A method for establishing a hybrid lithology pore interpretation model

Abstract

This invention discloses a method for establishing a porosity interpretation model based on mixed lithology. The method includes: converting surface porosity into formation porosity by overburden correction of porosity from core analysis; establishing a functional relationship reflecting the instability of the rock skeleton by repeatedly fitting the overburden-corrected porosity with porosity logging curves; and establishing a multivariate regression interpretation model using the porosity interpreted from the three-porosity curves. The porosity interpretation model obtained through multiple multivariate regression fitting shows a significant reduction in relative error compared to the single-variate fitting porosity interpretation model, thus improving interpretation accuracy.

Description

Technical Field

[0001] This invention relates to the field of oilfield exploration and development technology, and in particular to a method for establishing a porosity interpretation model based on mixed lithology. Background Technology

[0002] Porosity interpretation is an important research area in reservoir calculation. In near-shore source areas with underwater fan and fan delta facies, poor reservoir sorting often results in a mixture of various lithologies, such as conglomerate, gravelly sandstone, pebbly sandstone, and calcareous sandstone. Due to limited core data and physical property analysis data, it is difficult to establish porosity interpretation models for a single lithology. In addition, mixed rocks usually have complex rock compositions, often containing multiple minerals such as quartz, feldspar, limestone, and dolomite, with significant differences in their proportions. This leads to unstable logging parameter values ​​for different rock skeletons, resulting in large errors in porosity interpretation models established by linear fitting of porosity logging curves. Furthermore, due to the complex rock composition, the porosity variation trend in the same set of reservoir cores is not entirely consistent with the variation trends of sonic transit time, density, and compensated neutron curves, making it impossible for a single porosity curve to objectively reflect the formation porosity characteristics.

[0003] The aforementioned factors increase the difficulty of establishing porosity interpretation models in areas with mixed lithology. Patent CN112228037 A describes a porosity interpretation method and apparatus based on acoustic wave propagation theory. It establishes a multi-factor correction model for acoustic porosity by calculating clay content and processing anomalous measurements of acoustic wave time differences. However, this method is based on the premise that rock skeleton parameters are relatively stable, limiting its application in areas with complex lithology and unstable rock skeleton parameters. In 2022, the journal *Petroleum and Gas Geology* published a new method for porosity prediction based on variable skeleton parameters. This method uses the identification results of microscopic thin sections to determine skeleton parameters, but the process is relatively complex, limiting its application in areas lacking thin section analysis capabilities. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed to provide a method for establishing a porosity interpretation model based on mixed lithology to overcome or at least partially solve the above problems.

[0005] According to one aspect of the present invention, a method for establishing a porosity interpretation model based on mixed lithology is provided, the method comprising:

[0006] Based on the core repositioning, the ground porosity is converted into formation porosity by analyzing the porosity overburden pressure of the core.

[0007] By repeatedly fitting the porosity and porosity logging curves after overburden correction, a relationship that can reflect the instability characteristic function of the rock skeleton is established.

[0008] A multiple regression model was established to explain the porosity derived from the three-porosity curve.

[0009] Optionally, the step of converting ground porosity into formation porosity by correcting the porosity overburden pressure through core analysis, based on core repositioning, specifically includes:

[0010] In accordance with the principle of core repositioning, repositioning is controlled within the top and bottom boundaries of the wellbore based on marker layers and lithological combinations.

[0011] Porosity overburden correction includes: converting ground porosity into formation porosity through overburden correction, and establishing a porosity interpretation model based on three-porosity curves and core analysis porosity to more closely approximate the actual underground conditions;

[0012] Using overburden pressure correction data from the study block, an overburden pressure correction formula was established to convert the surface porosity from core analysis into formation porosity.

[0013] Optionally, the principle of core repositioning, within the top and bottom boundaries of the wellbore, specifically includes controlling the repositioning based on marker layers and lithological combinations, including:

[0014] Based on the principle of core repositioning, we start from the uppermost marker layer and push it upwards to the top of the core section, then proceed downwards in sequence to achieve a match between lithology and electrical properties.

