A method and device for identifying formation fluid types

Abstract

This invention discloses a method and apparatus for identifying formation fluid types. The method includes obtaining the mass percentage of various elements in a rock skeleton using lithological scanning logging data; determining the mass percentage of various minerals in the rock skeleton based on the mass percentage of various elements in the rock skeleton; obtaining the rock skeleton density and porosity; obtaining the volume fraction of each mineral in the rock based on the rock skeleton density, the mass percentage of each mineral in the rock skeleton, and the rock porosity; obtaining the rock resistivity; obtaining the electrical conductivity of the pore fluid in the rock based on the volume fraction of each mineral in the rock and the rock resistivity; and determining the formation fluid type based on the electrical conductivity of the pore fluid in the rock. The beneficial effect of the technical solution proposed in this invention is that the electrical conductivity of the pore fluid can be calculated separately, thereby eliminating the influence of high-conductivity minerals such as chlorite, muscovite, and pyrite on the calculation of pore fluid electrical conductivity, and improving the accuracy of the identification results.

Description

Technical Field

[0001] This invention relates to the field of open-hole logging evaluation technology for oil and natural gas, and in particular to a method and apparatus for identifying formation fluid types. Background Technology

[0002] As oilfield development deepens, more and more oilfields, both domestically and internationally, are facing increasing exploration and development difficulties and a growing number of complex lithologies and reservoirs. Influenced by factors such as block stress, the lithology of the formations is complex, and the physical and electrical properties of the rocks vary considerably. When evaluating fluid properties using the traditional Archie formula (such as the Chinese invention patent application number CN200910059741.6), the influence of high-conductivity minerals such as chlorite, muscovite, and pyrite on the calculation of pore fluid conductivity cannot be excluded, often leading to significant deviations in the determined fluid type.

[0003] With the promotion and application of Schlumberger's lithology scanning logging technology, more and more oilfields have introduced this technology, which has played an increasingly important role in lithology identification, geological research and other processes.

[0004] However, lithological scanning logging calculates the elemental content in the rock skeleton and cannot evaluate the fluids in the formation pores, which greatly limits the application scope of this technology. Summary of the Invention

[0005] In view of this, it is necessary to provide a method and apparatus for identifying formation fluid types to solve the technical problem that when evaluating fluid properties using the traditional Archie formula, the influence of high-conductivity minerals such as chlorite, muscovite, and pyrite on the calculation of pore fluid conductivity cannot be excluded, resulting in a large deviation in the identified fluid type.

[0006] To achieve the above objectives, the present invention provides a method for identifying formation fluid types, comprising:

[0007] The mass percentage of various elements in the rock skeleton is obtained by lithological scanning logging data.

[0008] The mass percentage of various minerals in the rock skeleton is obtained based on the mass percentage of various elements in the rock skeleton.

[0009] To obtain the skeletal density and porosity of the rock;

[0010] The volume fraction of each mineral in the rock is obtained based on the rock's framework density, the mass percentage of each mineral in the rock framework, and the rock's porosity.

[0011] Obtain the resistivity of the rock;

[0012] The electrical conductivity of pore fluids in rocks is obtained based on the volume fraction of each mineral in the rock and the resistivity of the rock.

[0013] The type of formation fluid can be determined based on the electrical conductivity of the pore fluid in the rock.

[0014] In some embodiments, the formation fluid type identification method further includes: the formation fluid type identification method can also be applied to elemental logging data. In wells lacking lithological scanning logging data but having elemental logging data, the formation fluid type can be determined by combining the mineral density obtained from the lithological scanning data of other wells.

[0015] In some embodiments, the mass percentage of various minerals in the rock framework is obtained based on the mass percentage of various elements in the rock framework, specifically including:

[0016] Determine the equations that express the relationship between the mass percentage of various minerals and the mass percentage of various elements in the rock framework;

[0017] Calculate the optimal solution of the equation under preset constraints to obtain the mass percentage of various minerals in the rock skeleton.

[0018] In some embodiments, the equation expressing the relationship between the mass percentage of various minerals and the mass percentage of various elements in the rock framework is as follows:

[0019]

[0020] Among them, C ij Let m be the content of the i-th element in the j-th mineral. j e represents the mass percentage of the j-th mineral in the rock framework. i Let be the mass percentage of the i-th element in the rock skeleton, p be the number of element types, q be the number of mineral types, and i and j be natural numbers greater than 0.

