Method, device, equipment and medium for identifying fluid of glauconite-containing sandstone reservoir

Abstract

The invention discloses a glauconite-containing sandstone reservoir fluid identification method, device and equipment and a medium. The method comprises the following steps: determining the porosity of a target glauconite-containing sandstone reservoir, and determining the reservoir type of the target glauconite-containing sandstone reservoir according to the porosity; if the reservoir type is a reservoir, characteristic parameters of a target glauconite-containing sandstone reservoir are obtained, the porosity and the characteristic parameters are input into an identification model, a fluid identification value of the target glauconite-containing sandstone reservoir is obtained, and the fluid type of the fluid of the glauconite-containing sandstone reservoir is determined according to the fluid identification value. The fluid types are divided according to the oil content of the reservoir. According to the technical scheme, the fluid identification value is accurately obtained through the constructed identification model, so that the fluid type of the glauconite-containing sandstone reservoir fluid is accurately determined according to the fluid identification value, the problem that the implementation cost is high when the reservoir fluid type is determined is solved, the fluid type division efficiency is improved, and the method is suitable for mass production. And a basis is provided for further research and explanation.

Description

Technical Field

[0001] The invention relates to the field of oil and gas exploration and development, and in particular to a method, device, equipment and medium for identifying fluid in a glauconite-containing sandstone reservoir. Background Art

[0002] In recent years, with the progress and development of domestic oil exploration and development technology, production tests of glauconite-containing sandstone reservoirs (such as the upper T layer of the Andes oil field) can also obtain higher oil and gas production.

[0003] At present, glauconite sandstone reservoirs are relatively rare in China, while the identification of fluids in glauconite sandstone reservoirs abroad relies on logging or coring, and the calculation of reservoir physical properties and oil content and other parameters relies on nuclear magnetic resonance logging or modeling with detailed core analysis and testing data. Although the calculation method is relatively reliable, the implementation cost of a single well is high and cannot be applied on a large scale. Summary of the invention

[0004] The present invention provides a method, device, equipment and medium for identifying fluid in a glauconite-containing sandstone reservoir, so as to solve the problem of high implementation cost when determining the type of reservoir fluid.

[0005] According to one aspect of the present invention, a method for identifying fluid in a glauconite-containing sandstone reservoir is provided, the method comprising:

[0006] Determine the porosity of the target glauconite-containing sandstone reservoir, and determine the reservoir type of the target glauconite-containing sandstone reservoir according to the porosity, wherein the reservoir type is a reservoir and a non-reservoir;

[0007] If the reservoir type is a reservoir, obtaining characteristic parameters of the target glauconite-containing sandstone reservoir, the characteristic parameters including deep lateral resistivity, natural gamma and compensated neutron;

[0008] Input the porosity and the characteristic parameters into an identification model to obtain a fluid identification value of a target glauconite-containing sandstone reservoir, wherein the identification model describes the relationship between the fluid identification value and the characteristic parameters, and the fluid identification value is used to indicate the amount of oil in the reservoir;

[0009] The fluid type of the glauconite-containing sandstone reservoir fluid is determined according to the fluid identification value, and the fluid type is divided according to the oil content of the reservoir.

[0010] According to another aspect of the present invention, a fluid identification device for a glauconite-containing sandstone reservoir is provided, the device comprising:

[0011] A reservoir type determination module, used to determine the porosity of a target glauconite-containing sandstone reservoir, and determine the reservoir type of the target glauconite-containing sandstone reservoir according to the porosity, wherein the reservoir type is a reservoir and a non-reservoir;

[0012] A characteristic parameter determination module, for obtaining characteristic parameters of the target glauconite-containing sandstone reservoir if the reservoir type is a reservoir, wherein the characteristic parameters include deep lateral resistivity, natural gamma and compensated neutron;

[0013] A fluid identification value determination module, used for inputting the porosity and the characteristic parameters into an identification model to obtain a fluid identification value of a target glauconite-containing sandstone reservoir, wherein the identification model is used to describe the relationship between the fluid identification value and the characteristic parameters, and the fluid identification value is used to indicate the amount of oil in the reservoir;

[0014] The fluid type determination module is used to determine the fluid type of the glauconite-containing sandstone reservoir fluid according to the fluid identification value, and the fluid type is divided according to the oil content of the reservoir.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for identifying fluid in a glauconite-containing sandstone reservoir as described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for identifying fluid in a glauconite-containing sandstone reservoir as described in any embodiment of the present invention when executed.

