Method for quantitatively recognizing low-resistivity oil layer based on logging curve difference reconstruction

Abstract

A method for quantitatively identifying a low-resistivity oil layer based on logging curve difference reconstruction belongs to the field of oil exploration and development, and comprises the following steps: collecting well data including core analysis data, logging data, production formation testing data and logging curves of interval transit time, deep induced resistivity and medium induced resistivity in a research area; selecting a research area standard layer, and standardizing a logging curve by using a trend surface analysis method; classifying the logging phase states of the target interval; performing multi-point valuing on three logging curves of different logging phase states; carrying out difference reconstruction on the obtained logging values, calculating JAC, JRT and # imgabs0 #, and respectively establishing a calcareous sandstone identification drawing board and a low-resistivity oil layer identification drawing board; and counting the distribution range of each parameter, and determining the identification standards of the calcareous sandstone, the same oil-water layer and the water layer. The method is simple in operation, easy in data acquisition, low in labor cost, capable of quantitatively distinguishing the calcareous sandstone interlayer, the same oil-water layer and the water layer at one time, clearer in boundary and higher in distinguishing degree.

Description

Technical Field

[0001] The invention belongs to the field of petroleum exploration and development, and particularly relates to a method for quantitatively identifying low-resistivity oil layers based on logging curve difference reconstruction. Background Art

[0002] With the continuous deepening of oil and gas exploration and development, various complex oil and gas reservoirs have gradually become the main exploration targets. With the continuous increase in production and reserves of low-resistance oil reservoirs, it has become a special development field in recent years. Low-resistance oil layer mainly refers to the oil layer resistivity close to or less than the water layer resistivity under the same geological conditions in the region. Therefore, the response characteristics of the resistivity logging curve are similar to the characteristics of the water layer, which makes it easy to lose the low-resistance oil layer during the logging interpretation process, causing great obstacles to the later development and production increase. In existing research, the identification of low-resistance oil layers is mainly concentrated in two aspects. On the one hand, the cause of low-resistance oil layer is studied and identified based on core analysis methods such as cast thin sections, scanning electron microscopy, X-ray diffraction, major and trace element analysis, and electron probe. For example, patent CN 109580678 A discloses a method for quickly identifying and evaluating low-resistance oil and gas layers using digital core technology. This method is relatively costly and is mainly suitable for areas with rich core data and small areas. At present, the commonly used methods are mainly based on the intersection diagram method, overlap method, multi-well comparison method and nuclear magnetic resonance logging method based on logging curves. For example, patent CN109667576 A discloses a logging identification method for low-resistance oil layers of high mineralization origin. Patent CN117627633 A discloses a method for identifying low-resistivity oil layers. Patent CN114086945 B discloses a logging identification method for low-resistance oil layers of low mineralization origin. Patent CN106285660 A discloses a method and device for identifying low-resistance oil layers in multi-layer sandstone reservoirs. Patent CN116699721 A discloses a method for identifying sweet spots in low-resistance oil layers based on nuclear magnetic logging. Patent CN 109707378 B discloses a method for identifying low-resistance oil layers based on mud invasion characteristics and longitudinal comparison. In 2012, Volume 42, Issue 2, Journal of Jilin University (Earth Science Edition), Yu Hongyan et al. determined the lower limit of each characteristic parameter in the low-resistivity oil layer based on the analysis of test data, realized the transition from qualitative identification to quantitative evaluation, and established a quantitative evaluation model for low-resistivity oil layers with an accuracy rate of 96.15%. In 2013, Volume 25, Issue 3, Lithologic Oil and Gas Reservoirs, Xie Qing et al. used the intersection diagram method, logging curve overlap method and adjacent well water layer comparison method to identify the Chang 6 low-resistivity oil layer in Zhidan and Ansai areas. In 2018, Volume 25, Issue 1, Fault Block Oil and Gas Fields, Zheng Hua et al. combined core, logging data, and oil and production test results, and used quantitative and qualitative evaluation techniques to identify the low-resistivity oil layer in the Bohai Luda A Oilfield, with an identification accuracy of more than 90%. In the 2022 issue of Volume 37, Issue 5, Progress in Geophysics, Chen Mingjiang et al. proposed a new method for identifying low-resistivity oil layers based on resistivity change rate, which is applicable to both clastic rocks and carbonate rocks. Calcareous sandstone refers to dense sandstone containing calcareous cement, which is commonly developed in clastic reservoirs. It increases the heterogeneity of the reservoir and affects the overall physical properties of the reservoir.In addition, due to the high resistivity and low acoustic time difference of calcareous sandstone, it is easy to be misjudged as an oil layer in the identification of oil and water in low-resistance oil layers, resulting in a decrease in the oil layer interpretation compliance rate in the area. This has hindered the improvement of the remaining oil potential of old oil fields, especially low-resistance oil reservoirs. At present, the identification methods of calcareous sandstone are mainly logging identification and inter-well modeling identification. For example, patent CN101793145 A discloses a method for determining calcareous interlayers and formation porosity by neutron lifetime and compensated neutron combined logging. Patent CN117390526 A discloses a method for distributed identification of interlayers. In 2016, Vol. 27, No. 6, Natural Gas Geoscience, Liu Li et al., based on the differences in sedimentation and diagenesis between calcareous sandstone and non-calcareous sandstone, explored the influence of petrological characteristics and diagenetic evolution process on the distribution of calcareous interlayers. In Vol. 31, No. 5, 2017, Petroleum Geology and Engineering, Wang Yuchen et al. systematically studied the lithology and logging response characteristics of calcareous sandstone interlayers based on core and logging data. In Vol. 47, No. 5, 2020, Journal of Chengdu University of Technology (Natural Science Edition), Qinar Hushan et al. achieved accurate characterization of calcareous sandstone based on high-density seismic data.

