Multi-element granite buried hill reservoir quantitative evaluation method
By using a multi-factor quantitative evaluation method, the problem of insufficient factors in the prediction of granite buried hill reservoirs has been solved, and an intuitive quantitative evaluation of the development degree of buried hill reservoirs has been achieved, thereby improving the reliability and accuracy of exploration.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-02-06
- Publication Date
- 2026-05-05
AI Technical Summary
Existing technologies lack multi-factor consideration in the prediction of granite buried hill reservoirs, resulting in insufficient quantitative prediction results that fail to meet the requirements for exploration accuracy and reliability. In particular, in areas with complex structures and strong fluid activity, the prediction results often deviate from the actual drilling results.
A multi-factor quantitative evaluation method for buried hill reservoirs of granite was adopted. By acquiring and normalizing data on the formation age of granite, late tectonic activity and deep fluid action, the development degree of buried hill reservoirs was comprehensively calculated, and a quantitative evaluation system was constructed.
It enables highly reliable and easy-to-operate quantitative evaluation of granite buried hill reservoirs, improves the reliability and universality of predictions, identifies the biggest risk factors affecting reservoir development, and provides a scientific basis for exploration decision-making.
Smart Images
Figure CN121976790A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of oilfield exploration technology, and in particular relates to a quantitative evaluation method for multi-factor granite buried hill reservoirs. Background Technology
[0002] As global oil and gas exploration continues to expand into deep and ultra-deep reservoirs and complex buried hills, granite buried hills have become an important replacement area and exploration hotspot. Unlike conventional clastic reservoirs, granite buried hill reservoirs are highly heterogeneous, and their reservoir space is the result of long-term, multi-factor geological processes. Although traditional single-factor evaluation methods can solve the prediction problem of buried hill reservoirs to a certain extent, they are not applicable to the granite buried hill field due to insufficient consideration of influencing factors, resulting in large fluctuations in prediction success rates and persistently high exploration risks.
[0003] While existing technologies for predicting granite buried hill reservoirs have developed methods for fracture prediction, weathering crust identification, vertical reservoir zoning, and lithological and lithofacies classification, they generally exhibit the following characteristics: First, these technologies often focus on single reservoir elements, such as relying on seismic data or imaging logging for fracture prediction, or classifying lithology and lithofacies based on core and logging data. Second, the methods are often qualitative or semi-quantitative, with reservoir prediction results typically categorized as "favorable," "relatively favorable," or "unfavorable," lacking quantitative data. Third, existing technologies largely consider fluid stimulation, a key reservoir-forming factor, or only provide qualitative descriptions, failing to quantitatively characterize the impact of fluid activity on buried hill reservoirs in different areas. Ultimately, limited by these characteristics, while existing technologies can predict reservoirs in technically constructed areas or under simple geological conditions, their universality is weak, and their extrapolation risks are high. Especially when facing granite buried hills with long weathering times, complex structures, and intense fluid activity, the prediction results often deviate significantly from actual drilling results, failing to meet the urgent needs of current buried hill exploration for reservoir prediction accuracy and reliability.
[0004] Therefore, developing a quantitative method for predicting granite buried hill reservoirs that considers multiple factors is of great significance for improving the success rate of granite buried hill reservoir prediction and for comparing and optimizing buried hill targets. Summary of the Invention
[0005] The problem this invention aims to solve is to provide a multi-factor quantitative evaluation method for granite buried hill reservoirs. This method considers the influence of multiple factors such as the formation age of granite buried hills, late tectonic activity, and deep fluid activity on the development of buried hill reservoirs. It has the characteristics of high reliability, simple operation, wide applicability, and quantification. It can intuitively and quantitatively carry out comprehensive ranking and evaluation of the development degree of target buried hill reservoirs, thereby providing a scientific basis for the decision-making and deployment of oil and gas exploration in granite buried hills.
[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a multi-factor quantitative evaluation method for granite buried hill reservoirs, comprising the following steps: S1: Acquire and statistically analyze data related to the age, structure, and fluids of the buried hills of granite in the target area. The age data of the buried hills includes the formation time of the granite and the time of uplift and exposure. The structural data includes the density of buried hill faults and the area of the erosion zone during key periods. The fluid data includes the distance to igneous rocks and the distance to deep faults. S2: Normalize the relevant data; S3: Based on the normalized data, a quantitative evaluation of the development degree of granite buried hill reservoirs is obtained through comprehensive calculation.
[0007] Furthermore, in S1, the formation time of the granite is obtained by radioisotope dating. By utilizing the decay law of radioisotopes and measuring the ratio of the parent body to the daughter body, the crystallization age of the rock is calculated, thus obtaining the absolute formation time of the granite.
