Oil-water property identification method for oil reservoir, electronic device, apparatus, and storage medium

Abstract

This invention discloses a method, electronic device, apparatus, and storage medium for identifying the oil-water properties of an oil reservoir. The identification method includes: Step 1: distinguishing between the reservoir and the surrounding rock; Step 2: determining the resistivity of the reservoir and the surrounding rock, and determining a resistivity discrimination coefficient based on the resistivity of the reservoir and the surrounding rock; Step 3: converting the sonic transit-time logging curve into a porosity curve; Step 4: calculating the oil-water identification curve based on the porosity curve and the resistivity discrimination coefficient; Step 5: determining the discrimination criteria for oil layers, oil-water co-layers, and water layers in the reservoir, and identifying the oil-water properties of the reservoir based on the value of the oil-water identification curve. This invention avoids the drawbacks of traditional oil-water identification methods based on resistivity and three-porosity logging, and can quickly and accurately identify low-resistivity oil layers, significantly improving the accuracy of logging technology in fluid identification in tight sandstone reservoirs.

Description

Technical Field

[0001] This invention relates to the field of petroleum exploration and development technology, and in particular to a method, electronic device, apparatus and storage medium for identifying the oil and water properties of an oil reservoir. Background Technology

[0002] Resistivity logging and three-porosity logging (sonic, neutron, and density) are commonly used logging methods in oil and gas reservoirs for identifying reservoir fluid properties. However, resistivity logging is easily affected by non-fluid factors such as lithology, wellbore conditions, and mud infiltration. This is especially true in tight sandstone reservoirs, where the factors influencing resistivity changes are complex, and special reservoir types such as high-resistivity water layers and low-resistivity oil layers are common. Using the conventional rule of "high resistivity in oil layers and low resistivity in water layers" in conventional reservoirs is insufficient to accurately identify reservoir fluid properties, with a success rate often below 50%. Neutron porosity logging curves exhibit a "dig-out effect" when the formation contains gas, and sonic porosity curves show a "cycle jump" phenomenon due to the influence of natural gas. However, these phenomena are not obvious in low-resistivity tight sandstone reservoirs, and sometimes the resistivity of the oil layer is even lower than that of the water layer. Directly using resistivity and the size and shape of the three-porosity curves to identify oil and water layers has a very low success rate, posing significant challenges to the effective development of tight sandstone reservoirs.

[0003] Therefore, for tight sandstone oil reservoirs with complex lithology, pore structure and low resistivity, how to effectively identify oil layers, water layers and oil-water co-layers is an urgent problem to be solved in this field. Summary of the Invention

[0004] The purpose of this invention is to provide a method, electronic device, apparatus, and storage medium for identifying the oil and water properties of an oil reservoir, which can effectively identify oil layers, water layers, and oil-water co-layers, thereby guiding the exploration and development of oil fields.

[0005] To achieve the above objectives, the present invention provides a method for identifying the oil-water properties of an oil reservoir, specifically for low-resistivity tight sandstone reservoirs. The method includes:

[0006] Step 1: Differentiate between the reservoir and the surrounding rock;

[0007] Step 2: Determine the resistivity of the reservoir and the resistivity of the surrounding rock, and determine the resistivity discrimination coefficient based on the resistivity of the reservoir and the resistivity of the surrounding rock;

[0008] Step 3: Convert the sonic transit time logging curve into a porosity curve;

[0009] Step 4: Calculate the oil-water identification curve based on the porosity curve and the resistivity discrimination coefficient. The formula for calculating the oil-water identification curve is as follows:

[0010]

[0011] in, The value of the oil-water identification curve. The resistivity discrimination coefficient is... Acoustic porosity;

[0012] Step 5: Determine the criteria for distinguishing between oil layers, oil-water co-layers, and water layers in the reservoir. Identify the oil and water properties of the reservoir based on the values ​​of the oil-water identification curves, including: in the layer segment where the value of the oil-water identification curve is greater than A and the porosity value is greater than C%, the reservoir fluid properties are identified as oil layers.

[0013] In the oil-water identification curve range where the value is B to A, B < A, and the porosity value is greater than C%, the reservoir fluid properties are identified as oil-water co-containment. In the oil-water identification curve range where the value is less than B, and the porosity value is greater than C%, the reservoir fluid properties are identified as water-bearing. In the range where the porosity value is less than C%, the reservoir is identified as dry.