[0003] After analyzing the above-mentioned existing technologies, the following problems are found: (1) The conventional intersection diagram method and overlap method have low distinction accuracy and cumbersome steps, especially it is difficult to clearly distinguish the oil layer and the oil-water layer in the low-resistivity reservoir; and the cost of nuclear magnetic resonance logging is high, which is not conducive to the oil-water identification of a large number of wells; (2) Conventional logging identification is difficult to distinguish calcareous sandstone, and inter-well modeling identification requires a large amount of well data, which has poor timeliness and high interpretation cost; (3) At present, there is no comprehensive method that can quickly and simultaneously identify calcareous sandstone and oil layers, and oil-water layers in low-resistivity reservoirs. Summary of the invention

[0004] The purpose of the present invention is to provide a method for quantitatively identifying low-resistivity oil layers based on logging curve difference reconstruction, so as to solve the problem that the prior art cannot accurately identify calcareous sandstone interlayers and low-resistivity oil layers at one time. This method uses formula reconstruction means and combines logging data, mud logging data and production test oil data to obtain an identification intersection chart with high accuracy; calcareous sandstone and low-resistivity oil layers can be identified at one time only through a simple intersection chart method, which is highly practical; only three types of logging data are used, the data is easy to obtain, the process is simple, and the interpretation cost is low, which can be applied to the identification of low-resistivity oil layers and calcareous sandstones over a large range.

