Oil shale and oil-rich coal symbiotic combination determination method based on dynamic ash content threshold value
By preprocessing and modeling well logging data, the coexistence of oil shale and oil-rich coal can be quickly and automatically distinguished, solving the problems of long time consumption and strong subjectivity in traditional methods, and achieving efficient and accurate coexistence identification.
Patent Information
- Application Number
- CN202511204798.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-27
- Publication Date
- 2026-01-13
AI Technical Summary
In existing technologies, determining the ash content thresholds of oil shale and oil-rich coal is time-consuming, costly, and highly subjective, making it difficult to quickly and accurately distinguish their symbiotic combinations.
By preprocessing the well logging data and resampling it to a uniform depth using a dynamic time warping algorithm, combined with a multi-parameter regression model and kernel density estimation method, a continuous ash yield curve is constructed. The optimal ash yield threshold is determined by applying a bimodal model, thereby achieving automatic differentiation between oil shale and oil-rich coal.
It enables rapid and automatic identification of oil shale and oil-rich coal coexistence combinations, reducing human intervention, improving efficiency and objectivity, and lowering costs.
Smart Images
Figure CN121322011A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of mineral resources exploration, more particularly, to a method for determining oil shale and oil-rich coal symbiotic combination based on dynamic ash content threshold. BACKGROUND
[0002] Oil shale, also known as oil mother shale, can obtain oil shale oil through low-temperature dry distillation, and the oil content of low-temperature dry distillation is usually greater than or equal to 3.5%. Oil shale often symbiotically combines with oil-rich coal, and the symbiotic combination form of the two is of great significance to the formulation of the later joint exploration and development plan. Oil shale and oil-rich coal have similar characteristics on hand specimens, and usually need to be distinguished by the ash content obtained through laboratory analysis. However, the large-scale ash content determination in the laboratory is time-consuming and costly, and the ash content threshold of oil shale and oil-rich coal varies in different regions. SUMMARY
[0003] The present application aims to provide a method for determining oil shale and oil-rich coal symbiotic combination based on dynamic ash content threshold, which can quickly and automatically determine the symbiotic combination pattern of oil shale and oil-rich coal, reduce human intervention, and improve efficiency and objectivity.
[0004] The present application provides a method for determining oil shale and oil-rich coal symbiotic combination based on dynamic ash content threshold, comprising the following steps: S1: Preprocessing the logging data to obtain preprocessed logging data; the logging data includes the natural gamma logging curve, the density logging curve, the acoustic time difference logging curve, and the resistivity logging curve of the target well section; S2: Using a multi-parameter regression model to obtain a continuous calculated ash yield curve of the target layer section according to the preprocessed logging data; S3: Using a kernel density estimation method and a valley bottom search method of a bimodal model to determine the best ash yield threshold for distinguishing oil shale and oil-rich coal according to the continuous calculated ash yield curve of the target layer section; S4: Performing lithology discrimination on the target layer section point by point according to the best ash yield threshold for distinguishing oil shale and oil-rich coal to obtain the symbiotic combination pattern of oil shale and oil-rich coal.
[0005] The present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the method for determining oil shale and oil-rich coal symbiotic combination based on dynamic ash content threshold described above.
[0006] The method for determining oil shale and oil-rich coal symbiotic combination based on dynamic ash content threshold provided by the present application has the following beneficial effects: This invention addresses the problems of long cycle times, high costs, and strong subjectivity in determining ash yield thresholds in traditional methods. It utilizes conventional logging data, such as natural gamma (GR), density (DEN), sonic transit time (AC), and resistivity (RT) logging curves for the target well section. Using the GR curve depth as a benchmark, the Dynamic Time Warping (DTW) algorithm is applied to resample other curves to a unified depth sequence. Using the preprocessed logging data, a multi-parameter regression model of ash yield and logging response is applied to obtain continuous ash yield curves for the target layer. A smoothed probability density curve for calculating ash yield is constructed using kernel density estimation (KDE). The valley search method of a bimodal model is applied to determine the optimal ash yield threshold for distinguishing oil shale from oil-rich coal. Using the optimal ash yield threshold, lithology is determined point-by-point in the target layer. Based on the vertical sequence, the coexistence patterns of oil shale and oil-rich coal are identified, thus solving the problems of long cycle times, high costs, and strong subjectivity in determining ash yield thresholds in traditional methods.
