Method for predicting current ground temperature field of broken basin

Abstract

The invention relates to a method for predicting a current ground temperature field of a broken basin. The method comprises the following steps: firstly, acquiring a ground temperature gradient mean value of a well point stable section in a research area, and dividing the research area into a plurality of sub-areas according to parameters such as a stratum compaction coefficient; then, establishing a preliminary prediction model between the ground temperature gradient and the weighted average value of the mourphy surface depth, the substrate depth and the stratum thermal conductivity in each sub-region; and systematically screening out a well location with a relatively high ground temperature gradient by identifying the residual error of the preliminary prediction model, and further quantitatively analyzing the enhancement effect of deep and large fractures on a ground temperature field, namely determining the temperature control influence range of each fracture based on three-dimensional seismic data, calculating the distance between the well location and the fracture, and obtaining the enhancement efficiency coefficient of the fracture through linear regression. Correcting the preliminary model to obtain a final prediction model; and finally, based on the regional basic map and the final model, performing gridding calculation and generating a high-precision ground temperature gradient distribution map.

Description

Technical Field

[0001] This invention belongs to the field of resource exploration engineering technology, specifically a method for predicting the current geothermal field in a rift basin. Background Technology

[0002] The exploration of oil and gas, as well as geothermal resources, is inseparable from the study of geothermal fields. Previously, the prediction of the planar distribution of the temperature field mainly relied on well-controlled interpolation, a common method but prone to significant errors. Because well-controlled interpolation only uses limited well data to extrapolate the temperature distribution of the entire area, the results often fail to reflect the complexity of underground temperature variations. This is especially true in areas with complex geological conditions or large well spacing, where the prediction error is substantial and fails to meet the needs of refined exploration. Particularly in complex geological contexts, relying solely on traditional well-controlled interpolation methods is insufficient to provide sufficiently accurate temperature field distributions, limiting the effective assessment and development of geothermal resources. Summary of the Invention

[0003] The purpose of this invention is to provide a method for predicting the current geothermal field in a rift basin, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for predicting the current geothermal field in a rift basin, characterized in that the method comprises the following steps: S1. Obtain the average geothermal gradient of the stable section of each well point in the study area; S2. The study area is divided into multiple sub-regions based on geological parameters; S3. Based on sub-regions, establish a preliminary prediction model between the mean geothermal gradient and the weighted average of Moho depth, basement depth, and formation thermal conductivity:

[0005] In the formula, Hmoho is the depth of the Moho surface, HT is the basement depth, λi is the thermal conductivity of the formation, Hi is the formation thickness, and E is a constant term. S4. Based on the comparison between the calculation results of the preliminary prediction model and the mean geothermal gradient, well locations with high geothermal gradients are identified. By analyzing the relationship between the distribution of deep faults and well locations with high geothermal gradients, the preliminary prediction model is corrected to obtain the final prediction model:

[0006] In the formula, Gg represents the predicted geothermal gradient, Hmoho is the depth of the Moho discontinuity, HT is the basement depth, λi is the thermal conductivity of the formation, Hi is the formation thickness, L is the influence range of the temperature-controlling fault, Lfault is the distance from the fault, and E is a constant term. S5. Based on the basic map of the study area and the revised final prediction model, generate a geothermal gradient distribution map of the study area.

[0007] As a preferred scheme of the present application, the method for determining the stable section in S1 is: analyzing the vertical distribution characteristics of the well point geothermal gradient in the study area, and determining the depth section where the geothermal gradient value tends to be stable with the change of depth as the stable section.

[0008] As a preferred scheme of the present application, the average geothermal gradient in S1 is calculated based on the static temperature data of the drill pipe test.

[0009] As a preferred scheme of the present application, the geological parameter in S2 is the compaction coefficient.

[0010] As a preferred scheme of the present application, the weighted average value of the formation thermal conductivity in S3 is calculated according to the formation thickness.

[0011] As a preferred scheme of the present application, the method for identifying the well site with high geothermal gradient in S4 is: calculating the residual between the geothermal gradient prediction value obtained by the preliminary prediction model and the actual average geothermal gradient, and identifying the well with positive residual as the well site with high geothermal gradient.

