Waveform-curve dynamic matching based thin coal seam and parting inversion method and system

Abstract

The present application relates to oil and gas geophysical exploration technology field, disclose a kind of based on waveform-curve dynamic matching's thin coal seam and interburden inversion method and system, using the sliding time window waveform matching based on dynamic time warping (DTW) algorithm, while overcoming the waveform distortion caused by local formation velocity difference, mining the thin layer lithology change contained in seismic waveform, introduce the dual constraint weighted formula of fusion waveform similarity and spatial distance, with DTW similarity as the centralization weight of matching, and use spatial distance weight constraint to limit the spatial smoothness of inversion result, so that the inversion result can not only better fit all known well points, but also can more reliably predict the unknown stratum mutation zone between wells, finally, inversion result optimizes horizontal well trajectory design, successfully guides drilling trajectory to effectively avoid interburden development section in bimodal coal seam, propose a kind of can improve thin coal seam and interburden prediction accuracy seismic inversion method.

Description

Technical Field

[0001] This invention relates to the field of oil and gas geophysical exploration technology, and in particular to a method and system for inverting thin coal seams and interbedded rock based on waveform-curve dynamic matching. Background Technology

[0002] Seismic prediction methods for thin coal seams and interbedded rock can be mainly classified into several categories. First, model-based constrained sparse pulse inversion utilizes well logging impedance constraints to obtain impedance data volumes for interpretation. Second, geostatistical inversion combines geostatistical simulation with seismic inversion to generate multiple equally probable high-resolution lithological models. Third, conventional waveform indication inversion uses seismic waveform similarity to spatially infer well information. In addition, frequency division interpretation and attribute analysis, among other seismic attribute-based qualitative identification techniques, are also frequently used.

[0003] However, current technologies face significant limitations in application. Model-based inversion methods suffer from low resolution due to seismic frequency band limitations, insufficient resolution of thin interbedded layers (especially sub-meter-thin coal seams and interbedded rock), and are highly dependent on the initial model and wavelet, exhibiting strong ambiguity. Geostatistical inversion is computationally expensive, and variograms struggle to accurately characterize complex thin interbedded layers. Conventional waveform indication methods, which compare only one or a few fixed waveforms, present challenges in achieving accurate matching of local stratigraphic stretching / compression, failing to guarantee precise matching. Furthermore, attribute analysis methods are largely qualitative, making it difficult to achieve the precise characterization of lithological boundaries. These shortcomings prevent them from meeting the current requirements for fine-grained geology of coalbed methane / coal-rock gas, hindering their practical application.

[0004] Therefore, there is an urgent need to develop a seismic inversion method that can improve the prediction accuracy of thin coal seams and interbedded rock. Summary of the Invention

[0005] This invention provides a method and system for inverting thin coal seams and interbedded rock based on waveform-curve dynamic matching, aiming to solve at least one of the above-mentioned technical problems.

[0006] To achieve the above objectives, this invention provides a method for inverting thin coal seams and interbedded rock based on waveform-curve dynamic matching, the method comprising the following steps:

[0007] Obtain logging data from several drilled wells within the work area, perform correction and standardization processing on the logging data, calculate the wave impedance curve of each drilled well in the time domain, and generate target curve data for distinguishing between thin coal seams and interbedded rock.

[0008] Acquire the wellbore seismic trace data and estimated wavelet data of the drilled well, perform well-seismic calibration operation on the target curve data to determine the top and bottom boundary times of the target segment, and perform time domain extension operation based on the top and bottom boundary times to generate the working segment.

[0009] Within the working section, seismic waveform data corresponding to each drilled well is extracted, and the seismic waveform data and target curve data are aligned in time and stored in pairs to construct a waveform-impedance curve database.

[0010] For any seismic trace to be inverted in the 3D seismic data volume, the seismic waveform data to be inverted extracted within the working segment is segmented based on the sliding time window strategy. The dynamic time warping algorithm is used to calculate the similarity between the segmented seismic waveform data to be inverted and the drilled well seismic waveform data in the database. The top N drilled wells with the highest similarity metric value are selected as the sample well group.

[0011] Based on the wave impedance curves corresponding to the sample well group, the similarity metric, and the spatial distance data between the sample wells and the seismic trace to be inverted, the fusion weight is calculated, and a weighted fusion operation is performed on the wave impedance curves of the sample well group to generate the wave impedance inversion curve of the seismic trace to be inverted.

[0012] Based on the impedance inversion curves of all seismic traces to be inverted, a three-dimensional impedance volume is established, and a coal seam structure plan view is generated using the three-dimensional impedance volume.

[0013] Optionally, logging data from several drilled wells within the work area are acquired, and the logging data is corrected and standardized to calculate the wave impedance curves of each drilled well in the time domain and generate target curve data for distinguishing between thin coal seams and interbedded rock. Specifically, this includes:

[0014] Acquire sonic transit time logging data and density logging data from several drilled wells within the work area;

[0015] Environmental correction and standardization are performed on the sonic transit time logging data and the density logging data. Based on the standardization results, the wave impedance sequence is calculated, and the wave impedance sequence is resampled at a uniform sampling interval to generate a wave impedance curve.

[0016] The wave impedance curve is used as the target curve data for distinguishing between thin coal seams and interbedded gangue.

[0017] Optionally, the wellbore seismic trace data and estimated wavelet data of the drilled well are acquired, and wellbore calibration calculations are performed on the target curve data to determine the top and bottom boundary times of the target layer, specifically including:

[0018] Acquire the well-through seismic trace data and estimated wavelet data of the drilled well, convert the wave impedance curve into a reflection coefficient sequence, and perform a convolution operation in combination with the estimated wavelet to generate a synthetic seismic record.

