Coal bed gas gas content prediction method based on cloud transformation

Abstract

The invention belongs to the technical field of seismic data interpretation in seismic exploration, and relates to a coal bed gas gas content prediction method based on cloud transformation. A coal bed gas gas content prediction method based on cloud transformation is characterized by comprising the steps of S1, well-seismic comprehensive calibration; s2, extracting wavelets; s3, carrying out PDF function analysis; s4, phase control geostatistics inversion is carried out; s5, the longitudinal wave impedance is converted into the Langmuir volume representing the gas content of the coal bed gas through cloud transformation, a three-dimensional data body of the Langmuir volume is obtained, the threshold value of the coal bed gas enrichment area is selected in combination with research on the condition of the selected area, and prediction of the coal bed gas enrichment area is carried out. According to the method, the reservoir space identification capability is improved, and the reservoir space distribution rule can be accurately predicted.

Description

Technical Field

[0001] The invention belongs to the technical field of seismic data interpretation in seismic exploration, and relates to a method for predicting the gas content of coalbed methane based on cloud transformation. Background Art

[0002] As one of the natural gas energy sources that can continue to increase production under the background of carbon peak and carbon neutrality, the resource-rich and economically exploitable areas and strata of coalbed methane have become a hot topic in the energy field. Coalbed methane-rich areas are the primary factor in the selection and evaluation of coalbed methane, and are also the material conditions for coalbed methane exploration and development. At present, the most commonly used post-stack inversion methods mainly include trace integral inversion, colored inversion, sparse pulse inversion, and geostatistical inversion. The basis for seismic inversion to find coalbed methane-rich areas is that the longitudinal wave impedance of coalbed methane-rich areas usually has obvious numerical differences from the surrounding strata. Therefore, a quantitative description of the distribution characteristics of coalbed methane can be achieved based on seismic inversion. However, due to geological factors, coal seams in many areas are usually thin (less than 10 meters). When solving the problem of predicting the distribution characteristics of such coalbed methane-rich areas, traditional trace integral inversion, colored inversion, and sparse pulse inversion are limited by various inversion methods themselves and are often not applicable. Summary of the invention

[0003] This application adopts the technical idea of ​​phase-controlled geostatistical inversion. Phase-controlled geostatistical inversion targets the control of sedimentary facies on reservoir development, establishes an inversion control model using constraints such as seismic attributes, improves the ability to identify reservoir space, and relies on the sensitivity of characteristic parameters to reservoirs to accurately predict the spatial distribution of reservoirs, especially in the quantitative prediction of the distribution law of coalbed methane-rich areas, which has certain technical advantages.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is as follows: A method for predicting gas content of coalbed methane based on cloud transformation includes: S1. Well seismic comprehensive calibration: well seismic calibration and drilling calibration synthesis are carried out in the selected area, and the correlation coefficient between the basic drilling data and the target layer section is above 85%; S2, wavelet extraction, extracting the actual well-side seismic wavelet from the well-seismic calibration, and fitting to obtain the average wavelet; S3, PDF function analysis, based on the longitudinal wave impedance and lithology information, the PDF functions of different lithologies are analyzed to obtain the PDF functions of different lithologies including coal seams, sandstone layers, and mudstone layers; S4. Phase-controlled geostatistical inversion: Conduct phase-controlled geostatistical inversion research by debugging inversion parameters to obtain geostatistical inversion results containing coalbed methane information; S5. Cloud transformation converts longitudinal wave impedance into Langmuir volume, which characterizes the gas content of coalbed methane, and obtains a three-dimensional data body of Langmuir volume. Combined with the situation in the selected research area, the threshold value of coalbed methane-rich area is selected to carry out the prediction of coalbed methane-rich area.

[0005] Further, the phase peak state of the seismic wavelet is consistent with the earthquake, and the similarity is greater than a first preset value; When used for well seismic calibration, the correlation is greater than a second preset value.

[0006] Furthermore, in the PDF function analysis, the PDF function is set in a hierarchical manner.

[0007] Furthermore, the specific method of the phase-controlled geostatistical inversion is: The vertical and horizontal ranges of the geological body are set through the variogram; The signal-to-noise ratio of the set value is selected to perform the evolution of the signal-to-noise ratio of the seismic data.

[0008] Furthermore, the specific method of converting the longitudinal wave impedance into the Langmuir volume characterizing the gas content of the coalbed methane by the cloud transformation is: ; in is the amplitude coefficient; is the cloud model function, For cloud model functions Expected value; For cloud model functions Entropy of For cloud model functions of super entropy; is the number of discrete concepts generated after transformation.

