Seismic waveform-based underwater shunt channel sand body prediction method

Abstract

A seismic waveform-based underwater shunt channel sand body prediction method belongs to the technical field of oil exploration and development, and comprises the following steps: S1, analyzing an oil outlet reservoir system disclosed by well drilling, and determining the position of an underwater shunt channel sand body on a seismic section; s2, seismic waveform typical features corresponding to different lithologic combinations are analyzed; s3, performing gradient transformation point by point on each seismic trace in the corresponding time window on the seismic section of the target interval; s4, performing normalization correction and neural network analysis on a gradient change set by using gradient change of the seismic waveform features corresponding to the lithologic combination as an input channel; s5, logging curve model constraint inversion is carried out on the target interval, and an inversion plane distribution number set of the target interval is extracted; and S6, performing normalization on the inversion plane graph of the extracted target interval and the seismic waveform feature similarity degree plane distribution graph, and performing mathematical operation. According to the method, the transverse resolution advantage of earthquakes and the longitudinal resolution advantage of logging are utilized, and the prediction precision of the underwater diversion river channel sand body is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of petroleum exploration and development, and in particular relates to a method for predicting underwater diversion channel sand bodies based on seismic waveforms. Background Art

[0002] The oil exploration and development process is a complex one that integrates multidisciplinary basic theoretical knowledge. It requires mutual support, complementation, and verification among various disciplines. Ultimately, each discipline must have fundamental research and technological innovation to achieve success. Fundamental research and technological innovation are equally important to the success of the research results. When faced with common problems, we usually need different specialized technologies and detailed summaries to address the complex and ever-changing geological targets faced during oil exploration and development. In this process, oil exploration and development workers need to continuously innovate technology, integrate development, and improve accuracy. At the same time, they need to conduct detailed analysis and compare and verify research results across various aspects, such as logging, geology, and seismic, to eliminate the adverse effects of technical shortcomings in individual disciplines. In continental lake basins, a large number of underwater distributary channel sand bodies are the main oil-producing strata. These underwater distributary channel sand bodies contain a large amount of oil and gas. If logging information, seismic technology, and geological sedimentation theory can be used to improve the prediction accuracy of underwater distributary channel sand bodies in continental lake basins, it will help improve the success rate of exploration and development in this layer.

[0003] The common prediction technology of underwater distributary channel sand bodies in continental lake basins is to extract the "amplitude, frequency, and phase" attributes of seismic data, or to perform mathematical operations on the three parameters of "amplitude, frequency, and phase" and then perform attribute extraction, analyze the degree of coincidence between the extraction results and the wells, calculate the coincidence rate, and achieve the purpose of reducing exploration risks. Although this method is relatively objective, it can only predict the lithologic combination changes of the target layer on the plane. Due to the limitations and multi-solutions of seismic wave theory, there will be large errors in the prediction of underwater distributary channel sand bodies. The other type is to use the advantage of the vertical resolution of the well to carry out logging curve model constraint inversion, based on the According to the sedimentary law, a certain algorithm is selected to calculate the difference model on the well as a constraint condition. At the same time, the interface information of the seismic reflection wave is deconvolved using various wavelets to obtain the inversion plane information of the target layer. Then, mathematical operations are performed on the well difference model and the inversion plane information according to certain weights to obtain the final result. These common practices or technologies are inefficient, computationally intensive, highly multi-solution prone, and have low prediction accuracy. Therefore, if the simple and easy-to-calculate waveform gradient information can be organically combined with the geological sedimentary law and then applied to the constraint conditions of the logging curve model constrained inversion, the above disadvantages will be greatly improved. Summary of the Invention

[0004] In order to solve the above problems, the present invention proposes a method for predicting underwater diversion channel sand bodies based on seismic waveforms, comprising the following steps:

[0005] S1. Analyze the oil-producing strata revealed by drilling and clarify the location of underwater distributary channel sand bodies on the seismic profile;

[0006] S2. Analyze the typical characteristics of seismic waveforms corresponding to different lithologic combinations;

[0007] S3, performing gradient transformation on each seismic trace within the time window corresponding to the target layer on the seismic section;

[0008] S4, using the gradient changes of the seismic waveform characteristics corresponding to the n lithologic combinations in step S2 as input traces, performing normalization correction and neural network analysis on the gradient change set in step S3;

[0009] S5. Performing well logging curve model constrained inversion on the target layer segment and extracting an inversion plane distribution data set of the target layer segment;

[0010] S6. Normalize the inversion plane map of the target layer segment extracted in step S5 with the plane distribution map of the similarity of seismic waveform characteristics generated in step S4, and perform mathematical operations.