[0019] Perform time alignment calculations on the synthetic seismic records and the well-pass seismic trace data, and output the calibration alignment results with the correlation coefficient maximized.

[0020] Based on the calibration and alignment results, the top and bottom boundary times of the target layer are determined.

[0021] Optionally, a time domain expansion operation is performed based on the top and bottom bound times to generate working segments, specifically including:

[0022] Calculate the time thickness of the target layer segment based on the top and bottom boundary times;

[0023] Based on the preset edging factor and the target layer time thickness, an expansion operation is performed on the top boundary time and the bottom boundary time to generate the top boundary time and the bottom boundary time of the working layer.

[0024] The working segment is determined based on the top boundary time and the bottom boundary time of the working segment.

[0025] Optionally, within the working section, seismic waveform data corresponding to each drilled well is extracted, and the seismic waveform data and target curve data are aligned in execution time and then stored in pairs to construct a waveform-impedance curve database, specifically including:

[0026] Based on the well vibration calibration alignment results, the depth domain to time domain mapping transformation is performed on the wave impedance curves of each drilled well to obtain the time domain wave impedance curves.

[0027] Seismic waveform data within the working segment is extracted from the three-dimensional seismic data volume along the trajectory of each drilled well;

[0028] Within the working segment, seismic waveform data and time-domain impedance curves are respectively extracted and time sampling points are aligned to generate well point waveform-curve data pairs.

[0029] The waveform-curve data of each well point are stored according to the well identifier to form a waveform-impedance curve database.

[0030] Optionally, for any seismic trace to be inverted in the 3D seismic data volume, the seismic waveform data extracted within the working segment is segmented based on a sliding time window strategy, specifically including:

[0031] Based on the time thickness set of the interbedded rock in the drilled well, the average time thickness of the interbedded rock is calculated, and based on the average time thickness, the sliding time window length and sliding step size are set.

[0032] Within the time between the top and bottom boundaries of the working layer, the seismic waveform data to be inverted is truncated using the sliding window length and the sliding step size to generate a short waveform sequence to be inverted.

[0033] Optionally, a dynamic time warping algorithm is used to calculate the similarity between the segmented seismic waveform data to be inverted and the drilled seismic waveform data in the database, and the top N drilled wells with the highest similarity metric values ​​are selected as the sample well group, specifically including:

[0034] Within each sliding time window, the short waveform to be inverted and the short waveform of each drilled well point are acquired respectively. Dynamic time warping is performed on the short waveform to be inverted and the short waveform of the well point to generate distance values.

[0035] An aggregation operation is performed on the distance values ​​of the same drilled well across multiple time windows to obtain the overall distance value, which is then used as a similarity metric.

[0036] Sort the drilled wells by similarity metric from smallest to largest and select the top N wells with the smallest total distance value to form a sample well group. At the same time, output the set of total distance values ​​of the sample well group.

[0037] Optionally, a comprehensive weight is calculated based on the wave impedance curves corresponding to the sample well group, the similarity metric, and the spatial distance data between the sample wells and the seismic traces to be inverted, specifically including:

[0038] The waveform similarity weight is calculated based on the similarity metric, and its expression is as follows:

[0039] ;

[0040] In the formula, For waveform similarity weights, For the first The overall distance value between each sample well and the waveform to be inverted. The standard deviation of candidate well spacing;

[0041] The spatial distance weight is calculated based on the planar distance between the sample well and the seismic trace to be inverted, and its expression is as follows:

[0042] ;

[0043] In the formula, Spatial distance weights For the first The planar distance between the sample well and the channel to be inverted. Geologically relevant radius;

[0044] The comprehensive weight is calculated based on waveform similarity weight and spatial distance weight, and its expression is as follows:

[0045] ;

[0046] In the formula, For comprehensive weighting, This is the weighting adjustment coefficient.

[0047] Optionally, a weighted fusion operation is performed on the wave impedance curves of the sample well group to generate the wave impedance inversion curve of the seismic trace to be inverted, specifically including:

[0048] At each time sampling point within the working segment, the wave impedance value of the corresponding time sampling point in the sample well group is obtained. The wave impedance value is then weighted and averaged according to the comprehensive weight corresponding to each sample well. The expression is as follows:

[0049] ;

[0050] In the formula, The generated pseudo-well curve in time wave impedance value, It is the first The weight of the sample wells, It is the first Sample well curve in time The value;

[0051] The calculation results are used as the inversion wave impedance values ​​of the seismic trace to be inverted at the sampling point of that time, forming a complete wave impedance inversion curve.

[0052] Furthermore, to achieve the above objectives, the present invention also provides a thin coal seam and interbedded rock inversion system based on waveform-curve dynamic matching, comprising:

[0053] The acquisition module is used to acquire logging data from several drilled wells in the work area, perform correction and standardization processing on the logging data, calculate the wave impedance curve of each drilled well in the time domain, and generate target curve data for distinguishing between thin coal seams and interbedded gangue.

[0054] The calculation module is used to acquire well-through seismic trace data and estimated wavelet data of the drilled well, perform well-seismic calibration calculation on the target curve data to determine the top and bottom boundary times of the target segment, and perform time domain extension calculation based on the top and bottom boundary times to generate the working segment.

[0055] The extraction module is used to extract seismic waveform data corresponding to each drilled well within the working segment, and to pair and store the seismic waveform data with the target curve data after aligning the execution time, thereby constructing a waveform-impedance curve database.

