A method for predicting permeability sand bodies in ultra-low permeability reservoirs

Abstract

The present application belongs to the field of oil and gas reservoir evaluation, and discloses a method for predicting permeability sand body of ultra-low permeability reservoir, so as to make up for the problem of lacking prediction of permeability sand body of ultra-low permeability reservoir. The method first carries out deep homing on the lithology parameters of the research well, and then establishes a litho-electric relationship model. The natural gamma ray and the acoustic time difference reflecting the lithology and the physical property are used to reconstruct the acoustic time difference curve through the natural gamma ray, and then the reconstructed acoustic time difference curve is fitted with the core analysis porosity and permeability to establish a fitting model. The single well permeability is calculated through the fitting model, and the corresponding permeability sand body thickness is extracted, so as to achieve the prediction of the permeability sand body under the macroscopic guidance, and lay a foundation for the next development design.

Description

Technical Field

[0001] This invention relates to the field of oil and gas reservoir evaluation, and in particular to a method for predicting permeable sand bodies in ultra-low permeability reservoirs. Background Technology

[0002] With the development of oil and gas exploration and development technologies, oil and gas development has shifted from conventional reservoirs to unconventional reservoirs that are low-permeability, tight, and difficult to develop. Currently, the classification standards for low-permeability reservoirs in China include ultra-low permeability reservoirs, super-low permeability reservoirs, and tight reservoirs. Low-permeability reservoirs are characterized by poor physical properties, small pore-throat scale, and non-Darcy flow. With increased exploration levels, rising oil and gas demand, and over-exploitation of conventional (including generally low-permeability) oil and gas resources, super-low permeability oil and gas resources have also been included in the development scope. Super-low permeability reserves account for a significant proportion of remaining low-permeability reserves, and their development is of great importance to the sustainable and stable development of the petroleum industry.

[0003] Sand bodies, as an important component of reservoirs, provide storage space for oil and gas. In ultra-low permeability reservoirs, due to their low porosity and poor permeability, oil and gas tend to accumulate more easily within sand bodies. However, identifying permeable sand bodies in ultra-low permeability reservoirs is difficult during reservoir development, making quantitative analysis impossible and posing a significant obstacle to the efficient development of ultra-low permeability oilfields. Clarifying the distribution patterns of permeable sand bodies in ultra-low permeability reservoirs is crucial and urgent for oilfield development; therefore, conducting permeable sand body prediction in ultra-low permeability reservoirs has important theoretical and practical significance.

[0004] Patent CN117197551A discloses a method, apparatus, and electronic equipment for characterizing sand body distribution. The method includes: identifying channel sedimentary facies in the study area and finding optimal logging curves; acquiring production data corresponding to all cored wells and classifying the identified channel sedimentary facies using the production data; constructing a classification model, training the constructed classification model, and classifying all wells using the trained classification model; and characterizing sand body distribution using a lithofacies model-controlled constraint method based on the well classification results. However, this technology does not disclose how to characterize and distribute permeable sand bodies in ultra-low permeability reservoirs.

[0005] Patent CN116299717A discloses a three-dimensional characterization method for deltaic distributary channel sand bodies. The method includes: performing frequency extension processing on raw seismic data to improve its vertical resolution; constructing a three-dimensional stratigraphic slice based on the preprocessed seismic data under the constraint of manually interpreted seismic horizons; selecting seismic attributes that can indicate distributary channel sand bodies through well-seismic calibration analysis; and conducting evolutionary analysis and three-dimensional sand body characterization based on the established three-dimensional stratigraphic slice and the selected seismic attributes. This method comprehensively utilizes geological and geophysical methods to intuitively and effectively characterize the evolutionary features and three-dimensional spatial distribution features of deltaic distributary channel sand bodies. However, this method cannot provide guidance for the distribution of permeable sand bodies in ultra-low permeability reservoirs.

[0006] Therefore, how to realize the distribution and prediction of permeable sand bodies in ultra-low permeability reservoirs has become an urgent problem to be solved in this field. Summary of the Invention

[0007] To address the above problems, this invention provides a method for predicting permeable sand bodies in ultra-low permeability reservoirs.

