Conglomerate layer identification method, device, equipment and storage medium

Abstract

The present application provides a method, device, equipment and storage medium for identifying gravel layers. In this method, the exploration well logging data of the target study area is obtained, and the rock cuttings corresponding to the exploration well logging data are analyzed to determine the main controlling factors of the gravel layer in the target study area. Then, according to the main controlling factors of the gravel layer and the pre-established gravel identification plate, the diagenetic stage of the gravel layer is identified. In this technical solution, by determining the main controlling factors and establishing the gravel identification plate, the gravel layer type in the target study area can be more accurately identified, making subsequent drilling operations more efficient and low-risk, and preventing possible malicious drilling fluid loss.

Description

Technical Field

[0001] The present application relates to the field of exploration and development technology, and in particular to a conglomerate layer identification method, device, equipment and storage medium. Background Art

[0002] During the drilling process, the presence of gravel layers can lead to complex engineering situations such as drill sticking and fish falling, making the operation more difficult and costly. The presence of high-speed gravel also poses a great challenge to the study of the velocity field of the target layer under the gravel.

[0003] In the prior art, the prediction of conglomerate layers is generally based on seismic geological methods, non-seismic methods and other methods. These methods treat the conglomerate layer as a whole and make macroscopic predictions.

[0004] However, in practical applications, there are large differences in cementation, morphology, and state between different diagenetic stages of conglomerate layers, and seismic geological methods, non-seismic methods, and other methods cannot accurately predict each diagenetic stage, so the safety of drilling cannot be guaranteed in subsequent drilling operations. Summary of the invention

[0005] The present application provides a conglomerate layer identification method, device, equipment and storage medium to solve the problem that the prior art cannot accurately determine the stage to which the conglomerate layer belongs.

[0006] In a first aspect, an embodiment of the present application provides a method for identifying a conglomerate layer, comprising:

[0007] Obtain exploration well logging data in the target study area;

[0008] Analyze the rock cuttings corresponding to the exploration well logging data to determine the main controlling factors of the conglomerate layer in the target research area, wherein the main controlling factors include: sedimentation mode, gravel composition and gravel particle size;

[0009] The diagenetic stage of the conglomerate layer is identified according to the main controlling factors of the conglomerate layer and a pre-established conglomerate identification plate.

[0010] In a possible design of the first aspect, analyzing the rock cuttings corresponding to the exploration well logging data to determine the main controlling factors of the conglomerate layer in the target research area includes:

[0011] Analyze the rock cuttings corresponding to the exploration well logging data to determine the attribute information of equally spaced conglomerate samples in the target research area;

[0012] Determining the cementation degree of the equally spaced conglomerate samples according to the property information of the equally spaced conglomerate samples;

[0013] Determining the sedimentary information of the conglomerate layer in the target research area according to the cementation degree of the gravel;

[0014] Determining a depositional pattern of the conglomerate layer according to the depositional information of the conglomerate layer;

[0015] The attribute information of the equally spaced conglomerate samples is analyzed using an element capture spectrum logging instrument to determine the gravel composition and gravel particle size of the conglomerate layer.

[0016] In another possible design of the first aspect, before identifying the diagenetic stage of the conglomerate layer according to the main controlling factors of the conglomerate layer and a pre-established conglomerate identification plate, the method further includes:

[0017] Processing the exploration well logging data to obtain a natural gamma curve, a resistivity curve and an acoustic time difference curve corresponding to the exploration well logging data;

[0018] The conglomerate identification chart is established according to the natural gamma curve, the resistivity curve and the acoustic wave time difference curve.

[0019] In this possible design, the method further includes:

[0020] Processing the exploration well logging data to obtain information on differences in cementation degree of alluvial fan conglomerate bodies at various stages and information on differences in drillability during logging drilling;

[0021] Establishing the development stage and type of the alluvial fan conglomerate body according to the natural gamma ray curve, the resistivity curve, the acoustic time difference curve, the cementation degree difference information of the alluvial fan conglomerate body at each stage, and the drillability difference information during logging drilling;

[0022] According to the development period and type of the alluvial fan conglomerate body, the characteristic difference information of each stage included in the gravel layer is determined.

[0023] In another possible design of the first aspect, before acquiring the exploration well logging data of the target research area, the method further includes:

[0024] Acquiring surface data of the target study area;

[0025] The surface data are analyzed to determine that thick conglomerates of the Quaternary or Neogene strata exist in the target study area.

[0026] In yet another possible design of the first aspect, the method further includes:

[0027] Obtain three-dimensional gravity, magnetic and electrical survey data and seismic depth domain data in the target study area;

[0028] Overlaying the three-dimensional gravity, magnetic and electrical survey data with the seismic depth domain data to obtain lithology interpretation data of the conglomerate layer;

[0029] Using the lithology interpretation data and the exploration well logging data, determine to update the three-dimensional electrical profile of the target research area;

[0030] The resistivity difference on the updated three-dimensional electrical profile is used to perform three-dimensional carving of the alluvial fan body in the target study area.