[0056] The selection module is used to segment the seismic waveform data extracted within the working layer based on the sliding time window strategy for any seismic trace to be inverted in the three-dimensional seismic data volume. The dynamic time warping algorithm is used to calculate the similarity between the segmented seismic waveform data to be inverted and the drilled seismic waveform data in the database, and the top N drilled wells with the highest similarity metric value are selected as the sample well group.

[0057] The generation module is used to calculate the fusion weight based on the wave impedance curves corresponding to the sample well group, the similarity metric value, and the spatial distance data between the sample wells and the seismic trace to be inverted, and to perform a weighted fusion operation on the wave impedance curves of the sample well group to generate the wave impedance inversion curve of the seismic trace to be inverted.

[0058] A module is established to create a three-dimensional wave impedance volume based on the wave impedance inversion curves of all seismic traces to be inverted, and to generate a coal seam structure plan view using the three-dimensional wave impedance volume.

[0059] The beneficial effects of this invention are as follows:

[0060] 1. The core of this method is the use of a sliding time window waveform matching based on the Dynamic Time Warping (DTW) algorithm. This overcomes waveform distortion caused by local differences in formation velocity while simultaneously revealing thin-layer lithological variations within the seismic waveform. Application examples show that this method can clearly demonstrate a 94.7% vertical sequence matching rate for a 1.8m thick interbedded rock layer.

[0061] 2. This method introduces a dual-constraint weighted formula that integrates waveform similarity and spatial distance. The formula uses DTW similarity as the centralized weight for matching, and spatial distance weights to limit the spatial smoothness of the inversion results, thus ensuring they meet the "close similarity" characteristic. This allows the inversion results to not only fit all known well points well but also reliably predict unknown formation abrupt change zones between wells.

[0062] 3. The inversion results of this invention were used to optimize the horizontal well trajectory design, successfully guiding the drilling trajectory to effectively avoid interbedded rock sections in bifurcation coal seams. Actual drilling statistics from 30 horizontal wells show that the average coal seam encounter rate increased to 96.8%, and the success rate of interbedded rock avoidance reached 91.4%. Attached Figure Description

[0063] Figure 1 This is a flowchart illustrating the overall technical process of the present invention.

[0064] Figure 2 This is a map showing the sensitive parameters of the coal-bearing strata reservoir in region D of this invention.

[0065] Figure 3 This is a schematic diagram of the target layer and working layer in this invention;

[0066] Figure 4 This is a schematic diagram of the seismic waveform and wave impedance curve database in this invention;

[0067] Figure 5 'a' represents the comparative effect of the conventional linear method in this invention. Figure 5 b shows the comparative effect of the Dynamic Time Warping (DTW) algorithm in this invention;

[0068] Figure 6 a represents the wave impedance curves of the five preferred sample wells in this invention. Figure 6 b is the inversion impedance curve of the inversion channel to be inverted in this invention;

[0069] Figure 7 This is a schematic diagram of the wave impedance volume inversion effect in this invention;

[0070] Figure 8 This is a plan view of the coal seam structure in this invention.

[0071] Figure 9 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0072] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0073] It should be noted that current seismic prediction technologies for thin coal seams and interbedded rock face face significant limitations in application. Model-based inversion methods suffer from low resolution due to seismic frequency band limitations, lacking sufficient resolution for thin interbedded layers (especially sub-meter-thick coal seams and interbedded rock), and are highly dependent on the initial model and wavelet, exhibiting strong ambiguity. Geostatistical inversion is computationally expensive, and variograms are insufficient for accurately characterizing complex thin interbedded layers. Conventional waveform indication methods, which compare only one or a few fixed waveforms, present challenges in achieving accurate matching of local stratigraphic stretching / compression, failing to guarantee precise matching. Furthermore, attribute analysis methods are largely qualitative, making it difficult to achieve the precise characterization of lithological boundaries. These shortcomings fail to meet the current requirements for refined coalbed methane / coal-rock gas geology, hindering their practical application.

[0074] To address the aforementioned issues, this embodiment proposes an inversion method and system for thin coal seams and interbedded rock based on waveform-curve dynamic matching. It employs a sliding time window waveform matching based on the Dynamic Time Warping (DTW) algorithm. This overcomes waveform distortion caused by local formation velocity differences while uncovering thin-layer lithological variations contained within the seismic waveform. Furthermore, a dual-constraint weighted formula integrating waveform similarity and spatial distance is introduced. DTW similarity serves as the central weight for matching, while spatial distance weights constrain the spatial smoothness of the inversion results. This allows the inversion results to not only fit all known well points well but also reliably predict unknown formation abrupt change zones between wells. Ultimately, the inversion results optimize horizontal well trajectory design, successfully guiding the drilling trajectory to effectively avoid interbedded rock sections within bifurcation coal seams.

[0075] Specifically, such as Figure 1 As shown, the inversion method for thin coal seams and interbedded rock proposed in this embodiment based on waveform-curve dynamic matching includes the following steps:

[0076] S1: Construct well logging rock physics charts using all drilled wells, and clearly distinguish the target curves for thin coal seams and interbedded gangue as wave impedance curves.

[0077] It should be noted that rock geophysical properties study the different characteristics exhibited by different reservoirs on well logging curves. Through analysis, relevant parameters of reservoir rock properties, pore structure, and hydrocarbon content can be obtained, which is of great significance to reservoirs. It can identify relatively sensitive logging parameters, thereby guiding the selection of appropriate parameters and algorithms for reservoirs using well-seismic combined methods to improve the reliability and accuracy of reservoir prediction.

