Seismic phase reliability evaluation method and evaluation system

Abstract

The present invention proposes a seismic phase reliability evaluation method and evaluation system. The evaluation method includes: comparing the well logging curve with the lithology of the target layer, selecting the target layer sensitive response well logging curve Q; performing synthetic recording waveform experimental analysis after removing the target layer data from the well logging data, and obtaining the waveform change rate parameter C; comparing the seismic waveform with the well logging synthetic recording waveform, and obtaining the first waveform matching relationship E; comparing the well logging acoustic wave curve waveform with the sensitive response well logging curve Q waveform, and obtaining the second waveform matching relationship F; based on the waveform change rate parameter C, the first waveform matching relationship E and the second waveform matching relationship F, calculating the seismic phase reliability factor K. The present invention provides a set of technical processes for seismic phase reliability evaluation methods other than the blind well verification method for the reliability of seismic phase calculation results, and can overcome the shortcomings of many blind well verification methods, thereby guiding the rational application of seismic phases in the later stage, and providing basic data for the accuracy of oil and gas exploration.

Description

Technical Field

[0001] The present invention belongs to the field of oil and gas geophysical exploration, and more specifically, relates to a seismic phase reliability evaluation method and evaluation system. Background Art

[0002] As an important technology of geophysical exploration technology, seismic phase has played a lot of roles in oil and gas reservoir characterization and oil and gas exploration, and has become an important technology in the field of oil exploration. Seismic phase also plays a very important role in geological research, especially in identifying sedimentary phase structure identification and boundary characterization. Seismic phase is the sum of the manifestations of sedimentary phase on the seismic profile, and is the seismic characteristics formed by the sedimentary environment. In the exploration and development of underground sedimentary minerals such as oil and coal, seismic phase is one of the bridges connecting seismic data and geological data, and is of great significance.

[0003] Seismic phase technology can also be called waveform classification technology. At present, the main features of waveform classification technology are: (1) Multidisciplinary characteristics, involving geography, computing, bionics and other disciplines. Make full use of seismic data information to divide and identify seismic phases, reduce the impact of human intervention, and provide a more reliable analysis basis for oil and gas reservoir prediction and geological structure analysis of rock formations; (2) Human participation is becoming less and less, mainly using computer processing to improve the efficiency of seismic phase division, reduce the impact of human factors on the results, and reduce the labor cost in seismic phase division and identification. The basic calculation methods of seismic phases are mostly derived from artificial intelligence algorithms.

[0004] The study of seismic phases began in the late 1970s. Widrow et al. proposed a weight adaptive theory with continuous value in 1962; then Minskey and Papert collaborated on < <perceptrons>>The book "Self-Organizing Map Neural Networks" was published in 1969; Finnish scientist Kohonen proposed the theory of self-organizing map neural networks in 1972; Fukushima proposed the cognitive machine principle of neural networks in 1975; Grossberg proposed the adaptive resonance mechanism of artificial neural networks in 1976. Hopfield proposed the HNN model in 1982, introduced the energy function theory into the HNN model, and obtained the method of judging the stability of artificial neural networks and the implementation of electronic circuits. This model greatly promoted the development of artificial neural network theory and then promoted the promotion of seismic phase technology. Hinton proposed the Boltzmann machine model in 1984. This method borrowed the simulated annealing technology in statistical physics to ensure that the learning process of artificial neural networks can converge globally. In the following period of time, the waveform classification method based on artificial neural networks has achieved rapid development and has been applied to many fields.

[0005] Around 2000, three seismic phase division methods, namely waveform classification, seismic structural attribute mapping and seismic geomorphology, began to appear in foreign countries. At this time, waveform classification became one of the main methods for seismic phase division abroad. In terms of the corresponding achievements in seismic research in China, Duan Yushun et al. proposed an automatic identification method and application of seismic phases in 2004, which can automatically identify seismic phases. Chen Fanghong et al. proposed a three-dimensional seismic phase analysis based on visualization in 2005 and applied it to the identification of sedimentary phases. Deng Chuanwei et al. proposed the application of waveform classification technology in reservoir microphase prediction in 2008, and used waveform classification algorithms to identify microseismic phases. In 2009, Iván Dimitri Marroquín et al. proposed waveform classification based on visualization data mining, and used it to compare with the results of traditional supervised waveform classification methods. This method shows that visualization data mining methods can also perform good waveform classification of seismic signals. In 2010, Zhang Linke et al. proposed a seismic image analysis idea based on seismic attributes. Liu Qingmin et al. proposed an empirical mode decomposition-based seismic phase analysis technique in 2010, and introduced the empirical mode decomposition method into seismic phase analysis. Atishroy Norman et al. analyzed the seismic phase distribution in the latent space in 2013.