[0020] The technical solution of the embodiment of the present invention determines the porosity of the target glauconite-containing sandstone reservoir, and determines the reservoir type of the target glauconite-containing sandstone reservoir according to the porosity; if the reservoir type is a reservoir, the characteristic parameters of the target glauconite-containing sandstone reservoir are obtained, the porosity and the characteristic parameters are input into the identification model, and the fluid identification value of the target glauconite-containing sandstone reservoir is obtained, and the fluid type of the glauconite-containing sandstone reservoir fluid is determined according to the fluid identification value, and the fluid type is divided according to the oil content of the reservoir. The technical solution of the present application accurately obtains the fluid identification value through the constructed identification model, so as to accurately determine the fluid type of the glauconite-containing sandstone reservoir fluid according to the fluid identification value, solves the problem of high implementation cost when determining the reservoir fluid type, improves the efficiency of fluid type division, and provides a basis for further research and interpretation.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 is a flow chart of a method for identifying fluid in a glauconite-containing sandstone reservoir provided in an embodiment of the present invention;

[0024] Figure 2 is a cross-plot of porosity and characteristic parameters of a core analysis of a glauconite-containing sandstone reservoir applicable to an embodiment of the present invention;

[0025] Figure 3 A cross-plot of the porosity of the core analysis and the porosity of the calculated porosity of the glauconite-containing sandstone reservoir in the target layer provided in the embodiment of the present invention;

[0026] Figure 4 A relationship diagram between the calculated porosity and saturation of a glauconite-containing sandstone reservoir provided in an embodiment of the present invention;

[0027] Figure 5 is a cross-plot of characteristic parameters of a glauconite-containing sandstone reservoir applicable according to an embodiment of the present invention;

[0028] Figure 6 is a cross-plot between characteristic parameters of a glauconite-containing sandstone reservoir and fluid types to which an embodiment of the present invention is applicable;

[0029] Figure 7 is a schematic diagram of reservoir type classification and fluid identification values ​​applicable to an embodiment of the present invention;

[0030] Figure 8 is a schematic structural diagram of a fluid identification device for a glauconite-containing sandstone reservoir provided according to an embodiment of the present invention;

[0031] Fig. 9 It is a schematic diagram of the structure of an electronic device for implementing the method for identifying fluid in a glauconite-containing sandstone reservoir according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first", "second" and "third" etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0034] Embodiment 1

[0035] Figure 1 The flow chart of a method for identifying fluid in a glauconite sandstone reservoir provided by an embodiment of the present invention is applicable to the case of identifying fluid in a glauconite sandstone reservoir. The method can be executed by a fluid identification device for a glauconite sandstone reservoir. The fluid identification device for a glauconite sandstone reservoir can be implemented in the form of hardware and / or software. The fluid identification device for a glauconite sandstone reservoir can be configured in an electronic device having a method for identifying fluid in a glauconite sandstone reservoir. Figure 1 As shown, the method includes:

[0036] S110. Determine the porosity of the target glauconite-containing sandstone reservoir, and determine the reservoir type of the target glauconite-containing sandstone reservoir according to the porosity. The reservoir type is a reservoir and a non-reservoir.

[0037] Specifically, the porosity of the target glauconite-containing sandstone reservoir can be obtained by analyzing the core of the target glauconite-containing sandstone reservoir, and then the porosity is compared with the lower limit value of the target glauconite-containing sandstone reservoir to obtain the reservoir type of the target glauconite-containing sandstone reservoir.

[0038] Optionally, determining the porosity of the target glauconite-containing sandstone reservoir may include: obtaining a compensated density, a compensated neutron, a natural gamma, a shale content, and a shale neutron of the target glauconite-containing sandstone reservoir; normalizing the compensated density, the compensated neutron, the shale neutron, the shale content, and the natural gamma to obtain a normalized value of the compensated density, a normalized value of the compensated neutron, a normalized value of the shale neutron, a normalized value of the shale content, and a normalized value of the natural gamma; and normalizing the compensated density The compensated density normalized value, the compensated neutron normalized value, the mudstone neutron normalized value, the mud content normalized value, the natural gamma normalized value and the mud content normalized value are input into a porosity model to obtain the porosity of the target glauconite-containing sandstone reservoir. The porosity model is a model that describes the relationship between the compensated density normalized value, the compensated neutron normalized value, the mudstone neutron normalized value, the mud content normalized value and the natural gamma normalized value and porosity.

