Method and system for identifying sand-lime mixed rock oil-water layer

Abstract

The invention discloses a method and system for identifying an oil-water layer of sand-lime mixed rock, and the method comprises the steps: determining stratum lithology under different unit depth step lengths according to coring experiment analysis data and logging data of a to-be-evaluated well, and preferably selecting a plurality of lithology identification sensitive curves through comparing various logging curves; lithology identification parameters under different unit depth step lengths are calculated, and lithology identification parameter threshold values used for distinguishing different lithology are determined; according to the logging item type of the well to be researched, the lithology identification parameter threshold value is utilized to calculate the grey matter content under different depth step lengths in the depth section meeting the lithology identification parameter grey matter condition; according to the ash content under different depth step lengths, the porosity under different depth step lengths and the oil saturation for removing the influence of ash components on the resistivity are calculated, the oil saturation threshold value under different oil-bearing states is determined, and therefore the oil-water layer of the to-be-evaluated well is recognized. According to the method, the porosity and the oil saturation are calculated more accurately, and the oil-water layer is judged accurately.

Description

Technical Field

[0001] The invention relates to the technical field of petroleum geological exploration and development, and in particular to a method and system for identifying sand-lime mixed rock oil-water layers. Background Art

[0002] Mixed rocks were previously called mixed sediments, which refer to rocks formed by mixed sedimentation of terrigenous clastics and carbonate components. In a broad sense, they also include a mixture of alternating interlayers or interlayers formed by terrigenous clastics and carbonate components in space (including vertical and / or planar). Studies have shown that mixed rocks can be widely developed in continental lakes, sea-continent transition, continental shelves, and slopes. Mixed rocks are developed in many basins at home and abroad, such as the Shahejie Formation in the Bohai Bay Basin, the Paleogene in the Qaidam Basin, the Permian in the Junggar Basin, the Transamerica Basin in South Africa, the DeWijk and Vannepofen oil fields in northeastern Australia, especially in the first section of the Shahe Formation in the Bohai Bay Basin, where a large number of sand-lime mixed rocks are developed, and there are a large number of literature reports at home and abroad.

[0003] The reservoir space type of mixed rock is more complex than that of clastic rock. The carbonate components in mixed rock are either filled between particles or distributed in layers in clastic rock, which often makes the rock porosity smaller and the resistivity larger. The rock electrical relationship becomes more complicated, which makes it more difficult to identify oil and water layers by logging. The identification results are less consistent with the oil test, which cannot meet the needs of oil and gas exploration and development. A large number of research has been carried out at home and abroad on the identification of oil and water layers in reservoirs, and a large number of oil layer logging identification methods have been formed. The commonly used methods are the intersection diagram method, curve overlap method, movable water identification method, differential identification method, resistivity time lapse logging curve identification method, neutron lifetime logging method, nuclear magnetic resonance method, etc. Among them, the most commonly used is the intersection diagram method, which uses resistivity-porosity intersection or resistivity-acoustic time difference, resistivity-density, resistivity-neutron intersection to identify oil and water layers, and divide oil and water layers according to the distribution area of ​​oil test conclusions. The above-mentioned methods for identifying oil and water layers, such as the intersection plot method, curve overlap method, movable water identification method, and micro-difference identification method, are based on the condition that the rock-electricity relationship of the rock changes little, and basically conform to the Archie formula. However, the rock-electricity relationship of the mixed rock changes greatly, so these methods are not suitable; the resistivity time lapse logging curve identification method, neutron lifetime logging method, and nuclear magnetic resonance method are not collected in most drillings due to the limitation of logging data collection projects, resulting in a small application area. None of the above methods solve the problem of difficult logging identification of mixed rock oil and water layers and low interpretation compliance rate.

[0004] The prior art (CN106370814B) discloses a method for well logging identification of lacustrine mixed rock reservoirs based on composition-structure classification. The scheme includes: a sample collection step, collecting basic data for lithology calibration and its corresponding conventional logging curves; a curve-sample lithology calibration step, extracting the logging curves in the corresponding well section according to the lithology calibration results; a sample classification step, classifying known lithology samples according to composition and structure; a lithology component-structure indicator sensitivity curve optimization step, the optimization method can use the box plot method and the intersection plot method; a composition-structure secondary classification lithology interpretation flow chart is established based on the optimization results; and the lithology of unknown lithology sections is identified based on the lithology identification flow chart. The patent method mainly discloses a method for identifying the lithology of mixed rocks, and does not involve the problem of well logging identification of mixed rock oil and water layers.

[0005] The prior art (CN109653725B) discloses a logging interpretation method for the degree of waterlogging in mixed reservoirs based on sedimentary microfacies and rock phases. The scheme includes: conducting detailed comparison and division of strata; conducting classification research on sedimentary microfacies-rock phases; establishing a four-property relationship chart for each phase belt, establishing a logging interpretation model suitable for the study area according to the sedimentary microfacies-rock phases, and processing the actual data based on the sedimentary microfacies-rock phase discrimination model, and analyzing the differences in logging interpretation of various types of reservoirs on this basis; using the entropy-based quantitative evaluation method for waterlogged layers to study the waterlogging status of mixed reservoirs, establish a comprehensive evaluation standard for waterlogged layers in mixed reservoirs, and divide the oil layer waterlogging level, and summarize the flooding pattern of the target layer segment. The patented method uses an entropy-based quantitative evaluation method for waterlogged layers to study the waterlogging status of mixed reservoirs, mainly for the interpretation of waterlogged layers, and does not solve the problem of logging identification of mixed rock oil and water layers.

[0006] In summary, the existing technology has not effectively solved the problem of well logging identification of mixed rock oil and water layers. Summary of the invention

[0007] The purpose of the present invention is to provide an oil-water layer identification scheme for sand-lime mixed sedimentary rock.

[0008] In order to solve the above technical problems, an embodiment of the present invention provides a method for identifying oil and water layers in sand-lime mixed rock, including: determining the formation lithology at different unit depth steps based on the core sampling experimental analysis data and logging data of the well to be evaluated, based on which, by comparing various logging curves, a number of lithology identification sensitivity curves are selected; based on the several lithology identification sensitivity curves, lithology identification parameters at different unit depth steps are calculated and lithology identification parameter limit values ​​for distinguishing different lithologies are determined; based on the logging project type of the well to be studied, the lithology identification parameter limit values ​​are used to calculate the ash content at different depth steps in the depth section that meets the ash condition of the lithology identification parameter; based on the ash content at different depth steps, the porosity at different depth steps and the oil saturation after removing the influence of the ash component on the resistivity are calculated; based on the oil saturation at different depth steps, the oil saturation limit values ​​under different oil content states are determined, based on which the oil and water layers of the well to be evaluated are identified.

[0009] Preferably, in the step of determining the formation lithology at different unit depth steps based on the core sampling experimental analysis data and logging data of the well to be evaluated, and based on this, by comparing various logging curves, preferably selecting several lithology identification sensitive curves, the step includes: comparing the formation lithology at the different unit depth steps with the various logging curves, analyzing the logging characteristics of different lithologies, and preliminarily screening out logging curves that are sensitive to lithology identification; intersecting several preliminary sensitive logging curves in pairs, and selecting several lithology identification sensitive curves based on the scattered intersection distribution diagram of the preliminary sensitive curves.

