A method for quantitatively identifying low-resistance oil layers based on well logging curve difference reconstruction

Abstract

The application discloses a method for quantitatively identifying low-resistance oil layers based on difference value reconstruction of logging curves, and belongs to the field of oil exploration and development, and comprises the following steps: collecting well data in a research area, wherein the well data comprises core analysis data, logging data, production test data, sonic time difference, deep induction resistivity and medium induction resistivity logging curves; selecting a standard layer in the research area; standardizing the logging curves by using a trend surface analysis method; classifying logging facies states of a target layer section; taking multi-point values of three kinds of logging curves of different logging facies states; carrying out difference value reconstruction on the obtained logging values; calculating JAC, JRT and; establishing calcium sandstone and low-resistance oil layer identification plates respectively; and determining identification standards of calcium sandstone, oil-water same layer and water layer by counting parameter distribution ranges. The application has the advantages of simple operation, easy data acquisition, low labor cost, one-time quantitative distinction of calcium sandstone interlayer, oil-water same layer and water layer, clearer boundary and higher distinction degree.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of oil exploration and development, and particularly relates to a method for quantitatively identifying low-resistance oil layers based on well logging curve difference reconstruction. BACKGROUND

[0002] With the deepening of oil and gas exploration and development work, various complex oil and gas reservoirs have gradually become the main exploration target. With the increasing of low-resistivity reservoir production and reserves, it has become a special development field in recent years. Low-resistivity 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, so the resistivity logging curve response characteristics are similar to those of the water layer, which leads to the loss of low-resistivity oil layer in the logging interpretation process, causing great obstacles to the later development and production increase. In the existing research, the identification of low-resistivity oil layer mainly focuses on two aspects. On the one hand, based on core analysis methods such as cast thin section, scanning electron microscope, X-ray diffraction, major and trace element analysis, and electron probe, the genesis and identification of low-resistivity oil layer are studied. For example, patent CN 109580678 A discloses a method for quickly identifying and evaluating low-resistivity oil and gas layers using digital core technology. This method is relatively expensive and is mainly suitable for areas with abundant core data and small regions. At present, the commonly used methods are mainly based on the intersection chart method, overlapping method, multi-well correlation method, and nuclear magnetic resonance logging method. For example, patent CN 109667576 A discloses a method for identifying low-resistivity oil layers caused by high salinity. Patent CN 117627633 A discloses a method for identifying low-resistivity oil layers. Patent CN 114086945 B discloses a mud logging identification method for low-resistivity oil layers caused by low salinity. Patent CN 106285660 A discloses a method and device for identifying low-resistivity oil layers in multi-layer sandstone reservoirs. Patent CN 116699721 A discloses a method for identifying low-resistivity oil layer sweet spots based on nuclear magnetic logging. Patent CN 109707378 B discloses a method for identifying low-resistivity oil layers based on mud invasion characteristics and longitudinal correlation. In 2012, Vol. 42, No. 2, Journal of Jilin University (Earth Science Edition), Yu Hongyan et al. determined the lower limit value of each characteristic parameter in low-resistivity oil layers based on analysis and test data, realizing the transition from qualitative identification to quantitative evaluation, and the accuracy of the established low-resistivity oil layer quantitative evaluation model reached 96.15%. In 2013, Vol. 25, No. 3, Lithologic Oil and Gas Reservoirs, Xie Qing et al. used the intersection chart method, logging curve overlapping method, and adjacent well water layer correlation method to identify low-resistivity oil layers in Chang 6 of Zhidan and Ansai areas. In 2018, Vol. 25, No. 1, Fault Block Oil and Gas Fields, Zheng Hua et al. combined core, logging data, and test results to identify low-resistivity oil layers in Bohai Lvda A oilfield using quantitative and qualitative evaluation techniques, with an identification accuracy of over 90%. In 2022, Vol. 37, No. 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 suitable for both clastic rocks and carbonate rocks. Calcareous sandstone refers to dense sandstone containing calcareous cement, which is widely developed in clastic reservoirs and increases the heterogeneity of the reservoir, affecting the overall reservoir properties.In addition, due to the characteristics of high resistivity and low acoustic time difference of calcareous sandstone, it is easy to be misjudged as an oil layer in the oil-water identification of low-resistivity oil layers, resulting in a decrease in the interpretation coincidence rate of oil layers in the region. This all hinders the improvement of the remaining oil potential of old oil fields, especially low-resistivity reservoirs. At present, the identification methods of calcareous sandstone mainly include logging identification and interwell modeling identification. For example, patent CN101793145A discloses a method for determining calcareous interlayer and formation porosity by combining neutron lifetime and compensated neutron logging. Patent CN117390526A discloses a method for identifying the distribution of interlayers. In 2016, Vol. 27, No. 6, Natural Gas Geosciences, Liu Li et al. discussed the influence of petrological characteristics and diagenetic evolution on the distribution of calcareous interlayers based on the differences in sedimentation and diagenesis between calcareous sandstone and non-calcareous sandstone. In 2017, Vol. 31, No. 5, 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 2020, Vol. 47, No. 5, Journal of Chengdu University of Technology (Natural Science Edition), Qinale Husan et al. realized the accurate characterization of calcareous sandstone based on high-density seismic data.