[0039] Among them, the porosity model can be expressed as:

[0040] POR=a×DEN0+b×(CNL0+c×CNL sh0 ×V sh0 )-d×GR0+e

[0041] Among them, DEN0 is the normalized value of density compensation in the treatment section, dimensionless; CNL0 is the normalized value of neutron compensation in the treatment section, dimensionless; CNL sh0 is the normalized value of mudstone neutrons in the processing section, dimensionless; GR0 is the normalized value of natural gamma in the processing section, dimensionless; a, b, c, d, e are regression coefficients, which are determined in the actual training model; V sh0 To process the normalized value of the mud content in the well section, it is dimensionless and its calculation formula is:

[0042]

[0043] Where GCUR is an empirical coefficient related to the age of the strata, 3.7 for new strata and 2 for old strata, V shMax is the maximum value of the mud content in the processing section, SH is the relative value of natural gamma, and its calculation formula is:

[0044]

[0045] Where, GR is the natural gamma logging value of the processed well section; GR max Natural gamma logging value of pure mudstone formation; GR min It is the natural gamma logging value of pure lithological formation.

[0046] Specifically, the process of constructing the porosity model is as follows: obtaining the historical characteristic parameters and historical porosity of the target glauconite-bearing sandstone reservoir, the historical characteristic parameters including natural gamma, acoustic wave time difference, compensated density, compensated neutron and deep lateral resistivity; based on the historical characteristic parameters and historical porosity, establishing a first relationship curve between the historical porosity and the first historical characteristic parameter, such as Figure 2 As shown; according to the first relationship curve, the first sensitive parameter of the target glauconite sandstone reservoir is determined, and the first sensitive parameter is the compensated density, compensated neutron and natural gamma. For example, the first sensitive parameter is accurately obtained by the relationship between the porosity and the characteristic parameter in the first relationship curve (such as a positive or negative correlation, which means that the characteristic parameter has a greater impact on the porosity), and then the first sensitive parameter is normalized to obtain the normalized value of the sensitive parameter.

[0047] In particular, in order to demonstrate the reliability of the selected sensitive parameters, the historical characteristic parameters were input into the convolutional neural network algorithm model in different combinations for calculation, and different results were obtained, as shown in Table 1, which proves that the first sensitive parameter determined is reliable. The results in Table 1 show that the calculated porosity obtained by inputting the three curves of GR, CNL, and DEN has the smallest error and the highest correlation with the core analysis porosity, and the result is the best.

[0048] Therefore, a linear multiple regression can be performed based on the historical porosity and the normalized value of the first sensitive parameter, and the shale content and mudstone neutrons can be used for shale correction to establish a porosity model.

[0049] In particular, the relationship curve between the calculated porosity results and the core porosity is established to obtain their correlation, thereby demonstrating the applicability of the model, such as Figure 3 shown.

[0050] Table 1 Comparison of porosity calculation errors when different curves are input into the convolutional neural network algorithm

[0051]

[0052] Optionally, the reservoir type of the target glauconite-containing sandstone reservoir is determined based on the porosity as follows:

[0053] Determine a preset lower limit value of porosity of the target glauconite-containing sandstone reservoir, wherein the preset lower limit value of porosity can be determined according to the distribution probability of the statistical results of porosity and saturation of the glauconite-containing sandstone reservoir, such as Figure 4 As shown; further, the classification identification value of the reservoir type is determined according to the preset porosity lower limit value and the porosity, which can be determined according to the following formula:

[0054]

[0055] Among them, Q1 is the classification identification value, POR is the porosity, and a0 is the preset porosity lower limit value.

[0056] Finally, the reservoir type of the target glauconite sandstone reservoir is determined based on the division identification value and the preset division identification value. The preset division identification values ​​are 0 and 2, 0 corresponds to non-reservoir, and 2 corresponds to reservoir. When the division identification value is 0 in the preset division identification value, the reservoir type of the target glauconite sandstone reservoir is non-reservoir, that is, there is no oil storage and no exploitation is carried out. When the division identification value is 2 in the preset division identification value, the reservoir type of the target glauconite sandstone reservoir is a reservoir, and it is necessary to further determine the oil content in the reservoir to determine how to exploit it later.

[0057] S120. If the reservoir type is a reservoir, characteristic parameters of the target glauconite-containing sandstone reservoir are obtained, the characteristic parameters including deep lateral resistivity, natural gamma, and compensated neutron.

