Secondary interpretation method for watered-out layers under small layer level oil-water interface understanding condition

Abstract

The application discloses a watered-out layer secondary interpretation method under the condition of small layer level oil-water interface understanding, and steps are as follows: step 1, small layer geological modeling and oil-water interface fine calibration; step 2, well logging data standardization and quality control; step 3, continuous calculation of small layer mixed formation water resistivity; step 4, completion of reservoir parameter quantitative calculation; step 5, obtaining of primary interpretation based on small layer oil-water interface; step 6, implementation of secondary iteration correction under the constraint of production dynamics; and step 7, output of interpretation results and application suggestions. The application belongs to the technical field of oilfield development well logging interpretation, and solves the problems that the prior art is not accurate in key parameter calculation, the interpretation process is single, and the precision and actual development demand are not matched.

Description

Technical Field

[0001] This invention belongs to the field of well logging interpretation technology in oilfield development. It is applicable to low-permeability, thin sand bodies, multiple fault blocks, and high water-cut water injection development oilfields, and involves a secondary interpretation method for water-flooded layers under the condition of recognizing the oil-water interface at the small layer level. Background Technology

[0002] Located at the western end of the Fuxin Uplift in the central depression of the Songliao Basin, this oilfield is a typical low-permeability fractured tectonic-lithological reservoir. The main development formations are the Quanzhou No. 4 and Quanzhou No. 3 Fuyang oil layers, characterized by fine geological stratification, divided into 8 sand groups and 26 sub-layers. Influenced by geological sedimentary conditions, the reservoir exhibits distinct low permeability and low porosity characteristics, along with thin sand bodies, rapid lateral distribution, dense fault blocks, and strong inter-layer heterogeneity. These inherent geological conditions present numerous challenges to oilfield development and well logging interpretation. To date, the oilfield has undergone nearly 40 years of water injection development, entering a medium-to-high water-cut stage, with an overall water cut exceeding 85%. Long-term water injection development has led to increasingly complex reservoir water flooding patterns, resulting in a scattered and fragmented distribution of remaining oil, no longer exhibiting a concentrated distribution. Against this backdrop, the accuracy of logging interpretation of water-flooded formations directly affects the scientific validity and effectiveness of tapping remaining oil potential and adjusting development plans, becoming a core technical challenge currently restricting the efficient development and adjustment of the oilfield, which urgently needs to be addressed in a targeted manner.

[0003] Based on the reservoir characteristics and current development status of this oilfield, existing well logging interpretation techniques for water-flooded formations have significant shortcomings and are unable to meet the needs of precise development. These shortcomings manifest in five aspects: First, fine oil-water interface calibration is not carried out for each sub-layer. Instead, a "one-size-fits-all" approach is used, applying uniform interface parameters to different sub-layers within the same sand group. This fails to accurately characterize the water-flooded heterogeneity within the layer, resulting in significant interpretation errors. Second, the methods for calculating the resistivity of mixed formation water are crude, often using fixed values ​​or simple averaging methods. These methods do not fully consider the dynamic changes in the mixing of injected water and native water at each point within the reservoir, directly affecting the accuracy of saturation calculations. Third, the resolution of the well logging series is too low, resulting in insufficient ability to identify thin and poorly developed layers and interlayers, which easily leads to lithological misjudgment and further affects the accuracy of water-flooded layer interpretation. Fourth, the interpretation mode is relatively simple, with most interpretations being static interpretations without iterative correction based on dynamic production data such as production profiles and water absorption profiles, resulting in a low interpretation accuracy rate, with the original interpretation accuracy rate being only 80.3%. Fifth, the accuracy of remaining oil identification and potential layer exploration is insufficient, failing to reach the small-scale level, and cannot provide refined data support for precise potential tapping and optimized development plans in oilfields, making it difficult to meet the high-efficiency development needs of low-permeability fractured reservoirs.

[0004] To address the prominent issues of existing technologies and considering the regional geological characteristics of this oilfield, which is primarily controlled by geological structures, there is an urgent need to establish a precise and efficient secondary interpretation method for water-flooded layers. This method should use the oil-water interface at the sub-layer level as a core constraint, deeply integrating well logging data, geological information, and production dynamics to achieve high-precision, small-layer coverage, and iterative optimization in water-flooded layer evaluation. This would solve the current well logging interpretation challenges and provide strong technical support for the oilfield's subsequent precise potential tapping and efficient development. Summary of the Invention

[0005] The purpose of this invention is to provide a secondary interpretation method for water-flooded layers under the condition of recognizing the oil-water interface at the small layer level, and to solve the problems of inaccurate calculation of key parameters, simple interpretation process, and mismatch between accuracy and actual development needs in the existing technology.

[0006] The technical solution adopted in this invention is a secondary interpretation method for water-flooded layers under the condition of recognizing the oil-water interface at a small layer level, which is implemented according to the following steps: Step 1: Layer-by-layer geological modeling and fine-scale calibration of the oil-water interface; Step 2: Standardization and quality control of well logging data; Step 3: Continuous calculation of water resistivity in mixed formations of different sub-layers; Step 4: Complete the quantitative calculation of reservoir parameters; Step 5: Obtain a primary interpretation based on the sublayered oil-water interface; Step 6: Implement a second-order iterative correction under production dynamic constraints; Step 7: Output the explanation results and application suggestions.

[0007] The beneficial effects of this invention are mainly reflected in the following four aspects: 1) The interpretation accuracy has been greatly improved. The average absolute error of porosity is controlled at 1.21 porosity units, the permeability error is maintained within one order of magnitude, and the interpretation accuracy of water-flooded layers has increased from 80.3% to 92.0%. 2) The thin layer identification capability is significantly enhanced, and it is compatible with high-resolution dual-lateral and density logging technologies. It can accurately identify thin and poor layers as thin as 0.2m thick, solving the problem of insufficient identification of thin and poor layers in the original technology. 3) The potential tapping targets are more targeted, and small-level remaining oil-rich areas can be systematically identified. A single study can tap 33 potential layers in 18 wells, providing a clear direction for precise potential tapping; 4) It has wide applicability and can be adapted to various complex reservoirs such as low permeability, thin sand bodies, multiple fault blocks, and high water cut, making it highly valuable for application. Attached Figure Description

[0008] Figure 1 This is a resistivity-acoustic transit time cross-plot of the method of the present invention in the second sand group of the third working area of ​​an oilfield. Figure 2 This is the resistivity-acoustic transit time cross plot of the method of the present invention in the resistivity-acoustic transit time of the third sand group in the third working area of ​​an oilfield (Plate 2). Figure 3 This is a typical well secondary interpretation effect diagram of the method of the present invention; Figure 4 It is a continuation Figure 3 A typical diagram illustrating the secondary interpretation effect of a well. Detailed Implementation