[0005] The technical solution adopted by the present invention is: A method for quantitatively identifying low-resistivity oil layers based on logging curve difference reconstruction includes the following steps: Step 1: Collect well data in the study area, including core analysis data, logging data, production test data, and acoustic transit time (AC), deep induction resistivity (RILD), and medium induction resistivity (RILM) logging curves; Step 2: From the collected data, select the standard layer of the study area. The standard layer is selected according to the principles of PetroChina logging data processing and interpretation (SY / T 6451-2017); Step 3: Perform trend surface analysis on the standard layer, and obtain trend surface equations of acoustic time difference, deep induction resistivity and medium induction resistivity corresponding to the standard layer by fitting; calibrate the acoustic time difference, deep induction resistivity and medium induction resistivity values ​​of the standard layer of each well to the trend surface equation, and obtain the standardized correction value of the logging data in the study area; Step 4: Classify the phase states of the logging curves of the target layer into high-peak type, medium-amplitude type and low-amplitude type; Step 5: Combine core analysis data, logging data, and oil test conclusions to perform multi-point sampling of deep induction resistivity, medium induction resistivity, and acoustic time difference logging curves of sandstone and calcareous sandstone interlayers according to different logging phases; Step 6: Reconstruct the difference between the deep induction resistivity and acoustic time difference logging values, establish the JAC-JRT calcareous sandstone identification chart, and determine the calcareous sandstone interlayer identification standard. The reconstruction formula is: (1) (2) Among them, JRT and JAC are the relative values ​​of logging curves; RILD max is the maximum value of deep induction resistivity logging curve; RILD is the deep induction resistivity logging value; AC max is the maximum value of the acoustic time difference; AC is the acoustic time difference value; Step 7: Reconstruct the difference between the deep induction resistivity value and the medium induction resistivity value taken from the phase state of the medium-amplitude and low-amplitude logging curves to establish the RILD- Low-resistivity oil layer identification chart, determine the fluid property identification standard, and reconstruct the formula as follows: (3) in, is the resistivity difference; RILD is the deep induction resistivity logging value; RILM is the medium induction resistivity logging value; Step 8. Use the established calcareous sandstone interlayer identification map and low-resistivity oil layer identification map to conduct a one-time quantitative identification of the calcareous sandstone interlayers and fluid properties in other target layers in the study area.

[0006] Furthermore, in step 2, the standard layer is widely distributed in the study area, and its logging characteristics are stable or change regularly, which is convenient for tracking and comparison in the whole area.

[0007] Furthermore, in step 2, the standard layer is a rock layer with a distribution area exceeding 90% in the study area and obvious lithological characteristics.

[0008] This method selects the stable mudstone at the top of the XI sand group as the standard layer, and its distribution range exceeds 90% of the total area of ​​the study area, with an average of 370 km 2 .

[0009] Furthermore, in step 3, obtaining the standardized correction value of the logging data includes the following steps: 1) Count the deep induction resistivity, medium induction resistivity and starting depth of the acoustic time difference logging curve of the standard layer of each well; 2) Calculate the average depth of the standard layer and the average deep induction resistivity, the average medium induction resistivity and the average acoustic time difference; 3) Linearly fit the average depth value of the standard layer of each well with the average deep induction resistivity value, the average medium induction resistivity value and the average acoustic wave time difference value to obtain the corresponding relationship (i.e., trend surface equation); 4) Obtain the logging value corresponding to the standard layer of each well according to the trend surface equation and compare it with the average value of the actual measurement to obtain the standardized correction value, thus completing the standardization of the logging curve; then use the trend surface equation to complete the standardization of the logging curves of other wells in the area.

[0010] Furthermore, in step 4, the phase classification of the logging curve refers to taking the thick mudstone section adjacent to the target layer with uniform and stable lithology and straight logging curve shape as a reference, and dividing the logging phase of the target layer (target sandstone layer) into three types: high-pointed type, medium-amplitude type and low-amplitude type according to the degree of deviation from the logging curve of the target layer; the selected logging curve is a deep induction resistivity curve with obvious characteristics and deep detection depth; deep induction resistivity curves are used for both the thick mudstone section and the target layer.

[0011] The specific classification standards are as follows: 1) The logging curve is straight, and the average amplitude is 1 to 2 times that of the logging curve in the mudstone section, which is a low-amplitude type; 2) The logging curve is straight, but overall higher than the logging curve of the mudstone section, presenting a "step" shape, and the average amplitude is 2 to 4 times that of the logging curve of the mudstone section, which is a medium-amplitude type; 3) The logging curve is "sharp" in shape, with no straight sections, and the average amplitude is 4 to 6 times that of the logging curve in the mudstone section, which is a high-sharp type.