[0058] Specifically, when the reservoir type of the target glauconite-containing sandstone reservoir is determined to be a reservoir, it is necessary to further determine the type of fluid in the reservoir of the target glauconite-containing sandstone reservoir. Therefore, it is necessary to obtain the characteristic parameters of the target glauconite-containing sandstone reservoir, so as to facilitate the subsequent accurate derivation of the fluid type based on the analysis results of the characteristic parameters.

[0059] S130, input the porosity and characteristic parameters into the identification model to obtain the fluid identification value of the target glauconite-containing sandstone reservoir. The identification model is used to describe the relationship between the fluid identification value and the characteristic parameters. The fluid identification value is used to indicate the oil content in the reservoir.

[0060] Among them, the recognition model can be expressed as:

[0061]

[0062] Where Qt is the fluid identification value, POR is the porosity, a0 is the preset lower limit of porosity; RESD is the deep lateral resistivity; (RESD) max is the maximum value of deep lateral resistivity in the processing section; b0 is taken according to the characteristic value after normalization of deep lateral resistivity; GR is natural gamma; (GR) max is the maximum value of natural gamma in the processing section; CNL is compensated neutron; (CNL) max Compensate for the maximum value of neutrons in the treatment section.

[0063] Optionally, the recognition model is constructed as follows:

[0064] The historical characteristic parameters and historical fluid types of the target glauconite-bearing sandstone reservoir are obtained. The historical characteristic parameters include natural gamma, acoustic wave time difference, compensated density, compensated neutron and deep lateral resistivity. The historical fluid types are divided according to the saturation and actual test production results, which are authentic and reliable, and provide a basis for the optimization of the second sensitive parameter. The fluid types include the first oil-producing layer, the second oil-producing layer, the third oil-producing layer, the first oil-water layer, the second oil-water layer and the water layer; the first oil-producing layer is a high oil layer, that is, the oil production is greater than the first preset value, the second oil-producing layer is a medium oil layer, the oil production is between the second preset value and the first preset value, the third oil-producing layer is a low oil layer, the first oil-water layer is an oil-water layer, but the oil content is more than the water content, the second oil-water layer is an oil-containing water layer, that is, the water content is right for the oil content, and the water layer contains only water but no oil. A second relationship curve between the historical characteristic parameters is established based on the historical characteristic parameters, for example Figure 6 ; Based on the historical characteristic parameters and historical fluid types, a third relationship curve between the characteristic parameters and the fluid type is established, for example Figure 5 ; Thus, the second sensitive parameter of the target glauconite-containing sandstone reservoir is determined according to the second relationship curve and the third relationship curve; finally, the historical porosity of the target glauconite-containing sandstone reservoir is obtained, and a porosity model is established based on the second sensitive parameter and the historical porosity.

[0065] S140. Determine the fluid type of the glauconite-containing sandstone reservoir fluid according to the fluid identification value, and classify the fluid type according to the oil content of the reservoir.

[0066] Specifically, the fluid identification value is determined. Different fluid types correspond to different fluid identification values. Then, the fluid type of the glauconite-containing sandstone reservoir fluid can be determined according to the fluid identification value.

[0067] Optionally, determining the fluid type of the glauconite sandstone reservoir fluid based on the fluid identification value can also be as follows: obtaining a fluid identification value classification standard table, such as Table 2, and comparing the fluid identification value with the fluid identification value classification standard table, the fluid identification value classification standard table being the identification value range of different fluid types, and determining the fluid type of the glauconite sandstone reservoir fluid based on the comparison result, that is, distinguishing which range the fluid identification value is in in the table and which type it corresponds to, and t1, t2, t3, t4, and t5 in the table are determined based on actual oil production.

[0068] Table 2 Fluid identification value classification standard table

[0069] The first oil-producing layer The second oil-producing layer The third oil-producing layer The first oil-water layer Second oil-water layer Water layer Qt >t1 (t2,t1] (t3,t2] (t4,t3] (t5,t4] (0,t5]

[0070] The technical solution of the embodiment of the present invention determines the porosity of the target glauconite-containing sandstone reservoir, and determines the reservoir type of the target glauconite-containing sandstone reservoir according to the porosity; if the reservoir type is a reservoir, the characteristic parameters of the target glauconite-containing sandstone reservoir are obtained, the porosity and the characteristic parameters are input into the identification model, and the fluid identification value of the target glauconite-containing sandstone reservoir is obtained, and the fluid type of the glauconite-containing sandstone reservoir fluid is determined according to the fluid identification value, and the fluid type is divided according to the oil content of the reservoir. The technical solution of the present application accurately obtains the fluid identification value through the constructed identification model, so as to accurately determine the fluid type of the glauconite-containing sandstone reservoir fluid according to the fluid identification value, solves the problem of high implementation cost when determining the reservoir fluid type, improves the efficiency of fluid type division, and provides a basis for further research and interpretation.