[0021] In some embodiments, the volume fraction of various minerals in a rock is obtained based on the rock's framework density, the mass percentage of various minerals in the rock framework, and the rock's porosity. This process specifically includes the following steps:

[0022] The density of each mineral component is obtained by measuring the skeletal density of the rock and the mass percentage of each mineral in the rock skeletal structure.

[0023] The volume fraction of each mineral is obtained based on the mass percentage of each mineral in the rock skeleton, the density of each mineral component, and the porosity of the rock.

[0024] In some embodiments, the density of each mineral component is obtained by the framework density of the rock and the mass percentage of various minerals in the rock framework, using the following formula:

[0025]

[0026] Among them, V ma D is the volume of a rock skeleton per unit mass. ma D is the skeleton density. i Let v be the density of the i-th mineral component. i Let m be the volume of the i-th mineral. i Let be the mass fraction of the i-th mineral, where i is a natural number greater than 0.

[0027] In some embodiments, the volume fraction of each mineral is obtained based on the mass percentage of each mineral in the rock framework, the density of each mineral component, and the porosity of the rock, using the following formula:

[0028]

[0029] Among them, v iN Let be the volume fraction of the i-th mineral. The porosity of the rock is expressed in m. i Let D be the mass fraction of the i-th mineral. i Let be the density of the i-th mineral component, where i is a natural number greater than 0.

[0030] In some embodiments, the electrical conductivity of the pore fluid in the rock is obtained based on the volume fraction of each mineral in the rock and the resistivity of the rock, using the following formula:

[0031]

[0032] Among them, c i Let c be the electrical conductivity of the i-th mineral. f Let be the electrical conductivity of the pore fluid, c be the electrical conductivity of the rock, and ρ be the resistivity of the rock. v represents the porosity of the rock. i Let be the volume fraction of the i-th mineral, where i is a natural number greater than 0.

[0033] In some embodiments, the formation fluid type is determined based on the electrical conductivity of the pore fluid in the rock, specifically as follows:

[0034] If the electrical conductivity of the pore fluid is greater than 2 S / m, the formation fluid type is water.

[0035] If the electrical conductivity of the pore fluid is between 1 and 2 S / m, the formation fluid type is gas-water co-layer.

[0036] If the electrical conductivity of the pore fluid is less than 1 S / m, the formation fluid type is gas layer.

[0037] The above judgment criteria were obtained by fitting data from this study area. In other regions, the judgment criteria may be adjusted adaptively due to different geological conditions.

[0038] The present invention also provides an apparatus for identifying formation fluid types, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method for identifying formation fluid types is implemented.

[0039] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for identifying formation fluid types is implemented.

[0040] Compared with the prior art, the beneficial effects of the technical solution proposed in this invention are: in this technical solution, the conductivity of pore fluid can be calculated separately, thereby eliminating the influence of high conductivity minerals such as chlorite, muscovite, and pyrite on the calculation of pore fluid conductivity, and improving the accuracy of the identification results. Attached Figure Description

[0041] Figure 1 This is a schematic flowchart of an embodiment of the method for identifying formation fluid types provided by the present invention;

[0042] Figure 2 yes Figure 1 A schematic diagram of the fluid flow in step S2.

[0043] Figure 3 This is the result of the optimal calculation of the mineral profile of well X1;

[0044] Figure 4 yes Figure 1 A schematic diagram of the fluid flow in step S4.

[0045] Figure 5 It is a rock and mineral resistance model;

[0046] Figure 6 This is the resistivity regression result of well X2;

[0047] Figure 7 This is the resistivity inversion result of well X2. Detailed Implementation

[0048] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0049] Please refer to Figure 1 This invention provides a method for identifying formation fluid types, comprising:

[0050] S1. Obtain the mass percentages of various elements in the rock matrix through litho-scanning logging data;

[0051] In this embodiment, the litho-scanning logging data processing software obtains the mass percentages of various elements in the rock matrix through spectral deconvolution calculation. Specifically, the mass percentages of various elements in the rock matrix can be obtained through the LithoScanner processing module under the Techlog platform.