[0012] As a preferred scheme of the present application, the analysis of the relationship between the distribution of deep faults and the abnormal area of geothermal gradient in S4 specifically includes the following steps: S4.1 determining the temperature control fault influence range L of each deep fault based on the three-dimensional seismic data interpretation results; S4.2 calculating the vertical distance Lfault between each well site with high geothermal gradient and the nearest deep fault, and screening out the well sites that meet Lfault≤L; S4.3 taking the L-Lfault value corresponding to the screened well site as the independent variable, and taking the high geothermal gradient value ΔGg thereof as the dependent variable, performing linear regression analysis to obtain the regression coefficient D, which is the enhancement efficiency coefficient of the fault on the geothermal field.

[0013] As a preferred scheme of the present application, the basic map in S5 includes the basement depth plane map, the Moho depth map, the deep fault distribution map and the weighted average value distribution map of the formation thermal conductivity.

[0014] As a preferred scheme of the present application, the geothermal gradient distribution map in S5 is an isogram or a color rendering map generated by interpolation calculation based on the regular grid data body.

[0015] To achieve the above-mentioned purpose, the present application provides the following technical scheme: compared with the prior art, the present application has the following beneficial effects: BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is the flowchart of the present application. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0018] One specific embodiment of the present application is a present geothermal field prediction method for a fault basin, comprising the following steps: S1. Obtain the average geothermal gradient of each well point in the study area; S2. Divide the study area into multiple sub-regions according to geological parameters; S3. Based on the sub-regions, establish a preliminary prediction model between the average geothermal gradient and the Moho depth, the basement depth, and the weighted average value of the formation thermal conductivity; S4. Based on the comparison between the calculation results of the preliminary prediction model and the average geothermal gradient, identify the geothermal gradient anomaly area, correct the preliminary prediction model by analyzing the relationship between the distribution of deep faults and the geothermal gradient anomaly area, and obtain the final prediction model; S5. Based on the corrected final prediction model, generate a geothermal gradient distribution map of the study area.

[0019] In order to further illustrate the specific process of implementing the above steps, in this embodiment, the present application discloses a present geothermal field prediction method for a fault basin, which is performed in the following manner: S1: Collect the drill pipe test static temperature data of all available drilling wells in the study area from the oilfield database, and obtain the depth and static temperature value list of each well. Process the original data of each well, select the continuous temperature-depth data points in the wellbore, and perform linear fitting by using the least square method. The slope of the fitting straight line is the geothermal gradient value of the well section. This calculation generates a new data set of the geothermal gradient change with depth for each well.

[0020] Analyze the vertical distribution characteristics of the geothermal gradient data of all wells. Plot the data in a scatter plot, with the vertical coordinate being the depth and the horizontal coordinate being the geothermal gradient. Analyze the vertical distribution characteristics of the geothermal gradient value. In this embodiment, at a depth of about 600 meters or less, the geothermal gradient value is scattered and changes sharply, while below this depth, the geothermal gradient value converges in a stable range with slight fluctuations. Accordingly, it is determined that 600 meters is the stable depth threshold of the region, and the depth of 600 meters or more is the stable section of the region.

[0021] For the well whose depth is more than 600 meters, all the calculated geothermal gradient values in the depth section of 600 meters are selected, the arithmetic mean of these values is calculated, and the mean value is taken as the geothermal gradient mean value of the well. The final results are arranged into a table, and the well number, geographic coordinates and geothermal gradient mean value are recorded.

[0022] S2: Extract the conventional logging curve data of representative well sites in the study area from the logging database, mainly including acoustic travel time, density, neutron porosity and natural gamma. Select the normally compacted shale section, draw the semi-logarithmic relationship graph of acoustic travel time and depth, and through linear fitting of the data points in the logarithmic coordinate system, the reciprocal of the slope of the obtained straight line is the compaction coefficient value of the well. The compaction coefficient values calculated from all representative well sites in the study area are summarized to form a spatial distribution data set.