[0009] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0010] The present invention provides a secondary interpretation method for water-flooded layers under the condition of recognizing the oil-water interface at a small-layer level, which is implemented according to the following steps: Step 1: Layer-by-layer geological modeling and fine-scale oil-water interface calibration. The specific process is as follows: 1.1) The oil-water interface of each sub-layer shall be determined according to the three-level calibration requirements of work area-sand group-sub-layer. The third-level calibration must strictly adhere to the principles of "graded control, cyclic comparison, facies constraint, and framework closure," using 8 sand groups and 26 sub-layers of the Fuyang oil reservoir in a certain oilfield as the basic unit. Combined with its geological and development characteristics, the entire process must be implemented to ensure that the calibration results accurately reflect the actual reservoir conditions. Specific requirements are: 1) Based on the work area-level calibration, it is necessary to combine the regional tectonic characteristics of the western end of the Fuxin Uplift in the central depression of the Songliao Basin, clarify the overall tectonic morphology, fault distribution pattern and sedimentary facies distribution characteristics of the work area, delineate the macroscopic range of the oil-water interface of different tectonic units, eliminate the influence of regional tectonic differences on the small layers, and delineate the boundaries for subsequent sand group and small layer-level calibration.

[0011] 2) The sand group-level calibration is a transitional step. Calibration is carried out separately for each of the eight sand groups. Combining the sedimentary cycle characteristics of the sand groups, the connectivity of the sand bodies and the heterogeneity of the reservoir, the commonalities and differences in the fluid distribution of different sub-layers within the same sand group are analyzed to determine the oil-water interface range of each sand group, so as to avoid confusion between interfaces between different sand groups. At the same time, the characteristics of rapid lateral changes in sand bodies are taken into account, and comparisons between sand groups and between wells are carried out.

[0012] 3) Focusing on sub-layer calibration, 26 sub-layers are used as basic units to complete fine sub-layer division and inter-well comparison. During the operation, high-resolution logging data is required to accurately identify sub-layer interfaces and interlayer distribution. Combined with core observation results, the thickness, lithological combination, porosity, and permeability distribution characteristics of each sub-layer sand body are clarified. Given the characteristics of thin sand bodies (mostly less than 1m thick) and well-developed interlayers, the sub-layer boundaries are carefully calibrated to avoid omissions or miscalibrations. Inter-well comparison needs to consider fracture development and correct for the influence of fault block movement on sub-layer distribution, ensuring accurate correspondence of the same sub-layer between different wells. This achieves uniformity and refinement of sub-layer division, laying the foundation for subsequent accurate oil-water interface calibration.

[0013] It is evident that the entire calibration process must be integrated with regional geological data and simultaneously reference production dynamic data to ensure that each level of calibration has a reliable basis and avoid deviations caused by subjective judgment.

[0014] 1.2) Utilize multiple data sources to validate and determine the principles for calibrating the original oil-water interface and the current oil-water interface. The calibration process must adhere to the core principles of "geology as the foundation, data as the basis, and dynamic verification as the verification method." Considering the low permeability and high water-cut development characteristics of the Fuyang oil layer in the Songliao Basin, it must strictly follow the calibration logic of dividing work areas, sand groups, and sub-layers to ensure accurate and realistic calibration of the original and current water-flooded interfaces. Specific implementation principles are as follows: 1) Geological Adaptation Principle: The calibration results must be consistent with the regional geological patterns by taking into account the sedimentary facies and structural characteristics of the Fuyang oil layer. For example, given the characteristics of fault block development, the blocking effect of faults on oil and water distribution should be considered, and different fault blocks should be calibrated separately to avoid errors caused by uniform calibration across fault blocks; given the strong heterogeneity between layers, different sub-layers within the same sand group should be calibrated separately based on their own reservoir conditions to avoid a "one-size-fits-all" approach.

[0015] 2) Data Priority and Multi-Source Verification Principle: Priority should be given to direct data such as oil testing and core analysis. Production data of oilfield blocks should be selected from original production data that have not been affected by water injection to accurately determine the original oil-water interface. Core analysis should focus on observing oil-bearing and water-bearing characteristics, and combine porosity, permeability data, and relative permeability data to help determine the location of the oil-water interface. Production data and production dynamic data (production profile, water absorption profile, etc.) should be used as verification basis. Combined with the water flooding characteristics of the high water-cut development stage, the current water flooding interface should be calibrated to avoid calibration deviation caused by relying on a single data.

[0016] 3) Grade correspondence principle: Strictly follow the graded calibration of work area, sand group, and sub-layer to ensure that the interfaces of each level are connected vertically and logically consistent. The work area level interface constrains the sand group level, and the sand group level interface guides the sub-layer level. At the same time, take into account the rapid lateral changes of the sub-layer, correct the differences between well interfaces in a timely manner, and ensure that the original and current water-flooded interfaces can accurately reflect the fluid distribution status of each level.

[0017] 1.3) The principle of establishing a three-level oil-water interface database of work area-sand group-sub-layer, The database establishment should adhere to the principles of "standardization and uniformity, accuracy and reliability, dynamic updability, and adaptability to development." Based on the geological and development characteristics of the Fuyang oil layer in a certain oilfield, the fluid boundaries and elevation constraints of each sub-layer should be clearly defined to provide stable and accurate data support for the secondary interpretation of water-flooded layers. This includes the following implementation principles: 1) Standardization and unification principle: The database needs to have a clear and unified naming standard, data format and accuracy requirements. Taking 8 sand groups and 26 sub-layers as the core unit, information such as oil-water interface elevation, fluid properties and calibration basis of each layer should be entered in a unified manner to ensure that data from different work areas, different sand groups and different sub-layers can be compared and retrieved.

[0018] 2) Accuracy and reliability principle: The entered data must be strictly verified, and the calibration results verified by multiple sources should be given priority, while abnormal data should be eliminated; In view of the characteristics of low permeability and complex water flooding pattern of Fuyang oil layer, the credibility of fluid boundary of each sub-layer should be marked, and the elevation constraint range should be clearly defined to avoid the impact of data deviation on the accuracy of subsequent interpretation.

[0019] 3) Dynamic adaptation principle: The database must be updatable and be supplemented with new oil testing and production dynamic data in a timely manner in combination with the dynamic situation of oilfield development. The current water-flooded interface and fluid boundary should be updated to adapt to the characteristics of scattered remaining oil distribution and rapid changes in water flooding patterns in the high water-cut development stage. At the same time, the database must be linked with the results of fine sub-layer division and inter-well comparison to ensure that the data is synchronized with the actual development status of the reservoir.

[0020] 4) Practicality principle: The database should clearly define the fluid boundaries and elevation constraints of each sublayer, clearly distinguish the original oil-water interface from the current water-flooded interface, provide accurate constraints for subsequent calculation of water-flooded layer saturation and identification of remaining oil, meet the actual needs of fine development of low-permeability oilfields and secondary interpretation of water-flooded layers, and ensure that the database can directly serve the precise tapping of potential measures in oilfields.

