A coal underground gasification furnace roof aquifer risk identification method and device

Abstract

The application provides a coal underground gasification furnace roof aquifer risk identification method and device, relates to the coal underground gasification technical field, and includes: obtaining three-dimensional seismic data of the area where the coal underground gasification furnace is located and the multiple logging curves of the drilling well in the area where the coal underground gasification furnace is located;The multiple logging curves are analyzed, the porosity prediction curve of the drilling well and the risk threshold are determined;Three-dimensional waveform indication simulation is carried out on the three-dimensional seismic data and the porosity prediction curve, and the porosity data of the area where the coal underground gasification furnace is located are obtained;From the porosity data, the porosity data of the roof is extracted as the target porosity data;The target porosity data is compared with the risk threshold, and the risk identification result of the roof is obtained.The application extracts the waveform indication simulation inversion plan of each potential aquifer respectively, divides the water-bearing risk area, and can accurately identify the risk of the aquifer in the area where the coal underground gasification furnace is located.

Description

Technical Field

[0001] This application relates to the field of underground coal gasification technology, and more specifically, to a method and apparatus for identifying the risk of aquifer on the roof of an underground coal gasification furnace. Background Technology

[0002] Existing aquifer risk identification methods generally distinguish aquifers based on lithological characteristics. For example, carbonate rocks with karst development or coarse-grained sandstone are considered common aquifers. However, the lithology of the same well is basically the same from top to bottom, but the physical properties are different at different depths. It is not accurate to distinguish aquifers based solely on lithology.

[0003] Current methods for determining aquifers in hydrogeological drilling primarily rely on a comprehensive analysis of factors such as drilling fluid consumption, drilling speed, and the lithology and fracture development of the extracted core samples. While these on-site assessments can identify aquifers, they require continuous observation during drilling, a solid foundation in hydrogeology, and a strong sense of responsibility from the engineers. Furthermore, this method can only identify whether the strata at the well point contain water, and cannot effectively identify aquifers between wells.

[0004] Geophysical prediction methods for the water content of the top and bottom plates of underground coal gasification coal seams mainly include well logging curve method, electromagnetic exploration method and seismic exploration method. Well logging curve method such as resistivity well logging curve cannot judge water content alone, and the accuracy of identification is not high.

[0005] Whether a rock stratum contains water is related to its physical properties; generally, sandstone with good physical properties is considered an aquifer. Existing seismic inversion techniques include sparse pulse inversion, pre-stack elastic impedance inversion, pre-stack simultaneous inversion, model-based post-stack inversion, and geostatistical inversion. These inversion techniques can predict lithology, physical properties, and gas-bearing properties of strata to a certain extent, but they also have limitations. Due to the limited resolution of seismic data, sparse pulse inversion, pre-stack elastic impedance inversion, and pre-stack simultaneous inversion are difficult to obtain high-resolution simulation results. Influenced by well interpolation, model-based inversion has low lateral resolution and is unsuitable for predicting strata with rapid lateral changes. Geostatistical inversion is constrained by the model and obtains high-frequency components through stochastic simulation, resulting in strong randomness, low lateral resolution, and high ambiguity. Therefore, conventional inversion methods are insufficient to meet the requirements for detailed aquifer prediction.

[0006] Coal underground gasification projects require that there be no aquifer within 150m above the coal seam. Therefore, how to effectively identify and delineate the aquifer in the area where the coal underground gasification furnace is located has always been a goal that the industry needs to improve. Summary of the Invention

[0007] The purpose of this application is to provide a method and apparatus for identifying the risk of aquifers on the roof of an underground coal gasifier, in order to solve the above-mentioned problems existing in the prior art, and to accurately identify the risk level of aquifers on the roof of an underground coal gasifier.

[0008] Firstly, a method for identifying the risk of aquifers on the roof of underground coal gasification furnaces is provided, the method including: Acquire 3D seismic data of the area where the underground coal gasification furnace is located, as well as various logging curves of wells drilled in the area where the underground coal gasification furnace is located; The various logging curves are analyzed to determine the porosity prediction curve and risk threshold of the well. A three-dimensional waveform indication simulation was performed on the three-dimensional seismic data and the porosity prediction curve to obtain the porosity data of the area where the underground coal gasification furnace is located. From the porosity data, the porosity data of the roof of the area where the underground coal gasification furnace is located is extracted as the target porosity data; By comparing the target porosity data with the risk threshold, the risk identification result of the roof of the area where the underground coal gasification furnace is located is obtained.

[0009] In an optional implementation, the multiple types of logging curves include: density logging curves, compensated neutron logging curves, and natural gamma logging curves; Analyzing the various types of logging curves to determine the porosity prediction curve of the well, including: Cross-plot analysis of the density logging curves and compensated neutron logging curves of the well was performed to obtain the rock physical parameters of the well. Based on the rock physical parameters, the pure rock porosity curve of the well is determined; based on the natural gamma logging curve, the clay content curve of the well is determined. The porosity curve of the pure rock is corrected using the clay content curve to obtain the porosity prediction curve of the well.