[0012] Furthermore, in step 5, the multi-point value acquisition refers to: referring to the core analysis data, logging data and oil test conclusions, the deep induction resistivity, medium induction resistivity and acoustic wave time difference value are acquired. The specific operation steps are as follows: 1) Select the maximum value of the deep induction resistivity curve and the maximum value of the acoustic time difference curve of the target layer respectively; 2) The deep induction resistivity logging data, medium induction resistivity logging data, and acoustic time difference logging data at different depths corresponding to the high-point, medium-amplitude, and low-amplitude logging types were averaged, with a sampling interval of 0.25 m to reduce the error caused by random sampling.

[0013] Furthermore, in step six, the specific process of determining the identification standard of calcareous sandstone interlayers is as follows: placing the relative values ​​JAC and JRT of the logging curves reconstructed by the difference into a rectangular coordinate system for intersection analysis, and establishing a JAC-JRT calcareous sandstone identification chart; and dividing the calcareous sandstone identification standard according to the distribution of high-pointed type (calcareous sandstone) and medium-low amplitude type sandstone (non-calcareous sandstone) in the coordinate system.

[0014] Furthermore, in step 7, the specific process of determining the fluid property identification standard is as follows: the resistivity difference after the difference reconstruction and RILD into a rectangular coordinate system for intersection analysis, and establish RILD- Low-resistivity oil layer identification chart; based on the distribution of oil-water layers and water layers in the coordinate system during oil testing, the oil-water layers and water zone identification standards are divided.

[0015] Furthermore, in step eight, the JAC and JRT values ​​calculated for the new target layer are , RILD were respectively put into the established JAC-JRT calcareous sandstone identification plate and RILD- The low-resistivity oil layer identification map identifies calcareous sandstone and oil-water layers and water layers.

[0016] The present invention is applied in low-resistance oil reservoirs to identify calcareous sandstone and oil-water layers and water layers.

[0017] Furthermore, the JAC value of the calcareous sandstone is between 0.7 and 0.85, and the JRT value is between 1.0 and 1.4; >0, RILD is between 9.1 and 17.5Ω·m; the water layer <0, RILD is between 5.1 and 11.7Ω·m.

[0018] Beneficial effects of the present invention: (1) The present invention selects a standard layer and uses the trend surface analysis method to standardize the logging data of other wells, eliminate the influence of non-geological factors, unify the calibration standard, and reduce errors. The trend surface analysis method combines the logging parameters of multiple wells to perform multivariate quantitative analysis, thereby improving the accuracy of the standardization process. It is particularly suitable for areas where the standard layer is stable in the whole area, but the vertical distribution of the burial depth is quite different and the heterogeneity is strong.

[0019] (2) The present invention uses a small amount of coring well data, production test data and logging curves (acoustic time difference, deep induction resistivity and medium induction resistivity) to establish calcareous sandstone identification charts and low-resistivity oil layer identification charts, and the data processing process is simple and fast. Compared with the charts established by other methods, the difference reconstruction formula only requires three logging curves: AC, RILD and RILM. The formula is simple and easy to operate. After difference reconstruction, the relative values ​​of the logging curves (JRT and JAC) of calcareous sandstone and non-calcareous sandstone are more different, and the resistivity difference between the oil-water layer and the water layer ( ) is more obvious. Therefore, the recognition board established based on it has clearer boundaries and higher differentiation.

[0020] (3) The calcareous sandstone reservoir is dense, and the resistivity is equal to or higher than the resistivity of the oil layer. Conventional methods are difficult to identify the characteristics of calcareous sandstone, low-resistivity oil layer and water layer. This method uses optimization measures such as standardization of three logging curves (AC, RILD, RILM), logging phase classification, multi-point value taking, and difference reconstruction to more quickly and directly deduct the non-effective calcareous sandstone reservoir, and then distinguish the oil-water layer and the water layer. This method is suitable for regional large-scale low-resistivity oil layer identification, which is conducive to further improving the development efficiency of old well areas.