[0006] In the past decade, the main research on seismic phases has actually been concentrated in the application field. In order to characterize the seismic sedimentary characteristics of different regions and different strata in different blocks of multiple oil fields, targeted application methods have been proposed for the characteristics of their respective regional strata in the application of seismic phases. In 2001, Zhao Limin and others applied the waveform classification method in the prediction study of Dawangzhuang concealed oil reservoirs in the Jizhong exploration area to improve the consistency rate of reservoir prediction. In 2004, Zeng Hongliu conducted an experiment on a certain research area, which included different river phases (direct current river, network river, meandering river). Since the seismic waveform characteristics in the area are relatively similar, the waveform classification method cannot be used to distinguish them well. Finally, only the geomorphic imaging method can better divide these three types of river channel deposits. It is concluded that the waveform classification method is a seismic phase division method based on waveform characteristics, which has the characteristics of simple classification and mature algorithm, and is suitable for the division of large-scale sedimentary phase belts. In 2007, Yin Jifeng and others began to apply waveform classification technology to the prediction of bioherm gas reservoirs in eastern Sichuan. He also used the neural network waveform classification technology to classify the waveforms of the three-dimensional seismic data in the eastern Sichuan region. The three seismic phases divided according to the waveform classification results were more accurate than the boundaries of the sedimentary phases studied by previous researchers, and the distribution range of bioherms was successfully predicted. By 2008-2010, waveform classification technology was widely used, and good results were achieved in various regions. At this time, Yang Zhanlong and others proposed a reclassification study on seismic waveform classification, which can effectively screen and quickly approach favorable exploration targets.

[0007] In order to further clarify the geological significance of the lateral changes of meaningful seismic signals, it is necessary to carry out reclassification research in the preferred favorable seismic facies type to clarify the details of geological changes in favorable exploration target areas and provide a direct basis for well deployment. Subsequently, in 2011, Zhang Wenqi et al. analyzed and studied the Daniudi gas field case. Based on the division of the stratigraphic framework, combined with the production information of the production wells, they conducted waveform difference analysis on the reservoir and obtained seismic waveform parameters with higher gas sensitivity. They analyzed and reconstructed the seismic waveform sensitivity information and constructed 7 new waveform difference parameters to establish the corresponding relationship between reservoirs with different production capacities and seismic waveforms. Zhang Shuai (2013) proposed a targeted application model for seismic facies for the sand bodies of the phreatic lake basin, Jiang Qingchun (2012) for the Pubei area, She Gang (2012) for the thin sandstone reservoirs in northern Hubei, Wang Jincheng (2011) for the sedimentary system of the Kongsan section of the Huanghua Depression, and Sun Jing (2013) for the Cretaceous Qingshuihe Formation on the northwest margin of the Junggar Basin.

[0008] The methods of seismic phase validity analysis mentioned in the above literature are still relatively simple, and the blind well verification method is mainly used, that is, when there are many wells in the target layer, some wells are used as constraints to participate in the seismic phase calculation. The calculated seismic phase results are compared with the wells that are not involved in the calculation (i.e., blind wells). The coincidence rate between the blind well and the seismic phase results is used as the reliability parameter of the seismic phase. This conventional blind well verification method has certain defects: on the one hand, when there are few wells (for example, new exploration blocks or new formations often have few wells, and the few wells are often far apart), the few wells are often all used to constrain the calculation of seismic phases, and then lack blind well constraints. At this time, if only some wells are used to calculate other wells for verification, there will be fewer well constraints, resulting in inaccurate seismic phases, and at the same time, the blind wells are often far away from the wells involved in the calculation, which makes the verification meaningless; on the other hand, the blind well verification method does not start from the basic principles of seismic phases and sedimentary phases, but only evaluates the accuracy of seismic phases from the application level of whether the results of seismic geology are consistent, and cannot provide a sufficiently convincing theoretical basis; on the other hand, this method lacks analysis of seismic phase classification and key parameters of seismic phases. There are many methods for calculating seismic phases, and the parameters involved include time window, number of waveform classifications, applicability of calculation methods, etc. This verification method ignores the evaluation of such parameters.