[0014] According to a preferred embodiment of the present invention, the distinction between reservoir and surrounding rock includes: identifying sandstone and mudstone using natural gamma logging curves, determining sandstone with a thickness greater than a first set value as the reservoir, and determining mudstone with a thickness greater than a second set value at the top or bottom of the reservoir as the surrounding rock.

[0015] According to a preferred embodiment of the present invention, the method for determining the resistivity of the reservoir includes: based on existing resistivity logging data of the oilfield and the development of the reservoir and the surrounding rock, reading the average deep-induction resistivity of the sandstone as the resistivity of the reservoir; and / or,

[0016] The methods for determining the resistivity of the surrounding rock include:

[0017] Based on the existing resistivity logging data of the oilfield and the development of the reservoir and the surrounding rock, the average deep-induction resistivity of the mudstone is selected as the resistivity of the surrounding rock.

[0018] According to a specific embodiment of the present invention, the resistivity discrimination coefficient is determined using the following formula:

[0019]

[0020] in, The resistivity discrimination coefficient is... The resistivity of the reservoir is... Let be the resistivity of the surrounding rock, C be the regional coefficient, and be a constant.

[0021] According to a specific embodiment of the present invention, the acoustic transit time logging curve is converted into an acoustic porosity curve using the following formula.

[0022]

[0023] in, The acoustic porosity, The target layer is the sonic transit time logging value. This represents the acoustic transit time value of the stratigraphic rock framework. This represents the time difference of acoustic waves in the formation fluid.

[0024] Another disclosed embodiment of the present invention provides an electronic device, comprising:

[0025] At least one processor; and,

[0026] A memory communicatively connected to the at least one processor; wherein,

[0027] The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to perform the above-described method for identifying the oil-water properties of a reservoir.

[0028] In one disclosed embodiment, the present invention also provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the above-described method for identifying the oil-water properties of a reservoir.

[0029] In one disclosed embodiment, the present invention also provides an oil-water property identification device for an oil reservoir, comprising:

[0030] The differentiation module is used to differentiate between the reservoir and the surrounding rock.

[0031] A resistivity discrimination coefficient module is used to determine the resistivity of the reservoir and the resistivity of the surrounding rock, and to determine the resistivity discrimination coefficient by the resistivity of the reservoir and the resistivity of the bottom surrounding rock.

[0032] The conversion module is used to convert the sonic transit time logging curve into a porosity curve. The calculation formula for the oil-water identification curve is as follows:

[0033]

[0034] in, The value of the oil-water identification curve. The resistivity discrimination coefficient is... Acoustic porosity;

[0035] An oil-water identification module is used to determine the criteria for identifying oil layers, oil-water co-layers, and water layers in a reservoir. Based on the values ​​of the oil-water identification curves, the module identifies the oil-water properties of the reservoir, including: for layers where the oil-water identification curve value is greater than A and the porosity value is greater than C%, the reservoir fluid properties are identified as oil layers; for layers where the oil-water identification curve value is between B and A, where B < A, and the porosity value is greater than C%, the reservoir fluid properties are identified as oil-water co-layers; for layers where the oil-water identification curve value is less than B and the porosity value is greater than C%, the reservoir fluid properties are identified as water layers; and for layers where the porosity value is less than C%, the reservoir is identified as a dry layer.

[0036] The beneficial effects of this invention are as follows:

[0037] This invention provides a method for identifying oil and water layers in low-resistivity tight sandstone reservoirs. It creatively combines resistivity and porosity logging data, applying it to fluid identification in low-resistivity reservoirs. This avoids the drawbacks of traditional oil-water identification methods based on resistivity and three-porosity logging, enabling rapid and accurate identification of low-resistivity oil layers and significantly improving the accuracy of logging technology in fluid identification within tight sandstone reservoirs. This method has a wide range of practical applications in both low-resistivity oil and gas reservoirs, demonstrating strong operability and greatly enhancing its guiding role in oil and gas field exploration and development.

[0038] The present invention has other features and advantages, which will be apparent from or will be set forth in detail in the accompanying drawings and the following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description

[0039] The above and other objects, features and advantages of the present invention will become more apparent from the more detailed description of exemplary embodiments of the invention in conjunction with the accompanying drawings.

[0040] Figure 1 This is a flowchart of a method for identifying the oil-water properties of an oil reservoir according to an embodiment of the present invention.

[0041] Figure 2 This is a fluid identification map of the Honghe Oilfield in the southern Ordos Basin of China, according to an embodiment of the present invention.

