Low-resistivity oil layer identification method and device

Abstract

The invention discloses a low-resistivity oil layer identification method and device. The method comprises the steps that on the basis of the layering result, porosity, permeability data, shale content and other data of a research block, irreducible water saturation of all reservoirs is obtained through calculation by means of an irreducible water saturation calculation model; substituting the irreducible water saturation of each reservoir into the relation model of the irreducible water saturation and the oil saturation to obtain the oil saturation of the oil layer of each reservoir; according to the oil saturation of the oil layer of each reservoir, the formation water resistivity, the rock electricity parameters of the research block and the reservoir porosity data, the Archie formula is used for back calculation to obtain the corrected formation resistivity, and the ratio of the corrected formation resistivity to the logging actually measured resistivity is the resistivity correction multiple; establishing a low-resistivity oil layer identification plate by adopting the test oil layer data, the resistivity correction multiple and the logging actual measurement resistivity, and obtaining a low-resistivity oil layer identification result according to the position of the data point on the plate; according to the method, the identification accuracy of the low-resistivity oil layer is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil exploration, and more particularly, to a method and device for identifying low-resistivity oil layers. Background Art

[0002] For low-resistivity oil layers, they are currently mainly defined by the ratio of the resistivity of the oil and gas layer to the resistivity when the formation is 100% water-bearing. Generally, a low-resistivity oil layer refers to an oil layer with a resistance increase rate less than or equal to 3. The resistivity of a low-resistivity oil layer is equivalent to that of the adjacent water layer, with a small difference from the resistivity of the upper and lower surrounding rocks, and is lower than the resistivity of oil layers in most oil fields in China (the resistivity value is between 3 Ω·m and 1000 Ω·m).

[0003] Due to the reduced contrast between the resistivity of low-resistivity oil layers and that of water layers, it is more difficult to identify them, and they are extremely likely to be misinterpreted as water layers and the oil layers are missed. From the analysis of the causes leading to low resistivity, there are mainly the following 5 physical causes: high immobile water (bound water) saturation, additional conductivity of clay, oil-water differentiation, difference in salinity between oil and water layers, and invasion of drilling fluid. Among them, the low-resistivity oil layers caused by high immobile water saturation are widely developed, which is one of the main causes of low-resistivity oil and gas layers and occupies a very important position.

[0004] Currently, the identification and evaluation of low-resistivity oil layers caused by high immobile water saturation are mainly carried out from the following 3 aspects.

[0005] (1) Identifying low-resistivity oil layers using logging curves

[0006] In terms of conventional logging, it is mainly to compare the electrical characteristics of oil layers and typical water layers in the same layer and adjacent layers, and find the differences in electrical characteristics between oil layers and water layers for qualitative interpretation of oil and water layers, such as the longitudinal resistivity comparison method, invasion factor method, etc. These methods mainly identify low-resistivity oil layers based on the resistivity level, and have a good identification effect on low-resistivity oil layers caused by the difference in salinity between oil and water layers and the invasion of drilling fluid. However, for low-resistivity oil layers caused by high immobile water saturation, the resistivity of the oil layer is often equivalent to that of the water layer, and the identification and evaluation method mainly based on resistivity often has an unclear effect.

[0007] In terms of special logging, using nuclear magnetic resonance logging can, to a certain extent, solve the problem of difficult identification of low-resistivity oil layers, especially solve the identification problem of low-resistivity oil and gas layers caused by high immobile water (bound water). Nuclear magnetic resonance logging can identify oil and gas and their types through differential spectrum and spectral shift analysis. Although nuclear magnetic resonance logging has a good effect in both identifying and evaluating low-resistivity oil layers, its disadvantages are high cost and difficult operation, especially in the oilfield development stage, which is not conducive to popularization and application.

[0008] (2) Developing multi-parameter qualitative interpretation charts

[0009] Most low-resistivity oil layers and water layers still have subtle differences. By conducting a detailed study and extraction of logging information and creating various crossplots, it is possible to effectively identify some low-resistivity oil layers caused by high immobile water (irreducible water), such as conventional porosity-deep lateral resistivity crossplots, porosity-saturation crossplots, resistivity-natural gamma relative value plots, water saturation-irreducible water saturation crossplots, etc. The basic principle is mainly to analyze the effects of changes in reservoir lithology, physical properties, and formation water properties on reservoir resistivity and saturation to achieve the purpose of identifying low-resistivity oil layers. The crossplot method mainly relies on the subtle differences in resistivity and saturation due to the subtle changes in lithology, physical properties, and oil-bearing properties between oil layers and water layers to identify oil-water layers. The differences in lithology and physical properties between low-resistivity oil layers caused by high immobile water (irreducible water) and water layers are often relatively subtle in logging responses. Coupled with the influence of multiple factors such as formation water properties, complex lithology, and complex physical property conditions, it is impossible to obtain accurate oil saturation values. Therefore, for specific research blocks, the identification of low-resistivity oil layers often poses great difficulties in evaluation.