[0008] According to one aspect of the present invention, a method for predicting permeable sand bodies in ultra-low permeability reservoirs is provided, the method comprising the following steps: Wells with core analysis and electrical logging data within the study area were selected as study wells. Lithological and electrical parameters of the study wells were collected. The lithological parameters included core analysis permeability and core analysis porosity, and the electrical parameters included measured sonic transit time and natural gamma value. The lithological parameters and electrical parameters of the study well are compared and analyzed to perform depth-based correction of the lithological parameters; The natural gamma values ​​of pure mudstone, pure sandstone, and the target layer are determined based on the corrected lithological parameters and the measured natural gamma values. The mud content of the target layer is determined based on the natural gamma value of the pure mudstone, the natural gamma value of the pure sandstone, and the natural gamma value of the target layer. The acoustic transit time value of the study well is corrected based on the clay content of the target layer to obtain the corrected acoustic transit time. A first model representing the relationship between the core analysis porosity and the corrected sonic transit time of the study well, and a second model representing the relationship between the core analysis porosity and the core analysis permeability are determined; The porosity and permeability of other wells were determined based on the first model, the second model, and the corrected sonic transit time of other wells in the study area. Based on the permeability values ​​of each well in the study area, planar characterization of permeability values ​​and planar quantitative characterization of permeable sand bodies are achieved.

[0009] According to one embodiment of the present invention, the method further includes collecting lithological and / or electrical parameters of each well in the study area and establishing an initial database.

[0010] According to one embodiment of the present invention, the depth repositioning correction adopts the repositioning diagram method and the correlation fitting comparison method. After the depth repositioning correction is completed, a rock-electric relationship database of the study well is established based on the corrected data.

[0011] According to one embodiment of the present invention, determining the clay content of the target layer based on the natural gamma value of the pure mudstone, the natural gamma value of the pure sandstone, and the natural gamma value of the target layer includes: The natural gamma value of the target layer is determined based on the natural gamma value of the pure mudstone, the natural gamma value of the pure sandstone, and the natural gamma value of the target layer. Then, the mud content of the target layer is determined based on the natural gamma relative value.

[0012] According to one embodiment of the present invention, the relative natural gamma value of the target layer is calculated using the following formula: , Where ΔGR is the relative natural gamma value of the target layer, in %; GR is the natural gamma value of the target layer, in API; GR max Natural gamma values ​​for pure mudstone, expressed in API; GR min The natural gamma value of pure sandstone is expressed in API. The clay content of the target layer is calculated using the following formula: , Among them, V sh % represents the clay content of the target layer; ΔGR represents the relative natural gamma of the target layer; GCUR represents the Hilchie index, which has no unit.

[0013] According to one embodiment of the present invention, correcting the acoustic transit time value of the study well based on the clay content of the target layer includes determining the corrected acoustic transit time value using the clay content, the acoustic transit time of pure mudstone, the acoustic transit time of sandstone skeleton, and the measured acoustic transit time value.

[0014] According to an embodiment of the present invention, the corrected acoustic time difference value is determined using the following formula: , Among them, V sh The clay content of the target layer is expressed in %; Δt eΔt is the corrected acoustic transit time value, in μs / m; Δt is the measured acoustic transit time value, in μs / m. sh The sonic transit time value for pure mudstone is expressed in μs / m; Δt ma The time difference of acoustic waves in the sandstone skeleton is expressed in μs / m.

[0015] According to one embodiment of the present invention, the method further includes: When determining the first model, a first chart is created showing the core analysis porosity and the corrected acoustic transit time. The first model is then determined by fitting the first chart.

[0016] According to one embodiment of the present invention, the method further includes: When determining the second model, a second chart of the core analysis permeability and the core analysis porosity is established, and the second model is determined by fitting the second chart.

[0017] According to one embodiment of the present invention, the corrected sonic transit time of other wells in the study area is determined in the following manner: Obtain the sonic transit time curve and natural gamma curve for each of the other wells; The natural gamma value of the target layer in each well is determined based on the natural gamma curve. The natural gamma value of the target layer is determined based on the natural gamma value of the pure mudstone, the natural gamma value of the pure sandstone, and the natural gamma value of the target layer. Then, the mud content of the target layer is determined based on the natural gamma relative value. The corrected sonic transit time for each well is determined based on the mud content of the target layer, the sonic transit time of pure mudstone, the sonic transit time of sandstone skeleton, and the measured sonic transit time values.

