known wells in the work area is in the depth domain and the seismic data is in the time domain, it is necessary to create a synthetic seismic record to establish a time-depth relationship, and then determine the seismic waveforms corresponding to different lithologies based on the time-depth relationship.

[0122] In some embodiments, determining the lithology corresponding to the seismic waveform of the target layer by calculating the similarity between the seismic waveform of the target layer and the seismic waveforms corresponding to different preset lithologies includes:

[0123] The lithology corresponding to the seismic waveform of the target layer is determined by calculating the distance between the vectors corresponding to multiple seismic sampling points on the seismic waveform of the target layer and the vectors corresponding to the seismic sampling points of the seismic waveforms of different preset lithologies.

[0124] Furthermore, the preset seismic waveforms corresponding to different lithologies include seismic waveforms corresponding to sandstone, silty mudstone, and mudstone.

[0125] In some embodiments, seismic sampling points are divided into the seismic waveforms of the target layer and the seismic waveforms corresponding to different preset lithologies based on a 60-millisecond time window. The seismic waveforms of the target layer have the same waveform length as the seismic waveforms corresponding to the different preset lithologies.

[0126] By selecting the average energy and energy half-decay time from the seismic attributes, a vector corresponding to multiple seismic sampling points on the seismic waveform is established.

[0127] Specifically, the seismic sampling point e corresponding to any seismic waveform of the target layer at time i. i The corresponding vector A i Let [X1, X2] be the seismic waveform corresponding to sandstone at time i, and the corresponding seismic sampling point m be the same. i The corresponding vector B i [Y1, Y2]; the vector C corresponding to the seismic sampling point at time i corresponding to the seismic waveform of the silty mudstone. i [K1, K2]; the vector D corresponding to the seismic sampling point at time i corresponding to the seismic waveform of the silty mudstone. i [Z1, Z2].

[0128] X1, Y1, K1, and Z1 all represent the values ​​corresponding to the average energy in the seismic attributes; X2, Y2, K2, and Z2 all represent the values ​​corresponding to the energy half-decay in the seismic attributes.

[0129] The vector a = (x1, x2, ..., x) can be calculated using the following formula. nand b = (y1, y2, ..., y n The distance d between them ab :

[0130]

[0131] Where, x i y represents the value of the i-th element in vector a; i This represents the value of the i-th element in vector a; n represents the number of elements in vector a or b.

[0132] Calculate the vector A corresponding to time i using the formula above. i Sum vector B i Distance between Vector A i Sum vector C i Distance between Vector A i Sum vector D i Distance between

[0133] Calculate the distance D between any seismic waveform of the target layer and the corresponding seismic waveform of the sandstone using the following formula. AB :

[0134]

[0135] in, Let e ​​be the seismic sampling point on the target layer seismic waveform at time i. i The corresponding vector A i The seismic waveform corresponding to sandstone at time i corresponds to the seismic sampling point m. i The corresponding vector B i The distance between them; n represents the number of seismic sampling points (i.e., the window length).

[0136] Similarly, the distance between any seismic waveform of the target seismic layer and the corresponding seismic waveforms of silty mudstone and mudstone can be calculated, and the lithology corresponding to the shortest distance can be used as the category of any seismic waveform. For example, if the selected seismic waveform has the shortest distance to the seismic waveform corresponding to silty mudstone, then the seismic waveform is classified as silty mudstone.

[0137] Figure 7 This is a cross-sectional view showing the result of predicting reservoir parameters of the target layer using the method provided in the embodiments of the present invention; Figure 8 This is a plan view showing the result of predicting reservoir parameters of the target layer using the method provided in the embodiments of the present invention.

[0138] Reference Figure 7 , Figure 8The lithology encountered in Well A mainly consists of three types: argillaceous siltstone, sandstone, and mudstone. Based on the drilling results, the profile classification is as follows: Blue-dark blue type 1 indicates predicted mudstone development; light green-green type 2 indicates predicted argillaceous siltstone development; and light yellow-yellow type 3 indicates predicted sandstone development. Based on the drilling results, the planar classification is as follows: Blue-dark blue type 1 indicates predicted mudstone development; light green-yellow type 2 indicates predicted argillaceous siltstone development; and dark red-red type 3 indicates predicted sandstone development.

