A comprehensive prediction method for oil-uranium-hot dry rock-shale oil in sedimentary basins

Abstract

This invention discloses a comprehensive prediction method for oil-uranium-hot dry rock-shale oil in sedimentary basins, belonging to the field of geological exploration technology. This method involves using effective geophysical methods to extract basic geological background information, constructing a three-dimensional spatial framework of the basin, clarifying the correlation between structures, strata, and igneous rocks, calculating the correlation coefficient and intercept of airborne gravity and magnetic anomalies, initially screening key target areas, extracting key information on energy resource accumulation / mineralization, performing two-dimensional or three-dimensional characterization of target geological bodies, combining magnetotelluric sounding inversion profiles with seismic and drilling data, adjusting core parameter settings, and overlaying oil-uranium-hot dry rock-shale oil resource accumulation / mineralization evaluation information to obtain comprehensive energy resource prediction results. This method can provide scientific and technical support for basic geological research on oil-uranium-hot dry rock-shale oil in sedimentary basins and provide comprehensive and reliable prediction of resources.

Description

Technical Field

[0001] This invention relates to the field of geological exploration technology, specifically to a comprehensive prediction method for oil-uranium-hot dry rock-shale oil in sedimentary basins. Background Technology

[0002] Multiple large sedimentary basins in my country contain a variety of resources, with organic (oil and gas) resources and inorganic resources (uranium ore and hot dry rocks) closely linked in their interdependent occurrence environment. These basins serve as natural testing grounds for my country to conduct collaborative exploration of energy resources.

[0003] Currently, the recoverable shallow oil resources in large sedimentary basins are decreasing, with insufficient reserve resources and unclear replacement areas. Exploring conventional or unconventional oil and gas resources or other alternative resources within and around the basins is crucial for maintaining the position of large oil fields in the national energy industry.

[0004] Compared with traditional fossil fuels, hot dry rock geothermal energy is a highly competitive renewable and clean energy source that can provide a reliable, stable, and secure energy supply. Taking the Songliao Basin as an example, which belongs to the western Pacific plate margin geothermal domain, the geothermal flow value within the basin is higher than the average value of Northeast China and even eastern my country. Focusing on the research and potential evaluation of hot dry rock reservoir formation conditions has significant theoretical value and practical significance.

[0005] Studies have shown that oil and gas and uranium deposits are symbiotic and interact within most sedimentary basins in my country, and the involvement of oil and gas in sandstone-type uranium mineralization can be used to locate sandstone-type uranium deposits. Although single-parameter geophysical methods strongly support energy resource prediction, they still have certain limitations and multiple solutions in solving certain geological problems.

[0006] Therefore, there is a need to provide a comprehensive prediction method for oil-uranium-hot dry rock-shale oil in sedimentary basins, which aims to solve the above problems. Summary of the Invention

[0007] To address the shortcomings of existing technologies, the purpose of this invention is to provide a comprehensive prediction method for oil-uranium-hot dry rock-shale oil in sedimentary basins, thereby solving the problems mentioned in the background.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A comprehensive prediction method for oil-uranium-hot dry rock-shale oil in sedimentary basins includes the following steps:

[0010] Step S1: Use effective geophysical methods to extract basic geological background information, including deep fault systems, deep geological structures and the spatiotemporal distribution of igneous rocks, and on this basis, construct a three-dimensional spatial framework of the basin to clarify the relationship between structure, strata and igneous rocks.

[0011] Step S2: Calculate the correlation coefficient and intercept value of airborne gravity and magnetic anomalies, preliminarily screen key target areas, and use improved coefficient wavelet multi-scale analysis, gravity and magnetic anomaly wavelet mode edge detection, and high-resolution gravity, magnetic and gradient joint inversion method based on cross-constraint. Use known drilling or seismic data for constraints to extract energy resource reservoir (mineralization) relationship information and perform two-dimensional or three-dimensional characterization of target geological bodies. Among them, the energy resource reservoir (mineralization) relationship information includes stratigraphic structure, fault structure, magmatic rock distribution and favorable structures.

