Method for identifying oil and gas reservoir thin layer based on dynamic mean amplitude difference ratio
By using the dynamic mean amplitude difference ratio method, combined with the natural potential and flushing zone resistivity curves, the problem of identifying thin layers in oil and gas reservoirs using conventional logging has been solved, achieving efficient and accurate thin layer identification and division, and improving identification efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- PETROCHINA CO LTD
- Filing Date
- 2024-11-25
- Publication Date
- 2026-05-26
AI Technical Summary
In existing technologies, conventional logging has many drawbacks when identifying thin layers in oil and gas reservoirs, including difficulty in identification, inconsistent classification standards, omissions and missing data in the 0.2-0.8m thin layer, and cumbersome and time-consuming data processing, which makes it difficult to meet the needs of large-scale surveys of old wells.
By employing a method based on dynamic mean amplitude difference ratio, the location, thickness, and upper and lower interfaces of thin oil and gas reservoir layers are identified by calculating the dynamic average value, relative amplitude, and multiple curves of the spontaneous potential and flushing zone resistivity curves, combined with high-resolution logging and core well verification.
It improved the recognition rate of thin sand bodies larger than 0.4m to over 90%, and the recognition rate of thin sand bodies between 0.2 and 0.4m to about 80%, achieving rapid and accurate thin-layer division, reducing layer loss, and improving recognition efficiency.
Smart Images
Figure CN122085387A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of oil and gas exploration and development technology, specifically relating to a method for identifying thin layers of oil and gas reservoirs based on dynamic mean amplitude difference ratio. Background Technology
[0002] In the exploration and development of oil and gas reservoirs, the vertical identification accuracy of sand bodies using conventional logging data is relatively low. Typically, logging interpretation mainly relies on manual stratification to identify and delineate thicker reservoir layers (over 0.8m thick). However, identifying thin sand bodies presents challenges due to difficulties in identification and inconsistent delineation standards, leading to common instances of missed delineation and interpretation of these thin layers. Current methods for thin layer identification primarily rely on deconvolution techniques based on reservoir response signals, employing mathematical calculations based on thin layer thickness and instrument parameters. However, these methods suffer from drawbacks such as cumbersome and time-consuming data processing. In oilfields in the later stages of development, there is a large amount of data from older wells, resulting in a significant workload for surveying. This is especially true for oil and gas reservoirs with long oil-bearing intervals and well-developed layered sand bodies, making the identification of thin layers using conventional logging data even more challenging. Summary of the Invention
[0003] The purpose of this invention is to provide a method for identifying thin layers in oil and gas reservoirs based on dynamic mean amplitude difference ratio, which solves the problems of omission and low identification efficiency of thin layers in oil and gas reservoirs, especially 0.2-0.8m thin layers, in the manual interpretation of a large amount of conventional logging data in the prior art.
[0004] The technical solution adopted in this invention is a method for identifying thin layers in oil and gas reservoirs based on the dynamic mean amplitude difference ratio, which specifically includes the following steps: Step 1: Calculate the dynamic average value curve of spontaneous potential and the dynamic average value curve of resistivity of flushed zone for the spontaneous potential curve and the flushed zone resistivity curve in conventional well logging, respectively, using the same step size; Step 2: Calculate the relative amplitude curve by combining the natural potential curve, the dynamic average value curve of natural potential, and the maximum amplitude of natural potential; calculate the multiple curve by combining the resistivity curve of the rinsing zone and the dynamic average value curve of the rinsing zone resistivity. Step 3: Select the thin-layer area to be tested in the jurisdiction, use high-resolution logging to divide the thin layer, determine the location and thickness of the thin layer, and use this result as a standard to compare the relative amplitude characteristics of spontaneous potential; Step 4: Based on Step 3, further compare the resistivity multiple characteristics of the rinsing band; Step 5: Based on the actual conditions of the core wells within the jurisdiction, compare and verify the value limits of the relative amplitude parameter of relative spontaneous potential and the resistivity multiple parameter of flushing zone. Step 6: Identify the location, thickness, and upper and lower interfaces of thin oil and gas reservoirs within the jurisdiction based on the relative amplitude parameters of the natural potential and the resistivity multiple parameters of the flushing zone.