[0023] The spatial distribution characteristics of the compaction coefficient values are analyzed, and the compaction coefficient planar contour map of the study area is generated by using the geological statistical interpolation method. According to the shape, gradient change and numerical distribution range of the contour map, combined with the regional geological structure background, the entire study area is divided into several homogeneous sub-regions with similar compaction coefficient values and consistent geological characteristics. Each divided sub-region is assigned a unique number, and its geographical boundary range and represented average compaction coefficient characteristics are recorded.

[0024] S3: Collect the three parameter data of all well points inside each sub-region divided in step S2: the Moho depth obtained from regional seismic data interpretation, the basement depth determined by joint interpretation of drilling and seismic data, and the weighted average value of formation thermal conductivity calculated based on core measurement and logging data. At the same time, the geothermal gradient mean value of these well points calculated in step S1 is extracted.

[0025] In each sub-region, the geothermal gradient mean value of the above-mentioned well points is taken as the dependent variable, and its corresponding Moho depth, basement depth and weighted average value of formation thermal conductivity are taken as three independent variables to form a modeling data set. A multiple linear regression analysis is performed on the modeling data set of each sub-region. Through least squares fitting, a linear regression equation with three parameters as independent variables and geothermal gradient as dependent variable is obtained, which has the form of:

[0026] In the formula, Gg represents the predicted geothermal gradient, Hmoho is the Moho depth; HT is the basement depth; λi is the formation thermal conductivity; Hi is the formation thickness; E is the constant term.

[0027] The goodness of fit and significance level of each sub-region regression model are evaluated and recorded to verify the reliability of the formula. The regression coefficients A, B, C and constant term E of each sub-region finally determined are recorded corresponding to the number of the sub-region to form the basic prediction formula of each partition.

[0028] S4: Apply each sub-area based prediction formula established in step S3 to all well locations in the study area. Input the Moho depth, basement depth and weighted average of formation thermal conductivity for each well to calculate the preliminary predicted value of geothermal gradient.

[0029] Compare the preliminary predicted value of each well with the measured average value of geothermal gradient of the stable section of the well obtained in step S1, and calculate the residual as the high value ΔGg of geothermal gradient. Systematically identify all well locations with positive residuals, i.e. those with preliminary predicted values systematically lower than measured values, define these well locations as high geothermal gradient well locations, and record their well numbers and geographic coordinates.

[0030] Overlay the spatial distribution of the above-identified high geothermal gradient well locations with the distribution map of deep faults obtained from the interpretation of regional 3D seismic data. Based on the structural plan obtained from 3D seismic data, measure the width of the geothermal field anomaly on both sides of the deep fault zone, take the maximum vertical distance, and define this maximum vertical distance value as the temperature control fault influence range L of the fault. For each high geothermal gradient well location, calculate its vertical distance from the nearest deep fault, and define this value as Lfault. Select all well locations with Lfault≤L, and use the L-Lfault of these well locations as the independent variable and their corresponding high geothermal gradient value ΔGg as the dependent variable to perform linear regression analysis. Determine the coefficient D through the regression, which represents the enhancement efficiency of the fault on the geothermal field.

[0031] Include the above-determined correction term ΔGg =D*(L-Lfault) into the basic preliminary prediction model in step S3 to form the final geothermal gradient prediction formula, which has the final form:

[0032] where Gg represents the predicted geothermal gradient, Hmoho is the Moho depth, HT is the basement depth, λi is the formation thermal conductivity, Hi is the formation thickness, L is the temperature control fault influence range, Lfault is the distance from the fault, and E is a constant term.

[0033] S5: Collect and organize the basement depth plan, Moho depth map, deep fault distribution map and weighted average of formation thermal conductivity distribution covering the entire study area, import the above four digital maps into software with grid calculation and visualization functions, and divide the entire study area into regular grids on the plane.