[0007] This invention enables rapid and automatic determination of the symbiotic combination patterns of oil shale and oil-rich coal, reducing human intervention and improving efficiency and objectivity, and has good application prospects in the field of mineral exploration. Attached Figure Description
[0008] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This is a flowchart of the method for determining the symbiotic combination of oil shale and oil-rich coal based on dynamic ash threshold provided by the present invention; Figure 2 The present invention provides an application of the dynamic time warping algorithm to resample four types of logging curves in the target layer to a unified depth map; Figure 3 It is a smooth probability density curve constructed using the kernel density estimation method provided by this invention; Figure 4 This invention provides a symbiotic combination pattern of oil shale and oil-rich coal identified based on the optimal ash yield threshold. Detailed Implementation
[0009] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
[0010] Figure 1 A schematic diagram of the method for determining the symbiotic combination of oil shale and oil-rich coal based on a dynamic ash threshold is shown in this embodiment. In this embodiment, the method for determining the symbiotic combination of oil shale and oil-rich coal based on a dynamic ash threshold includes the following steps: S1: Preprocess the logging data to obtain preprocessed logging data; the logging data includes the natural gamma logging curve, density logging curve, sonic transit time logging curve, and resistivity logging curve of the target well section; In one exemplary embodiment, step S1 specifically includes: S11: Verify the data format of the logging data and confirm the data units. Remove outliers such as spikes, jumps, and straight sections that are obviously inconsistent with geological laws to obtain the verified logging data. S12: Using the natural gamma logging curve in the logging data as the reference depth, the dynamic time warping algorithm is used to perform depth correction on all curves in the logging data to ensure that all types of logging data are strictly aligned at the same depth sampling point, and the preprocessed logging data is obtained. In one exemplary embodiment, the dynamic time warping algorithm is as follows: , in, Represents from point Time The minimum cumulative distance; Represents the reference curve. Values of each sampling point; Represents the curve to be aligned. Values of each sampling point; Represents Euclidean distance; This indicates taking the minimum value; S2: Based on the preprocessed logging data, a multi-parameter regression model is used to obtain the continuous ash yield curve of the target layer. In one exemplary embodiment, step S2 specifically includes: S21: Based on the preprocessed logging data, candidate oil-rich coal points are selected. Based on the candidate oil-rich coal points, the cumulative probability distribution of each curve in the preprocessed logging data is calculated, and the value of the preset cumulative probability of oil-rich coal is taken as the benchmark value of pure oil-rich coal. In one exemplary embodiment, the preset cumulative probability of oil-rich coal is: or ; S22: Based on the preprocessed logging data, candidate oil shale points are selected. Based on the candidate oil shale points, the cumulative probability distribution of each curve in the preprocessed logging data is calculated, and the value of the preset cumulative probability of oil shale is taken as the pure oil shale benchmark value. In one exemplary embodiment, the preset oil shale accumulation probability is: or ; S23: Obtain ash yield experimental data, and based on the ash yield experimental data and the logging values at the corresponding depth points, use a multi-parameter regression model to obtain a continuous calculated ash yield curve for the target layer. In one exemplary embodiment, the multiparameter regression model is as follows: , in, This represents the calculation of ash yield; Represents the weighting coefficients of each logging parameter; , , , These represent the pre-processed natural gamma ray logging curve, density logging curve, sonic transit time logging curve, and resistivity logging curve, respectively, i.e., the pre-processed logging data. , , , These