[0005] The invention is further characterized by: In step 1, the step size is determined by combining the thickness characteristics of the thin layer of the target area to be identified. The specific method for determining the step size is as follows: The selected thin layer thickness is 0.6m; based on the actual formation distribution, the surrounding rock resistivity is selected as 1.0 ohm-meter, the thin layer resistivity as 6.0 ohm-meter, and the vertical resolution of the logging instrument as 30cm; Based on the above parameters, the theoretical model was used to calculate and compare the results with the actual logging response. Under the principle that the contribution rate of the surrounding rock is not less than 80% and the shorter the step length, the better the effect of interlayer interference, a step length of 3.0m was selected.
[0006] The method for calculating the relative amplitude curve in step 2 is as follows: .
[0007] The calculation method for the multiple curve in step 2 is as follows: .
[0008] Step 3 specifically involves: Based on the correspondence between the thickness of the thin layer (i.e., the thickness of the sand body) and the relative amplitude of the spontaneous potential, the characteristics of the relative amplitude of the spontaneous potential are obtained, and the limit value of the relative amplitude parameter of the spontaneous potential is determined.
[0009] Step 4 specifically involves: Plot the corresponding point graphs of the relative amplitude of natural potential and the resistivity multiple of the rinsing zone under different thin layer thicknesses. Based on the limit value of the relative amplitude parameter of natural potential determined in step 3, and the thin layers with low potential value in actual production, determine the limit value of the resistivity multiple parameter of the rinsing zone.
[0010] Step 5 specifically involves: Take the logging map of at least one core well in the jurisdiction, and combine it with the location and thickness of thin layer development in the core description to determine the universality of the natural potential relative amplitude parameter and the flushing zone resistivity multiple parameter calculated in steps 3 to 4 in the jurisdiction.
[0011] Step 6 specifically involves: For the well logging maps of oil and gas reservoirs within the jurisdiction, based on the relative amplitude parameter of spontaneous potential and the resistivity multiple parameter of flushing zone determined in step 5, thin layer identification is performed at the corresponding well depth: when the relative amplitude of spontaneous potential is greater than the relative amplitude parameter of spontaneous potential and the resistivity multiple parameter of flushing zone is greater than the resistivity multiple parameter of flushing zone, the starting point of the depth is the upper boundary of the thin layer, and the ending point of the depth is the lower boundary of the thin layer; the thickness of the thin layer is the difference between the lower boundary and the upper boundary of the thin layer.
[0012] The beneficial effects of this invention are: This invention presents a method for identifying thin layers in oil and gas reservoirs based on the dynamic mean amplitude difference ratio. Utilizing the dynamic amplitude difference ratio of spontaneous potential and flushed zone resistivity, it solves problems such as low logging response amplitude in thin layers, significant influence from surrounding rock leading to difficulties in artificial stratification, and inconsistent stratification standards. Through verification using high-resolution logging comparisons and core well comparisons, this method achieves an identification rate of over 90% for thin sand bodies longer than 0.4m; it also shows good identification results for thin sand bodies of 0.2-0.4m, with an identification rate of approximately 80%. This method allows for rapid detection and surveying of large amounts of old well data using a computer, enabling thin layer delineation, reducing stratification loss of thin sand bodies, and significantly improving the identification efficiency of thin layers. It serves as a crucial support for tapping potential and increasing reserves. Attached Figure Description
[0013] Figure 1 This is a flowchart of the method for identifying thin layers of oil and gas reservoirs based on the dynamic mean amplitude difference ratio of the present invention; Figure 2 This is a step size analysis diagram in the method for identifying thin layers of oil and gas reservoirs based on the dynamic mean amplitude difference ratio of this invention; Figure 3 This is a diagram illustrating the method for determining FSP and FRX curves in this invention; Figure 4 This invention is based on the relationship between the relative amplitude of natural potential and the thickness of sand body to determine the relative amplitude limit value analysis diagram; Figure 5 This is a graph showing the relative amplitude curve of spontaneous potential and the resistivity multiple curve of flushed zone in the method for identifying thin layers of oil and gas reservoirs based on the dynamic mean amplitude difference ratio of this invention. Figure 6 This is a comparison chart of thin-layer logging identification results from a core well; Figure 7 This is a diagram showing the results of the thin-layer test production section of the Yue 214-4 well in the Gaskule oilfield, Example 7. Detailed Implementation
[0014] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.
