A method for determining the lower limit depth of sandstone reservoir exploration based on rock evolution constraint

Abstract

This invention relates to the field of oil and gas exploration technology, and discloses a method for determining the lower limit depth of sandstone reservoir exploration based on diagenetic evolution constraints. Based on the measured porosity, permeability, and corresponding measured depth of sandstone of different lithologies within the study area, compaction evolution equations and permeability evolution equations for each lithology are fitted and constructed according to the diagenetic evolution mechanism. The economic lower limit of stable daily production per well is determined according to the economic evaluation of oil and gas development in the study area. Based on oil testing data, quantitative relationships between production capacity and porosity, and between production capacity and permeability are established. The economic lower limit is converted into corresponding lower limits of reservoir porosity and permeability, which are then substituted into the compaction evolution equations and permeability evolution equations for each lithology to obtain the lower limit depth of exploration for that lithology using current technology. This invention reveals the evolution law of reservoir properties with depth for different lithologies from a genetic perspective, organically coupling economic and technical conditions with geological laws to achieve accurate and differentiated prediction of the lower limit depth of exploration.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology, and in particular to a method for determining the lower limit depth of sandstone reservoir exploration based on diagenetic evolution constraints. Background Technology

[0002] In oil and gas exploration, accurately determining the lower limit depth of reservoir exploration—that is, the maximum burial depth with commercial exploitability under current economic and technological conditions—is crucial for reducing exploration risks and optimizing well placement. The lower limit depth of reservoir exploration is mainly controlled by two factors: first, economic and technological factors, namely, the minimum economic production required for development investment per well; and second, geological factors, namely, whether reservoir properties (such as porosity and permeability) can support this production capacity. Currently, the conventional method for determining the lower limit depth of reservoir exploration is as follows: first, determine the lower limit of physical properties through economic evaluation; then, establish a statistical relationship chart between porosity and depth (usually linear or exponential regression) based on a large amount of drilling data from the work area; and finally, read the corresponding depth value from the chart based on the lower limit of physical properties.

[0003] However, this method has the following technical drawbacks: First, it lacks a clear mechanism and has significant limitations in static statistics. Simple statistical regression oversimplifies the complex diagenetic evolution process, failing to reveal the intrinsic law of porosity decay with depth from a mechanistic perspective, resulting in predictions lacking a physical basis. Second, it fails to adequately characterize lithological differences. Sandstones of different grain sizes (such as siltstone, fine sandstone, and medium sandstone) exhibit significantly different compaction and porosity reduction rates during burial due to differences in their original composition and structure. Traditional methods typically mix different lithologies statistically or perform only simple grouped regressions, failing to explain and quantify these differences from the perspective of diagenetic mechanisms. Third, it carries high extrapolation risk and poor reliability. Based on the statistical relationships of limited drilling data, when applied to new exploration targets exceeding existing drilling depths or far from the well-controlled area, the reliability of the extrapolation results is difficult to guarantee, posing significant exploration risks.

[0004] To address the aforementioned issues, it is necessary to develop a method for determining the lower limit depth of reservoir exploration that starts from the diagenetic evolution mechanism, can accurately characterize lithological differences, and organically couples economic and technical conditions with geological evolution laws. Summary of the Invention

[0005] This invention provides a method for determining the lower limit depth of sandstone reservoir exploration based on diagenetic evolution constraints, which solves the technical problems in the prior art that lead to inaccurate prediction of the lower limit depth and high extrapolation risk due to the lack of diagenetic mechanism constraints and the neglect of lithological differences.

[0006] This invention provides a method for determining the lower limit depth of sandstone reservoir exploration based on diagenetic evolution constraints, including:

[0007] S1. Obtain the measured porosity, measured permeability, and measured depth corresponding to the measured porosity and measured permeability of sandstone of different lithologies within the target area; wherein, a measured porosity and its corresponding measured depth constitute a set of measured porosity data pairs, and a measured permeability and its corresponding measured depth constitute a set of measured permeability data pairs.

[0008] S2. Based on the measured porosity data and the measured permeability data, and according to the diagenetic evolution mechanism that porosity and permeability of different lithological sandstones decrease exponentially with increasing depth, compaction evolution equations and permeability evolution equations for different lithological sandstones are fitted and constructed respectively; wherein, the compaction evolution equation represents the exponential decay evolution relationship between the porosity of each lithological sandstone and the initial porosity, compaction coefficient, and depth, and the permeability evolution equation represents the exponential decay evolution relationship between the permeability of each lithological sandstone and the initial permeability, permeability decay coefficient, and depth;

[0009] S3. Obtain the economic evaluation parameters for oil and gas development in the economic evaluation report of the target area, so as to determine the lower limit of the economic production per well per day based on the economic evaluation parameters for oil and gas development.

[0010] S4. Obtain the oil testing data of the target area, and use the oil testing data to establish a first quantitative relationship model between the daily production of a single well and the reservoir porosity, and a second quantitative relationship model between the daily production of a single well and the reservoir permeability. Substitute the economic lower limit value into the first quantitative relationship model and the second quantitative relationship model respectively to obtain the reservoir porosity lower limit value and the reservoir permeability lower limit value that meet the requirements of the economic lower limit value.

[0011] S5. Substitute the lower limit values ​​of reservoir porosity and reservoir permeability into the compaction evolution equation and permeability evolution equation corresponding to each lithological sandstone to obtain the first depth corresponding to the lower limit value of reservoir porosity and the second depth corresponding to the lower limit value of reservoir permeability. Take the minimum or weighted average value of the first depth and the second depth as the lower limit depth of reservoir exploration for the corresponding lithological sandstone.

[0012] Furthermore, the different lithological sandstones include at least siltstone, fine sandstone, and medium sandstone.