[0008] Furthermore, in S1, the bulging and exfoliation time is calculated using the following formula: T=At i Where T is the uplift and exposure time, Ma; A is the granite formation time, Ma; t i Ma represents the burial time of the target in the hill.
[0009] Furthermore, in S1, the density of the buried hill fracture is calculated using the following formula: Where F is the fracture density, in fragments / km 2 N represents the number of fractures; R represents the statistical radius (km).
[0010] Furthermore, in S1, the critical period erosion area is determined based on seismic profiles and stratigraphic contact relationships, and the tectonic events, interfaces, intra-basin erosion areas, and continuous uplift erosion areas that cause stratigraphic erosion are interpreted. The corresponding erosion area is measured based on the interpretation range.
[0011] Furthermore, in S1, when a volcano or magmatic intrusion is developed around the buried hill target, the distance to the magmatic rock is the planar distance between the buried hill target and the nearest volcano or magmatic intrusion; when no volcano or magmatic intrusion is developed around the buried hill target, the distance to the magmatic rock is not considered.
[0012] Furthermore, in S1, when multiple deep faults develop near the target, the distance to the deep faults is the vertical distance between the buried hill target and the nearest deep fault in the plane.
[0013] Furthermore, in step S2, the normalization formula is as follows: in, The normalized value of the data; X is the statistical value of the data; The minimum value of a certain data item; This represents the maximum statistical value of a certain data item.
[0014] Furthermore, in S3, the calculation formula for the quantitative evaluation result of the development degree of granite buried hill reservoirs is as follows: P = A1 + T1 + F1 + S1 + D1 + D2 In the formula, P is the comprehensive score of reservoir development degree, dimensionless; A1 is the normalized value of granite formation time, dimensionless; T1 is the normalized value of uplift and exposure time, dimensionless; F1 is the normalized value of fault density, dimensionless; F2 is the normalized value of erosion area, dimensionless; D1 is the normalized value of distance from igneous rocks, dimensionless; D2 is the normalized value of distance from deep and large faults, dimensionless.
[0015] Furthermore, the present invention also provides a multi-element quantitative evaluation system for granite buried hill reservoirs, which runs the above-mentioned multi-element quantitative evaluation method for granite buried hill reservoirs.
[0016] The advantages and positive effects of this invention are: This invention overcomes the limitations of single-factor analysis in granite buried hill reservoir prediction. It innovatively integrates age, structure, and fluid as a unified, coupled system, fully considering the entire reservoir development process since the formation of the granite buried hill. Quantitative characterization based on fluid origin enhances the geological logic and reliability of the evaluation results. Technically, this invention parameterizes all influencing factors throughout the entire process, constructing a simple, transparent, and repeatable quantitative calculation method. This method not only identifies the greatest risk factors affecting buried hill reservoir development but also overcomes the limitations of traditional "black box" prediction models relying on statistical correlations, making the prediction process more interpretable. In summary, the methodology developed in this invention improves the reliability and universality of granite buried hill reservoir prediction, enabling intuitive quantitative evaluation and comprehensive ranking of different target buried hill reservoir development levels, thus providing a scientific basis for decision-making and deployment in granite buried hill oil and gas exploration. Attached Figure Description
[0017] Figure 1 This is a schematic diagram of the overall process of an embodiment of the present invention.
[0018] Figure 2 This is a planar distribution prediction map of buried hill lithology in the Qiongdongnan Basin according to an embodiment of the present invention.
[0019] Figure 3 This is a schematic diagram of the method for determining the burial time of a granite buried hill target according to an embodiment of the present invention.
[0020] Figure 4 This is a statistical diagram of fractures in a granite buried hill target according to an embodiment of the present invention.
[0021] Figure 5 This is a schematic diagram illustrating the critical period erosion zone interpretation method in an embodiment of the present invention. Detailed Implementation
[0022] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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.
[0023] The embodiments of the present invention will be further described below with reference to the accompanying drawings: like Figure 1 As shown, a quantitative evaluation method for multi-factor granite buried hill reservoirs includes the following steps.
[0024] S1: Acquisition and statistical analysis of age, structure, and fluid-related data for granite buried hills. Specifically, regarding the age of the buried hills, this mainly involves analyzing and statistically analyzing the formation time of the granite and the time of uplift and exposure; regarding late-stage tectonic activity, this mainly involves statistically analyzing the density of buried hill faults and the area of erosion zones during key periods; regarding the impact of deep fluid activity on buried hill reservoirs, this mainly involves statistically analyzing the distances to fluid migration channels such as volcanoes and deep faults. Specifically, S1 includes the following steps.