[0015] This invention relates to a method for identifying thin layers in oil and gas reservoirs based on the dynamic mean amplitude difference ratio, such as... Figure 1 As shown, the specific steps include the following: Step 1: Calculate the dynamic average value curve of spontaneous potential and the dynamic average value curve of resistivity of flushed zone for the spontaneous potential curve and the resistivity curve of flushed zone respectively, using the same step size.
[0016] Among them, the spontaneous potential curve is part of the electrical logging results. Because this curve shows obvious anomalies in the permeable layer, it is an important method for classifying and evaluating reservoirs.
[0017] The resistivity curve of the rinsing strip has a shallow detection depth, but a high longitudinal resolution, which can divide thin layers down to a few centimeters. It is generally used to divide thin layers and calculate their accurate thickness.
[0018] However, due to the influence of lithology and fluid on the above two logging curves, a dynamic average curve reflecting the reservoir characteristics at that point is obtained through calculation with a specific step size, in order to accurately describe the permeability and layer thickness of the formation in that well interval. The step size is determined in conjunction with the thickness characteristics of the thin layer in the target area. The specific determination method is as follows: A thin layer thickness of 0.6m was selected, and the optimal value of the identification step size was verified by numerical simulation based on this thickness. The simulated formation focused on analyzing the resistivity of the flushed zone to identify parameters. Combined with the actual distribution of the formation, the surrounding rock resistivity was selected as 1.0 ohm-meter, and the thin layer resistivity was selected as 6.0 ohm-meter. The average value of the thin layer resistivity in this area was taken here, and the vertical resolution of the logging instrument was selected as 30cm.
[0019] Based on the above parameters, in principle, the shorter the window step size, the better the effect of avoiding interlayer interference; however, a shorter step size also results in a smaller contribution from the surrounding rock and a larger contribution from the sand body itself; through theoretical model calculations, such as Figure 2 As shown, a step size of 3.0m results in a decrease of approximately 10.0% in the baseline response value compared to 2.0m, indicating an increase in the contribution of the surrounding rock. A step size of 4.0m results in a decrease of approximately 5.0% in the response value compared to 3.0m, and the two values are now quite similar. Further increasing the step size yields minimal changes. Considering that a shorter step size avoids interference from adjacent layers, a step size of 5.0 times the thickness is chosen, ensuring that the contribution of the surrounding rock is no less than 80%, thus better reflecting the response value of the surrounding rock. Therefore, the study demonstrates that a step size of 3.0m is reasonable and has universal applicability in identifying thin layers of 0.2-0.4m.
[0020] Step 2: Calculate the relative amplitude curve by combining the natural potential curve with the dynamic average value of natural potential and the maximum amplitude curve of natural potential; calculate the multiple curve by combining the resistivity curve of the flushing zone with the dynamic average value curve of the resistivity of the flushing zone.
[0021] The calculation method for the relative amplitude curve FSP is as follows:
[0022] The calculation method for the multiple curve FRX is as follows:
[0023] The relative amplitude curve and multiple curve obtained by calculating using the above formulas are as follows: Figure 3 As shown, the multiple curve reduces the influence of the surrounding rock and better highlights the logging response of thin layers. At the same time, the relative amplitude curve can also better indicate permeability and evaluate the characteristics of thin reservoirs.
[0024] Step 3: Select the thin-layer area to be tested, use high-resolution logging to divide the thin layer, determine the location and thickness of the thin layer, and use this result as a standard to compare the relative amplitude characteristics of spontaneous potential.
[0025] Specifically, based on the correspondence between the thickness of the thin layer (i.e., the thickness of the sand body) and the relative amplitude of the spontaneous potential, the characteristics of the relative amplitude of the spontaneous potential are obtained, and the limit value of the relative amplitude parameter of the spontaneous potential is determined.