[0010] (3) Conduct multi-well evaluation based on fine single-well interpretation

[0011] Judging the fluid properties of low-resistivity reservoirs not only requires comprehensively studying a large number of single logging interpretation results, but more importantly, organically integrating logging information with geological and reservoir understanding. Under the guidance of understanding the reservoir lithology characteristics, spatial distribution laws, and reservoir characteristics, the multi-solution nature of logging interpretation should be excluded as much as possible, such as identifying low-resistivity oil layers through the judgment of the high and low parts of the structure. The applicable research objects for multi-well evaluation are limited, and often only a few individual low-resistivity oil layers (such as high parts of the structure) can be effectively identified.

[0012] Therefore, there is an urgent need to develop a low-resistivity oil layer identification method and device to solve one or more of the above problems. Summary of the Invention

[0013] An object of the present invention is to provide a new technical solution for a low-resistivity oil layer identification method and device.

[0014] According to the first aspect of the present invention, there is provided a low-resistivity oil layer identification method, the method comprising:

[0015] Step S1: Obtain reservoir porosity data, reservoir permeability data, and reservoir shale content data of the research block;

[0016] Step S2: Based on the stratification results of the research block, the reservoir porosity data, the reservoir permeability data, and the reservoir shale content data, and using a pre-established irreducible water saturation calculation model, calculate the irreducible water saturation of each reservoir;

[0017] Step S3: Substitute the irreducible water saturation of each reservoir into the relationship model between irreducible water saturation and oil saturation, and calculate the oil saturation of the oil-bearing layer in each reservoir;

[0018] Step S4: Based on the oil saturation of the oil-bearing layer in each reservoir, the formation water resistivity of each reservoir, the petrophysical parameters of the research block, and the reservoir porosity data, and using Archie's formula to inversely calculate the corrected formation resistivity, the ratio of the corrected formation resistivity to the measured resistivity of the well logging is the resistivity correction multiple;

[0019] Step S5: Establish an identification chart for low resistivity oil layers using the test oil layer data, the resistivity correction multiple, and the measured resistivity of the well logging;

[0020] Step S6: Project the data points of the layer to be evaluated onto the identification chart for low resistivity oil layers, and obtain the identification result of the low resistivity oil layer according to the position of the data points on the identification chart for low resistivity oil layers.

[0021] In some embodiments, in the step S1, for the cored well section, the reservoir porosity data, reservoir permeability data, and reservoir shale content data of the research block are obtained through the physical property analysis data, thin section identification data, nuclear magnetic experiment data, mercury injection data, and relative permeability experiment data of the research block analyzed by core experiments;

[0022] For the non-cored well section, the reservoir porosity data is calculated from the density and acoustic well logging curves, the reservoir permeability data is calculated from the reservoir porosity data, and the reservoir shale content data is calculated from the natural gamma well logging curve through an empirical formula.

[0023] In some embodiments, in the step S2, the research block is stratified using the automatic stratification method or manual stratification method of principal component activity weighting and variance optimization based on conventional well logging data to obtain the stratification result of the research block.

[0024] In some embodiments, in the step S2, the establishment process of the irreducible water saturation calculation model is as follows:

[0025] Through single-parameter sensitivity analysis, it is determined that there is a single correlation between the irreducible water saturation and the reservoir porosity data, reservoir permeability data, and reservoir shale content data, and the irreducible water saturation calculation model is established using multiple regression.

[0026] In some embodiments, the irreducible water saturation calculation model is expressed as:

[0027]

[0028] where S wiSwi represents the irreducible water saturation; Φ represents the effective porosity of the reservoir, in %; K represents the effective permeability of the reservoir, in mD; Vsh represents the shale content of the reservoir, in %.

[0029] In some embodiments, in the step S3, the relationship model between the irreducible water saturation and the oil saturation is expressed as:

[0030] S wi +S o = 100 (2)

[0031] Wherein, S wi represents the irreducible water saturation, and S o represents the oil saturation.

[0032] In some embodiments, the petrophysical parameters of the research block are obtained from the petrophysical experiment analysis data of the core wells in the study area. Among them, the petrophysical parameters include coefficient a, coefficient b, pore index m, and saturation index n.

[0033] In some embodiments, in the step S4, the Archie's formula is expressed as:

[0034]

[0035] Wherein, S w represents the water saturation, which is a decimal. The oil saturation S o is expressed as S o = 1 - S w ; a and b are both coefficients; m and n respectively represent the pore index and the saturation index; Φ represents the effective porosity of the reservoir, in %; Rt 校正 represents the corrected formation resistivity, in Ω·m; Rw represents the formation water resistivity, in Ω·m.

[0036] In some embodiments, in the step S4, the resistivity correction factor A is expressed as:

[0037]

[0038] Wherein, Rt 校正 represents the corrected formation resistivity, in Ω·m; Rt represents the measured logging resistivity.

[0039] In some embodiments, in the step S4, the formation water salinity is obtained according to the water analysis data, and then the formation water resistivity is obtained;

[0040] Alternatively, the formation water resistivity is obtained by using the apparent formation water resistivity method.