represent the benchmark values for pure oil-rich coal; , , , These represent the baseline values for pure oil shale; Represents the intercept; S3: Based on the continuous ash yield curve of the target layer, the optimal ash yield threshold for distinguishing oil shale from oil-rich coal is determined using the kernel density estimation method and the valley search method of the bimodal model. In one exemplary embodiment, step S3 specifically includes: S31: Based on the continuous ash yield curve of the target section, select the strata with a ash yield greater than the preset ash yield to obtain a candidate sample set; In one exemplary embodiment, the candidate sample set is as follows: , in, Represents the candidate sample set; Indicates the selected first Ash yield of strata at a depth point; This indicates the number of gray values at depth points; S32: Calculate the ash yield statistics of the candidate sample set, and construct a smooth probability density curve for calculating the ash yield curve using the kernel density estimation method. In one exemplary embodiment, the statistics of the ash yield include the minimum, maximum, mean, and standard deviation; In one exemplary embodiment, the kernel density estimation method is as follows: , , in, This represents the probability density function of ash yield, that is, the smoothed probability density curve for calculating the ash yield curve. Represents the ash yield variable; Representing the The gray value of each sample; Represents the total number of candidate samples; Represents the bandwidth for kernel density estimation; This represents the standard deviation of ash yield; Represents the interquartile range of ash yield; S33: Based on the smooth probability density curve of the calculated ash yield curve, the valley search method of the bimodal model is used to determine the optimal ash yield threshold for distinguishing oil shale from oil-rich coal. As an exemplary embodiment, in step S33, in The upper part identifies local maxima (peaks), and the two main peaks correspond to oil-rich coal (low-oil coal) and low-oil coal, respectively. Peak) and oil shale (high) The main distribution of the peaks; between the two main peaks Search within the value range The minimum point, the point corresponding to The value is the optimal separation threshold between oil shale and oil-rich coal. ); S4: Based on the optimal ash yield threshold for distinguishing between oil shale and oil-rich coal, lithology is determined point by point in the target layer to obtain the symbiotic combination pattern of oil shale and oil-rich coal. In one exemplary embodiment, step S4 specifically includes: S41: Based on the optimal ash yield threshold for distinguishing between oil shale and oil-rich coal, the lithology of the target layer is determined point by point to obtain the lithology of each point in the target layer. In an exemplary embodiment, step S41 specifically includes: based on the optimal ash yield threshold for distinguishing oil shale from oil-rich coal, performing lithology determination point by point in the target stratum to obtain the lithology of each point in the target stratum, as shown in the formula:
[0011] in, To differentiate between oil shale and oil-rich coal, the optimal ash yield threshold is required. S42: Based on the lithology of each point in the target stratum, the symbiotic combination pattern of oil shale and oil-rich coal is obtained; In one exemplary embodiment, the symbiotic combination pattern of oil shale and oil-rich coal includes interbedded, interlayered, gradient, and composite types; the interbedded type represents a single layer thickness of oil shale and oil-rich coal of [missing information]. The interlayered type represents oil shale and oil-rich coal. Interlayers containing oil-rich coal or oil shale are smaller than The gradual change type represents a gradual change in ash content between oil shale and oil-rich coal, and between oil-rich coal and oil shale, without clear thin interlayers, but rather a transition zone; the composite type represents a combination of at least two of the interlayered, interbedded, and gradual types.
[0012] In some embodiments, the above-described method for determining the symbiotic combination of oil shale and oil-rich coal based on dynamic ash thresholds can also be implemented in the following ways.