[0018] According to one embodiment of the present invention, the method further includes: Before performing the permeability value plane characterization, the sand body distribution characterization is carried out first. Based on the thickness value of each sand body layer, the sand body plane distribution map of each small layer is characterized, and the boundary of each small layer of sandstone in the sand body plane distribution map is used as the boundary of the permeability value plane characterization.

[0019] According to one embodiment of the present invention, the planar quantitative characterization of the permeable sand body includes: The planar quantitative characterization of the permeable sand body is achieved by selecting the lower limit of permeability value of the study area as a benchmark.

[0020] By employing the above technical solutions, the permeability sand body prediction method for ultra-low permeability reservoirs provided by this invention can extract permeability characterization of ultra-low permeability reservoirs under multi-parameter constraints. Through comparative verification, it can accurately restore the permeability of ultra-low permeability reservoirs, showing good agreement with core analysis permeability and meeting the accuracy requirements of the method. Combined with permeability sand body numerical extraction and planar characterization, it can predict the distribution of permeable sand bodies in ultra-low permeability reservoirs with high accuracy. Attached Figure Description

[0021] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings: Figure 1 A flowchart of a method for predicting permeable sand bodies in ultra-low permeability reservoirs according to an embodiment of the present invention is shown; Figure 2 A comparison diagram of core porosity and acoustic transit time curve depth repositioning according to an embodiment of the present invention is shown; Figure 3 A schematic diagram for fitting a first model according to an embodiment of the present invention is shown; Figure 4 A schematic diagram for fitting a second model according to an embodiment of the present invention is shown. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0023] The terms "comprising" and "having," and any variations thereof, used in the specification and accompanying drawings of this invention are intended to cover non-exclusive inclusion; the terms "first," "second," etc., used in the specification, claims, or accompanying drawings of this invention are used to distinguish different objects, not to describe a particular order. "A plurality of" means two or more, unless otherwise explicitly specified.

[0024] Furthermore, the reference to "embodiment" herein means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0025] One objective of this invention is to provide a method for predicting the permeability of sand bodies in ultra-low permeability reservoirs. For example... Figure 1 As shown, the method generally includes: Step S1: Select wells with core analysis and electrical logging data in the study area as study wells, and collect lithological parameters and electrical parameters of the study wells. The lithological parameters include core analysis permeability and core analysis porosity, and the electrical parameters include measured sonic transit time and natural gamma value. Step S2: Compare and analyze the lithological and electrical parameters of the study well to perform depth-based correction of the lithological parameters; Step S3: Determine the natural gamma value of pure mudstone, pure sandstone, and the target layer based on the corrected lithological parameters and the measured natural gamma value; Step S4: Determine the clay content of the target layer based on the natural gamma value of pure mudstone, the natural gamma value of pure sandstone, and the natural gamma value of the target layer; Step S5: Correct the sonic transit time value of the study well based on the clay content of the target layer to obtain the corrected sonic transit time; Step S6: Determine the first model representing the relationship between core analysis porosity and corrected sonic transit time of the study well, and the second model representing the relationship between core analysis porosity and core analysis permeability; Step S7: Determine the porosity and permeability of other wells based on the first model, the second model, and the corrected sonic transit time of other wells in the study area; Step S8: Based on the permeability values ​​of each well in the study area, realize the planar characterization of permeability values ​​and the planar quantitative characterization of permeable sand bodies.

[0026] The method for predicting permeable sand bodies in ultra-low permeability reservoirs provided by this invention can extract permeability characterization of ultra-low permeability reservoirs under multi-parameter constraints. Through comparative verification, it can accurately restore the permeability of ultra-low permeability reservoirs, showing good agreement with the permeability analyzed from core samples, thus meeting the accuracy requirements of the method. Combined with numerical extraction and planar characterization of permeable sand bodies, it can predict the distribution of permeable sand bodies in ultra-low permeability reservoirs with high accuracy.

[0027] The following provides a detailed explanation of each step of the above method using examples.

[0028] In step S1, wells with core analysis and electrical logging data within the study area are selected as study wells, and lithological and electrical parameters of the study wells are collected. The lithological parameters include core analysis permeability and core analysis porosity, while the electrical parameters include measured acoustic transit time and natural gamma value.