[0031] In this possible design, the method further includes:

[0032] Analyze the seismic depth domain data to obtain the wave impedance propagation information of the chronological seismic profile, the current seismic profile and the low-frequency seismic reflection coefficient data body;

[0033] By superimposing the seismic profile of the said age and the current seismic profile, the interpretation data of the alluvial fan subfacies and the interpretation data of the top and bottom surfaces of the said conglomerate layer are obtained;

[0034] Obtaining sequence stratigraphic data of well logging and exploration lithology based on the interpretation data of the alluvial fan subfacies and the interpretation data of the top and bottom surfaces of the conglomerate layer;

[0035] Under the constraint conditions of the logging and exploration of well lithology, the seismic depth domain data is analyzed to obtain the attribute information of the seismic data volume;

[0036] The diagenetic stage of the alluvial fan body is determined based on the sequence stratigraphic data of the well logging lithology, the attribute information of the seismic data body and the wave impedance propagation information of the low-frequency seismic reflection coefficient data body.

[0037] In a second aspect, an embodiment of the present application provides a conglomerate layer identification device, including: an acquisition module, a determination module and an identification module;

[0038] The acquisition module is used to acquire the exploration well logging data of the target research area;

[0039] The determination module is used to analyze the rock cuttings corresponding to the exploration well logging data to determine the main controlling factors of the conglomerate layer in the target research area, wherein the main controlling factors include: deposition mode, gravel composition and gravel particle size;

[0040] The identification module is used to identify the diagenetic stage of the conglomerate layer according to the main controlling factors of the conglomerate layer and a pre-established conglomerate identification plate.

[0041] In a possible design of the second aspect, the determining module is specifically configured to:

[0042] Analyze the rock cuttings corresponding to the exploration well logging data to determine the attribute information of equally spaced conglomerate samples in the target research area;

[0043] Determining the cementation degree of the equally spaced conglomerate samples according to the property information of the equally spaced conglomerate samples;

[0044] Determining the sedimentary information of the conglomerate layer in the target research area according to the cementation degree of the gravel;

[0045] Determining a depositional pattern of the conglomerate layer according to the depositional information of the conglomerate layer;

[0046] The attribute information of the equally spaced conglomerate samples is analyzed using an element capture spectrum logging instrument to determine the gravel composition and gravel particle size of the conglomerate layer.

[0047] In another possible design of the second aspect, the identification module is further used to:

[0048] Processing the exploration well logging data to obtain a natural gamma curve, a resistivity curve and an acoustic time difference curve corresponding to the exploration well logging data;

[0049] The conglomerate identification chart is established according to the natural gamma curve, the resistivity curve and the acoustic wave time difference curve.

[0050] In this possible design, the identification module is further used to:

[0051] Processing the exploration well logging data to obtain information on differences in cementation degree of alluvial fan conglomerate bodies at various stages and information on differences in drillability during logging drilling;

[0052] Establishing the development stage and type of the alluvial fan conglomerate body according to the natural gamma ray curve, the resistivity curve, the acoustic time difference curve, the cementation degree difference information of the alluvial fan conglomerate body at each stage, and the drillability difference information during logging drilling;

[0053] According to the development period and type of the alluvial fan conglomerate body, the characteristic difference information of each stage included in the gravel layer is determined.

[0054] In another possible design of the second aspect, the acquisition module is further used to acquire surface data of the target research area;

[0055] The determination module is further used to analyze the surface data to determine whether there is a thick conglomerate in the Quaternary or Neogene strata in the target research area.

[0056] In yet another possible design of the second aspect, the acquisition module is further used to acquire three-dimensional gravity, magnetic and electrical survey data and seismic depth domain data of the target research area;

[0057] The determining module is further used for:

[0058] Overlaying the three-dimensional gravity, magnetic and electrical survey data with the seismic depth domain data to obtain lithology interpretation data of the conglomerate layer;

[0059] Using the lithology interpretation data and the exploration well logging data, determine to update the three-dimensional electrical profile of the target research area;

[0060] The resistivity difference on the updated three-dimensional electrical profile is used to perform three-dimensional carving of the alluvial fan body in the target study area.

[0061] In this possible design, the determining module is further used to:

[0062] Analyze the seismic depth domain data to obtain the wave impedance propagation information of the chronological seismic profile, the current seismic profile and the low-frequency seismic reflection coefficient data body;

[0063] By superimposing the seismic profile of the said age and the current seismic profile, the interpretation data of the alluvial fan subfacies and the interpretation data of the top and bottom surfaces of the said conglomerate layer are obtained;

[0064] Obtaining sequence stratigraphic data of well logging and exploration lithology based on the interpretation data of the alluvial fan subfacies and the interpretation data of the top and bottom surfaces of the conglomerate layer;

[0065] Under the constraint conditions of the logging and exploration of well lithology, the seismic depth domain data is analyzed to obtain the attribute information of the seismic data volume;

[0066] The diagenetic stage of the alluvial fan body is determined based on the sequence stratigraphic data of the well logging lithology, the attribute information of the seismic data body and the wave impedance propagation information of the low-frequency seismic reflection coefficient data body.

[0067] In a third aspect, an embodiment of the present application provides a computer device, including: a processor, a memory, a display, and a computer program stored in the memory and executable on the processor;

[0068] When the processor executes the computer program instructions, the conglomerate layer identification method provided in the first aspect and various possible designs is implemented.

[0069] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer program instructions are stored. When the computer program instructions are executed by a processor, they are used to implement the conglomerate layer identification method provided in the first aspect and various possible designs.

[0070] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, is used to implement the conglomerate layer identification method provided in the first aspect and various possible designs.