[0003] Through the analysis of the above-mentioned prior art, the following problems are found: (1) The conventional intersection chart method and overlapping method have low precision, complicated steps, and it is difficult to clearly distinguish oil layers and oil-water layers in low-resistivity reservoirs; and the cost of nuclear magnetic resonance logging is high, which is not conducive to oil-water identification of a large number of wells; (2) The conventional logging identification method cannot distinguish calcareous sandstone, and the interwell modeling identification method requires a large amount of well data, has poor timeliness, and has high interpretation cost; (3) At present, there is no comprehensive method that can quickly identify calcareous sandstone and oil layers and oil-water layers in low-resistivity reservoirs at the same time. SUMMARY

[0004] The purpose of the present application is to provide a method for quantitatively identifying low-resistivity oil layers based on logging curve difference reconstruction, which solves the problem that calcareous sandstone interlayers and low-resistivity oil layers cannot be accurately identified at one time in the prior art. The method uses formula reconstruction means combined with logging data, mud logging data and production testing data to obtain an identification intersection chart with high precision; the calcareous sandstone and low-resistivity oil layers can be identified at one time by using only a simple intersection chart method, which has strong practicality; only three types of logging data are used, the data is easy to obtain, the process is simple, the interpretation cost is low, and it can be applied to the identification of low-resistivity oil layers and calcareous sandstone in a large area.

[0005] The technical scheme adopted by the present application is as follows:

[0006] A method for quantitatively identifying low-resistivity oil layers based on logging curve difference reconstruction, comprising the following steps:

[0007] Step one, collect well data in the study area including core analysis data, mud logging data, production test data, and acoustic time difference (AC), deep induction resistivity (RILD) and middle induction resistivity (RILM) logging curves;

[0008] Step two, from the collected data, select the standard layer in the study area, and the selection of the standard layer is based on the principle of logging data processing and interpretation of China Petroleum Corporation (SY / T 6451-2017);

[0009] Step three, trend surface analysis is performed on the standard layer, and the trend surface equation of the acoustic time difference, deep induction resistivity and middle induction resistivity corresponding to the standard layer is fitted; the acoustic time difference, deep induction resistivity and middle induction resistivity values of the standard layer of each well are corrected to the trend surface equation, and the standardized correction value of the logging data in the study area is obtained;

[0010] Step four, the facies classification of the logging curves of the target layer is classified into high sharp type, medium amplitude type and low amplitude type;