[0042] Figure 3 This is an illustration of the oil layer identification effect of well HH373 in the Honghe Oilfield in the southern Ordos Basin of China, according to an embodiment of the present invention.

[0043] Figure 4 This is an illustration of the water layer identification effect of well HH71 in the Honghe Oilfield in the southern Ordos Basin of China, according to an embodiment of the present invention. Detailed Implementation

[0044] This invention innovates a scientific research method in the exploration and development of tight sandstone oil reservoirs by using the ratio of reservoir resistivity to that of adjacent surrounding rock and the product of resistivity and reservoir porosity. This method amplifies the logging response characteristics of low-resistivity sandstone oil layers and avoids the drawbacks of traditional oil-water identification methods based on resistivity and three-porosity logging. It can quickly and accurately identify oil layers, water layers, and oil-water co-layers in low-resistivity tight sandstone reservoirs, significantly improving the accuracy of logging technology in identifying fluids in tight sandstone reservoirs, thereby guiding oilfield exploration and development.

[0045] This invention addresses the difficulty in identifying oil-water layers in low-resistivity tight sandstone reservoirs by proposing a well logging method for identifying oil-water properties. Specifically, it uses the ratio of reservoir resistivity to the resistivity of adjacent surrounding rocks, multiplied by the reservoir porosity, to determine the fluid properties of low-resistivity reservoirs. This method amplifies the well logging signal of low-resistivity oil layers, enabling intuitive, rapid, and accurate identification of oil layers in low-resistivity tight sandstone reservoirs.

[0046] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings and examples, thereby enabling a full understanding of how the present invention uses technical means to solve technical problems and achieve technical effects, and allowing for implementation accordingly. It should be noted that, as long as there is no conflict, the various embodiments and features in each embodiment of the present invention can be combined with each other, and the resulting technical solutions are all within the protection scope of the present invention.

[0047] This embodiment discloses a method for identifying the oil-water properties of an oil reservoir, specifically for low-resistivity tight sandstone reservoirs, referring to... Figure 1 The identification method includes:

[0048] Step 1: Differentiate between the reservoir and the surrounding rock;

[0049] Step 2: Determine the resistivity of the reservoir and the resistivity of the surrounding rock, and determine the resistivity discrimination coefficient based on the resistivity of the reservoir and the resistivity of the surrounding rock;

[0050] Step 3: Convert the sonic transit time logging curve into a porosity curve;

[0051] Step 4: Calculate the oil-water identification curve based on the porosity curve and the resistivity discrimination coefficient;

[0052] Step 5: Determine the criteria for distinguishing between oil layers, oil-water co-layers, and water layers in the reservoir, and identify the oil-water properties of the reservoir based on the values ​​of the oil-water identification curves.

[0053] In this embodiment, distinguishing the reservoir and surrounding rock of an oil reservoir includes: using natural gamma logging curves to identify sandstone and mudstone, determining sandstone with a thickness greater than a first set value as the reservoir, and determining mudstone with a thickness greater than a second set value at the top or bottom of the reservoir as the surrounding rock.

[0054] In this embodiment, the method for determining the resistivity of the reservoir includes: based on existing resistivity logging data of the oilfield and the development of the reservoir and the surrounding rock, reading the average deep-induction resistivity of the sandstone as the resistivity of the reservoir; and / or,

[0055] The methods for determining the resistivity of the surrounding rock include:

[0056] Based on the existing resistivity logging data of the oilfield and the development of the reservoir and the surrounding rock, the average deep induction resistivity of the mudstone is selected as the resistivity of the surrounding rock.

[0057] In this embodiment, the resistivity discrimination coefficient is determined using the following formula.

[0058]

[0059] in, The resistivity discrimination coefficient is... The resistivity of the reservoir is... Let be the resistivity of the surrounding rock, C be the regional coefficient, and be a constant.

[0060] In this embodiment, the acoustic transit time logging curve is converted into the acoustic porosity curve using the following formula.

[0061]

[0062] in, The acoustic porosity, The target layer is the sonic transit time logging value. This represents the acoustic transit time value of the stratigraphic rock framework. This represents the time difference of acoustic waves in the formation fluid.

[0063] In this embodiment, the calculation formula for the oil-water identification curve is:

[0064]

[0065] in, The value of the oil-water identification curve. The resistivity discrimination coefficient is... The acoustic porosity is described above.