[0010] Preferably, in the step of intersecting a plurality of preliminary sensitive logging curves in pairs and selecting a plurality of lithology identification sensitive curves according to the scatter intersection distribution diagram of the preliminary sensitive curves, the steps include: drawing corresponding pure sandstone logging curves according to the pure sandstone logging data of the adjacent well sections at different depths within each lithology preliminary sensitive logging curve; drawing a comparison curve for comparing each lithology preliminary sensitive logging curve with the corresponding pure sandstone logging curve, thereby drawing a plurality of intersecting scatter intersection diagrams by intersecting the comparison curves of the logging curves in pairs; and analyzing the correlation between the preliminary sensitive logging items according to the plurality of intersecting scatter intersection diagrams, so as to screen out the lithology identification sensitive curve from the plurality of preliminary sensitive logging curves.

[0011] Preferably, the lithology identification parameters under the different unit depth steps are calculated using the following expression:

[0012] F 岩性 =A 1 *(X 1 -X 1纯砂岩 )+A 2 *(X 2 -X 2纯砂岩 )+A3 *(X 3 / X 3纯砂岩 )……

[0013] Among them, F 岩性 represents the lithology identification parameter, X 1 , X 2 , X 3 ...represents the logging values ​​at the specified depth in each lithology identification sensitivity curve, X 1纯砂岩 , X 2纯砂岩 , X 3纯砂岩 ...represent the various logging values ​​corresponding to the pure sandstone in the adjacent well section of the current lithology to be identified layer, A 1 , A 2 , A 3 ...respectively represent the lithology discrimination ability coefficients of various preliminary sensitive logging items.

[0014] Preferably, in the step of calculating the gray matter content at different depth steps within the depth segment that meets the gray matter condition of the lithology identification parameter according to the logging project type of the well to be studied and using the lithology identification parameter limit value, the step includes: determining the specified depth segment of the rock containing gray matter according to the lithology identification parameters at different unit depth steps and using the lithology identification parameter limit value; calculating the gray matter content at different depth steps within the specified depth segment according to the type of logging project currently possessed by the well to be studied, wherein, in the case that the well to be studied currently has the ability to collect at least two logging curves of acoustic time difference, density and neutron, the gray matter content is calculated by intersecting the at least two logging curves; in the case that the well to be studied currently has the ability to collect only acoustic time difference logging curves, according to the collected acoustic time difference logging curves, using a preset gray matter content calculation formula, the gray matter content data at different depths are calculated, wherein the gray matter content calculation formula is obtained by analyzing the correlation fitting between the gray matter content data in the coring experiment analysis data and the collected acoustic time difference logging curve.

[0015] Preferably, the gray matter content calculation formula is expressed using the following expression:

[0016] V 灰质 =B*(X 纯砂岩 -X) 2 +C*(X 纯砂岩 -X)+D

[0017] Among them, V 灰质 represents the gray matter content, X represents the data point in the measured acoustic time difference logging curve, and X 纯砂岩 It represents the average value of the data in the pure sandstone acoustic time difference logging curve of the adjacent well section where the current data point is located, and B, C, and D represent the fitting coefficients respectively.

[0018] Preferably, the step of calculating the porosity at different depth steps according to the ash content at the different depth steps includes: calculating the porosity at different depth steps in the specified depth segment according to the type of logging items of the current well to be studied, wherein, when the current well to be studied has the ability to collect at least two logging curves of acoustic time difference, density and neutron, the porosity is calculated by intersecting the at least two logging curves; when the current well to be studied only has the ability to collect acoustic time difference logging curves, the porosity at the corresponding depth step is calculated according to the ash content at different depth steps and the collected acoustic time difference logging curves using a preset porosity calculation formula.

[0019] Preferably, the porosity calculation formula is expressed by the following expression:

[0020]

[0021] △t ma =V 砂岩 ×Tm 砂岩 +V 灰质 ×Tm 灰岩 +V 泥岩 ×Tsh 泥岩

[0022]

[0023]

[0024] in, represents porosity, △t represents the data value in the acoustic time difference, △t ma Indicates the time difference of the comprehensive rock mineral acoustic skeleton, △t f represents the time difference of fluid sound waves, CP represents the compaction coefficient, Tm 砂岩 Represents the sandstone mineral skeleton, V 砂岩 Indicates the sandstone content, V 灰质 Indicates gray matter content, Tm 灰岩 Represents the limestone mineral skeleton, V 泥质 Indicates the mud content, Tsh 泥岩 Indicates the mudstone acoustic time difference, SMX i 、SMN i Indicates the logging value of pure mudstone and pure sandstone, GCUR i It represents the formation coefficient of the calculated shale content, and TSHi represents the logging value of the target curve selected when calculating the shale parameters at a certain depth point.

[0025] Preferably, the oil saturation at different depth steps is calculated using the following expression:

[0026]

[0027] S O =1-S w

[0028] Among them, S o Indicates oil saturation, S w represents water saturation, represents porosity, a represents rock electrical coefficient, m represents cementation index, R w represents the resistivity of formation water, Rt represents the resistivity of formation, R 灰质 Represents the resistivity of pure gray matter, V 灰质 Indicates gray matter content.

[0029] On the other hand, an embodiment of the present invention further provides a system for identifying sand-lime mixed rock oil-water layers, including: a sensitivity analysis module, which is configured to determine the formation lithology at different unit depth steps based on the coring experimental analysis data and logging data of the well to be evaluated, based on which, by comparing various logging curves, a plurality of lithology identification sensitivity curves are selected; a lithology identification classification module, which is configured to calculate the lithology identification parameters at different unit depth steps and determine the lithology identification parameter limit values ​​for distinguishing different lithologies based on the plurality of lithology identification sensitivity curves; and a ash content calculation module. It is configured to calculate the gray matter content at different depth steps in the depth section that meets the gray matter condition of the lithology identification parameter according to the logging project type of the well to be studied and using the limit value of the lithology identification parameter; the oil saturation calculation module is configured to calculate the porosity at different depth steps and the oil saturation after removing the influence of the gray matter component on the resistivity according to the gray matter content at different depth steps; the oil-water identification module is configured to determine the oil saturation limit value under different oil content states according to the oil saturation at different depth steps, and based on this, identify the oil and water layer of the well to be evaluated.

[0030] Compared with the prior art, one or more embodiments of the above scheme may have the following advantages or beneficial effects:

[0031] The present invention proposes a method and system for identifying oil and water layers in sand-lime mixed sedimentary rocks. The method and system include: optimizing lithology identification sensitivity curves; establishing lithology identification methods and determining lithology identification limit values ​​using sensitivity curves; optimizing or establishing porosity calculation methods for sand-lime mixed sedimentary rock layers according to the type of logging project; establishing an oil saturation calculation model that removes the influence of gray components on resistivity; and determining the oil and water layer identification limit value based on oil saturation. The present invention uses a new porosity calculation model and an oil saturation calculation model that removes the influence of gray content on resistivity through lithology identification and gray content calculation, and highlights the influence of oil and gas on well logging. It can more accurately calculate porosity and oil saturation, thereby more accurately identifying oil and water layers, achieving more accurate estimation of oil and gas geological reserves, and providing more accurate geological data and better technical support for oil testing and development plan formulation. Therefore, the present invention can calculate porosity and oil saturation more accurately, identify oil and water layers more accurately, and is mainly used in petroleum geological exploration and development. It can provide technical support for drilling deployment and oil and gas development plan formulation, and improve the economic benefits of oil and gas exploration and development. In addition, the present invention has been applied in many regions and achieved good results.

[0032] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0034] Figure 1 This is a step diagram of a method for identifying oil and water layers in sand-limestone mixed sedimentary rock according to an embodiment of the present application.