[0041] According to a second aspect of the present invention, there is provided a low-resistivity oil layer identification device, the device comprising:

[0042] a data acquisition module configured to acquire reservoir porosity data, reservoir permeability data, and reservoir shale content data of a research block;

[0043] a first calculation module configured to calculate the irreducible water saturation of each reservoir based on the stratification result of the research block, the reservoir porosity data, the reservoir permeability data, and the reservoir shale content data, and by using a pre-established irreducible water saturation calculation model;

[0044] a second calculation module configured to substitute the irreducible water saturation of each reservoir into a relationship model between the irreducible water saturation and the oil saturation, and calculate the oil saturation of the oil layer of each reservoir;

[0045] a correction multiple acquisition module configured to, based on the oil saturation of the oil layer of each reservoir, the formation water resistivity of each reservoir, the rock-electric parameters of the research block, and the reservoir porosity data, and by using Archie's formula, inversely calculate the corrected formation resistivity, and the ratio of the corrected formation resistivity to the measured well logging resistivity is the resistivity correction multiple;

[0046] a chart establishment module configured to establish a low-resistivity oil layer identification chart by using the oil test layer data, the resistivity correction multiple, and the measured well logging resistivity;

[0047] an identification module configured to project the data points of the layer to be evaluated onto the low-resistivity oil layer identification chart, and obtain the low-resistivity oil layer identification result according to the position of the data points on the low-resistivity oil layer identification chart.

[0048] In some embodiments, the data acquisition module is further specifically configured to, for the cored well section, obtain the reservoir porosity data, reservoir permeability data, and reservoir shale content data of the research block through physical property analysis data, thin section identification data, nuclear magnetic experiment data, mercury injection data, and relative permeability experiment data of the research block obtained by core experiment analysis;

[0049] For the non-cored well section, the reservoir porosity data is calculated from density and acoustic well logging curves, the reservoir permeability data is calculated from the reservoir porosity data, and the reservoir shale content data is calculated from the natural gamma well logging curve through an empirical formula.

[0050] In some embodiments, the first calculation module is further specifically configured to perform stratification processing on the research block by using an automatic stratification method or a manual stratification method of principal component activity weighting and variance optimization according to conventional well logging data, so as to obtain the stratification result of the research block.

[0051] In some embodiments, through single-parameter sensitivity analysis, it is determined that there is a single correlation between irreducible water saturation and reservoir porosity data, reservoir permeability data, and reservoir shale content data, and a calculation model for the irreducible water saturation is established using multiple regression, expressed as:

[0052]

[0053] where S wi represents irreducible water saturation; Φ represents effective reservoir porosity, in %; K represents effective reservoir permeability, in mD; and Vsh represents reservoir shale content, in %.

[0054] In some embodiments, the relationship model between irreducible water saturation and oil saturation is expressed as:

[0055] S wi + S o = 100 (2)

[0056] where S wi represents irreducible water saturation, and S o represents oil saturation.

[0057] In some embodiments, the correction multiple acquisition module is further specifically configured to obtain the petrophysical parameters of the research block through the petrophysical experiment analysis data of the core well section in the research area, where the petrophysical parameters include coefficient a, coefficient b, porosity exponent m, and saturation exponent n.

[0058] In some embodiments, the Archie's formula is expressed as:

[0059]

[0060] where S w represents water saturation, which is a decimal, and oil saturation S o is expressed as S o = 1 - S w ; a and b are both coefficients; m and n respectively represent porosity exponent and saturation exponent; Φ represents effective reservoir porosity, in %; Rt 校正 represents corrected formation resistivity, in Ω·m; and Rw represents formation water resistivity, in Ω·m.

[0061] According to the third aspect of the present invention, an electronic device is provided, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps in a low-resistivity oil layer identification method as described in the first aspect of the present invention above are implemented.

[0062] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in a low-resistivity oil layer identification method as described in the first aspect of the present invention above are implemented.

[0063] According to a fifth aspect of the present invention, a computer program product includes a computer program, and when the computer program is executed by a processor, the steps in a low-resistivity oil layer identification method as described in the first aspect of the present invention above are implemented. According to an embodiment disclosed by the present invention, it has the following beneficial effects:

[0064] The low-resistivity oil layer identification method of the present invention studies through the calculation method of irreducible water saturation in the reservoir to obtain the accurate oil saturation of the oil layer, and then corrects the resistivity of the reservoir through the oil saturation correction. By comparing the corrected resistivity with the measured resistivity, the correction multiple is obtained to achieve the purpose of identifying low-resistivity oil layers; this method is based on a large amount of oil layer test data and is established through the research of a large number of well data. Applying this method for oil layer identification, the results are more accurate. Therefore, the low-resistivity oil layer identification method of the present invention provides a new idea for the identification and evaluation of low-resistivity oil layers, and has a good application effect for low-resistivity oil layers formed by high irreducible water saturation. This method can be directly used in practice, effectively improving the accuracy of low-resistivity oil layer identification.

[0065] Other features and advantages of the present invention will become clear through the following detailed description of exemplary embodiments of the present invention with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The drawings incorporated in the specification and constituting a part of the specification illustrate embodiments of the present invention and, together with the description, are used to explain the principles of the present invention.