[0071] Embodiment 2

[0072] This embodiment describes the above embodiments by way of example and does not limit the protection scope of the present invention.

[0073] Taking the upper T layer of a well area in Ecuador as an example, the historical characteristic parameters and historical porosity of the T layer are obtained. The historical characteristic parameters include natural gamma, acoustic wave time difference, compensated density, compensated neutron and deep lateral resistivity. Based on the historical characteristic parameters and historical porosity, the first relationship curve between the historical porosity and the first historical characteristic parameter is established, such as Figure 2 As shown, by analyzing the relationship between the characteristic parameters and the porosity in the first relationship curve, the compensated density, compensated neutron and natural gamma are determined as the first sensitive parameter, and then the first sensitive parameter is normalized to obtain the normalized value of the sensitive parameter of the first sensitive parameter, and the mud content and mudstone neutron are used to perform mud correction, so as to establish a porosity model POR=a×DEN0+b×(CNL0+c×CNL0) in which the porosity and the normalized value of the first sensitive parameter are associated. sh0 ×V sh0 )-d×GR0+e, in this embodiment, a=-37.436, b=2.02, c=-3, d=-0.242, e=119.

[0074] On the basis of porosity calculation, the preset lower limit of porosity of T layer is determined according to the historical core data of T layer and the distribution probability of the calculated porosity and saturation statistical results, such as Figure 3 As shown, in the present invention, the preset porosity lower limit a0=0.1 is used to establish a reservoir and non-reservoir division model, and the division model Q1 can be:

[0075]

[0076] On the basis of the reservoir and non-reservoir division model, combined with the response characteristics of different fluids in the T-layer test production layer, the historical characteristic parameters and historical fluid types of the T-layer are obtained. The historical characteristic parameters include natural gamma, acoustic time difference, compensated density, compensated neutron and deep lateral resistivity; the historical fluid types are divided according to saturation and actual test production results (actual oil production), which are authentic and reliable. Saturation and oil production are important bases for judging fluid types. The relationship between historical characteristic parameters of different fluid types and their relationship with saturation determine the accuracy of fluid identification. Therefore, the accuracy and reliability of historical fluid type division provides a basis for the optimization of the second sensitive parameter. The historical fluid types include the first oil-producing layer, the second oil-producing layer, the third oil-producing layer, the first oil-water layer, the second oil-water layer and the water layer. Then, based on the historical characteristic parameters, a second relationship curve between the historical characteristic parameters is established, such as Figure 6 As shown, based on the historical characteristic parameters and the historical fluid types, a third relationship curve between the characteristic parameters and the fluid types is established. Finally, by analyzing the second relationship curve and the third relationship curve, deep lateral resistivity, natural gamma and compensated neutron are selected as the second sensitive parameters, so that the historical porosity of the T layer and the second sensitive parameters are used to establish an identification model. The identification model is:

[0077]

[0078] Particularly, in this embodiment, b0=0.25.

[0079] After the above porosity model, division model and identification model are established, the fluid type of the T layer can be accurately determined, specifically:

[0080] Obtain the compensated density, compensated neutron and natural gamma of the T layer, and then use the porosity model to determine the porosity of the T layer. Then input the porosity of the T layer into the partitioning model to obtain the reservoir type of the T layer. The partitioning model outputs 0 for non-reservoir and 2 for reservoir.

[0081] If the reservoir type is a reservoir, the characteristic parameters of the T layer are obtained, including deep lateral resistivity, natural gamma and compensated neutron. Then the porosity and characteristic parameters are input into the identification model to obtain the fluid identification value of the T layer. Then the fluid identification value is compared with the classification standard table of T layer fluid identification values ​​(Table 3) to determine the fluid type of the T layer.

[0082] Among them, the T-layer fluid identification value classification standard table (Table 3) states that the first oil-producing layer is a high-yield oil-producing layer, the second oil-producing layer is a medium-yield oil-producing layer, the third oil-producing layer is a low-yield oil-producing layer, the first oil-water layer is an oil-water layer, and the second oil-water layer is an oil-bearing water layer. The fluid types are divided into six types of fluid layers: high-yield oil-producing layer, medium-yield oil-producing layer, low-yield oil-producing layer, oil-water layer, oil-bearing water layer, and water layer, and are given digital labels respectively: "1, 2, 3, 4, 5, 6, 7, ".