[0066] In this embodiment, identifying the oil-water properties of the reservoir based on the value of the oil-water identification curve includes:

[0067] In the layer segment where the value of the oil-water identification curve is greater than A and the porosity value is greater than C%, the reservoir fluid properties are identified as an oil layer.

[0068] The reservoir fluid properties are determined to be oil and water co-layers when the values ​​of the oil-water identification curves are from B to A, B < A, and the porosity value is greater than C%.

[0069] In the layer segment where the value of the oil-water identification curve is less than B and the porosity value is greater than C%, the reservoir fluid properties are identified as water layer.

[0070] In the layer section where the porosity value is less than C%, the reservoir is identified as a dry layer.

[0071] This invention provides a method for identifying oil and water layers in low-resistivity tight sandstone reservoirs. It creatively combines resistivity and porosity logging data, applying it to fluid identification in low-resistivity reservoirs. This avoids the drawbacks of traditional oil-water identification methods based on resistivity and three-porosity logging, enabling rapid and accurate identification of low-resistivity oil layers and significantly improving the accuracy of logging technology in fluid identification within tight sandstone reservoirs. This method has a wide range of practical applications in both low-resistivity oil and gas reservoirs, demonstrating strong operability and greatly enhancing its guiding role in oil and gas field exploration and development.

[0072] The invention will be further described below using the Honghe Oilfield in the southern Ordos Basin of China as an example.

[0073] Example 1

[0074] 1. Use natural gamma logging curves to identify sandstone and mudstone, and classify reservoirs and surrounding rocks.

[0075] Mudstone, due to the presence of radioactive minerals, exhibits high gamma logging response characteristics, while sandstone, lacking radioactive minerals, exhibits relatively low gamma logging response characteristics. Based on core observations and thin section identification data, natural gamma curves are used to distinguish between sandstone and mudstone. Taking the Chang 8 formation of the Yanchang Formation in the Honghe Oilfield as an example, a natural gamma curve value less than 50 API indicates sandstone, while a value greater than 50 API indicates mudstone. Based on this lithological classification, sandstone thicker than 2 meters is identified as reservoir, and mudstone thicker than 2 meters at the top or bottom of the reservoir is identified as surrounding rock.

[0076] 2. Read the reservoir resistivity and the bottom surrounding rock resistivity to determine the resistivity discrimination coefficient.

[0077] Based on existing resistivity logging data and reservoir and surrounding rock development in the oilfield, the average deep-induction resistivity of sandstone with a thickness greater than 2 meters within the reservoir is taken as the reservoir resistivity. The average deep inductive resistivity of mudstone with a thickness greater than 2 meters at the bottom of the reservoir was selected as the surrounding rock resistivity. The resistivity discrimination coefficient is obtained by dividing the reservoir resistivity by the surrounding rock resistivity and multiplying it by the regional coefficient C. Its specific expression is:

[0078]

[0079] In the formula:

[0080] : Resistivity discrimination coefficient, dimensionless; Reservoir resistivity, Ω m; Rock resistivity, Ω m; C: regional coefficient, a constant, C=2.0 for the Chang 8 stratum in the Honghe Oilfield of the Ordos Basin.

[0081] Taking the Chang 8 formation of well HH373 in Honghe Oilfield as an example, the reservoir resistivity value was read. 8Ω m, resistivity value of the surrounding rock at the bottom 16Ω m, the regional coefficient C of the Honghe Oilfield is 2.0, and the resistivity discrimination coefficient of this well is calculated. It equals 1.0.

[0082] Taking the Chang 8 formation of well HH71 in Honghe Oilfield as an example, the reservoir resistivity value was read. 5Ω m, resistivity value of the surrounding rock at the bottom 20Ω m, the regional coefficient C of the Honghe Oilfield is 2.0, and the resistivity discrimination coefficient of this well is calculated. It equals 0.5.

[0083] 3. The porosity curve is calculated using the sonic transit time logging curve.

[0084] Sonic logging is a logging method that determines the geological characteristics of a formation by measuring the speed of sound waves propagating through the formation within the wellbore. The following formula converts the sonic transit time curve into an sonic porosity curve. :

[0085]

[0086] In the formula:

[0087] Acoustic porosity, % Target layer sonic transit time logging values, in units ; : Sonic transit time value of the stratigraphic rock skeleton, unit ; : Formation fluid acoustic transit time, unit .