[0067] Figure 1 It is a schematic flowchart of a low-resistivity oil layer identification method provided according to an embodiment;

[0068] Figure 2 It is a schematic diagram of a low-resistivity oil layer identification chart in a low-resistivity oil layer identification method provided according to an embodiment;

[0069] Figure 3 It is a schematic structural diagram of a low-resistivity oil layer identification device provided according to an embodiment;

[0070] Figure 4 It is a schematic diagram of an electronic device. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0071] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention.

[0072] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way a limitation on the present invention or its application or use.

[0073] Technologies, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such technologies, methods, and devices should be regarded as part of the specification.

[0074] In all the examples shown and discussed herein, any specific values should be construed as merely exemplary and not as a limitation. Accordingly, other examples of the exemplary embodiments may have different values.

[0075] Embodiment 1:

[0076] Referring to Figure 1 as shown, this embodiment provides a method for identifying low-resistivity oil layers, the method comprising:

[0077] Step S1: Obtain reservoir porosity data, reservoir permeability data, and reservoir shale content data of the study block;

[0078] In some embodiments, in step S1 of the method for identifying low-resistivity oil layers of this embodiment, for the cored well section, the reservoir porosity data, reservoir permeability data, and reservoir shale content data of the study block are obtained from the physical property analysis data, thin section identification data, nuclear magnetic experiment data, mercury injection data, and relative permeability experiment data of the study block through core experiment analysis;

[0079] For non-cored well sections, the reservoir porosity data is calculated from density and acoustic logging curves, the reservoir permeability data is calculated from the reservoir porosity data, and the reservoir shale content data is calculated from the natural gamma logging curve through an empirical formula.

[0080] Step S2: Based on the stratification result, reservoir porosity data, reservoir permeability data, and reservoir shale content data of the study block, and using a pre-established irreducible water saturation calculation model, calculate the irreducible water saturation of each reservoir;

[0081] In some embodiments, in step S2 of the method for identifying low-resistivity oil layers of this embodiment, the study block is stratified according to conventional logging data using an automatic stratification method or a manual stratification method of principal component activity weighting and variance optimization to obtain the stratification result of the study block.

[0082] In some embodiments, in step S2 of the low-resistivity oil layer identification method of this embodiment, through single-parameter sensitivity analysis, it is determined that there is a single correlation between irreducible water saturation and reservoir porosity data, reservoir permeability data, and reservoir shale content data, and a calculation model for irreducible water saturation is established using multiple regression, expressed as:

[0083]

[0084] where S wi represents irreducible water saturation; Φ represents effective reservoir porosity, in %; K represents effective reservoir permeability, in mD; Vsh represents reservoir shale content, in %.

[0085] Step S3: Substitute the irreducible water saturation of each reservoir into the relationship model between irreducible water saturation and oil saturation, and calculate the oil saturation of the oil layer in each reservoir;

[0086] In some embodiments, in step S3 of the low-resistivity oil layer identification method of this embodiment, the relationship model between irreducible water saturation and oil saturation is expressed as:

[0087] S wi +S o = 100 (2)

[0088] where, S wi represents irreducible water saturation, S o represents oil saturation.

[0089] Step S4: Based on the oil saturation of the oil layer in each reservoir, the formation water resistivity of each reservoir, the petrophysical parameters of the study block, and the reservoir porosity data, and using Archie's formula to inversely calculate the corrected formation resistivity, the ratio of the corrected formation resistivity to the measured well logging resistivity is the resistivity correction multiple;

[0090] In some embodiments, in the low-resistivity oil layer identification method of this embodiment, the petrophysical parameters of the study block are obtained through petrophysical experiment analysis data of the cored well section in the study area. Among them, the petrophysical parameters include coefficient a, coefficient b, porosity exponent m, and saturation exponent n.

[0091] In some embodiments, in step S4 of the low-resistivity oil layer identification method of this embodiment, Archie's formula is expressed as:

[0092]

[0093] where, S w represents water saturation, which is a decimal, and oil saturation S o is expressed as S o = 1 - S w; a and b are both coefficients; m and n represent the porosity index and saturation index respectively; Φ represents the effective porosity of the reservoir, in %; Rt 校正 represents the corrected formation resistivity, in Ω·m; Rw represents the formation water resistivity, in Ω·m.

[0094] Step S5: Establish a low resistivity oil layer identification chart using the data of the oil testing layer, the resistivity correction multiple, and the measured resistivity of well logging;

[0095] Step S6: Project the data points of the layer to be evaluated onto the low resistivity oil layer identification chart, and obtain the low resistivity oil layer identification result according to the position of the data points on the low resistivity oil layer identification chart.

[0096] In some embodiments, in the low resistivity oil layer identification method of this embodiment, if the data points fall in the oil layer area of the low resistivity oil layer identification chart, it is determined that the layer to be evaluated is an oil layer; if the data points fall in the water layer area of the low resistivity oil layer identification chart, it is determined that the layer to be evaluated is a water layer; if the data points fall in the oil-water coexisting layer area of the low resistivity oil layer identification chart, it is determined that the layer to be evaluated is an oil-water coexisting layer.