[0035] Figure 2 It is a schematic diagram of the process of calculating the lithology identification parameters of a single well in the method for identifying sand-lime mixed rock oil-water layers in an embodiment of the present application.

[0036] Figure 3 This is an example of a scatter intersection diagram formed by intersecting a comparison curve regarding acoustic wave time difference with a comparison curve regarding natural gamma in a method for identifying sand-limestone mixed rock oil-water layers in an embodiment of the present application.

[0037] Figure 4 This is an example of a scatter intersection diagram formed by intersecting a comparison curve regarding acoustic wave time difference with a comparison curve regarding micropotential in a method for identifying sand-limestone mixed rock oil-water layers in an embodiment of the present application.

[0038] Figure 5 It is a schematic diagram of the fitting process of the calculation formula for ash content in the method for identifying sand-lime mixed rock oil-water layers according to an embodiment of the present application.

[0039] Figure 6 This is an example diagram of the porosity calculation results in the method for identifying sand-limestone mixed rock oil-water layers in an embodiment of the present application.

[0040] Figure 7 This is an example of a resistivity and porosity cross-plot for identifying oil and water layers drawn using existing technology.

[0041] Figure 8 This is an example diagram of the intersection of oil and water layers using oil saturation to identify oil and water layers in a method for identifying oil and water layers in sand-limestone mixed sedimentary rock in an embodiment of the present application.

[0042] Fig. 9 This is an example diagram of the calculation results of ash content, porosity and oil saturation in the method for identifying oil-water layers in sand-lime mixed sedimentary rocks according to an embodiment of the present application.

[0043] Fig.10 This is another example diagram of the calculation results of ash content, porosity and oil saturation in the method for identifying oil-water layers in sand-lime mixed sedimentary rocks according to an embodiment of the present application.

[0044] Fig.11 This is a schematic diagram of the structure of a system for identifying oil-water layers in sand-limestone mixed sedimentary rock according to an embodiment of the present application. DETAILED DESCRIPTION

[0045] The following will describe the implementation methods of the present invention in detail with reference to the accompanying drawings and embodiments, so that the implementation process of how the present invention applies technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly. It should be noted that as long as there is no conflict, the various embodiments of the present invention and the various features in the embodiments can be combined with each other, and the technical solutions formed are all within the protection scope of the present invention.

[0046] In addition, the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions. Also, although a logical sequence is shown in the flowchart, in some cases, the steps shown or described can be performed in a sequence different from that here.

[0047] The terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments. Unless the context clearly indicates otherwise, the singular forms "one", "one" and "item" used herein are also intended to include plural numbers. It should also be understood that the terms "include" and / or "comprise" used herein specify the existence of stated features, integers, steps, operations, units and / or components, without excluding the existence or addition of one or more other features, integers, steps, operations, units, components and / or combinations thereof.

[0048] Mixed rocks were previously called mixed sediments, which refer to rocks formed by mixed sedimentation of terrigenous clastics and carbonate components. In a broad sense, they also include a mixture of alternating interlayers or interlayers formed by terrigenous clastics and carbonate components in space (including vertical and / or planar). Studies have shown that mixed rocks can be widely developed in continental lakes, sea-continent transition, continental shelves, and slopes. Mixed rocks are developed in many basins at home and abroad, such as the Shahejie Formation in the Bohai Bay Basin, the Paleogene in the Qaidam Basin, the Permian in the Junggar Basin, the Transamerica Basin in South Africa, the DeWijk and Vannepofen oil fields in northeastern Australia, especially in the first section of the Shahe Formation in the Bohai Bay Basin, where a large number of sand-lime mixed rocks are developed, and there are a large number of literature reports at home and abroad.

[0049] The reservoir space type of mixed rock is more complex than that of clastic rock. The carbonate components in mixed rock are either filled between particles or distributed in layers in clastic rock, which often makes the rock porosity smaller and the resistivity larger. The rock electrical relationship becomes more complicated, which makes it more difficult to identify oil and water layers by logging. The identification results are less consistent with the oil test, which cannot meet the needs of oil and gas exploration and development. A large number of research has been carried out at home and abroad on the identification of oil and water layers in reservoirs, and a large number of oil layer logging identification methods have been formed. The commonly used methods are the intersection diagram method, curve overlap method, movable water identification method, differential identification method, resistivity time lapse logging curve identification method, neutron lifetime logging method, nuclear magnetic resonance method, etc. Among them, the most commonly used is the intersection diagram method, which uses resistivity-porosity intersection or resistivity-acoustic time difference, resistivity-density, resistivity-neutron intersection to identify oil and water layers, and divide oil and water layers according to the distribution area of ​​oil test conclusions. The above-mentioned methods for identifying oil and water layers, such as the intersection plot method, curve overlap method, movable water identification method, and micro-difference identification method, are based on the condition that the rock-electricity relationship of the rock changes little, and basically conform to the Archie formula. However, the rock-electricity relationship of the mixed rock changes greatly, so these methods are not suitable; the resistivity time lapse logging curve identification method, neutron lifetime logging method, and nuclear magnetic resonance method are not collected in most drillings due to the limitation of logging data collection projects, resulting in a small application area. None of the above methods solve the problem of difficult logging identification of mixed rock oil and water layers and low interpretation compliance rate.

[0050] The prior art (CN106370814B) discloses a method for well logging identification of lacustrine mixed rock reservoirs based on composition-structure classification. The scheme includes: a sample collection step, collecting basic data for lithology calibration and its corresponding conventional logging curves; a curve-sample lithology calibration step, extracting the logging curves in the corresponding well section according to the lithology calibration results; a sample classification step, classifying known lithology samples according to composition and structure; a lithology component-structure indicator sensitivity curve optimization step, the optimization method can use the box plot method and the intersection plot method; a composition-structure secondary classification lithology interpretation flow chart is established based on the optimization results; and the lithology of unknown lithology sections is identified based on the lithology identification flow chart. The patent method mainly discloses a method for identifying the lithology of mixed rocks, and does not involve the problem of well logging identification of mixed rock oil and water layers.

[0051] The prior art (CN109653725B) discloses a logging interpretation method for the degree of waterlogging in mixed reservoirs based on sedimentary microfacies and rock phases. The scheme includes: conducting detailed comparison and division of strata; conducting classification research on sedimentary microfacies-rock phases; establishing a four-property relationship chart for each phase belt, establishing a logging interpretation model suitable for the study area according to the sedimentary microfacies-rock phases, and processing the actual data based on the sedimentary microfacies-rock phase discrimination model, and analyzing the differences in logging interpretation of various types of reservoirs on this basis; using the entropy-based quantitative evaluation method for waterlogged layers to study the waterlogging status of mixed reservoirs, establish a comprehensive evaluation standard for waterlogged layers in mixed reservoirs, and divide the oil layer waterlogging level, and summarize the flooding pattern of the target layer segment. The patented method uses an entropy-based quantitative evaluation method for waterlogged layers to study the waterlogging status of mixed reservoirs, mainly for the interpretation of waterlogged layers, and does not solve the problem of logging identification of mixed rock oil and water layers.

[0052] In summary, the existing technology has not effectively solved the problem of well logging identification of mixed rock oil and water layers.