[0013] Furthermore, prior to S2, the following is also included:

[0014] Anomaly detection is performed on the measured porosity data pairs and the measured permeability data pairs. A multidimensional outlier detection method is used to identify and remove abnormal data points that are affected by non-compaction effects of later dissolution or cementation. The effective porosity data pairs and effective permeability data pairs that reflect the normal diagenetic evolution trend are retained as the measured porosity data pairs and the measured permeability data pairs, respectively.

[0015] Furthermore, a multidimensional outlier detection method is employed to identify and remove the outlier data points, specifically including:

[0016] Using depth and porosity, and depth and permeability as feature dimensions respectively, an algorithm based on the local outlier factor is employed to calculate the ratio of the local reachability density of each data point to the local reachability density of its neighbors. If the ratio exceeds a preset threshold, the data point is identified as an outlier and removed; and / or,

[0017] Based on the geological background, isolated points with similar depths but whose physical properties deviate from the regional evolution trend and exceed twice the standard deviation of the fitted residuals are removed as outlier data points.

[0018] Furthermore, in S2, both the compaction evolution equation and the permeability evolution equation are in exponential form;

[0019] The compaction evolution equation is: ,in, Porosity Where c is the initial porosity, z is the compaction coefficient, and z is the depth.

[0020] The permeability evolution equation is as follows: Where K is the penetration rate. d is the initial permeability, z is the permeability decay coefficient, and z is the depth.

[0021] The process of fitting and constructing the compaction evolution equation and the permeability evolution equation respectively includes:

[0022] Taking the natural logarithm of both sides of the compaction evolution equation and the permeability evolution equation respectively, we obtain their respective linear forms;

[0023] Using the depth as the independent variable and the natural logarithm of porosity and the natural logarithm of permeability as the dependent variables, a univariate linear regression method is used to fit the measured porosity data pairs and the measured permeability data pairs corresponding to each lithological sandstone to obtain regression coefficients, thereby determining the initial porosity, compaction coefficient, initial permeability, and permeability decay coefficient of each lithological sandstone.

[0024] Furthermore, in S3, the economic evaluation parameters for oil and gas development include oil and gas prices, development costs, and extraction methods. The extraction methods include natural energy extraction, water injection development, or fracturing stimulation, and different extraction methods correspond to different economic lower limits.

[0025] Furthermore, in S4, both the first quantitative relationship model and the second quantitative relationship model are exponential models;

[0026] The first quantitative relationship model is Where Q is the daily production of the single well. The reservoir porosity is given by a and b, which are the first regression coefficients.

[0027] The second quantitative relationship model is Where Q is the daily production of the single well, K is the reservoir permeability, and a' and b' are the second regression coefficients.

[0028] This invention also provides a device for determining the lower limit depth of sandstone reservoir exploration based on diagenetic evolution constraints. Based on the method for determining the lower limit depth of sandstone reservoir exploration based on diagenetic evolution constraints described above, the device includes:

[0029] The acquisition module is used to acquire the measured porosity, measured permeability, and measured depth corresponding to the measured porosity and measured permeability of sandstone of different lithologies within the target area; wherein, a measured porosity and its corresponding measured depth constitute a set of measured porosity data pairs, and a measured permeability and its corresponding measured depth constitute a set of measured permeability data pairs;

[0030] The fitting module is used to fit and construct compaction evolution equations and permeability evolution equations for different lithological sandstones based on the measured porosity data pairs and the measured permeability data pairs, according to the diagenetic evolution mechanism in which porosity and permeability of different lithological sandstones decrease exponentially with increasing depth. The compaction evolution equation represents the exponential decay evolution relationship between the porosity of each lithological sandstone and its initial porosity, compaction coefficient, and depth; the permeability evolution equation represents the exponential decay evolution relationship between the permeability of each lithological sandstone and its initial permeability, permeability decay coefficient, and depth.

[0031] The determination module is used to obtain the economic evaluation parameters of oil and gas development in the economic evaluation report of oil and gas development in the target area, so as to determine the economic lower limit of daily production of a single well based on the economic evaluation parameters of oil and gas development.

[0032] The conversion module is used to acquire the oil testing data of the target area, establish a first quantitative relationship model between the daily production of a single well and the reservoir porosity, and a second quantitative relationship model between the daily production of a single well and the reservoir permeability using the oil testing data, and substitute the economic lower limit value into the first quantitative relationship model and the second quantitative relationship model respectively to obtain the reservoir porosity lower limit value and the reservoir permeability lower limit value that meet the requirements of the economic lower limit value;

[0033] The solution module is used to substitute the lower limit values ​​of reservoir porosity and reservoir permeability into the compaction evolution equation and permeability evolution equation corresponding to each lithological sandstone to obtain the first depth corresponding to the lower limit value of reservoir porosity and the second depth corresponding to the lower limit value of reservoir permeability. The minimum value or weighted average value of the first depth and the second depth is taken as the lower limit depth of reservoir exploration for the corresponding lithological sandstone.

[0034] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0035] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method.

[0036] The beneficial effects of this invention are as follows:

[0037] This invention constructs a compaction evolution model constrained by diagenetic evolution mechanisms, giving clear physical meaning to the relationship between porosity, permeability, and depth. It reveals the genetic patterns of these variations, and the predicted results are more scientific and accurate than pure statistical regression. Separate models are built for lithologies of different grain sizes, quantifying the control effect of lithological differences on the compaction and porosity reduction process, enabling differentiated evaluation of the lower exploration depth, which is more consistent with geological realities. By coupling economic and technical parameters with the geological model through the productivity-physical property relationship, the determined lower exploration depth simultaneously meets geological laws and economic requirements, resulting in more practical value and significantly reduced extrapolation risk. The data required for this invention are all conventionally obtained measured data such as porosity, permeability, depth, and oil testing data from the exploration phase, and can be flexibly extended to other physical property parameters such as permeability. The calculation process is simple and easy to promote and apply within the industry. Attached Figure Description

[0038] Figure 1 This is a flowchart illustrating the method for determining the lower limit depth of sandstone reservoir exploration based on diagenetic evolution constraints according to the present invention.