[0009] The present invention proposes a set of solutions to the above-mentioned deficiencies in the accuracy evaluation of seismic phases. Summary of the invention

[0010] The present invention provides a set of evaluation methods for the reliability of seismic phase calculation results, overcomes the shortcomings of the traditional blind well verification method mentioned above, and further guides the rational application of seismic phases, providing basic data for the accuracy of oil and gas exploration.

[0011] The present invention provides a set of evaluation methods for the reliability of seismic phase calculation results based on waveform basic data and seismic phase basic calculation parameters. When the seismic phase waveform approximation theory is applied, the main basic parameters involved in calculating the seismic phase include layer accuracy, time window selection based on the layer for calculating the waveform length, number of waveform classifications, etc. Layer accuracy belongs to the evaluation category of layer interpretation work, which is not involved in the present invention. The present invention proposes a targeted optimization plan for the time window and waveform classification number therein. When applying seismic phases, it involves many aspects such as well logging, geological deposition, and waveforms. The present invention also proposes a targeted evaluation method for these contents.

[0012] According to one aspect of the present invention, there is provided a method for evaluating seismic phase reliability, comprising:

[0013] Compare the logging curve with the lithology of the target layer and select the sensitive response logging curve Q of the target layer;

[0014] After removing the target layer data from the logging data, the synthetic recording waveform experimental analysis was performed to obtain the waveform change rate parameter C;

[0015] Compare the seismic waveform with the well logging synthetic record waveform to obtain a first waveform matching relationship E;

[0016] Comparing the waveform of the logging acoustic wave curve with the waveform of the sensitive response logging curve Q to obtain a second waveform matching relationship F;

[0017] Based on the waveform change rate parameter C, the first waveform matching relationship E and the second waveform matching relationship F, the seismic phase reliability factor K is calculated.

[0018] Furthermore, the seismic phase reliability factor K=C*E*F, wherein the value range of K is [0, 1], and the closer K is to 1, the higher the reliability of the seismic phase representation of the target layer.

[0019] Furthermore, the waveform change rate parameter C=Ab*At, the value range of C is [0, 1], wherein At is the number of wavelength periods, and Ab is the ratio of the maximum amplitude value change.

[0020] Furthermore, At=A / T, A is the time range of waveform change, and T is the period of a single wavelength of the seismic wave where the target layer is located.

[0021] Furthermore, Ab=B / BB, where B is the maximum amplitude value within the variation range before removing the target layer, and BB is the maximum amplitude value within the variation range after removing the target layer.

[0022] Further, obtaining the first waveform matching relationship E includes: selecting the well logging synthetic record in the time range A of the target layer and comparing it with the seismic trace waveform in the simultaneous window range of the point;

[0023] E=E2 / E1, the value range of E is [0, 1], where E1 is the amplitude change rate within the time range A of the well logging synthetic record, E2 is the amplitude change rate within the time range A of the seismic waveform, and E1>E2.

[0024] Further, the second waveform matching relationship F is calculated using the ratio of the amplitude change rates;

[0025] F=|F2 / F1|, the value range of F is [0, 1], F1 is the amplitude change rate of the sensitive response logging curve Q within the time range A, F2 is the amplitude change rate of the logging acoustic wave waveform within the time range A, and the absolute value of F1 is greater than F2.

[0026] Furthermore, it also includes selecting accurate time window parameters:

[0027] If only the seismic phase within a certain time window range above and below a single layer is to be analyzed, then the time range A is selected as the time window;

[0028] If you need to make an interlayer seismic phase of two layers, just select these two layers, one as the top and the other as the bottom, and there is no need to drift the time window up and down.

[0029] Furthermore, it also includes selecting the number D of waveform classifications;

[0030] D>=DD, DD is the number of sedimentary microfacies interpreted by well logging and geological sedimentation.