[0053] In order to solve the problems in the above-mentioned background technology, the embodiment of the present application proposes a method and system for identifying oil and water layers in sand-lime mixed sedimentary rocks. The method and system include: optimizing lithology identification sensitivity curves; establishing lithology identification methods and determining lithology identification limit values ​​using sensitivity curves; optimizing or establishing a sand-lime mixed sedimentary rock layer porosity calculation method according to the logging project type; establishing an oil saturation calculation model that removes the influence of gray components on resistivity; and determining the oil and water layer identification limit value based on oil saturation. The present invention can more accurately calculate the porosity and oil saturation of sand-lime mixed sedimentary rocks, thereby more accurately identifying oil and water layers.

[0054] Embodiment 1

[0055] Figure 1 This is a step diagram of the method for identifying the oil-water layer in the sand-lime mixed sedimentary rock according to the embodiment of the present application. Figure 1The specific steps of the method for identifying oil and water layers in sand-lime mixed sedimentary rock (also referred to as "oil and water layer identification method") described in an embodiment of the present invention are described.

[0056] Step S110 determines the formation lithology at different unit depth steps based on the coring experimental analysis data and logging data of the well to be evaluated. Based on this, several lithology identification sensitivity curves are selected by comparing various logging curves.

[0057] In step S110, first, the coring experimental analysis data, cuttings logging, well logging data, oil test data and production test data of each well in the study area are collected. According to the coring experimental analysis data and logging data of the well to be evaluated, the lithology is matched with each logging curve in depth to obtain the formation lithology under different unit depth steps, so as to draw a comprehensive map of a single well (including but not limited to: multiple logging curves of each single well, depth and (logging) lithology relationship), see Figure 2 .

[0058] After obtaining the comprehensive map of a single well, the formation lithology at different unit depth steps corresponding to the current well to be evaluated is compared with various logging curves, and the logging characteristics of different lithologies are analyzed (for example, the amplitude level of logging data of each logging curve under different lithologies). The logging curves that are sensitive to lithology identification are preliminarily screened out from all the logging curves that can be collected from the current single well, and several preliminary sensitive logging curves are obtained.

[0059] Then, the obtained several preliminary sensitive logging curves are intersected in pairs, and according to the preliminary sensitive curve scatter intersection distribution diagram, several lithology identification sensitive curves are further selected from the several preliminary sensitive logging curves.

[0060] Specifically, in one embodiment, in the first step, for a single well, pure sandstone logging data of adjacent well sections at different depths in each lithology preliminary sensitive logging curve are plotted into pure sandstone logging curves of corresponding logging curve types (e.g., AC 纯砂岩 、DEN 纯砂岩 etc., A.C. 纯砂岩 Indicates the pure sandstone acoustic time difference logging curve of the adjacent well section at the current depth, DEN 纯砂岩 Indicates the pure sandstone density logging curve of the adjacent well section at the current depth).

[0061] The second step is to draw a comparison curve for comparing each lithology preliminary sensitive logging curve with the pure sandstone logging curve at the corresponding depth according to the pure sandstone logging curves of different logging curve types. It should be noted that in the embodiment of the present invention, the comparison curve can be, for example: AC 纯砂岩 -AC, DEN-DEN 纯砂岩The comparison curves may be difference comparison curves, ratio comparison curves, or different comparison curve forms determined according to the logging item type. The embodiment of the present invention does not specifically limit the comparison form of the comparison curves of each logging type. Among them, AC represents the measured acoustic time difference logging curve of the current well to be evaluated, and DEN represents the measured density logging curve of the current well to be evaluated. Then, by intersecting the comparison curves of each logging curve type in pairs, multiple intersecting scatter plots are drawn (for example: AC 纯砂岩 -AC and GR-GR 纯砂岩 Scatter intersection plot; AC 纯砂岩 -AC and RN-RN 纯砂岩 Scatter intersection plot), see Figure 3 and Figure 4 .

[0062] The third step is to analyze the correlation between the preliminary sensitive logging items based on the multiple intersecting scatter plots drawn in the second step, and select the logging curve with better lithology discrimination as the sensitive curve to screen out several lithology identification sensitive curves from several preliminary sensitive logging curves.

[0063] In this way, after selecting several logging curves with good sensitivity to lithology identification, the process proceeds to step S120.

[0064] Step S120 calculates lithology identification parameters at different unit depth steps based on the plurality of lithology identification sensitivity curves selected in step S110, and determines the lithology identification parameter limit values ​​for distinguishing different lithologies.

[0065] In step S120, the lithology identification parameters at different unit depth steps in the target well section (well section to be evaluated) of the well to be evaluated are calculated according to the several lithology identification sensitivity curves selected in step S110. Figure 2 The calculation results of the 7th column in .

[0066] In one embodiment, the lithology identification parameters at different unit depth steps are calculated using the following expression:

[0067] F 岩性 =A 1 *(X 1 -X 1纯砂岩 ) + A 2 *(X 2 -X 2纯砂岩 ) + A 3 *(X 3 / X 3纯砂岩 )…… (1)

[0068] Among them, F 岩性 represents the lithology identification parameter, X 1 , X2 , X 3 ...represents the measured logging values ​​at the specified depth in each lithology identification sensitivity curve, X 1纯砂岩 , X 2纯砂岩 , X 3纯砂岩 ...represent the various logging values ​​corresponding to the pure sandstone in the adjacent well section of the current lithology to be identified layer, A 1 , A 2 , A 3 ...respectively represent the lithology discrimination coefficients of various preliminary sensitive logging items. Among them, the lithology discrimination coefficients of various preliminary sensitive logging items are calculated by analyzing the correlation between various preliminary sensitive logging items.

[0069] After calculating the lithology identification parameters at different depths, step S120 described in the embodiment of the present invention also determines the range of lithology identification parameters corresponding to each lithology type based on different lithology types and corresponding lithology identification parameters at different unit depth steps, for example, by cluster analysis, so as to obtain the boundary values ​​of lithology identification parameters for distinguishing different lithologies, and then enters step S130.

[0070] In the embodiment of the present invention, different lithology types include sandy limestone, sandy limestone, gray sandstone, gray sandstone and sandstone.

[0071] Step S130 calculates the gray matter content at different depth steps within the depth section that meets the gray matter condition of the lithology identification parameter according to the logging project type of the well to be studied and using the lithology identification parameter limit value obtained in step S120.

[0072] In step S130, step 1, firstly, according to the lithology identification parameters at different unit depth steps obtained in step S120, the designated depth section of the rock containing lime is determined by using the lithology identification parameter limit value set in step S120. That is, firstly, the depth section that meets the sandstone lithology identification parameters is determined, and then the remaining depth sections in the target well section except the sandstone lithology depth section are used as the designated depth sections, so as to screen out all the designated depth sections that meet the core identification parameter conditions containing lime lithology.

[0073] Then (step 2), the gray matter content at different depth steps within the specified depth section is calculated according to the type of well logging items of the current well to be studied.

[0074] In actual application, the configuration types of logging projects of different wells to be studied may be different, so that multiple types of logging curves can be collected in the logging projects of some wells, while some logging projects can only collect acoustic wave time difference logging curves. Therefore, for different types of logging projects, the embodiments of the present invention use different methods to calculate the gray matter content at different depths in the specified depth section and the following porosity.

[0075] In the process of calculating the gray matter content, in one embodiment, when the well to be studied has the capability of acquiring at least two logging curves of acoustic wave transit time, density and neutron, the gray matter content is calculated by intersecting at least two logging curves.

[0076] In another embodiment, when the well to be studied currently has the capability of only collecting acoustic time difference logging curves, the gray matter content data at different depths are calculated based on the collected acoustic time difference logging curves using a preset gray matter content calculation formula.