[0052] S2. Obtain the mass percentages of various minerals in the rock matrix based on the mass percentages of various elements in the rock matrix;

[0053] Please refer to Figure 2 , and step S2 specifically includes the following steps:

[0054] S21. Determine the equation expressing the relationship between the mass percentages of various minerals and the mass percentages of various elements in the rock matrix;

[0055] Let the mass percentage matrix of various elements and the mass percentage matrix of various minerals be represented by E and M respectively. Then, there is a simple linear relationship between the formation mineral content M and the elemental dry weight E:

[0056] C·M = E (1)

[0057] Where C is the conversion coefficient matrix, and equation (1) can be expanded as:

[0058]

[0059] Where C ij is the content of the i-th element in the j-th mineral, m j is the mass percentage of the j-th mineral in the rock matrix, e i is the mass percentage of the i-th element in the rock matrix, p is the number of element types, q is the number of mineral types, and both i and j are natural numbers greater than 0.

[0060] S22. Calculate the optimal solution of the equation under preset constraint conditions to obtain the mass percentages of various minerals in the rock matrix.

[0061] In equation (2), usually, p < q. Therefore, equation (2) has infinitely many solutions, and it is necessary to calculate the optimal solution under constraint conditions, forming a multi-objective linear programming problem, that is:

[0062]

[0063] Where F is the objective function to be optimized, e i is the mass percentage of the i-th element in the rock matrix, is the model prediction value, w iLet m be the dry weight weight coefficient of the i-th element in the rock framework. j Let be the mass percentage of the j-th mineral in the rock skeleton, and st be the two constraints of the problem, namely, the content of each mineral is not less than 0, and the sum is 1.

[0064] Constrained optimization algorithms can be used to calculate the mass percentage of various minerals in a rock skeleton. This algorithm utilizes data from multiple data acquisition points at different depths, overcoming the limitation of single-point data in determining the mass percentage of various minerals. Taking well X1 as an example, by writing a processing program, the lithological profile of the well, expressed as a percentage of mineral mass, can be calculated. Figure 3 As shown, the first channel is the depth channel, the second channel is the natural gamma curve, and the third channel is the fine mineral content profile. In general applications, minerals of the same type are combined, such as illite and montmorillonite, which constitute the clay part of the entire rock, forming a simplified fourth lithological profile.

[0065] S3. Obtain the skeleton density and porosity of the rock;

[0066] The skeleton density of a rock can be obtained using the skeleton density processing module in Lithoscanner. The porosity of the rock can be obtained from density logging data; another method for calculating porosity will be provided later.

[0067] S4. Based on the rock's framework density, the mass percentage of various minerals in the rock framework, and the rock's porosity, obtain the volume fraction of various minerals in the rock.

[0068] Please refer to Figure 4 Step S4 specifically includes the following steps:

[0069] S41. The density of each mineral component is obtained by using the skeleton density of the rock and the mass percentage of each mineral in the rock skeleton.

[0070] The specific formula is as follows:

[0071]

[0072] Among them, V ma D is the volume of a rock skeleton per unit mass. ma D is the skeleton density. i Let v be the density of the i-th mineral component. i Let m be the volume of the i-th mineral. i Let be the mass fraction of the i-th mineral, where i is a natural number greater than 0.

[0073] The solution to equation (4) can be obtained using multiple linear regression. That is, by using data from multiple data collection points at different depths, the deficiency of data from a single data collection point in being unable to solve the density of each mineral component is solved. For example, through regression, the regression results of some mineral densities in well X1 in this example are shown in Table 1, and the correlation coefficient reaches R. 2 =0.9981, the calculation result is very reasonable and can be used for subsequent calculations.

[0074] Table 1: Regression results of partial mineral densities in Well X1

[0075] Mineral Name <![CDATA[Mineral density regression results (g / cm 3 )]]> quartz 2.650107 Potassium feldspar 2.66851 Sodium feldspar 2.677062 calcium feldspar 2.632653 … … muscovite 2.673432 Biotite 2.94843 pyrite 4.860995 plaster 2.9814

[0076] After the mineral component density regression is completed, the variable skeleton porosity can be calculated by combining it with conventional density logging data. The calculation formula is the same as that for conventional skeleton porosity, except that the skeleton density becomes a variable at this time, as shown in equation (5):

[0077]

[0078] Among them, D ma D represents the skeletal density, and D represents the density measured from density logging data. f For a given pore fluid density, the value of Df can be determined by matching it with conventional porosity.