[0078] In this embodiment of the invention, based on the logging results of 12 core wells in Block D, cross-plot templates are established using parameters related to lithology, such as acoustic transit time (AC), density (DEN), logarithmic resistivity (logRT), natural gamma (GR), and acoustic impedance (AI), to achieve quantitative classification of multiple lithologies and provide rock physical constraints for subsequent inversion. Four sets of cross-plots (AC-logRT, AI-GR, logRT-DEN, and AI-CNL) are used (e.g., ...). Figure 2As shown in Table 1, four main lithologies can be clearly divided: Coal seam: AI ≤ 6838 m / sg / cm³, AC > 200 µs / m, DEN ≤ 2.0 g / cm³, GR > 32 API, logRT shows obvious high resistance; parting: 6838 < AI ≤ 9493 m / sg / cm³, AC 150–200 µs / m, DEN 2.0–2.37 g / cm³, 17 < GR ≤ 32 API, logRT medium resistance; sandstone: AI > 9493 m / sg / cm³, AC ≤ 150 µs / m, DEN > 2.37 g / cm³, GR ≤ 116 API, CNL ≤ 17%; mudstone: AI > 9493 m / sg / cm³, AC ≤ 150 µs / m, DEN > 2.37 g / cm³, GR > 116 API, 17% < CNL < 32%. Low AI, high AC, and low DEN are characteristics of low organic matter content, high porosity, and low density; the AI, AC, and DEN values of the parting are in the middle because it contains more ash and mineral impurities; high AI and low GR in sandstone indicate that the detrital grains are mainly quartz and less argillaceous; high GR and high CNL in mudstone indicate a large content of clay minerals. These differences can provide a quantitative reference basis for rock physics in inversion. Although multiple curves above can identify thin coal seams and partings, considering that the wave impedance curve has the highest correlation with seismic data, the target curve for distinguishing thin coal seams and partings is determined to be the wave impedance curve.

[0079] Table 1 Sensitive parameter table of coal-bearing formation reservoirs in Area D

[0080]

[0081] S2: Determine the target interval through well-seismic calibration, and trim the top and bottom of the target interval (expand 10% of the thickness of the target interval at both the top and bottom) to obtain the working interval.

[0082] In this embodiment of the invention, acoustic transit time (AC) and density (DEN) logging curves of all drilled wells within the work area are collected. The curves undergo rigorous environmental correction (such as correction for wellbore enlargement and mud intrusion effects) and standardization to eliminate systematic errors caused by instrumentation, operation, and other factors between different wells, ensuring data consistency. Wave impedance curves are calculated using the corrected acoustic and density curves, and a reflection coefficient sequence is synthesized. The reflection coefficient sequence is convolved with the estimated wavelet to generate a theoretical synthetic seismic record. On a dedicated software platform, the synthetic seismic record is displayed side-by-side with the actual well-passing seismic traces for manual interactive comparison, identifying marker layers (such as strongly reflective standard layers) for preliminary alignment. Based on this preliminary alignment, the top (T_top) and bottom (T_bottom) interfaces of the target thin coal seam segment are focused. By slightly stretching or compressing the time-depth relationship of the synthetic record, and possibly fine-tuning the wavelet phase or frequency, the correlation coefficient between the reflection characteristics generated by the coal seam in the synthetic record and the corresponding in-phase axis in the seismic trace is maximized, thus completing the well-seismic calibration.

[0083] After accurate calibration, the seismic time-domain thickness of the target segment of the thin coal seam is directly calculated: ΔT = T_bottom - T_top. Since the seismic waveform is a comprehensive response of the underground strata, the local reflection characteristics (thin coal seam reflection) are inevitably affected by the interference and tuning effects of adjacent strata. To mitigate the adverse effects of the inversion boundary and adapt to the requirements of this sliding time-window analysis method, appropriate borders are added to both ends of the target segment. T_top' = T_top - ΔT × C, T_bottom' = T_bottom + ΔT × C, where C is the border factor, typically taken as 10%, so that the expanded segment range can accommodate as much of the reflection amplitude near the target reflection phase axis as possible and cover at least one adjacent period; the new expanded segment thickness is obtained as {T_top', T_bottom'}. This is used as the working segment for this waveform-curve dynamic matching inversion; all subsequent database establishment, waveform extraction, sliding comparison, and curve fusion are performed within this segment. Figure 3 The diagram shows the target layer and the working layer.

[0084] S3: Within the working section, establish a database of seismic waveforms and wave impedance curves using all drilled wells.

[0085] In this embodiment of the invention, within the working segment determined in step S2, a "seismic waveform-impedance curve" database is constructed for all drilled wells. During implementation, it is first necessary to ensure a unified data foundation: the wave impedance curves of all drilled wells are converted to the time domain using time-depth relationships. Subsequently, seismic waveforms within the working segment {T_top', T_bottom'} are extracted along the well trajectory of each drilled well. Simultaneously, wave impedance value curves between working segments are extracted from the wave impedance curves of each drilled well. Finally, a "seismic waveform-impedance curve" database is constructed. Figure 4 The seismic waveform serves as the input feature, representing the comprehensive response of the stratigraphic combination at a specific location; the wave impedance curve aligned with it serves as the target output, revealing the true lithological impedance structure underground.

[0086] S4: Extract the seismic waveform of the trace to be inverted within the working segment, and compare it with the waveforms of all drilled wells in the database based on the sliding time window and dynamic time warping algorithm. Select the top N drilled wells with the highest similarity to the seismic waveform of the trace to be inverted as the sample well group for the inverted trace.