[0077] Furthermore, the gray matter content calculation formula is expressed by the following expression:

[0078] V 灰质 =B*(X 纯砂岩 -X) 2 +C*(X 纯砂岩 -X)+D (2)

[0079] Among them, V 灰质 represents the gray matter content, X represents the data point in the measured acoustic time difference logging curve, and X 纯砂岩 represents the average value of the data in the pure sandstone acoustic time difference logging curve of the adjacent well section of the layer where the current data point is located, and B, C, and D represent the fitting coefficients respectively. In the embodiment of the present invention, the gray matter content calculation formula is obtained by fitting by analyzing the correlation between the gray matter content data in the coring experimental analysis data of the well to be evaluated and the collected acoustic time difference logging curve.

[0080] In this way, the embodiment of the present invention can calculate the gray matter content data at different depth steps within the specified depth according to the above expression (2), see Figure 5 , thereby entering step S140.

[0081] Step S140 calculates the porosity at different depth steps and the oil saturation of the resistivity after removing the ash component at different depth steps according to the ash content at different depth steps in the specified depth segment calculated in step S130.

[0082] In step S140, the porosity at different depth steps is calculated based on the gray matter content at different depth steps in the specified depth segment calculated in step S130. Specifically, the porosity at different depth steps in the specified depth segment is calculated based on the type of well logging project currently being studied.

[0083] In the process of calculating porosity, in one embodiment, when the well to be studied is capable of collecting at least two logging curves of acoustic wave transit time, density and neutron, the porosity is calculated by intersecting the at least two logging curves.

[0084] In another embodiment, when the well to be studied is only capable of collecting acoustic time difference logging curves, the porosity at the corresponding depth step is calculated using a preset porosity calculation formula according to the gray matter content at different depth steps and the collected acoustic time difference logging curves, see Figure 6 The porosity calculation results in column 9.

[0085] Furthermore, the porosity calculation formula is expressed using the following expression:

[0086]

[0087] △t ma =V 砂岩 ×Tm 砂岩 +V 灰质 ×Tm 灰岩 +V 泥岩 ×Tsh 泥岩 (4)

[0088]

[0089]

[0090] in, Indicates porosity, decimal; △t indicates the data value in the acoustic time difference, unit is μs / m; △t ma It indicates the time difference of the comprehensive rock mineral acoustic skeleton, in μs / m; △t f represents the time difference of fluid sound waves, CP represents the compaction coefficient, Tm 砂岩 Represents the sandstone mineral skeleton, in μs / m; V 砂岩 Indicates sandstone content, decimal; V 灰质 Indicates gray matter content, decimal; Tm 灰岩 Represents the limestone mineral skeleton, in μs / m; V 泥质 Indicates the mud content, decimal; Tsh 泥岩 Indicates the time difference of mudstone sonic waves, in μs / m; SMX i 、SMN i Indicates the logging values ​​of pure mudstone and pure sandstone, generally GR and SP are used; GCUR i The formation coefficient that represents the calculated mud content. Since the target layer is the Tertiary stratum, usually, GCUR iTake 3.7; TSHi represents the logging value of the target curve selected when calculating the mud parameters at a certain depth point.

[0091] Then, step S140 described in the embodiment of the present invention further calculates the oil saturation at different depth steps according to the calculated porosity and ash content at different depth steps using a preset oil saturation calculation formula:

[0092]

[0093] S O =1-S w (8)

[0095] Among them, S o Indicates oil saturation, decimal; S w Indicates water saturation, decimal; represents porosity, a decimal; a represents rock electrical coefficient, dimensionless; m represents cementation index, dimensionless; R w Represents the formation water resistivity, in Ω.m; Rt represents the formation resistivity, in Ω.m; R 灰质 Represents the resistivity of pure gray matter, in Ω.m; V 灰质 Represents gray matter content, decimal. It should be noted that R 灰质 According to the calculation result of expression (2), V 灰质 The resistivity value at the depth corresponding to the maximum value or the resistivity value at the depth known to be pure limestone (specifically, if there is logging or other data showing that it is limestone, the resistivity value at this depth is R 灰质 ; In the absence of logging or other data, according to the corresponding characteristics of pure limestone logging, the logging curve shows the pure limestone depth point; the curve characteristics are not obvious and there is no logging or other data. According to the calculation results of formula (2), V 灰质 The resistivity value corresponding to the maximum depth).

[0096] Thus, step S140 of the embodiment of the present invention obtains the oil saturation at different depths within the specified depth segment according to the above expressions (7) and (8) to remove the influence of the gray matter component on the resistivity, thereby entering step S150.

[0097] Step S150 determines the oil saturation limit values ​​under different oil content states according to the oil saturation at different depth steps calculated in step S140, thereby identifying the oil and water layer of the well to be evaluated according to the oil saturation limit values ​​under different oil content states.

[0098] In step S150, according to the oil saturation at different depth steps in the specified depth segment, combined with the oil testing data, a cross-plot of oil saturation and porosity under different oil content states is drawn (see Figure 8 ), using, for example, a cluster analysis method, to determine the numerical range of oil saturation corresponding to each oil-bearing state, thereby obtaining the oil saturation limit value for distinguishing different oil-bearing states, and then identifying the oil and water layers of the well to be evaluated. In the embodiment of the present invention, different oil-bearing states include: oil layer, oil and water layer, oil-water layer, and water layer.

[0099] Embodiment 2

[0100] Based on the oil-water layer identification method formed in the above embodiment 1, the embodiment of the present invention applies the above oil-water layer identification method to a certain area in the Dongying Depression of the Bohai Bay Basin. The oil-water layer identification method involved in this example is as follows:

[0101] To prepare for the optimal lithology identification sensitivity curve, first, collect the core sampling and experimental analysis data, cuttings logging, logging data, oil test and production test data in the study area, match the lithology with the logging curve in depth, and draw a comprehensive map of a single well. Figure 2 .

[0102] The formation lithology is accurately determined by using coring and experimental analysis data and logging data, and the re-determined lithology is compared with the logging curve to analyze the logging characteristics of different lithologies. The logging characteristics of different lithologies are shown in Table 1, and the sensitivity curve is preliminarily optimized. Among them, the preliminarily optimized sensitivity curves mainly include: natural gamma (GR), acoustic time difference (AC), density (DEN), neutron (CNL), RL (micro gradient), micro potential (RN), shallow detection resistivity (RXO), deep detection resistivity (RT), among which RXO and RT are affected by oil content to a certain extent.