[0021] Step 2: Standardization and quality control of well logging data. The Fuyang oil reservoir in the Songliao Basin is a typical low-permeability to ultra-low-permeability reservoir. Due to its complex sedimentary environment and long-term water injection development, the reservoir's lithology, physical properties, and electrical properties have changed significantly. Well logging data is easily affected by factors such as mud invasion, wellbore enlargement, and instrument inaccuracy, leading to deviations in the logging curves and directly impacting the accuracy of reservoir evaluation and remaining oil prediction. Therefore, it is necessary to strictly adhere to the Fuyang oil reservoir well logging data processing specifications, conducting standardization and quality control work. The core focus should be on three key aspects: mixed formation water resistivity calculation, resistivity correction, and sonic transit time correction, to ensure the data is authentic, reliable, and highly comparable.

[0022] 2.1) The calculation of mixed formation water resistivity is a fundamental parameter for well logging interpretation. After long-term water injection development, the formation water in the Fuyang oil layer exhibits a complex state where primary water, injected water, capillary-bound water, and clay-bound water coexist. A single calculation method cannot meet the accuracy requirements; therefore, the four-dimensional equivalent conductivity model of primary water-injected water-capillary-bound water-clay-bound water, as described below, and a continuous iterative method are used for point-by-point calculation. The specific operation is as follows: 1) Collect regional geological data, core analysis data and oil test and production data of the Fuyang oil layer, and determine the basic parameters of the four types of water. The volume of mud-bound water is taken according to regional experience, and generally accounts for about 20% of the mud volume. 2) The formation is equivalent to a volumetric model of four types of water conducting in parallel. Combining this with the Indonesian formula for dispersed clay, a correlation equation is established between the resistivity of mixed formation water and the resistivity and volume content of each type of water. The expression is as follows: ; 3) The continuous iterative method is adopted to solve the problem point by point. The mixed formation water resistivity verified by adjacent wells is used as the initial value and substituted into the equation for iterative calculation until the error between the calculated value and the measured value is less than 3%. This ensures that the calculation results at each point are consistent with the actual water drive characteristics of the Fuyang oil layer in a certain oilfield, and provides accurate parameter support for subsequent saturation calculation.

[0023] 2.2) The resistivity and sonic transit time are standardized using the standard layer coefficient correction method to eliminate data biases from different wells and logging cycles, achieving a unified comparison of logging curves across the entire area. Based on the geological characteristics of the Fuyang oil layer, the specific operation is as follows: Resistivity correction: The core is to restore the true formation resistivity based on environmental and lithological parameters such as mud invasion, wellbore size, and lithology, using Archie's formula, invasion correction model, and geometric factor correction. Specific steps are tailored to the actual conditions of the Fuyang oil layer: 1) Data preprocessing: Screening logging curves for the main sandstone sections of the Fuyang oil layer, removing abnormal data such as curve breaks and jumps, verifying environmental parameters such as mud density, resistivity, and wellbore diameter (CAL), and marking abnormal sections where the wellbore diameter exceeds 0.5 inches of the drill bit size; 2) Invasion correction: Based on the characteristics of fresh mud invasion in the Fuyang oil layer, identifying the resistivity difference between the flushed zone and the original formation, calculating the invasion depth using the invasion correction model, and combining this with the modified form of Archie's formula Rt=Rxo×(Rmf / Rw). (m / n) (where m is the cementation index and n is the saturation index, combined with the values ​​obtained from regional rock electrical experiments), correcting the influence of mud invasion on resistivity; 3) Geometric factor correction: in view of the development characteristics of thin interbedded sand and mud in the Fuyang oil layer, a geometric factor is introduced to correct the resistivity distortion caused by wellbore enlargement and surrounding rock interference, with a focus on correcting the abnormal well diameter section; 4) Verify the correction effect: select known oil-testing or production sections of regional standard wells, compare the corrected resistivity with the measured true resistivity of the formation, and control the error within 5% to ensure that the corrected curve can truly reflect the oil-bearing and lithological characteristics of the reservoir.

[0024] Sonic transit time correction: Using stable mudstone sections in the region as standard layers for unified correction, the depth of logging curves is realigned, outlier removal is performed, and core data is matched. Specific operations conform to the actual conditions of the Fuyang oilfield: First, standard layer selection: Based on the regional geological data of the Fuyang oilfield, mudstone sections with wide distribution, stable lithology, and unaffected by tectonic activity and water drive (spontaneous potential SP≥90, stable well diameter) are selected as standard layers to ensure clear responses in all wells throughout the region. Second, depth realignment: Using the natural gamma and resistivity curves of the standard mudstone sections as benchmarks, the depth of the sonic transit time curves is adjusted to ensure clear sonic responses in all wells within the standard layers. The first step is to ensure consistent time difference depth, eliminating depth errors in logging instruments. The second step is to eliminate outliers by using a recursive formula combined with density (DEN) and neutron (CNL) curves to identify sonic transit time distortion points. Outliers caused by cycle jumps and wellbore enlargement are corrected by recursively averaging similar curves, and severely distorted sections of the curves are eliminated. The third step is to match core data by collecting measured sonic transit time data from cores of each well in the Fuyang oil layer and comparing it with the corrected logging curves. The correction coefficients are adjusted to ensure that the curves fit the core data to a good degree of over 85%, ensuring that the corrected sonic transit time accurately reflects the reservoir porosity characteristics.

[0025] 2.3) Complete quality control: After completing the above standardization process, conduct a comprehensive verification of the logging data, focusing on checking the rationality of the mixed formation water resistivity calculation and the effect of resistivity and sonic transit time correction. Remove unqualified data and reprocess them. At the same time, establish a standardized data ledger to record correction parameters, standard layer information and verification results, which shall comply with the standardization specifications of logging data of Fuyang oil layer in Songliao Basin, and lay a solid foundation for subsequent fine reservoir evaluation and prediction of remaining oil distribution.

[0026] Step 3: Continuous calculation of water resistivity in sub-layered mixed formations. 3.1) Construct a four-dimensional equivalent volumetric conductivity model of primary water, injected water, capillary-bound water, and mud-bound water. 1) Measurement and correction of basic parameters: Collect core samples from different well sections and formations of the Fuyang oil layer, measure the resistivity and salinity of the original formation water (original formation water) and injected water (actual injected water on site), determine the volume fraction of capillary bound water and clay bound water in combination with well logging data, correct the additional conductivity coefficient of clay in low-permeability formations, and eliminate the error of uneven distribution of bound water caused by small pore throats.