[0026] according to Figure 4 As shown in the example, the statistical results show that for thin layers of 0.2m to 0.4m, the relative amplitude of spontaneous potential is concentrated below 5%, which is difficult to distinguish from the noise signal of the logging curve, resulting in poor identification effect; for thin layers above 0.4m, the relative amplitude of spontaneous potential is generally in the range of 5%, and the identification accuracy is 92.8%.
[0027] Step 4: Based on Step 3, further compare the resistivity multiple characteristics of the rinsing band.
[0028] Specifically, the following steps are taken: plot the corresponding points of the relative amplitude of the natural potential and the resistivity multiple of the rinsing zone under different thin layer thicknesses; determine the limit value of the resistivity multiple parameter of the rinsing zone based on the limit value of the relative amplitude of the natural potential parameter determined in step 3 and the thin layers with low potential value in actual production.
[0029] like Figure 5 As shown in the example, considering the guiding role of natural potential in reservoir evaluation due to its permeability characteristics, and the low potential value of 0.2m~0.4m thin layers in production practice, the multiple of the resistivity of the flushing zone of thin layers above 0.4m to the dynamic baseline value, i.e. the multiple of the resistivity of the flushing zone, is concentrated above 1.25 times. This can effectively separate the thin layer response value from the baseline and noise signal, with a recognition rate of 93.3%.
[0030] Step 5: Based on the actual conditions of the core wells in the jurisdiction, compare and verify the value limits of the relative amplitude parameter of the spontaneous potential and the resistivity multiple parameter of the flushing zone.
[0031] Specifically, this involves: obtaining the logging data from at least one cored well within the jurisdiction, and combining this data with the location and thickness of thin layers in the core description to determine the universality of the relative amplitude parameters of spontaneous potential and the resistivity multiple parameters of the flushed zone calculated in steps 3 and 4 within the jurisdiction. For example... Figure 6 As shown, for thin layers between 1734 and 1736 m, the corresponding relative amplitude parameter of spontaneous potential is above 5%, and the resistivity multiple parameter of the flushing zone is above 1.25; for thin layers between 1738 and 1739 m and between 1740 and 1741 m, the corresponding relative amplitude parameter of spontaneous potential and the resistivity multiple parameter of the flushing zone are applicable.
[0032] Among them, the relative amplitude parameter of spontaneous potential can effectively identify the location, thickness, and upper and lower interfaces of thin layers larger than 0.2m; the resistivity multiple parameter of the flushing zone can indicate the existence of thin layers larger than 0.4m, and has an auxiliary role. The two indicators can be combined and used in computer stratification to automatically identify and classify thin layers in long well sections and large amounts of logging data.
[0033] Step 6: Identify the location, thickness, and upper and lower interfaces of thin oil and gas reservoirs within the jurisdiction based on the relative amplitude parameters of the natural potential and the resistivity multiple parameters of the flushing zone.
[0034] Specifically, for the well logging maps of oil and gas reservoirs within the jurisdiction, based on the relative amplitude parameter of spontaneous potential and the resistivity multiple parameter of the flushing zone determined in step 5, thin layers are identified at the corresponding well depths: when the relative amplitude of spontaneous potential is greater than the relative amplitude parameter of spontaneous potential and the resistivity multiple parameter of the flushing zone is greater than the resistivity multiple parameter of the flushing zone, the starting point of the depth is the upper boundary of the thin layer, and the ending point of the depth is the lower boundary of the thin layer; the thickness of the thin layer is the difference between the lower boundary and the upper boundary of the thin layer.
[0035] This invention presents a method for identifying thin layers in oil and gas reservoirs based on dynamic mean amplitude difference ratios. It addresses issues such as low logging response amplitude in thin layers, significant influence from surrounding rock leading to difficulties in manual stratification, and inconsistent stratification standards. Through comparison with high-resolution logging and core wells, this method achieves an identification rate of over 90% for thin sand bodies longer than 0.4m; it also shows good identification results for thin sand bodies between 0.2 and 0.4m, with an identification rate of approximately 80%. This method allows for rapid computer-based detection and surveying of large amounts of old well data, enabling thin layer delineation, reducing stratification loss of thin sand bodies, and significantly improving thin layer identification efficiency. It serves as a crucial support for tapping potential and increasing reserves.