[0088] Taking the Chang 8 formation of well HH373 in Honghe Oilfield as an example, the sonic transit time of the formation rock skeleton The value is 182 Formation fluid acoustic transit time Value 620 Substituting into the above formula, we can obtain the acoustic time difference curve of the 8-layer formation. Converted to acoustic porosity curve .

[0089] 4. The oil-water identification curve is obtained by multiplying the acoustic porosity curve by the resistivity discrimination coefficient.

[0090] Acoustic porosity curve Multiply by resistivity discrimination coefficient This allows us to obtain the oil-water identification curve for low-resistivity tight sandstone reservoirs. Its specific expression is:

[0091]

[0092] 5. Establish oil-water discrimination criteria to determine reservoir fluid properties.

[0093] Using oil and water identification curves based on oil and water test data The magnitude of the value is used to establish criteria for distinguishing target oil layers, oil-water co-layers, and water layers, so as to effectively identify the properties of oil and water.

[0094] The criteria for determining effective reservoirs are as follows, for reference. Figure 2 :

[0095] Oil-water identification curve If the value is greater than 0.1 and the porosity value is greater than 7%, the reservoir fluid properties are identified as oil-bearing layers.

[0096] Oil-water identification curve If the value is 0.07-0.1 and the porosity value is greater than 7%, the reservoir fluid properties are identified as oil-water co-layers.

[0097] Oil-water identification curve If the value is less than 0.07 and the porosity value is greater than 7%, the reservoir fluid properties are identified as water-bearing layers.

[0098] In the layer section with a porosity value of less than 7%, regardless of the oil-water identification curve Regardless of the value, the reservoir is classified as a dry reservoir.

[0099] refer to Figure 3 and Figure 4 Taking the identification of oil-water layers in wells HH373 and HH73 in the Honghe Oilfield in the southern Ordos Basin of China as an example.

[0100] In well HH373, the SRRP curve values ​​of the 1984.5-1990m, 1991-1993.2m, and 1995-2003m sections of the Chang 8 reservoir are all greater than 0.1, averaging 0.133. According to the oil-water identification standard for low-resistivity tight sandstone in the Honghe Oilfield, these sections are all identified as oil-bearing layers. The well's daily oil production during testing was 3.23 tons, with a water cut of 5.8%, and a cumulative oil production of 2081 tons and a cumulative water production of 12 tons. Based on this comprehensive assessment, it is determined to be an oil-bearing layer, consistent with the oil-water identification curve. The judgment results are consistent.

[0101] The SRRP curve values ​​of the 2431.3-2453m section of the Chang 8 reservoir in well HH71 are all less than 0.1, with an average of only 0.034. According to the oil-water identification standard for low-resistivity tight sandstone in the Honghe Oilfield, these are all identified as water-bearing layers. The well's daily oil production during testing was 0.12 tons, with a water cut of 99.6%, and a cumulative oil production of 0.22 tons and a cumulative water production of 404.3 tons. Based on this comprehensive assessment, it is determined to be a water-bearing layer, consistent with the oil-water identification curve. The judgment results are consistent.

[0102] Example 2

[0103] This disclosure also provides an electronic device, which includes:

[0104] At least one processor; and,

[0105] A memory that is communicatively connected to at least one processor; wherein,

[0106] The memory stores instructions that can be executed by at least one processor, which enables the at least one processor to perform the above-described method for identifying the oil-water properties of a reservoir.

[0107] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0108] This memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0109] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of this disclosure, the processor is used to execute computer-readable instructions stored in the memory.

[0110] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0111] Example 3

[0112] This disclosure provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the above-described method for identifying the oil and water properties of a reservoir.

[0113] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments of the present disclosure are performed.

[0114] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0115] Example 4

[0116] This disclosure provides an oil-water property identification device for an oil reservoir, the device comprising:

[0117] A differentiation module, which is used to differentiate between the reservoir and the surrounding rock;

[0118] A resistivity discrimination coefficient module is used to determine the resistivity of the reservoir and the resistivity of the surrounding rock, and to determine the resistivity discrimination coefficient by the resistivity of the reservoir and the resistivity of the bottom surrounding rock.

[0119] The conversion module is used to convert the sonic transit time logging curve into a porosity curve;

[0120] The oil-water identification module is used to determine the criteria for identifying oil layers, oil-water co-layers, and water layers in a reservoir, and to identify the oil-water properties of the reservoir based on the value of the oil-water identification curve.