[0079] It should be understood that porosity can also be obtained directly from density logging data.

[0080] S42. Based on the mass percentage of various minerals in the rock skeleton, the density of each mineral component, and the porosity of the rock, the volume fraction of each mineral is obtained.

[0081] The mineral volume v is calculated according to equation (4). i The data does not include pore volume and is not normalized. To ensure that the rock volume after adding pores is 1, it needs to be normalized. The normalization formula is as follows:

[0082]

[0083] Among them, v iN Let be the volume fraction of the i-th mineral. The porosity of the rock is expressed in m. i Let D be the mass fraction of the i-th mineral. i Let be the density of the i-th mineral component, where i is a natural number greater than 0.

[0084] v at this time iN The pore volume has already been included, meaning that the pore fluid, together with the rock skeleton, constitutes a rock profile with a total volume of 1, such as... Figure 3As shown in the fifth diagram, the final grayish-white portion represents the volume of pore fluid within the entire rock, i.e., porosity.

[0085] S5. Obtain the resistivity of the rock;

[0086] The resistivity of rocks can be calculated from conventional well logging data.

[0087] S6. Based on the volume fraction of each mineral in the rock and the resistivity of the rock, obtain the electrical conductivity of the pore fluid in the rock.

[0088] To obtain the fluid properties within formation pores, the following assumptions need to be made during the research process:

[0089] Assumption 1: The strata are horizontally isotropic, and the minerals are relatively uniformly distributed in the horizontal direction. Most of the strata meet this condition during the diagenesis process.

[0090] Assumption 2: Vertical well conditions, rock resistivity satisfies the parallel model. Because in highly deviated and horizontal wells, the resistivity curve is significantly affected by the vertical anisotropy of the formation, and the inversion results cannot reflect the true situation, it is considered unsuitable in this invention.

[0091] Under the two assumptions above, the rock resistance satisfies the following parallel relationship:

[0092]

[0093] Where R is the resistance of the entire rock, R i R is the resistance of the i-th mineral component. f The resistance of the pore fluid.

[0094] At the same time, to illustrate the point, we can consider simplifying the strata and rocks as follows: Figure 5 The model shown is given. Where m... i The mass fraction of the i-th mineral component has been calculated in equation (2), c i Let S be the electrical conductivity of the i-th mineral component. i Let S be the cross-sectional area occupied by the i-th mineral component, and let S be the sum of these components. The entire rock volume can be considered as a thin cuboid with a cross-sectional area of ​​S and a thickness of Δh. Since the strata are horizontally isotropic, the depth of the rock's side surface can be disregarded. According to basic physics, the electrical resistance R, resistivity ρ, and conductivity c of an object satisfy the following relationship:

[0095]

[0096] At the same time:

[0097] v i =S i ·Δh (9)

[0098] Combining equations (7), (8), and (9), we obtain:

[0099]

[0100] Among them, c i Let c be the electrical conductivity of the i-th mineral. f Let be the electrical conductivity of the pore fluid, c be the electrical conductivity of the rock, and ρ be the resistivity of the rock. v represents the porosity of the rock. i Let be the volume fraction of the i-th mineral, where i is a natural number greater than 0.

[0101] Equation (10) shows that, under the assumption that rock minerals satisfy parallel connection, rock conductivity satisfies a simple linear relationship, which, like density acoustic logging data, satisfies a volume model relationship. Unlike density acoustic logging data, resistivity curves are probed at greater depths and are affected by many factors such as surrounding rock, intrusion, and dip angle. Therefore, directly regressing equation (10) would not yield ideal calculation results. Taking well X2 as an example, we regressed the resistivity of this well, and the regression results are as follows: Figure 6 As shown.

[0102] As can be seen from the figure, the resistivity calculated by regression method has a good overall correlation with the actual resistivity, but there are also large differences in some well sections, with a correlation coefficient of only 0.403. This indicates that calculating the mineral conductivity by regression method is only a theoretical possibility.