[0087] In this embodiment of the invention, this step, based on the core of waveform-curve dynamic matching inversion, aims to find the most similar "neighbor" well for each seismic trace to be inverted within the work area. The complete seismic waveform of the current trace to be inverted within the working segment is extracted from the 3D seismic data volume.

[0088] Specifically, a sliding time window is used for refined comparison: to avoid masking local details despite overall waveform similarity, a sliding time window strategy is employed. The waveform of the entire working section is divided into several overlapping short time windows. The length of the time window is determined by three times the average thickness of interbedded rock thicknesses from all drilled wells. This ensures that the waveform and curve length within a single time window can encompass a complete coal seam-interbedded rock-coal seam cycle, guaranteeing no interbedded rock is missed. The sliding step size can be half the time window length. Within each short time window, waveform similarity is calculated separately.

[0089] Specifically, the Dynamic Time Warping (DTW) algorithm is used for waveform and curve comparison: within each sliding time window, the DTW algorithm compares the short waveform of the trace to be inverted with the waveforms of the corresponding time windows of each well in the database one by one. The advantage of the DTW algorithm is that it can non-linearly "stretch" or "compress" two time series to find the optimal matching path between them, thereby overcoming waveform time distortion caused by small local velocity changes and calculating an optimal similarity metric (distance value) that is resistant to local stretching. Figure 5 The image shows the seismic waveform of the seismic trace to be inverted and the seismic waveform of a certain drilled well. Figure 5 'a' represents the comparison effect of the conventional linear method, and the correlation coefficient between the two is 0.79. Figure 5b. Comparison of the Dynamic Time Warping (DTW) algorithm results: The correlation coefficient between the two is 0.94, which shows that the Dynamic Time Warping (DTW) algorithm can significantly improve the comparison accuracy of the two seismic waveforms, and help to more accurately select sample wells.

[0090] Finally, based on the similarity scores from high to low (i.e., the distance values ​​from small to large), the top N wells (assuming 5 or 10) are selected as the sample well group for the current trace to be inverted. These N wells can be considered as the known cases where the underground wave impedance structure is most similar to the seismic waveform response under the current seismic waveform characteristics to be inverted.

[0091] S5: Weighted fusion of all wave impedance curves within the sample well group to obtain the inverted wave impedance curve of the trace to be inverted.

[0092] S51: Obtain sample well curves: From the database in S3, extract the wave impedance curves of these N wells in the sample well group determined in S4 within the working interval, such as... Figure 6 Figure a shows the wave impedance curves of the five selected sample wells.

[0093] S52: Calculate the fusion weight: Introduce waveform similarity constraints and spatial distance constraints to better represent the characteristics of thin interlayers and obtain prediction weights with better geological rationality and spatial continuity.

[0094] Waveform similarity weight The similarity score or distance value is obtained from the DTW calculation in step S4, expressed by the formula:

[0095] ;

[0096] In the formula, For the first The overall distance value between each sample well and the waveform to be inverted. The standard deviation of candidate well distances is used to control the decay rate. This invention converts distance into an exponentially decaying similarity weight; the greater the similarity, the greater the weight, and the more prominent the advantage.

[0097] Spatial distance weight The constraint determines the spatial smoothness, rationality, and reduction of interference from distant wells in the inversion results. The formula is expressed as:

[0098] ;

[0099] In the formula, For the first The planar distance between the sample well and the channel to be inverted. The geologically relevant radius is obtained based on the rate of change or variation function analysis of sedimentary facies within the work area, ensuring that the influence of nearby wells on the current well is greater than that of distant wells, which is in line with geological laws.

[0100] Overall weight It is obtained by multiplying the weights from the previous two methods. Here, geometric mean is used to address the weighting problem caused by a single parameter and its single value, aiming to ensure that the factors mutually constrain and restrain each other. The formula is expressed as follows:

[0101] ;

[0102] In the formula, the exponent The weight adjustment coefficient can be used to fine-tune the weight of each item according to the actual situation (the default value is 1). Based on the data of different work areas, the weight index can be adjusted accordingly.

[0103] S53: Weighted linear fusion yields the inverted wave impedance curve: At each time sampling point, the values ​​of the N wave impedance curves at that point are weighted and averaged. The formula can be simplified to:

[0104] ;

[0105] In the formula, The generated pseudo-well curve in time wave impedance value, It is the first The weight of the sample wells, It is the first Sample well curve in time The value of .

[0106] Through the above fusion, a high-resolution wave impedance inversion curve with the same length as the working segment and for the current channel to be inverted is obtained. Figure 6 b). This curve integrates information from multiple wells with similar geological conditions and uses seismic waveforms as a bridge for spatial extrapolation.

[0107] S6: Repeat S4 and S5 for each channel to be inverted in the entire three-dimensional system to obtain the wave impedance curve of each channel to be inverted, and finally combine them to form the wave impedance volume of the entire three-dimensional system.

[0108] In this embodiment of the invention, a process is to transform the intelligent calculation results of the track to be inverted into a three-dimensional continuous geological model. In this process, the logic of the above-mentioned local inversion is systematically implemented for the entire work area by utilizing all the traversed work area locations.

[0109] In practice, hundreds of thousands or even millions of seismic traces in the 3D seismic data volume are processed one by one. For each trace to be inverted, S4 is repeated individually and automatically—first, the waveform of the trace within the working segment is extracted, and N sample wells with similar geological responses are selected from the established sample database based on real-time waveform matching and intelligent search; then, S5—the wave impedance curves of the N sample wells are weighted and fused to obtain a high-resolution wave impedance estimation curve for the (X,Y) point corresponding to the seismic trace. Because a large amount of data needs to be calculated, after processing each seismic trace, the parallel computation task of wave impedance estimation can be completed through high-performance computing.