[0097] Specifically, the low resistivity oil layer identification method of the embodiment of the present invention is further described in detail:

[0098] A low resistivity oil layer identification method includes the following steps:

[0099] (1) Data collection.

[0100] Collect and sort out the data of physical property analysis, thin section identification, nuclear magnetic experiment, mercury injection, relative permeability experiment, etc. of the research block, and obtain the reservoir porosity data, reservoir permeability data, and reservoir shale content data of the core samples in the research block;

[0101] (2) Divide the reservoir.

[0102] Automatically or manually layer according to the conventional well logging data using the layer method of principal component activity weighting and variance optimization. If the research block has been interpreted once, the layer result of the first interpretation can be directly used.

[0103] (3) Establish the irreducible water saturation S wi Calculation model.

[0104] Through single parameter sensitivity analysis, the irreducible water saturation analyzed by core has a good single correlation with the shale content and pore structure index Through multiple regression, the calculation formula for irreducible water saturation is obtained: where Φ represents the effective porosity of the reservoir, %, K represents the effective permeability of the reservoir, mD, and Vsh represents the shale content of the reservoir, %.

[0105] Among them, for the cored section, the effective porosity Φ, effective permeability K, and shale content Vsh of the reservoir can be directly obtained from the physical properties, thin sections, etc. of core experiments. For the uncored well section, the effective porosity Φ of the reservoir can be calculated from density and acoustic logging curves, the effective permeability K of the reservoir can be calculated from the effective porosity of the reservoir, and the shale content Vsh of the reservoir can be calculated from the natural gamma logging curve through an empirical formula.

[0106] (4) Obtain the oil saturation So of the oil layer based on the irreducible water saturation. o .

[0107] Since the irreducible water saturation Sw wi + the oil saturation So o = 100, based on this, the oil saturation So of the oil layer can be obtained o ;

[0108] (5) Calculate the formation water resistivity Rw.

[0109] Obtain the formation water salinity from water analysis data and then get the formation water resistivity Rw, or use the apparent formation water resistivity method to get the formation water resistivity Rw;

[0110] (6) Obtain the values of Archie parameters a, b, m, and n.

[0111] Obtain the values of Archie parameters a, b, m, and n from the Archie experiment analysis data of the cored well section in the study area;

[0112] (7) Correct the formation resistivity and obtain the resistivity correction factor A.

[0113] Based on the obtained oil saturation So o , Archie parameters a, b, m, n, and formation water resistivity Rw, use Archie's formula to back-calculate the true formation resistivity, i.e., the corrected formation resistivity Rt 校正 , and calculate the ratio of it to the measured logging resistivity Rt to obtain the resistivity correction factor A.

[0114] Among them, the resistivity correction factor is expressed as:

[0115]

[0116] Archie's formula is expressed as:

[0117] Among them, the oil saturation: So = 1 - Sw; Φ represents the effective porosity of the reservoir, in decimal; Rt represents the measured logging resistivity, in Ω·m; Rw represents the formation water resistivity, in Ω·m; Sw represents the water saturation, in decimal; a and b are both coefficients; m and n represent the porosity index and saturation index respectively; So represents the oil saturation, in decimal.

[0118] (8) Establish a low-resistivity oil layer identification chart.

[0119] Use the data points of the oil layers, oil-water layers, and water layers from well testing to plot a crossplot of the measured logging resistivity Rt and the resistivity correction multiple A, as Figure 2 shown. Determine the distribution areas of the data points of the oil layers, oil-water layers, and water layers on the chart according to the data distribution characteristics, and establish a low-resistivity oil layer identification chart and standards;

[0120] Figure 2 is the low-resistivity oil layer identification chart. The abscissa is the resistivity correction multiple A, and the ordinate is the measured logging resistivity. In the figure, the oil layers with Rt > 2 Ω·m on the ordinate are normal oil layers (non-low-resistivity oil layers), and their resistivity is significantly separated from that of the oil-water layers and water layers, making them relatively easy to identify; the area where 0.8 < Rt < 1 is the low-resistivity oil layer identification area. Simply relying on resistivity, it can be distinguished from the water layer, but it is difficult to distinguish from the oil-water layer. However, through the resistivity correction multiple A on the abscissa, it can be distinguished from the oil-water layer. For low-resistivity oil layers, 1 < resistivity correction multiple A < 2, while for oil-water layers and water layers, resistivity correction multiple A > 2.

[0121] (9) Project the data points of the layer to be evaluated onto the low-resistivity oil layer identification chart, and identify whether it is an oil layer according to the position of the data points. If the data points fall within the oil layer area, the layer to be evaluated can be determined as an oil layer; otherwise, the layer to be evaluated is determined as a water layer or an oil-water layer.