[0012] Step S3: Combine the magnetotelluric sounding inversion profile with seismic and drilling data to adjust the core parameter settings;

[0013] Step S4: Overlay oil-uranium-hot dry rock-shale oil resource accumulation (mineralization) evaluation information to obtain comprehensive energy resource prediction results.

[0014] As a further aspect of the present invention, the basic geological background information extraction in step S1 is based on the fluctuation and regional characteristics of gravity and magnetic anomalies. By combining multiple geophysical methods, the intensity of regional tectonic changes, the variability of sedimentary facies, and the development of magmatic bodies are revealed through the variability of anomaly gradients, the development of cascade zones, and the regularity and multidirectionality of anomaly trends.

[0015] As a further aspect of the present invention, the key target area selection in step S2 includes the following:

[0016] a. Calculate the correlation coefficient and intercept of airborne gravity and magnetic anomalies;

[0017] b. Identify homologous anomalies, semi-homologous anomalies, and non-homologous anomalies.

[0018] As a further aspect of the present invention, the correlation coefficient is set as R, and the location of the correlation coefficient is as follows:

[0019] And -1 ≤ R ≤ 1,

[0020] In practice, the formula generally used for gravity and magnetic anomaly correspondence analysis is:

[0021] A represents the component of the measured field weight that is related to the background field.

[0022]

[0023]

[0024] As a further aspect of the present invention, the two-dimensional or three-dimensional characterization of the target geological body in step S2 includes structural stratification, spatial distribution characteristics of magmatic rocks, and fault distribution characteristics.

[0025] As a further aspect of the present invention, the structural layering includes the calculation of the depth of the bottom surface of the Cenozoic strata, the calculation of the depth of the bottom surface of the Upper Cretaceous strata, the calculation of the depth of the bottom surface of the Upper Jurassic-Lower Cretaceous strata, and the calculation of the depth of the top surface of the magnetic basement.

[0026] As a further aspect of the present invention, the spatial distribution characteristics of the magmatic rocks include shallow magnetic geological bodies and concealed magnetic geological bodies.

[0027] As a further aspect of the present invention, the fracture distribution characteristics include gravity anomaly indicators, aeromagnetic anomaly indicators, and quantitative indicators.

[0028] As a further aspect of the present invention, the core parameter adjustment in step S3 involves refining the two-dimensional and three-dimensional geological models and adjusting the geophysical forward and inverse modeling parameters based on the constraints of drilling, seismic, and geodetic sounding.

[0029] As a further aspect of the present invention, the comprehensive prediction of energy resources in step S4 includes extraction of key information on mineral deposits, summary of mineral exploration indicators, and comprehensive prediction of multiple factors.

[0030] In summary, the embodiments of the present invention have the following beneficial effects compared with the prior art:

[0031] This invention extracts basic geological background information through a combination of effective geophysical methods, and constructs a three-dimensional spatial framework of the basin based on this information. It elucidates the correlations between structures, strata, and igneous rocks, calculates the correlation coefficients and intercepts of airborne gravity and magnetic anomalies, preliminarily screens key target areas, and uses known drilling or seismic data for constraints to extract key information on energy resource accumulation (mineralization). It performs two-dimensional or three-dimensional characterization of target geological bodies, combines magnetotelluric sounding inversion profiles with seismic and drilling data, adjusts core parameter settings, and overlays oil-uranium-hot dry rock-shale oil resource accumulation (mineralization) evaluation information to obtain comprehensive energy resource prediction results. This can provide geological-geophysical methodological support for the basic material research of large sedimentary basins, and simultaneously enable comprehensive and reliable prediction of basic geological resources of oil-uranium-hot dry rock-shale oil in sedimentary basins.

[0032] To more clearly illustrate the structural features and effects of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0033] Figure 1 This is a technical diagram of an embodiment of the invention.

[0034] Figure 2 This is a three-dimensional tectonic layering diagram of a large sedimentary basin in an embodiment of the invention.

[0035] Figure 3 This is a three-dimensional physical property inversion diagram of a typical magmatic rock mass.