[0039] Figure 2 This is a schematic diagram showing the fitting results of the compaction evolution model for sandstone of different lithologies in this invention.

[0040] Figure 3 This is a schematic diagram of the fitting results of the production capacity (daily oil production)-porosity relationship in this invention.

[0041] Figure 4 This is a schematic diagram illustrating the principle of determining the lower limit depth of sandstone reservoir exploration based on diagenetic evolution constraints in this invention.

[0042] Figure 5 This is a schematic diagram of the device for determining the lower limit depth of sandstone reservoir exploration based on diagenetic evolution constraints according to the present invention.

[0043] Figure 6 This is a schematic diagram of the internal structure of a computer device according to an embodiment of the present invention.

[0044] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0045] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0046] like Figure 1 As shown, this invention provides a method for determining the lower limit depth of sandstone reservoir exploration based on diagenetic evolution constraints, applicable to predicting the lower limit depth of shallow to medium-depth sandstone reservoirs in the Bohai Bay Basin and other similar rift basins. The method specifically includes the following steps:

[0047] S1. Obtain the measured porosity, measured permeability, and measured depth corresponding to the measured porosity and measured permeability of different lithologies of sandstone (including at least siltstone, fine sandstone, and medium sandstone) within the target area; wherein, a measured porosity and its corresponding measured depth constitute a set of measured porosity data pairs, and a measured permeability and its corresponding measured depth constitute a set of measured permeability data pairs;

[0048] S2. Based on the measured porosity data pair and the measured permeability data pair, and according to the diagenetic evolution mechanism that porosity and permeability of different lithological sandstones decrease exponentially with increasing depth, compaction evolution equations and permeability evolution equations for different lithological sandstones are fitted and constructed respectively; wherein, the compaction evolution equation represents the exponential decay evolution relationship between the porosity of each lithological sandstone and the initial porosity, compaction coefficient, and depth, and the permeability evolution equation represents the exponential decay evolution relationship between the permeability of each lithological sandstone and the initial permeability, permeability decay coefficient, and depth.

[0049] Before performing S2, to ensure the accuracy of the compaction evolution model, abnormal data points that are clearly affected by non-compaction effects such as later dissolution or cementation need to be removed. Specifically,

[0050] Anomaly detection is performed on the measured porosity data pairs and the measured permeability data pairs. A multidimensional outlier detection method is used to identify and remove outlier data points affected by non-compaction processes caused by later dissolution or cementation. Effective porosity data pairs and effective permeability data pairs reflecting normal diagenetic evolution trends are retained as the measured porosity data pairs and the measured permeability data pairs, respectively. Specifically, the multidimensional outlier detection method for identifying and removing outlier data points includes:

[0051] Using depth and porosity, and depth and permeability as feature dimensions, respectively, an algorithm based on the Local Outlier Factor (LOF) is employed to calculate the ratio of the local reachable density of each data point to the local reachable density of its neighboring points. If the ratio exceeds a preset threshold (usually 1.5), the data point is identified as an anomaly and removed. Alternatively, considering the geological background, isolated points with similar depths but whose physical properties deviate from the regional evolution trend and exceed twice the standard deviation of the fitted residual are also removed as anomalies. This method effectively filters out noise caused by non-compaction processes, retaining valid data reflecting normal compaction trends.

[0052] In S2, according to the theory of diagenesis, the porosity of sandstone decreases exponentially with depth under the dominance of mechanical compaction. Therefore, both the compaction evolution equation and the permeability evolution equation are in exponential form.

[0053] The compaction evolution equation is: ,in, Porosity Where c is the initial porosity, z is the compaction coefficient, and z is the depth.

[0054] The permeability evolution equation is as follows: Where K is the penetration rate. d is the initial permeability, z is the permeability decay coefficient, and z is the depth.

[0055] The process of fitting and constructing the compaction evolution equation and the permeability evolution equation respectively includes:

[0056] Taking the natural logarithm of both sides of the compaction evolution equation and the permeability evolution equation, respectively, yields their respective linear forms. With the depth as the independent variable and the natural logarithm of porosity and the natural logarithm of permeability as the dependent variables, a univariate linear regression method is used to fit the measured porosity data pairs and the measured permeability data pairs corresponding to each lithological sandstone to obtain the regression coefficients, thereby determining the initial porosity, compaction coefficient, initial permeability, and permeability decay coefficient of each lithological sandstone.

[0057] More specifically, the process of fitting and constructing the compaction evolution equation is as follows:

[0058] Taking the natural logarithm of both sides of the compaction evolution equation, we obtain the linear form. Using the depth z as the independent variable, and the natural logarithm of the porosity as the variable... Using the measured data of each lithological sandstone as the dependent variable, a univariate linear regression method was used to fit the regression coefficients to determine the initial porosity of each lithological sandstone. By combining the compaction coefficient c, the compaction evolution equation is obtained. Similarly, the steps for fitting and constructing the permeability evolution equation are the same as those for constructing the compaction evolution equation, ultimately yielding both the compaction evolution equation and the permeability evolution equation.