[0071] The embodiment of the present application provides a method, device, equipment and storage medium for identifying gravel layers. In the method, the exploration well logging data of the target study area is obtained, and the rock cuttings corresponding to the exploration well logging data are analyzed to determine the main controlling factors of the gravel layer in the target study area, and then the diagenetic stage of the gravel layer is identified according to the main controlling factors of the gravel layer and the pre-established gravel identification plate. In this technical solution, by determining the main controlling factors and establishing the gravel identification plate, the gravel layer type in the target study area can be more accurately identified, making subsequent drilling operations more efficient and low-risk, and preventing possible malicious drilling fluid leakage. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 A schematic diagram of a flow chart of a first embodiment of a conglomerate layer identification method provided in an embodiment of the present application;

[0073] Figure 2 A schematic diagram of a flow chart of Embodiment 2 of a conglomerate layer identification method provided in an embodiment of the present application;

[0074] Figure 3 A schematic diagram of a flow chart of Embodiment 3 of a conglomerate layer identification method provided in an embodiment of the present application;

[0075] Figure 4 A schematic diagram of a flow chart of a fourth embodiment of a conglomerate layer identification method provided in an embodiment of the present application;

[0076] Figure 5 A schematic diagram of the structure of a conglomerate layer identification device provided in an embodiment of the present application;

[0077] Figure 6 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0078] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0079] Before introducing the embodiments of the present application, the background technology and application scenarios of the present application are first explained.

[0080] During the exploration and development process, the presence of gravel layers can lead to drill sticking, fish falling and other situations during operations. The presence of high-speed gravel also poses a great challenge to the study of the velocity field of the target layer under the gravel. Currently, the prediction of gravel is mainly based on seismic geological methods, non-seismic methods and other methods.

[0081] However, there are great differences in cementation, morphology and state between different diagenetic stages of conglomerate from the surface downwards, and a comprehensive prediction method is not conducive to reservoir prediction, safe drilling and prediction of conglomerate thickness under conglomerate. At present, there is no prediction method for conglomerate layers in the diagenetic stage.

[0082] In response to the above technical problems, the inventor's technical conception of this application is: the existing technology roughly predicts the conglomerate layer as a whole, so by conducting a specific analysis of the rock fragments of the conglomerate layer, the influencing factors affecting the conglomerate layer can be determined, thereby judging the diagenetic stage of the conglomerate layer.

[0083] The technical solution of the present application is described in detail below through specific embodiments. It should be noted that the following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below.

[0084] Figure 1 A schematic flow chart of Embodiment 1 of a conglomerate layer identification method provided in an embodiment of the present application.

[0085] like Figure 1 As shown, the conglomerate layer identification method may include the following steps:

[0086] Step 11: Obtain the exploration well logging data in the target study area.

[0087] In this step, the conglomerate layer in the target study area needs to be identified. First, basic information about the conglomerate layer in the target study area is obtained, which may be exploration well logging data.

[0088] In a possible implementation, the exploratory well logging data may be obtained from exploratory well logging in the target research area.

[0089] Among them, wells drilled to obtain geological data required for oil and gas development are called exploratory wells; mud logging is the use of rock and mineral analysis, geophysics, geochemistry and other methods to observe, sample, collect, record and analyze information on wellbore return materials such as solids, liquids and gases during the drilling process; the target research area is the area that requires exploration and development operations, that is, the area where gravel layer identification is required.

[0090] Prior to this step, the surface data of the target study area is obtained and analyzed to determine the presence of thick conglomerates of the Quaternary or Neogene strata in the target study area.

[0091] Optionally, by analyzing the surface outcrop data of the target study area, it is determined whether there are thick conglomerates of the Quaternary or Neogene strata in the target study area. If so, the following steps are performed.

[0092] The surface data may be surface outcrop data.

[0093] Step 12: Analyze the rock cuttings corresponding to the exploration well logging data to determine the main controlling factors of the conglomerate layer in the target study area.

[0094] Among them, the main controlling factors include: sedimentation mode, gravel composition and gravel particle size.

[0095] In this step, the physical properties of the conglomerate layer are related to the rock cuttings. The corresponding rock cuttings can be obtained from the exploration well logging data. By performing corresponding analysis on the rock cuttings, the identification factors affecting the conglomerate layer can be determined. The analysis method is given in the following scheme.

[0096] Among them, the sedimentary model refers to the physical and chemical model of the sedimentary environment and its sediments summarized on the basis of comprehensive research on the physical, chemical and biological characteristics of modern sediments in a certain environment. The gravel composition refers to the component material of the gravel in the conglomerate layer, and the gravel particle size refers to the size of the gravel in the conglomerate layer.

[0097] Step 13: Identify the diagenetic stage of the conglomerate layer based on the main controlling factors of the conglomerate layer and the pre-established conglomerate identification plate.

[0098] In this step, various factors affecting the diagenetic stage of the conglomerate layer are input into the pre-established conglomerate identification plate to obtain the diagenetic stage to which the target study area belongs.

[0099] Optionally, the diagenetic stage may include a pre-diagenetic stage, a quasi-diagenetic stage, and a diagenetic stage.

[0100] After knowing the diagenetic stage of the conglomerate layer, the composition of the conglomerate and the engineering complexities encountered during drilling in the target study area are predicted and prompted, and the selection of the drill bit is optimized to avoid the occurrence of drilling engineering complexities.