[0027] 2) Construct a four-dimensional equivalent volumetric conductivity model: Based on the parallel conductivity model, introduce the volume weight coefficients of each water body (combining the porosity and permeability stratification data of the low-permeability reservoir of Fuyang oil layer to determine the proportion of different water bodies), clarify that the native water and injected water are the mobile conductive phases, and the capillary bound water and mud bound water are the fixed conductive phases, establish a four-dimensional equivalent equation for water body conductivity, incorporate the mud conductive contribution term, and correct the conductivity enhancement effect of bound water in low-permeability reservoir.

[0028] Rt=1 / [α1 / Rw+α2 / Rinj+η(β1 / Rcb+β2 / Rsh)], Key parameters: x, y, z represent three dimensions of the reservoir space; t represents one dimension of development time. Coefficient definition: α1: Original water volume weighting coefficient; α2: Dynamic volume weighting coefficient of injected water; β1: Percentage of capillary-bound water volume; β2: Percentage of water bound by mud.

[0029] η(φ,K): Correction coefficient for enhanced conductivity of bound water in low-permeability reservoir of Fuyang oil layer η=1+λ·φ / lgK volume conservation constraint (layered porosity and permeability data calibration): α1+α2+β1+β2=φ; 3) Model verification and optimization: Select the measured data of the unflooded section of the Fuyang oil layer, substitute it into the model to calculate the conductivity parameters, compare it with the measured values, adjust the weight coefficients, and ensure that the model error is controlled within 5% to adapt to the resistivity characteristics of the oil layer.

[0030] 3.2) The resistivity Rwz of the mixed formation water was calculated using a continuous iterative method. 1) Initial parameter setting: Based on the actual situation of the Fuyang oil layer, determine the initial iteration range of Rwz (based on the resistivity range of native water and injected water, usually 0.01~10Ω·m), set the iteration step size to 0.01, and clarify the iteration termination condition (the difference of Rwz between two adjacent iterations ≤ 0.001Ω·m). 2) Iterative calculation point by point: with a step size of 0.01, the four-dimensional equivalent volume conductivity model is substituted one by one to calculate the comprehensive conductivity of the formation under the corresponding Rwz. At the same time, the Indonesian formula (adapted to the calculation of oil saturation in low-permeability reservoirs) is combined with the parameters such as porosity and oil saturation interpreted from well logging to solve for the theoretical value of the corresponding formation resistivity. 3) Optimal solution selection: The theoretical formation resistivity calculated in each iteration is compared with the measured formation resistivity of the well section, and the Rwz value with the smallest error is selected as the optimal solution; In view of the coexistence of multiple formation waters in the Fuyang oil layer, the iteration is divided into small layers to avoid calculation deviations caused by the mixing of water in different layers.

[0031] 3.3) Verification of water flooding degree correlation: Based on the logging data of water flooded wells in the Fuyang oil layer, the Rwz values ​​corresponding to different water flooding levels (weak water flooding, medium water flooding, and strong water flooding) are compared with the injected water resistivity to verify the rule that "the higher the water flooding degree, the closer the Rwz value is to the injected water resistivity". The iteration parameters are further corrected to ensure that the calculation results are consistent with the actual water flooding evaluation needs of the oilfield.

[0032] Step 4: Complete the quantitative calculation of reservoir parameters. This step involves quantitative calculations of reservoir parameters according to sand groups and sub-regions, clarifying the calculation standards and interpretation models for three key parameters: porosity, permeability, and saturation. This ensures that the calculation results are accurate, reliable, and logically rigorous, providing data support for subsequent reservoir analysis and development plan formulation.

[0033] The core principle of quantitative calculation of reservoir parameters is: porosity and permeability are established separately for sand groups to ensure that the parameter calculation matches the geological characteristics of the sand groups; saturation calculation is performed by selecting the optimal model according to the work area to adapt to the differences in reservoir pore structure and fluid distribution in different regions.

[0034] The porosity interpretation model was constructed separately for each sand group, covering a total of nine sand groups: Sand Group I (layers 1-2), Sand Group II (layers 3-6), Sand Group III (layers 7-8), Sand Group IV (layers 9-12), Sand Group V (layers 13-14), Sand Group VI (layers 15-17), Sand Group VII (layers 18-20), Sand Group VIII (layers 21-23), and Sand Group IX (layers 24-26). The division of layers 1-26 strictly followed the layer division principles of a certain oilfield in the Songliao Basin, using stratigraphic sedimentary cycles, lithological assemblage characteristics, reservoir continuity, and thickness distribution as core division criteria. This ensured the uniformity and rationality of the sand group and layer divisions, providing a foundation for establishing porosity interpretation models for each sand group and guaranteeing the specificity of porosity calculations for different sand groups.

[0035] The permeability interpretation model is also constructed separately for each sand group, and the following formula is used uniformly: Lg(K) = A × φ B×IGR C, Where K is permeability, φ is porosity, IGR is the relative value of natural gamma, and A, B, and C are the fitting coefficients for each sand group; By fitting and calibrating the measured data of each sand group, the correlation coefficients of all sand group permeability models are greater than 0.90, indicating that the model fit is high and the calculation results can truly reflect the actual distribution characteristics of permeability of each sand group.

[0036] The saturation model adopts a zone-based optimization approach, adapting corresponding formulas to the differences in reservoir characteristics across different regions: In one case, the reservoir pore structure in the peripheral region is relatively complex and the fluid distribution is uneven, so the Simandoux formula is used for calculation; in another case, the reservoir pores in the internal region are uniformly developed and the fluid properties are stable, so the Indonesian or Nigerian formula is used. The model focuses on calculating the bound water saturation Swb and the residual oil saturation Sor, while also referring to the calculation of the relative oil-water permeability, providing key parameters for reservoir oil content evaluation and development potential analysis.

[0037] Step 5: Obtain a primary interpretation based on the sublayered oil-water interface. Reference Figure 1 and Figure 2 The water production rate Fw is a key parameter calculated from well logging data. Its calculation is based on measured well logging parameters such as resistivity and sonic transit time, combined with Archie's formula and relative permeability curves (which are curves drawn from a series of core experimental data). It comprehensively considers geological parameters such as reservoir porosity and permeability, and is obtained after correction. It needs to be verified by resistivity-sonic transit time cross plot to ensure that the calculation accuracy meets the specifications.

[0038] Based on the water flooding patterns and development practices of the Fuyang oil reservoir, five levels of water flooding are classified according to the water production rate (Fw). The classification principle strictly follows the actual production data of the oilfield and the electrical logging interpretation standards. The five levels of water flooding are as follows: No water flooding, Fw < 10%, the reservoir basically maintains its original oil-bearing state, and the electrical response is close to that of the original oil layer; weak water flooding, 10% ≤ Fw < 40%, the reservoir is partially water-washed, the oil saturation decreases slightly, and the resistivity decreases slightly; moderate water flooding, 40% ≤ Fw < 60%, the reservoir is moderately water-washed, the oil saturation decreases significantly, the resistivity decreases significantly, and the acoustic transit time increases slightly; strong water flooding, 60% ≤ Fw < 80%, the reservoir is deeply water-washed, the oil saturation decreases significantly, the resistivity decreases significantly, and the acoustic transit time increases significantly; extremely strong water flooding, Fw ≥ 80%, the reservoir is almost completely water-washed, the oil saturation is extremely low, the resistivity is close to that of a water layer, and the acoustic transit time reaches its maximum value.