[0010] In an optional implementation, the multiple logging curves further include: well porosity measurement data and logging interpretation results; Analyzing the various types of well logging curves to determine the risk threshold includes: Histogram analysis was performed on the porosity measurement data and the well logging interpretation results to obtain the risk threshold.

[0011] In an optional implementation, the risk threshold includes: a first risk threshold, a second risk threshold, and a third risk threshold.

[0012] In an optional implementation, the multiple types of logging curves further include: time-depth relationship data; Before performing a three-dimensional waveform indication simulation on the three-dimensional seismic data and the porosity prediction curve to obtain the porosity data of the area where the underground coal gasification furnace is located, the method further includes: Based on the time-depth relationship data, the porosity prediction curve is converted into a time-domain porosity prediction curve; Perform a three-dimensional waveform indication simulation on the three-dimensional seismic data and the porosity prediction curve, including: A three-dimensional waveform indication simulation is performed on the three-dimensional seismic data and the time-domain porosity prediction curve.

[0013] In an optional implementation, before extracting the porosity data of the roof of the area where the underground coal gasification furnace is located from the porosity data, the method further includes: Obtain seismic horizon data for different regions of the roof slab; From the porosity data, the porosity data of the roof of the area where the underground coal gasification furnace is located is extracted, including: For any region of the roof, based on the seismic horizon data of the region, the porosity data of the region is extracted from the porosity data and used as the target porosity data for the region of the roof; Based on the target porosity data of different regions of the roof, the target porosity data of the roof is determined.

[0014] In an optional implementation, the target porosity data is compared with the risk threshold to obtain the risk identification result of the roof of the area where the underground coal gasification furnace is located, including: For any region of the top plate, if the target porosity data of the region is less than the first risk threshold, the risk identification result of the region is no risk. If the target porosity data of the region is greater than or equal to the first risk threshold and the target porosity data of the region is less than the second risk threshold, then the risk identification result of the region is low risk. If the target porosity data of the region is greater than or equal to the second risk threshold and the target porosity data of the region is less than the third risk threshold, then the risk identification result of the region is medium risk. If the target porosity data of the region is greater than the third risk threshold, then the risk identification result of the region is high risk; Based on the risk identification results of different areas of the roof, the risk identification results of the roof of the area where the underground coal gasification furnace is located are determined.

[0015] Secondly, a risk identification device for aquifers on the roof of an underground coal gasification furnace is provided, the device comprising: The acquisition unit is used to acquire three-dimensional seismic data of the area where the underground coal gasification furnace is located, as well as various logging curves of wells drilled in the area where the underground coal gasification furnace is located. The analysis unit is used to analyze the various logging curves to determine the porosity prediction curve and risk threshold of the well. The simulation unit is used to perform three-dimensional waveform indication simulation on the three-dimensional seismic data and the porosity prediction curve to obtain the porosity data of the area where the underground coal gasification furnace is located. The comparison unit is used to extract the porosity data of the roof of the area where the underground coal gasifier is located from the porosity data, and use it as the target porosity data; compare the target porosity data with the risk threshold to obtain the risk identification result of the roof of the area where the underground coal gasifier is located.

[0016] Thirdly, an electronic device is provided, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in memory, it implements any of the steps described in the first aspect above.

[0017] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements the steps of any of the methods described in the first aspect above.

[0018] This application determines the initial porosity threshold by predicting the porosity curve of the area where the underground coal gasifier is located, and then combining it with the porosity curve and water saturation curve of the selected standard well. This allows for the division of the porosity prediction curves of all wells in the area where the underground coal gasifier is located, thus obtaining a reasonably distributed aquifer.

[0019] Based on the porosity curve, this application performs a three-dimensional waveform indication simulation and inversion of porosity in the area where the underground coal gasification furnace is located. The inversion results are accurate and reliable.