[0139] Step S104: Determine the reservoir parameters of the target layer based on the category corresponding to the seismic waveform of the target layer.

[0140] In some embodiments, when the seismic waveform of the target layer is classified as argillaceous siltstone, the numerical range of the hydrocarbon content, porosity, and reservoir thickness corresponding to the preset data of argillaceous siltstone is used as the numerical range of the hydrocarbon content, porosity, and reservoir thickness corresponding to the seismic waveform of the target layer.

[0141] In some embodiments, when the seismic waveform of the target layer is classified as sandstone, the numerical range of the oil and gas content, porosity, and reservoir thickness corresponding to the preset sandstone is used as the numerical range of the oil and gas content, porosity, and reservoir thickness corresponding to the seismic waveform of the target layer.

[0142] Among them, the preset data on the hydrocarbon content, porosity, and sandstone thickness of silty mudstone, sandstone, or mudstone can be used to determine the reservoir parameters of known wells in the work area.

[0143] This invention also provides an apparatus for predicting reservoir parameters. The apparatus includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it runs the method for predicting reservoir parameters based on seismic attributes.

[0144] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the method for predicting reservoir parameters based on seismic attributes.

[0145] The method provided in this invention statistically analyzes the reservoir parameter information of known wells in the target work area, performs correlation analysis on the reservoir parameters of the known wells and / or the seismic attributes of the target layer, optimizes the seismic attributes, classifies the target layer based on the optimized seismic attributes, and finally predicts the reservoir parameters based on the classification results. This method can effectively extrapolate the lithological information of some known wells in the target work area to the entire work area, thereby avoiding the development of a large number of drilling wells in the target work area, reducing construction costs and improving operational efficiency.

[0146] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for predicting reservoir parameters based on seismic attributes, characterized in that, The method includes: Correlation analysis is performed on the reservoir parameters and / or the first seismic attribute of the target layer of the known well, and the first seismic attribute is optimized to generate the second seismic attribute; Based on the second seismic attribute corresponding to multiple seismic sampling points on the seismic waveform of the target layer, a vector corresponding to multiple seismic sampling points of the target layer is established. The vectors corresponding to multiple seismic sampling points on the seismic waveform of the target layer are input into the prediction model for classification to determine the category of the seismic waveform of the target layer; and Based on the category corresponding to the seismic waveform of the target layer, the reservoir parameters of the target layer are determined.

2. The method according to claim 1, characterized in that, The process of performing correlation analysis on the reservoir parameters and / or the first seismic attributes of the known well and / or the target layer, and then performing seismic attribute optimization on the first seismic attributes to generate the second seismic attribute includes: When the number of wells drilled in the work area is greater than or equal to a preset threshold, the correlation between the first seismic attributes is analyzed, and based on the correlation between the first seismic attributes, the first seismic attributes are filtered to generate a second seismic attribute. When the number of wells drilled in the work area is less than a preset threshold, the correlation between the first seismic attribute and the reservoir parameter is analyzed, and based on the correlation between the first seismic attribute and the reservoir parameter, the first seismic attribute is screened, and the screened first seismic attribute is combined to generate a second seismic attribute.

3. The method according to claim 2, characterized in that, When the number of wells drilled in the work area is greater than or equal to a preset threshold, the correlation between the first seismic attributes is analyzed, and based on the correlation between the first seismic attributes, the first seismic attributes are filtered to generate a second seismic attribute, including: The correlation coefficient between any two first earthquake attributes is calculated in the following manner: the two first earthquake attributes include first earthquake attribute S1 and first earthquake attribute S2; Calculate the covariance and standard deviation of the sample data corresponding to the first seismic attribute S1 and the first seismic attribute S2, respectively; Based on the covariance and standard deviation of the sample data corresponding to the first earthquake attribute S1 and the first earthquake attribute S2, the correlation coefficient between the first earthquake attribute S1 and the first earthquake attribute S2 is determined. Based on the correlation coefficient between any two of the first earthquake attributes, the first earthquake attributes are filtered to generate the second earthquake attributes.