[0101] The process of establishing the conglomerate identification plate is given in the following embodiment.

[0102] The conglomerate layer identification method provided in the embodiment of the present application obtains the exploration well logging data in the target study area, and analyzes the rock cuttings corresponding to the exploration well logging data to determine the main controlling factors of the conglomerate layer in the target study area, and then identifies the diagenetic stage of the conglomerate layer according to the main controlling factors of the conglomerate layer and the pre-established conglomerate identification plate. In this technical solution, by determining the main controlling factors and establishing the conglomerate identification plate, the conglomerate layer type in the target study area can be more accurately identified, making subsequent drilling operations more efficient and low-risk, and preventing possible malicious drilling fluid loss.

[0103] Based on the above embodiments, Figure 2 The flowchart of the second embodiment of the conglomerate layer identification method provided in the present application is as follows. Figure 2 As shown, the above step 12 can be implemented by the following steps:

[0104] Step 21: Analyze the rock cuttings corresponding to the exploration well logging data to determine the attribute information of equally spaced conglomerate samples in the target study area.

[0105] In this step, the rock cuttings obtained from the exploration well logging data are first analyzed to obtain the gravel particle size, gravel roundness, and sorting degree of equally spaced conglomerate samples in the target study area.

[0106] Among them, the attribute information includes: gravel particle size, gravel roundness, and sorting degree, which refers to the physical properties of gravel at different intervals.

[0107] Step 22: Determine the cementation degree of the equally spaced conglomerate samples according to the attribute information of the equally spaced conglomerate samples.

[0108] In this step, the equally spaced conglomerate samples may be processed using a scanning electron microscope, a cast thin section, etc., to obtain the cementation degree of the equally spaced conglomerate samples.

[0109] Among them, the degree of cementation refers to the degree to which the separated conglomerate samples are consolidated together. Starting from the early diagenetic stage, the degree of cementation of equally spaced conglomerate samples deepens.

[0110] Step 23: Determine the sedimentary information of the conglomerate layer in the target study area according to the cementation degree of the gravel.

[0111] In this step, the conglomerate layer is divided into sedimentary facies zones according to the cementation degree of the gravel, and the sedimentary environment and sedimentary sequence of the conglomerate layer are analyzed and obtained.

[0112] That is to say, the sedimentary facies belts, sedimentary environments and sedimentary sequences divided are the sedimentary information of conglomerate layers.

[0113] Step 24: Determine the sedimentary pattern of the conglomerate layer according to the sedimentary information of the conglomerate layer.

[0114] In this step, the conglomerate deposition model is established according to the sedimentary facies, sedimentary environment and sedimentary sequence of the conglomerate layer, and the influence of the location, period and intensity of structural deformation on the change of the conglomerate layer thickness is analyzed.

[0115] Among them, the sedimentary pattern of the conglomerate layer can be a physical and chemical model of the sedimentary environment and its sedimentary sequence summarized on the basis of a comprehensive study of the physical, chemical and biological characteristics of sediments in a certain sedimentary environment.

[0116] Step 25: Analyze the property information of the equally spaced conglomerate samples using an element capture spectrum logging instrument to determine the gravel composition and gravel particle size of the conglomerate layer.

[0117] In this step, the volume content of silicon, calcium, iron, sulfur and other elements in the main minerals in the formation in the equally spaced conglomerate samples is calculated by the Elemental Capture Spectroscopy (ECS) instrument, and then the gravel composition and gravel particle size of the conglomerate layer are determined.

[0118] The conglomerate layer identification method provided in the embodiment of the present application determines the attribute information of equally spaced conglomerate samples in the target study area by analyzing the rock cuttings corresponding to the exploration well logging data, and determines the cementation degree of the equally spaced conglomerate samples based on the attribute information of the equally spaced conglomerate samples, and then determines the sedimentary information of the conglomerate layer in the target study area based on the cementation degree of the gravel, and then determines the sedimentary mode of the conglomerate layer based on the sedimentary information of the conglomerate layer, and finally uses the element capture spectrum logging instrument to analyze the attribute information of the equally spaced conglomerate samples to determine the gravel composition and gravel particle size of the conglomerate layer. In this technical solution, the relevant information of the gravel in the conglomerate layer is obtained by analyzing the rock cuttings, which provides a basis for the subsequent division of the conglomerate layer.

[0119] Based on the above embodiments, Figure 3 This is a flow chart of Embodiment 3 of the conglomerate layer identification method provided in the present application. Figure 3 As shown, before the above step 13, the conglomerate layer identification method may further include the following steps:

[0120] Step 31: Process the exploration well logging data to obtain a natural gamma curve, a resistivity curve, and an acoustic time difference curve corresponding to the exploration well logging data.

[0121] In this step, the natural gamma curve, resistivity curve and sonic time difference curve corresponding to the exploration well logging data are determined respectively according to the natural gamma, resistivity and sonic time difference of gravel in the exploration well logging data.

[0122] Step 32: Create a conglomerate identification chart based on the natural gamma curve, resistivity curve, and acoustic time difference curve.

[0123] In this step, the first two-dimensional intersection map of conglomerate (natural gamma-resistivity) is established based on the relationship between the natural gamma curve and the resistivity curve; the second two-dimensional intersection map of conglomerate (deep resistivity-shallow resistivity) is established based on the relationship between the resistivity curve and the acoustic wave time difference curve.