[0020] This application extracts waveform indicator simulation inversion plane maps of each potential aquifer and divides the water-rich risk areas, which can more accurately identify the risk of aquifers in the area where underground coal gasification furnaces are located. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart illustrating a method for identifying the risk of aquifer aquifer on the roof of an underground coal gasification furnace, provided in this application embodiment; Figure 2 A logging curve and a logging interpretation diagram are provided for embodiments of this application; Figure 3 A density logging curve distribution histogram provided in this application embodiment; Figure 4 A histogram of compensated neutron logging curve distribution provided in an embodiment of this application; Figure 5 A gamma logging curve distribution histogram provided in this application embodiment; Figure 6 A porosity logging and logging interpretation result intersection histogram provided in this application embodiment; Figure 7 A statistical histogram of gamma logging curve distribution is provided for embodiments of this application; Figure 8 A mud content curve provided for an embodiment of this application; Figure 9 A neutron-density cross-plot provided for an embodiment of this application; Figure 10 A schematic diagram of a predicted porosity curve provided in an embodiment of this application; Figure 11 A waveform indication simulation flowchart is provided for an embodiment of this application; Figure 12 A waveform indication simulation result cross-sectional view provided in an embodiment of this application; Figure 13 A schematic diagram of seismic horizon profiles K1, K2, and K3 provided for an embodiment of this application; Figure 14 A diagram showing the simulation results of porosity and the prediction results of water content risk in the K1 layer provided in this application embodiment; Figure 15 A diagram showing the simulation results of porosity and the prediction results of water content risk in the K2 layer provided in this application embodiment; Figure 16 A diagram showing the porosity simulation results and water-bearing risk prediction results of the K3 layer provided in this application embodiment; Figure 17 A schematic diagram of a risk identification device for aquifer on the roof of an underground coal gasification furnace provided in this application embodiment; Figure 18 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0024] The risk identification method for aquifers on the roof of underground coal gasification furnaces provided in this application can be applied to servers or terminals with strong computing capabilities. The server can be a physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The terminal can be user equipment (UE) such as mobile phones, smartphones, laptops, digital radio receivers, personal digital assistants (PDAs), and tablet computers (PADs), handheld devices, in-vehicle devices, wearable devices, computing devices or other processing devices connected to a wireless modem, mobile stations (MS), mobile terminals, etc. The terminal and server can be directly or indirectly connected via wired or wireless communication methods, which is not limited herein.

[0025] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0026] Figure 1 This is a flowchart illustrating a method for identifying the risk of aquifers on the roof of an underground coal gasification furnace, as provided in an embodiment of this application. Figure 1 As shown, the method may include: Step S110: Obtain three-dimensional seismic data of the area where the underground coal gasification furnace is located, as well as various logging curves of wells drilled in the area where the underground coal gasification furnace is located; analyze the various logging curves to determine the porosity prediction curve and risk threshold of the wells.

[0027] In this embodiment of the application, it is necessary to obtain multiple types of logging curves for all wells in the area where the underground coal gasification furnace is located; the multiple types of logging curves include: density logging curves, compensated neutron logging curves, natural gamma logging curves, drilling time-depth relationship data, porosity measurement data, and logging interpretation results.

[0028] In this embodiment of the application, multiple types of logging curves are analyzed to determine the porosity prediction curve of the well, including: Cross-plot analysis of density logging curves and compensated neutron logging curves of the well is performed to obtain the rock physical parameters of the well. Based on the rock physical parameters, the pure rock porosity curve of the well is determined. Based on the natural gamma logging curve, the clay content curve of the well is determined. The pure rock porosity curve is corrected using the clay content curve to obtain the porosity prediction curve of the well.

[0029] In the embodiments of this application, the formation of the well generally includes pure mudstone layers, pure sandstone layers, mixed layers (i.e., mixed mudstone and sandstone layers) and coal seams.

[0030] In this embodiment of the application, the rock physical parameters include: rock skeleton density value, clay density value, fluid neutron value, rock skeleton neutron value, clay neutron value, and fluid neutron value.

[0031] In this embodiment of the application, porosity calculation based on rock physical parameters includes: Since the rock framework itself is essentially hydrogen-free, the hydrogen content index of pure freshwater rock is determined by the porosity of the pure rock filled with freshwater. For a unit volume of rock, the hydrogen content can be converted into a compensated neutron value. For a formation with porosity φ, the relationship between the rock's compensated neutron porosity and mineral content and porosity is as follows: ; in, CNL ma It is the density of the rock skeleton, V sh The content of clay, CNL sh The neutron value of the regional clay. CNL φ φ represents the fluid neutron value, and φ represents the calculated porosity. The logging parameters of dry and wet clay points are determined by using neutron-density intersection. The logging parameters are then substituted into the above-mentioned relationship between rock compensated neutron porosity and mineral content and porosity to obtain the porosity.

[0032] In this embodiment, the area where the underground coal gasification furnace is located is determined as the target area. Based on the geological characteristics of the target area, a stratigraphic volume model of the water-bearing sandstone and mudstone lithology profile is established. That is, the total volume of the rock strata in the target area is equal to the sum of the sandstone volume, mudstone volume and rock strata pore volume. In practical applications, as long as the lithology of the target area is mainly sandstone and mudstone, the stratigraphic volume model is established based on the total volume of a certain rock stratum being equal to the sum of the sandstone volume, mudstone volume and stratigraphic pore volume.