[0122] Table 1 Identification results of low-resistivity oil layers, oil-water layers, and water layers in some wells of H Oilfield

[0123]

[0124] In summary, the low-resistivity oil layer identification method of the embodiment of the present invention studies through the calculation method of the irreducible water saturation of the reservoir to obtain the accurate oil saturation of the oil layer, and then obtains the true resistivity of the reservoir through the correction of the oil saturation. By comparing the corrected resistivity with the measured resistivity, the correction multiple is obtained to achieve the purpose of identifying the low-resistivity oil layer; this method is established based on a large amount of oil layer test data and through the research of a large number of well data. Applying this method for oil layer identification, the results are more accurate. Therefore, the low-resistivity oil layer identification method of the embodiment of the present invention provides a new idea for the identification and evaluation of low-resistivity oil layers, has a good application effect for low-resistivity oil layers formed by high irreducible water saturation, and this method can be directly used in practice, effectively improving the accuracy of low-resistivity oil layer identification.

[0125] Example 2:

[0126] See Figure 3 shown. This embodiment provides a low-resistivity oil layer identification device 1, and the device 1 includes:

[0127] A data acquisition module 10, configured to acquire reservoir porosity data, reservoir permeability data, and reservoir shale content data of a research block;

[0128] A first calculation module 20, configured to calculate the irreducible water saturation of each reservoir based on the stratification result of the research block, reservoir porosity data, reservoir permeability data, and reservoir shale content data, and by using a pre-established irreducible water saturation calculation model;

[0129] A second calculation module 30, configured to substitute the irreducible water saturation of each reservoir into the relationship model between irreducible water saturation and oil saturation, and calculate the oil-bearing saturation of each reservoir;

[0130] A correction factor acquisition module 40, configured to calculate the corrected formation resistivity by using Archie's formula based on the oil-bearing saturation of each reservoir, the formation water resistivity of each reservoir, the petrophysical parameters of the research block, and reservoir porosity data, and the ratio of the corrected formation resistivity to the measured resistivity of the well logging is the resistivity correction factor;

[0131] A chart establishment module 50, configured to establish a low-resistivity oil layer identification chart by using the test oil layer data, resistivity correction factor, and measured resistivity of the well logging;

[0132] An identification module 60, configured to project the data points of the layer to be evaluated onto the low-resistivity oil layer identification chart, and obtain the low-resistivity oil layer identification result according to the position of the data points on the low-resistivity oil layer identification chart.

[0133] In some embodiments, the data acquisition module 10 in the low-resistivity oil layer identification device 1 of this embodiment is further specifically configured to, for the cored well section, obtain the reservoir porosity data, reservoir permeability data, and reservoir shale content data of the research block through the physical property analysis data, thin section identification data, nuclear magnetic experiment data, mercury injection data, and relative permeability experiment data of the research block analyzed by core experiments;

[0134] For the non-cored well section, the reservoir porosity data is calculated from the density and acoustic well logging curves, the reservoir permeability data is calculated from the reservoir porosity data, and the reservoir shale content data is calculated from the natural gamma well logging curve through an empirical formula.

[0135] In some embodiments, the first calculation module 20 in the low-resistivity oil layer identification device 1 of this embodiment is further specifically configured to perform stratification processing on the research block by using the principal component activity weighting and variance optimization automatic stratification method or manual stratification method according to the conventional well logging data, so as to obtain the stratification result of the research block.

[0136] In some embodiments, in the low-resistivity oil layer identification device 1 of this embodiment, through single-parameter sensitivity analysis, it is determined that there is a single correlation between the irreducible water saturation and the reservoir porosity data, reservoir permeability data, and reservoir shale content data, and a calculation model for the irreducible water saturation is established using multiple regression, expressed as:

[0137]

[0138] Where S wi represents the irreducible water saturation; Φ represents the effective porosity of the reservoir, in %; K represents the effective permeability of the reservoir, in mD; Vsh represents the shale content of the reservoir, in %.

[0139] In some embodiments, the relationship model between the irreducible water saturation and the oil saturation in the low-resistivity oil layer identification device 1 of this embodiment is expressed as:

[0140] S wi +S o = 100 (2)

[0141] Where, S wi represents the irreducible water saturation, S o represents the oil saturation.

[0142] In some embodiments, the correction factor acquisition module 40 in the low-resistivity oil layer identification device 1 of this embodiment is further specifically configured to obtain the petrophysical parameters of the study block through the petrophysical experiment analysis data of the cored well section in the study area. Among them, the petrophysical parameters include coefficient a, coefficient b, pore index m, and saturation index n.

[0143] In some embodiments, the Archie formula in the low-resistivity oil layer identification device 1 of this embodiment is expressed as:

[0144]

[0145] Where, S w represents the water saturation, which is a decimal, and the oil saturation S o is expressed as S o = 1 - S w ; a and b are both coefficients; m and n respectively represent the pore index and saturation index; Φ represents the effective porosity of the reservoir, in %; Rt 校正 represents the corrected formation resistivity, in Ω·m; Rw represents the formation water resistivity, in Ω·m.

[0146] Example 3:

[0147] The present invention discloses an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps in a low-resistivity oil layer identification method according to any one of the disclosed embodiments 1 of the present invention are implemented.

[0148] Figure 4 As shown in the structural diagram of an electronic device according to an embodiment of the present invention, Figure 4 the electronic device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be implemented through WIFI, a carrier network, near field communication (NFC), or other technologies. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen. The input device of the electronic device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the electronic device, or an external keyboard, a touchpad, or a mouse, etc.