[0013] The figure shows a flowchart of a method for determining the symbiotic combination of oil shale and oil-rich coal based on a dynamic ash threshold. In this embodiment, the method for determining the symbiotic combination of oil shale and oil-rich coal based on a dynamic ash threshold includes: Step 1: Input the natural gamma of the target well section ( ),density( ), sound wave time difference ( ), resistivity ( Well logging curves, with Based on the curve depth, the Dynamic Time Warping (DTW) algorithm is applied to resample other curves to a unified depth sequence; Step 2: Using the preprocessed logging data, apply a multi-parameter regression model of ash content and logging response to obtain a continuous ash yield curve for the target layer. Step 3: Use the kernel density estimation (KDE) method to construct a smooth probability density curve for calculating ash yield curve, and apply the valley search method of the bimodal model to determine the optimal ash yield threshold for distinguishing oil shale from oil-rich coal. Step 4: Using the optimal ash yield threshold, lithology is determined point by point in the target layer, and the coexistence pattern of oil shale and oil-rich coal is identified based on the vertical sequence.
[0014] Optionally, in step 1, the input target well section's natural gamma ( ) ),density( ), sound wave time difference ( ), resistivity ( Well logging curves, with Using curve depth as a baseline, the Dynamic Time Warping (DTW) algorithm is applied to resample other curves to a unified depth sequence, specifically including: Step 11: Load the target well section , , , Four types of original well logging data files were used to verify the data format and confirm the data units, and outliers such as spikes, jumps, and straight sections that clearly did not conform to geological patterns were removed. Step 12: Select Using well logging curves as the baseline depth, a Dynamic Time Warping (DTW) algorithm is employed to perform depth correction on all curves, ensuring strict alignment of the four types of well logging data at the same depth sampling point. The correction accuracy reaches [a certain level]. The sampling interval for well logging data.
[0015] Preferably, in step 12, the DTW algorithm is used as follows:
[0016] In the formula Represents from point Time The minimum cumulative distance is dimensionless. Represents the reference curve. Each sampling point value, in units ; Represents the curve to be aligned. Each sampling point value, with the same unit as the logging curve to be aligned; Represents Euclidean distance, dimensionless.
[0017] like Figure 2 The image shows the application of the dynamic time warping algorithm to resample four types of logging curves in the target layer to a unified depth map; Optionally, in step 2, the step of using the preprocessed logging data and applying a multi-parameter regression model of ash content and logging response to obtain a continuous ash yield curve for the target layer specifically includes: Step 21: Utilize oil-rich coal at lower prices ,Low ,high ,high Features are used to screen candidate oil- and coal-rich points. The cumulative probability distribution of each curve on the candidate point set is calculated, and the cumulative probability is taken. (or The value at () is used as the benchmark value for pure oil-rich coal; Step 22: Utilizing the high efficiency of oil shale higher ,high ,Low Based on the characteristics, candidate oil shale points are selected. The cumulative probability distribution of each curve on the candidate point set is calculated, and the cumulative probability is taken. (or The value at () is used as the benchmark value for pure oil shale; Step 23: Using laboratory data of known ash yield and well logging values at corresponding depth points, perform multiple linear regression to determine the weight coefficients and intercepts of the multi-parameter regression model of ash and well logging response. Calculate the ash yield at each point through the regression model to obtain a continuous calculated ash yield curve for the target layer.
[0018] Preferably, in step 23, the multi-parameter regression model of ash content and well logging response is:
[0019] In the formula This represents the ash yield calculation, in units. ; , , , Represents the pre-processed logging values, in units of , , , .
[0020] like Figure 3 The figure shows a smooth probability density curve constructed using the kernel density estimation method.
[0021] Optionally, in step 3, the process of constructing a smooth probability density curve for calculating the ash yield curve using the kernel density estimation (KDE) method and applying the valley search method of the bimodal model to determine the optimal ash yield threshold for distinguishing oil shale from oil-rich coal specifically includes: Step 31: Set the ash yield screening criteria (usually...) ), excluding obviously irrelevant strata, to obtain a stratum containing Candidate sample set of gray values at depth points .
[0022] Step 32: Calculate the sample set The ash yield statistics, including minimum, maximum, mean, and standard deviation, are used to construct a smooth probability density curve for Ad using the kernel density estimation (KDE) method. .