[0033] In this embodiment of the application, determining the clay content curve of the well based on the natural gamma logging curve includes: The natural gamma logging curve of any well contains natural gamma values ​​at different locations within the well. Histograms are used to statistically analyze the natural gamma logging curves of the well, resulting in a natural gamma histogram for that well. From this histogram, natural gamma readings are taken from different locations within the well. The maximum natural gamma reading is selected from all locations to obtain the maximum natural gamma value for the pure mudstone layer (excluding the coal seam). The minimum natural gamma reading is selected from all locations to obtain the minimum natural gamma value for the pure mudstone layer (excluding the coal seam). Based on the maximum and minimum natural gamma values ​​for the pure mudstone layer and the natural gamma readings at different locations, the clay content index is calculated for each location. Finally, the clay content curve for the well is obtained based on the clay content index at each location.

[0034] In this embodiment, the formula for calculating the mud content index is as follows: ; Among them, I GR GR represents the relative value of natural gamma at any point in the well, also known as the clay content index, and is dimensionless; GR represents the natural gamma reading at any point in the well. min and GR max These represent the minimum and maximum natural gamma values ​​of the well drilling, respectively.

[0035] In practical applications, the relative value of the clay volume content of the target layer reflects the level of clay volume content in the formation. A larger relative value indicates a higher clay volume content in the formation, while a smaller relative value indicates a lower clay volume content.

[0036] In this embodiment of the application, the porosity curve of pure rock is corrected using the clay content curve to obtain the porosity prediction curve of the well, including: By combining the natural gamma logging curves of the well and using the formation volume model as a basis, the Hilch index is used to correct the calculated clay content index at different locations of the well, resulting in the corrected clay content index at different locations. Based on the corrected clay content index at different locations of the well, the porosity prediction curve of the well is obtained.

[0037] In this embodiment of the application, the correction formula is as follows: ; Among them, V sh It represents the volumetric mud content at different drilling locations, i.e., the corrected mud content index; GCUR represents the Hillch index, which is related to the formation age and can be determined statistically by combining core analysis results with natural gamma logging values.

[0038] In this embodiment of the application, the risk threshold includes: a first risk threshold, a second risk threshold, and a third risk threshold; specifically, the risk threshold is a porosity risk threshold, the first risk threshold can be 12%, the second risk threshold can be 16%, and the third risk threshold can be 20%.

[0039] In this application embodiment, multiple types of logging curves are analyzed to determine risk thresholds, including: performing histogram analysis on porosity measurement data and logging interpretation results to obtain risk thresholds.

[0040] Step S120: Perform a three-dimensional waveform indication simulation on the three-dimensional seismic data and porosity prediction curve to obtain porosity data for the area where the underground coal gasification furnace is located.

[0041] In this embodiment of the application, based on the porosity prediction curves of all wells within the target area and combined with three-dimensional seismic data, a three-dimensional indicator simulation is used to predict the porosity of the target area in three dimensions.

[0042] In this embodiment of the application, the porosity prediction curve of the well obtained by analyzing multiple types of logging curves is actually a depth domain porosity prediction curve; when performing three-dimensional waveform indication simulation, a time domain porosity prediction curve is required. Therefore, before performing three-dimensional waveform indication simulation, the porosity prediction curve needs to be converted into a time domain porosity prediction curve using time-depth data.

[0043] In this embodiment of the application, the acquisition of porosity data includes: filtering the time-domain porosity prediction curve, extracting the low-frequency information of the time-domain porosity prediction curve, constructing a simulation model for three-dimensional waveform indication simulation based on the extracted low-frequency information; acquiring the configured simulation parameters, and using the three-dimensional seismic data, the time-domain porosity prediction curve and the simulation parameters to perform three-dimensional waveform indication simulation on the simulation model to obtain the porosity data of the area where the underground coal gasification furnace is located.

[0044] Specifically, the simulation parameters include: effective sample number, smoothing coefficient, and medium frequency; the effective sample number can be 3; the smoothing coefficient can be 3; the cutoff frequency can be set as follows: the high-pass frequency of the low-frequency part is 8Hz, the high-cutoff frequency is 10Hz, the low-cutoff frequency of the high-frequency part is 45Hz, the high-pass frequency is 150Hz, and the high-cutoff frequency is 200Hz.

[0045] In this embodiment of the application, the result obtained by the three-dimensional waveform indicator simulation is a three-dimensional porosity data volume; after obtaining the three-dimensional porosity data volume, it needs to be converted into a two-dimensional planar diagram.

[0046] In this embodiment of the application, the standard well can be a well whose porosity and water content are known and interpreted by well logging within the target area; or it can be a well outside the target area that is less than a preset distance threshold and has a high correlation with the target area; the specific distance threshold can be 100m.

[0047] Step S130: Extract the porosity data of the roof of the area where the underground coal gasifier is located from the porosity data, and use it as the target porosity data; compare the target porosity data with the risk threshold to obtain the risk identification result of the roof of the area where the underground coal gasifier is located.