[0015] Optionally, the step of using overburden pressure correction data from the study block to establish an overburden pressure correction formula and converting the surface porosity from core analysis into formation porosity specifically includes:

[0016] The overburden correction formula is: Ф=a*Фo b ;

[0017] In the formula: a and b are fitting coefficients; Ф—porosity of core analysis after overburden correction, %; Фo—porosity of core analysis, %.

[0018] Optionally, the step of establishing a functional relationship between the porosity after overburden correction and the porosity logging curve through multiple fittings to reflect the instability of the rock skeleton specifically includes:

[0019] Based on the core location and porosity overburden correction of the core data from the core wells in the study block, the correspondence between the sonic transit time logging curve and the porosity of the core after overburden correction was analyzed. A porosity interpretation model was established by fitting the sonic transit time logging curve and the porosity of the core after overburden correction multiple times, and its porosity was interpreted.

[0020] The correlation between density logging curves and core analysis porosity after overburden correction was analyzed. A porosity interpretation model was established by repeatedly fitting the density logging curves and core analysis porosity after overburden correction to interpret the porosity.

[0021] The correspondence between compensated neutron logging curves and core analysis porosity after overburden correction was analyzed. A porosity interpretation model was established by repeatedly fitting the compensated neutron logging curves and core analysis porosity after overburden correction, and the porosity was interpreted.

[0022] Optionally, the step of establishing a porosity interpretation model by repeatedly fitting the acoustic transit time curve and the porosity analysis of the core after overburden correction, and interpreting its porosity specifically includes:

[0023] By repeatedly fitting the sonic transit time curve with the porosity of the core after overburden correction, the fitting coefficients were determined, and a model for interpreting porosity using the sonic transit time curve was established.

[0024] Ф=A n *△t n +A n-1 *△t n-1 +……A*△t+A0

[0025] In the formula, A n ...A0 - Fitting coefficient; Ф - Porosity of core analysis after overburden correction, %; Δt - Sonic transit time logging value, µs / m;

[0026] Substituting the sonic transit time logging values ​​at the depth corresponding to the core analysis porosity into the interpretation model, the interpreted porosity values ​​are calculated:

[0027] Ф △t =A n *△t n +A n-1 *△t n-1 +……A*△t+A0

[0028] Ф △t - Sonic transit time logging interpretation of porosity, %.

[0029] Optionally, the correlation between the density logging curve and the core porosity analysis is analyzed. A porosity interpretation model is established through multiple fitting operations using the density logging curve and the core porosity after overburden correction. This model explains the specific porosity components, including:

[0030] The correlation between density logging curves and core analysis porosity was analyzed. Through multiple fitting operations using density logging values ​​and core analysis porosity corrected for overburden pressure, the fitting coefficients were determined, and a model for interpreting porosity using density logging curves was established.

[0031] Ф=B n *ρ n +B n-1 *ρ n-1 +……B*ρ+B0

[0032] In the formula B n ...B0 - Fitting coefficient, ρ - Density logging value, g / cm³ 3 ; Ф - Porosity of core analysis after overburden correction, %;

[0033] Substituting the density logging value at the depth corresponding to the core analysis porosity into the model for interpreting porosity using density logging curves, the interpreted porosity value is calculated:

[0034] Ф ρ =B n *ρ n +B n-1 *ρ n-1 +……B*ρ+B0

[0035] Among them, Ф ρ Interpreting porosity for density logging, %.

[0036] Optionally, the analysis of the correspondence between the compensated neutron logging curve and the core analysis porosity after overburden correction is performed. A porosity interpretation model is established through multiple fitting operations using the compensated neutron logging curve and the core analysis porosity after overburden correction, and the porosity is specifically explained as including:

[0037] The correlation between compensated neutron logging curves and core analysis porosity after overburden correction was analyzed. Through multiple fitting operations using compensated neutron logging values ​​and overburden correction-corrected core analysis porosity, the fitting coefficients were determined, and a model for interpreting porosity using compensated neutron logging curves was established.