[0036] Figure 4 This is a diagram illustrating the tomographic effect of airborne gravity and magnetic wavelet mode identification in an embodiment of the invention.

[0037] Figure 5 This is a graph showing the effect of gravity-magnetic correlation regression analysis in an embodiment of the invention.

[0038] Figure 6 This is a distribution diagram of the correlation coefficient R in the correlation analysis of airborne gravity and magnetic fields in an embodiment of the invention.

[0039] Figure 7 This is a distribution diagram of the intercept A in the correlation analysis of airborne gravity and magnetic fields in an embodiment of the invention.

[0040] Figure 8 This is a comparison diagram of the correlation coefficient R of homologous anomalies and gravity and magnetic anomalies in the embodiments of the invention.

[0041] Figure 9 This is a comparison diagram of the semi-homogeneous abnormality line coefficient R and gravity and magnetic anomalies in an embodiment of the invention.

[0042] Figure 10 This is a comparison diagram of the correlation coefficient R of non-homogeneous anomalies and gravity and magnetic anomalies in an embodiment of the invention.

[0043] Figure 11 This is a schematic diagram of comprehensive energy resource prediction in an embodiment of the invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0045] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0046] In one embodiment of the present invention, see Figure 1 , Figure 2 , Figure 3 , Figure 4 and Figure 11 The aforementioned integrated prediction method for oil-uranium-hot dry rock-shale oil in sedimentary basins includes the following steps:

[0047] Step S1: Use effective geophysical methods to extract basic geological background information, including deep fault systems, deep geological structures and the spatiotemporal distribution of igneous rocks, and on this basis, construct a three-dimensional spatial framework of the basin to clarify the relationship between structure, strata and igneous rocks.

[0048] Step S2: Calculate the correlation coefficient and intercept value of airborne gravity and magnetic anomalies, preliminarily screen key target areas, and use improved coefficient wavelet multi-scale analysis, gravity and magnetic anomaly wavelet mode edge detection, and high-resolution gravity, magnetic and gradient joint inversion method based on cross-constraint. Use known drilling or seismic data for constraints to extract key information on energy resource accumulation (mineralization), and perform two-dimensional or three-dimensional characterization of target geological bodies. Among them, key information on energy resource accumulation (mineralization) includes stratigraphic structure, fault structure, distribution of magmatic rocks and favorable structures.

[0049] Step S3: Combine the magnetotelluric sounding inversion profile with seismic and drilling data to adjust the core parameter settings;

[0050] Step S4: Overlay oil-uranium-hot dry rock-shale oil resource accumulation (mineralization) evaluation information to obtain comprehensive energy resource prediction results.

[0051] Specifically:

[0052] The basic geological background information extraction in step S1 is based on the fluctuation and regional characteristics of gravity and magnetic anomalies. It uses a combination of multiple geophysical methods to reveal the intensity of regional tectonic changes, the variability of sedimentary facies, and the development of magmatic bodies through the variability of anomaly gradients, the development of cascade zones, and the regularity and multidirectionality of anomaly trends.

[0053] See Figure 5 , Figure 6 , Figure 7 , Figure 8 , Figure 9 and Figure 10 The key target area selection in step S2 includes the following:

[0054] a. Calculate the correlation coefficient and intercept of airborne gravity and magnetic anomalies.

[0055] When gravity and magnetism originate from the same source, the relationship between the vertical component of the magnetic anomaly with perpendicular magnetization and the vertical first derivative of the gravitational anomaly is as follows:

[0056]

[0057] In the formula, μ0 is the vacuum permeability, G is the gravitational constant, ρ is the remnant density, and M is the magnetization. In practical applications, and in analyzing the correspondence between gravity and magnetic anomalies, the following formula is generally used:

[0058]

[0059] In the formula, A represents the component in the measured field that is related to the background field.