[0103] Table 1 Logging characteristics of different lithofacies

[0104]

[0105] Based on the preliminary optimization of sensitive logging curves, the distribution diagram of lithology and preliminary optimization of sensitive logging curves and the correlation scatter diagram between the two curves are drawn. Figure 3 Lithology and AC 纯砂岩 -AC and GR-GR 纯砂岩 Scatter intersection diagram, where AC 纯砂岩 -AC has good lithology differentiation, GR-GR 纯砂岩 The lithological distinction is slightly poor; Figure 4 Lithology and AC 纯砂岩 -AC and RN-RN 纯砂岩 Scatter intersection diagram, where AC 纯砂岩-AC has good lithology differentiation, RN-RN 纯砂岩 The lithology is better. 纯砂岩 With CNL 纯砂岩 -CNL scatter intersection diagram, RXO-RXO 纯砂岩 With AC 纯砂岩 -AC scatter intersection diagram and other scattered point intersections, through correlation analysis, the final sensitive logging curves are selected as AC, DEN, RN, and RL, thus establishing the lithology identification parameters (F 岩性 ) calculation model. Among them, the fitting coefficients to be determined are determined according to the correlation, F 岩性 The expression is:

[0106] F 岩性 =0.35*(AC 纯砂岩 -AC) 归一化 +0.3*(DEN-DEN 纯砂岩 ) 归一化 +0.2*(RN / RN 纯砂岩 ) 归一化

[0107] +0.15*(RL / RL 纯砂岩 ) 归一化 (9)

[0108] Where: F 岩性 It represents the lithology identification parameter, dimensionless; AC represents the measured acoustic time difference, unit is μs / m; DEN represents the measured density, unit is g / cm 3 ; RL represents the measured micro gradient, the unit is Ω.m; RN represents the measured micro potential, the unit is Ω.m; AC 纯砂岩 、DEN 纯砂岩 , RL 纯砂岩 , RN 纯砂岩 They respectively represent the logging values ​​of various logging types corresponding to the pure sandstone in the adjacent well section of the lithology to be identified layer.

[0109] Using the lithology identification parameter (F 岩性 ) is compared with the lithology statistics obtained from core and experimental analysis and logging lithology to determine the lithology identification limit value. Table 2 is the lithology identification limit value data table.

[0110] Table 2. Table of lithology values ​​identified by logging curves

[0111]

[0112] Figure 2 The seventh column in the formula is the lithology identification parameter (F 岩性 ), it can be seen from the figure that the higher the ash content, the higher the rock type F 岩性In addition, the F 岩性 The limit values ​​for identifying lithology are shown in Table 3.

[0113] Table 3 Lithology identification parameters (F 岩性 ) Lithology Data Table

[0114]

[0115] In the process of logging calculation of ash content, under the condition that two or more logging curves among the three porosities of AC, DEN and CNL are collected, the intersection of two of the curves DEN-CNL, DEN-AC and AC-CNL is used to calculate the ash content.

[0116] When only AC is collected without collecting DEN and CNL logging curves, the cross plot of ash content and AC obtained from core experimental analysis is made, see Figure 5 , the gray matter content calculation model was obtained by mathematical fitting, see formula 10:

[0117] V 灰质 =0.0025*(AC 纯砂岩 -AC) 2 -0.0391*(AC 纯砂岩 -AC)+1.7165 (10)

[0118] Where: V 灰质 Indicates gray matter content, unit is %; AC indicates the measured acoustic time difference, unit is μs / m; AC 纯砂岩 It indicates the acoustic time difference of pure sandstone in adjacent well sections, in μs / m.

[0119] A porosity calculation method for sand-lime mixed rock formations is selected or established according to the logging project. Under the condition of collecting two or more logging curves among the three porosities of AC, DEN and CNL, the porosity is calculated by the intersection of two of the curves.

[0120] If most of the wells in the work area of ​​the second embodiment do not collect DEN and CNL, and only collect AC porosity logging curves, the above expressions (3) to (6) are used to calculate the porosity.

[0121] Figure 6 The 9th column is the porosity calculated by the above expressions (3) to (6), and the 8th column is the porosity calculated by the original method. Overall, the porosity calculated by the above expressions (3) to (6) is slightly larger than the porosity originally obtained by using the experimental rock skeleton value, which is mainly more reasonable for reservoirs containing ash.

[0122] Next, an oil saturation calculation model was established to remove the influence of the gray component on resistivity. The oil saturation calculated from the core sampling experimental analysis data of the well to be evaluated was used to determine the unknown coefficients a=1, n=1.87, and m=2.21*ln(Rt) in the above expressions (7) and (8). 0.1548 ; Then, using the above expressions (7) and (8), the non-oil and gas effects of the gray component on resistivity are removed, the effect of formation oil content on resistivity is restored, and the oil saturation (So) is calculated.

[0123] Finally, the oil-water layer identification limit value based on oil saturation is determined. The oil well test data is projected onto the newly calculated oil saturation (So) and porosity intersection diagram to study and determine the So limit value corresponding to the oil-water layer.

[0124] Figure 7 It is a commonly used resistivity-porosity cross-plot, from which it can be seen that the oil layer, oil-water layer, oil-water layer, and water layer cannot be distinguished. Misjudgment errors may occur during logging interpretation, resulting in missed oil layers or misjudgment of oil layers. Figure 8 This is the cross-plot of the newly calculated oil saturation (So) and porosity. It can be seen that the ability to distinguish between oil and water layers has improved significantly. Figure 8 The oil-water layer identification limit values ​​were determined, see Table 4.

[0125] Table 4 Oil saturation (So) identification data table of oil and water layers

[0126]

[0127] According to the statistics of the research area of ​​the second embodiment, the original logging interpretation of oil and water layers was 65% consistent, and the new calculation of oil saturation (So) to identify oil and water layers reached a consistent rate of 86%, which significantly improved the consistency of oil and water layer identification.

[0128] Embodiment 3

[0129] Based on the oil-water layer identification method formed in the above embodiment 1, the embodiment of the present invention applies the above oil-water layer identification method to another area of ​​the Dongying Depression in the Bohai Bay Basin. The oil-water layer identification method involved in this example is as follows:

[0130] To prepare for the optimal lithology identification sensitivity curve, first, collect the core sampling and experimental analysis data, cuttings logging, well logging data, oil testing and production testing data in the study area, perform depth matching of lithology and logging curves, and draw a comprehensive map of a single well.

[0131] The core sampling and experimental analysis data and logging data are used to accurately determine the lithology of the formation. The re-determined lithology is compared with the logging curve, and the logging characteristics of different lithologies are analyzed. The sensitive curves are preliminarily optimized, mainly including natural gamma (GR), acoustic time difference (AC), density (DEN), neutron (CNL), RL (micro gradient), and micro potential (RN).

[0132] Based on the preliminary optimization of sensitive logging curves, the distribution diagram of lithology and preliminary optimization of sensitive logging curves and the correlation scatter diagram of two curves were drawn. Through correlation analysis, the optimal sensitive logging curves were AC, DEN, RN, and RL, and the lithology identification parameters (F 岩性 ), determine the unknown coefficient according to the correlation, F 岩性 The expression is:

[0133] F 岩性 =0.55*(AC 纯砂岩 -AC) 归一化 +0.25*(RN / RN 纯砂岩 ) 归一化 +0.20*(RL / RL 纯砂岩 ) 归一化 (11)

[0134] Where: F 岩性 It represents the lithology identification parameter, dimensionless; GR represents the measured natural gamma, the unit is API; AC represents the measured acoustic time difference, the unit is μs / m; RL represents the measured micro gradient, the unit is Ω.m; RN represents the measured micro potential, the unit is Ω.m; AC 纯砂岩 , RL 纯砂岩 , RN 纯砂岩 They respectively represent the logging values ​​of various logging types corresponding to the pure sandstone in the adjacent well section of the lithology to be identified layer.

[0135] Using the lithology identification parameter (F 岩性 ) is compared with the lithology statistics obtained from core and experimental analysis and logging lithology to determine the lithology identification limit value.

[0136] In the working area of ​​Example 3, most wells did not collect DEN and CNL logging curves but only collected AC. The cross-plot of ash content and AC obtained by core experimental analysis was made, and a mathematical method was used to fit the ash content calculation model, see formula 12:

[0137] V 灰质 =0.0025*(AC 纯砂岩 -AC) 2 -0.0389*(AC 纯砂岩 -AC)+1.727 (12)

[0138] Where: V灰质 Indicates gray matter content, unit is %; AC indicates the measured acoustic time difference, unit is μs / m; AC 纯砂岩 It indicates the acoustic time difference of pure sandstone in adjacent well sections, in μs / m.