[0083] Table 3 Classification standard table of T layer fluid identification values

[0084] The first oil-producing layer The second oil-producing layer The third oil-producing layer The first oil-water layer Second oil-water layer Water layer Qt >50 (35,50] (18,35] (15,18] (8,15] (0,8] Label 6 5 4 3 2 1

[0085] The technical solution of the embodiment of the present invention first determines the porosity of the target glauconite-containing sandstone reservoir, and determines the reservoir type of the target glauconite-containing sandstone reservoir according to the porosity. If the reservoir type is a reservoir, characteristic parameters of the target glauconite-containing sandstone reservoir are obtained, and the porosity and characteristic parameters are input into an identification model to obtain a fluid identification value of the target glauconite-containing sandstone reservoir. The fluid type of the glauconite-containing sandstone reservoir fluid is determined according to the fluid identification value, which solves the problem of high implementation cost when determining the reservoir fluid type, improves the efficiency of fluid type classification, and provides a basis for further research and interpretation.

[0086] Embodiment 3

[0087] Figure 8 The schematic diagram of the structure of a fluid identification device for a glauconite-containing sandstone reservoir provided by an embodiment of the present invention. Figure 8 As shown, the device comprises:

[0088] A reservoir type determination module 210 is used to determine the porosity of a target glauconite-containing sandstone reservoir, and determine the reservoir type of the target glauconite-containing sandstone reservoir according to the porosity, wherein the reservoir type is a reservoir and a non-reservoir;

[0089] The characteristic parameter determination module 220 is used to obtain characteristic parameters of the target glauconite-containing sandstone reservoir if the reservoir type is a reservoir, wherein the characteristic parameters include deep lateral resistivity, natural gamma and compensated neutron;

[0090] A fluid identification value determination module 230 is used to input the porosity and the characteristic parameters into an identification model to obtain a fluid identification value of a target glauconite-containing sandstone reservoir, wherein the identification model describes the relationship between the fluid identification value and the characteristic parameters, and the fluid identification value is used to indicate the amount of oil in the reservoir;

[0091] The fluid type determination module 240 is used to determine the fluid type of the glauconite-containing sandstone reservoir fluid according to the fluid identification value, and the fluid type is divided according to the oil content of the reservoir.

[0092] Optionally, the reservoir type determination module includes a porosity determination unit, which is used to:

[0093] Obtain the compensated density, compensated neutron, mudstone neutron, mud content and natural gamma of the target glauconite-bearing sandstone reservoir;

[0094] Normalizing the compensation density, the compensation neutron, the mudstone neutron, the mud content and the natural gamma to obtain a normalized value of the compensation density, a normalized value of the compensation neutron, a normalized value of the mudstone neutron, a normalized value of the mud content and a normalized value of the natural gamma;

[0095] The compensated density normalized value, the compensated neutron normalized value, the mudstone neutron normalized value, the mud content normalized value, the natural gamma normalized value and the mud content normalized value are input into a porosity model to obtain the porosity of the target glauconite-containing sandstone reservoir. The porosity model is a model that describes the relationship between the compensated density normalized value, the compensated neutron normalized value, the mudstone neutron normalized value, the mud content normalized value and the natural gamma normalized value and porosity.

[0096] Optionally, the porosity model is constructed as follows:

[0097] Obtaining historical characteristic parameters and historical porosity of the target glauconite-bearing sandstone reservoir, wherein the historical characteristic parameters include natural gamma, acoustic wave time difference, compensated density, compensated neutron and deep lateral resistivity;

[0098] Establishing a first relationship curve between the historical porosity and the first historical characteristic parameter based on the historical characteristic parameter and the historical porosity;

[0099] Determine a first sensitive parameter related to the target glauconite-containing sandstone reservoir according to the first relationship curve, wherein the first sensitive parameter includes compensated density, compensated neutron and natural gamma;

[0100] Normalizing the first sensitive parameter to obtain a normalized sensitive parameter value of the first sensitive parameter;

[0101] Obtaining a shale correction parameter, and establishing the porosity model according to the historical porosity, the normalized value of the first sensitive parameter and the shale correction parameter, wherein the shale correction parameter includes shale content and mudstone neutrons.