[0009] Compared with the prior art, the present invention has the following beneficial effects: The present invention transforms the longitudinal wave impedance cloud into a Langmuir volume that can directly characterize the gas content of coalbed methane, obtains a three-dimensional data body of the Langmuir volume, selects a suitable Langmuir volume threshold value according to the gas content of coalbed methane in known wells in the work area, and then extracts the plane thickness distribution characteristics of the coalbed methane enrichment area of ​​the target coal seam, so as to achieve accurate vertical and horizontal prediction of the gas content of coalbed methane. The method combining phase-controlled geostatistical inversion and cloud transformation can better achieve quantitative description of coalbed methane enrichment areas and achieve better application effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 is a flow chart of the method of the present invention; Figure 2 This is a schematic diagram of the comprehensive well-seismic calibration of the present invention; Figure 3 It is a superposition diagram of sub-waves of the side channels of multiple wells of the present invention; Figure 4 It is the well logging well point and high-speed layer thickness map of the present invention; Figure 5 It is a schematic diagram of the variogram analysis of the present invention; Figure 6 It is a signal-to-noise ratio analysis diagram of seismic data of the present invention; Figure 7 This is an analysis diagram of the relationship between longitudinal wave impedance and Langmuir volume of the present invention; Figure 8 is the Langmuir volume profile through the well; Fig. 9 It is the time-thickness map of the coalbed methane-rich area of ​​the first-time target coal seam; Fig.10 It is the time thickness map of the coalbed methane enrichment area of ​​the second time target coal seam; Fig.11 It is the time thickness map of the coalbed methane enrichment area of ​​the third time target coal seam. DETAILED DESCRIPTION

[0011] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0012] like Figure 1-Figure 9 As shown, the technical solution adopted by the present invention is as follows: A method for predicting gas content of coalbed methane based on cloud transformation includes: S1. Comprehensive well seismic calibration: well seismic calibration and drilling calibration synthesis are carried out in the selected area. The correlation coefficient between the basic drilling data and the target layer is above 85%.

[0013] S2. Wavelet extraction: extract the actual wellbore seismic wavelet from the well seismic calibration and fit it to get the average wavelet.

[0014] S3. PDF function analysis: PDF function analysis of different lithologies is performed based on longitudinal wave impedance and lithology information to obtain PDF functions of different lithologies including coal seams, sandstone layers, and mudstone layers.

[0015] S4. Phase-controlled geostatistical inversion: Through the debugging of inversion parameters, phase-controlled geostatistical inversion research is carried out to obtain geostatistical inversion results containing coalbed methane information.

[0016] S5. Cloud transformation converts longitudinal wave impedance into Langmuir volume, which characterizes the gas content of coalbed methane, and obtains a three-dimensional data body of Langmuir volume. Combined with the situation in the selected research area, the threshold value of coalbed methane-rich area is selected to carry out the prediction of coalbed methane-rich area.

[0017] like Figure 2 As shown in the figure, basic data such as logging and drilling are collected, and well seismic calibration is performed on the wells. From the calibration effect, the wells with a correlation coefficient with the target layer greater than 85% have a better calibration effect, and the calibration synthetic records of each well have a high correlation with the actual seismic trace.

[0018] like Figure 3 As shown, wavelet is the core of all technical parameters related to inversion prediction. Generally, wavelets are seismic wavelets of actual well bypass channels extracted from well seismic calibration. The wavelet phase peak state is selected to be consistent with the earthquake, and the similarity is greater than the first preset value; when used for well seismic calibration, the correlation is greater than the second preset value. The similarity is calculated by Euclidean distance, and the correlation is analyzed by NLP model. Finally, the average wavelet is selected from multiple wells as the inversion wavelet. Judging from the distribution morphology of the well bypass wavelets of 10 boreholes in the selected area, the wavelet morphology is good, the polarity is negative, and it is close to the theoretical wavelet morphology. like Figure 4 As shown in the figure, the PDF functions of different lithologies are analyzed based on the longitudinal wave impedance and lithology information, and the PDF functions of different lithologies including coal seams, sandstone layers, and mudstone layers are obtained. The inversion lithology classification data uses the logging interpretation results of the wells drilled in the selected area. The main lithologies are divided into three types: coal, sandstone, and mudstone. The lithology ratio is statistically calculated according to the lithology classification scheme, and the PDF function is set in layers. The key inversion parameters directly control the inversion results.

[0019] like Figure 5 As shown, the variogram controls the vertical and horizontal resolution of the inversion effect. The horizontal range determines the distribution range of the geological body, and the vertical range determines the resolution of the thickness of the geological body. Generally, the determination of the horizontal range requires the use of the results of deterministic inversion plus the researchers' geological understanding of the selected area for comprehensive judgment. Usually, seismic attributes are used for constraints, such as root mean square amplitude, to ensure the horizontal resolution. The vertical range is determined based on the resolution of the seismic data plus the sample analysis data of the well point measurement. The vertical range and horizontal range of the geological body are set by the variogram. After many rounds of debugging, the inventor uniformly uses the inversion parameters: vertical range 4ms, horizontal range 800m. The study found that some coal seams in the selected area are thinner and have stronger lateral heterogeneity. This parameter is consistent with the geological requirements in the selected area.