[0031] According to another aspect of the present invention, there is provided a seismic phase reliability evaluation system, comprising:

[0032] A memory storing executable instructions;

[0033] A processor runs the executable instructions in the memory to implement the seismic phase reliability evaluation method.

[0034] The present invention starts from basic theoretical data and basic calculation parameters, optimizes and calculates the basic calculation parameters of seismic phases, follows the principle of integration of seismic, logging and sedimentation, evaluates the reliability of the interrelationship between the three data, and finally calculates the seismic phase reliability factor K, providing a set of evaluation methods for the reliability of seismic phase calculation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The above and other objects, features and advantages of the present disclosure will become more apparent through a more detailed description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, wherein like reference numerals generally represent like components in the exemplary embodiments of the present disclosure.

[0036] Figure 1 The figure is a flow chart of the seismic phase reliability evaluation method according to the present invention.

[0037] Figure 2 The waveform comparison diagrams are of the four target layers before and after the sand removal experiment in the well according to the embodiment of the present invention.

[0038] Figure 3 This is a comparison diagram of the synthetic recording waveform of the sand 1 section according to an embodiment of the present invention and the seismic waveform of the corresponding depth section of the well point.

[0039] Figure 4 It is a comparison diagram of the AC curve waveform of the sand 1 section and the GR curve waveform of the well logging according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0041] The present invention starts from the comparison of basic waveform data of seismic and well logging and the basic calculation parameters of seismic phases, follows the principle of integration of seismic, well logging and sedimentation, evaluates the correlation between the three data, and then calculates the reliability of seismic phase characterization. The relevant parameters A, At, Ab, C, D, E, F, K are designed, and the seismic phase reliability factor K is finally calculated, which provides a set of technical processes for seismic phase reliability evaluation methods other than the blind well verification method for the reliability of seismic phase calculation results, and can overcome the shortcomings of many blind well verification methods, thereby guiding the rational application of seismic phases in the later stage and providing basic data for the accuracy of oil and gas exploration.

[0042] like Figure 1 As shown, the present invention discloses a method for evaluating the reliability of seismic phases, comprising:

[0043] Compare the logging curve with the lithology of the target layer and select the sensitive response logging curve Q of the target layer;

[0044] After removing the target layer data from the logging data, the synthetic recording waveform experimental analysis was performed to obtain the waveform change rate parameter C;

[0045] Compare the seismic waveform with the well logging synthetic record waveform to obtain a first waveform matching relationship E;

[0046] Comparing the waveform of the logging acoustic wave curve with the waveform of the sensitive response logging curve Q to obtain a second waveform matching relationship F;

[0047] Based on the waveform change rate parameter C, the first waveform matching relationship E and the second waveform matching relationship F, the seismic phase reliability factor K is calculated.

[0048] More preferably, the specific steps may include:

[0049] First, compare the logging curve with the lithology of the target layer: select the sensitive response logging curve Q of the target layer.

[0050] There are many types of logging curves, and it is necessary to select the logging curves that are more sensitive to the target layer in order to effectively identify the target layer. This step can use the results of conventional logging interpretation.

[0051] Next, a synthetic record experiment is performed on the target layer of the logging data: a waveform change rate parameter C is defined, and the value range of C is [0, 1].

[0052] In the well logging curve, synthetic records can be made based on the existing well logging curves. In order to clarify the impact of this target layer on the seismic waveform, the well curve within the depth range of this target layer section can be directly intercepted and straightened. By comparing the changes in the synthetic records before and after straightening, it can be seen that the scope and magnitude of the impact of the existence of this target layer on the synthetic record waveform can be seen. There are two results:

[0053] 1) If the synthetic record changes, it can be directly concluded that the seismic waveform has sensitive response characteristics to this target layer. Let the waveform change time range be A, let the period of a single wavelength of the seismic wave in the target layer be T, and define the parameter At, At = A / T, At is the number of wavelength periods. Within the waveform change range, let the maximum amplitude value in the time range A before the target layer is B, and after the change, the maximum amplitude value in the time range A is BB, define Ab, Ab = B / BB, then Ab is the ratio of the maximum amplitude value change.