[0121] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for identifying oil-water properties of an oil reservoir, for low-resistivity tight sandstone reservoirs, characterized in that, The method includes: Step 1: Differentiate between the reservoir and the surrounding rock; Step 2: Determine the resistivity of the reservoir and the resistivity of the surrounding rock, and determine the resistivity discrimination coefficient based on the resistivity of the reservoir and the resistivity of the surrounding rock; Step 3: Convert the sonic transit time logging curve into a porosity curve; Step 4: Calculate the oil-water identification curve based on the porosity curve and the resistivity discrimination coefficient. The formula for calculating the oil-water identification curve is as follows: wherein, is the value of the oil-water identification curve, is the resistivity discrimination coefficient, is the acoustic porosity; Step 5: Determine the criteria for distinguishing between oil layers, oil-water co-layers, and water layers in the reservoir. Identify the oil and water properties of the reservoir based on the values ​​of the oil-water identification curves, including: in the layer segment where the value of the oil-water identification curve is greater than A and the porosity value is greater than C%, the reservoir fluid properties are identified as oil layers. In the oil-water identification curve range where the value is B to A, B < A, and the porosity value is greater than C%, the reservoir fluid properties are identified as oil-water co-containment. In the oil-water identification curve range where the value is less than B, and the porosity value is greater than C%, the reservoir fluid properties are identified as water-bearing. In the range where the porosity value is less than C%, the reservoir is identified as dry.

2. The method of identifying oil-water properties of a reservoir according to claim 1, wherein, The distinction between reservoir and surrounding rock includes: using natural gamma logging curves to identify sandstone and mudstone, identifying sandstone with a thickness greater than a first set value as the reservoir, and identifying mudstone with a thickness greater than a second set value at the top or bottom of the reservoir as the surrounding rock.

3. The method of identifying oil-water properties of a reservoir according to claim 2, wherein, The method for determining the resistivity of the reservoir includes: based on existing resistivity logging data of the oilfield and the development of the reservoir and the surrounding rock, reading the average deep-induction resistivity of the sandstone as the resistivity of the reservoir; and / or, The methods for determining the resistivity of the surrounding rock include: Based on the existing resistivity logging data of the oilfield and the development of the reservoir and the surrounding rock, the average deep-induction resistivity of the mudstone is selected as the resistivity of the surrounding rock.

4. The method of identifying oil-water properties of a reservoir according to claim 1, wherein, The resistivity discrimination coefficient is determined using the following formula. in, The resistivity discrimination coefficient is... The resistivity of the reservoir is... Let be the resistivity of the surrounding rock, C be the regional coefficient, and be a constant.

5. The method for identifying the oil-water properties of an oil reservoir according to claim 1, characterized in that, The sonic transit time logging curve is converted into a sonic porosity curve using the following formula. in, The acoustic porosity, The target layer is the sonic transit time logging value. This represents the acoustic transit time value of the stratigraphic rock framework. This represents the time difference of acoustic waves in the formation fluid.

6. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed, enables the at least one processor to perform the oil-water property identification method for reservoirs according to any one of claims 1-5.

7. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions for causing a computer to execute the oil-water property identification method for reservoirs as described in any one of claims 1-5.

8. A device for identifying the oil-water properties of an oil reservoir, characterized in that, include: The differentiation module is used to differentiate between the reservoir and the surrounding rock. A resistivity discrimination coefficient module is used to determine the resistivity of the reservoir and the resistivity of the surrounding rock, and to determine the resistivity discrimination coefficient by the resistivity of the reservoir and the resistivity of the bottom surrounding rock. The conversion module is used to convert the sonic transit time logging curve into a porosity curve. The calculation formula for the oil-water identification curve is as follows: in, The value of the oil-water identification curve. The resistivity discrimination coefficient is... Acoustic porosity; An oil-water identification module is used to determine the criteria for identifying oil layers, oil-water co-layers, and water layers in a reservoir. Based on the values ​​of the oil-water identification curves, the module identifies the oil-water properties of the reservoir, including: for layers where the oil-water identification curve value is greater than A and the porosity value is greater than C%, the reservoir fluid properties are identified as oil layers; for layers where the oil-water identification curve value is between B and A, where B < A, and the porosity value is greater than C%, the reservoir fluid properties are identified as oil-water co-layers; for layers where the oil-water identification curve value is less than B and the porosity value is greater than C%, the reservoir fluid properties are identified as water layers; and for layers where the porosity value is less than C%, the reservoir is identified as a dry layer.

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