[0021] (4) The present invention is still applicable to areas lacking logging data and wells without oil test conclusions. It is only necessary to calculate JRT, JAC and By casting points on the calcareous sandstone identification plate and the low-resistivity oil layer identification plate respectively, the calcareous sandstone interlayer and the oil-water layer and water layer can be distinguished at one time. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flowchart of the method of the present invention; Figure 2 is the fitting relationship between the acoustic time difference (AC) and depth; Figure 3 is the fitting relationship of medium induction resistivity (RILM) with depth; Figure 4 is the fitting relationship of deep induction resistivity (RILD) changing with depth; Figure 5 Identification plate for established calcareous sandstone; Figure 6 To establish a low-resistivity oil layer identification plate; Figure 7 This is a schematic diagram of the calcareous sandstone casting point; Figure 8 This is a schematic diagram of low-resistivity oil layer casting points; Fig. 9 This is a schematic diagram of the identification and verification of fluid properties of the calcareous sandstone interlayer and low-resistivity oil layer in Well W21; Fig.10 Schematic diagram of the identification and verification of fluid properties of the calcareous sandstone interlayer and low-resistivity oil layer in Well W22. DETAILED DESCRIPTION

[0023] With the continuous advancement of oil and gas development, most of my country's oil fields have entered the middle and late stages of development, and the importance of unrecognized low-resistivity oil reservoirs as remaining potential has become increasingly prominent. However, due to the influence of structural sedimentation, skeleton conductivity and fluid conductivity, the resistivity of the oil layer is low and difficult to distinguish from the water layer. In addition, low-resistivity oil reservoirs with calcareous sandstone interlayers are easily misjudged as oil layers in oil-water identification due to the logging characteristics of calcareous sandstone. Therefore, it is particularly important to quickly and accurately identify calcareous sandstone and low-resistivity oil layers. At present, the existing methods have large data requirements, high costs, slow speeds, and cannot quantitatively identify calcareous sandstone and low-resistivity oil layers at one time. Based on this, the existing logging identification method was improved. By taking the values ​​of the logging curves and reconstructing the difference, the calcareous sandstone identification map and the low-resistance oil layer identification map of the study area were established, realizing the rapid quantitative identification of calcareous sandstone interlayers and oil-water layers.

[0024] In this example, by performing phase classification, multi-point value selection and difference reconstruction on the acoustic time difference, deep induction resistivity and medium induction resistivity logging curves in the core analysis data, logging data and oil test data in the study area, a calcareous sandstone identification map and a low-resistivity oil layer identification map were established, and the identification criteria were quantitatively divided. For well areas that lack core, logging and oil test data, points can be directly cast for identification, so that calcareous sandstone interlayers, oil-water layers and water layers can be accurately and quickly identified at one time. The problem of quantitative identification of calcareous sandstone and low-resistivity oil layers at one time has been solved.

[0025] Among them, the difference reconstruction formula is based on the classification of logging phases and combined with the oil test conclusions to calculate the acoustic time difference, the relative values ​​of deep induction resistivity and medium induction resistivity logging curves (JRT, JAC), the difference in resistivity between the oil and water layer and the water layer ( ). After reconstruction, the resistivity difference is more obvious, and the range boundary of the established calcareous sandstone and low-resistivity oil layer identification map is clearer.

[0026] In an old oil field, the early development degree is high, the water content is high, and the formation water mineralization is high, resulting in low resistance of the oil layer and a large number of calcareous sandstone interlayers. As a result, it is difficult to remove the calcareous sandstone interlayers that affect the reservoir properties and accurately identify the oil and water layers in actual work. However, the logging data of the oil field is relatively rich, so the present invention is used to identify the calcareous sandstone and oil and water layers through the logging data.

[0027] A method for quantitatively identifying low-resistivity oil layers based on logging curve difference reconstruction, such as Figure 1 As shown, the following steps are included: Step 1: Collect well data in the study area, including core analysis data, logging data, production test data, and acoustic transit time (AC), deep induction resistivity (RILD), and medium induction resistivity (RILM) logging curves; Step 2: According to the PetroChina Well Logging Data Processing and Interpretation Standards (SY / T 6451-2017), the stable mudstone at the top of the XI sand group was selected as the standard layer. Its distribution range exceeds 90% of the total area of ​​the study area, with an average of 370 km 2 .