[0025] S11: Determination of the formation time of granite.
[0026] Primarily based on radiometric dating techniques, this method utilizes the decay patterns of radioactive isotopes (parent isotopes decay into daughter isotopes) to calculate the crystallization age of rocks by determining the ratio of parent to daughter isotopes. Commonly used methods include the uranium-lead (U-Pb) method, the potassium-argon (K-Ar) method, and the rubidium-strontium (Rb-Sr) method, which can determine the absolute formation time of granites.
[0027] S12: Calculation of the exposure time for bulging.
[0028] The uplift and exposure time of a granite buried hill refers to the time from its formation to its burial by sedimentary basin strata. Based on seismic profiles passing through the buried hill target, the final burial line of the target can be identified. Combined with the regional chronostratigraphic framework, the burial time of different buried hill targets can be obtained. The formula for calculating the uplift and exposure time of a buried hill target is as follows: T=At i In the formula, T is the uplift and exposure time, Ma; A is the granite formation time, Ma; t i Ma represents the burial time for each buried hill target.
[0029] S13: Statistics on the density of buried mountain faults.
[0030] The statistical analysis of fracture density in granite buried hills is primarily based on the interpretation of buried hill faults and fault planar distribution maps, or on planar maps of the fracture system depicted by buried hill seismic curvature body slices. When conducting fracture statistics, it is crucial to ensure that the fracture system for each target body is characterized using the same method or technique. During the statistical analysis, a circle with radius R is drawn, using the target well point or structural center as the center. The number of fractures within the circle is counted. The statistical radius R should remain consistent across all target bodies. Since fractures farther from the target contribute less to the fracture development of the target buried hill reservoir, it is recommended that the statistical radius R not exceed 5 km. The formula for calculating fracture density is as follows: In the formula, F is the fracture density, in units per km. 2 N represents the number of fractures; R represents the statistical radius (km).
[0031] S14: Area of erosion zone during critical period.
[0032] The intense uplift or deformation of late-stage tectonics is conducive to the development of reservoir spaces such as buried hill fractures. Therefore, the area of the erosion zone can indirectly reflect the degree of influence of tectonic activity on buried hill reservoirs.
[0033] This embodiment primarily uses seismic profiles and stratigraphic contact relationships to identify the tectonic events and interfaces that cause stratigraphic erosion, and interprets the resulting intra-basin erosion zones and continuously uplifted erosion zones. Figure 5 As shown, the area of the corresponding erosion zone is measured based on the interpretation range. If multiple strongly eroded interfaces exist, the area of the largest erosion zone after merging is measured; if no obvious erosion is observed in the buried hill target area, and no erosion zone exists during the key tectonic period, then this value is 0.
[0034] S15: Distance to igneous rocks. Magmatic activity since the formation of sedimentary basins can create numerous volcanoes and intrusive bodies, accompanied by hydrothermal fluid activity, which can fill fractures in buried hills. Generally, the farther away from igneous rocks, the weaker the influence of hydrothermal fluids on buried hill reservoirs. Based on the basin's seismic data, combined with the seismic reflection characteristics of volcanoes and intrusive bodies, it is possible to determine whether volcanoes or intrusive bodies are developed around the buried hill target. If volcanoes or magmatic intrusive bodies are developed, the planar distance between them and the buried hill target is measured. When multiple volcanoes or intrusive bodies exist, the nearest one is selected for distance measurement; if no volcanoes or intrusive bodies are developed in the basin, this data can be disregarded.
[0035] S16: Distance to deep faults. Deep faults connect to the deep crust and facilitate the migration of deep fluids to shallower layers and buried hills. Their influence spreads outwards from the deep fault as the center line, weakening with distance. Based on seismic data, deep faults around buried hills can be identified, with a focus on detachment tensional faults, faults extending to the Moho discontinuity, and concave-controlling faults that easily lead to the migration of deep fluids to shallower layers. When multiple deep faults are present near the target, the vertical distance between the target and the nearest deep fault should be measured.
[0036] S2: Normalization of age, tectonics, and fluid data. Specifically, to avoid excessive weighting of single factors, the age, tectonics, and fluid-related data of the granite buried hills in S1 are normalized to ensure that the weights of each data point are similar and that the values are between 0 and 1. The calculation formula is as follows: In the formula, The normalized value of the data; X is the statistical value of the data; It represents the statistical minimum value of a certain data point. This represents the maximum statistical value of a certain data item.