[0038]

[0039] In the formula, C n ...C0 - Fit coefficient; Compensated neutron logging values, %; Ф - core analysis porosity after overburden correction, %;

[0040] Substituting the compensated neutron logging values ​​at the depth corresponding to the core analysis porosity into the above interpretation model, the logging interpretation porosity value is calculated:

[0041]

[0042] Among them, Ф Nf To compensate for the porosity interpretation of neutron logging, %.

[0043] Optionally, the establishment of a multiple regression explanation model using the porosity explained by the three-porosity curve specifically includes:

[0044] A multivariate regression interpretation model was established using porosity interpreted from acoustic transit time, density, and compensated neutron curve logging, along with core analysis porosity corrected for overburden pressure.

[0045] Optionally, the establishment of a multiple regression interpretation model using the porosity explained by acoustic transit time, density, and compensated neutron curve, and the core analysis porosity corrected for overburden pressure, specifically includes:

[0046] Porosity interpreted from acoustic transit time, density, and compensated neutron logging curves was compared with core analysis porosity after overburden correction using multiple regression analysis. The fitting coefficients were determined, and a multiple regression interpretation model was established.

[0047] Ф=A*Ф △t +B*Ф ρ +C*Ф Nf +DФ 多元拟合 =A*Ф △t +B*Ф ρ +C*Ф Nf +D

[0048] In the formula, A, B, C, and D are fitting coefficients; Ф is the core analysis porosity after overburden correction, in %; Ф 多元拟合 Porosity, %; Ф, explained by multivariate regression logging. △t Interpreting porosity (%) for acoustic transit time logging; Ф ρ Interpreting porosity (%) for density logging; Ф Nf To compensate for the porosity interpretation of neutron logging, %.

[0049] This invention provides a method for establishing a porosity interpretation model based on mixed lithology. The method includes: converting surface porosity into formation porosity by overburden correction of porosity obtained from core analysis; establishing a functional relationship reflecting the instability of the rock skeleton by repeatedly fitting the overburden-corrected porosity with porosity logging curves; and establishing a multivariate regression interpretation model using the porosity interpreted from the three-porosity curves. The porosity interpretation model obtained through multiple multivariate regression fitting shows a significant reduction in relative error compared to the single-variate fitting porosity interpretation model, thus improving interpretation accuracy.

[0050] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0051] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 A flowchart illustrating a method for establishing a porosity interpretation model based on mixed lithology, provided in an embodiment of the present invention;

[0053] Figure 2 This is an illustration of core repositioning in a specific embodiment of the present invention;

[0054] Figure 3 This is a schematic diagram of the porosity overburden correction relationship in a specific embodiment of the present invention;

[0055] Figure 4 This is a schematic diagram illustrating the establishment of a multiple-fit porosity interpretation model using acoustic time-of-flight curves in a specific embodiment of the present invention.

[0056] Figure 5 This is a schematic diagram illustrating the establishment of a linear regression porosity interpretation model using acoustic time difference curves in a specific embodiment of the present invention.

[0057] Figure 6 This is a schematic diagram of a multiple-fit porosity interpretation model established using density curves in a specific embodiment of the present invention;

[0058] Figure 7 This is a schematic diagram of a linear regression porosity interpretation model established using density curves in a specific embodiment of the present invention;

[0059] Figure 8 This is a schematic diagram illustrating the establishment of a multiple-fit porosity interpretation model using compensated neutron curves in a specific embodiment of the present invention.

[0060] Figure 9 This is a schematic diagram illustrating the linear regression porosity interpretation model established using the compensated neutron curve in a specific embodiment of the present invention;

[0061] Figure 10 A schematic diagram illustrating the establishment of a multivariate regression porosity interpretation model for a specific embodiment of the present invention;

[0062] Figure 11 This is a schematic diagram comparing the relative errors in pore interpretation according to a specific embodiment of the present invention. Detailed Implementation

[0063] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0064] The terms "comprising" and "having," and any variations thereof, in the specification, embodiments, claims, and drawings of this invention are intended to cover non-exclusive inclusion, such as including a series of steps or units.