[0054] Define the waveform change rate parameter C, C = Ab*At, the larger the C value is, the stronger the response of the seismic waveform to the target layer is, and therefore the more reliable the analysis of the target layer based on the seismic waveform is (including seismic phase).

[0055] 2) If the synthetic record does not change, it can be directly concluded that the seismic waveform is insensitive to the target layer and cannot reflect the properties of the target layer. Therefore, the analysis of the target layer based on the seismic waveform (including any one or any combination of seismic amplitude, frequency, and phase attributes) is unreliable (including seismic phase, in this case C = 0).

[0056] Next, we can evaluate and analyze the factors of the earthquake phase time window range and select accurate time window parameters:

[0057] If only the seismic phases within a certain time window range above and below a single layer are to be analyzed, the above-mentioned time range A is selected as the time window.

[0058] If it is required to make two layers of interlayer seismic phases, then select these two layers, one as the top and the other as the bottom, and there is no need to drift the time window up and down.

[0059] Next, we can do a waveform classification number selection analysis: let the number of waveform classifications be D.

[0060] Assume that the number of sedimentary microfacies interpreted by well logging and geological sedimentary interpretation is DD. The number of waveform classifications D>=DD is sufficient. Because the waveform change sensitivity of seismic phases is affected by changes in multiple factors such as sedimentary microfacies, lithology, and structure, the seismic waveform sensitivity is generally greater than that of well logging and geological analysis, so D>=DD can ensure that the waveform classification of seismic phases includes the classification of sedimentary microfacies as much as possible.

[0061] Next, the seismic waveform is compared with the synthetic record waveform: define the waveform matching relationship E, and the value range of E is [0, 1].

[0062] Select the synthetic logging record in the time window A range of the target layer and compare it with the seismic waveform in the window range of the point. The higher the degree of agreement between the two, the more effective the analysis of the target layer based on the seismic waveform. This degree of agreement can be expressed by a variety of parameters that can reflect the degree of agreement between the two waveforms, such as the amplitude change rate ratio, waveform change rate, phase change rate ratio, etc., and let this parameter be E. However, it should be noted that the value should be within the range of [0, 1]. The closer to 1, the higher the degree of agreement between the two waveforms.

[0063] Example: Using the ratio of amplitude change rates to find E

[0064] Assume that the amplitude change rate of the synthetic record within the time range A is E1, the amplitude change rate of the seismic waveform within the time range A is E2, and E1>E2, then E=E2 / E1.

[0065] Next, the waveform of the well logging acoustic wave AC (acoustic) curve is compared with the waveform of the sensitive well logging curve Q: the waveform matching relationship F is defined, and the value range of F is [0, 1].

[0066] The synthetic logging waveform is generally derived from the convolution calculation of the sub-wave and the reflection coefficient. The reflection coefficient is calculated from the AC logging curve, and the reflection coefficient has a relatively good positive correlation with the AC logging curve. The sensitive logging curve Q has a good recognition ability for the lithology of the target layer. If the AC logging curve and the Q curve also have a relatively good linear correspondence, it means that the synthetic record and the Q curve have a good correspondence, which further indicates that the seismic waveform has a good response relationship with the target layer. This waveform fit is similar to the waveform fit calculation method in step 5. This fit value can also be expressed by a variety of parameters that can reflect the fit of the two waveforms, such as the amplitude change rate ratio, waveform change rate, phase change rate ratio, etc., and this parameter is set to F. It should be noted that because the AC curve and the Q curve can be either linearly positively correlated or linearly negatively correlated, the value should be within the range of [-1, 1]. However, in order to calculate the final seismic reliability factor, the absolute value of the F value here should be taken. The closer F is to 1, the higher the fit of the two waveforms.

[0067] Example: Using the ratio of amplitude change rates to find F

[0068] Assume that the amplitude change rate of curve Q in time range A is F1, the amplitude change rate of curve AC in time range A is F2, and the absolute value of F1 is greater than F2, then F=|F2 / F1|.

[0069] Finally, calculate the seismic phase reliability factor K: define the reliability factor K, K = C*E*F.