[0028] Step 3: Statistically collect the starting depths of the deep induction resistivity, medium induction resistivity and acoustic time difference logging curve standard layers of 20 wells with even distribution in the study area and rich core analysis data, logging data and oil testing data, and calculate the average depth of the standard layer, the average deep induction resistivity, the average medium induction resistivity and the average acoustic time difference.

[0029] Then, the average depth value of the standard layer of each well is linearly fitted with the average deep induction resistivity value, the average medium induction resistivity value and the average acoustic wave time difference value, and the corresponding relationship (i.e., trend surface equation) is obtained (see Figure 2-Figure 4 ); in this way, the best approximation value of each logging curve near the standard layer and the standardized correction value of each logging curve are calculated (see Table 1). And this trend surface equation is used to complete the data standardization processing of other wells in the area.

[0030] Table 1 Logging values ​​and correction values ​​obtained based on fitting relationship Step 4: Using the thick mudstone section at the top of the target layer (XI sand layer) as a reference standard, quantitatively divide the logging phase. First, calculate the average deep induction resistivity of the thick mudstone section of each well, and define the curve segment with a similar logging shape to the mudstone section and an average amplitude of deep induction resistivity of 1 to 2 times that of the mudstone section as a low-amplitude type. The curve segment with a "step" shape and an average amplitude of 2 to 4 times that of the mudstone section is classified as a medium-amplitude type. The curve segment with a "sharp" shape, no straight section, and an average amplitude of 4 to 6 times that of the mudstone section is classified as a high-sharp type.

[0031] Step 5: Select the maximum values ​​of the deep induction resistivity curve and the acoustic time difference curve of the XI and XII sand layers. Combined with the core analysis data, logging data, and oil test conclusions, the deep induction resistivity logging data, medium induction resistivity logging data, and acoustic time difference logging data at different depths corresponding to the high-point, medium-amplitude, and low-amplitude logging are averaged, with a sampling interval of 0.25m.

[0032] Step 6: Reconstruct the difference between the deep induction resistivity and acoustic time difference logging values ​​to establish the JAC-JRT calcareous sandstone identification map (see Figure 5 ), quantitatively characterizes the identification criteria of calcareous sandstone: when the JAC value is between 0.7~0.85 and the JRT value is between 1.0~1.4, it is calcareous sandstone. The reconstruction formula is: (1) (2) Among them, the relative value of JRT and JAC logging curves; RILD max is the maximum value of deep induction resistivity logging curve; RILD is the deep induction resistivity logging value; AC max is the maximum value of the acoustic time difference; AC is the acoustic time difference value; Step 7: Reconstruct the difference between the deep induction resistivity value and the medium induction resistivity value taken from the phase state of the medium-amplitude and low-amplitude logging curves to establish the RILD- Low-resistivity oil layer identification plate (see Figure 6 ), quantitatively characterizes the fluid property identification standard: when >0, RILD is between 9.1~17.5Ω·m, the oil and water are in the same layer; <0, RILD is between 5.1~11.7Ω·m, it is the water layer; the reconstruction formula is: (3) in, is the resistivity difference; RILD is the deep induction resistivity logging value; RILM is the medium induction resistivity logging value; Step 8: Use the established calcareous sandstone interlayer identification map (see Figure 5 ) and low resistivity oil layer identification plate (see Figure 6 ) A one-time quantitative identification of the calcareous sandstone interlayers and fluid properties in other target layers in the study area was carried out.

[0033] For other wells in the area that have no cores, lack logging data and oil testing results, this method is used to identify calcareous sandstone interlayers and low-resistivity oil layers. Taking W21 and W22 wells as examples, according to the above steps (steps 3 to 7), JAC, JRT and And cast points on the identification board to quickly identify the calcareous sandstone interlayer and the oil-water layer, such as Figure 7 , Figure 8 shown.