[0065] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0066] This invention provides a method for establishing a porosity interpretation model based on mixed lithology, the method comprising:

[0067] Step 1: Core repositioning. Due to geological, drilling technology, and process factors, there is often a deviation between the logging depth and the actual depth. Relatively speaking, the depth recorded by well logging is much more accurate. Following the principle of core repositioning, the core is repositioned in sections within the top and bottom boundaries of the wellbore, based on marker layers and lithological combinations.

[0068] Step 2: Porosity Overburden Correction. Since the porosity analyzed in the core is surface porosity, and the three-porosity logging curve reflects the main formation information, only by converting the surface porosity from the core analysis into formation porosity through overburden correction can the porosity interpretation model established using the three-porosity curve more closely approximate the actual underground conditions. Using overburden correction data from the study block, an overburden correction formula is established to convert the surface porosity from the core analysis into formation porosity.

[0069] Step 3: Based on the core location and overburden correction of the core data from the core wells in the study area, analyze the correspondence between sonic transit time and the porosity of the core after overburden correction, establish a porosity interpretation model using the sonic transit time curve, and interpret its porosity.

[0070] Step 4: Analyze the correspondence between the density logging curve and the porosity of the core analysis after overburden correction, establish a porosity interpretation model using the density curve, and interpret its porosity.

[0071] Step 5: Analyze the correspondence between the compensated neutron logging curve and the core analysis porosity after overburden correction, establish a porosity interpretation model using the neutron curve, and interpret its porosity.

[0072] Step 6: Establish a multivariate regression interpretation model using the porosity interpreted from the sonic transit time, density, and compensated neutron logging curves, and the core analysis porosity after overburden correction.

[0073] like Figure 1 As shown, in step 101, core repositioning, according to the core repositioning principle, starting from the uppermost marker layer, the core is pushed upwards to the top of the cored section, and then downwards sequentially, until the lithology and electrical properties match. Figure 2 After completing the core placement of a single well, proceed to step 102.

[0074] Step 102: Porosity overburden correction. Using overburden correction data from the study block, establish the overburden correction formula (…). Figure 3 The overburden correction formula is:

[0075] Ф=0.9995*Ф o 0.9853

[0076] Ф—Porosity of core analysis after overburden correction, %;

[0077] Ф o —Core analysis porosity, %.

[0078] Using the overburden pressure correction formula established above, the surface porosity from the core analysis is converted into formation porosity (Table 1), and then proceed to step 103.

[0079] Table 1. Porosity data obtained from core analysis and after overburden correction.

[0080]

[0081]

[0082] Step 103: Based on the core location of the core data from the core wells in the study block, the correspondence between sonic transit time and lithological analysis porosity is analyzed. Using the data in the example (Table 2), during the process of fitting the sonic transit time curve with the lithological analysis porosity after overburden correction multiple times, a quadratic fitting relationship is found that can meet the interpretation accuracy requirements of this example.

[0083] Table 2. Basic data for establishing a porosity interpretation model using acoustic transit time curves.

[0084]

[0085] Quadratic fitting was used to determine the various correlation coefficients in the explanatory model. Figure 4 (Table 2), the formula for explaining the model is:

[0086] Ф=-0.0012*△t 2 +0.7668*△t-95.612 △t

[0087] Wherein, Ф is the porosity of the core analysis after overburden correction, in %; Δt is the acoustic transit time, in µs / m.

[0088] The porosity interpretation model established using quadratic fitting is better than that of linear fitting. Figure 5 The correlation is good, and the accuracy of the explanation is also improved.

[0089] Substituting the sonic transit time logging values ​​(Table 2) corresponding to the depth of core analysis porosity into the above interpretation model, the logging interpretation porosity values ​​(Table 2) were calculated:

[0090] Ф △t = -0.0012*△t 2 +0.7668*△t-95.612 △t

[0091] Ф △t - Sonic transit time logging interpretation of porosity, %.