[0036] Example 1 This embodiment provides a method for identifying thin layers in oil and gas reservoirs based on the dynamic mean amplitude difference ratio, such as... Figure 1 As shown, the specific steps include the following: Step 1: Calculate the dynamic average value curve of spontaneous potential and the dynamic average value curve of resistivity of flushed zone for the spontaneous potential curve and the flushed zone resistivity curve in conventional well logging, respectively, using the same step size; Step 2: Calculate the relative amplitude curve by combining the natural potential curve, the dynamic average value curve of natural potential, and the maximum amplitude of natural potential; calculate the multiple curve by combining the resistivity curve of the rinsing zone and the dynamic average value curve of the rinsing zone resistivity. Step 3: Select the thin-layer area to be tested in the jurisdiction, use high-resolution logging to divide the thin layer, determine the location and thickness of the thin layer, and use this result as a standard to compare the relative amplitude characteristics of spontaneous potential; Step 4: Based on Step 3, further compare the resistivity multiple characteristics of the rinsing band; Step 5: Based on the actual conditions of the core wells within the jurisdiction, compare and verify the value limits of the relative amplitude parameter of relative spontaneous potential and the resistivity multiple parameter of flushing zone. Step 6: Identify the location, thickness, and upper and lower interfaces of thin oil and gas reservoirs within the jurisdiction based on the relative amplitude parameters of the natural potential and the resistivity multiple parameters of the flushing zone.
[0037] Example 2 Based on Example 1, the step size mentioned in step 1 is determined by combining the thickness characteristics of the thin layer of the target area to be identified. The specific determination method is as follows: The selected thin layer thickness is 0.6m; based on the actual formation distribution, the surrounding rock resistivity is selected as 1.0 ohm-meter, the thin layer resistivity as 6.0 ohm-meter, and the vertical resolution of the logging instrument as 30cm; Based on the above parameters, the theoretical model was used to calculate and compare the results with the actual logging response. Under the principle that the contribution rate of the surrounding rock is not less than 80% and the shorter the step length, the better the effect of interlayer interference, a step length of 3.0m was selected.
[0038] A step size of 3.0m is generally applicable to thin-layer identification in the range of 0.2 to 0.4m.
[0039] Example 3 Based on Example 2, The relative amplitude curve is calculated as follows: ; The calculation method for the multiple curve is as follows: .
[0040] Example 4 Based on Example 3, in step 3, the characteristics of the relative amplitude of the natural potential are obtained according to the correspondence between the thickness of the thin layer, i.e., the thickness of the sand body, and the relative amplitude of the natural potential, and the limit value of the relative amplitude parameter of the natural potential is determined.
[0041] like Figure 4 As shown, for thin layers of 0.2m to 0.4m, the relative amplitude of spontaneous potential is concentrated below 5%; for thin layers above 0.4m, the relative amplitude of spontaneous potential is generally 5% or above. Step 4 specifically involves: plotting the corresponding point graphs of the relative amplitude of the natural potential and the resistivity multiple of the rinsing zone under different thin layer thicknesses; and determining the limit value of the resistivity multiple parameter of the rinsing zone based on the limit value of the relative amplitude of the natural potential parameter determined in Step 3, as well as the thin layers with low potential value in actual production.
[0042] like Figure 5As shown, considering the guiding role of spontaneous potential in reservoir evaluation due to its permeability characteristics, and the relatively low potential value of thin layers (0.2m~0.4m) in production practice, the multiples of the resistivity of the flushing zone to the dynamic baseline value for thin layers above 0.4m, i.e., the multiples of the resistivity of the flushing zone, are concentrated above 1.25. Example 5 Based on Example 4, step 5 specifically includes: Take the logging map of at least one core well in the jurisdiction, and combine it with the location and thickness of thin layer development in the core description to determine the universality of the natural potential relative amplitude parameter and the flushing zone resistivity multiple parameter calculated in steps 3 to 4 in the jurisdiction.