[0029] For example, core analysis data from cored wells can be collected to obtain lithological parameters, and logging data can be collected to obtain electrical parameters. Based on the collected lithological and electrical parameters of the study well, an initial database can be established that corresponds in depth to the acoustic curves (AC), natural gamma curve (GR), true resistance curve (RT), spontaneous potential curve (SP), and other electrical logging curves, as well as the core analysis data for porosity (POR) and permeability (PERM). Table 1 below shows the initial database corresponding to a certain study well.

[0030] Table 1. Corresponding data of core analysis porosity and permeability sampling depth and electrical conductivity curves.

[0031] The collection of a large amount of data is beneficial for discovering the correspondence between electrical measurement curves such as acoustic curve AC, natural gamma curve GR, true resistance curve RT, and spontaneous potential curve SP, and core analysis of porosity POR and permeability PERM.

[0032] In step S2, the lithological and electrical parameters of the study well are compared and analyzed to perform depth correction of the lithological parameters and improve the consistency between the core porosity and the logging data.

[0033] Due to various factors affecting the core sampling process, discrepancies may exist between the logging depth and the core sampling depth. Therefore, to accurately interpret the logging data, depth realignment of the core data is necessary. Depth realignment correction ensures consistency in depth between the core analysis data and the logging data, thereby establishing an accurate rock-electrical relationship.

[0034] In some embodiments, depth repositioning correction can be performed, for example, using the repositioning chart method and / or the correlation fitting comparison method. The repositioning chart method typically plots core analysis data (such as porosity and permeability) as a depth-related bar chart and compares it with well logging curves (such as sonic transit time logging curves, natural gamma curves, etc.). By comparing, the difference between the logging depth and the core depth can be found, thus determining the value requiring repositioning correction. The repositioning chart method can visually display the depth relationship between the core and logging data, helping to improve the accuracy of well logging interpretation. The correlation fitting comparison method, by giving a certain window length and step size, calculates the correlation function values ​​at various locations during the movement of the core segment. The location of the maximum value of the correlation function can be found; this location is the best position for comparison between the core and the logging curve. The difference between their depths is the depth movement amount to be determined, thus performing depth repositioning. This method can quantify the correlation between the core and logging data, further improving the accuracy of well logging interpretation.

[0035] Figure 2A comparison diagram of core porosity and sonic transit time curve depth repositioning according to an embodiment of the present invention is shown. The diagram shows that the core analysis porosity and permeability shifted upwards in depth relative to the sonic transit time curve AC during the repositioning process. After completing the depth repositioning correction, a rock-electrical relationship database for the study well was established based on the corrected data to prepare for the subsequent establishment of the first and second models.

[0036] In step S3, the natural gamma value (GR) of pure mudstone is determined based on the corrected lithological parameters and the measured natural gamma value. max Natural gamma value (GR) of pure sandstone min And the natural gamma value (GR) of the target layer.

[0037] Since the natural gamma values ​​of pure sandstone and pure mudstone are typically extreme values ​​within their respective ranges, these extreme values ​​can be determined by analyzing the natural gamma values ​​and natural gamma curves obtained from well logging. Mudstone generally contains more radioactive material; therefore, pure mudstone typically has a higher natural gamma value, while pure sandstone usually has a lower natural gamma value. Further lithological analysis of core samples taken from the study well at the depth corresponding to the extreme values ​​(depth after depth correction) can help verify whether the determined natural gamma values ​​for pure mudstone and pure sandstone are appropriate.

[0038] The natural gamma value of the target layer can be determined based on the depth of the target layer and the natural gamma curve obtained from well logging.

[0039] Next, in step S4, based on the natural gamma value GR of pure mudstone... max Natural gamma value (GR) of pure sandstone min The natural gamma value (GR) of the target layer determines the clay content (V) of the target layer. sh .

[0040] Determining the clay content V of the target layer sh In this case, it is necessary to first determine the natural gamma relative value ΔGR of the target layer. Specifically, the natural gamma relative value ΔGR of the target layer can be calculated using the following formula:

[0041] Where ΔGR is the relative natural gamma value of the target layer, in %; GR is the natural gamma value of the target layer, in API; GR max Natural gamma values ​​for pure mudstone, expressed in API; GR min The value represents the natural gamma value of pure sandstone, expressed in API.