[0092] Proceed to step 104.

[0093] Step 104: Analyze the correspondence between density logging curves and core analysis porosity. Using data from the implementation (Table 3), during the process of repeatedly fitting the density logging curves with the lithological analysis porosity after overburden correction, a quadratic fitting relationship was found to meet the interpretation accuracy requirements of this example. Therefore, quadratic fitting was used to determine the various correlation coefficients in the interpretation model. Figure 6 Table 3), the formula for explaining the model is:

[0094] Ф=-74.926*ρ 2 +325.7*ρ-325.05

[0095] ρ - Density logging value, g / cm³ 3

[0096] Ф - Porosity of core analysis after overburden correction, %.

[0097] The porosity interpretation model established using quadratic fitting is better than that of linear fitting. Figure 7 The correlation is good, and the accuracy of the explanation is also improved.

[0098] Substituting the density logging values ​​at the depths corresponding to the core analysis porosity (Table 3) into the above interpretation model, the logging interpretation porosity values ​​were calculated (Table 3):

[0099] Ф ρ =-74.926*ρ 2 +325.7*ρ-325.05

[0100] Ф ρ - Density logging interprets porosity, %.

[0101] Table 3. Basic data for establishing a porosity interpretation model using density logging curves.

[0102]

[0103] Proceed to step 105.

[0104] Step 105: Analyze the correspondence between the compensated neutron logging curve and the core analysis porosity. Using the data from the implementation (Table 4), during the process of repeatedly fitting the compensated neutron logging curve and the core analysis porosity after overburden correction, it was found that the quadratic fitting relationship can meet the interpretation accuracy requirements of this example. Therefore, quadratic fitting was used to determine the various correlation coefficients in the interpretation model. Figure 8 Table 4), the formula for explaining the model is:

[0105]

[0106] Compensated neutron logging values;

[0107] Ф - Porosity of core analysis after overburden correction, %

[0108] The porosity interpretation model established using quadratic fitting is better than that of linear fitting. Figure 9 The correlation is good, and the accuracy of the explanation is also improved.

[0109] Substituting the compensated neutron logging values ​​(Table 4) corresponding to the depths of each core analysis porosity into the above interpretation model, the logging interpretation porosity values ​​are calculated (Table 4):

[0110]

[0111] Ф Nf - Compensated neutron logging interpretation of porosity, %.

[0112] Table 4. Basic data for establishing a porosity interpretation model using compensated neutron logging curves.

[0113]

[0114] Proceed to step 106.

[0115] Step 106: The porosity interpreted from steps 103, 104, and 105 using sonic transit time, density, and compensated neutron logging curves, along with the core analysis porosity corrected for overburden pressure (Table 5), is subjected to multiple regression to determine the fitting coefficients and establish a multiple regression interpretation model. Figure 10 (Table 5), the formula is:

[0116] Ф 多元拟合 =-0.03716*Ф △t +0.399233*Ф ρ +0.721398*Ф Nf -2.1002

[0117] Among them, Ф 多元拟合 -Multivariate regression fitting of well logging interpretation of porosity, %; Ф △t- Acoustic transit time logging interprets porosity; Ф ρ - Density logging interprets porosity, %; Ф Nf - Compensated neutron logging interpretation of porosity, %.

[0118] Table 5. Basic data for establishing a multiple regression porosity interpretation model using the three-porosity curve.

[0119]

[0120] This invention discloses a method for establishing a porosity interpretation model based on mixed lithology. The porosity interpretation model established by this invention using multiple multivariate fitting better reflects the porosity development characteristics in mixed lithology than existing single-variate fitting models. Based on core repositioning and overburden correction of core analysis porosity, the correlation between the multiple fitting relationship established between the three-porosity curves and the overburden-corrected core analysis porosity is significantly improved compared to the single linear relationship. Furthermore, the porosity interpretation model using multiple regression of the porosity interpreted from the three-porosity curves reduces errors caused by inconsistencies between the single porosity curve and the porosity variation trend in core analysis (Table 6). Figure 11 ).