[0034] like Fig. 9 As shown in the figure, the cores of Well W21 were observed to be calcareous sandstone at 1898m-1901.6m and 1922m-1927m, which were judged to be calcareous sandstone using the calcareous sandstone identification chart; 1902m-1922m was judged to be a water layer using the low-resistivity oil layer identification chart. The daily water production of this layer was 24.6t and the daily oil production was 0t in the initial oil test. The oil test conclusion is consistent with the identification result.

[0035] like Fig.10 As shown in the figure, the core of Well W22 was observed to be calcareous sandstone at 2090m-2094.5m, and it was judged to be calcareous sandstone using the calcareous sandstone identification chart; 2095m-2115.6m was judged to be an oil-water layer using the low-resistivity oil layer identification chart. The daily oil production of this layer was 8.9t and the daily water production was 23.6t in the initial oil test. The oil test conclusion is consistent with the identification result.

[0036] It can be seen that the calcareous sandstone interlayer identified by the present invention is consistent with the coring observation and judgment, and the identified fluid properties are basically consistent with the actual production situation, which further illustrates the feasibility of the method of the present invention.

Claims

1. A method for quantitatively identifying low-resistivity oil layers based on logging curve difference reconstruction, characterized in that: The following steps are involved: Step 1: Collect well data in the study area, including core analysis data, logging data, production test data, and acoustic time difference, deep induction resistivity and medium induction resistivity logging curves; Step 2: From the collected data, select the standard layer of the study area. The standard layer is selected according to the principles of CNPC logging data processing and interpretation standards; Step 3: Perform trend surface analysis on the standard layer, and obtain trend surface equations of acoustic time difference, deep induction resistivity and medium induction resistivity corresponding to the standard layer by fitting; calibrate the acoustic time difference, deep induction resistivity and medium induction resistivity values ​​of the standard layer of each well to the trend surface equation, and obtain the standardized correction value of the logging data in the study area; Step 4: Classify the phase states of the logging curves of the target layer into high-peak type, medium-amplitude type and low-amplitude type; Step 5: Combine core analysis data, logging data, and oil test conclusions to perform multi-point sampling of deep induction resistivity, medium induction resistivity, and acoustic time difference logging curves of sandstone and calcareous sandstone interlayers according to different logging phases; Step 6: Reconstruct the difference between the deep induction resistivity and acoustic time difference logging values, establish the JAC-JRT calcareous sandstone identification chart, and determine the calcareous sandstone interlayer identification standard. The reconstruction formula is: （1） （2） Among them, JRT and JAC are the relative values ​​of logging curves; RILD max is the maximum value of deep induction resistivity logging curve; RILD is the deep induction resistivity logging value; AC max is the maximum value of the acoustic time difference; AC is the acoustic time difference value; Step 7: Reconstruct the difference between the deep induction resistivity value and the medium induction resistivity value taken from the phase states of the medium-amplitude and low-amplitude logging curves to establish the RILD- Low-resistivity oil layer identification chart, determine the fluid property identification standard, and reconstruct the formula as follows: （3） in, is the resistivity difference; RILD is the deep induction resistivity logging value; RILM is the medium induction resistivity logging value; Step 8. Use the established calcareous sandstone interlayer identification map and low-resistivity oil layer identification map to conduct a one-time quantitative identification of the calcareous sandstone interlayers and fluid properties in other target layers in the study area.

2. The method for quantitatively identifying low-resistivity oil layers based on logging curve difference reconstruction according to claim 1, characterized in that: In step 2, the standard layer is widely distributed in the study area, with stable logging characteristics or regular changes, which is convenient for tracking and comparison in the entire area.

3. The method for quantitatively identifying low-resistivity oil layers based on logging curve difference reconstruction according to claim 1, characterized in that: In step 2, the standard layer is a rock layer with a distribution area of ​​more than 90% in the study area and obvious lithological characteristics.