[0048] In this embodiment of the application, the roof is within 150m above the coal seam.

[0049] In this embodiment of the application, before extracting the porosity data of the roof of the area where the underground coal gasification furnace is located from the porosity data, the method further includes: Obtain seismic horizon data for different regions of the roof; extract porosity data of the roof in the region where the underground coal gasification furnace is located from the porosity data, including: for any region of the roof, extract the porosity data of the region from the porosity data based on the seismic horizon data of that region, and use it as the target porosity data of the roof region; determine the target porosity data of the roof based on the target porosity data of different regions of the roof.

[0050] In the embodiments of this application, the seismic stratigraphic data of different regions of the roof refers to several aquifers within 150 meters above the coal seam; the seismic stratigraphic data of different regions of the roof is obtained directly from geological data or by other methods.

[0051] In this embodiment, the target porosity data is compared with a risk threshold to obtain the risk identification result of the roof of the area where the underground coal gasification furnace is located. In practice, different areas of the roof are determined as risk-free, low-risk, medium-risk, and high-risk areas according to the risk threshold; that is, the roof is divided into anhydrous sandstone, weakly hydrous sandstone, moderately hydrous sandstone, and water-rich sandstone. The specific steps are as follows: For any region of the roof, if the target porosity data of that region is less than the first risk threshold, the risk identification result of the region is no risk; if the target porosity data of the region is greater than or equal to the first risk threshold and less than the second risk threshold, the risk identification result of the region is low risk; if the target porosity data of the region is greater than or equal to the second risk threshold and less than the third risk threshold, the risk identification result of the region is medium risk; if the target porosity data of the region is greater than the third risk threshold, the risk identification result of the region is high risk; based on the risk identification results of different regions of the roof, the risk identification result of the roof of the region where the underground coal gasification furnace is located is determined.

[0052] In one embodiment of this application, the target porosity data is compared with a risk threshold to obtain the risk identification result of the roof of the area where the underground coal gasification furnace is located, including: if the target porosity is <12%, it is an anhydrous rock layer, i.e., a risk-free area; if the target porosity is greater than or equal to 12% and less than 16%, it is a weakly ahydrous rock layer, i.e., a low-risk area; if the target porosity is greater than or equal to 16% and less than 20%, it is a moderately ahydrous rock layer, i.e., a medium-risk area; and if the target porosity is greater than or equal to 20%, it is a water-rich rock layer, i.e., a high-risk area.

[0053] Taking a test area containing three wells—BaiMei 001, Huang 4, and BaiQuan 5—as an example, the risk identification method for the aquifers on the top and bottom plates of the underground coal gasification furnace proposed in this application includes the following steps: 1. Predict the porosity curves of Baimei 001 Well, Huang 4 Well, and Baiquan 5 Well: like Figure 2 As shown, cross-plot analysis was performed on the natural gamma, neutron, and density curves of the test area to obtain the drilling porosity curve; the density logging curve of the test area is shown in the figure. Figure 3 As shown, Figure 3 DEN represents density; the compensated neutron logging curves of the test area are as follows: Figure 4 As shown, Figure 4 CNL represents compensated neutrons; the natural gamma logging curves in the test area are as follows: Figure 5 As shown, Figure 5 GR stands for gamma; 1.1 Calculation of clay content When a muddy stratum contains no other radioactive minerals besides mud, the natural radioactivity of the rock is mainly determined by the radioactive elements adsorbed by the mud. Therefore, natural gamma logging is commonly used to determine the mud content of the rock. To calculate the mud content using GR (Grammatic Reduction), the GR values ​​for pure mudstone and pure rock must first be determined. Comprehensive analysis suggests that histograms from multiple wells can be used to determine the GR values ​​for pure mudstone and pure sandstone in different geological strata.

[0054] The amplitude of the natural gamma ray logging curve varies depending on the formation's clay content. Using the gamma ray logging value of pure mudstone as the maximum and the gamma ray logging value of pure sandstone as the minimum, the gamma ray logging value of the target formation is compared to these values ​​to calculate the relative value of the formation's clay volume content. The magnitude of the relative value reflects the formation's clay volume content; a larger relative value indicates a higher clay volume content, and a smaller relative value indicates a lower clay volume content. Figure 6 As shown, Figure 6 In the medium POR (Porosity), 12%, 16%, and 20% represent the first, second, and third risk thresholds, respectively. Figure 6 The right side shows rock strata with different water-bearing properties as interpreted by well logging.

[0055] After removing the ultra-low GR anomalies from the coal seam, histogram statistics were performed on the GR curve of the well, such as... Figure 7 As shown. From Figure 7 It can be seen that the maximum and minimum values ​​of natural gamma ray in the target formation are 76 and 43 API, respectively. Based on these two values ​​and the natural gamma ray logging curve, the Hillch index is used to correct the above formula. The Hillch index is related to the geological age of the strata. In the test area, the coal-bearing target strata are Jurassic strata, and the Hillch index is taken as 2. The formation clay volume content of this borehole is calculated, and the results are as follows: Figure 8 As shown.