[0121] Table 6. Comparison of relative errors between the multivariate regression porosity interpretation model and the porosity interpretation model established using single porosity curves.

[0122]

[0123] Beneficial effects: The porosity interpretation model fitted by multiple regressions has significantly improved the interpretation accuracy compared with the porosity interpretation models established by single-fitting and multiple-fitting of single porosity curves, thereby improving the economic benefits of oil and gas exploration.

[0124] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for establishing a porosity interpretation model based on mixed lithology, characterized in that, The establishment method includes: Based on the core repositioning, the ground porosity is converted into formation porosity by analyzing the porosity overburden pressure of the core. By repeatedly fitting the porosity and porosity logging curves after overburden correction, a relationship that can reflect the instability characteristic function of the rock skeleton is established. A multiple regression model was established to explain the porosity derived from the three-porosity curve.

2. The method for establishing a porosity interpretation model based on mixed lithology according to claim 1, characterized in that, Based on the core repositioning, the conversion of ground porosity into formation porosity through core analysis and overburden pressure correction specifically includes: In accordance with the principle of core repositioning, repositioning is controlled within the top and bottom boundaries of the wellbore based on marker layers and lithological combinations. Porosity overburden correction includes converting ground porosity into formation porosity through overburden correction. Only by using a porosity interpretation model established by combining the three-porosity curve with core analysis porosity can the actual underground conditions be more closely approximated. Using overburden pressure correction data from the study block, an overburden pressure correction formula was established to convert the surface porosity from core analysis into formation porosity.

3. The method for establishing a porosity interpretation model based on mixed lithology according to claim 2, characterized in that, The principle of core repositioning, within the top and bottom boundaries of the wellbore, specifically includes controlling repositioning based on marker layers and lithological combinations, including: Based on the principle of core repositioning, we start from the uppermost marker layer and push it upwards to the top of the core section, then proceed downwards in sequence to achieve a match between lithology and electrical properties.

4. The method for establishing a porosity interpretation model based on mixed lithology according to claim 2, characterized in that, The process of using overburden pressure correction data from the study block to establish an overburden pressure correction formula, and converting the surface porosity from core analysis into formation porosity, specifically includes: The formula for the overlay correction is: Φ = a * Φo b ; In the formula: a and b are fitting coefficients; Ф—porosity of core analysis after overburden correction, %; Фo—porosity of core analysis, %.

5. The method for establishing a porosity interpretation model based on mixed lithology according to claim 1, characterized in that, The specific steps of establishing a functional relationship between the porosity after overburden correction and the porosity logging curve through multiple fittings to reflect the instability of the rock skeleton include: Based on the core location and porosity overburden correction of the core data from the core wells in the study block, the correspondence between the sonic transit time logging curve and the porosity of the core after overburden correction was analyzed. A porosity interpretation model was established by fitting the sonic transit time curve and the porosity of the core after overburden correction multiple times, and its porosity was interpreted. The correlation between density logging curves and core analysis porosity after overburden correction was analyzed. A porosity interpretation model was established by repeatedly fitting the density logging curves and core analysis porosity after overburden correction to interpret the porosity. The correspondence between compensated neutron logging curves and core analysis porosity after overburden correction was analyzed. A porosity interpretation model was established by repeatedly fitting the compensated neutron logging curves and core analysis porosity after overburden correction, and the porosity was interpreted.

6. The method for establishing a porosity interpretation model based on mixed lithology according to claim 5, characterized in that, The porosity interpretation model is established by repeatedly fitting the acoustic transit time curve and the core analysis porosity after overburden correction, and the porosity is specifically interpreted as follows: By repeatedly fitting the sonic transit time curve with the porosity of the core after overburden correction, the fitting coefficients were determined, and a model for interpreting porosity using the sonic transit time curve was established. Φ = A n Δt n + A n-1 Δt n-1 +... A Δt + A0 wherein A n … A0- fitting coefficient, Ф - core analysis porosity corrected for overburden pressure, %; Δt - acoustic traveltime log value, us / m; Substituting the sonic transit time logging values ​​at the depth corresponding to the core analysis porosity into the interpretation model, the interpreted porosity values ​​are calculated: Φ △t = A n * Δt n + A n-1 * Δt n-1 +... A * Δt + A0 Ф △t - Sonic transit time logging interpretation of porosity, %.