[0042] This method fully considers the influence of mudstone. The natural gamma value of pure sandstone is the minimum, and the natural gamma value of pure mudstone is the maximum. The difference between the natural gamma values ​​of pure mudstone and pure sandstone is the maximum difference in gamma values ​​and can be used as a baseline. By comparing the difference between the natural gamma values ​​of the target layer and the natural gamma values ​​of pure sandstone with this baseline, the relative value of the natural gamma value of the mudstone in the target layer can be calculated.

[0043] Then, the clay content V of the target layer is calculated using the following formula. sh : , Among them, V sh % represents the clay content of the target layer; ΔGR represents the relative natural gamma of the target layer; GCUR represents the Hilchie index, which has no unit.

[0044] In step S5, based on the clay content V of the target layer sh The sonic transit time value of the study well was corrected to obtain the corrected sonic transit time. The calculated clay content V was then used... sh Pure mudstone acoustic transit time Δt sh Sandstone skeleton acoustic transit time Δt ma And determine the corrected acoustic time difference value Δt based on the measured acoustic time difference value. e Specifically, the corrected acoustic time difference value can be determined using the following formula: , Among them, V sh The content of clay is expressed as %; Δt e Δt is the corrected acoustic transit time value, in μs / m; Δt is the measured acoustic transit time value, in μs / m. sh The sonic transit time value for pure mudstone is expressed in μs / m; Δt ma The sonic transit time (Δt) is the value for the sandstone skeleton, expressed in μs / m. The sonic transit time Δt for pure mudstone can be determined by analyzing the sonic transit time curve and related well logging data. sh Acoustic transit time Δt between sandstone skeleton and sandstone skeleton ma .

[0045] This step requires considering the acoustic transit time of pure mudstone and pure sandstone. Based on this, the influence of mud is removed, and the true acoustic transit time of the entire well is calculated, thereby drawing the acoustic transit time curve of the entire well.

[0046] In step S6, the core analysis porosity φ and the corrected acoustic transit time Δt of the well are determined. e The first model represents the relationship between core analysis porosity φ and core analysis permeability K, and the second model represents the relationship between core analysis porosity φ and core analysis permeability K.

[0047] For example, the core porosity value can be analyzed based on the rock electrical relationship database established after depth repositioning correction in step S2, and the corrected acoustic transit time Δt determined in step S5. e Data was used to create a chart, and the acoustic transit time Δt was added to correct for mudstone. e The trend line between the core analysis porosity φ and the trend line is fitted to obtain the formula, i.e., the first model. Figure 3 A schematic diagram for fitting a first model according to an embodiment of the present invention is shown. The fitting results, as shown in the diagram, indicate that: φ=0.0937Δt e -9.857, where φ is the core analysis porosity in percentage (%), and Δt e The acoustic time difference is corrected for mudstone.

[0048] R 2 =0.8163, where R is the correlation coefficient.

[0049] Furthermore, a chart can be created based on the core analysis porosity value φ and core analysis permeability value K in the rock-electric relationship database established after the depth repositioning correction in step S2. A trend line can be added, and the relationship formula between core analysis permeability K and core analysis porosity φ can be fitted, which is the second model. Figure 4 A schematic diagram for fitting a second model according to an embodiment of the present invention is shown. The fitting results, as shown in the diagram, indicate that: K = 0.0038e 0.4021φ Where K is the permeability value of the core analysis, in μm²; and φ is the porosity of the core analysis.

[0050] R 2 =0.8463, where R is the correlation coefficient.

[0051] Determining the two relationships mentioned above can guide the connection between acoustic transit time (AC), porosity (POR), and permeability (PERM). Using the connection between AC, POR, and PERM, permeability values ​​from other uncored wells can be obtained.

[0052] In step S7, the porosity and permeability of other wells are determined based on the first model, the second model, and the corrected sonic transit time of other wells in the study area.

[0053] Other wells within the study area refer to wells within the study area other than the study well (especially those without core analysis). To calculate the permeability of other wells within the study area, mudstone correction needs to be performed on their sonic transit times. This correction step is similar to steps S2 to S5 above. When performing mudstone correction on sonic transit times for other wells without core analysis, the clay content of the target layer needs to be determined first. Similar to the study well, the natural gamma value of the target layer can be determined based on the natural gamma curve of the selected well. Since the natural gamma values ​​of pure mudstone and pure sandstone in the same block are similar, the natural gamma values ​​of pure mudstone and pure sandstone determined for the study well can be used directly. The relative natural gamma value of the target layer is calculated based on these parameters. After obtaining the relative natural gamma value of the target layer, the clay content of the target layer is calculated, and subsequent sonic transit time correction is performed.