[0110] Each seismic trace processed results in a wave impedance curve suspended under the geographical coordinates of that trace. Once all seismic traces have been processed, a set of independent wave impedance curves, of equal number and spatially regularly distributed, is obtained.

[0111] The next step is data reorganization and integration. Based on the original spatial grid architecture of the seismic data volume (a coordinate system based on inline and crossline lines), these independent curves are rearranged and spliced ​​together to ultimately form a complete and continuous three-dimensional data volume—that is, the wave impedance volume.

[0112] In this final result, the information in each data unit (also known as a "channel") has been converted from the original seismic reflection amplitude to the inferred wave impedance. This conversion improves the vertical resolution and reveals the very small differences in wave impedance in geological targets such as thin coal seams, interbedded rock, and thin interbedded layers.

[0113] Figure 7 The acoustic impedance volume inversion can effectively reflect the subtle lateral variations in thin coal seams and interbedded rock. For example, taking well D6-5 as an example, the actual drilling results show a typical bipartite coal seam (coal seam-interbedded rock-coal seam), with an upper coal seam 2 meters thick, a lower coal seam 4 meters thick, and an interbedded rock layer reaching 1.8 meters in thickness. The resulting inverted acoustic impedance profile generally matches the actual vertical sequence well. Figure 7 The method (left) can effectively distinguish the superposition relationship between two low-impedance coal seams (5200–6500 m / s·g / cm3) and a high-impedance interbedded rock (7600–8900 m / s·g / cm3), with a vertical sequence matching degree of 94.7%. This proves that the method can accurately distinguish the wave impedance of the interbedded rock being much higher than that of the coal seams, and can also identify the response signal of the interbedded rock at this scale from the data. However, the actual drilling results of well D5-1 show that its interbedded rock is only 0.5 meters thick, and no obvious interbedded rock reflection is shown in the inversion results, only a continuous low-resistivity reflection, indicating that this method cannot identify thin layers below the sub-meter level.

[0114] In addition, this method can also capture lateral abrupt changes in coal seam structure. In the actual drilling of well D7-6, it was a single-type coal seam. Figure 7 (b) The inversion profile on its eastern side revealed a bifurcation phenomenon in the coal seam: west of the boundary point, the wave impedance remained relatively stable at around 6200 m / s·g / cm³, while east of the boundary point, there was a structure consisting of two low-resistivity coal seams (5800–6300 m / s·g / cm³) sandwiching a medium-resistivity layer (8200–8500 m / s·g / cm³), with a predicted interbedded rock thickness of 2.6–3 m and an area of ​​approximately 8.5 km². This was later confirmed by the infill well D7-9 (actually drilled with an interbedded rock thickness of 2.7 m), demonstrating that the seismic waveform-curve dynamic matching inversion method can accurately identify abrupt changes in thin interbedded rock in the transverse direction of the coal seam.

[0115] S7: Based on the wave impedance volume results, conduct fine structure interpretation and planar mapping of coal seams.

[0116] In practical applications, the Benxi Formation No. 8 coal seam is a lagoon-tidal flat facies deposit, controlled by high-frequency sea-level fluctuations. Internally, it contains two sets of mudstone-carbonaceous mudstone interbedded with rock in the upper and lower parts, respectively, with local facies transitions to continuous mudstone interlayers. Based on sedimentary cycles, it can be subdivided into three smaller layers: 8-1#, 8-2#, and 8-3#, and further classified into three structural types: a "one-part type" without interbedded rock, a "two-part type" with one interbedded rock, and a "three-part type" with two interbedded rock. Using the three-dimensional impedance volume obtained in step S6, a planar diagram of the coal seam structure is derived (…). Figure 8 The spatial distribution of coal seam structures can be observed: ① A stable unific structure is developed in the central part of the study area, accounting for about 60% of the area, with a continuous coal seam thickness of more than 8m, indicating stable deposition of low-energy lacustrine facies; ② The northern and southeastern parts of the study area are mainly dific, accounting for about 38%, with interbedded rock thickness of 0.8-3.0m. Due to the continuous fluctuation of sea level, the sedimentary environment is unstable, resulting in a bifurcation structure of interbedded rock; ③ A trific structure is developed in the northwest, accounting for a small proportion, only about 2%, with a cumulative interbedded rock thickness of 2-7m, which is caused by local compression of the negative microstructure development zone.

[0117] Based on the embodiments of this invention, two pilot test areas were successively implemented in the study area, deploying 30 horizontal wells, basically covering both type I and type II coal seams. During the drilling of type II horizontal wells, the horizontal well trajectory design was adjusted according to the inversion results, effectively avoiding interbedded rock layers and mitigating the risk of stuck drill pipe. The actual coal seam encounter rate was 96.8%, and the success rate of interbedded rock avoidance was 91.4%.

[0118] Reference Figure 9 , Figure 9 This is a schematic diagram of the structure of the thin coal seam and interbedded gangue inversion system based on waveform-curve dynamic matching according to an embodiment of the present invention.

[0119] like Figure 9As shown, the thin coal seam and interbedded rock inversion system based on waveform-curve dynamic matching proposed in this embodiment of the invention includes:

[0120] The acquisition module 10 is used to acquire logging data of several drilled wells in the work area, perform correction and standardization processing on the logging data, calculate the wave impedance curve of each drilled well in the time domain, and generate target curve data for distinguishing thin coal seams from interbedded gangue.