[0139] Fig. 9 The first column is the ash content calculated by formula 12, which is basically consistent with the core experimental data. The porosity is calculated using the above expressions (3) to (6).

[0140] Fig. 9 The seventh column shows the porosity calculated using formula (3) to (6), which is very close to the porosity obtained from core experiments.

[0141] Using the above expressions (7) and (8), a method for calculating oil saturation is formed to remove the influence of gray matter on resistivity. In which, the oil saturation is analyzed by core analysis to determine the unknown coefficients a=1, n=1.87, and m=1.67.

[0142] By projecting the oil well test data onto the newly calculated oil saturation (So) and porosity intersection diagram, the study determined the So limit values ​​corresponding to the oil and water layers, oil layer: So ≥ 50%; oil and water in the same layer: 52% ≥ So> 38%; oil-containing water layer: 40% ≥ So> 10%; oil-containing water layer: So<15%.

[0143] Through statistics on the research area of ​​Example 3, the oil-water layer coincidence rate of logging interpretation has increased from 61% to 82%, which significantly improves the oil-water layer identification coincidence rate.

[0144] Embodiment 4

[0145] Based on the oil-water layer identification method formed in the above embodiment one, the embodiment of the present invention is implemented mainly with reference to embodiment three for an exploration well in the neighborhood of the above embodiment three, where there is little surrounding data.

[0146] First, the cuttings logging and well logging data in the study area were collected, the lithology was matched with the logging curves in depth, and a comprehensive map of the single well was drawn.

[0147] The logging data is used to accurately determine the lithology of the formation, and the lithology is compared with the logging curve to preliminarily optimize the sensitive curves, mainly natural gamma (GR), acoustic time difference (AC), RL (micro gradient), and micro potential (RN).

[0148] Establishing lithology identification parameters (F 岩性 ) Using F in Example 3 岩性 expression.

[0149] The ash content is calculated using the calculation model of Example 3, see Expression 12. The porosity is calculated using the above Expressions (3) to (6).

[0150] Fig.10 The 7th column is the porosity calculated using the above expressions (3) to (6).

[0151] Using the above expressions (7) and (8), a method for calculating oil saturation is formed to remove the influence of gray matter on resistivity. Among them, the undetermined coefficients a=1, n=1.87, m=2.21*ln(Rt) are determined by core analysis of oil saturation. 0.1548 .

[0152] The oil-water layer identification adopts the limit values ​​of Example 3, oil layer: So≥50%; oil-water layer: 52%≥So>38%; oil-water layer: 40%≥So>10%; oil-water layer: So<15%.

[0153] The target layer So of the fourth embodiment is basically greater than 55%. Fig.10 In the 7th column, it is judged as an oil layer according to the oil-water layer identification limit value, see Fig.10 Column 8.

[0154] Embodiment 5

[0155] Based on the oil-water layer identification method formed in the above-mentioned embodiments 1 to 4, the embodiment of the present invention also provides a system for identifying sand-lime mixed rock oil-water layers (also called "oil-water layer identification system"). Fig.11 This is a schematic diagram of the structure of a system for identifying oil-water layers in sand-limestone mixed sedimentary rock according to an embodiment of the present application.

[0156] like Fig.11 As shown, the oil-water layer identification system described in the embodiment of the present invention includes: a sensitivity analysis module 1101, a lithology identification and classification module 1102, a gray matter content calculation module 1103, an oil saturation calculation module 1104 and an oil-water identification module 1105.

[0157] Specifically, the sensitivity analysis module 1101 is implemented according to the method described in step S110 above, and is configured to determine the formation lithology at different unit depth steps based on the coring experimental analysis data and logging data of the well to be evaluated. Based on this, by comparing various logging curves, several lithology identification sensitivity curves are selected; the lithology identification division module 1102 is implemented according to the method described in step S120 above, and is configured to calculate the lithology identification parameters at different unit depth steps and determine the lithology identification parameter limit values ​​for distinguishing different lithologies based on several lithology identification sensitivity curves; the ash content calculation module 1103 is implemented according to the method described in step S130 above, and is configured to According to the logging project type of the well to be studied, the limit value of the lithology identification parameter is used to calculate the gray matter content at different depth steps in the depth segment that meets the gray matter condition of the lithology identification parameter; the oil saturation calculation module 1104 is implemented according to the method described in the above step S140, and is configured to calculate the porosity at different depth steps and the oil saturation after removing the influence of the gray matter component on the resistivity based on the gray matter content at different depth steps; the oil-water identification module 1105 is implemented according to the method described in the above step S150, and is configured to determine the oil saturation limit value under different oil content states based on the oil saturation at different depth steps, based on which the oil and water layers of the well to be evaluated are identified.

[0158] The present invention discloses a method and system for identifying oil and water layers in sand-lime mixed sedimentary rocks. The method and system include: optimizing lithology identification sensitivity curves; establishing lithology identification methods and determining lithology identification limit values ​​using sensitivity curves; optimizing or establishing sand-lime mixed sedimentary rock layer porosity calculation methods according to the logging project type; establishing an oil saturation calculation model that removes the influence of gray components on resistivity; and determining the oil and water layer identification limit value based on oil saturation. The present invention uses a new porosity calculation model and an oil saturation calculation model that removes the influence of gray content on resistivity through lithology identification and gray content calculation, and highlights the influence of oil and gas on well logging. It can more accurately calculate porosity and oil saturation, thereby more accurately identifying oil and water layers, achieving more accurate estimation of oil and gas geological reserves, and providing more accurate geological data and better technical support for oil testing and development plan formulation. Therefore, the present invention can calculate porosity and oil saturation more accurately, identify oil and water layers more accurately, and is mainly used in petroleum geological exploration and development. It can provide technical support for drilling deployment and oil and gas development plan formulation, and improve the economic benefits of oil and gas exploration and development. In addition, the present invention has been applied in many regions and achieved good results.

[0159] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any person familiar with the technology within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0160] In the description of the present invention, unless otherwise specified, "plurality" means two or more than two; the orientations or positional relationships indicated by the terms "upper", "lower", "left", "right", "inner", "outer", "front end", "rear end", "head", "tail", etc. are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0161] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "connected" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0162] It should be understood that the embodiments disclosed in the present invention are not limited to the specific structures, processing steps or materials disclosed herein, but should be extended to equivalent substitutions of these features understood by ordinary technicians in the relevant field. It should also be understood that the terms used herein are only used for the purpose of describing specific embodiments and are not meant to be limiting.

[0163] The "one embodiment" or "embodiment" mentioned in the specification means that the specific features, structures or characteristics described in conjunction with the embodiment are included in at least one embodiment of the present invention. Therefore, the phrases "one embodiment" or "embodiment" appearing in various places throughout the specification do not necessarily refer to the same embodiment.

[0164] Although the embodiments disclosed in the present invention are as above, the contents described are only embodiments adopted for facilitating the understanding of the present invention and are not intended to limit the present invention. Any technician in the technical field to which the present invention belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present invention, but the patent protection scope of the present invention shall still be subject to the scope defined in the attached claims.