[0059] S3. Obtain the economic evaluation parameters for oil and gas development in the target area's economic evaluation report (including oil and gas prices, development costs, and extraction methods; extraction methods include natural energy extraction, water injection development, or fracturing stimulation, with different extraction methods corresponding to different economic lower limits). The lower limit of the daily production Q of a single well is determined based on the aforementioned oil and gas development economic evaluation parameters. ;

[0060] S4. Obtain oil testing data for the target area, and use the oil testing data to establish a relationship between the daily production Q of a single well and reservoir porosity. The first quantitative relationship model between the single-well daily production Q and the reservoir permeability K, and the second quantitative relationship model between the single-well daily production Q and the reservoir permeability K, and the economic lower limit value Substituting the values ​​into the first and second quantitative relationship models respectively, we obtain the economic lower limit value. Required lower limit of reservoir porosity and reservoir permeability lower limit .

[0061] Both the first quantitative relationship model and the second quantitative relationship model are exponential models;

[0062] The first quantitative relationship model is Where Q is the daily production of the single well. Let be the reservoir porosity, and a and b be the first regression coefficients.

[0063] The second quantitative relationship model is Where Q is the daily production of the single well, K is the reservoir permeability, and a' and b' are the second regression coefficients.

[0064] For the first quantitative relationship model, the lower limit of economic production capacity will be... Substituting into the first quantitative relationship model, the corresponding lower limit of reservoir porosity can be obtained. : Finally, the lower limit of porosity was set. Substituting these known values ​​into the established compaction evolution equations for different lithologies: Solving the equation yields: That is, the first depth corresponding to when the lithology reaches the lower limit of the reservoir porosity.

[0065] For the second quantitative relationship model, a method similar to that used for porosity is adopted, namely, obtaining the measured permeability and corresponding measured depth data of sandstones of different lithologies within the target area, and constructing a permeability evolution model with depth for different sandstones based on the diagenetic evolution mechanism. The permeability evolution model with depth is as follows: Where K is the permeability (mD). Let d be the initial permeability, d be the permeability decay coefficient, and z be the depth; based on the oil testing data, a second quantitative relationship model (exponential model) is established between the daily production Q of the single well and the reservoir permeability. ), and the economic lower limit value Substitute the values ​​to obtain the lower limit of reservoir permeability. Lower limit of reservoir permeability Substitute into the permeability evolution model of the corresponding lithology sandstone. The solution is obtained by solving the problem based on the lower limit constraint of reservoir permeability, resulting in the second depth. .

[0066] S5. Set the lower limit value of the reservoir porosity. and reservoir permeability lower limit Substituting the values ​​into the compaction evolution equation and permeability evolution equation corresponding to each lithology of sandstone, respectively, we can obtain the first depth corresponding to when each lithology of sandstone reaches the lower limit of reservoir porosity, and the second depth corresponding to when it reaches the lower limit of reservoir permeability; that is, solving... Obtain the first depth Solve Obtain the second depth ;

[0067] The first depth Second depth The minimum or weighted average value is taken as the lower limit depth for reservoir exploration of the corresponding sandstone lithology.

[0068] In a specific example of the present invention, taking a certain uplifted area in the Bohai Bay Basin as an example, the specific implementation process of the method of the present invention will be described in detail.

[0069] 1. Regional Geological Overview

[0070] The study area is located in a raised area of ​​the Bohai Bay Basin. The main target strata are the lower section of the Neogene Minghuazhen Formation and the Guantao Formation. The reservoir lithology is mainly siltstone, fine sandstone, and medium sandstone, with depths mostly shallower than 2000m. Currently, the main extraction method used in this area is natural energy water drive.

[0071] 2. Construct compaction evolution models for different lithologies

[0072] Measured porosity-depth data of siltstone, fine sandstone, and medium sandstone in the study area were collected (Table 1). First, outlier data points affected by local cementation or dissolution were removed based on the geological background. Then, according to the formula... Linear regression analysis was performed on effective data from different lithologies. The x-axis was set to depth z. The ordinate is calculated according to the formula Linear fitting was performed to obtain the regression equation. The initial porosity of each lithology was then calculated. The compaction coefficient c and the results are shown in Table 2. The fitted curve is shown in... Figure 2 .

