A method for predicting present geothermal field of a faulted basin

Abstract

The present application relates to a kind of rift basin present geothermal field prediction method.The method first obtains the mean of well point stable section geothermal gradient in study area, and according to stratigraphic compaction coefficient and other parameters, the study area is divided into multiple sub-regions;Subsequently, the preliminary prediction model between geothermal gradient and Moho depth, basement depth, stratigraphic thermal conductivity weighted average value is established in each sub-region;By identifying the residual of preliminary prediction model, the geothermal gradient high well site is systematically screened out, and then the quantitative analysis of the enhancement effect of deep fault on geothermal field is carried out: that is, based on three-dimensional seismic data, the temperature control influence range of each fault is determined, the distance of well site from fault is calculated, and the enhancement efficiency coefficient of fault is obtained by linear regression, and the preliminary model is corrected, to obtain the final prediction model;Finally, based on regional base map and the final model, high-precision geothermal gradient distribution map is generated by gridding calculation.

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:

[0005] S1. Obtain the average geothermal gradient of the stable section of each well point in the study area;

[0006] S2. The study area is divided into multiple sub-regions based on geological parameters;

[0007] 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:

[0008]

[0009] 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;

[0010] 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:

[0011]

[0012] In the formula, Gg represents the predicted geothermal gradient, Hmoho is the depth of the Moho surface, HT is the basement depth, λi is the formation thermal conductivity, Hi is the formation thickness, L is the influence range of the temperature-controlling fault, Lfault is the vertical distance from the well location to the nearest deep fault, D is the enhancement efficiency coefficient of the fault on the geothermal field, and E is a constant term.

[0013] 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.

[0014] As a preferred embodiment of the present invention, the method for determining the stable segment in S1 is as follows: analyze the vertical distribution characteristics of the geothermal gradient at well points in the study area, and determine the depth segment where the geothermal gradient value tends to stabilize with depth as the stable segment.

[0015] As a preferred embodiment of the present invention, the average geothermal gradient in S1 is calculated based on the static temperature data of the drill pipe test.

[0016] In a preferred embodiment of the present invention, the geological parameter in S2 is the compaction coefficient.

[0017] As a preferred embodiment of the present invention, the weighted average value of the formation thermal conductivity in S3 is calculated by weighting the formation thickness.

[0018] As a preferred embodiment of the present invention, the method for identifying well locations with high geothermal gradients in S4 is as follows: calculate the residual between the predicted geothermal gradient value obtained from the preliminary prediction model and the actual average geothermal gradient value, and identify wells with positive residual values ​​as well locations with high geothermal gradients.

[0019] As a preferred embodiment of the present invention, the analysis of the relationship between the distribution of deep and large faults and the geothermal gradient anomaly zone in step S4 specifically includes the following steps:

[0020] S4.1 Based on the interpretation results of 3D seismic data, determine the influence range L of temperature-controlled faults for each deep and large fault;

[0021] S4.2 Calculate the vertical distance Lfault between each well location with a high temperature gradient and the nearest deep fault, and select well locations that satisfy Lfault≤L. S4.3 Using the L - Lfault value corresponding to the selected well location as the independent variable and the higher geothermal gradient value ΔGg as the dependent variable, a linear regression analysis is performed to obtain the regression coefficient D. This coefficient D is the efficiency coefficient of the fault's enhancement of the geothermal field.

[0022] As a preferred embodiment of the present invention, the basic maps in S5 include a basement depth plan map, a Moho surface depth map, a deep fault distribution map, and a formation thermal conductivity weighted average distribution map.

[0023] As a preferred embodiment of the present invention, the geothermal gradient distribution map in S5 is a contour map or color rendering map generated by interpolation calculation based on a regular grid data volume. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the process of this invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] One specific embodiment of the present invention is: a method for predicting the current geothermal field of a rift basin, comprising the following steps:

[0027] S1. Obtain the average geothermal gradient at each well point in the study area;

[0028] S2. The study area is divided into multiple sub-regions based on geological parameters;

[0029] 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.

[0030] S4. Based on the comparison between the calculation results of the preliminary prediction model and the mean geothermal gradient, geothermal gradient anomaly areas are identified. The preliminary prediction model is then corrected by analyzing the relationship between the distribution of deep faults and geothermal gradient anomaly areas to obtain the final prediction model.