[0103] Based on this, we use the constrained least squares method to invert the electrical conductivity of rocks, and write equation (10) in matrix form:

[0104] f(c) = C = c T V (11)

[0105] The objective function can now be written as:

[0106]

[0107] Where F is the objective function to be optimized, and st is the constraint condition of the problem, namely, the electrical conductivity of each mineral component is not less than 0.

[0108] By solving equation (12), the optimal solution that minimizes the objective function can be calculated, thereby enabling the calculation of the electrical conductivity of pore fluids and rock skeleton minerals. Furthermore, based on the electrical conductivity of the pore fluids, the purpose of identifying the properties of pore fluids in the formation can be achieved.

[0109] S7. Determine the type of formation fluid based on the electrical conductivity of pore fluids in the rock. Specifically:

[0110] If the electrical conductivity of the pore fluid is greater than 2 S / m, the formation fluid type is water.

[0111] If the electrical conductivity of the pore fluid is between 1 and 2 S / m, the formation fluid type is gas-water co-layer.

[0112] If the electrical conductivity of the pore fluid is less than 1 S / m, the formation fluid type is gas layer.

[0113] It should be noted that the above judgment criteria were obtained by fitting data from this study area. In other regions, due to different geological conditions, the judgment criteria may be adjusted accordingly.

[0114] The above identification method is consistent with the conclusions of oil testing or MDT (The Modular Formation Dynamics Tester Tool), which effectively solves the problem of difficulty in evaluating the fluid properties of complex oil and gas reservoirs with low porosity and low permeability.

[0115] In this embodiment, taking well X2 as an example, such as Figure 7 As shown, the dark gray and light gray curves in the fifth, sixth, and seventh channels are the logging values ​​and inversion values ​​of the skeleton density, porosity, and resistivity curves, respectively. According to the conductivity calculation results of well X2, the apparent conductivity of the pore fluid in well X2 calculated by equation (12) is 0.54283 S / m, which is a typical gas layer characteristic. Based on this, it can be determined that the reservoir fluid of well X2 is a gas layer.

[0116] In addition, the electrical conductivity of other minerals in the rock skeleton was also calculated, and the final results for some minerals are shown in Table 2.

[0117] Table 2: Inversion results of electrical conductivity of some minerals in Well X2

[0118] Mineral Name Mineral conductivity inversion results (S / m) quartz 0 Sodium feldspar 0.0196 chlorite 0.681 muscovite 0.4449 … … Biotite 0.1008 pyrite 0.5687 plaster 0 Pore ​​fluid 0.5428

[0119] As shown in Table 2, the minerals in this well's framework, such as chlorite, muscovite, and pyrite, have relatively high electrical conductivity, significantly impacting the overall resistivity. This makes it impossible to eliminate the influence of these mineral components when using the traditional Archie formula for formation fluid evaluation. The calculated results often fail to reflect the true formation conditions, leading to unsuccessful fluid evaluation. This also illustrates the complexity of formation fluid evaluation. The technical solution provided by this invention can calculate the conductivity of pore fluids separately, thus eliminating the influence of high-conductivity minerals such as chlorite, muscovite, and pyrite on the calculation of pore fluid conductivity, improving the accuracy of the identification results.

[0120] This embodiment also identified the fluid type for other open-hole wells, and the identification results are shown in Table 3.

[0121] In some embodiments, the formation fluid type identification method further includes: the formation fluid type identification method can also be applied to elemental logging data. In wells lacking lithological scanning logging data but having elemental logging data, the formation fluid type can be determined by combining the mineral density obtained from the lithological scanning data of other wells.

[0122] Table 3: Comparison of Conductivity Inversion Results

[0123] well name Well section (m) Test conclusions (including MDT) Inverted conductivity (S / m) Well Y1 8072.79, MDT water layer 4.08952 Y2 well 7736-7810 water layer 2.85611 Y3 well upper part 7452.5-7490 Gas and water in the same layer 1.19062 Y3 well bottom section 7553,MDT water layer 2.75212 Y4 well 4940-4967 Aquifer 1.68833 Y5 well 8022-8143.35 air layer 0.67199

[0124] The present invention also provides an apparatus for identifying formation fluid types, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method for identifying formation fluid types is implemented.