[0023] Step 33: In The system identifies local maxima (peaks), with two main peaks corresponding to the main distributions of oil-rich coal (low Ad peak) and oil shale (high Ad peak), respectively. Within the Ad value interval between the two main peaks, the system searches... The minimum value point, the corresponding Ad value is the optimal separation threshold between oil shale and oil-rich coal. ).
[0024] Preferably, in step 32, the kernel density estimation (KDE) method is as follows:
[0025] In the formula Represents the probability density function of ash yield, in units ; Represents the ash yield variable, in units ; Representing the Ash value of each sample, in units ; Represents the total number of candidate samples; Represents the bandwidth for kernel density estimation, in units of ; The kernel density estimation bandwidth Choosing the Silverman criterion:
[0026] In the formula Represents the standard deviation of ash yield, in units ; Represents the interquartile range of ash yield, in units .
[0027] Optionally, in step 4, the step of using the optimal ash yield threshold to perform lithological discrimination on the target layer point by point, and identifying the co-occurrence pattern of oil shale and oil-rich coal based on the vertical sequence, specifically includes: Step 41: Utilize the optimal separation threshold ( ) Lithological identification is performed point by point in the target stratum. At that time, the point was determined to be rich in oil and coal. At that time, the point was determined to be oil shale; Step 42: Classify the identified oil shale and oil-rich coal co-occurrence assemblages into predefined typical co-occurrence patterns. Preferably, in step 42, the predefined typical symbiotic combination patterns include four types: interlayered, sandwiched, gradient, and composite. Interbedded type, representing oil shale / oil-rich coal, has a single layer thickness of [missing information]. ; Interlayer type represents oil shale / oil-rich coal It contains interlayers of oil-rich coal / oil shale. ; The gradual change in ash content between oil shale / oil-rich coal and oil-rich coal / oil shale is characterized by a gradual change in ash content, without clear thin interbedded layers, but rather as a transition zone. Composite type represents two or three combinations of interlayered, sandwiched, and gradient types.
[0028] likeFigure 4 The diagram shows the symbiotic combination patterns of oil shale and oil-rich coal identified based on the optimal ash yield threshold.
[0029] This embodiment provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for determining the symbiotic combination of oil shale and oil-rich coal based on a dynamic ash threshold.
[0030] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.
Claims
1. A method for determining the symbiotic combination of oil shale and oil-rich coal based on a dynamic ash threshold, characterized in that, Includes the following steps: S1: Preprocess the logging data to obtain preprocessed logging data; the logging data includes the natural gamma logging curve, density logging curve, sonic transit time logging curve, and resistivity logging curve of the target well section; S2: Based on the preprocessed logging data, a multi-parameter regression model is used to obtain the continuous ash yield curve of the target layer. S3: Based on the continuous ash yield curve of the target layer, the optimal ash yield threshold for distinguishing oil shale from oil-rich coal is determined using the kernel density estimation method and the valley search method of the bimodal model. S4: Based on the optimal ash yield threshold for distinguishing between oil shale and oil-rich coal, lithology is determined point by point in the target layer to obtain the symbiotic combination pattern of oil shale and oil-rich coal.
2. The method for determining the symbiotic combination of oil shale and oil-rich coal based on dynamic ash threshold according to claim 1, characterized in that, Step S1 specifically includes: S11: Verify the data format of the logging data and confirm the data units. Remove outliers such as spikes, jumps, and straight sections that are obviously inconsistent with geological laws to obtain the verified logging data. S12: Using the natural gamma logging curve in the logging data as the reference depth, the dynamic time warping algorithm is used to perform depth correction on all curves in the logging data to ensure that all types of logging data are strictly aligned at the same depth sampling point, thus obtaining the preprocessed logging data.