[0039] Reference Figure 3 and Figure 4 Secondary interpretation of water-flooded layers must be based on the oil-water interface of the sub-layers and strictly follow the electrical logging interpretation specifications for the Fuyang oil layer: 1) Preprocess logging data such as resistivity and sonic transit time to correct depth deviations and remove abnormal data to ensure data accuracy; 2) Divide single sandstone sub-layers with stable mudstone interlayers as boundaries, clarify the oil-water interface of each sub-layer, and define the interpretation boundary; 3) Use resistivity-sonic transit time cross plots to project the preprocessed logging data, and combine it with Fw classification thresholds to complete the preliminary determination of the water-flood level of the sub-layers; 4) Calculate the oil saturation using the Archie formula, and combine it with relative permeability curves (these are sets of core experimental data, very conventional data, those who work with reservoirs will understand) to correct the water production rate. After verification by the oil-water interface constraint, the final output of the three core interpretation results—the water-flood level of the sub-layers, oil saturation, and water production rate—provides a reliable basis for oilfield development adjustments or potential tapping measures.

[0040] Step 6: Implement a second-order iterative correction under production dynamic constraints. 6.1) Preliminary preparations, 1) Basic data: primary interpretation results (oil saturation So, effective thickness, permeability, water resistivity Rwz), single-well sub-layer division results, sub-layer oil-water interface data; 2) Dynamic data: production profile, water absorption profile, wellhead water cut (≤95% is valid data), stratified water cut, stratified oil production / water production, pressure recovery data; 3) Supporting data: core analysis report, relative permeability curve, block injection-production profile data.

[0041] 6.2) Dynamic and static data access and quality verification. 1) Data processing: Organize all dynamic and static data according to the "single well-sub-layer-time" sequence and associate them with the corresponding sub-layer numbers; the time series should be continuous without gaps; the sub-layer numbers should be consistent with the first interpretation; missing data should be filled in and the sub-layer numbers should be unified; 2) Data validation: Verify the consistency between the stratified water cut and the wellhead water cut, and the matching between the production profile and the stratified production volume; The total water cut of each layer deviates from the water cut at the wellhead by ≤5%; the error in the production profile is ≤8%. Data with deviations exceeding the threshold are removed and replaced with adjacent valid data from the same layer segment.

[0042] 3) Targeted verification: The key points of verification are the differences in the stratification and uneven water absorption, and the rationality of the data is verified in combination with the distribution of the interlayer. Areas with a water absorption difference ≥30% require special attention; abnormal liquid production / water absorption near the interlayer need to be verified. Based on the core data, it was confirmed that the abnormal data was caused by the reservoir itself or by measurement error.

[0043] 6.3) Comparison of primary interpretation with production dynamics (with the oil-water interface of the small layer as the boundary). 1) The comparison criteria are shown in Table 1: Table 1. Comparison of Primary Explanation and Production Dynamics

[0044] 2) Key points of operation: Using the oil-water interface of the small layer as a rigid boundary, the layers are compared well by well and layer by layer. Each well is marked with the deviation segment number and deviation type (optimistic / conservative / physical property deviation) to form a list of deviation segments.

[0045] 6.4) Correction of deviation segment parameters to obtain Rwz+ saturation. 1) Correct formation water resistivity Rwz, Correction formula: Rwz= Rw×(1-Sw) n Where Rwz represents the corrected formation water resistivity, Sw represents the true water saturation, and n is the saturation index, with a value of 2.0-2.8 for the Fuyang oil layer, especially the upper limit for low-permeability layers.

[0046] Operating steps: 1.1) The true water saturation Sw is inferred from the layered water content, Sw = layered water content × 1.05, which is suitable for the high bound water characteristics of the Fuyang oil layer. 1.2) Combine the core analysis with the formation water resistivity Rw, and substitute it into the formula to calculate the corrected Rwz; 1.3) Low-permeability tight layer and high bound water layer (bound water saturation ≥25%), additional rock electrical experiment correction: Rwz after correction is equal to 1.1-1.2 times the calculated value of Rwz; 1.4) Compare Rwz before and after correction. If the deviation is ≤10%, it is acceptable. If it exceeds the deviation, refit the n value.

[0047] 2) Adjust the saturation parameter. 2.1) Correction of the original oil saturation So: Corrected So = So (first interpretation) × K, K is a correction coefficient. When interpreting a more optimistic range: K = 0.85-0.95; when interpreting a more conservative range: K = 1.05-1.15. The reasonable range of So after correction for the Fuyang oil layer is 30%-65% (if it exceeds the range, it needs to be re-verified).

[0048] 2.2) Adjust the remaining oil saturation (Sor): Water-drive effective layer (annual water cut increase rate ≥3%): Sor = Sor single explanation of fluctuation (+2%-5%). Unused / weakly used layer: Sor = Sor is interpreted once (preserving the original value); Standard range: ≥18% after Sor correction (minimum residual oil threshold of Fuyang oil layer).

[0049] Iteration requirements: First, correct the deviation layer of a single well, and then extend it to the well group to ensure that the Rwz, saturation, and oil-water interface data are consistent within the well group. The number of iterations should be ≤2.

[0050] 6.5) Marking of remaining oil-rich layers and potential layers, as shown in Table 2.

[0051] Table 2. Marking of Remaining Oil-Enriched Layers and Potential Layers

[0052] 6.6) Finishing touches: After organizing and correcting all parameters, a secondary interpretation result table (single well + well group) is generated. The basis and source of parameters for the correction of deviation layers should be clearly marked to ensure traceability; Output a list of remaining oil-rich layers and potential layers, and clarify the direction of subsequent modification (layered water injection, perforation, fracturing, etc.).

[0053] Step 7: Output the explanation results and application suggestions. Based on the reservoir and utilization characteristics of the Fuyang oil layer, including low porosity and permeability, superimposed channel sand bodies, development of interlayers, large vertical utilization differences, and uneven water drive, a targeted stratified exploitation and water injection adjustment strategy is proposed. The output of small-level interpretation results can provide accurate basis for oilfield development adjustment and optimization, ensuring that the results can be directly applied.