[0124] That is, the conglomerate identification plate includes a first two-dimensional intersection plate and a second two-dimensional intersection plate.

[0125] In this embodiment, the following steps may also be included:

[0126] Step 1: Process the exploration well logging data to obtain the difference information of cementation degree of alluvial fan conglomerate at different stages and the difference information of drillability during logging drilling.

[0127] Among them, the cementation degree difference information describes the different cementation degrees of the alluvial fan conglomerate at different stages, and the drillability difference information describes the difficulty of drilling the alluvial fan conglomerate at different stages.

[0128] Step 2: Establish the development stage and type of alluvial fan conglomerate based on the natural gamma curve, resistivity curve, acoustic time difference curve, cementation degree difference information of alluvial fan conglomerate at different stages, and drillability difference information during logging drilling.

[0129] Among them, the development stages and types of alluvial fan conglomerate bodies can be the undiagenetic stage, which is the stage of unconsolidated conglomerate layers, the quasi-diagenetic stage, which refers to the stage when the conglomerate begins to cement but still has a certain degree of undercompaction, and the diagenetic stage, which refers to the stage when the rock is completely consolidated and diagenesized.

[0130] Generally speaking, in the pre-diagenetic stage, the resistivity is 95-480Ω.m, the acoustic time difference is 65-80us / m, the calcium carbonate content is 10%-15%, the average porosity is 4%-10%, and the average drilling time is 15-40min / m. It is common to encounter complex engineering problems such as lost circulation, obstruction, stuck drilling, and fish dropping; in the quasi-diagenetic stage, the resistivity is 30-90Ω.m, the acoustic time difference is 55-65us / m, the calcium carbonate content is 15%-35%, the average porosity is 3%-5%, and the average drilling time is 33-60min / m. It is common to encounter complex engineering problems such as lost circulation, obstruction, stuck drilling, and fish dropping; in the diagenetic stage, the resistivity is 25-100Ω.m, the acoustic time difference is 50-70us / m, the calcium carbonate content is 20%-45%, the average porosity is 2%-4%, and the average drilling time is 40-100min / m. It is common to encounter complex engineering problems such as obstruction, stuck drilling, stuck drilling, and fish dropping.

[0131] Step 3: According to the development period and type of the alluvial fan conglomerate body, determine the characteristic difference information of each stage included in the gravel layer.

[0132] According to the corresponding data of the above-mentioned different stages, the characteristic difference information of each stage included in the gravel layer is obtained.

[0133] The conglomerate layer identification method provided in the embodiment of the present application processes the exploration well logging data to obtain the natural gamma curve, resistivity curve and acoustic time difference curve corresponding to the exploration well logging data, and establishes a conglomerate identification chart based on the natural gamma curve, resistivity curve and acoustic time difference curve. In this technical solution, the conglomerate identification chart drawn based on the exploration well logging data provides a basis for identifying the diagenetic stage of the conglomerate layer.

[0134] Based on the above embodiments, Figure 4 This is a flow chart of Embodiment 4 of the conglomerate layer identification method provided in the present application. Figure 4 As shown, the conglomerate layer identification method may further include the following steps:

[0135] Step 41: Obtain three-dimensional gravity, magnetic and electrical survey data and seismic depth domain data in the target study area.

[0136] In this step, the three-dimensional gravity, magnetic and electrical exploration data and seismic depth domain data are obtained by comprehensively analyzing the geological characteristics of complex areas, the surface strata distribution and the three-dimensional characteristics of underground structures through three-dimensional gravity, magnetic and electrical exploration technology.

[0137] Step 42: Overlay the three-dimensional gravity, magnetic and electrical survey data with the seismic depth domain data to obtain lithology interpretation data for the conglomerate layer.

[0138] In this step, the three-dimensional gravity, magnetic and electrical survey data are processed to obtain the resistivity data volume, and the resistivity profile is superimposed with the seismic depth domain data.

[0139] In one possible implementation, with the interpreted seismic depth domain data as the background, the resistivity profile at the same position is processed into a transparent color and directly superimposed on the seismic profile. The superimposed profile simultaneously has resistivity values, seismic phase parameter sedimentary phase information, and geological history information, and a lithological interpretation of the lithology of the conglomerate layer is performed to obtain lithological interpretation data of the conglomerate layer.

[0140] Step 43: Using the lithology interpretation data and exploration well logging data, determine to update the three-dimensional electrical profile of the target study area.

[0141] In this step, the stratigraphic positions of the field outcrop data in the exploration well logging data, the lithology implementation and its planar distribution law in the lithology interpretation data are studied, and the logging and well logging characteristics of the three diagenetic stages in the target study area are combined to jointly constrain the three-dimensional electrical profile of the target study area, that is, to obtain an updated three-dimensional electrical profile.

[0142] Step 44: Utilize the resistivity difference on the updated three-dimensional electrical profile to perform three-dimensional carving on the alluvial fan body in the target study area.

[0143] In this step, due to the formation stage of the conglomerate layer and the differences in gravel, there are certain differences in their resistivity. According to the resistivity differences on the updated three-dimensional electrical profile, the alluvial fan body in the target study area can be three-dimensionally carved to form an alluvial fan body with different carving patterns.