[0149] Those skilled in the art can understand that Figure 4 the structure shown in is only the structural diagram of the part related to the technical solution of the present disclosure, and does not constitute a limitation on the electronic device to which the application solution of the present application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have a different component layout.

[0150] Embodiment 4:

[0151] The embodiment of the present invention discloses a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps in a low-resistivity oil layer identification method according to any one of the embodiments 1 of the present invention are implemented.

[0152] Embodiment 5:

[0153] The embodiment of the present invention discloses a computer program product, including a computer program. When the computer program is executed by a processor, the steps in a low-resistivity oil layer identification method as described in Embodiment 1 of the present invention above are implemented.

[0154] Please note that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification. The above embodiments only represent several implementation manners of the present application, and their descriptions are relatively specific and detailed, but should not be construed as a limitation on the scope of the invention patent. It should be pointed out that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

[0155] The embodiments of the subject matter and the functional operations described in this specification can be implemented in the following: digital electronic circuits, tangible computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or a combination of one or more of them. The embodiments of the subject matter described in this specification can be implemented as one or more computer programs, that is, one or more modules in computer program instructions encoded on a tangible non-transitory program carrier to be executed by a data processing device or to control the operation of the data processing device. Alternatively or additionally, the program instructions can be encoded on an artificially generated propagated signal, such as a machine-generated electrical, optical or electromagnetic signal, which is generated to encode and transmit information to a suitable receiver device for execution by the data processing device. A computer storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of them.

[0156] The processes and logical flows described in this specification can be executed by one or more programmable computers executing one or more computer programs to perform corresponding functions by operating on input data and generating output. The processes and logical flows can also be executed by dedicated logic circuits, such as FPGA (Field Programmable Gate Array) or ASIC (Application Specific Integrated Circuit), and the device can also be implemented as dedicated logic circuits.

[0157] Computers suitable for executing computer programs include, for example, general and / or special purpose microprocessors, or any other type of central processing unit. Generally, the central processing unit will receive instructions and data from read-only memory and / or random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include one or more mass storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, etc., or the computer will be operably coupled to such mass storage devices to receive data therefrom or transfer data thereto, or both. However, a computer is not necessarily required to have such devices. In addition, a computer may be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive, to name just a few.

[0158] Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, such as including semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal hard disks or removable disks), magneto-optical disks, and CD ROM and DVD-ROM disks. Processors and memories may be supplemented by, or incorporated in, special purpose logic circuitry.

[0159] Although this specification contains many specific implementation details, these should not be construed as limiting the scope of any invention or the scope of what is claimed, but rather as mainly describing the features of specific embodiments of a particular invention. Certain features described in multiple embodiments in this specification may also be implemented in combination in a single embodiment. On the other hand, the various features described in a single embodiment may also be implemented separately in multiple embodiments or in any suitable sub-combination. In addition, although features may operate in certain combinations as described above and even be claimed as such initially, one or more features from a claimed combination may in some cases be removed from that combination, and the claimed combination may be directed to a sub-combination or a variation of a sub-combination.

[0160] Similarly, although operations are depicted in the drawings in a particular order, this should not be construed as requiring that the operations be performed in the particular order shown or sequentially, or that all illustrated operations be performed, to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. In addition, the separation of various system modules and components in the above embodiments should not be construed as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0161] Thus, particular embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the acts recited in the claims can be performed in a different order and still achieve the desired result. Further, the processes depicted in the figures are not necessarily in the particular order or sequential order shown to achieve the desired result. In some implementations, multitasking and parallel processing may be advantageous.

[0162] The foregoing is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the scope of protection of the present invention.

[0163] Although some specific embodiments of the present invention have been described in detail by way of examples, those skilled in the art should understand that the above examples are for illustrative purposes only and not for limiting the scope of the present invention. Those skilled in the art should understand that the above embodiments can be modified without departing from the scope and spirit of the present invention. The scope of the present invention is defined by the appended claims.

Claims

1. A method for identifying low-resistivity oil layers, characterized in that, The method includes: Step S1: Obtain the reservoir porosity data, reservoir permeability data, and reservoir shale content data of the research block; Step S2: Based on the stratification result of the research block, the reservoir porosity data, the reservoir permeability data, and the reservoir shale content data, and using a pre-established irreducible water saturation calculation model, calculate the irreducible water saturation of each reservoir; Step S3: Substitute the irreducible water saturation of each reservoir into the relationship model between irreducible water saturation and oil saturation, and calculate the oil saturation of each reservoir; Step S4: According to the oil saturation of each reservoir, the formation water resistivity of each reservoir, the rock-electric parameters of the research block, and the reservoir porosity data, and use Archie's formula to inversely calculate the corrected formation resistivity. The ratio of the corrected formation resistivity to the measured logging resistivity is the resistivity correction multiple; Step S5: Establish a low-resistivity oil layer identification chart using the oil test layer data, the resistivity correction multiple, and the measured logging resistivity; Step S6: Project the data points of the layer to be evaluated onto the low-resistivity oil layer identification chart, and obtain the low-resistivity oil layer identification result according to the position of the data points on the low-resistivity oil layer identification chart.