[0054] 7.1) Standardize the output interpretation results. Based on the single well-well group-block level, four core results are output, all detailed down to the sub-layer level, to meet the development needs of the Fuyang oilfield: 1) Sub-layer water flooding level, divided into four levels: no water flooding, weak water flooding, medium water flooding, and strong water flooding, clearly defining the water flooding range and degree of each sub-layer; 2) Precise data on remaining oil saturation, marking the Sor value and distribution characteristics of each sub-layer, distinguishing between dispersed and enriched remaining oil; 3) Precise location of potential layers, clearly defining the potential sub-layer number, effective thickness, physical property parameters, and distribution range; 4) Supporting results, including a single well interpretation result table, a distribution map of remaining oil and potential layers in the well group, and marking the correction basis.

[0055] 7.2) Strategies for stratified mining and water injection adjustment are proposed. 1) Layered production strategy: In view of the multi-layered and unevenly utilized characteristics of the Fuyang oil layer, a "layered production allocation and differentiated transformation" model is adopted: For the remaining oil-rich layer (So≥45%), layered perforation is prioritized, with small-scale fracturing transformation, to control the production of single layer and avoid premature water flooding; for the potential layer (So≥35%, permeability≥5mD), a low-production and stable production mode is adopted to gradually release production capacity; for the medium-to-strong water flooding layer, production allocation is reduced, and water shut-off operations are prioritized to reduce ineffective water production.

[0056] 2) Water Injection Adjustment Strategy: Adapting to the low porosity, low permeability, and uneven water absorption characteristics of the Fuyang oil reservoir, implement "layered injection and precise profile control". For weakly absorbent and untapped potential layers, increase the single-layer water injection volume and adopt a mild water injection mode (injection intensity 0.8-1.2 m³ / (d·m)) to avoid high pressure damage to the reservoir; for strongly absorbent and moderately water-flooded layers, reduce the water injection volume and add profile control agents to block high-permeability channels and improve water drive effect; for areas with interlayer development, optimize the water injection interval to avoid cross-layer water flow and ensure that water injection accurately corresponds to the remaining oil-rich layers.

[0057] 3) Supporting recommendations: Establish a dynamic monitoring ledger for single wells, regularly retest production and water absorption profiles, and track changes in remaining oil; for thin and poor potential layers, prioritize reservoir stimulation to enhance utilization; for well groups, coordinate and allocate resources to achieve "injection-production balance," promote efficient development of the Fuyang oil layer, and extend the development cycle.

[0058] The key innovations of this invention include the following: 1) Fine constraint of oil-water interface at the sub-layer level: For the first time, the calibration of the three-level oil-water interface at the work area-sand group-sub-layer level is closely integrated with the interpretation of water-flooded layers, accurately characterizing the water-flooded heterogeneity within the layer; 2) Calculation of resistivity of continuously mixed formation water: A four-dimensional conductivity model + point-by-point iteration significantly improves the reliability of saturation calculation; 3) Secondary iterative interpretation process: Static primary interpretation + dynamic secondary correction form a closed-loop interpretation system; 4) Residual oil and potential tapping at the sub-layer level: Directly outputs potential sub-layers that can be implemented, supporting precise development.

[0059] Example 1 Secondary interpretation of the water-flooded layer No. 8 in the III work area of ​​a certain oilfield (single well).

[0060] The steps of the method of the present invention described above are briefly described as follows: Sub-layer division and interface calibration: Sub-layer No. 8 of the III sand group of Fuyu oil layer was selected, inter-well sub-layer comparison was completed, the oil-water interface elevation of the sub-layer was calibrated, and fault block boundary constraints were established.

[0061] Standardized logging was implemented, and the resistivity and sonic transit time of the study well were corrected using the standard layer method to complete curve repositioning and quality control.

[0062] The resistivity of mixed formation water was calculated using a four-dimensional conductivity model and a continuous iteration method, with Rwz calculated point by point. In the heavily flooded section, Rwz approaches the resistivity of the injected water.

[0063] Reservoir parameters were calculated using the porosity and permeability model of Sand Group III, and saturation was calculated using the Indonesian formula.

[0064] Based on the interpretation of resistivity-sonic transit time plot, it was determined that the No. 8 sublayer was moderately flooded with an oil saturation of 45.3%.

[0065] After a second correction, the production and water absorption profiles were accessed, and it was found that the layer was not washed by water in some areas. After correcting the Rwz, it was reinterpreted as weak to moderate water flooding, with the remaining oil enriched and the oil saturation at 48.3%.

[0066] The results were applied, and the layer was designated as a potential layer. The lower high water-cut layer was sealed and the layer was subjected to stratified fracturing. The daily oil production increased from 0.5t to 3.3t, and the water cut decreased from 96.8% to 51.5%.

[0067] Example 2 Secondary interpretation of 32 wells in the No. 8 water-flooded layer of a certain oilfield's III work area.

[0068] The steps of the method of the present invention described above are briefly described as follows: Sub-layer division and interface calibration: Sub-layer No. 8 of the III sand group of Fuyu oil layer was selected, inter-well sub-layer comparison was completed, the oil-water interface elevation of the sub-layer was calibrated, and fault block boundary constraints were established.

[0069] Standardized logging was implemented, and the resistivity and sonic transit time of the study well were corrected using the standard layer method to complete curve repositioning and quality control.

[0070] The resistivity of mixed formation water was calculated using a four-dimensional conductivity model and a continuous iteration method, with Rwz calculated point by point. In the heavily flooded section, Rwz approaches the resistivity of the injected water.

[0071] Reservoir parameters were calculated using the porosity and permeability model of Sand Group III, and saturation was calculated using the Indonesian formula.

[0072] Based on the interpretation, using the resistivity-sonic transit time cross plot, the flooding level of sub-layer 8 was re-determined, with an average oil saturation of 46.1%.

[0073] A second correction was made, incorporating production and water absorption profile data. It was found that 15 wells in this layer were partially unwashed or weakly washed. After correcting the Rwz, the results were reinterpreted as weak to moderate water flooding, with the remaining oil enriched at an oil saturation of 47.8%.

[0074] The results were applied to interpret the No. 8 sub-layer of 32 wells using this method, with an overall interpretation accuracy rate of 91.8%. Eleven remaining oil-rich sub-layers were identified, guiding oilfields to implement adjustment wells in 7 wells. The average daily oil production per well increased by 0.9t, and the average daily oil production increased from 0.6t to 1.5t, while the water cut decreased from 95.8% to 73.5%.

[0075] Example 3 Secondary interpretation of 29 wells in the No. 5 water-flooded layer of a certain oilfield's II work area.

[0076] The steps of the method of the present invention described above are briefly described as follows: Sub-layer division and interface calibration: Sub-layer No. 5 of the II sand group of Fuyu oil layer was selected, inter-well sub-layer comparison was completed, the oil-water interface elevation of the sub-layer was calibrated, and fault block boundary constraints were established.

[0077] Standardized logging was implemented, and the resistivity and sonic transit time of the study well were corrected using the standard layer method to complete curve repositioning and quality control.