[0056] 1.2 Calculation of porosity Based on the sensitivity curve analysis results, porosity was calculated using density-neutron curve cross-plot analysis. Density-neutron cross-plots were generated to obtain the aforementioned values. Figure 9 This is a density-neutron cross-section diagram. Figure 9 SS represents the water-saturated pure sandstone line, LS represents the water-saturated limestone line, and DOL represents the water-saturated dolomite line. Point A is the rock skeleton point, showing a rock skeleton density of 2.65 g / cc and a neutron value of ( CNL ma The value is -0.02; point B is the free water point, i.e., the fluid density is 1 g / cc, and the neutron value is ( CNL w Point C is the bound water point; point D is the wet clay point with a density and a neutron value of 0.45; point E is the dry clay point, i.e., the clay density is 2.66 g / cc, and the clay neutron value is ( ). CNL sh The value is 0.4. Substituting the above values ​​into the equation system, the porosity curves of Baimei 001 well, Huang 4jin well, and Baiquan 5 well are obtained, as shown in the following figures. Figure 10 .

[0057] 2. Three-dimensional prediction of water-rich risk areas in the experimental zone Based on the existing single-well porosity curves of three wells, waveform indicator simulation technology was used to predict the porosity of the entire test area in three dimensions. Waveform indicator simulation is an inversion method developed from geostatistical inversion. It adopts a waveform phase control approach, using seismic waveforms to drive well logging curves for simulation. It does not require uniform well location distribution, has higher resolution, and better conforms to geological sedimentary patterns. Compared with conventional simulation techniques, waveform indicator simulation fully utilizes the correlation between seismic data and well logging information, breaking through the seismic resolution limitation of λ / 4 (λ represents the wavelength of the seismic wavelet), achieving high-resolution inversion. Simultaneously, waveform indicator simulation technology overcomes the limitation of traditional geostatistical inversion methods, which can only predict P-wave impedance data, and can directly predict the physical properties of underground rock strata (such as porosity). Therefore, waveform indicator simulation is used to characterize the distribution of porosity in underground rock strata to identify water-bearing risks within 150m of the upper part of the coal seam.

[0058] 2.1 Principle of Waveform Indication Simulation Method Seismic waveform indication simulation is based on the Seismic Waveform Indication Markov Chain Monte Carlo Stochastic Simulation (SMCMC) algorithm. It employs a phase-controlled stochastic simulation approach, selecting wells with close spatial distance and high correlation to the prediction points to establish the initial model, guided by seismic waveform characteristics. Combining seismic waveform classification techniques with fundamental principles of seismic sedimentology, the lateral variations in seismic waveforms can characterize the facies transition features of sedimentary environments, thereby reflecting the combined characteristics of lithologies. This demonstrates the advantages of waveform phase control, enhances the reliability of simulation results, eliminates the influence of uneven well logging distribution on simulation results, and improves the accuracy of reservoir simulation. It is suitable for predicting thin layers with rapid lateral variations.

[0059] The specific steps for waveform indication simulation are as follows: Figure 11 As shown, the process includes: selecting wells with high correlation to the seismic traces to be identified to establish an initial model; using the porosity of the effective sample wells (i.e., Baiquan 5, Huang 4, and Baimei 001) as prior information; calculating the relative porosity using seismic data; combining this with well logging data to obtain the absolute porosity; performing matched filtering on the initial model and porosity to obtain the likelihood function; and, based on Bayesian theory, obtaining the posterior probability distribution according to the prior probability and the likelihood function. The mean of the solution where the posterior probability density is maximized is taken as the final expected value, i.e., the final porosity.

[0060] 2.2 Waveform Indication Simulation Results Based on the porosity curve, waveform indication simulation was performed on the test area to obtain the three-dimensional porosity prediction results of the test area. Then, the waveform indication simulation profile was extracted, such as... Figure 12As shown in the waveform indicator simulation profile, the red area represents high-porosity sandstone, and the blue area represents coal seams or mudstone. The waveform indicator simulation profile shows that the simulation results match the porosity curves of all wells wells wells well, with high horizontal and vertical resolution and clear detail. Overall, the waveform indicator simulation results show a good correlation with seismic waveforms, indicating that seismic data plays a certain controlling role in the simulation, resulting in higher resolution and greater reliability, making it suitable for predicting porosity in the test area.