[0060] 1) Regression analysis:

[0061] The above equation shows that when gravity and magnetism are of different origins, the vertical component ΔZ of the magnetic anomaly in perpendicular magnetization ⊥ The magnetic anomaly has a linear relationship with the first vertical derivative Δg / z of the gravity anomaly. Therefore, by treating the magnetic anomaly as a pole-switching process, calculating the first vertical derivative of the gravity anomaly, and then applying ΔZ... ⊥ By performing linear regression analysis with Δg / z, the slope can be obtained. And the intercept A, from which the Poisson ratio can be calculated.

[0062] A can be used as a criterion for determining whether gravity and magnetism originate from the same source, and it is also a standard for testing the reliability of the calculated Poisson's ratio. When A = 0, it is the ideal situation where gravity and magnetism originate from the same source. In reality, A is generally not equal to zero, but as long as the value of A is not large or the change is not significant, gravity and magnetism can be considered to originate from the same source, and the calculated Poisson's ratio is meaningful. Otherwise, it indicates that the background field in the study area changes greatly, or that there is strong mutual interference between anomalies, or that there is residual magnetization intensity in a direction different from the geomagnetic field.

[0063] 2) Correlation coefficient calculation

[0064] reaction ΔZ ⊥ The quantity that is closely related to the linearity of Δg / z is the correlation coefficient R, which is defined as:

[0065]

[0066] In the formula:

[0067]

[0068]

[0069]

[0070] b. Differentiating between homologous anomalies, semi-homologous anomalies, and non-homologous anomalies.

[0071] R represents the degree of linear correlation between gravity and magnetic anomalies within the calculation window, with a value of -1 ≤ R ≤ 1. A value close to 1 indicates a positive correlation, signifying that high (or low) gravity corresponds to high (or low) magnetic force; a value close to -1 indicates a negative correlation, suggesting a correspondence between high gravity and low magnetic force, or vice versa; a value close to ±1 indicates that gravity and magnetic anomalies originate from the same source; a value close to zero (generally -0.3 to 0.3) suggests that gravity and magnetic anomalies originate from different sources, or that there is strong mutual interference between the anomalies, or that the remnant magnetism is inconsistent with the direction of the geomagnetic field. Therefore, the magnitude of R can be used to determine the reliability of the Poisson's ratio obtained from regression analysis (or, in other words, the correlation coefficient is a method for testing linear correlation).

[0072] The two-dimensional or three-dimensional characterization of the target geological body in step S2 includes tectonic stratification, spatial distribution characteristics of magmatic rocks, and fault distribution characteristics, wherein:

[0073] 1) Structural stratification involves using a combination of geophysical methods to calculate the depth of different sections in the study area, mainly including:

[0074] (1) Depth of the bottom surface of the new interface

[0075] By combining known drilling data, seismic data, and regional airborne gravity and magnetic data, the depth of the bottom surface of the newly formed solution interface was determined;

[0076] (2) Depth of the Upper Cretaceous base

[0077] First, forward modeling is performed using the depth of the bottom surface of the newborn interface to calculate the gravity anomaly caused by the newborn interface (the density of the newborn stratum is 2.05 g / cm³). 3 Then, the gravity anomaly caused by the newborn boundary is removed to obtain the Bouguer gravity anomaly caused by the pre-newborn boundary. Based on the gridded data of the vertical first derivative of Bouguer gravity, the following Euler equation is established:

[0078]

[0079] By using the field values ​​at different coordinate points (x, y, z) and the gradient values ​​in the three directions, the unknown variables x0, y0, and z0 are solved, thereby determining the tectonic tracks and locations, and calculating the depth value of the Upper Cretaceous bottom interface; finally, the calculated data are locally corrected using known drilling and seismic depth data as constraints.