[0011] Step five, combined with core analysis data, mud logging data and test oil conclusion, the deep induction resistivity, middle induction resistivity and acoustic time difference logging curves of sandstone and calcareous sandstone interlayer are taken for different logging facies;

[0012] Step six, the deep induction resistivity and acoustic time difference logging values obtained are differentially reconstructed, the JAC-JRT calcareous sandstone identification plate is established, the identification standard of calcareous sandstone interlayer is determined, and the reconstruction formula is:

[0013] (1)

[0014] (2)

[0015] Wherein, JRT and JAC are 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 acoustic time difference; AC is the acoustic time difference value;

[0016] Step seven, the deep induction resistivity value and middle induction resistivity value taken in the medium amplitude type and low amplitude type logging curve facies are differentially reconstructed, the RILD- low resistance oil layer identification plate of tested oil layer is established, the fluid property identification standard is determined, and the reconstruction formula is:

[0017] (3)

[0018] Wherein, RILD is the deep induction resistivity logging value; RILM is the medium induction resistivity logging value;

[0019] Step eight, using the established calcareous sandstone interlayer identification chart and low-resistance oil layer identification chart to quantitatively distinguish the calcareous sandstone interlayer and fluid properties of other target layers in the study area.

[0020] Further, in step two, the standard layer is a layer that is widely distributed in the study area, has stable or regular changes in logging characteristics, and is convenient for tracking and comparing in the whole area.

[0021] Further, in step two, the standard layer is a rock layer that is distributed in more than 90% of the study area and has obvious lithological characteristics.

[0022] The method selects the top stable development mudstone of XI sand group as the standard layer, which is distributed in more than 90% of the total area of the study area, with an average of 370Km 2 .

[0023] Further, in step three, the calculation of the standardization correction value of the logging data includes the following steps:

[0024] 1) Statistics of the starting depth of the deep induction resistivity, medium induction resistivity and acoustic travel time logging curves of the standard layer of each well;

[0025] 2) Calculate the average depth and average deep induction resistivity, average medium induction resistivity and average acoustic travel time of the standard layer;

[0026] 3) Linear fitting is performed on the average depth value of the standard layer of each well and the average deep induction resistivity value, average medium induction resistivity value and average acoustic travel time value, respectively, to obtain the corresponding relationship formula (i.e. trend surface equation);

[0027] 4) According to the trend surface equation, the logging values corresponding to the standard layer of each well are calculated and compared with the actual average values to obtain the standardization correction value, and the standardization of the logging curve is completed; then the trend surface equation is used to complete the standardization processing of the logging curves of other wells in the region.

[0028] Further, in step four, the facies classification of the logging curve refers to taking the thick layer of mudstone with uniform and stable lithology adjacent to the target layer as a reference, and according to the deviation degree of the logging curve of the target layer segment (the target sandstone layer segment), the logging facies of the target layer segment is divided into three categories: high sharp type, medium amplitude type and low amplitude type; the selected logging curve is the deep induction resistivity curve with obvious characteristics and deep detection depth; the thick layer of mudstone and the target layer segment both use the deep induction resistivity curve.

[0029] The specific classification criteria are as follows:

[0030] 1) the average amplitude of the well logging curve is 1-2 times of the well logging curve of the mudstone section, and the well logging curve is low-amplitude type;

[0031] 2) the well logging curve is flat, but the overall value is higher than the well logging curve of the mudstone section, and the average amplitude of the well logging curve is 2-4 times of the well logging curve of the mudstone section, and the well logging curve is medium-amplitude type;

[0032] 3) the well logging curve is sharp, without flat section, and the average amplitude of the well logging curve is 4-6 times of the well logging curve of the mudstone section, and the well logging curve is high-sharp type.