[0061] 2.3 Prediction results of water-rich risk areas in the experimental zone Based on the waveform simulation results and combined with the single-well aquifer prediction results, porosity of 12%, 16%, and 20% were selected as low, medium, and high risk thresholds, respectively, to predict the potential water-bearing risk in the test area. Areas with porosity less than 12% have no water-bearing risk; areas with porosity greater than or equal to 12% but less than 16% have weak water-bearing risk; areas with porosity greater than or equal to 16% but less than 20% have medium water-bearing risk; and areas with porosity greater than or equal to 20% have high water-bearing risk. Based on the stratigraphic position of the collected sandstone, three water-bearing sandstone layers can be identified within 150m above the coal seam: K3 potential sandstone aquifer, K2 potential sandstone aquifer, and K1 potential sandstone aquifer. The seismic stratigraphic profiles of K1, K2, and K3 are shown below. Figure 13 As shown.

[0062] from Figure 14 It can be seen that the northern and southern parts of the K1 test area have a higher risk of water content, while the central part of the test area has a lower risk.

[0063] from Figure 15 It can be seen that the overall water content risk of layer K2 is low, while the water content risk is moderate near Baiquan 5 jin.

[0064] from Figure 16 It can be seen that almost the entire region is at risk of moisture content, with the entire region at medium risk of moisture content. This application embodiment determines the aquifer risk porosity threshold by analyzing the well logging interpretation and porosity of the Maye 2 well. Using existing gamma and neutron curves as input data, porosity curves of the Baimei 001, Huang 4, and Baiquan 5 wells in the test area are predicted. Based on the aquifer risk threshold classification results, the predicted curves of the three wells are divided, resulting in a reasonable aquifer distribution. Using the porosity curves as a basis, a three-dimensional waveform indication simulation of porosity is performed on the test area, and the simulation results are accurate and reliable. Based on the collected seismic horizons, the upper 150m of the coal seam is divided into three potential aquifers: K1, K2, and K3. Waveform indication simulation porosity plane maps of each potential aquifer are extracted to delineate the aquifer risk area.

[0065] Corresponding to the above method, this application also provides a risk identification device for aquifers on the roof of an underground coal gasification furnace, such as... Figure 17 As shown, the risk identification device for the aquifer on the roof of the underground coal gasification furnace includes: The acquisition unit 1710 is used to acquire three-dimensional seismic data of the area where the underground coal gasification furnace is located, as well as various logging curves of wells drilled in the area where the underground coal gasification furnace is located. Analysis unit 1720 is used to analyze multiple types of logging curves to determine the porosity prediction curve and risk threshold of the well. Simulation unit 1730 is used to perform three-dimensional waveform indication simulation on three-dimensional seismic data and porosity prediction curves to obtain porosity data of the area where the underground coal gasification furnace is located. The comparison unit 1740 is used to extract the porosity data of the roof of the area where the underground coal gasifier is located from the porosity data, and use it as the target porosity data; the target porosity data is compared with the risk threshold to obtain the risk identification result of the roof of the area where the underground coal gasifier is located.

[0066] The functions of each functional unit of the coal underground gasifier roof aquifer risk identification device provided in the above embodiments of this application can be realized through the above-described methods and steps. Therefore, the specific working process and beneficial effects of each unit in the coal underground gasifier roof aquifer risk identification device provided in the embodiments of this application will not be repeated here.

[0067] This application also provides an electronic device, such as... Figure 18 As shown, it includes a processor 1810, a communication interface 1820, a memory 1830, and a communication bus 1840, wherein the processor 1810, the communication interface 1820, and the memory 1830 communicate with each other through the communication bus 1840.

[0068] Memory 1830 is used to store computer programs; When processor 1810 executes a program stored in memory 1830, it performs the following steps: Acquire 3D seismic data of the area where the underground coal gasification furnace is located, as well as various logging curves of wells drilled in the area where the underground coal gasification furnace is located; Analyze various logging curves to determine the porosity prediction curve and risk threshold for drilling; Three-dimensional waveform indicator simulation was performed on three-dimensional seismic data and porosity prediction curves to obtain porosity data for the area where the underground coal gasification furnace is located. Porosity data of the roof of the area where the underground coal gasification furnace is located is extracted from the porosity data and used as the target porosity data. By comparing the target porosity data with the risk threshold, the risk identification results of the roof of the area where the underground coal gasification furnace is located are obtained.

[0069] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0070] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0071] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0072] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0073] The implementation methods and beneficial effects of the various components of the electronic device in the above embodiments for solving the problem can be found in [reference needed]. Figure 1 The steps in the illustrated embodiments are used to implement the electronic device. Therefore, the specific working process and beneficial effects of the electronic device provided in this application will not be repeated here.

[0074] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform any of the coal underground gasification furnace roof aquifer risk identification methods described in the above embodiments.

[0075] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the coal underground gasification furnace roof aquifer risk identification methods described in the above embodiments.