[0037] S3: Quantitative evaluation of granite buried hill reservoirs. Specifically, S3 includes the following steps: Based on the method in S2, the data affecting granite buried hill reservoirs are normalized. Then, by summing the normalized values of each influencing factor, the reservoir development degree of the granite buried hill target can be quantitatively evaluated. The calculation formula is as follows: P = A1 + T1 + F1 + S1 + D1 + D2 In the formula, P is the comprehensive score of reservoir development degree, dimensionless; A1 is the normalized value of granite formation time, dimensionless; T1 is the normalized value of uplift and exposure time, dimensionless; F1 is the normalized value of fault density, dimensionless; F2 is the normalized value of erosion area, dimensionless; D1 is the normalized value of distance from igneous rocks, dimensionless; D2 is the normalized value of distance from deep and large faults, dimensionless.
[0038] Based on the comprehensive score, the reservoir development degree of granite buried hill targets can be ranked. The higher the score, the more developed the buried hill reservoir.
[0039] The invention will now be specifically illustrated using the granite buried hill reservoir in the Qiongdongnan Basin as an example: S1: Using zircon dating via the uranium-lead (U-Pb) method, the ages of the basement granite in well YL8-1 of the Songnan Low Uplift in the Qiongdongnan Basin were determined to be 226 Ma, 236 Ma, and 262 Ma respectively; the ages of the basement granite in well ST34-3 of the Songtao Uplift were determined to be 98.4 Ma, and 101 Ma respectively. In summary, combined with the regional buried hill lithology planar distribution prediction map, as shown... Figure 2 As shown, the average formation time of granites in different regions can be obtained. For example, the average formation time of the Songnan low-uplift granite in the study area is 241 Ma; the average formation time of the Songtao uplift granite is 99.7 Ma.
[0040] like Figure 3 As shown, based on the seismic profile of the buried hill target, the final burial line of the buried hill target is identified, and combined with the regional chronostratigraphic framework, the burial time of different buried hill targets is obtained.
[0041] like Figure 4 As shown, during the statistics, a circle is drawn with the target well point or structural center as the center and a radius of R=5km. The number of fractures within the circle is counted. The statistical radius R should be consistent across all target bodies. The fracture target is calculated using the fracture density calculation formula.
[0042] like Figure 5 As shown, based on seismic profiles and stratigraphic contact relationships, the tectonic events and interfaces causing stratigraphic erosion are identified. The resulting intra-basin erosion zones and continuously uplifted erosion zones are then interpreted, and the corresponding erosion zone area is measured based on the interpretation range. If multiple strongly eroded interfaces exist, the area of the largest combined erosion zone is measured; if no obvious erosion is observed in the buried hill target area, and no erosion zone exists during the key tectonic period, this value is 0.
[0043] The determination is made as to whether volcanoes or intrusive bodies are developed around the target buried hill, and the deep and large faults around the buried hill are identified.
[0044] S2: Using the analysis and statistical methods for age, structure, and fluid-related data of granite buried hills in S1, as shown in Table 1, the data affecting the development of buried hill reservoirs in the Qiongdongnan Basin were statistically analyzed. The results show that the data exhibit orders of magnitude differences in their value ranges. To avoid excessive weighting of single factors, the data were normalized to ensure similar weights and values between 0 and 1. Table 1: Statistical Analysis, Normalization, and Comprehensive Score of Granite Buried Hill Reservoirs in the Qiongdongnan Basin The normalization method used for the buried hill target data not only ensured similar weights for each data point but also enabled rapid identification of the greatest risk factors affecting buried hill reservoir development. For example, after normalization, the Yongle 10 target shows a proximity to deep faults, indicating a risk of late-stage fluid injection and cementation filling in the buried hill fractures. In contrast, the Lingshui 32 and Lingshui 32W target areas have relatively low fault densities, suggesting weak late-stage tectonic activity and low fracture density in the buried hill reservoirs.
[0045] S3: Calculations of the reservoir development levels of various buried hill targets in the Qiongdongnan Basin revealed that Yongle 10, Lingshui 32, Lingshui 32W, Yongle 1, and Lingshui 29 have relatively high scores and well-developed reservoirs, making them favorable exploration targets. Currently, Yongle 10 has been drilled, and drilling data reveals that its lithology is monzogranite with well-developed fractures and localized calcite filling. The effective reservoir ratio reaches 90%, with a porosity of 6.3%–18.5%, averaging 11.8%, indicating a high-quality buried hill reservoir. This data is in good agreement with the evaluation results of this method.