[0033] Further, in step five, the multiple-point value refers to: referring to core analysis data, logging data and oil test conclusion, the deep induction resistivity, the medium induction resistivity and the acoustic time difference value are valued, and the specific operation steps are as follows:

[0034] 1) the maximum value of the deep induction resistivity curve and the maximum value of the acoustic time difference curve of the target layer section are selected respectively;

[0035] 2) the deep induction resistivity logging data, the medium induction resistivity logging data and the acoustic time difference logging data of different depths corresponding to the high-sharp type, the medium-amplitude type and the low-amplitude type logging are averaged, and the sampling interval is 0.25 m, so as to reduce the error caused by random sampling.

[0036] Further, in step six, the specific process of determining the identification standard of the calcareous sandstone interlayer is: the relative value JAC and JRT of the difference value reconstructed logging curve are put into the rectangular coordinate system for intersection analysis, and the JAC-JRT calcareous sandstone identification plate is established; according to the distribution of the high-sharp type (calcareous sandstone) and the medium-low amplitude type sandstone (non-calcareous sandstone) in the coordinate system, the calcareous sandstone identification standard is divided.

[0037] Further, in step seven, the specific process of determining the identification standard of the fluid property is: the difference value and RILD of the difference value reconstructed resistivity are put into the rectangular coordinate system for intersection analysis, and the RILD- low-resistance oil layer identification plate is established; according to the distribution of the oil-water layer and the water layer in the oil test in the coordinate system, the oil-water layer and the water layer identification standard is divided.

[0038] Further, in step eight, the JAC, JRT values and , RILD calculated from the new target layer section are respectively put into the JAC-JRT calcareous sandstone identification plate and the RILD- low-resistance oil layer identification plate, and the calcareous sandstone and the oil-water layer and the water layer are identified.

[0039] The application of the application in the low-resistance oil reservoir can identify the calcareous sandstone and the oil-water layer and the water layer.

[0040] Further, 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; the oil-water same layer of the >0, and the RILD is between 9.1 and 17.5 Ω·m; the water layer of the <0, and the RILD is between 5.1 and 11.7 Ω·m.

[0041] The beneficial effects of the present application are as follows:

[0042] (1) The present application selects a standard layer, and uses the trend surface analysis method to standardize the logging data of other wells, eliminates the influence of non-geological factors, unifies the scale standard, and reduces the error. The trend surface analysis method combines the logging parameters of multiple wells for multivariate quantitative analysis, thereby improving the accuracy of the standardization process. It is especially suitable for regions where the standard layer is stably developed throughout the region, but the vertical distribution of the buried depth is greatly different, and the heterogeneity is strong.

[0043] (2) The present application uses a small amount of coring well data, production test oil data and logging curves (acoustic travel time, deep induction resistivity and medium induction resistivity) to establish a calcareous sandstone identification plate and a low-resistance oil layer identification plate, and the data processing process is simple and fast. Compared with the plates established by other methods, the difference reconstruction formula only needs three kinds of logging curves, AC, RILD and RILM, and the formula is simple and easy to operate. After difference reconstruction, the relative values (JRT and JAC) of the logging curves of calcareous sandstone and non-calcareous sandstone are more different, and the resistivity difference (RILD-RILM) of the oil-water same layer and the water layer is more obvious. Therefore, the boundary of the identification plate established according to the same is clearer, and the distinction is higher.

[0044] (3) In view of the characteristics that the calcareous sandstone reservoir is dense, the resistivity is equal to or higher than that of the oil layer, and it is difficult to identify the calcareous sandstone, the low-resistance oil layer and the water layer by the conventional method. The present method optimizes the standardization of three kinds of logging curves (AC, RILD and RILM), logging facies classification, multi-point selection, difference reconstruction and other measures, and preferentially excludes the calcareous sandstone non-effective reservoir more quickly and directly, and then distinguishes the oil-water same layer and the water layer. The method is suitable for large-scale low-resistance oil layer identification work in the region, and is conducive to further improving the development efficiency of the old well area.