Claims

1. A method for identifying sand-lime mixed rock oil-water layer, characterized in that: include: According to the core sampling experimental analysis data and logging data of the well to be evaluated, the formation lithology at different unit depth steps is determined. Based on this, several lithology identification sensitivity curves are selected by comparing various logging curves; According to the plurality of lithology identification sensitivity curves, lithology identification parameters at different unit depth steps are calculated and lithology identification parameter limit values ​​for distinguishing different lithologies are determined; According to the logging project type of the well to be studied, the ash content at different depth steps in the depth section that meets the ash condition of the lithology identification parameter is calculated using the lithology identification parameter limit value; According to the ash content at the different depth steps, the porosity at the different depth steps and the oil saturation after removing the influence of the ash component on the resistivity are calculated; According to the oil saturation at different depth steps, the oil saturation limit values ​​under different oil content states are determined, based on which the oil and water layers of the well to be evaluated are identified.

2. The method according to claim 1, characterized in that: In the step of determining the formation lithology at different unit depth steps according to the coring experimental analysis data and logging data of the well to be evaluated, and based on this, by comparing various logging curves, selecting several lithology identification sensitivity curves, the steps include: Compare the formation lithology at the different unit depth steps with various logging curves, analyze the logging characteristics of different lithologies, and preliminarily screen out logging curves that are sensitive to lithology identification; Several preliminary sensitive logging curves are intersected in pairs, and several lithology identification sensitive curves are selected based on the preliminary sensitive curve scatter intersection distribution diagram.

3. The method according to claim 2, characterized in that The step of intersecting a plurality of preliminary sensitive logging curves in pairs and selecting a plurality of lithology identification sensitive curves according to the preliminary sensitive curve scatter intersection distribution diagram includes: According to the pure sandstone logging data of adjacent well sections at different depths in each lithology preliminary sensitive logging curve, a corresponding pure sandstone logging curve is drawn; Draw a comparison curve for comparing each lithology preliminary sensitive logging curve with the corresponding pure sandstone logging curve, so as to draw a plurality of intersecting scattered point intersection diagrams by intersecting the comparison curves of various logging curves in pairs; According to the plurality of intersecting scatter cross plots, the correlation between the various preliminary sensitive logging items is analyzed to select the lithology identification sensitive curve from a plurality of preliminary sensitive logging curves.

4. The method according to any one of claims 1 to 3, characterized in that The lithology identification parameters under different unit depth steps are calculated using the following expression: F 岩性 =A1*(X1-X 1纯砂岩 )+A2*(X2-X 2纯砂岩 )+A3*(X3 / X 3纯砂岩 )…… Among them, F 岩性 represents the lithology identification parameter, X1, X2, X3, etc. represent the logging values ​​at the specified depth in each lithology identification sensitivity curve, X 1纯砂岩 , X 2纯砂岩 , X 3纯砂岩 ...respectively represent the various logging values ​​corresponding to the pure sandstone in the adjacent well section of the current lithology to be identified layer, and A1, A2, A3...respectively represent the lithology differentiation ability coefficients of various preliminary sensitive logging items.

5. The method according to any one of claims 1 to 4, characterized in that: The step of calculating the gray matter content at different depth steps in the depth section satisfying the gray matter condition of the lithology identification parameter by using the lithology identification parameter limit value according to the logging project type of the well to be studied includes: According to the lithology identification parameters at different unit depth steps, the designated depth section of the rock containing lime is determined by using the limit value of the lithology identification parameters; According to the type of logging project of the current well to be studied, the gray matter content at different depth steps within the specified depth section is calculated, where: In the case that the well to be studied currently has the capability of acquiring at least two logging curves of acoustic wave time difference, density and neutron, the gray matter content is calculated by intersecting the at least two logging curves; In the case that the well to be studied currently only has the ability to collect sonic time difference logging curves, the gray matter content data at different depths are calculated based on the collected sonic time difference logging curves using a preset gray matter content calculation formula, wherein the gray matter content calculation formula is obtained by analyzing the correlation fitting between the gray matter content data in the coring experiment analysis data and the collected sonic time difference logging curves.

6. The method according to claim 5, characterized in that The gray matter content calculation formula is expressed by the following expression: V 灰质 =B*(X 纯砂岩 -X) 2 +C*(X 纯砂岩 -X)+D Among them, V 灰质 represents the gray matter content, X represents the data point in the measured acoustic time difference logging curve, and X 纯砂岩 It represents the average value of the data in the pure sandstone acoustic time difference logging curve of the adjacent well section where the current data point is located, and B, C, and D represent the fitting coefficients respectively.

7. The method according to claim 5 or 6, characterized in that: The step of calculating the porosity at different depth steps according to the gray matter content at the different depth steps includes: According to the type of logging items of the well to be studied, the porosity at different depth steps within the specified depth section is calculated, where: In the case that the well to be studied has the capability of acquiring at least two logging curves of acoustic wave time difference, density and neutron, the porosity is calculated by intersecting the at least two logging curves; When the well to be studied is only capable of collecting acoustic time difference logging curves, the porosity at the corresponding depth step is calculated using a preset porosity calculation formula according to the gray matter content at different depth steps and the collected acoustic time difference logging curves.

8. The method according to claim 7, characterized in that The porosity calculation formula is expressed by the following expression: △t ma =V 砂岩 ×Tm 砂岩 +V 灰质 ×Tm 灰岩 +V 泥岩 ×Tsh 泥岩 in, represents porosity, △t represents the data value in the acoustic time difference, △t ma Indicates the time difference of the comprehensive rock mineral acoustic skeleton, △t f represents the time difference of fluid sound waves, CP represents the compaction coefficient, Tm 砂岩 Represents the sandstone mineral skeleton, V 砂岩 Indicates the sandstone content, V 灰质 Indicates gray matter content, Tm 灰岩 Represents the limestone mineral skeleton, V 泥质 Indicates the mud content, Tsh 泥岩 Indicates the mudstone acoustic time difference, SMX i 、SMN i Indicates the logging value of pure mudstone and pure sandstone, GCUR i It represents the formation coefficient of the calculated shale content, and TSHi represents the logging value of the target curve selected when calculating the shale parameters at a certain depth point.

9. The method according to any one of claims 1 to 8, characterized in that: The oil saturation at different depth steps is calculated using the following expression: S O =1-S w Among them, S o Indicates oil saturation, S w represents water saturation, represents porosity, a represents rock electrical coefficient, m represents cementation index, R w represents the resistivity of formation water, Rt represents the resistivity of formation, R 灰质 Represents the resistivity of pure gray matter, V 灰质 Indicates gray matter content.

10. A system for identifying sand-lime mixed rock oil-water layers, characterized in that: include: The sensitivity analysis module is configured to determine the formation lithology at different unit depth steps based on the core sampling experimental analysis data and logging data of the well to be evaluated. Based on this, several lithology identification sensitivity curves are selected by comparing various logging curves; A lithology identification and classification module, configured to calculate lithology identification parameters at different unit depth steps and determine lithology identification parameter limit values ​​for distinguishing different lithologies according to the plurality of lithology identification sensitivity curves; A gray matter content calculation module is configured to calculate the gray matter content at different depth steps within a depth section that meets the gray matter condition of the lithology identification parameter according to the logging project type of the well to be studied and using the lithology identification parameter limit value; An oil saturation calculation module, configured to calculate the porosity at different depth steps and the oil saturation after removing the influence of the ash component on the resistivity according to the ash content at different depth steps; The oil-water identification module is configured to determine the oil saturation limit values ​​under different oil content states according to the oil saturation under the different depth steps, and based on this, identify the oil and water layers of the well to be evaluated.

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