[0020] like Figure 6As shown in the figure, the signal-to-noise ratio of seismic data is also a key parameter of pre-stack inversion, which controls the proportion of seismic information introduced and the degree of constraint. The signal-to-noise ratio of the set value is selected to evolve the signal-to-noise ratio of seismic data. The seismic data used in this inversion is the data after the gather is optimized, and the signal-to-noise ratio is high, which is basically concentrated in the range of 9-12db. After analyzing the post-stack data, the application of the 10db signal-to-noise ratio parameter is the optimal solution.

[0021] Cloud transformation is a nonlinear random simulation method. For two parameter variables with nonlinear relationships, cloud transformation can transform one parameter variable into another parameter variable through probability field simulation method under the premise of following the nonlinear relationship between the two parameter variables.

[0022] The mathematical definition of cloud transformation is: the frequency distribution statistical function of a data attribute X in a given domain , according to the attribute value Frequency distribution Automatically generate several clouds with different granularity Each cloud represents a discrete, qualitative concept. Its mathematical expression is as follows: ; in is the amplitude coefficient; is the cloud model function, For cloud model functions Expected value; For cloud model functions Entropy of For cloud model functions of super entropy; is the number of discrete concepts generated after transformation.

[0023] like Figures 7 to 11 As shown in the figure, the present invention transforms the longitudinal wave impedance cloud into a Langmuir volume that can directly characterize the gas content of coalbed methane, and obtains a three-dimensional data body of the Langmuir volume. According to the gas content of coalbed methane in known wells in the selected area, an appropriate Langmuir volume threshold value is selected. For example, if a Langmuir volume of 5m 3 / t is the threshold value of the coalbed methane-rich area, and then the plane thickness distribution characteristics of the coalbed methane-rich area of ​​the target coal seam are extracted to achieve accurate vertical and horizontal prediction of the gas content of the coalbed methane.

[0024] Practice shows that the method combining phase-controlled geostatistical inversion and cloud transformation can better realize the quantitative description of coalbed methane enrichment areas and achieve good application results.

[0025] The present invention is suitable for quantitative distribution prediction of coalbed methane-rich areas in relatively thin or thick coal seams.

[0026] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting the gas content of coalbed methane based on cloud transformation, characterized in that: Included are: S1. Well seismic comprehensive calibration: well seismic calibration and drilling calibration synthesis are carried out in the selected area, and the correlation coefficient between the basic drilling data and the target layer section is above 85%; S2, wavelet extraction, extracting the actual well-side seismic wavelet from the well-seismic calibration, and fitting to obtain the average wavelet; S3, PDF function analysis, based on the longitudinal wave impedance and lithology information, the PDF functions of different lithologies are analyzed to obtain the PDF functions of different lithologies including coal seams, sandstone layers, and mudstone layers; S4. Phase-controlled geostatistical inversion: Conduct phase-controlled geostatistical inversion research by debugging inversion parameters to obtain geostatistical inversion results containing coalbed methane information; S5. Cloud transformation converts longitudinal wave impedance into Langmuir volume, which characterizes the gas content of coalbed methane, and obtains a three-dimensional data body of Langmuir volume. Combined with the situation in the selected research area, the threshold value of coalbed methane-rich area is selected to carry out the prediction of coalbed methane-rich area.

2. The method for predicting gas content of coalbed methane based on cloud transformation according to claim 1 is characterized in that: The phase peak state of the seismic wavelet is consistent with the earthquake, and the similarity is greater than a first preset value; When used for well seismic calibration, the correlation is greater than a second preset value.

3. The method for predicting gas content of coalbed methane based on cloud transformation according to claim 1, characterized in that: In PDF function analysis, the PDF function is set in a hierarchical manner.

4. The method for predicting gas content of coalbed methane based on cloud transformation according to claim 1, characterized in that: The specific method of the phase-controlled geostatistical inversion is: The vertical and horizontal ranges of the geological body are set through the variogram; The signal-to-noise ratio of the set value is selected to perform the evolution of the signal-to-noise ratio of the seismic data.

5. The method for predicting gas content of coalbed methane based on cloud transformation according to claim 1 is characterized in that: The specific method of converting the longitudinal wave impedance into the Langmuir volume representing the gas content of coalbed methane by cloud transformation is: ； in is the amplitude coefficient; is the cloud model function, For cloud model functions Expected value; For cloud model functions Entropy of For cloud model functions of super entropy; is the number of discrete concepts generated after transformation.

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