[0045] (4) The present application is still applicable to the region lacking mud logging data and having no test oil conclusion well. Only JRT, JAC and RILD are calculated according to the difference reconstruction formula, and points are cast on the calcareous sandstone identification plate and the low-resistance oil layer identification plate, respectively, so that the calcareous sandstone interlayer and the oil-water same layer and the water layer can be distinguished at one time. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 The present application is a method flowchart.​​

[0047] Figure 2 a fitting relationship of acoustic travel time (AC) changing with depth;

[0048] Figure 3 a fitting relationship of middle induced resistivity (RILM) changing with depth;

[0049] Figure 4 a fitting relationship of deep induced resistivity (RILD) changing with depth;

[0050] Figure 5 a calcium sandstone identification chart established;

[0051] Figure 6 a low-resistance oil layer identification chart established;

[0052] Figure 7 a calcium sandstone point-throw schematic diagram;

[0053] Figure 8 a low-resistance oil layer point-throw schematic diagram;

[0054] Figure 9 a calcium sandstone and low-resistance oil layer fluid property identification and verification schematic diagram of W21 well;

[0055] Figure 10 a calcium sandstone and low-resistance oil layer fluid property identification and verification schematic diagram of W22 well. DETAILED DESCRIPTION

[0056] With the continuous development of oil and gas, most of the oilfields in China have entered the middle and late stages of development. The un-identified low-resistance oil reservoirs as an important part of the remaining potential are increasingly prominent. However, due to the influence of tectonic sedimentation, skeleton conductivity and fluid conductivity, the oil layer resistivity is low, which is difficult to distinguish from water layer. In addition, the low-resistance oil reservoir containing calcium sandstone interlayer is easily misjudged as oil layer in oil-water identification due to the logging characteristics of calcium sandstone. Therefore, it is particularly important to quickly and accurately identify calcium sandstone and low-resistance oil layer. At present, the existing methods have large data requirements, high cost and slow speed, and cannot quantitatively identify calcium sandstone and low-resistance oil layer at one time. Based on this, the existing logging identification method is improved, the logging curve value is taken and difference reconstructed, the calcium sandstone identification chart and low-resistance oil layer identification chart of the study area are established, and the rapid quantitative identification of calcium sandstone interlayer and oil-water layer is realized.

[0057] In the present example, by phase state classification, multi-point taking and difference reconstruction on the acoustic travel time, deep induction resistivity and medium induction resistivity logging curves in the core analysis data, mud logging data and oil test data of the study area, a calcareous sandstone identification plate and a low-resistance oil layer identification plate are established, and the identification criteria are quantitatively divided. For the well area lacking core, mud logging and oil test data, the identification can be directly carried out by point projection, and the calcareous sandstone interlayer, oil-water layer and water layer can be accurately and quickly identified at one time. The problem of one-time quantitative identification of calcareous sandstone and low-resistance oil layer is solved.

[0058] Among them, the difference reconstruction formula is based on the phase state classification of logging and combined with the oil test conclusion, to calculate the relative value (JRT, JAC) of acoustic travel time, deep induction resistivity and medium induction resistivity logging curves, the difference (Rw-Rw) of oil-water layer and water layer resistivity After reconstruction, the resistivity difference is more obvious, and the range boundary of the established calcareous sandstone and low-resistance oil layer identification plate is clearer.

[0059] In a certain old oilfield, the early development degree is high, the water cut is high, and the formation water salinity is high, which leads to low-resistance oil layer and contains a large number of calcareous sandstone interlayers. It is difficult to remove the calcareous sandstone interlayer which affects the reservoir property in actual work, and accurately identify oil-water layer. The logging data of the oilfield is relatively rich, so the invention is used to identify calcareous sandstone and oil-water layer through logging data.