[0078] The resistivity of mixed formation water was calculated using a four-dimensional conductivity model and a continuous iteration method, with Rwz calculated point by point. In the heavily flooded section, Rwz approaches the resistivity of the injected water.

[0079] Reservoir parameters were calculated using the porosity and permeability model of the II sand group, and saturation was calculated using the Indonesian formula.

[0080] Based on the interpretation, using the resistivity-sonic transit time cross plot, the flooding level of sub-layer 5 was re-determined, with an average oil saturation of 47.2%.

[0081] After a second correction, the production and water absorption profiles were accessed, revealing that 19 wells in this layer were either not water-washed or only weakly water-washed. After correcting the Rwz, the results were reinterpreted as weak to moderate water flooding, with the remaining oil enriched to an oil saturation of 48.6%.

[0082] The results were applied to interpret the No. 5 sub-layer of 29 wells using this method, with an overall interpretation accuracy rate of 90.2%. Fifteen remaining oil-rich sub-layers were identified, guiding oil production plants to implement adjustment wells in 10 wells. The average daily oil production per well increased by 1.5t, and the average daily oil production increased from 0.6t to 2.1t, while the water cut decreased from 90.8% to 68.5%.

[0083] Example 4 Secondary interpretation of 25 wells in the No. 8 water-flooded layer of a certain oilfield's II work area.

[0084] The steps of the method of the present invention described above are briefly described as follows: Sub-layer division and interface calibration: Sub-layer No. 8 of the III sand group of Fuyu oil layer was selected, inter-well sub-layer comparison was completed, the oil-water interface elevation of the sub-layer was calibrated, and fault block boundary constraints were established.

[0085] Standardized logging was implemented, and the resistivity and sonic transit time of the study well were corrected using the standard layer method to complete curve repositioning and quality control.

[0086] The resistivity of mixed formation water was calculated using a four-dimensional conductivity model and a continuous iteration method, with Rwz calculated point by point. In the heavily flooded section, Rwz approaches the resistivity of the injected water.

[0087] Reservoir parameters were calculated using the porosity and permeability model of Sand Group III, and saturation was calculated using the Indonesian formula.

[0088] Based on the interpretation, using the resistivity-sonic time difference intersection chart, the water flooding level of the No. 8 sub-layer in the II work area was re-determined, with an average oil saturation of 45.6%.

[0089] A second correction was made, incorporating production and water absorption profile data. It was found that 12 wells in this layer were partially unwashed or weakly washed. After correcting the Rwz, the results were reinterpreted as weak to moderate water flooding, with the remaining oil enriched at an oil saturation of 46.2%.

[0090] The results were applied to interpret the No. 8 sub-layer of 25 wells using this method, with an overall interpretation accuracy rate of 91.0%. Twelve remaining oil-rich sub-layers were identified, guiding oil production plants to implement adjustment wells in 5 wells, resulting in an average daily oil increase of 1.0t per well, raising the average daily oil production from 0.5t to 1.5t, and reducing the water cut from 88.6% to 72.5%.

[0091] Example 5 Secondary interpretation of 19 wells in the No. 14 water-flooded layer of a certain oilfield's II work area.

[0092] The steps of the method of the present invention described above are briefly described as follows: Sub-layer division and interface calibration: Sub-layer No. 14 of the V sand group of Fuyu oil layer was selected, inter-well sub-layer comparison was completed, the oil-water interface elevation of the sub-layer was calibrated, and fault block boundary constraints were established.

[0093] Standardized logging was implemented, and the resistivity and sonic transit time of the study well were corrected using the standard layer method to complete curve repositioning and quality control.

[0094] The resistivity of mixed formation water was calculated using a four-dimensional conductivity model and a continuous iteration method, with Rwz calculated point by point. In the heavily flooded section, Rwz approaches the resistivity of the injected water.

[0095] Reservoir parameters were calculated using the porosity and permeability model of the V sand group, and saturation was calculated using the Indonesian formula.

[0096] Based on the interpretation, using the resistivity-sonic transit time intersection chart, the water flooding level of the No. 14 sub-layer of the V sand group in the II work area was re-determined, with an average oil saturation of 46.4%.

[0097] After a second correction, the production profile and water absorption profile data were incorporated, revealing that 10 wells in this layer were partially unwashed or weakly washed. After correcting the Rwz, the results were reinterpreted as weak to moderate water flooding, with the remaining oil enriched at an oil saturation of 46.8%.

[0098] The results were applied to interpret the No. 14 sub-layer of 19 wells using this method, with an overall interpretation accuracy of 90.0%. Ten remaining oil-rich sub-layers were identified, guiding oil production plants to implement adjustment wells in 5 wells. The average daily oil production per well increased by 1.1t, and the average daily oil production increased from 0.5t to 1.6t, while the water cut decreased from 91.6% to 71.8%.

[0099] Example 6 Layer 10 of Section III of a certain oilfield.

[0100] The steps of the method of the present invention described above are briefly described as follows: This method was used to interpret the No. 10 sub-layer of 15 wells, with an overall interpretation accuracy rate of 89.8%. Ten remaining oil-rich sub-layers were identified, guiding the oil production plant to implement adjustment wells in 4 wells, with an average daily increase of 0.8t of oil per well.

[0101] Secondary interpretation of 15 wells in the No. 10 water-flooded layer of a certain oilfield's III work area.

[0102] Sub-layer division and interface calibration: Sub-layer No. 10 of the No. 4 sand group of Fuyu oil layer was selected, inter-well sub-layer comparison was completed, the oil-water interface elevation of the sub-layer was calibrated, and fault block boundary constraints were established.

[0103] Standardized logging was implemented, and the resistivity and sonic transit time of the study well were corrected using the standard layer method to complete curve repositioning and quality control.

[0104] The resistivity of mixed formation water was calculated using a four-dimensional conductivity model and a continuous iteration method, with Rwz calculated point by point. In the heavily flooded section, Rwz approaches the resistivity of the injected water.

[0105] Reservoir parameters were calculated using the porosity and permeability model of Sand Group IV, and saturation was calculated using the Indonesian formula.

[0106] In one interpretation, using resistivity-sonic transit time intersection charts, the water flooding level of sub-layer 14 in sand group IV of work area III was re-determined, with an average oil saturation of 45.8%.

[0107] A second correction was made, incorporating production and water absorption profile data. It was found that 10 wells in this layer were partially unwashed or weakly washed. After correcting the Rwz, the results were reinterpreted as weak to moderate water flooding, with the remaining oil enriched at an oil saturation of 46.3%.

[0108] The results were applied to interpret the No. 10 sub-layer of 15 wells using this method, with an overall interpretation accuracy of 90.8%. Ten remaining oil-rich sub-layers were identified, guiding the oilfield to implement adjustment wells in 4 wells. The average daily oil production per well increased by 0.8t, and the average daily oil production increased from 0.6t to 1.4t, while the water cut decreased from 86.2% to 75.8%.