[0102] Optionally, a reservoir type determination module is used to

[0103] Determining a preset lower limit value of porosity of the target glauconite-containing sandstone reservoir;

[0104] Determining a reservoir type classification identification value according to the preset porosity lower limit value and the porosity;

[0105] The reservoir type of the target glauconite-containing sandstone reservoir is determined according to the classification identification value and the preset classification identification value.

[0106] Optionally, the recognition model is constructed as follows:

[0107] Acquiring historical characteristic parameters and historical fluid types of the target glauconite-containing sandstone reservoir, wherein the historical characteristic parameters include natural gamma, acoustic wave time difference, compensated density, compensated neutron and deep lateral resistivity, and the fluid types include a first oil-producing layer, a second oil-producing layer, a third oil-producing layer, a first oil-water layer, a second oil-water layer and a water layer;

[0108] Establishing a second relationship curve between the historical characteristic parameters according to the historical characteristic parameters;

[0109] Establishing a third relationship curve between the characteristic parameter and the fluid type according to the historical characteristic parameter and the historical fluid type;

[0110] Determine a second sensitive parameter of the target glauconite-containing sandstone reservoir according to the second relationship curve and the third relationship curve;

[0111] The historical porosity of the target glauconite-containing sandstone reservoir is obtained, and the identification model is established based on the second sensitive parameter and the historical porosity.

[0112] Optionally, a fluid type determination module is used to:

[0113] Obtaining a fluid identification value classification standard table, and comparing the fluid identification value with the fluid identification value classification standard table, wherein the fluid identification value classification standard table is a range of identification values ​​for different fluid types;

[0114] The fluid type of the glauconite-containing sandstone reservoir fluid is determined according to the comparison result.

[0115] The device for identifying fluid in glauconite-containing sandstone reservoirs provided in the embodiment of the present invention can execute the method for identifying fluid in glauconite-containing sandstone reservoirs provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0116] Embodiment 4

[0117] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0118] Fig. 9A schematic diagram of the structure of an electronic device that can be used to implement the method for identifying fluid in a glauconite-containing sandstone reservoir according to an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0119] like Fig. 9 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0120] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0121] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for identifying fluids in a glauconite-containing sandstone reservoir.

[0122] In some embodiments, the method for identifying fluids in a glauconite-containing sandstone reservoir may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for identifying fluids in a glauconite-containing sandstone reservoir described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for identifying fluids in a glauconite-containing sandstone reservoir by any other appropriate means (e.g., by means of firmware).

[0123] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0124] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0125] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0126] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0127] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0128] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0129] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0130] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for identifying fluid in a glauconite-containing sandstone reservoir, characterized in that: include: Determine the porosity of the target glauconite-containing sandstone reservoir, and determine the reservoir type of the target glauconite-containing sandstone reservoir according to the porosity, wherein the reservoir type is a reservoir and a non-reservoir; If the reservoir type is a reservoir, obtaining characteristic parameters of the target glauconite-containing sandstone reservoir, the characteristic parameters including deep lateral resistivity, natural gamma and compensated neutron; Input the porosity and the characteristic parameters into an identification model to obtain a fluid identification value of a target glauconite-containing sandstone reservoir, wherein the identification model describes the relationship between the fluid identification value and the characteristic parameters, and the fluid identification value is used to indicate the amount of oil in the reservoir; The fluid type of the glauconite-containing sandstone reservoir fluid is determined according to the fluid identification value, and the fluid type is divided according to the oil content of the reservoir.

2. The method according to claim 1, characterized in that Determine the porosity of the target glauconite-bearing sandstone reservoir, including: Obtain the compensated density, compensated neutron, mudstone neutron, mud content and natural gamma of the target glauconite-bearing sandstone reservoir; Normalizing the compensation density, the compensation neutron, the mudstone neutron, the mud content and the natural gamma to obtain a normalized value of the compensation density, a normalized value of the compensation neutron, a normalized value of the mudstone neutron, a normalized value of the mud content and a normalized value of the natural gamma; The compensated density normalized value, the compensated neutron normalized value, the mudstone neutron normalized value, the mud content normalized value, the natural gamma normalized value and the mud content normalized value are input into a porosity model to obtain the porosity of the target glauconite-containing sandstone reservoir. The porosity model is a model that describes the relationship between the compensated density normalized value, the compensated neutron normalized value, the mudstone neutron normalized value, the mud content normalized value and the natural gamma normalized value and porosity.