[0070] From the above definition of K, it can be seen that the calculation parameters C, E, and F of the reliability factor K of the seismic phase are all within the range of [0, 1], so K is also within this numerical range. The closer K is to 1, the higher the reliability of the seismic phase characterization of the target layer. At the same time, from the above definition of K, it can be seen that the calculation of the reliability factor of the seismic phase is the result of the effective integration of seismic, well logging, and geological sedimentary interpretation.

[0071] To facilitate understanding of the solution and effects of the embodiments of the present invention, a specific application example is given below. Those skilled in the art should understand that this example is only for facilitating understanding of the present invention, and any specific details thereof are not intended to limit the present invention in any way.

[0072] There are only two wells in the target layer of a certain oil field block. It is necessary to conduct a seismic phase reliability evaluation on the results calculated based on the seismic phase waveform, and then determine the distribution range and boundary of the sand body deposition.

[0073] Step 1: Comparison between logging curve and target layer lithology: Since the predicted target layer is a sandstone lithology target layer, the target lithology that needs to be characterized is sandstone. From the known logging interpretation, it can be seen that the logging gamma (GR) curve has a better effect on distinguishing sandstone and mudstone. Therefore, the sensitive response Q curve here is the logging GR curve.

[0074] Step 2: Perform synthetic logging experiment analysis on the target layer of logging data: Calculate the waveform change rate parameter C.

[0075] like Figure 2 As shown in Figure 2, there are four target layers in this area, located at 1540ms, 1575ms, 1595ms and 1620ms respectively. The thickness of the sand body is shown in Figure 2. Figure 2 As shown in the middle gray rectangle. The four target layers from top to bottom are Sand 1, Sand 2, Sand 3 and Sand 4. The sand removal response experiment on the well curve was carried out separately for each layer of sand, and the synthetic record curves before and after sand removal are marked in the figure. The first column of curves Track9 is a comparison of synthetic records before and after sand removal 1. It can be found that the two curves change very obviously, that is, the existence of sand bodies has a great influence on the waveform, and the seismic waveform has a good response to sand bodies. The waveform change rate parameter C1 of this sand 1 is calculated to be 0.63. Similarly, the waveform change of Sand 2 is large, while the waveform change of the synthetic record of Sand 3 and Sand 4 before and after sand removal is very small. C2 = 0.55 for Sand 2, C3 = 0.05 for Sand 3, and C4 = 0.04 for Sand 4.

[0076] It can be seen that the seismic waveforms of Sand 1 and Sand 2 segments respond more obviously to the sand bodies; while the existence of the sand bodies of Sand 3 and Sand 4 has little effect on the seismic waves, and the synthetic recording waveforms change little before and after sand removal. Therefore, the sand body distribution range of the seismic phase calculated by Sand 3 and Sand 4 is very unreliable. Therefore, the seismic phase reliability factor of the target layer segments of Sand 3 and Sand 4 does not need to be calculated again, and it is directly concluded that the reliability of the seismic phase characterization of the sand body distribution range of the target layer segments of Sand 3 and Sand 4 is very low, and it is not worth providing a reference for subsequent work. The seismic phase evaluation work of Sand 1 and Sand 2 segments can continue.

[0077] For the convenience of illustration, this embodiment only obtains the reliability factor of sand 1.

[0078] Step 3: Evaluate and analyze the seismic phase time window factors, and then select the accurate time window parameter A.

[0079] Depend on Figure 2 From the first column of curves Track9, it can be found that the time window range of the inconsistent changes in the synthetic recorded waveform before and after sand removal is (1510ms, 1560ms), which is approximately 30ms above and 20ms below sand 1. Therefore, the range of parameter A is within the range of (-30ms, 20ms) based on the sand 1 layer.

[0080] Step 4: Analysis of waveform classification number selection: Calculate the number of waveform classifications D

[0081] Through well logging and sedimentary microfacies analysis of the area, eight types of sedimentary microfacies are mainly developed in the target layer of the sand 1 section, so the value of D can be D>=8. In this embodiment, the value of D is taken as 9.

[0082] Step 5: Compare the seismic waveform with the synthetic recorded waveform: Obtain the waveform matching relationship E

[0083] like Figure 3 As shown in the figure, the curve segment (1510ms, 1560ms) on the left is the synthetic logging waveform. The seismic waveform of the seismic data at the well point location and depth range is shown in the waveform in the figure on the right. The ratio of the amplitude change rates of the two is used to obtain E = 0.83.