[0011] Furthermore, in step S1, the oil-producing strata revealed by drilling are analyzed, and the sedimentary range of the underwater distributary channel is determined through curve characteristics and lateral comparative analysis. The three-dimensional seismic data of the area are used to establish a seismic work area, and the well-seismic calibration is used to determine the well-seismic time-depth relationship, further clarifying the position of the underwater distributary channel sand body on the seismic profile.

[0012] Furthermore, in step S2, the typical characteristics of the seismic waveforms corresponding to different lithologic combinations are analyzed, and the number of types is recorded as n. Gradient transformation is performed on the seismic traces of these different characteristics, that is, G=dx / dy, where G is the gradient transformation value, dx is the amplitude of the next sample point minus the amplitude of the previous sample point, and dy is the time of the next sample point minus the time of the previous sample point. The ratio of the two is used as a description of the change in the seismic waveform, and the sequence of this ratio is used as an array of the typical characteristics of the seismic waveform, and each array is assigned an independent natural number identifier from 1 to n.

[0013] Furthermore, in step S3, a gradient transformation is performed point by point on each seismic trace within the time window corresponding to the target layer segment on the seismic profile, that is, G=dx / dy, where G is the gradient transformation value, dx is the amplitude of the next sample point minus the amplitude of the previous sample point, and dy is the time of the next sample point minus the time of the previous sample point, forming a gradient change set for each seismic trace and participating in subsequent calculations as an array.

[0014] Furthermore, in step S4, the gradient changes of the seismic waveform characteristics corresponding to the n lithologic combinations in step S2 are used as input channels to perform normalization correction and neural network analysis on the gradient change set in step S3. According to the n lithologic combinations corresponding to the underwater distributary channels, they are classified and assigned values ​​based on the similarity of the seismic waveform characteristics, and are assigned increasing natural numbers 1-n, respectively, and the number of the lithologic combinations corresponding to the underwater distributary channels is kept consistent.

[0015] Furthermore, in step S5, based on the completion of well seismic calibration and stratigraphic interpretation, well logging curve connection analysis is carried out according to the geological characteristics of underwater distributary channel sand body deposition, and a reasonable geological model is established. Well logging curve model constrained inversion is carried out on the target layer segment, and the inversion plane distribution data set of the target layer segment is extracted.

[0016] Furthermore, in step S6, the inversion plane map of the target layer segment extracted in step S5 is normalized with the plane distribution map of the seismic waveform characteristics similarity generated in step S4, and a mathematical operation is performed. The formula is z=x*y, where z is the final result, x is the normalized logging curve model constrained inversion plane data set, and y is the normalized seismic waveform characteristics similarity plane distribution data set.

[0017] Furthermore, the final well logging curve model constraint inversion and underwater distributary channel correlation plane distribution map is obtained to realize the prediction of underwater distributary channel sand bodies.

[0018] The beneficial effects of the present invention are as follows: the present invention reduces the problem of multi-solution in seismic prediction of underwater distributary channel sand bodies in the process of improving the accuracy of oil and gas exploration and development from the perspective of underwater distributary channel sand bodies in continental lake basins. The present invention has the characteristics of simple operation, flexibility and convenience, and low multi-solution. It can fully utilize the lateral resolution advantage of seismic and the vertical resolution advantage of well logging. Multiple disciplines can verify and complement each other to improve the prediction accuracy of underwater distributary channel sand bodies in continental lake basins, thereby reducing the drilling risk of exploration and development. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The five different seismic waveform characteristic patterns corresponding to the five lithologic combinations of the underwater distributary channel of the present invention are shown;

[0020] Figure 2 A plane diagram of the seismic waveform classification set corresponding to the lithologic combination of the present invention;

[0021] Figure 3 This is a plane distribution diagram of the well logging curve model constraint inversion of the present invention;

[0022] Figure 4 This is a predicted thickness map of underwater distributary channel sand bodies based on seismic waveforms of the present invention; DETAILED DESCRIPTION