[0054] Once the corrected sonic transit times of other wells are determined, the porosity of the well can be determined using the first model based on the corrected sonic transit times, and then the permeability of the well can be determined using the second model based on the porosity. The permeability of each well in the study area can be determined by following step S7.

[0055] In step S8, permeability values ​​are characterized in a plane and permeable sand bodies are quantitatively characterized in a plane based on the permeability values ​​of each well in the study area.

[0056] In some embodiments, before performing planar characterization of permeability values, a control sand body distribution characterization is performed. The planar distribution map of each sub-layer of sand body is characterized based on the thickness values ​​of each sub-layer, and the boundaries of each sub-layer of sandstone in the planar distribution map are used as the boundaries for the planar characterization of permeability values. For example, the planar distribution map of each sub-layer of sand body is characterized based on the thickness values ​​of each sub-layer. This sand body distribution map serves as the boundary for subsequent physical property parameter characterization and permeability sand body characterization. In subsequent planar characterization, neither physical property nor permeability sand bodies can exceed this boundary. The boundaries of each sub-layer of sandstone in the planar distribution map serve as the boundaries for subsequent permeability value and permeability sand body characterization. The permeability values ​​determined in step S7 are applied only within the area defined by the boundaries. Using the control lines of each sub-layer of sandstone boundary, planar characterization of permeability values ​​is achieved, and the quantitative display of permeability values ​​is realized.

[0057] Next, a quantitative planar characterization of the permeable sand body can be performed. Based on the different permeability measurements in different regions, a lower limit value K for permeability to be selected for the study area is given. o Using this lower limit value K o The planar characterization of permeability values ​​is selected as a benchmark, with permeability higher than K. oThe sand bodies can be considered as permeable sand bodies. The distribution of the sand bodies and the permeability value are mutually controlled to achieve a quantitative planar characterization of permeable sand bodies, laying the foundation for the prediction and research of reservoir productivity of sand bodies with different permeability.

[0058] This method first performs depth localization and then establishes a rock-electrical relationship model. Utilizing natural gamma rays and sonic transit time, which reflect lithology and physical properties, the sonic transit time curve is reconstructed using natural gamma rays. A fitting model is then established with core analysis of porosity and permeability. This fitting model is used to calculate the permeability of a single well and extract the corresponding thickness of the permeable sand body. Under macroscopic guidance, this method achieves permeability sand body prediction, laying the foundation for the next stage of development design.

[0059] The embodiments described above are merely illustrative of implementation methods of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A method for predicting permeability sand bodies in ultra-low permeability reservoirs, characterized in that, Includes the following steps: Wells with core analysis and electrical logging data within the study area were selected as study wells. Lithological and electrical parameters of the study wells were collected. The lithological parameters included core analysis permeability and core analysis porosity, and the electrical parameters included measured sonic transit time and natural gamma value. The lithological parameters and electrical parameters of the study well are compared and analyzed to perform depth-based correction of the lithological parameters; The natural gamma values ​​of pure mudstone, pure sandstone, and the target layer are determined based on the corrected lithological parameters and the measured natural gamma values. The mud content of the target layer is determined based on the natural gamma value of the pure mudstone, the natural gamma value of the pure sandstone, and the natural gamma value of the target layer. The acoustic transit time value of the study well is corrected based on the clay content of the target layer to obtain the corrected acoustic transit time. A first model representing the relationship between the core analysis porosity and the corrected sonic transit time of the study well, and a second model representing the relationship between the core analysis porosity and the core analysis permeability are determined; The porosity and permeability of other wells were determined based on the first model, the second model, and the corrected sonic transit time of other wells in the study area. Based on the permeability values ​​of each well in the study area, planar characterization of permeability values ​​and planar quantitative characterization of permeable sand bodies are achieved.

2. The method for predicting permeability sand bodies in ultra-low permeability reservoirs according to claim 1, characterized in that, The method also includes collecting lithological and / or electrical parameters of each well in the study area and establishing an initial database.