[0076] Those skilled in the art will understand that the embodiments in this application can be provided as methods, systems, or computer program products. Therefore, the embodiments in this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments in this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0077] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0078] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0079] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0080] Although preferred embodiments have been described in this application, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.

[0081] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims in this application and their equivalents, then this application also intends to include these modifications and variations.

Claims

1. A method for identifying the risk of aquifers on the roof of an underground coal gasification furnace, characterized in that, The method includes: Acquire 3D seismic data of the area where the underground coal gasification furnace is located, as well as various logging curves of wells drilled in the area where the underground coal gasification furnace is located; The various logging curves are analyzed to determine the porosity prediction curve and risk threshold of the well. A three-dimensional waveform indication simulation was performed on the three-dimensional seismic data and the porosity prediction curve to obtain the porosity data of the area where the underground coal gasification furnace is located. From the porosity data, the porosity data of the roof of the area where the underground coal gasification furnace is located is extracted as the target porosity data; By comparing the target porosity data with the risk threshold, the risk identification result of the roof of the area where the underground coal gasification furnace is located is obtained.

2. The method as described in claim 1, characterized in that, The various types of logging curves include: density logging curves, compensated neutron logging curves, and natural gamma logging curves; Analyzing the various logging curves to determine the porosity prediction curve of the well, including: Cross-plot analysis of the density logging curves and compensated neutron logging curves of the well was performed to obtain the rock physical parameters of the well. Based on the rock physical parameters, the pure rock porosity curve of the well is determined; based on the natural gamma logging curve, the clay content curve of the well is determined. The porosity curve of the pure rock is corrected using the clay content curve to obtain the porosity prediction curve of the well.

3. The method as described in claim 1, characterized in that, The various types of logging curves also include: well porosity measurement data and logging interpretation results; Analyzing the various types of well logging curves to determine the risk threshold includes: Histogram analysis was performed on the porosity measurement data and the well logging interpretation results to obtain the risk threshold.

4. The method as described in claim 3, characterized in that, The risk thresholds include: a first risk threshold, a second risk threshold, and a third risk threshold.

5. The method as described in claim 1, characterized in that, The various types of logging curves also include: time-depth relationship data; Before performing a three-dimensional waveform indication simulation on the three-dimensional seismic data and the porosity prediction curve to obtain the porosity data of the area where the underground coal gasification furnace is located, the method further includes: Based on the time-depth relationship data, the porosity prediction curve is converted into a time-domain porosity prediction curve; Perform a three-dimensional waveform indication simulation on the three-dimensional seismic data and the porosity prediction curve, including: A three-dimensional waveform indication simulation is performed on the three-dimensional seismic data and the time-domain porosity prediction curve.

6. The method as described in claim 4, characterized in that, Before extracting the porosity data of the roof of the area where the underground coal gasification furnace is located from the porosity data, the method further includes: Obtain seismic horizon data for different regions of the top plate; From the porosity data, the porosity data of the roof of the area where the underground coal gasification furnace is located is extracted, including: For any region of the roof, based on the seismic horizon data of the region, the porosity data of the region is extracted from the porosity data and used as the target porosity data for the region of the roof; Based on the target porosity data of different regions of the roof, the target porosity data of the roof is determined.

7. The method as described in claim 6, characterized in that, The target porosity data is compared with the risk threshold to obtain the risk identification result of the roof of the area where the underground coal gasification furnace is located, including: For any region of the top plate, if the target porosity data of the region is less than the first risk threshold, the risk identification result of the region is no risk. If the target porosity data of the region is greater than or equal to the first risk threshold and the target porosity data of the region is less than the second risk threshold, then the risk identification result of the region is low risk. If the target porosity data of the region is greater than or equal to the second risk threshold and the target porosity data of the region is less than the third risk threshold, then the risk identification result of the region is medium risk. If the target porosity data of the region is greater than the third risk threshold, then the risk identification result of the region is high risk; Based on the risk identification results of different areas of the roof, the risk identification results of the roof of the area where the underground coal gasification furnace is located are determined.

8. A risk identification device for aquifer on the roof of an underground coal gasification furnace, characterized in that, The device includes: The acquisition unit is used to acquire three-dimensional seismic data of the area where the underground coal gasification furnace is located, as well as various logging curves of wells drilled in the area where the underground coal gasification furnace is located. The analysis unit is used to analyze the various logging curves to determine the porosity prediction curve and risk threshold of the well. The simulation unit is used to perform three-dimensional waveform indication simulation on the three-dimensional seismic data and the porosity prediction curve to obtain the porosity data of the area where the underground coal gasification furnace is located. The comparison unit is used to extract the porosity data of the roof of the area where the underground coal gasifier is located from the porosity data, and use it as the target porosity data; compare the target porosity data with the risk threshold to obtain the risk identification result of the roof of the area where the underground coal gasifier is located.

9. An electronic device, characterized in that, The electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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