[0060] A method for quantitatively identifying low-resistance oil layer based on difference reconstruction of logging curves, as shown in Figure 1 , comprising the following steps:

[0061] Step one, collect well data containing core analysis data, mud logging data, production oil test data and acoustic travel time (AC), deep induction resistivity (RILD) and medium induction resistivity (RILM) logging curves in the study area;

[0062] Step two, according to the principle of logging data processing and interpretation of China Petroleum Corporation (SY / T 6451-2017), select the mudstone stably developed at the top of XI sand group as the standard layer, which is distributed over 90% of the total area of the study area, with an average of 370Km 2 .

[0063] Step three, calculate the average depth, deep induction resistivity average, medium induction resistivity average and acoustic travel time average of the standard layer by statistically analyzing the starting depth of the deep induction resistivity, medium induction resistivity and acoustic travel time logging curves of the standard layer of 20 wells in the study area which are evenly distributed, have rich core analysis data, mud logging data and oil test data.

[0064] Then, the average depth value of each well standard layer is linearly fitted with the average induced deep resistivity value, the average induced medium resistivity value and the average acoustic time difference value respectively, to obtain the corresponding relationship (i.e. the trend surface equation) (see Figures 2-4 );to calculate the best approximation value of each logging curve near the standard layer and the standard correction amount of each logging curve (see Table 1). And use this trend surface equation to complete the data standardization processing of other wells in the region.

[0065] Table 1 Logging values and correction amounts obtained according to the fitting relationship

[0066] Step four, taking the thick shale section at the top of the target layer (XI sand layer) as the reference standard, the logging facies is quantitatively divided. First, the average deep induced resistivity of each well thick shale section is calculated, and the curve segment with similar logging shape and average amplitude of deep induced resistivity 1~2 times of the shale section is defined as low amplitude type. The curve segment with "step" shape and average amplitude of 2~4 times of the shale section is classified as medium amplitude type. The curve segment with "sharp protrusion" shape, no flat section and average amplitude of 4~6 times of the shale section is classified as high sharp type.

[0067] Step five, select the maximum value of deep induced resistivity curve and the maximum value of acoustic time difference curve in XI and XII sand layer sections. Combined with core analysis data, logging data and oil test conclusion, the average value of deep induced resistivity logging data, medium induced resistivity logging data and acoustic time difference logging data at different depths corresponding to high sharp type, medium amplitude type and low amplitude type logging facies is taken, and the sampling interval is 0.25m.

[0068] Step six, difference reconstruction is carried out on the obtained deep induced resistivity and acoustic time difference logging values, to establish JAC-JRT calcareous sandstone identification chart (see Figure 5 ), to quantitatively depict the identification standard of calcareous sandstone: when JAC value is between 0.7~0.85 and JRT value is between 1.0~1.4, it is calcareous sandstone. The reconstruction formula is:

[0069] (1)

[0070] (2)

[0071] Wherein, JRT and JAC are relative values of logging curves; RILD max is the maximum value of deep induced resistivity logging curve; RILD is the deep induced resistivity logging value; AC max is the maximum value of acoustic time difference; AC is the acoustic time difference value;

[0072] Step seven, the difference reconstruction of deep induction resistivity value and medium induction resistivity value taken from the phase state of medium amplitude type and low amplitude type logging curves, and the establishment of RILD of tested oil layers Low resistivity oil layer identification chart (see Figure 6 ), quantitative description of fluid property identification criteria: when > 0, RILD is between 9.1-17.5 Ω·m, which is oil-water layer; when < 0, RILD is between 5.1-11.7 Ω·m, which is water layer; the reconstruction formula is:

[0073] (3)

[0074] Among them, is the resistivity difference; RILD is the deep induction resistivity logging value; RILM is the medium induction resistivity logging value;

[0075] Step eight, using the established calcareous sandstone interlayer identification chart (see Figure 5 ) and low resistivity oil layer identification chart (see Figure 6 ) to quantitatively distinguish the calcareous sandstone interlayer and fluid properties of other target layers in the study area.