[0034] The four attribute values of the point, i.e. the basement depth, the Moho depth, the weighted average of the formation thermal conductivity and the distance to the nearest deep fault, are read from the four maps respectively. According to the sub-region where the grid point is located, the final prediction model corresponding to the sub-region is called. The four attribute values read and the coefficients and constants determined are substituted into the formula to calculate the predicted geothermal gradient value of the grid point. After the calculation of all grid points is completed, a new data volume containing the predicted geothermal gradient values of all grid points will be generated in the software. The gridding and contour drawing functions of the software are used to generate the predicted geothermal gradient plane map of the study area.

[0035] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary and that changes in form and detail can be made without departing from the principles and spirit of the application. The scope of the application is therefore defined by the appended claims and their equivalents.

Claims

1. A method for predicting present geotemperature field of a rifted basin, characterized in that, The method comprises the following steps: S1. obtaining the average geothermal gradient of each well point in the study area; S2. dividing the study area into multiple sub-regions according to geological parameters; S3. based on the sub-regions, establishing a preliminary prediction model between the average geothermal gradient and the Moho depth, the basement depth, and the weighted average value of the formation thermal conductivity: wherein Hmoho is the Moho depth, HT is the basement depth, λi is the formation thermal conductivity, Hi is the formation thickness, and E is a constant term; S4. based on the comparison between the calculation results of the preliminary prediction model and the average geothermal gradient, identifying the well sites with high geothermal gradient, modifying the preliminary prediction model by analyzing the relationship between the distribution of deep faults and the well sites with high geothermal gradient, and obtaining the final prediction model: wherein Gg represents the predicted geothermal gradient, Hmoho is the Moho depth, HT is the basement depth, λi is the formation thermal conductivity, Hi is the formation thickness, L is the temperature control fault influence range, Lfault is the fault distance, and E is a constant term; S5. generating the geothermal gradient distribution map of the study area based on the basic map of the study area and the modified final prediction model.

2. The method of predicting present geotemperature field of a rift basin according to claim 1, characterized in that, The determination method of the stable section in S1 is to analyze the vertical distribution characteristics of the well point geothermal gradient in the study area, and to determine the depth section where the geothermal gradient value changes with depth as a stable section.

3. The method of predicting present geotemperature field of a rift basin according to claim 1, wherein, The average geothermal gradient in S1 is calculated based on the drill pipe test static temperature data.

4. The method of predicting present geotemperature field of rift basin according to claim 1, characterized in that, The geological parameter in S2 is the compaction coefficient.

5. The method of predicting present geotemperature field of rift basin according to claim 1, characterized in that, The weighted average value of the formation thermal conductivity in S3 is calculated according to the formation thickness.

6. The method of predicting present geotemperature field of rift basin according to claim 1, characterized in that, The method for identifying the well sites with high geothermal gradient in S4 is to calculate the residual between the predicted geothermal gradient value obtained by the preliminary prediction model and the actual average geothermal gradient, and to identify the well sites with positive residual as the well sites with high geothermal gradient.

7. The method of predicting present geotemperature field of rift basin according to claim 1, characterized in that, The analysis of the relationship between the distribution of deep faults and the geothermal gradient anomaly area in S4 specifically comprises the following steps: S4.1 determining the temperature control fault influence range L of each deep fault based on the three-dimensional seismic data interpretation results; S4.2 calculating the vertical distance Lfault between each well site with high geothermal gradient and the nearest deep fault, and screening out the well sites that satisfy Lfault≤L; S4.3 taking the L - Lfault value corresponding to the screened well sites as the independent variable, and taking the high geothermal gradient value ΔGg of the well sites as the dependent variable, performing linear regression analysis, and obtaining the regression coefficient D, which is the enhancement efficiency coefficient of the fault on the geothermal field.

8. The method of predicting present geotemperature field of rift basin according to claim 1, characterized in that, The basic map in S5 includes the basement depth plan, the Moho depth map, the deep fault distribution map, and the weighted average value distribution map of the formation thermal conductivity.

9. The method of predicting present geotemperature field of rift basin according to claim 1, characterized in that, The geothermal gradient distribution map in S5 is an isogram or a color rendering map generated by interpolation calculation based on the regular grid data body.

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