[0144] In this embodiment, the conglomerate layer identification method may further include the following steps:

[0145] Step 1: Analyze the seismic depth domain data to obtain the wave impedance propagation information of the chronological seismic profile, current seismic profile and low-frequency seismic reflection coefficient data body.

[0146] By analyzing the seismic depth domain data obtained above, we can obtain the chronological seismic profile, that is, the historical seismic record profile, the current seismic profile, that is, the current seismic record profile, and the wave impedance propagation information of the low-frequency seismic reflection coefficient data body.

[0147] Step 2: By superimposing the chronological seismic profile and the current seismic profile, the interpretation data of the alluvial fan subfacies and the top and bottom surface interpretation data of the conglomerate layer are obtained.

[0148] Among them, the alluvial fan subfacies can be further divided into three subfacies: fan root (top), fan middle and fan edge (end) according to the modern alluvial fan landforms and sediment distribution characteristics, and there are no obvious boundaries between the three subfacies.

[0149] Step 3: Based on the interpretation data of the alluvial fan subfacies and the top and bottom surfaces of the conglomerate layer, the sequence stratigraphic data of the logging exploration lithology are obtained.

[0150] The interpretation of alluvial fan subfacies and the top and bottom surfaces of conglomerate layers is mainly carried out by superposition of chronoseismic sections and seismic sections.

[0151] Step 4: Under the constraints of logging and exploration of well lithology, analyze the seismic depth domain data to obtain the attribute information of the seismic data volume.

[0152] The attribute information of the seismic data volume may include acoustic wave impedance information, acoustic wave root mean square amplitude information, and seismic reflection characteristic information.

[0153] Step 5: Determine the diagenetic stage of the alluvial fan body based on the sequence stratigraphic data of the well logging lithology, the attribute information of the seismic data body, and the wave impedance propagation information of the low-frequency seismic reflection coefficient data body.

[0154] Based on the joint constraints of sequence stratigraphic data of well logging lithology, attribute information of seismic data body and wave impedance propagation information of low-frequency seismic reflection coefficient data body, the conglomerate layers of different diagenetic stages of alluvial fan bodies are identified.

[0155] Furthermore, if the errors of the three constraint conditions are greater than a certain threshold, such as 20%, then the relevant parameters of the pre-established conglomerate identification plate need to be readjusted.

[0156] The conglomerate layer identification method provided in the embodiment of the present application first obtains the three-dimensional gravity, magnetic and electrical survey data and seismic depth domain data of the target study area, and superimposes the three-dimensional gravity, magnetic and electrical survey data and the seismic depth domain data to obtain the lithology interpretation data of the conglomerate layer, and then uses the lithology interpretation data and exploration well logging data to determine whether to update the three-dimensional electrical profile of the target study area, and finally uses the resistivity difference on the updated three-dimensional electrical profile to perform three-dimensional carving of the alluvial fan body in the target study area. In this scheme, starting from the three-dimensional gravity, magnetic and electrical survey data and the seismic depth domain data, the three-dimensional carving of the alluvial fan body is realized, which provides a basis for the identification of the diagenetic stage of the conglomerate layer.

[0157] Based on the above embodiment of the method for identifying conglomerate layers, Figure 5 This is a schematic diagram of the structure of the conglomerate layer identification device provided in the embodiment of the present application. Figure 5 As shown, the conglomerate layer identification device includes: an acquisition module 51, a determination module 52 and an identification module 53;

[0158] An acquisition module 51 is used to acquire the exploration well logging data of the target research area;

[0159] A determination module 52 is used to analyze the rock cuttings corresponding to the exploration well logging data to determine the main controlling factors of the conglomerate layer in the target research area, the main controlling factors including: deposition mode, gravel composition and gravel particle size;

[0160] The identification module 53 is used to identify the diagenetic stage of the conglomerate layer according to the main controlling factors of the conglomerate layer and the pre-established conglomerate identification plate.

[0161] In a possible design of the embodiment of the present application, the determination module 52 is specifically configured to:

[0162] Analyze the rock cuttings corresponding to the exploration well logging data to determine the attribute information of equally spaced conglomerate samples in the target study area;

[0163] Determine the cementation degree of the equally spaced conglomerate samples according to the attribute information of the equally spaced conglomerate samples;

[0164] According to the cementation degree of gravel, the sedimentary information of the conglomerate layer in the target research area is determined;

[0165] Determine the sedimentary pattern of the conglomerate layer based on its sedimentary information;

[0166] The element capture spectrum logging tool is used to analyze the attribute information of equally spaced conglomerate samples to determine the gravel composition and gravel size of the conglomerate layer.

[0167] In another possible design of the embodiment of the present application, the identification module 53 is further used to:

[0168] Process the exploration well logging data to obtain the natural gamma curve, resistivity curve and acoustic time difference curve corresponding to the exploration well logging data;

[0169] A conglomerate identification chart is established based on the natural gamma curve, resistivity curve and sonic time difference curve.

[0170] In this possible design, the identification module 53 is further used to:

[0171] The logging data of the exploration wells were processed to obtain the information on the difference in cementation degree of the alluvial fan conglomerate at each stage and the difference in drillability during logging drilling;

[0172] The development period and type of alluvial fan conglomerate are established based on natural gamma curves, resistivity curves, acoustic time difference curves, cementation degree difference information of alluvial fan conglomerate at different stages, and drillability difference information during logging drilling.