[0023] In order to make the technical means adopted by the present invention and the purpose achieved easy to understand, the present invention is further described below in conjunction with specific implementation methods. A method for predicting underwater distributary channel sand bodies based on seismic waveforms, a method for predicting underwater distributary channel sand bodies in terrestrial lake basin sediments based on seismic waveforms, the method first determines the well-seismic time-depth relationship through well-seismic calibration, and clarifies the position of the underwater distributary channel sand bodies on the seismic profile; performs gradient transformation on each seismic trace in the target layer segment point by point to form a gradient change set; performs normalization correction and neural network analysis on the gradient change set, and classifies the gradient change set according to the gradient change amount according to the required number to form the required number of seismic trace gradient change number sets; gives each seismic trace gradient change number set an independent the identification, such as numbers or letters; analyzing the seismic waveform characteristics corresponding to different lithologic combinations, and taking the seismic traces with these different characteristics as typical characteristics; calculating the gradients of the seismic traces with these different characteristics, comparing and analyzing them with the above-mentioned gradient change number sets of each seismic trace, and determining the distribution range of different lithologic combinations of underwater distributary channel sand bodies in the target layer; performing logging curve model constrained inversion on the target layer on the basis of well-seismic calibration, and extracting the inversion plane distribution number set of the target layer; then mutually constraining the plane distribution number set of the waveform classification corresponding to the different lithologic combinations of the underwater distributary channel sand bodies and the plane distribution number set of the logging curve model constrained inversion to perform mathematical operations, so as to achieve a more accurate and reliable prediction of the underwater distributary channel sand bodies.

[0024] This method innovatively leverages the lateral resolution advantage of seismic waveform plane changes, combined with the high accuracy of well point-constrained inversion using well logging curve models, to improve the accuracy of underwater distributary channel sand body prediction. The implementation process mainly includes six steps:

[0025] (1) Analyze the oil-producing strata revealed by drilling, determine the sedimentary range of the underwater distributary channel through curve characteristics and lateral comparison analysis, establish a seismic work area using the three-dimensional seismic data of the area, determine the time-depth relationship between the well and seismic calibration, and further clarify the location of the underwater distributary channel sand body on the seismic profile;

[0026] (2) Analyze the typical characteristics of seismic waveforms corresponding to different lithologic combinations, and record the number of types as n. Perform gradient transformation on these seismic traces with different characteristics, i.e., G = dx / dy, where G is the gradient transformation value, dx is the amplitude of the next sample point minus the amplitude of the previous sample point, and dy is the time of the next sample point minus the time of the previous sample point. Use the ratio of the two as a description of the change in seismic waveform, and use the sequence of this ratio as an array of the typical characteristics of the seismic waveform. Assign each array an independent natural number identifier from 1 to n.

[0027] (3) Perform a gradient transformation on each seismic trace within the time window corresponding to the target layer on the seismic profile, i.e., G = dx / dy, where G is the gradient transformation value, dx is the amplitude of the next sample point minus the amplitude of the previous sample point, and dy is the time of the next sample point minus the time of the previous sample point. This forms a gradient change set for each seismic trace and serves as an array for subsequent calculations.

[0028] (4) Using the gradient changes of the seismic waveform characteristics corresponding to the n lithologic combinations in step (2) as input, the gradient change set in step (3) is normalized and corrected and analyzed by a neural network. According to the similarity of the seismic waveform characteristics of the n lithologic combinations corresponding to the underwater distributary channels, they are classified and assigned values, and are assigned with increasing natural numbers 1-n, and the number of lithologic combinations corresponding to the underwater distributary channels is kept consistent;

[0029] (5) On the basis of completing well-seismic calibration and horizon interpretation, carry out well logging curve connection analysis according to the geological characteristics of underwater distributary channel sand body deposition, establish a reasonable geological model, carry out well logging curve model constraint inversion for the target layer, and extract the inversion plane distribution data set of the target layer;

[0030] (6) The inversion plane map of the target layer segment extracted in step (5) is normalized with the plane distribution map of the seismic waveform characteristics similarity generated in step (4), and a mathematical operation is performed. The formula is z = x*y, where z is the final result, x is the normalized well logging curve model constrained inversion plane data set, and y is the normalized seismic waveform characteristics similarity plane distribution data set. The final plane distribution map of the correlation between the well logging curve model constrained inversion and the underwater distributary channel is obtained to realize the prediction of the underwater distributary channel sand body.