2. The method for identifying low-resistivity oil layers according to claim 1, characterized in that, In the step S1, for the cored well section, the reservoir porosity data, reservoir permeability data, and reservoir shale content data of the research block are obtained through the physical property analysis data, thin section identification data, nuclear magnetic experiment data, mercury injection data, and relative permeability experiment data of the research block analyzed by core experiments.

3. The method for identifying low-resistivity oil layers according to claim 1, characterized in that, In the step S1, for the non-cored well section, the reservoir porosity data is calculated from the density and acoustic logging curves, the reservoir permeability data is calculated from the reservoir porosity data, and the reservoir shale content data is calculated from the natural gamma logging curve through an empirical formula.

4. The method for identifying low-resistivity oil layers according to claim 1, characterized in that, In the step S2, the research block is stratified using the automatic stratification method or manual stratification method of principal component activity weighting and variance optimization based on conventional logging data to obtain the stratification result of the research block.

5. The method for identifying low-resistivity oil layers according to claim 1, characterized in that, In the step S2, the establishment process of the irreducible water saturation calculation model is as follows: Determine the single correlation between irreducible water saturation and reservoir porosity data, reservoir permeability data, and reservoir shale content data through single-parameter sensitivity analysis; Use multiple regression to establish the irreducible water saturation calculation model.

6. The method for identifying low-resistivity oil layers according to claim 5, characterized in that, The irreducible water saturation calculation model is expressed as: where S wi represents the irreducible water saturation; Φ represents the effective porosity of the reservoir, in %; K represents the effective permeability of the reservoir, with the unit of mD; Vsh represents the reservoir shale content, with the unit of %.

7. The method for identifying low-resistivity oil layers according to claim 1, characterized in that, In the step S3, the relationship model between irreducible water saturation and oil saturation is expressed as: S wi +S o = 100 Among them, S wi represents the irreducible water saturation, and S o represents the oil saturation.

8. The method for identifying low-resistivity oil layers according to claim 1, characterized in that, The rock-electric parameters of the research block are obtained through the rock-electric experiment analysis data of the cored well section in the research area. Among them, the rock-electric parameters include coefficient a, coefficient b, pore index m, and saturation index n.

9. The method for identifying low-resistivity oil layers according to claim 8, characterized in that, In the step S4, Archie's formula is expressed as: Among them, S w represents the water saturation, which is a decimal. The oil saturation S o is expressed as S o = 1 - S w ; a and b are both coefficients; m and n represent the pore index and the saturation index respectively; Φ represents the effective porosity of the reservoir, with the unit of %; Rt 校正 represents the corrected formation resistivity, with the unit of Ω·m; Rw represents the formation water resistivity, with the unit of Ω·m.

10. The method for identifying low-resistivity oil layers according to claim 1, characterized in that, In the step S4, the resistivity correction multiple A is expressed as: where Rt 校正 represents the corrected formation resistivity in the unit of Ω·m; Rt represents the measured resistivity from well logging.

11. The method for identifying low-resistivity oil layers according to claim 1, characterized in that, In the step S4, obtain the formation water salinity according to the water analysis data, and then obtain the formation water resistivity; Alternatively, the formation water resistivity is obtained by using the apparent formation water resistivity method.

12. An apparatus for identifying low-resistivity oil layers, characterized in that, The device includes: a data acquisition module configured to acquire reservoir porosity data, reservoir permeability data, and reservoir shale content data of a research block; a first calculation module configured to calculate the irreducible water saturation of each reservoir based on the stratification result of the research block, the reservoir porosity data, the reservoir permeability data, and the reservoir shale content data, and by using a pre-established irreducible water saturation calculation model; a second calculation module configured to substitute the irreducible water saturation of each reservoir into the relationship model between irreducible water saturation and oil saturation to calculate the oil-bearing saturation of each reservoir; a correction factor acquisition module configured to calculate the corrected formation resistivity by using the Archie formula based on the oil-bearing saturation of each reservoir, the formation water resistivity of each reservoir, the petrophysical parameters of the research block, and the reservoir porosity data, and the ratio of the corrected formation resistivity to the measured logging resistivity is the resistivity correction factor; a chart establishment module configured to establish a low resistivity oil layer identification chart by using the oil test layer data, the resistivity correction factor, and the measured logging resistivity; an identification module configured to project the data points of the layer to be evaluated onto the low resistivity oil layer identification chart and obtain the low resistivity oil layer identification result according to the position of the data points on the low resistivity oil layer identification chart.

13. An electronic device, characterized in that, The electronic device includes a memory and a processor. When the processor executes the computer program stored in the memory, the steps in a low resistivity oil layer identification method according to any one of claims 1 to 11 are implemented.

14. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium. When the computer program is executed by the processor, the steps in a low resistivity oil layer identification method according to any one of claims 1 to 11 are implemented.

15. A computer program product, comprising a computer program which, when executed by a processor, implements the steps in an oil layer identification method according to any one of claims 1 to 11.

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