[0080] (3) Depth of the base of the Upper Jurassic-Lower Cretaceous

[0081] First, the gravity anomaly caused by the Upper Cretaceous is forward modeled using the depth of the Upper Cretaceous floor. Then, using wavelet decomposition, the discretization coherence of the scale parameter a and the translation parameter b in the continuous wavelet transform is denoted as follows: And j∈Z, the expansion step size a0≠0 is a fixed value, and it is always assumed that a0>0, through the discrete wavelet function expression:

[0082]

[0083] The regional gravity anomaly caused by the deep basin (mainly the Moho) was obtained using wavelet decomposition approximation coefficients. Based on the airborne Bouguer gravity anomaly, the gravity anomalies caused by the shallow layer (Upper Cretaceous) and the deep layer (mainly the Moho) were separated to obtain the Bouguer gravity anomaly caused by the Precambrian-Upper Jurassic-Lower Cretaceous. Then, the Parker iteration formula was used:

[0084]

[0085] (Where G is the gravitational constant, and ρ is the average density difference across the interface.) u and v are the wave numbers in the x and y directions, respectively. The depth of the Upper Jurassic-Lower Cretaceous bottom surface is calculated (the density difference between the Upper Jurassic-Lower Cretaceous and the Paleozoic is determined by statistical results of physical property measurements). The calculated results are compared with and corrected with drilling and seismic data to obtain the depth of the Upper Jurassic-Lower Cretaceous bottom surface.

[0086] (4) Depth of the top surface of the magnetic substrate

[0087] Using known seismic, drilling, and magnetotelluric data as constraints, the depth data calculated by the aeromagnetic anomaly derivative tangent method is corrected, and the corrected data is used as the final depth of the top surface of the magnetic base.

[0088] 2) Spatial distribution characteristics of igneous rocks

[0089] Given that the magnetic body and the surrounding rock have differences in magnetism or density, a combination of methods is used to delineate the boundary of the magnetic body, including improved coefficient wavelet multi-scale analysis, wavelet mode edge detection of gravity and magnetic anomalies, and high-resolution gravity and magnetic gradient joint inversion based on cross-constraints.

[0090] (1) Shallow magnetic geological bodies

[0091] For shallow magnetic geological bodies, their boundaries are mainly defined by the step zones of the aeromagnetic ΔT polarization contour map, the zero line of the aeromagnetic ΔT polarization vertical first derivative contour map, the depth abrupt change zone (line) of the aeromagnetic ΔT polarization Euler deconvolution map, and the wavelet mode maxima linear zone, while also referring to the aeromagnetic ΔT profile map, residual anomaly profile map, gravity and magnetic data, etc.

[0092] (2) Concealed magnetic geological bodies

[0093] For magnetic bodies hidden at a certain depth, their boundary delineation is mainly based on the plane map of the aeromagnetic ΔT pole contour lines, the plane map of ...

[0094] 3) Fault distribution characteristics

[0095] The distribution characteristics of fractures include gravity anomaly characteristics, aeromagnetic anomaly indicators, and quantitative indicators. Gravity anomaly characteristics of fracture structures can be summarized as: linear Bouguer gravity gradient zones, lines connecting extreme points of the Bouguer gravity directional derivative bands, boundaries between gravity anomalies of different characteristics, unidirectional or homomorphic curvature of Bouguer gravity anomaly contour lines, and edge lines connecting blocky Bouguer gravity anomalies with a certain orientation. Aeromagnetic anomaly indicators reflecting fracture structures in this area mainly include the following eight types: boundaries between magnetic anomalies of different characteristics, magnetic anomaly gradient zones, beaded magnetic anomaly zones, chaotic linear magnetic anomaly zones, linear positive magnetic anomaly zones, linear negative magnetic anomaly zones, magnetic anomaly misalignment zones, and edge lines connecting blocky positive and negative magnetic anomalies with a certain orientation. Quantitative indicators of fracture structures include gravity and magnetic wavelet mode maxima zones, and abrupt changes in density values ​​or magnetic values ​​or related parameters obtained through joint forward and inverse airborne gravity and magnetic analysis.

[0096] Based on the constraints of drilling, seismic and geodetic sounding, the two-dimensional and three-dimensional geological body models are improved and the geophysical forward and inverse modeling parameters are adjusted to complete the core parameter adjustment work.