[0031] S5. Based on the revised final prediction model, generate a geothermal gradient distribution map of the study area.

[0032] To further illustrate the specific process of implementing the above steps, in this embodiment, the method for predicting the current geothermal field of a rift basin disclosed in this patent is carried out in the following manner:

[0033] S1: Collect static temperature data from drill pipe tests of all available wells in the study area from the oilfield database to obtain a list of depth and static temperature values ​​for each well. Process the raw data of each well, select continuous temperature-depth data points in the wellbore, and perform linear fitting using the least squares method. The slope of the fitted line is the geothermal gradient value of that well section. This calculation generates a new dataset of geothermal gradient variation with depth for each well.

[0034] The vertical distribution characteristics of all well geothermal gradient data were analyzed. The data were plotted in a scatter plot, with depth on the vertical axis and geothermal gradient on the horizontal axis. Analyzing the vertical distribution characteristics of the geothermal gradient values, in this embodiment, at depths shallower than approximately 600 meters, the geothermal gradient values ​​are dispersed and fluctuate drastically, while below this depth, the geothermal gradient values ​​converge to a stable range with slight fluctuations. Based on this, 600 meters was determined as the stable depth threshold for this area, and depths deeper than 600 meters were considered the stable segment of the area.

[0035] For wells with a depth exceeding 600 meters, select all calculated geothermal gradient values ​​within the depth range above 600 meters, calculate the arithmetic mean of these values, and use this mean as the geothermal gradient mean for that well. Compile the final results into a table, recording the well number, geographic coordinates, and geothermal gradient mean.

[0036] S2: Extract conventional logging curve data from representative well locations within the study area from the logging database, mainly including sonic transit time, density, neutron porosity, and natural gamma. Select normally compacted mudstone sections and plot a semi-logarithmic relationship between sonic transit time and depth. Perform linear fitting on the data points in a logarithmic coordinate system; the reciprocal of the slope of the resulting straight line is the compaction coefficient value for that well. Summarize the compaction coefficient values ​​calculated from all representative well locations within the study area to form a spatially distributed dataset.

[0037] The spatial distribution characteristics of compaction coefficient values ​​were analyzed, and geostatistical interpolation methods were used to generate a planar contour map of the compaction coefficient for the study area. Based on the shape, gradient changes, and numerical distribution range of the contour maps, and combined with the regional geological tectonic background, the entire study area was divided into several homogeneous sub-regions with similar compaction coefficient values ​​and consistent geological characteristics. Each sub-region was assigned a unique number, and its geographical boundary range and the average compaction coefficient characteristics it represented were recorded.

[0038] S3: Collect three parameter data for all well points within each sub-region defined in step S2: the Moho depth obtained from the interpretation of regional seismic data, the basement depth determined by the joint interpretation of drilling and seismic data, and the weighted average of formation thermal conductivity calculated based on core measurements and logging data. Simultaneously, extract the average geothermal gradient values ​​calculated for these well points in step S1.

[0039] Within each sub-region, the mean geothermal gradient of the aforementioned well points is used as the dependent variable, and the weighted average of the corresponding Moho depth, basement depth, and formation thermal conductivity is used as the three independent variables to form the modeling dataset. A multiple linear regression analysis is performed on the modeling dataset for each sub-region. Through least squares fitting, a linear regression equation with three parameters as independent variables and geothermal gradient as the dependent variable is obtained, in the form:

[0040] 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, and E is a constant term.

[0041] The goodness of fit and significance level of the regression model for each subregion were evaluated and recorded to verify the reliability of the formula. The regression coefficients A, B, C and the constant term E of each subregion were then mapped to the subregion number and recorded to form the basic prediction formula for each partition.

[0042] S4: Apply the basic prediction formulas for each sub-region established in step S3 to all well locations within the study area. Input the weighted average of the Moho depth, basement depth, and formation thermal conductivity for each well to calculate the preliminary predicted value of the geothermal gradient.