[0121] The calculation module 20 is used to acquire the well-through seismic tunnel data and estimated wavelet data of the drilled well, perform well-seismic calibration calculation on the target curve data to determine the top and bottom boundary times of the target segment, and perform time domain expansion calculation based on the top and bottom boundary times to generate the working segment.

[0122] Extraction module 30 is used to extract seismic waveform data corresponding to each drilled well within the working section, and to pair and store the seismic waveform data with the target curve data after aligning the execution time, thereby constructing a waveform-wave impedance curve database.

[0123] Module 40 is selected for any seismic trace to be inverted in the three-dimensional seismic data volume. Based on the sliding time window strategy, the seismic waveform data extracted within the working segment to be inverted is segmented. The dynamic time warping algorithm is used to calculate the similarity between the segmented seismic waveform data to be inverted and the drilled seismic waveform data in the database. The top N drilled wells with the highest similarity metric value are selected as the sample well group.

[0124] The generation module 50 is used to calculate the fusion weight based on the wave impedance curves corresponding to the sample well group, the similarity metric value, and the spatial distance data between the sample wells and the seismic trace to be inverted, and to perform a weighted fusion operation on the wave impedance curves of the sample well group to generate the wave impedance inversion curve of the seismic trace to be inverted.

[0125] Module 60 is established to create a three-dimensional wave impedance volume based on the wave impedance inversion curves of all seismic traces to be inverted, and to generate a coal seam structure plan view using the three-dimensional wave impedance volume.

[0126] Other embodiments or specific implementations of the thin coal seam and interbedded gangue inversion system based on waveform-curve dynamic matching of the present invention can be referred to the above-described method embodiments, and will not be repeated here.

[0127] It is understood that in the description of this specification, references to terms such as "one embodiment," "another embodiment," "other embodiments," or "first embodiment to Nth embodiment," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0128] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0129] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for inverting thin coal seams and interbedded rock based on waveform-curve dynamic matching, characterized in that, The method includes the following steps: Obtain logging data from several drilled wells within the work area, perform correction and standardization processing on the logging data, calculate the wave impedance curve of each drilled well in the time domain, and generate target curve data for distinguishing between thin coal seams and interbedded rock. Acquire the wellbore seismic trace data and estimated wavelet data of the drilled well, perform well-seismic calibration operation on the target curve data to determine the top and bottom boundary times of the target segment, and perform time domain extension operation based on the top and bottom boundary times to generate the working segment. Within the working section, seismic waveform data corresponding to each drilled well is extracted, and the seismic waveform data and target curve data are aligned in time and stored in pairs to construct a waveform-impedance curve database. For any seismic trace to be inverted in the 3D seismic data volume, the seismic waveform data to be inverted extracted within the working segment is segmented based on the sliding time window strategy. The dynamic time warping algorithm is used to calculate the similarity between the segmented seismic waveform data to be inverted and the drilled well seismic waveform data in the database. The top N drilled wells with the highest similarity metric value are selected as the sample well group. Based on the wave impedance curves corresponding to the sample well group, the similarity metric, and the spatial distance data between the sample wells and the seismic trace to be inverted, the fusion weight is calculated, and a weighted fusion operation is performed on the wave impedance curves of the sample well group to generate the wave impedance inversion curve of the seismic trace to be inverted. Based on the impedance inversion curves of all seismic traces to be inverted, a three-dimensional impedance volume is established, and a coal seam structure plan view is generated using the three-dimensional impedance volume.

2. The method for inverting thin coal seams and interbedded rock based on waveform-curve dynamic matching as described in claim 1, characterized in that, Obtain logging data from several drilled wells within the work area, perform correction and standardization processing on the logging data, calculate the wave impedance curves of each drilled well in the time domain, and generate target curve data for distinguishing between thin coal seams and interbedded rock. Specifically, this includes: Acquire sonic transit time logging data and density logging data from several drilled wells within the work area; Environmental correction and standardization are performed on the sonic transit time logging data and the density logging data. Based on the standardization results, the wave impedance sequence is calculated, and the wave impedance sequence is resampled at a uniform sampling interval to generate a wave impedance curve. The wave impedance curve is used as the target curve data for distinguishing between thin coal seams and interbedded gangue.

3. The method for inverting thin coal seams and interbedded rock based on waveform-curve dynamic matching as described in claim 1, characterized in that, Acquire the wellbore seismic trace data and estimated wavelet data of the drilled well, and perform well-seismic calibration calculations on the target curve data to determine the top and bottom boundary times of the target layer, specifically including: Acquire the well-through seismic trace data and estimated wavelet data of the drilled well, convert the wave impedance curve into a reflection coefficient sequence, and perform a convolution operation in combination with the estimated wavelet to generate a synthetic seismic record. Perform time alignment calculations on the synthetic seismic records and the well-pass seismic trace data, and output the calibration alignment results with the correlation coefficient maximized. Based on the calibration and alignment results, the top and bottom boundary times of the target layer are determined.

4. The method for inverting thin coal seams and interbedded rock based on waveform-curve dynamic matching as described in claim 1, characterized in that, Perform time-domain extension operations based on the top and bottom bound times to generate working segments, specifically including: Calculate the time thickness of the target layer segment based on the top and bottom boundary times; Based on the preset edging factor and the target layer time thickness, an expansion operation is performed on the top boundary time and the bottom boundary time to generate the top boundary time and the bottom boundary time of the working layer. The working segment is determined based on the top boundary time and the bottom boundary time of the working segment.