4. The method according to claim 2, characterized in that, When the number of wells drilled in the work area is less than a preset threshold, the correlation between the first seismic attribute and the reservoir parameters is analyzed, and the first seismic attribute is screened based on the correlation between the first seismic attribute and the reservoir parameters, including: The correlation coefficient between the first seismic attribute S1 and the reservoir parameter A1 is calculated in the following manner; Sort the sample data corresponding to the first seismic attribute S1 and the reservoir parameter A1 in ascending order respectively; calculate the rank of the sample data corresponding to the first seismic attribute S1 and the reservoir parameter A1 respectively. Based on the rank of the sample data corresponding to the first seismic attribute S1 and the reservoir parameter A1, the correlation coefficient between the first seismic attribute S1 and the reservoir parameter A1 is determined. Based on the correlation coefficient between the first seismic attribute and the reservoir parameters, the first seismic attribute is filtered to generate a second seismic attribute.

5. The method according to claim 2, characterized in that, The step of combining the selected first seismic attributes to generate the second seismic attribute includes: Based on the selected first earthquake attribute, generate multiple sets of first earthquake attribute collections, each set of first earthquake attribute collections including at least one selected first earthquake attribute. For any of the first set of seismic attributes Q1, calculate the first set of seismic attributes Q. 14 The corresponding comprehensive evaluation score of the information medium, the first earthquake attribute set Q 14 The corresponding information medium evaluation score is determined by calculating the single information medium evaluation score corresponding to all the first earthquake attributes in the first earthquake attribute set Q1; The single evaluation score of the information medium corresponding to any first seismic attribute e1 in the first seismic attribute set Q1 is determined according to the following method: Calculate the correlation coefficients between the first earthquake attribute e1 and the remaining first earthquake attributes in the first earthquake attribute set Q1 respectively; and determine the single evaluation score of the information medium corresponding to e1 based on the correlation coefficients between the first earthquake attribute e1 and the remaining first earthquake attributes in the first earthquake attribute set Q1, as well as the correlation coefficients between the first earthquake attributes e1 and e1. Based on the comprehensive evaluation score of information media from multiple sets of first earthquake attributes, earthquake attributes are combined to generate second earthquake attributes.

6. The method according to claim 1, characterized in that, The first seismic attribute of the target layer is determined based on seismic data through a comprehensive structural geological interpretation of the target layer; The reservoir parameters of the known wells were determined based on seismic data through well logging formation comparison and well logging curve interpretation.

7. The method according to claim 1, characterized in that, The reservoir parameters of the known wells include: lithology, reservoir thickness, hydrocarbon content, and porosity.

8. The method according to claim 1, characterized in that, The step of inputting the vectors corresponding to multiple seismic sampling points on the seismic waveform of the target layer into the prediction model for classification, and determining the category of the seismic waveform of the target layer includes: The lithology corresponding to the seismic waveform of the target layer is determined by calculating the similarity between the seismic waveform of the target layer and the seismic waveforms corresponding to different preset lithologies.

9. The method according to claim 8, characterized in that, The step of determining the lithology corresponding to the seismic waveform of the target layer by calculating the similarity between the seismic waveform of the target layer and the seismic waveforms corresponding to different preset lithologies includes: The lithology corresponding to the seismic waveform of the target layer is determined by calculating the distance between the vectors corresponding to multiple seismic sampling points on the seismic waveform of the target layer and the vectors corresponding to the seismic sampling points of the seismic waveforms of different preset lithologies.

10. The method according to claim 1, characterized in that, Multiple seismic sampling points on the seismic waveform of the target layer are determined based on time windows; As the time interval corresponding to the time window increases, the number of peaks and troughs of the seismic waveform contained in the time window also increases. As the time interval corresponding to the time window decreases, the number of peaks and troughs of the seismic waveform contained in the time window also decreases.

11. The method according to claim 1, characterized in that, The first earthquake attribute includes: time-derived attributes, amplitude and frequency-derived attributes, and mixed attributes.

12. The method according to claim 1, characterized in that, The prediction model is an unsupervised classification model.

13. The method according to claim 12, characterized in that, The unsupervised classification model is a clustering model.

14. A device for predicting reservoir parameters, characterized in that, The device includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it runs the method for predicting reservoir parameters based on seismic attributes as described in any one of claims 1 to 13.

15. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method for predicting reservoir parameters based on seismic attributes according to any one of claims 1 to 13.

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