[0043] The preliminary predicted value of each well is compared with the measured average value of the geothermal gradient in the stable section of the well obtained in step S1. The residual is calculated as the geothermal gradient overshoot value ΔGg. All well locations with positive residual values ​​are systematically identified, that is, those well locations whose preliminary predicted values ​​are systematically lower than the measured values. These well locations are defined as geothermal gradient overshoot well locations, and their well numbers and geographical coordinates are recorded. The spatial distribution of well locations with high geothermal gradients identified above was overlaid with the distribution map of deep and large faults obtained from the interpretation of regional 3D seismic data. Based on the structural planar map obtained from the 3D seismic data, the width of the significantly anomaly in the geothermal field on both sides of the deep and large fault zone was measured, and the maximum vertical distance was taken. This maximum vertical distance value was defined as the temperature-controlling fault influence range L of the fault. For each well location with a high geothermal gradient, its vertical distance to the nearest deep and large fault was calculated, and this value was defined as Lfault. All well locations with Lfault ≤ L were selected. Using L-Lfault of these well locations as the independent variable and the corresponding geothermal gradient elevation value ΔGg as the dependent variable, a linear regression analysis was performed. The coefficient D was determined through this regression, which represents the enhancement efficiency of the fault on the geothermal field.

[0044] The determined correction term ΔGg =D*(L-Lfault) is incorporated into the basic preliminary prediction model in step S3 to form the final geothermal gradient prediction formula, which has the following final form:

[0045]

[0046] 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.

[0047] S5: Collect and organize the basement depth plan map, Moho depth map, deep fault distribution map, and formation thermal conductivity weighted average distribution map 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.

[0048] The software reads four attribute values ​​for each point from four maps: basement depth, Moho depth, weighted average formation thermal conductivity, and distance to the nearest deep fault. Based on the sub-region where the grid point is located, it calls the corresponding final prediction model for that sub-region. Substituting the four attribute values, along with the determined coefficients and constants, into the formula, the predicted geothermal gradient value for that grid point is calculated. After completing the calculation for all grid points, the software generates a new data volume containing the predicted geothermal gradient values ​​for all grid points. Using the software's gridding and contour plotting functions, a geothermal gradient plane prediction map of the study area is generated.

[0049] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for predicting the current geothermal field in a rift basin, characterized in that, The method includes 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: 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: In the formula, Gg represents the predicted geothermal gradient, Hmoho is the depth of the Moho surface, HT is the basement depth, λi is the formation thermal conductivity, Hi is the formation thickness, L is the influence range of the temperature-controlling fault, Lfault is the vertical distance from the well location to the nearest deep fault, D is the enhancement efficiency coefficient of the fault on the geothermal field, 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.

2. The method for predicting the current geothermal field of a rift basin according to claim 1, characterized in that, The method for determining the stable segment in S1 is as follows: analyze the vertical distribution characteristics of the geothermal gradient at well points in the study area, and determine the depth segment where the geothermal gradient value tends to stabilize with depth as the stable segment.

3. The method for predicting the current geothermal field in a rift basin according to claim 1, characterized in that, The average geothermal gradient in S1 is calculated based on static temperature data from drill pipe testing.

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

5. The method for predicting the current geothermal field of a rift basin according to claim 1, characterized in that, The weighted average thermal conductivity of the formation in S3 is calculated by weighting the formation thickness.

6. The method for predicting the current geothermal field of a rift basin according to claim 1, characterized in that, The method for identifying well locations with high geothermal gradients in S4 is as follows: calculate the residual between the predicted geothermal gradient value obtained from the preliminary prediction model and the actual average geothermal gradient value, and identify well locations with positive residual values ​​as well locations with high geothermal gradients.

7. The method for predicting the current geothermal field of a rift basin according to claim 1, characterized in that, The analysis of the relationship between the distribution of deep faults and geothermal gradient anomaly zones in S4 specifically includes the following steps: S4.1 Based on the interpretation results of three-dimensional seismic data, determine the influence range L of temperature-controlled faults for each deep and large fault; S4.2 Calculate the vertical distance Lfault between each well location with a high temperature gradient and the nearest deep fault, and select well locations that satisfy Lfault≤L. S4.3 Using the L - Lfault value corresponding to the selected well location as the independent variable and the higher geothermal gradient value ΔGg as the dependent variable, a linear regression analysis is performed to obtain the regression coefficient D. This coefficient D is the efficiency coefficient of the fault's enhancement of the geothermal field.

8. The method for predicting the current geothermal field of a rift basin according to claim 1, characterized in that, The basic maps in S5 include a basement depth plan map, a Moho surface depth map, a deep fault distribution map, and a formation thermal conductivity weighted average distribution map.

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

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