5. The method for inverting thin coal seams and interbedded rock based on waveform-curve dynamic matching as described in claim 3, characterized in that, Within the working section, seismic waveform data corresponding to each drilled well is extracted, and the seismic waveform data and target curve data are aligned in execution time and then stored in pairs to construct a waveform-impedance curve database, specifically including: Based on the well vibration calibration alignment results, the depth domain to time domain mapping transformation is performed on the wave impedance curves of each drilled well to obtain the time domain wave impedance curves. Seismic waveform data within the working segment is extracted from the three-dimensional seismic data volume along the trajectory of each drilled well; Within the working segment, seismic waveform data and time-domain impedance curves are respectively extracted and time sampling points are aligned to generate well point waveform-curve data pairs. The waveform-curve data of each well point are stored according to the well identifier to form a waveform-impedance curve database.

6. The method for inverting thin coal seams and interbedded rock based on waveform-curve dynamic matching as described in claim 1, characterized in that, For any seismic trace to be inverted in a 3D seismic data volume, the extracted seismic waveform data within the working segment is segmented based on a sliding time window strategy, specifically including: Based on the time thickness set of the interbedded rock in the drilled well, the average time thickness of the interbedded rock is calculated, and based on the average time thickness, the sliding time window length and sliding step size are set. Within the time between the top and bottom boundaries of the working layer, the seismic waveform data to be inverted is truncated using the sliding window length and the sliding step size to generate a short waveform sequence to be inverted.

7. The method for inverting thin coal seams and interbedded rock based on waveform-curve dynamic matching as described in claim 6, characterized in that, The dynamic time warping algorithm is used to calculate the similarity between the segmented seismic waveform data to be inverted and the drilled seismic waveform data in the database. The top N drilled wells with the highest similarity metric values ​​are selected as the sample well group, specifically including: Within each sliding time window, the short waveform to be inverted and the short waveform of each drilled well point are acquired respectively. Dynamic time warping is performed on the short waveform to be inverted and the short waveform of the well point to generate distance values. Aggregate the distance values ​​of the same drilled well across multiple time windows to obtain the overall distance value, and use the overall distance value as the similarity metric. Sort the drilled wells by similarity metric from smallest to largest and select the top N wells with the smallest total distance value to form a sample well group. At the same time, output the set of total distance values ​​of the sample well group.

8. The method for inverting thin coal seams and interbedded rock based on waveform-curve dynamic matching as described in claim 7, characterized in that, The comprehensive weight is calculated based on the wave impedance curves corresponding to the sample well groups, the similarity metric, and the spatial distance data between the sample wells and the seismic traces to be inverted. Specifically, it includes: The waveform similarity weight is calculated based on the similarity metric, and its expression is as follows: ； In the formula, For waveform similarity weights, For the first The overall distance value between each sample well and the waveform to be inverted. The standard deviation of candidate well spacing; The spatial distance weight is calculated based on the planar distance between the sample well and the seismic trace to be inverted, and its expression is as follows: ； In the formula, Spatial distance weights For the first The planar distance between the sample well and the channel to be inverted. Geologically relevant radius; The comprehensive weight is calculated based on waveform similarity weight and spatial distance weight, and its expression is as follows: ； In the formula, For comprehensive weighting, This is the weighting adjustment coefficient.

9. The method for inverting thin coal seams and interbedded rock based on waveform-curve dynamic matching as described in claim 8, characterized in that, A weighted fusion operation is performed on the impedance curves of the sample well group to generate the impedance inversion curve of the seismic trace to be inverted, specifically including: At each time sampling point within the working segment, the wave impedance value of the corresponding time sampling point in the sample well group is obtained. The wave impedance value is then weighted and averaged according to the comprehensive weight corresponding to each sample well. The expression is as follows: ； In the formula, The generated pseudo-well curve in time wave impedance value, It is the first The weight of the sample wells, It is the first Sample well curve in time The value; The calculation results are used as the inversion wave impedance values ​​of the seismic trace to be inverted at the sampling point of that time, forming a complete wave impedance inversion curve.

10. A thin coal seam and interbedded rock inversion system based on waveform-curve dynamic matching, characterized in that, The system includes: The acquisition module is used to acquire logging data from several drilled wells in the work area, perform correction and standardization processing on the logging data, calculate the wave impedance curve of each drilled well in the time domain, and generate target curve data for distinguishing between thin coal seams and interbedded gangue. The calculation module is used to acquire well-through seismic trace data and estimated wavelet data of the drilled well, perform well-seismic calibration calculation on the target curve data to determine the top and bottom boundary times of the target segment, and perform time domain extension calculation based on the top and bottom boundary times to generate the working segment. The extraction module is used to extract seismic waveform data corresponding to each drilled well within the working segment, and to pair and store the seismic waveform data with the target curve data after aligning the execution time, thereby constructing a waveform-impedance curve database. The selection module is used to segment the seismic waveform data extracted within the working layer based on the sliding time window strategy for any seismic trace to be inverted in the three-dimensional seismic data volume. The dynamic time warping algorithm is used to calculate the similarity between the segmented seismic waveform data to be inverted and the drilled seismic waveform data in the database, and the top N drilled wells with the highest similarity metric value are selected as the sample well group. The generation module is used to calculate the fusion weight based on the wave impedance curves corresponding to the sample well group, the similarity metric value, and the spatial distance data between the sample wells and the seismic trace to be inverted, and to perform a weighted fusion operation on the wave impedance curves of the sample well group to generate the wave impedance inversion curve of the seismic trace to be inverted. A module is established to create a three-dimensional wave impedance volume based on the wave impedance inversion curves of all seismic traces to be inverted, and to generate a coal seam structure plan view using the three-dimensional wave impedance volume.

Images