[0173] According to the development period and type of alluvial fan conglomerate body, the characteristic difference information of each stage included in the gravel layer is determined.

[0174] In another possible design of the embodiment of the present application, the acquisition module 51 is further used to acquire surface data of the target research area;

[0175] The determination module 52 is also used to analyze the surface data to determine whether there is a thick conglomerate in the Quaternary or Neogene strata in the target study area.

[0176] In another possible design of the embodiment of the present application, the acquisition module 51 is also used to acquire three-dimensional gravity, magnetic and electrical survey data and seismic depth domain data of the target study area;

[0177] The determination module 52 is further used for:

[0178] The lithology interpretation data of the conglomerate layer is obtained by superimposing the 3D gravity, magnetic and electrical survey data and the seismic depth domain data;

[0179] Using lithology interpretation data and exploration well logging data, determine to update the 3D electrical profile of the target study area;

[0180] The resistivity difference on the updated 3D electrical profile is used to perform 3D carving of the alluvial fan in the target study area.

[0181] In this possible design, the determination module 52 is further configured to:

[0182] Analyze the seismic depth domain data to obtain the wave impedance propagation information of the chronological seismic profile, current seismic profile and low-frequency seismic reflection coefficient data volume;

[0183] By superimposing the chronological seismic profile and the current seismic profile, the interpretation data of the alluvial fan subfacies and the top and bottom surface interpretation data of the conglomerate layer are obtained;

[0184] Based on the interpretation data of alluvial fan subfacies and the top and bottom surfaces of conglomerate layers, the sequence stratigraphic data of logging exploration lithology are obtained;

[0185] Under the constraints of logging and exploration of well lithology, the seismic depth domain data is analyzed to obtain the attribute information of the seismic data volume;

[0186] The diagenetic stage of the alluvial fan is determined based on the sequence stratigraphic data of well logging lithology, the attribute information of the seismic data body and the wave impedance multiplication information of the low-frequency seismic reflection coefficient data body.

[0187] The conglomerate layer identification device provided in the embodiment of the present application can be used to implement the technical solution of the conglomerate layer identification method in the above embodiment. Its implementation principle and technical effect are similar and will not be repeated here.

[0188] It should be noted that it should be understood that the division of the various modules of the above device is only a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. And these modules can all be implemented in the form of software calling through processing elements; they can also be all implemented in the form of hardware; some modules can also be implemented in the form of software called by processing elements, and some modules can be implemented in the form of hardware. In addition, all or part of these modules can be integrated together, or they can be implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each module above can be completed by an integrated logic circuit of hardware in the processor element or instructions in the form of software.

[0189] Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present application. Figure 6 As shown, the computer device may include: a processor 60 , a memory 61 , and computer program instructions stored in the memory 61 and executable on the processor 60 .

[0190] Among them, the computer device can correspond to the WCS system.

[0191] Optionally, the computer device may further include: a transceiver 62 .

[0192] The processor 60 executes the computer execution instructions stored in the memory, so that the processor 60 executes the scheme in the above embodiment. The processor 60 can be a general-purpose processor, including a central processing unit CPU, a network processor (NP), etc.; it can also be a digital signal processor DSP, an application-specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0193] The memory 61 and the transceiver 62 are connected to the processor 60 via a system bus and communicate with each other. The memory 61 is used to store computer program instructions.

[0194] The transceiver 62 is used to communicate with other devices, and the transceiver 62 constitutes a communication interface. Optionally, in hardware implementation, when the computer device is a receiving terminal, the above Figure 5 The acquisition module 51 in the illustrated embodiment corresponds to the transceiver 62 in this embodiment.

[0195] The system bus can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The system bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or only one type of communication between buses.

[0196] The computer device provided in the embodiment of the present application can be used to execute the technical solution of the conglomerate layer identification method in the above embodiment. Its implementation principle and technical effect are similar and will not be repeated here.

[0197] The embodiment of the present application also provides a chip for running instructions, which is used to execute the technical solution of the conglomerate layer identification method in the above embodiment.

[0198] The embodiment of the present application further provides a computer-readable storage medium, in which computer instructions are stored. When the computer instructions are executed on a computer, the computer executes the technical solution of the conglomerate layer identification method in the above embodiment.