[0076] For other wells in the region without coring, lacking of logging data and oil test conclusion, this method is used to identify calcareous sandstone interlayer and low resistivity oil layer. Taking W21 and W22 wells as examples, according to the above steps (steps three to seven), JAC, JRT and are calculated and plotted on the identification chart to quickly identify the calcareous sandstone interlayer and oil-water layer, as shown in Figure 7 , Figure 8 .

[0077] As shown in Figure 9 , the coring observation of W21 well at 1898m-1901.6m and 1922m-1927m is calcareous sandstone, which is judged as calcareous sandstone by using the calcareous sandstone identification chart; 1902m-1922m is judged as water layer by using the low resistivity oil layer identification chart, and the initial oil test of this layer produces 24.6t of water and 0t of oil per day. The oil test conclusion is consistent with the identification result.

[0078] As shown in Figure 10 , the coring observation of W22 well at 2090m-2094.5m is calcareous sandstone, which is judged as calcareous sandstone by using the calcareous sandstone identification chart; 2095m-2115.6m is judged as oil-water layer by using the low resistivity oil layer identification chart, and the initial oil test of this layer produces 8.9t of oil and 23.6t of water per day. The oil test conclusion is consistent with the identification result.

[0079] It can be seen that the calcareous sandstone interlayer identified by the application is consistent with the judgment of coring observation, and the fluid property identified is basically consistent with the actual production situation, which further illustrates the feasibility of the method of the application.

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, well logging data, production test data, and acoustic wave transit time, deep induction resistivity, and medium induction resistivity logging curves; Step 2: From the collected data, select the standard layer of the study area. The selection of the standard layer is based on the principles of CNPC well logging data processing and interpretation. Step 3: Perform trend surface analysis on the standard layer and obtain trend surface equations of acoustic transit time, deep induction resistivity, and medium induction resistivity corresponding to the standard layer by fitting; calibrate the acoustic transit time, deep induction resistivity, and medium induction resistivity values ​​of the standard layer of each well to the trend surface equations to obtain standardized correction values ​​of the well 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 results to obtain multiple-point values ​​of the deep induction resistivity, medium induction resistivity, and acoustic transit time logging curves of the sandstone and calcareous sandstone interlayers based on different logging phases; Step 6: Reconstruct the difference between the deep induction resistivity and acoustic transit time 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 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.

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 a layer that is widely distributed in the study area, has stable logging characteristics or changes regularly, and 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 normalized correction value of the logging data includes the following steps: 1) Calculate the deep induction resistivity, medium induction resistivity and starting depth of the acoustic transit time logging curve of each standard layer of each well; 2) Calculate the average depth of the standard layer and the average deep induction resistivity, average medium induction resistivity and average acoustic wave time difference; 3) Perform linear fitting on the average depth value of the standard layer of each well and the average deep induction resistivity value, average medium induction resistivity value and average acoustic wave transit time value to obtain the corresponding relationship; 4) Calculate 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 logging curve phase classification refers to the classification of the target layer logging phase into three types: high-peak type, medium-amplitude type, and low-amplitude type according to the degree of deviation from the target layer logging curve, with the thick mudstone section adjacent to the target layer having uniform and stable lithology and straight logging curve as a reference. The selected logging curve is a deep induction resistivity curve with obvious characteristics and deep detection depth. The specific classification criteria 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-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) If the logging curve is sharp and has no straight sections, and the average amplitude is 4 to 6 times that of the logging curve in the mudstone section, it 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 results, 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 transit time logging data at different depths corresponding to high-point, medium-amplitude, and low-amplitude logging 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 curve 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 and medium-low amplitude sandstones in the coordinate system, the identification standard of calcareous sandstone is divided.

8. The method for quantitatively identifying low-resistivity oil layers based on logging curve difference reconstruction according to 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 the 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 eight, the JAC value, JRT value and , RILD were respectively put into the established JAC-JRT calcareous sandstone identification plate and RILD- Low-resistivity oil layer identification map, identifying 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 according to 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 ranged from 5.1 to 11.7 Ω·m.

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