[0043] Example 6 Based on Example 5, step 6 specifically includes: For the well logging maps of oil and gas reservoirs within the jurisdiction, based on the relative amplitude parameter of spontaneous potential and the resistivity multiple parameter of flushing zone determined in step 5, thin layer identification is performed at the corresponding well depth: when the relative amplitude of spontaneous potential is greater than the relative amplitude parameter of spontaneous potential and the resistivity multiple parameter of flushing zone is greater than the resistivity multiple parameter of flushing zone, the starting point of the depth is the upper boundary of the thin layer, and the ending point of the depth is the lower boundary of the thin layer; the thickness of the thin layer is the difference between the lower boundary and the upper boundary of the thin layer.
[0044] This invention presents a method for identifying thin layers in oil and gas reservoirs based on dynamic mean amplitude difference ratios. It addresses issues such as low logging response amplitude in thin layers, significant influence from surrounding rock leading to difficulties in manual stratification, and inconsistent stratification standards. Through comparison with high-resolution logging and core wells, this method achieves an identification rate of over 90% for thin sand bodies longer than 0.4m; it also shows good identification results for thin sand bodies between 0.2 and 0.4m, with an identification rate of approximately 80%. This method allows for rapid computer-based detection and surveying of large amounts of old well data, enabling thin layer delineation, reducing stratification loss of thin sand bodies, and significantly improving thin layer identification efficiency. It serves as a crucial support for tapping potential and increasing reserves.
[0045] Example 7 The Upper Tertiary strata in the Gaskule oilfield consist of long-section layered sandstone formations, with an oil-bearing section approximately 1300m thick, divided into 19 oil groups and 187 sub-layers. Comparative analysis of four cored wells revealed significant leakage in thin sand bodies (0.2m-0.8m thick), accounting for 12.2% of the total sand body thickness in the cored section. The lithology of the thin sand bodies to be supplemented is mainly composed of argillaceous siltstone, siltstone, and fine sandstone, accounting for 94.3%. The thickness of the thin layers showing oil traces or higher within these oil layers is 53.3%, indicating significant oil potential.
[0046] Based on the method of this invention, a step size of 3.0m is proposed for dynamic mean amplitude difference ratio detection in this area. This accelerates the computer processing speed of historical data of the target oilfield and improves the thin-layer identification effect. The automated computer processing solves the problems of large workload in historical data review and low efficiency of manual review. The identification capability for thin layers larger than 0.4m reaches over 90%. See the results of some thin-layer test production sections as follows. Figure 7 As shown, the relative amplitude parameter of the spontaneous potential is taken as 1.6, and the resistivity multiple parameter of the flushing zone is taken as 9. The computer identified and processed the historical drilling data of 477 wells, reclassified more than 12,000 thin sand bodies, and further interpreted the data to add an oil layer thickness of 3.8m in a single well.
[0047] Currently, test production and water injection work has been carried out on thin layers less than 1.0m thick. The test wells that have already been tested have produced approximately 2.0 cubic meters of oil per day in thin layers. For example, the Yue 214-4 well, which separately tested thin sand bodies in the V oil group where thin layers are concentrated, produced 1.97 cubic meters of oil and 1.21 cubic meters of water per day, with a water cut of 38%. The results of this new round of thin layer identification and classification provide strong technical support for tapping the potential of old oil reservoirs.