Claims

1. A secondary interpretation method for water-flooded layers under the condition of recognizing the oil-water interface at a small-scale level, characterized in that, Follow these steps: Step 1: Layer-by-layer geological modeling and fine-scale calibration of the oil-water interface; Step 2: Standardization and quality control of well logging data; Step 3: Continuous calculation of water resistivity in mixed formations of different sub-layers; Step 4: Complete the quantitative calculation of reservoir parameters; Step 5: Obtain a primary interpretation based on the sublayered oil-water interface; Step 6: Implement a second-order iterative correction under production dynamic constraints; Step 7: Output the explanation results and application suggestions.

2. The secondary interpretation method for water-flooded layers under the condition of recognizing the oil-water interface at a small layer level according to claim 1, characterized in that, In step 1, the specific process is as follows: 1.1) The oil-water interface of the sub-layers shall be determined according to the three-level calibration requirements of work area-sand group-sub-layer; 1.2) Utilize multiple data validations to determine the principles for calibrating the original oil-water interface and the current oil-water interface; 1.3) The principle of establishing a three-level oil-water interface database of work area-sand group-sub-layer.

3. The secondary interpretation method for water-flooded layers under the condition of recognizing the oil-water interface at a small layer level according to claim 1, characterized in that, Step 2, the specific process is as follows: 2.1) The calculation of the resistivity of mixed formation water is a fundamental parameter for well logging interpretation; 2.2) The resistivity and acoustic transit time are standardized using the standard layer coefficient correction method; 2.3) Complete quality control.

4. The secondary interpretation method for water-flooded layers under the condition of recognizing the oil-water interface at a small layer level according to claim 1, characterized in that, Step 3, the specific process is as follows: 3.1) Construct a four-dimensional equivalent volumetric conductivity model of primary water, injected water, capillary-bound water, and mud-bound water; 3.2) The resistivity Rwz of the mixed formation water was calculated using the continuous iteration method; 3.3) Validation of the correlation between the degree of flooding.

5. The secondary interpretation method for water-flooded layers under the condition of recognizing the oil-water interface at a small layer level according to claim 1, characterized in that, Step 4, the specific process is as follows: Quantitative calculations of reservoir parameters were carried out according to sand groups and functional areas, clarifying the calculation standards and interpretation models for three key parameters: porosity, permeability, and saturation. The core principle of quantitative calculation of reservoir parameters is: porosity and permeability are interpreted by establishing interpretation models for each sand group to ensure that the parameter calculation matches the geological characteristics of the sand group; The saturation calculation model is selected according to the working area to adapt to the differences in reservoir pore structure and fluid distribution in different regions.

6. The secondary interpretation method for water-flooded layers under the condition of recognizing the oil-water interface at a small-scale level according to claim 1, characterized in that, Step 5, the specific process is as follows: The water production rate Fw is a key parameter calculated from well logging data. Its calculation is based on the measured parameters of resistivity and sonic transit time, combined with Archie's formula and relative permeability curve, taking into account reservoir porosity and permeability, and is obtained after correction. It needs to be verified by resistivity-sonic transit time cross plot.

7. The secondary interpretation method for water-flooded layers under the condition of recognizing the oil-water interface at a small layer level according to claim 6, characterized in that, Flooding is classified into five levels based on the water production rate (Fw). The five levels of flooding are as follows: Without water flooding, Fw < 10%, the reservoir basically maintains its original oil-bearing state, and the electrical response is close to that of the original oil layer; Weak water flooding, 10%≤Fw<40%, partial water washing of the reservoir, oil saturation slightly decreased, resistivity slightly decreased; In moderate water flooding, with 40%≤Fw<60%, the reservoir is moderately water washed, resulting in a significant decrease in oil saturation, a significant reduction in resistivity, and a slight increase in acoustic transit time. Strong water flooding, 60%≤Fw<80%, deep water washing of the reservoir, oil saturation decreases significantly, resistivity decreases significantly, and acoustic transit time increases significantly; Extremely strong water flooding, Fw≥80%, the reservoir is almost completely washed by water, the oil saturation is extremely low, the resistivity is close to that of the water layer, and the acoustic transit time reaches its maximum value.

8. The secondary interpretation method for water-flooded layers under the condition of recognizing the oil-water interface at a small layer level according to claim 6, characterized in that, Secondary interpretation of water-flooded layers should be based on the oil-water interface of the sub-layer as the core constraint, and should be carried out in accordance with the following oil layer electrical logging interpretation specifications: 1) Preprocess the logging data of resistivity and sonic transit time to correct depth deviation and remove abnormal data; 2) Divide single sandstone layers into smaller layers using stable mudstone interlayers as boundaries, clarify the oil-water interface of each layer, and define the interpretation boundaries; 3) Using resistivity-sonic transit time cross plots, the pre-processed well logging data is plotted and combined with the Fw classification threshold to complete the preliminary determination of the small-level water flooding level; 4) The oil saturation is calculated using the Archie formula, and the water production rate is corrected by the relative permeability curve. After verification by the oil-water interface constraint, the three core interpretation results of small-level flooding level, oil saturation, and water production rate are finally output.

9. The secondary interpretation method for water-flooded layers under the condition of recognizing the oil-water interface at a small layer level according to claim 1, characterized in that, Step 6 involves the following specific steps: 6.1) Preliminary preparations; 6.2) Dynamic and static data access and quality verification; 6.3) Comparison of initial explanation with production dynamics; 6.4) Correction of deviation segment parameters to obtain Rwz+ saturation; 6.5) Marking of remaining oil-rich layers and potential layers; 6.6) Finishing work.

10. The secondary interpretation method for water-flooded layers under the condition of recognizing the oil-water interface at a small layer level according to claim 1, characterized in that, Step 7 involves the following steps: Considering the reservoir characteristics of low porosity and permeability, superimposed channel sand bodies, well-developed interlayers, large vertical variability in water drive, and uneven water drive, a layered development and water injection adjustment strategy is proposed. The specific process is as follows: 7.1) Standardize the output interpretation results. Based on the single well-well group-block level, four core results are output, all detailed down to the sub-layer level, to meet the needs of oil reservoir development: 1) Sub-layer water flooding level, divided into four levels: no water flooding, weak water flooding, medium water flooding, and strong water flooding, clearly defining the water flooding range and degree of each sub-layer; 2) Precise data on remaining oil saturation, marking the Sor value and distribution characteristics of each sub-layer, distinguishing between dispersed and enriched remaining oil; 3) Precise location of potential layers, clearly defining the potential sub-layer number, effective thickness, physical property parameters, and distribution range; 4) Supporting results, including a single well interpretation result table, a distribution map of remaining oil and potential layers in the well group, and marking the correction basis. 7.2) Propose strategies for stratified mining and water injection adjustment.

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