[0031] In order to make the key links, core technologies, implementation effects and advantages of the present invention clearer, the present invention will be further described in detail below in accordance with the specific steps of the present invention, taking the I sand group of the fourth section of the Fuyu oil layer in a seismic work area in the southern Songliao Basin as an example, with reference to the accompanying drawings.

[0032] (1) A typical seismic work area with a full coverage area of ​​150 square kilometers was selected. Through well-seismic calibration and well-connected comparison, the location of the underwater distributary channel of the main target layer q4-I on the seismic profile was determined;

[0033] (2) Analysis shows that there are five lithologic combinations in the q4-I underwater distributary channel, corresponding to five different typical characteristics of seismic waveforms. These seismic traces with different characteristics are gradient transformed, i.e., G = dx / dy. In the formula, G is the gradient transformation value, dx is the amplitude of the next sample point minus the amplitude of the previous sample point, and dy is the time of the next sample point minus the time of the previous sample point. The ratio of the two is used as a description of the change of the seismic waveform, and the sequence of this ratio is used as an array of the typical characteristics of the seismic waveform, and is assigned numbers 1, 2, 3, 4, and 5 in sequence, such as Figure 1 As shown, Figure 1 These are the five different seismic waveform characteristics corresponding to the five lithologic combinations of underwater distributary channels. This figure is a model diagram that schematically describes the five basic seismic reflection waveforms corresponding to different sedimentary combinations of underwater distributary channels, which can be used as a model channel for neural network analysis.

[0034] (3) Perform a gradient transformation on each seismic trace within the time window corresponding to q4-I on the seismic profile, i.e., G = dx / dy, where G is the gradient transformation value, dx is the amplitude of the next sample point minus the amplitude of the previous sample point, and dy is the time of the next sample point minus the time of the previous sample point. The ratio of the two is used as a description of the seismic waveform change, and the sequence of this ratio is used as an array of the seismic waveform characteristics to form a gradient change set for each seismic trace;

[0035] (4) Using the gradient changes of the typical characteristics of the seismic waveforms corresponding to the five lithologic combinations in step (2) as input channels, the gradient change set of each seismic channel in step (3) is normalized and corrected and subjected to neural network analysis. The five typical characteristics of the seismic waveforms corresponding to the lithologic combinations of the q4-I underwater distributary channel are classified and assigned values ​​according to their similarity. The five characteristics are assigned numbers 1, 2, 3, 4, and 5 respectively, and a plane map containing five colors is generated, as shown in FIG. Figure 2 As shown in the figure, this figure shows the planar distribution of five types of seismic waveforms obtained by summarizing and classifying seismic reflection waveforms and calculating them using a neural network algorithm in a certain layer of underwater distributary channel sedimentary environment in a certain work area in Songnan. It will eventually be used as a constraint condition to predict the distribution of underwater distributary channel sand bodies in the target layer.

[0036] (5) Based on the well-seismic calibration, the logging curve model constraint inversion is carried out for the target layer q4-I underwater distributary channel, and the inversion plane distribution data set of the target layer is extracted, such as Figure 3 As shown in the figure, this is a planar distribution diagram of the characteristic parameter curve values ​​extracted by logging curve model constraint inversion in a certain layer of underwater distributary channel sedimentary environment in a certain work area in Songnan. It will eventually serve as the basic map of the distribution of underwater distributary channel sand bodies and participate in the prediction process.

[0037] (6) The logging curve model constraint inversion plane map of the target layer extracted in step (5) is normalized with the seismic waveform feature similarity plane distribution map generated in step (4), and a mathematical operation is performed. The formula is z = x*y, where z is the final result, x is the normalized logging curve model constraint inversion plane data set, and y is the normalized seismic waveform feature similarity plane distribution data set. The final logging curve model constraint inversion and underwater distributary channel correlation plane distribution map is obtained to achieve the prediction of underwater distributary channel sand bodies, such as Figure 4 The figure shows the predicted thickness distribution of the final underwater distributary channel sand body.