3. The method for determining the symbiotic combination of oil shale and oil-rich coal based on dynamic ash threshold according to claim 2, characterized in that, The dynamic time warping algorithm is as follows: ， in, Represents from point Time The minimum cumulative distance; Represents the reference curve. Values of each sampling point; Represents the curve to be aligned. Values of each sampling point; Represents Euclidean distance; This indicates taking the minimum value.
4. The method for determining the symbiotic combination of oil shale and oil-rich coal based on dynamic ash threshold according to claim 1, characterized in that, Step S2 specifically includes: S21: Based on the preprocessed logging data, candidate oil-rich coal points are selected. Based on the candidate oil-rich coal points, the cumulative probability distribution of each curve in the preprocessed logging data is calculated, and the value of the preset cumulative probability of oil-rich coal is taken as the benchmark value of pure oil-rich coal. S22: Based on the preprocessed logging data, candidate oil shale points are selected. Based on the candidate oil shale points, the cumulative probability distribution of each curve in the preprocessed logging data is calculated, and the value of the preset cumulative probability of oil shale is taken as the pure oil shale benchmark value. S23: Obtain ash yield experimental data, and based on the ash yield experimental data and the logging values at the corresponding depth points, use a multi-parameter regression model to obtain a continuous calculated ash yield curve for the target layer.
5. The method for determining the symbiotic combination of oil shale and oil-rich coal based on dynamic ash threshold according to claim 4, characterized in that, The multi-parameter regression model is shown in the formula: ， in, This represents the calculation of ash yield; Represents the weighting coefficients of each logging parameter; , , , These represent the pre-processed natural gamma ray logging curve, density logging curve, sonic transit time logging curve, and resistivity logging curve, respectively, i.e., the pre-processed logging data. , , , These represent the benchmark values for pure oil-rich coal; , , , These represent the baseline values for pure oil shale; Represents the intercept.
6. The method for determining the symbiotic combination of oil shale and oil-rich coal based on dynamic ash threshold according to claim 1, characterized in that, Step S3 specifically includes: S31: Based on the continuous ash yield curve of the target section, select the strata with a ash yield greater than the preset ash yield to obtain a candidate sample set; S32: Calculate the ash yield statistic of the candidate sample set, and construct a smooth probability density curve for calculating the ash yield curve using the kernel density estimation method. S33: Based on the smoothed probability density curve of the calculated ash yield curve, the valley search method of the bimodal model is used to determine the optimal ash yield threshold for distinguishing between oil shale and oil-rich coal.
7. The method for determining the symbiotic combination of oil shale and oil-rich coal based on dynamic ash threshold according to claim 6, characterized in that, The kernel density estimation method is as follows: ， ， in, This represents the probability density function of ash yield, that is, the smoothed probability density curve for calculating the ash yield curve. Represents the ash yield variable; Representing the The ash value of each sample; Represents the total number of candidate samples; Represents the bandwidth for kernel density estimation; This represents the standard deviation of ash yield; This represents the interquartile range of ash yield.
8. The method for determining the symbiotic combination of oil shale and oil-rich coal based on dynamic ash threshold according to claim 1, characterized in that, Step S4 specifically includes: S41: Based on the optimal ash yield threshold for distinguishing between oil shale and oil-rich coal, the lithology of the target layer is determined point by point to obtain the lithology of each point in the target layer. S42: Based on the lithology of each point in the target stratum, the symbiotic combination pattern of oil shale and oil-rich coal is obtained.
9. The method for determining the symbiotic combination of oil shale and oil-rich coal based on dynamic ash threshold according to claim 8, characterized in that, Step S41 specifically includes: based on the optimal ash yield threshold for distinguishing oil shale from oil-rich coal, performing lithology determination point by point in the target stratum to obtain the lithology of each point in the target stratum, as shown in the formula: ， in, The optimal ash yield threshold is used to distinguish between oil shale and oil-rich coal.
10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method for determining the symbiotic combination of oil shale and oil-rich coal based on dynamic ash threshold as described in any one of claims 1-9.