[0046] The advantages and positive effects of this invention are: This invention overcomes the limitations of single-factor analysis in granite buried hill reservoir prediction. It innovatively integrates age, structure, and fluid as a unified, coupled system, fully considering the entire reservoir development process since the formation of the granite buried hill. Quantitative characterization based on fluid origin enhances the geological logic and reliability of the evaluation results. Technically, this invention parameterizes all influencing factors throughout the entire process, constructing a simple, transparent, and repeatable quantitative calculation method. This method not only identifies the greatest risk factors affecting buried hill reservoir development but also overcomes the limitations of traditional "black box" prediction models relying on statistical correlations, making the prediction process more interpretable. In summary, the methodology developed in this invention improves the reliability and universality of granite buried hill reservoir prediction, enabling intuitive quantitative evaluation and comprehensive ranking of different target buried hill reservoir development levels, thus providing a scientific basis for decision-making and deployment in granite buried hill oil and gas exploration.
[0047] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.
Claims
1. A multi-factor quantitative evaluation method for granite buried hill reservoirs, characterized in that: Includes the following steps, S1: Acquire and statistically analyze data related to the age, structure, and fluids of the buried hills of granite in the target area. The age data of the buried hills includes the formation time of the granite and the time of uplift and exposure. The structural data includes the density of buried hill faults and the area of the erosion zone during key periods. The fluid data includes the distance to igneous rocks and the distance to deep faults. S2: Normalize the relevant data; S3: Based on the normalized data, a quantitative evaluation of the development degree of granite buried hill reservoirs is obtained through comprehensive calculation.
2. The method for quantitative evaluation of multi-factor granite buried hill reservoirs according to claim 1, characterized in that: In S1, the formation time of the granite is obtained by radioisotope dating. By utilizing the decay law of radioisotopes and measuring the ratio of parent to daughter rocks, the crystallization age of the rock is calculated, thus obtaining the absolute formation time of the granite.
3. A method for quantitative evaluation of multi-factor granite buried hill reservoirs according to claim 1 or 2, characterized in that: In S1, the ridge exposure time is calculated using the following formula: T=A-t i Where T is the uplift and exposure time, Ma; A is the granite formation time, Ma; t i Ma represents the burial time of the target in the hill.
4. A quantitative evaluation method for multi-factor granite buried hill reservoirs according to claim 1 or 2, characterized in that: In S1, the density of the buried hill fracture is calculated using the following formula: Where F is the fracture density, in fragments / km 2 N represents the number of fractures; R represents the statistical radius (km).
5. A method for quantitative evaluation of multi-factor granite buried hill reservoirs according to claim 1 or 2, characterized in that: In S1, the critical period erosion area is determined based on seismic profiles and stratigraphic contact relationships. The tectonic events, interfaces, intra-basin erosion areas, and continuously uplifted erosion areas that caused stratigraphic erosion are interpreted, and the corresponding erosion area is measured based on the interpretation range.
6. A method for quantitative evaluation of multi-factor granite buried hill reservoirs according to claim 1 or 2, characterized in that: In S1, when a volcano or magmatic intrusion is developed around the buried hill target, the distance to the magmatic rock is the planar distance between the buried hill target and the nearest volcano or magmatic intrusion; when no volcano or magmatic intrusion is developed around the buried hill target, the distance to the magmatic rock is not considered.
7. A method for quantitative evaluation of multi-factor granite buried hill reservoirs according to claim 1 or 2, characterized in that: In S1, when multiple deep faults develop near the target, the distance to the deep faults is the vertical distance between the buried hill target and the nearest deep fault in the plane.
8. A method for quantitative evaluation of multi-factor granite buried hill reservoirs according to claim 1 or 2, characterized in that: In step S2, the normalization formula is as follows: in, The normalized value of the data; X is the statistical value of the data; The minimum value of a certain data item; This represents the maximum statistical value of a certain data item.
9. A method for quantitative evaluation of multi-factor granite buried hill reservoirs according to claim 1 or 2, characterized in that: In S3, the calculation formula for the quantitative evaluation result of the development degree of granite buried hill reservoir is as follows: P = A1 + T1 + F1 + S1 + D1 + D2 In the formula, P is the comprehensive score of reservoir development degree, which is dimensionless; A1 is the normalized value of granite formation time, which is dimensionless. T1 is the normalized value of uplift exposure time, dimensionless; F1 is the normalized value of fault density, dimensionless; F1 is the normalized value of erosion area, dimensionless; D1 is the normalized value of distance from igneous rocks, dimensionless; D2 is the normalized value of distance from deep faults, dimensionless.
10. A multi-factor quantitative evaluation system for granite buried hill reservoirs, characterized in that: The method for quantitative evaluation of multi-factor granite buried hill reservoirs as described in any one of claims 1 to 9 is applied.