7. The method for establishing a porosity interpretation model based on mixed lithology according to claim 5, characterized in that, The analysis establishes a correlation between density logging curves and core porosity analysis. A porosity interpretation model is built using multiple fitting operations with the density logging curves and the core porosity after overburden correction. This model explains the specific components of porosity, including: The correlation between density logging curves and core analysis porosity was analyzed. Through multiple fitting operations using density logging values ​​and core analysis porosity corrected for overburden pressure, the fitting coefficients were determined, and a model for interpreting porosity using density logging curves was established. Ф=B n *ρ n +B n-1 *ρ n-1 +……B*ρ+B0 In the formula B n ...B0 - Fitting coefficient, ρ - Density logging value, g / cm³ 3 ; Ф - Porosity of core analysis after overburden correction, %; Substituting the density logging value at the depth corresponding to the core analysis porosity into the model for interpreting porosity using density logging curves, the interpreted porosity value is calculated: Ф ρ =B n *ρ n +B n-1 *ρ n-1 +……B*ρ+B0 Among them, Ф ρ Interpreting porosity for density logging, %.

8. The method for establishing a porosity interpretation model based on mixed lithology according to claim 5, characterized in that, The analysis establishes a correlation between the compensated neutron logging curves and the core analysis porosity after overburden correction. A variable skeleton porosity interpretation model is built using the compensated neutron curves, and the porosity is specifically explained as including: The correlation between compensated neutron logging curves and core analysis porosity after overburden correction was analyzed. Through multiple fitting operations using compensated neutron logging values ​​and overburden correction-corrected core analysis porosity, the fitting coefficients were determined, and a model for interpreting porosity using compensated neutron logging curves was established. F=C n *f Nf n +C n-1 *f Nf n-1 +……C*φ Nf +C0 In the formula, C n ...C0 - Fitting coefficient; φ Nf - Compensated neutron logging value, f; Ф - Porosity of core analysis after overburden correction, %; Substituting the compensated neutron logging values ​​at the depth corresponding to the core analysis porosity into the above interpretation model, the logging interpretation porosity value is calculated: F Nf =C n *f Nf n +C n-1 *f Nf n-1 +……C*φ Nf +C0 Among them, Ф Nf To compensate for the porosity interpretation of neutron logging, %.

9. The method for establishing a porosity interpretation model based on mixed lithology according to claim 1, characterized in that, The specific steps of establishing a multiple regression explanation model using the porosity explained by the three-porosity curve include: A multivariate regression interpretation model was established using porosity interpreted from sonic transit time, density, and compensated neutron logging curves, along with core analysis porosity corrected for overburden pressure.

10. The method for establishing a porosity interpretation model based on mixed lithology according to claim 9, characterized in that, The establishment of a multivariate regression interpretation model using porosity interpreted from acoustic transit time, density, and compensated neutron logging curves, along with core analysis porosity corrected for overburden pressure, specifically includes: Porosity interpreted using acoustic transit time, density, and compensated neutrons was compared with core analysis porosity corrected for overburden pressure using multiple regression to determine the fitting coefficients and establish a multiple regression interpretation model. F=A*F △t +B*Ф ρ +C*Ф Nf +DФ 多元拟合 =A*Ф △t +B*Ф ρ +C*Ф Nf +D In the formula, A, B, C, and D are fitting coefficients; Ф is the core analysis porosity after overburden correction, in %; Ф 多元拟合 Porosity, %; Ф, explained by multivariate regression logging. △t Porosity, %; Ф, explained by acoustic transit time. ρ Interpret the porosity (%) for the density curve; Ф Nf Porosity is explained for the neutron curve, %.

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