[0073] Table 1 Measured porosity-depth data

[0074] Depth / m Lithology Porosity / % Depth / m Lithology Porosity / % Depth / m Lithology Porosity / % 704.32 fine sandstone 35.2 882.3 fine sandstone 27.3 1223.5 medium sandstone 28.5 704.34 medium sandstone 32.9 882.31 fine sandstone 27.7 1229 fine sandstone 22.1 704.52 medium sandstone 35 882.31 medium sandstone 28.3 1229 medium sandstone 26.9 704.54 medium sandstone 30.3 882.32 fine sandstone 24.2 1235 fine sandstone 23.3 704.72 medium sandstone 34.3 882.32 medium sandstone 30.4 1235 medium sandstone 25.1 704.74 medium sandstone 35.5 882.33 fine sandstone 26.3 1246 fine sandstone 22.4 704.91 medium sandstone 34 882.33 medium sandstone 31.3 1246 medium sandstone 24 704.93 fine sandstone 35.8 882.34 fine sandstone 24.6 1247.5 fine sandstone 21 705.3 fine sandstone 32.4 882.34 medium sandstone 28.1 1247.5 medium sandstone 27.3 705.65 fine sandstone 36.2 922 siltstone 20.5 1249 fine sandstone 22.3 706.18 fine sandstone 27.3 922.3 siltstone 19.1 1249.5 fine sandstone 20.4 706.21 fine sandstone 31.3 922.4 siltstone 18.8 1387.5 siltstone 14.9 706.37 fine sandstone 27.5 923.1 medium sandstone 33.8 1388.5 siltstone 15.8 706.42 fine sandstone 31.7 923.15 fine sandstone 27.7 1389.56 fine sandstone 20.2 706.54 fine sandstone 31.4 923.27 fine sandstone 28.7 1389.58 fine sandstone 18.4 706.55 fine sandstone 28.7 923.32 fine sandstone 24 1389.6 fine sandstone 20.6 706.73 fine sandstone 28.7 923.42 fine sandstone 27.8 1389.62 fine sandstone 21.2 706.75 fine sandstone 30.9 923.43 fine sandstone 29.3 1389.64 fine sandstone 18.2 706.95 siltstone 24.8 923.68 medium sandstone 31.9 1389.67 medium sandstone 24.3 706.97 siltstone 23.2 923.7 medium sandstone 30.1 1389.73 medium sandstone 22.3 844.12 fine sandstone 26 923.8 medium sandstone 31.5 1524 medium sandstone 25.8 844.33 medium sandstone 30.9 923.82 medium sandstone 28.3 1524.4 medium sandstone 24.2 844.36 medium sandstone 31.1 924.09 medium sandstone 30.8 1532 medium sandstone 22.4 844.47 medium sandstone 30.7 924.12 medium sandstone 29.1 1532.2 fine sandstone 24.4 844.51 medium sandstone 31.4 924.26 medium sandstone 29.6 1532.2 fine sandstone 24.7 844.71 medium sandstone 30.5 924.27 medium sandstone 28.1 1532.2 medium sandstone 21.1 844.75 medium sandstone 31.7 924.5 medium sandstone 29.2 1532.3 fine sandstone 20.9 844.87 medium sandstone 31.6 924.52 medium sandstone 28.8 1532.4 fine sandstone 21.7 844.91 medium sandstone 30.2 924.65 medium sandstone 27.8 1532.4 medium sandstone 20.7 845.3 fine sandstone 32.2 924.67 medium sandstone 31 1532.5 fine sandstone 18.2 845.32 medium sandstone 33.9 924.88 medium sandstone 25 1549 fine sandstone 22.2 845.5 medium sandstone 29.8 924.92 medium sandstone 27.9 1549.3 fine sandstone 19.6 845.53 fine sandstone 30.6 925.12 medium sandstone 30.4 1549.32 fine sandstone 21.4 845.63 medium sandstone 29 925.13 fine sandstone 27.6 1608 fine sandstone 20.5 845.64 medium sandstone 29.8 967 fine sandstone 25.6 1612 fine sandstone 19 845.88 medium sandstone 29.6 967.12 fine sandstone 26.4 1673.81 fine sandstone 18.9 845.89 medium sandstone 31.4 967.13 fine sandstone 24.2 1673.87 fine sandstone 19.1 845.91 medium sandstone 32.9 967.14 fine sandstone 20.3 1673.93 fine sandstone 18.4 846.1 fine sandstone 31.5 967.14 medium sandstone 31.3 1673.99 fine sandstone 17.2 846.11 medium sandstone 29.9 967.15 fine sandstone 24.4 1674.05 fine sandstone 16.6 846.25 medium sandstone 32.5 967.15 fine sandstone 24.6 1674.05 medium sandstone 22.9 846.26 fine sandstone 31 967.16 medium sandstone 28.3 1674.11 fine sandstone 19.3 846.48 fine sandstone 28.9 967.17 medium sandstone 28.9 1833 fine sandstone 17.2 846.49 medium sandstone 30.1 967.8 siltstone 20.5 1833 medium sandstone 21.5 846.64 fine sandstone 21.5 969.6 siltstone 21.3 1833.5 fine sandstone 16.4 846.69 medium sandstone 31.6 1000 fine sandstone 25.7 1833.5 medium sandstone 18.9 846.73 siltstone 22.2 1026 fine sandstone 23.9 1833.8 fine sandstone 17.9 846.9 medium sandstone 36.7 1026 medium sandstone 26.6 1834.1 fine sandstone 17.1 846.93 medium sandstone 29.8 1029 fine sandstone 25.3 1834.4 fine sandstone 15.4 847.31 fine sandstone 31.1 1029 medium sandstone 28.4 1892.12 fine sandstone 15.1 847.32 fine sandstone 29.7 1086 fine sandstone 23.4 1892.13 fine sandstone 18.1 847.35 fine sandstone 27.9 1086 medium sandstone 32.2 1892.14 fine sandstone 18.5 847.5 medium sandstone 32.2 1095 fine sandstone 27.4 1892.15 fine sandstone 16.2 847.52 fine sandstone 32.3 1095 medium sandstone 26.1 1892.16 fine sandstone 17.8 847.7 medium sandstone 31.8 1099 fine sandstone 26.3 1892.23 medium sandstone 17.8 847.72 medium sandstone 31.8 1105 fine sandstone 23.9 1892.25 medium sandstone 19.8 847.88 medium sandstone 33.8 1118.5 fine sandstone 27.2 1892.27 medium sandstone 20.2 847.9 fine sandstone 32.4 1123.5 fine sandstone 25.3 1892.31 medium sandstone 22.5 847.95 medium sandstone 33 1129 siltstone 18.1 1932.35 fine sandstone 15.9 848.2 medium sandstone 32.9 1129 siltstone 15.1 1932.35 medium sandstone 19.1 848.24 medium sandstone 30.8 1130 siltstone 18.7 1932.37 fine sandstone 16.5 848.4 fine sandstone 30.1 1132.8 fine sandstone 26 1932.37 medium sandstone 18.3 848.43 medium sandstone 34.9 1132.8 medium sandstone 29.4 1932.39 fine sandstone 15.4 848.58 medium sandstone 32.6 1133 fine sandstone 23.6 1932.39 medium sandstone 16.7 848.6 medium sandstone 35.5 1133 medium sandstone 26.3 1932.41 fine sandstone 18.2 848.74 medium sandstone 30.6 1149 siltstone 16.9 1932.43 fine sandstone 14.6 848.75 fine sandstone 28.9 1223.5 fine sandstone 23.8 1932.45 fine sandstone 17.6 882 fine sandstone 29.8