[0097] See Figure 11 Step S4, the comprehensive prediction of energy resources, includes the extraction of key information on reservoir (mineralization), the summary of prospecting indicators, and multi-factor comprehensive prediction. Among them, the extraction of reservoir (mineralization) relationship information is based on the regional geological background, and selects effective geophysical forward and inverse modeling methods to extract key information on reservoir (mineralization) such as equilibrium gravity anomalies, fault structures, tectonic units, spatial distribution characteristics of magmatic rocks, tectonic stratification, and favorable structures. The summary of prospecting indicators is based on geophysical prospecting indicators, including aeromagnetic prospecting indicators, aeromagnetic prospecting indicators, and geological indicators. The multi-factor comprehensive prediction is based on the comparative analysis of the geological background of energy resource reservoir (mineralization), favorable mineralization locations, and gravity and magnetic anomaly response characteristics, and superimposed with oil-uranium-hot dry rock-shale oil resource mineralization (reservoir) evaluation information to obtain the comprehensive prediction results of energy resources.

[0098] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A comprehensive prediction method for oil-uranium-hot dry rock-shale oil in sedimentary basins, characterized in that, Includes the following steps: Step S1: Use effective geophysical methods to extract basic geological background information, including deep fault systems, deep geological structures and the spatiotemporal distribution of igneous rocks, and on this basis, construct a three-dimensional spatial framework of the basin to clarify the relationship between structure, strata and igneous rocks. Step S2: Calculate the correlation coefficient and intercept value of airborne gravity and magnetic anomalies, preliminarily screen key target areas, and use improved coefficient wavelet multi-scale analysis, gravity and magnetic anomaly wavelet mode edge detection, and high-resolution gravity, magnetic and gradient joint inversion method based on cross-constraint. Use known drilling or seismic data for constraints to extract key information on energy resource accumulation / mineralization, and perform two-dimensional or three-dimensional characterization of the target geological body. Among them, key information on energy resource accumulation / mineralization includes stratigraphic structure, fault structure, distribution of magmatic rocks and favorable structures. Step S3: Combine the magnetotelluric sounding inversion profile with seismic and drilling data to adjust the core parameter settings; Step S4: Overlay oil-uranium-hot dry rock-shale oil resource accumulation / mineralization evaluation information to obtain comprehensive energy resource prediction results; The basic geological background information extraction in step S1 is based on the fluctuation and regional characteristics of gravity and magnetic anomalies. It uses a combination of multiple geophysical methods to reveal the intensity of regional tectonic changes, the variability of sedimentary facies, and the development of magmatic bodies through the variability of anomaly gradients, the development of step zones, and the regularity and multidirectionality of anomaly trends. The key target area selection in step S2 includes the following: a. Calculate the correlation coefficient and intercept of airborne gravity and magnetic anomalies; b. Identify homologous anomalies, semi-homologous anomalies, and non-homologous anomalies; The correlation coefficient is denoted as R, and the correlation coefficient is defined as follows: And -1 ≤ R ≤ 1, In practice, the formula used for gravity and magnetic anomaly correspondence analysis is: , This indicates the components of the measured field that are related to the background field. In the formula, The permeability of vacuum. The gravitational constant is... For the remaining density, The magnetization intensity; The vertical component of the magnetic anomaly in vertical magnetization. The vertical first derivative of gravity anomalies; The two-dimensional or three-dimensional characterization of the target geological body in step S2 includes structural stratification, spatial distribution characteristics of magmatic rocks, and fault distribution characteristics. The structural stratification includes the calculation of the depth of the bottom surface of the Cenozoic, the bottom surface of the Upper Cretaceous, the bottom surface of the Upper Jurassic-Lower Cretaceous, and the top surface of the magnetic basement. The spatial distribution characteristics of the magmatic rocks include shallow magnetic geological bodies and concealed magnetic geological bodies; The fracture distribution characteristics include gravity anomaly indicators, aeromagnetic anomaly indicators, and quantitative indicators; The core parameter debugging in step S3 is to improve the two-dimensional and three-dimensional geological body models and debug the geophysical forward and inverse modeling parameters based on the constraints of drilling, seismic and geodetic sounding. The comprehensive energy resource prediction in step S4 includes the extraction of key information on mineral deposits / ore deposits, the summary of mineral exploration indicators, and comprehensive prediction of multiple factors.

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