4. The method for quantitatively identifying low-resistivity oil layers based on logging curve difference reconstruction according to claim 1, characterized in that: In step 3, obtaining the standardized correction value of the logging data includes the following steps: 1) Count the deep induction resistivity, medium induction resistivity and starting depth of the acoustic time difference logging curve of the standard layer of each well; 2) Calculate the average depth of the standard layer and the average deep induction resistivity, the average medium induction resistivity and the average acoustic wave time difference; 3) Linearly fit the average depth value of the standard layer of each well with the average deep induction resistivity value, the average medium induction resistivity value and the average acoustic wave time difference value to obtain the corresponding relationship; 4) Obtain the logging value corresponding to the standard layer of each well according to the trend surface equation and compare it with the average value of the actual measurement to obtain the standardized correction value, thus completing the standardization of the logging curve; then use the trend surface equation to complete the standardization of the logging curves of other wells in the area.

5. The method for quantitatively identifying low-resistivity oil layers based on logging curve difference reconstruction according to claim 1, characterized in that: In step 4, the phase classification of the logging curve refers to taking the thick mudstone section adjacent to the target layer with uniform and stable lithology and straight logging curve as a reference, and classifying the logging phase of the target layer into three types: high-pointed type, medium-amplitude type and low-amplitude type according to the degree of deviation from the logging curve of the target layer; the selected logging curve is a deep induction resistivity curve with obvious characteristics and deep detection depth; the specific classification standards are as follows: 1) The logging curve is straight, and the average amplitude is 1 to 2 times that of the logging curve in the mudstone section, which is a low-amplitude type; 2) The logging curve is straight, but overall higher than the logging curve of the mudstone section, in a step-like shape, and the average amplitude is 2 to 4 times that of the logging curve of the mudstone section, which is a medium-amplitude type; 3) The logging curve is sharp, without straight sections, and the average amplitude is 4 to 6 times that of the logging curve in the mudstone section, which is a high-sharp type.

6. The method for quantitatively identifying low-resistivity oil layers based on logging curve difference reconstruction according to claim 1, characterized in that: In step 5, the multi-point value acquisition refers to: referring to the core analysis data, logging data and oil test conclusions, the deep induction resistivity, medium induction resistivity and acoustic wave time difference values ​​are acquired. The specific operation steps are as follows: 1) Select the maximum value of the deep induction resistivity curve and the maximum value of the acoustic time difference curve of the target layer respectively; 2) The deep induction resistivity logging data, medium induction resistivity logging data, and acoustic time difference logging data at different depths corresponding to the high-point, medium-amplitude, and low-amplitude logging types were averaged, with a sampling interval of 0.25 m to reduce the error caused by random sampling.

7. The method for quantitatively identifying low-resistivity oil layers based on logging curve difference reconstruction according to claim 1, characterized in that: In step six, the specific process of determining the identification standard of calcareous sandstone interlayers is as follows: the relative values ​​JAC and JRT of the logging curves reconstructed by the difference are placed in the rectangular coordinate system for intersection analysis, and a JAC-JRT calcareous sandstone identification chart is established; according to the distribution of high-pointed, medium-low amplitude sandstones in the coordinate system, the calcareous sandstone identification standard is divided.

8. The method for quantitatively identifying low-resistivity oil layers based on logging curve difference reconstruction as claimed in claim 1, characterized in that: In step 7, the specific process of determining the fluid property identification standard is as follows: the resistivity difference after the difference reconstruction and RILD into a rectangular coordinate system for intersection analysis, and establish RILD- Low-resistivity oil layer identification chart; based on the distribution of oil-water layers and water layers in the coordinate system during oil testing, the oil-water layers and water zone identification standards are divided.

9. The method for quantitatively identifying low-resistivity oil layers based on logging curve difference reconstruction according to claim 1, characterized in that: In step 8, the JAC value, JRT value and , RILD were respectively put into the established JAC-JRT calcareous sandstone identification plate and RILD- The low-resistivity oil layer identification map identifies calcareous sandstone and oil-water layers and water layers.

10. The method for quantitatively identifying low-resistivity oil layers based on logging curve difference reconstruction as claimed in claim 9, characterized in that: The JAC value of the calcareous sandstone is between 0.7 and 0.85, and the JRT value is between 1.0 and 1.4; >0, RILD is between 9.1 and 17.5Ω·m; the water layer <0, RILD is between 5.1 and 11.7Ω·m.

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