Claims
1. A method for identifying thin layers of oil and gas reservoirs based on dynamic mean amplitude difference ratio, characterized in that, Specifically comprising the following steps: Step 1: Calculate the spontaneous potential dynamic average curve and the flushed zone resistivity dynamic average curve according to the same step length respectively on the basis of the spontaneous potential curve and the flushed zone resistivity curve in the conventional well logging; Step 2: Calculate the relative amplitude curve in combination with the spontaneous potential curve and the spontaneous potential dynamic average curve and the maximum amplitude of the spontaneous potential; and calculate the multiple curve in combination with the flushed zone resistivity curve and the flushed zone resistivity dynamic average curve; Step 3: Select the thin layer to be measured in the jurisdiction, divide the thin layer by using the high-resolution well logging, determine the position and thickness of the thin layer, and compare the relative amplitude characteristics of the spontaneous potential on the basis of the results; Step 4: Further compare the flushed zone resistivity multiple characteristics on the basis of Step 3; Step 5: Compare and verify the value limits of the relative spontaneous potential relative amplitude parameter and the flushed zone resistivity multiple parameter according to the actual situation of the coring well in the jurisdiction; Step 6: Identify the position, thickness and upper and lower boundaries of the thin layer of the oil and gas reservoir in the jurisdiction according to the relative amplitude parameter of the spontaneous potential and the flushed zone resistivity multiple parameter.
2. The method for identifying oil and gas reservoir thin layer based on dynamic mean amplitude difference ratio according to claim 1, characterized in that, The step length in Step 1 is determined in combination with the thickness characteristics of the thin layer to be identified, and the determination method is specifically as follows: Select the thin layer thickness of 0.6 m; in combination with the actual stratum distribution, select the surrounding rock resistivity of 1.0 ohm meter, the thin layer resistivity of 6.0 ohm meter, and the longitudinal resolution of the well logging instrument of 30 cm; In combination with the above parameters, the step length of 3.0 m is selected by calculating the theoretical model and comparing with the actual well logging response under the principle that the contribution rate of the surrounding rock is not less than 80% and the shorter the step length, the better the interlayer interference effect.
3. The method for identifying thin layers of oil and gas reservoirs based on dynamic mean amplitude difference ratio according to claim 1, characterized in that, The calculation method of the relative amplitude curve in Step 2 is as follows: 。 4. The method for identifying oil and gas reservoir thin layer based on dynamic mean amplitude difference ratio according to claim 1 or 3, characterized in that, The calculation method of the multiple curve in Step 2 is as follows: 。 5. The method for identifying thin layers of oil and gas reservoirs based on dynamic mean amplitude difference ratio according to claim 1, characterized in that, Step 3 is specifically as follows: According to the corresponding relationship between the thickness of the thin layer, i.e. the thickness of the sand body, and the relative amplitude of the spontaneous potential, the characteristics of the relative amplitude of the spontaneous potential are obtained, and the parameter limit value of the relative amplitude of the spontaneous potential is determined.
6. The method for identifying thin layers of oil and gas reservoirs based on dynamic mean amplitude difference ratio according to claim 1, characterized in that, Step 4 is specifically as follows: Draw the corresponding point graph of the relative amplitude of the spontaneous potential and the flushed zone resistivity multiple under different thin layer thicknesses, determine the parameter limit value of the flushed zone resistivity multiple according to the parameter limit value of the relative amplitude of the spontaneous potential determined in Step 3, and the thin layer with low potential value in actual production.
7. The method for identifying thin layers of oil and gas reservoirs based on dynamic mean amplitude difference ratio according to claim 1, characterized in that, Step 5 is specifically as follows: Determine the universality of the relative amplitude parameter of the spontaneous potential and the flushed zone resistivity multiple parameter calculated in Steps 3-4 in the jurisdiction by taking the well logging graph of at least one coring well in the jurisdiction and combining the thin layer development position and thickness in the core description.
8. The method for identifying thin layers of oil and gas reservoirs based on dynamic mean amplitude difference ratio according to claim 1, characterized in that, Step 6 is specifically as follows: According to the relative amplitude parameter of the spontaneous potential and the flushed zone resistivity multiple parameter determined in Step 5, identify the thin layer at the corresponding well depth on the well logging graph of the oil and gas reservoir in the jurisdiction: when the relative amplitude of the spontaneous potential is greater than the relative amplitude parameter of the spontaneous potential and the flushed zone resistivity multiple is greater than the flushed zone resistivity multiple parameter, the depth starting point is the upper boundary of the thin layer, and the depth ending point is the lower boundary of the thin layer; the thickness of the thin layer is the difference between the lower boundary of the thin layer and the upper boundary of the thin layer.