[0199] The computer-readable storage medium mentioned above can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0200] Optionally, a readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0201] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for identifying a conglomerate layer, characterized in that: include: Obtain exploration well logging data in the target study area; Analyze the rock cuttings corresponding to the exploration well logging data to determine the main controlling factors of the conglomerate layer in the target research area, wherein the main controlling factors include: sedimentation mode, gravel composition and gravel particle size; Analyze the rock cuttings corresponding to the exploration well logging data to determine the attribute information of equally spaced conglomerate samples in the target study area; determine the degree of cementation of the equally spaced conglomerate samples based on the attribute information of the equally spaced conglomerate samples; determine the sedimentary information of the conglomerate layer in the target study area based on the cementation degree of the gravel; determine the sedimentary pattern of the conglomerate layer based on the sedimentary information of the conglomerate layer; analyze the attribute information of the equally spaced conglomerate samples using an element capture spectrum logging instrument to determine the gravel composition and gravel grain size of the conglomerate layer; obtain three-dimensional gravity, magnetic and electrical exploration data and seismic depth domain data of the target study area; The three-dimensional gravity, magnetic and electrical survey data and the seismic depth domain data are superimposed to obtain lithology interpretation data of the conglomerate layer; the three-dimensional electrical profile of the target study area is updated by using the lithology interpretation data and the exploration well logging data; the alluvial fan body in the target study area is three-dimensionally carved by using the resistivity difference on the updated three-dimensional electrical profile; The seismic depth domain data are analyzed to obtain the wave impedance propagation information of the chronological seismic profile, the current seismic profile and the low-frequency seismic reflection coefficient data body; the interpretation data of the alluvial fan subfacies and the top and bottom surface interpretation data of the conglomerate layer are obtained by superimposing the chronological seismic profile and the current seismic profile; the sequence stratigraphic data of the well logging exploration lithology are obtained based on the interpretation data of the alluvial fan subfacies and the top and bottom surface interpretation data of the conglomerate layer; under the constraint condition of the well logging exploration lithology, the seismic depth domain data are analyzed to obtain the attribute information of the seismic data body; the diagenetic stage of the alluvial fan body is determined based on the sequence stratigraphic data of the well logging exploration lithology, the attribute information of the seismic data body and the wave impedance propagation information of the low-frequency seismic reflection coefficient data body; The diagenetic stage of the conglomerate layer is identified according to the main controlling factors of the conglomerate layer and a pre-established conglomerate identification plate.

2. The method according to claim 1, characterized in that Before identifying the diagenetic stage of the conglomerate layer according to the main controlling factors of the conglomerate layer and the pre-established conglomerate identification plate, the method further includes: Processing the exploration well logging data to obtain a natural gamma curve, a resistivity curve and an acoustic time difference curve corresponding to the exploration well logging data; The conglomerate identification chart is established according to the natural gamma curve, the resistivity curve and the acoustic wave time difference curve.

3. The method according to claim 2, characterized in that The method further comprises: Processing the exploration well logging data to obtain information on differences in cementation degree of alluvial fan conglomerate bodies at various stages and information on differences in drillability during logging drilling; Establishing the development stage and type of the alluvial fan conglomerate body according to the natural gamma ray curve, the resistivity curve, the acoustic time difference curve, the cementation degree difference information of the alluvial fan conglomerate body at each stage, and the drillability difference information during logging drilling; According to the development period and type of the alluvial fan conglomerate body, the characteristic difference information of each stage included in the gravel layer is determined.

4. The method according to any one of claims 1 to 3, characterized in that: Before obtaining the exploration well logging data in the target research area, the method further includes: Acquiring surface data of the target study area; The surface data are analyzed to determine that thick conglomerates of the Quaternary or Neogene strata exist in the target study area.

5. A conglomerate layer identification device, characterized in that: include: Acquisition module, determination module and identification module; The acquisition module is used to acquire the exploration well logging data of the target research area; The determination module is used to analyze the rock cuttings corresponding to the exploration well logging data to determine the main controlling factors of the conglomerate layer in the target research area, wherein the main controlling factors include: deposition mode, gravel composition and gravel particle size; The determination module is specifically used to analyze the rock cuttings corresponding to the exploration well logging data to determine the attribute information of the equally spaced conglomerate samples in the target study area; determine the cementation degree of the equally spaced conglomerate samples according to the attribute information of the equally spaced conglomerate samples; determine the sedimentation information of the conglomerate layer in the target study area according to the cementation degree of the gravel; determine the sedimentation mode of the conglomerate layer according to the sedimentation information of the conglomerate layer; analyze the attribute information of the equally spaced conglomerate samples using an element capture spectrum logging instrument to determine the gravel composition and gravel particle size of the conglomerate layer; The determination module is further used to obtain three-dimensional gravity, magnetic and electrical exploration data and seismic depth domain data of the target study area; to perform superposition processing on the three-dimensional gravity, magnetic and electrical exploration data and the seismic depth domain data to obtain lithology interpretation data of the conglomerate layer; to determine to update the three-dimensional electrical profile of the target study area using the lithology interpretation data and the exploration well logging data; to perform three-dimensional carving of the alluvial fan body in the target study area using the resistivity difference on the updated three-dimensional electrical profile; The determination module is further used to analyze the seismic depth domain data to obtain the wave impedance propagation information of the chronological seismic profile, the current seismic profile and the low-frequency seismic reflection coefficient data body; obtain the interpretation data of the alluvial fan subfacies and the top and bottom surface interpretation data of the conglomerate layer by superimposing the chronological seismic profile and the current seismic profile; obtain the sequence stratigraphic data of the well logging exploration lithology according to the interpretation data of the alluvial fan subfacies and the top and bottom surface interpretation data of the conglomerate layer; analyze the seismic depth domain data under the constraint of the well logging exploration lithology to obtain the attribute information of the seismic data body; determine the diagenetic stage of the alluvial fan body according to the sequence stratigraphic data of the well logging exploration lithology, the attribute information of the seismic data body and the wave impedance propagation information of the low-frequency seismic reflection coefficient data body; The identification module is used to identify the diagenetic stage of the conglomerate layer according to the main controlling factors of the conglomerate layer and a pre-established conglomerate identification plate.

6. A computer device, characterized in that: The method comprises a processor, a memory, a display and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for identifying a conglomerate layer as claimed in any one of claims 1 to 4 is implemented.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the conglomerate layer identification method according to any one of claims 1 to 4.

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