3. The method according to claim 2, characterized in that The construction process of the porosity model is: Obtaining historical characteristic parameters and historical porosity of the target glauconite-bearing sandstone reservoir, wherein the historical characteristic parameters include natural gamma, acoustic wave time difference, compensated density, compensated neutron and deep lateral resistivity; Establishing a first relationship curve between the historical porosity and the first historical characteristic parameter based on the historical characteristic parameter and the historical porosity; Determine a first sensitive parameter related to the target glauconite-containing sandstone reservoir according to the first relationship curve, wherein the first sensitive parameter includes compensated density, compensated neutron and natural gamma; Normalizing the first sensitive parameter to obtain a normalized sensitive parameter value of the first sensitive parameter; Obtaining a shale correction parameter, and establishing the porosity model according to the historical porosity, the normalized value of the first sensitive parameter and the shale correction parameter, wherein the shale correction parameter includes shale content and mudstone neutrons.

4. The method according to claim 1, characterized in that: Determining the reservoir type of the target glauconite-containing sandstone reservoir according to the porosity includes: Determining a preset lower limit value of porosity of the target glauconite-containing sandstone reservoir; Determining a reservoir type classification identification value according to the preset porosity lower limit value and the porosity; The reservoir type of the target glauconite-containing sandstone reservoir is determined according to the classification identification value and the preset classification identification value.

5. The method according to claim 1, characterized in that The construction process of the recognition model is as follows: Acquiring historical characteristic parameters and historical fluid types of the target glauconite-containing sandstone reservoir, wherein the historical characteristic parameters include natural gamma, acoustic wave time difference, compensated density, compensated neutron and deep lateral resistivity, and the fluid types include a first oil-producing layer, a second oil-producing layer, a third oil-producing layer, a first oil-water layer, a second oil-water layer and a water layer; Establishing a second relationship curve between the historical characteristic parameters according to the historical characteristic parameters; Establishing a third relationship curve between the characteristic parameter and the fluid type according to the historical characteristic parameter and the historical fluid type; Determine a second sensitive parameter of the target glauconite-containing sandstone reservoir according to the second relationship curve and the third relationship curve; The historical porosity of the target glauconite-containing sandstone reservoir is obtained, and the identification model is established based on the second sensitive parameter and the historical porosity.

6. The method according to claim 1, characterized in that Determining the fluid type of the glauconite-containing sandstone reservoir fluid according to the fluid identification value includes: Obtaining a fluid identification value classification standard table, and comparing the fluid identification value with the fluid identification value classification standard table, wherein the fluid identification value classification standard table is a range of identification values ​​for different fluid types; The fluid type of the glauconite-containing sandstone reservoir fluid is determined according to the comparison result.

7. A fluid identification device for a glauconite-containing sandstone reservoir, characterized in that: include: A reservoir type determination module, used to determine the porosity of a target glauconite-containing sandstone reservoir, and determine the reservoir type of the target glauconite-containing sandstone reservoir according to the porosity, wherein the reservoir type is a reservoir and a non-reservoir; A characteristic parameter determination module, for obtaining characteristic parameters of the target glauconite-containing sandstone reservoir if the reservoir type is a reservoir, wherein the characteristic parameters include deep lateral resistivity, natural gamma and compensated neutron; A fluid identification value determination module, used for inputting the porosity and the characteristic parameters into an identification model to obtain a fluid identification value of a target glauconite-containing sandstone reservoir, wherein the identification model is used to describe the relationship between the fluid identification value and the characteristic parameters, and the fluid identification value is used to indicate the amount of oil in the reservoir; The fluid type determination module is used to determine the fluid type of the glauconite-containing sandstone reservoir fluid according to the fluid identification value, and the fluid type is divided according to the oil content of the reservoir.

8. The method according to claim 7, characterized in that The reservoir type determination module includes a porosity determination unit for: Obtain compensated density, compensated neutron and natural gamma of target glauconite-bearing sandstone reservoir; Normalizing the compensation density, the compensation neutrons, and the natural gamma to obtain a normalized value of the compensation density, a normalized value of the compensation neutrons, and a normalized value of the natural gamma; The compensated density normalized value, the compensated neutron normalized value and the natural gamma normalized value are input into a porosity model to obtain the porosity of the target glauconite-containing sandstone reservoir. The porosity model is a model that describes the relationship between the compensated density normalized value, the compensated neutron normalized value, the natural gamma normalized value and porosity.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the method for identifying fluid in a glauconite-containing sandstone reservoir according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for identifying fluid in a glauconite-containing sandstone reservoir according to any one of claims 1 to 6 when the instructions are executed.

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