[0038] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed in the present invention, who makes equivalent replacements or changes based on the technical solutions and concepts of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for predicting underwater diversion channel sand bodies based on seismic waveforms, characterized in that: The steps include: S1. Analyze the oil-producing strata revealed by drilling and clarify the location of underwater distributary channel sand bodies on the seismic profile; S2. Analyze the typical characteristics of seismic waveforms corresponding to different lithologic combinations; S3, performing gradient transformation on each seismic trace within the time window corresponding to the target layer on the seismic section; S4, using the gradient changes of the seismic waveform characteristics corresponding to the n lithologic combinations in step S2 as input traces, performing normalization correction and neural network analysis on the gradient change set in step S3; S5. Performing well logging curve model constrained inversion on the target layer segment and extracting an inversion plane distribution data set of the target layer segment; S6. Normalize the inversion plane map of the target layer segment extracted in step S5 with the plane distribution map of the similarity of seismic waveform characteristics generated in step S4, and perform mathematical operations.

2. The method for predicting underwater distributary channel sand bodies based on seismic waveforms according to claim 1, characterized in that: In step S1, the oil-producing strata revealed by drilling are analyzed, and the sedimentary range of the underwater distributary channel is determined through curve characteristics and lateral comparative analysis. The three-dimensional seismic data of the area are used to establish a seismic work area, and the well-seismic calibration is used to determine the well-seismic time-depth relationship, further clarifying the position of the underwater distributary channel sand body on the seismic profile.

3. The method for predicting underwater distributary channel sand bodies based on seismic waveforms according to claim 1, characterized in that: In step S2, the typical characteristics of the seismic waveforms corresponding to different lithologic combinations are analyzed, and the number of types is recorded as n. Gradient transformation is performed on the seismic traces of these different characteristics, i.e., G=dx / dy, where G is the gradient transformation value, dx is the amplitude of the next sample point minus the amplitude of the previous sample point, and dy is the time of the next sample point minus the time of the previous sample point. The ratio of the two is used as a description of the change in the seismic waveform, and the sequence of this ratio is used as an array of the typical characteristics of the seismic waveform, and each array is assigned an independent natural number identifier from 1 to n.

4. The method for predicting underwater diversion channel sand bodies based on seismic waveforms according to claim 3, characterized in that: In step S3, a gradient transformation is performed point by point on each seismic trace within the time window corresponding to the target layer segment on the seismic profile, that is, G=dx / dy, where G is the gradient transformation value, dx is the amplitude of the next sample point minus the amplitude of the previous sample point, and dy is the time of the next sample point minus the time of the previous sample point. A gradient change set of each seismic trace is formed and used as an array in subsequent calculations.

5. The method for predicting underwater diversion channel sand bodies based on seismic waveforms according to claim 4, characterized in that: In step S4, the gradient changes of the seismic waveform characteristics corresponding to the n lithologic combinations in step S2 are used as input channels, and the gradient change set in step S3 is normalized and corrected and subjected to neural network analysis. According to the n lithologic combinations corresponding to the underwater distributary channels, they are classified and assigned values ​​based on the similarity of the seismic waveform characteristics, and are assigned with increasing natural numbers 1-n, respectively, and the number of the lithologic combinations is kept consistent with the number of the lithologic combinations corresponding to the underwater distributary channels.

6. The method for predicting underwater distributary channel sand bodies based on seismic waveforms according to claim 5, characterized in that: In step S5, based on the completion of well seismic calibration and stratigraphic interpretation, well logging curve connection analysis is carried out according to the geological characteristics of the underwater distributary channel sand body deposition, and a reasonable geological model is established. Well logging curve model constrained inversion is carried out for the target layer segment, and the inversion plane distribution data set of the target layer segment is extracted.

7. The method for predicting underwater distributary channel sand bodies based on seismic waveforms according to claim 6, characterized in that: In step S6, the inversion plane map of the target layer segment extracted in step S5 is normalized with the plane distribution map of the seismic waveform characteristics similarity generated in step S4, and a mathematical operation is performed. The formula is z=x*y, where z is the final result, x is the normalized logging curve model constrained inversion plane data set, and y is the normalized seismic waveform characteristics similarity plane distribution data set.

8. The method for predicting underwater distributary channel sand bodies based on seismic waveforms according to claim 7, characterized in that: The final well logging curve model constraint inversion and underwater distributary channel correlation plane distribution map are obtained to realize the prediction of underwater distributary channel sand bodies.

Images