3. The method for predicting permeability sand bodies in ultra-low permeability reservoirs according to claim 1, characterized in that, The depth repositioning correction adopts the repositioning diagram method and the correlation fitting comparison method. After the depth repositioning correction is completed, a rock-electric relationship database of the study well is established based on the corrected data.

4. The method for predicting permeability sand bodies in ultra-low permeability reservoirs according to claim 1, characterized in that, Determining the clay content of the target layer based on the natural gamma value of the pure mudstone, the natural gamma value of the pure sandstone, and the natural gamma value of the target layer includes: The natural gamma value of the target layer is determined based on the natural gamma value of the pure mudstone, the natural gamma value of the pure sandstone, and the natural gamma value of the target layer. Then, the mud content of the target layer is determined based on the natural gamma relative value.

5. The method for predicting permeability sand bodies in ultra-low permeability reservoirs according to claim 4, characterized in that, The relative natural gamma value of the target layer is calculated using the following formula: ， Where ΔGR is the relative natural gamma value of the target layer, in %; GR is the natural gamma value of the target layer, in API; GR max Natural gamma values ​​for pure mudstone, expressed in API; GR min The natural gamma value of pure sandstone is expressed in API. The clay content of the target layer is calculated using the following formula: ， Among them, V sh % represents the clay content of the target layer; ΔGR represents the relative natural gamma of the target layer; GCUR represents the Hilchie index, which has no unit.

6. The method for predicting permeability sand bodies in ultra-low permeability reservoirs according to claim 1, characterized in that, Correcting the acoustic transit time value of the study well based on the clay content of the target layer includes determining the corrected acoustic transit time value using the clay content, the acoustic transit time of pure mudstone, the acoustic transit time of sandstone skeleton, and the measured acoustic transit time value.

7. The method for predicting permeability sand bodies in ultra-low permeability reservoirs according to claim 6, characterized in that, The corrected acoustic time difference value is determined using the following formula: ， Among them, V sh The clay content of the target layer is expressed in %; Δt e Δt is the corrected acoustic transit time value, in μs / m; Δt is the measured acoustic transit time value, in μs / m. sh The sonic transit time value for pure mudstone is expressed in μs / m; Δt ma The time difference of acoustic waves in the sandstone skeleton is expressed in μs / m.

8. The method for predicting permeability sand bodies in ultra-low permeability reservoirs according to claim 1, characterized in that, The method further includes: When determining the first model, a first chart is created showing the core analysis porosity and the corrected acoustic transit time. The first model is then determined by fitting the first chart.

9. The method for predicting permeability sand bodies in ultra-low permeability reservoirs according to claim 1, characterized in that, The method further includes: When determining the second model, a second chart of the core analysis permeability and the core analysis porosity is established, and the second model is determined by fitting the second chart.

10. The method for predicting permeability sand bodies in ultra-low permeability reservoirs according to claim 1, characterized in that, The corrected sonic transit time of other wells in the study area was determined in the following manner: Obtain the sonic transit time curve and natural gamma curve for each of the other wells; The natural gamma value of the target layer in each well is determined based on the natural gamma curve. The natural gamma value of the target layer is determined based on the natural gamma value of the pure mudstone, the natural gamma value of the pure sandstone, and the natural gamma value of the target layer. Then, the mud content of the target layer is determined based on the natural gamma relative value. The corrected sonic transit time for each well is determined based on the mud content of the target layer, the sonic transit time of pure mudstone, the sonic transit time of sandstone skeleton, and the measured sonic transit time values.

11. The method for predicting permeability sand bodies in ultra-low permeability reservoirs according to claim 1, characterized in that, The method further includes: Before performing the permeability value plane characterization, the sand body distribution characterization is carried out first. Based on the thickness value of each sand body layer, the sand body plane distribution map of each small layer is characterized, and the boundary of each small layer of sandstone in the sand body plane distribution map is used as the boundary of the permeability value plane characterization.

12. The method for predicting permeability sand bodies in ultra-low permeability reservoirs according to claim 1, characterized in that, The planar quantitative characterization of the permeable sand body includes: The planar quantitative characterization of the permeable sand body is achieved by selecting the lower limit of permeability value of the study area as a benchmark.

Images