[0084] Step 6: Compare the waveform of the well logging acoustic wave AC curve with the waveform of the well logging GR curve: obtain the waveform matching relationship F.

[0085] like Figure 4 As shown, the AC curve and the GR curve are smoothed respectively, and the resolution scale of the smoothed curves is close to the resolution scale of seismic waves. It should be pointed out that whether it is smooth or not, and the degree of smoothness, requires specific analysis of specific circumstances. The depth range of the sand 1 section is determined by the depth section defined by the two bottom horizontal lines. After the curves are smoothed, it can be seen that the two have a relatively good negative correlation. According to the calculation method of the present invention, the waveform change rate value of the two is calculated to be -0.57, and the absolute value is taken, F=0.57.

[0086] Step 7: Define the reliability factor K, K = C * E * F = 0.63 * 0.83 * 0.57 = 0.298

[0087] Example calculation results: The seismic phase calculations of Sand 3 and Sand 4 are unreliable, and the reliability factor of the sand body range represented by the seismic phase near the well in the Sand 2 layer is 0.298.

[0088] Those skilled in the art should understand that the purpose of the above description of the embodiments of the present invention is only to exemplarily illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any given examples.

[0089] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.< / perceptrons>

Claims

1. A method for evaluating the reliability of earthquake phases, characterized in that: include: Compare the logging curve with the lithology of the target layer and select the sensitive response logging curve Q of the target layer; After removing the target layer data from the logging data, the synthetic recording waveform experimental analysis was performed to obtain the waveform change rate parameter C; Compare the seismic waveform with the well logging synthetic record waveform to obtain a first waveform matching relationship E; Comparing the waveform of the logging acoustic wave curve with the waveform of the sensitive response logging curve Q to obtain a second waveform matching relationship F; Calculate the seismic phase reliability factor K based on the waveform change rate parameter C, the first waveform matching relationship E and the second waveform matching relationship F; The seismic phase reliability factor K=C*E*F, wherein the value range of K is [0, 1], and the closer K is to 1, the higher the reliability of the seismic phase representation of the target layer; Wherein, the waveform change rate parameter C=Ab*At, the value range of C is [0, 1], where At is the number of wavelength periods, and Ab is the ratio of the maximum amplitude value change; Wherein, obtaining the first waveform matching relationship E includes: selecting the well logging synthetic record of the target layer in the time range A and comparing it with the seismic trace waveform in the same time range A; E = E2 / E1, the value range of E is [0, 1], where E1 is the amplitude change rate within the time range A of the well logging synthetic record, E2 is the amplitude change rate within the time range A of the seismic waveform, and E1>E2; Wherein, the second waveform matching relationship F is calculated using the ratio of the amplitude change rates; F= |F2 / F1|, the value range of F is [0, 1], F1 is the amplitude change rate of the sensitive response logging curve Q within the time range A, F2 is the amplitude change rate of the logging acoustic wave waveform within the time range A, and the absolute value of F1 is greater than F2.

2. The earthquake phase reliability evaluation method according to claim 1, characterized in that: At=A / T, A is the time range of waveform change, and T is the period of a single wavelength of the seismic wave in the target layer.

3. The earthquake phase reliability evaluation method according to claim 1, characterized in that: Ab=B / BB, B is the maximum amplitude value within the change range before removing the target layer, and BB is the maximum amplitude value within the change range after removing the target layer.

4. The earthquake phase reliability evaluation method according to claim 1, characterized in that: Further includes selecting accurate time window parameters: If only the seismic phases within a certain time range above and below a single layer are to be analyzed, then the time range A is selected as the time window; If you need to make an interlayer seismic phase of two layers, just select these two layers, one as the top and the other as the bottom, and there is no need to drift the time window up and down.

5. The earthquake phase reliability evaluation method according to claim 1, characterized in that: Further comprising selecting the number D of waveform classifications; D>=DD, DD is the number of sedimentary microfacies interpreted by well logging and geological sedimentation.

6. A seismic phase reliability evaluation system, characterized in that: include: A memory storing executable instructions; A processor, wherein the processor runs the executable instructions in the memory to implement the seismic phase reliability evaluation method according to any one of claims 1 to 5.

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