[0125] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for identifying formation fluid types is implemented.

[0126] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying formation fluid types, characterized in that, include: The mass percentage of various elements in the rock skeleton is obtained by lithological scanning logging data. The mass percentage of various minerals in the rock skeleton is obtained based on the mass percentage of various elements in the rock skeleton. To obtain the skeletal density and porosity of the rock; The volume fraction of each mineral in the rock is obtained based on the rock's framework density, the mass percentage of each mineral in the rock framework, and the rock's porosity. Obtain the resistivity of the rock; The electrical conductivity of pore fluids in rocks is obtained based on the volume fraction of each mineral in the rock and the resistivity of the rock. Determine the type of formation fluid based on the electrical conductivity of pore fluids in rocks; The method of obtaining the mass percentage of various minerals in the rock framework based on the mass percentage of various elements in the rock framework specifically includes: Determine the equations that express the relationship between the mass percentage of various minerals and the mass percentage of various elements in the rock framework; Calculate the optimal solution of the equation under preset constraints to obtain the mass percentage of various minerals in the rock skeleton; The equation expressing the relationship between the mass percentage of various minerals and the mass percentage of various elements in the rock framework is as follows: in, Let be the content of the i-th element in the j-th mineral. Let be the mass percentage of the j-th mineral in the rock framework. denoted as , where is the mass percentage of the i-th element in the rock framework, p is the number of element types, q is the number of mineral types, and i and j are both natural numbers greater than 0. The conductivity of the pore fluid in the rock is obtained based on the volume fraction of each mineral and the resistivity of the rock. The specific formula is as follows: in, Let be the electrical conductivity of the i-th mineral. The electrical conductivity of the pore fluid. The electrical conductivity of the rock, The resistivity of the rock, For rock porosity, Let be the volume fraction of the i-th mineral, where i is a natural number greater than 0.

2. The method for identifying formation fluid types according to claim 1, characterized in that, Also includes: The method for identifying formation fluid types can also be applied to elemental logging data. In wells lacking lithological scanning logging data but possessing elemental logging data, the mineral density obtained from lithological scanning data of other wells can be combined to determine the formation fluid type.

3. The method for identifying formation fluid types according to claim 1, characterized in that, Based on the rock's framework density, the mass percentage of various minerals in the rock framework, and the rock's porosity, the volume fraction of various minerals in the rock is obtained, specifically including the following steps: The density of each mineral component is obtained by measuring the skeletal density of the rock and the mass percentage of each mineral in the rock skeletal structure. The volume fraction of each mineral is obtained based on the mass percentage of each mineral in the rock skeleton, the density of each mineral component, and the porosity of the rock.

4. The method for identifying formation fluid types according to claim 3, characterized in that, The density of each mineral component is obtained by using the framework density of the rock and the mass percentage of various minerals in the rock framework. The specific formula is as follows: Among them, V ma D is the volume of a rock skeleton per unit mass. ma D is the skeleton density. i Let v be the density of the i-th mineral component. i Let m be the volume of the i-th mineral. i Let be the mass fraction of the i-th mineral, where i is a natural number greater than 0.

5. The method for identifying formation fluid types according to claim 4, characterized in that, The volume fraction of each mineral is obtained based on the mass percentage of various minerals in the rock framework, the density of each mineral component, and the porosity of the rock. The specific formula is as follows: Among them, v iN Let be the volume fraction of the i-th mineral. The porosity of the rock is expressed in m. i Let D be the mass fraction of the i-th mineral. i Let be the density of the i-th mineral component, where i is a natural number greater than 0.

6. The method for identifying formation fluid types according to claim 1, characterized in that, The type of formation fluid is determined based on the electrical conductivity of pore fluids in rocks, specifically: If the electrical conductivity of the pore fluid is greater than 2 S / m, the formation fluid type is water. If the electrical conductivity of the pore fluid is between 1 and 2 S / m, the formation fluid type is gas-water co-layer. If the electrical conductivity of the pore fluid is less than 1 S / m, the formation fluid type is gas layer.

7. A device for identifying formation fluid types, characterized in that, The system includes a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the method for identifying formation fluid types as described in any one of claims 1-6.

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