[0075] Table 2 Initial porosity and compaction coefficient

[0076] Lithology Data points <![CDATA[Intercept A = ln(φ0)]]> <![CDATA[Slope - c (×10⁻ 4 m⁻¹)]]> <![CDATA[Compaction coefficient c (× 10⁻ 4 m⁻¹)]]> <![CDATA[Initial porosity φ0 (%)]]> Goodness of fit R² siltstone 14 3.497 -5.21 5.21 33.0 0.89 fine sandstone 99 3.639 -4.20 4.20 38.1 0.91 medium sandstone 89 3.736 -4.01 4.01 41.9 0.93

[0077] As shown in Table 2, the initial porosity decreases with increasing sandstone grain size. The increase in the compaction coefficient c and the decrease in the compaction coefficient c indicate that the coarse-grained sandstone has a stronger resistance to compaction, which is completely consistent with the diagenetic evolution mechanism and verifies the rationality of the model of this invention.

[0078] 3. Calibration of the production capacity-porosity relationship and conversion of the lower limit of physical properties

[0079] Based on the economic evaluation report of the referenced study area (crude oil price of $60 / barrel, drilling and completion cost of approximately 20 million yuan per well, and operating cost of approximately 500 yuan / ton), and combined with the natural energy extraction method, the economic lower limit of stable daily production per well was calculated. =10.0 tons / day.

[0080] After collecting oil test data in the study area and removing outliers, nonlinear regression was performed on the production capacity and porosity data using formula (3) to obtain the following relationship: That is, a=1.80, b=0.09, and the goodness of fit R²=0.81, as shown. Figure 3 As shown.

[0081] Will Substituting 10.0 into the formula The corresponding lower limit of porosity can be obtained by solving the problem. ≈19.0%. This means that under the current economic and technological conditions in this region, the reservoir porosity needs to reach above 19.0% to guarantee commercial production capacity.

[0082] 4. Determine the lower limit depth for exploration of each lithology.

[0083] Lower limit of porosity Substituting 19.0% and the model parameters for each lithology in Table 2 into the formula Calculate the lower limit depth for exploration of each lithology. :

[0084] Silty sandstone: =-ln(19.0 / 33.0) / (5.21×10 -4 )≈1060m

[0085] Fine sandstone: =-ln(19.0 / 38.1) / (4.20×10 -4 )≈1657m

[0086] Medium sandstone: =-ln(19.0 / 41.9) / (4.01×10 -4 )≈1972m

[0087] The calculation results are as follows Figure 4 As shown.

[0088] 5. Results Analysis and Application Recommendations

[0089] To further illustrate the application of this invention in terms of permeability, this embodiment supplements the analysis with relevant permeability data. Based on the measured permeability-depth data of the study area, an exponential model is also used. The fit is performed, where K is the permeability (mD). Let be the initial permeability, and d be the permeability decay coefficient. Taking fine sandstone as an example, the initial permeability is obtained through regression. =1200mD, permeability attenuation coefficient d=2.30×10 -3 m -1 Based on the oil trial data from the study area, the relationship between production capacity and penetration rate was established using an exponential model. The regression yielded a'=1.35, b'=0.10, and a goodness-of-fit R²=0.78. The lower bound of economic output was then determined. Substituting 10.0 tons / day into the equation, we obtain the lower limit of permeability. =ln(10.0 / 1.35) / 0.10≈20.0mD. Substituting the permeability evolution model of fine sandstone, the lower limit of exploration depth based on permeability constraints is obtained. =-ln(20.0 / 1200) / (2.30×10 -3 The depth is approximately 1780m. Compared to 1657m based on porosity, the difference is 123m, reflecting the varying constraints imposed by different physical properties on reservoir effectiveness. In this application, the relationship between productivity and porosity shows a better fit (R²=0.81), and considering the principle of conservative exploration depth, the smaller 1657m can be chosen as the lower limit for exploration of fine sandstone, which can also be determined based on a weighted approach. This example demonstrates that this invention can be flexibly extended to other physical properties such as permeability to achieve comprehensive evaluation under multi-parameter constraints.

[0090] The exploration depth limits differ significantly among different lithologies, with medium sandstone reaching a depth of approximately 912 m deeper than siltstone, demonstrating the advantage of the differentiated evaluation method of this invention. In explorations shallower than 2000 m in the study area, it is recommended that fine sandstone (exploration limit 1657 m) be the primary exploration target; medium sandstone (1972 m) can be considered for deeper prospective exploration; and siltstone (1060 m) is suitable as a target for shallow exploration. The depth values ​​determined by this invention provide clear geological and economic constraints for well placement.

[0091] The technical principle of this invention is based on classical diagenetic evolution theory. During burial diagenesis, mechanical compaction is one of the most significant factors leading to a decrease in sandstone porosity, and its effect intensifies with increasing depth, resulting in an exponential decay of porosity. This decay law is controlled by lithology: the coarser the grain size, the stronger the rock skeleton's resistance to compaction, and the smaller the compaction coefficient c, thus preserving more primary porosity at the same depth. This is achieved by independently calibrating different lithologies. The methods described in sections c and d meticulously depict the vertical evolution trajectory of porosity from a causal perspective, overcoming the limitations of purely statistical methods. Furthermore, this invention establishes a production capacity-physical property relationship, incorporating economic evaluation parameters... Convert to geological parameters This approach achieves an organic coupling between economic and technological conditions and geological evolution laws. The final determined lower limit depth for exploration not only conforms to the basic diagenetic evolution laws but also meets the rigid requirements for economical mining.

[0092] It should be noted that the above embodiments are based on natural energy extraction and the lower limit of porosity. For production enhancement measures such as water injection or fracturing, the lower limit of economic production capacity is different. This will decrease accordingly, leading to a lower limit for physical properties. Lowering the limit will increase the final exploration depth. Those skilled in the art can adjust the method according to the core principles of this invention, combined with specific mining methods and evaluation parameters; these adjustments all fall within the scope of this invention.

[0093] like Figure 5As shown, the present invention also provides a device for determining the lower limit depth of sandstone reservoir exploration based on diagenetic evolution constraints. Based on the method for determining the lower limit depth of sandstone reservoir exploration based on diagenetic evolution constraints described above, the device includes:

[0094] The acquisition module 1 is used to acquire the measured porosity, measured permeability, and measured depth corresponding to the measured porosity and measured permeability of sandstone of different lithologies in the target area; wherein, a measured porosity and a corresponding measured depth constitute a set of measured porosity data pairs, and a measured permeability and a corresponding measured depth constitute a set of measured permeability data pairs;

[0095] Fitting module 2 is used to fit and construct compaction evolution equations and permeability evolution equations for different lithological sandstones based on the measured porosity data pairs and the measured permeability data pairs, according to the diagenetic evolution mechanism that porosity and permeability of different lithological sandstones decrease exponentially with increasing depth. The compaction evolution equation represents the exponential decay evolution relationship between the porosity of each lithological sandstone and its initial porosity, compaction coefficient, and depth; the permeability evolution equation represents the exponential decay evolution relationship between the permeability of each lithological sandstone and its initial permeability, permeability decay coefficient, and depth.

[0096] Module 3 is used to obtain the economic evaluation parameters of oil and gas development in the economic evaluation report of oil and gas development in the target area, so as to determine the economic lower limit of daily production of a single well based on the economic evaluation parameters of oil and gas development.

[0097] The conversion module 4 is used to acquire the oil testing data of the target area, establish a first quantitative relationship model between the daily production of a single well and the reservoir porosity, and a second quantitative relationship model between the daily production of a single well and the reservoir permeability using the oil testing data, and substitute the economic lower limit value into the first quantitative relationship model and the second quantitative relationship model respectively to obtain the reservoir porosity lower limit value and the reservoir permeability lower limit value that meet the requirements of the economic lower limit value;

[0098] The solution module 5 is used to substitute the lower limit value of reservoir porosity and the lower limit value of reservoir permeability into the compaction evolution equation and permeability evolution equation corresponding to each lithological sandstone to obtain the first depth corresponding to the lower limit value of reservoir porosity and the second depth corresponding to the lower limit value of reservoir permeability. The minimum value or weighted average value of the first depth and the second depth is taken as the lower limit depth of reservoir exploration for the corresponding lithological sandstone.

[0099] Each of the above modules is used to perform the corresponding steps in the above method for determining the lower limit depth of sandstone reservoir exploration based on diagenetic evolution constraints. The specific implementation method is as described in the above method embodiment, and will not be repeated here.

[0100] like Figure 6 As shown, the present invention also provides a computer device, which may be a server, and its internal structure may be as follows: Figure 6 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores all data required for the process of determining the lower limit depth of sandstone reservoir exploration based on diagenetic evolution constraints. The network interface is used for communication with external terminals via a network connection. The computer program is executed by the processor to implement the method for determining the lower limit depth of sandstone reservoir exploration based on diagenetic evolution constraints.

[0101] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer equipment on which the present application is applied.

[0102] An embodiment of this application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements any of the above-described methods for determining the lower limit depth of sandstone reservoir exploration based on diagenetic evolution constraints.

[0103] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by hardware related to computer program instructions. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the above method embodiments. Any references to memory, storage, databases, or other media provided in this application and used in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM), such as dynamic RAM (used as main storage) or static RAM (commonly used as cache memory). By way of illustration and not limitation, RAM has various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), and Rambus DRAM (RDRAM).

[0104] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0105] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for determining the lower limit depth of sandstone reservoir exploration based on diagenetic evolution constraints, characterized in that, include: S1. Obtain the measured porosity, measured permeability, and measured depth corresponding to the measured porosity and measured permeability of sandstone of different lithologies within the target area; wherein, a measured porosity and its corresponding measured depth constitute a set of measured porosity data pairs, and a measured permeability and its corresponding measured depth constitute a set of measured permeability data pairs. S2. Based on the measured porosity data and the measured permeability data, and according to the diagenetic evolution mechanism that porosity and permeability of different lithological sandstones decrease exponentially with increasing depth, compaction evolution equations and permeability evolution equations for different lithological sandstones are fitted and constructed respectively; wherein, the compaction evolution equation represents the exponential decay evolution relationship between the porosity of each lithological sandstone and the initial porosity, compaction coefficient, and depth, and the permeability evolution equation represents the exponential decay evolution relationship between the permeability of each lithological sandstone and the initial permeability, permeability decay coefficient, and depth; S3. Obtain the economic evaluation parameters for oil and gas development in the economic evaluation report of the target area, so as to determine the lower limit of the economic production per well per day based on the economic evaluation parameters for oil and gas development. S4. Obtain the oil testing data of the target area, and use the oil testing data to establish a first quantitative relationship model between the daily production of a single well and the reservoir porosity, and a second quantitative relationship model between the daily production of a single well and the reservoir permeability. Substitute the economic lower limit value into the first quantitative relationship model and the second quantitative relationship model respectively to obtain the reservoir porosity lower limit value and the reservoir permeability lower limit value that meet the requirements of the economic lower limit value. S5. Substitute the lower limit values ​​of reservoir porosity and reservoir permeability into the compaction evolution equation and permeability evolution equation corresponding to each lithological sandstone to obtain the first depth corresponding to the lower limit value of reservoir porosity and the second depth corresponding to the lower limit value of reservoir permeability. Take the minimum or weighted average value of the first depth and the second depth as the lower limit depth of reservoir exploration for the corresponding lithological sandstone.

2. The method for determining the lower limit depth of sandstone reservoir exploration based on diagenetic evolution constraints according to claim 1, characterized in that, The different lithologies of sandstone include at least siltstone, fine sandstone, and medium sandstone.

3. The method for determining the lower limit depth of sandstone reservoir exploration based on diagenetic evolution constraints according to claim 1, characterized in that, Before S2, it also includes: Anomaly detection is performed on the measured porosity data pairs and the measured permeability data pairs. A multidimensional outlier detection method is used to identify and remove abnormal data points that are affected by non-compaction effects of later dissolution or cementation. The effective porosity data pairs and effective permeability data pairs that reflect the normal diagenetic evolution trend are retained as the measured porosity data pairs and the measured permeability data pairs, respectively.

4. The method for determining the lower limit depth of sandstone reservoir exploration based on diagenetic evolution constraints according to claim 3, characterized in that, The outlier data points are identified and removed using a multidimensional outlier detection method, specifically including: Using depth and porosity, and depth and permeability as feature dimensions respectively, an algorithm based on the local outlier factor is employed to calculate the ratio of the local reachability density of each data point to the local reachability density of its neighbors. If the ratio exceeds a preset threshold, the data point is identified as an outlier and removed; and / or, Based on the geological background, isolated points with similar depths but whose physical properties deviate from the regional evolution trend and exceed twice the standard deviation of the fitted residuals are removed as outlier data points.

5. The method for determining the lower limit depth of sandstone reservoir exploration based on diagenetic evolution constraints according to claim 1, characterized in that, In S2, both the compaction evolution equation and the permeability evolution equation are in exponential form; The compaction evolution equation is: ,in, Porosity Where c is the initial porosity, z is the compaction coefficient, and z is the depth. The permeability evolution equation is as follows: Where K is the penetration rate. d is the initial permeability, z is the permeability decay coefficient, and z is the depth. The process of fitting and constructing the compaction evolution equation and the permeability evolution equation respectively includes: Taking the natural logarithm of both sides of the compaction evolution equation and the permeability evolution equation respectively, we obtain their respective linear forms; Using the depth as the independent variable and the natural logarithm of porosity and the natural logarithm of permeability as the dependent variables, a univariate linear regression method is used to fit the measured porosity data pairs and the measured permeability data pairs corresponding to each lithological sandstone to obtain regression coefficients, thereby determining the initial porosity, compaction coefficient, initial permeability, and permeability decay coefficient of each lithological sandstone.

6. The method for determining the lower limit depth of sandstone reservoir exploration based on diagenetic evolution constraints according to claim 1, characterized in that, In S3, the economic evaluation parameters for oil and gas development include oil and gas prices, development costs, and extraction methods. The extraction methods include natural energy extraction, water injection development, or fracturing. Different extraction methods correspond to different economic lower limits.

7. The method for determining the lower limit depth of sandstone reservoir exploration based on diagenetic evolution constraints according to claim 1, characterized in that, In S4, both the first quantitative relationship model and the second quantitative relationship model are exponential models. The first quantitative relationship model is Where Q is the daily production of the single well. The reservoir porosity is given by a and b, which are the first regression coefficients. The second quantitative relationship model is Where Q is the daily production of the single well, K is the reservoir permeability, and a' and b' are the second regression coefficients.

8. A device for determining the lower limit depth of sandstone reservoir exploration based on diagenetic evolution constraints, based on the method for determining the lower limit depth of sandstone reservoir exploration based on diagenetic evolution constraints according to any one of claims 1 to 7, characterized in that, The device includes: The acquisition module is used to acquire the measured porosity, measured permeability, and measured depth corresponding to the measured porosity and measured permeability of sandstone of different lithologies within the target area; wherein, a measured porosity and its corresponding measured depth constitute a set of measured porosity data pairs, and a measured permeability and its corresponding measured depth constitute a set of measured permeability data pairs; The fitting module is used to fit and construct compaction evolution equations and permeability evolution equations for different lithological sandstones based on the measured porosity data pairs and the measured permeability data pairs, according to the diagenetic evolution mechanism in which porosity and permeability of different lithological sandstones decrease exponentially with increasing depth. The compaction evolution equation represents the exponential decay evolution relationship between the porosity of each lithological sandstone and its initial porosity, compaction coefficient, and depth; the permeability evolution equation represents the exponential decay evolution relationship between the permeability of each lithological sandstone and its initial permeability, permeability decay coefficient, and depth. The determination module is used to obtain the economic evaluation parameters of oil and gas development in the economic evaluation report of oil and gas development in the target area, so as to determine the economic lower limit of daily production of a single well based on the economic evaluation parameters of oil and gas development. The conversion module is used to acquire the oil testing data of the target area, establish a first quantitative relationship model between the daily production of a single well and the reservoir porosity, and a second quantitative relationship model between the daily production of a single well and the reservoir permeability using the oil testing data, and substitute the economic lower limit value into the first quantitative relationship model and the second quantitative relationship model respectively to obtain the reservoir porosity lower limit value and the reservoir permeability lower limit value that meet the requirements of the economic lower limit value; The solution module is used to substitute the lower limit values ​​of reservoir porosity and reservoir permeability into the compaction evolution equation and permeability evolution equation corresponding to each lithological sandstone to obtain the first depth corresponding to the lower limit value of reservoir porosity and the second depth corresponding to the lower limit value of reservoir permeability. The minimum value or weighted average value of the first depth and the second depth is taken as the lower limit depth of reservoir exploration for the corresponding lithological sandstone.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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