Thin-layer gas-bearing sandstone lithologic trap identification method and system

Abstract

The invention provides a thin-layer gas-bearing sandstone lithologic trap identification method and system. The identification method comprises the following steps: analyzing lithologic trap development geological conditions according to collected thin-layer sandstone geological data; processing the existing seismic data to enable the processed seismic data to meet the conditions of lithologic trap description and fluid identification; according to the processed seismic data, determining seismic reflection characteristics of a sequence section of a target layer, comprehensively calibrating a well seismic, establishing an isochronous stratigraphic framework, researching seismic facies characteristics of the target layer series under the isochronous stratigraphic framework, and automatically tracking and explaining lithologic traps by utilizing a sand body automatic tracking technology; well-seismic combination: utilizing a well logging response mode of a well-drilled gas-bearing layer and combining a gas-bearing layer seismic forward modeling model; and the favorable lithologic trap target is optimized, and a drillable exploration target is provided for oil-gas exploration. The thin-layer lithologic trap description precision is improved, and a foundation is laid for efficient exploration of hidden lithologic traps.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration, and particularly to a method and system for identifying thin-layer gas-bearing sandstone lithologic traps. Background Art

[0002] With the continuous increase in exploration degree, large and medium-sized structural oil reservoirs have been almost drilled out, and the difficulty of oil and gas exploration is increasing. Subtle lithologic oil and gas reservoirs have become the most potential field for oil and gas reserve growth in China's oil and gas exploration. The identification and description of traps in thin-layer sandstone and fluid identification are the current research hotspots and difficulties in the field of applied geophysics.

[0003] Endless identification techniques for sandstone lithologic traps have emerged, mostly concentrated in two aspects: one is to establish an isochronous stratigraphic framework and, on this basis, establish a geological model for the development of lithologic traps; the other is to use seismic data to carry out time-frequency analysis, interpretive processing, multi-attribute analysis, well-logging constrained inversion, prestack inversion, etc. to identify subtle lithologic traps. Most existing patents focus on the delineation of lithologic trap boundaries, but there is a lack of systematic and effective research methods for the description of thin-layer sandstone lithologic traps and fluid identification. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed to provide a method and system for identifying thin-layer gas-bearing sandstone lithologic traps that overcome the above problems or at least partially solve the above problems.

[0005] According to one aspect of the present invention, there is provided a method for identifying thin-layer gas-bearing sandstone lithologic traps, the identification method comprising:

[0006] Analyzing the geological conditions for the development of lithologic traps based on the collected geological data of thin-layer sandstone;

[0007] Processing the existing seismic data so that the processed seismic data meets the conditions for lithologic trap description and fluid identification;

[0008] Determining the seismic reflection characteristics of the target layer sequence segment based on the processed seismic data, performing well-seismic comprehensive calibration, establishing an isochronous stratigraphic framework, studying the seismic facies characteristics of the target layer series under the isochronous framework, using the optimized seismic attributes to depict the spatial distribution of sedimentary microfacies in the target layer segment, and automatically tracking and interpreting lithologic traps using the sand body automatic tracking technology;

[0009] Combining well and seismic data, using the logging response mode of the gas-bearing layer in the drilled wells, combining with the seismic forward model of the gas-bearing layer, and the seismic attribute characteristics of the gas-bearing layer and the frequency-domain fluid identification to comprehensively detect the gas-bearing property of the target layer segment;

[0010] Optimizing the favorable lithologic trap targets to provide drillable exploration targets for oil and gas exploration.

[0011] Optionally, the analysis of the geological conditions for lithologic trap development based on the collected thin-layer sandstone geological data includes:

[0012] Regional geological survey to investigate the sedimentary background, tectonic evolution, and fault system of lithologic traps;

[0013] Subdivision and correlation of small layers of drilling strata inside and outside the area to obtain analysis results.

[0014] Optionally, the specific analysis results include: marker beds, lithologic combinations of target intervals, characteristics of electric logging curves and hydrocarbon shows, core descriptions, and analysis and testing characteristics of cored intervals.

[0015] Optionally, the optimization of favorable lithologic trap targets specifically includes:

[0016] Optimizing favorable lithologic trap targets based on the geological conditions for lithologic trap development, sedimentary microfacies types and spatial distributions, reservoir physical properties, and fluid distribution characteristics.

[0017] Optionally, the analysis of the geological conditions for lithologic trap development based on the collected thin-layer sandstone geological data further includes:

[0018] Analysis of single-well facies and connected-well facies of drilled wells inside and outside the area to determine the single-well microfacies and connected-well microfacies characteristics of sandstone reservoirs through mud logging, core, logging, and thin-section data analysis;

[0019] Analysis of reservoir space types to determine the reservoir space types of sandstone reservoirs in the target formation through field outcrops, cores, and thin-section data, and establish a longitudinal and planar development framework model of reservoir space;

[0020] Analysis of the main controlling factors for gas-bearing properties of lithologic traps to determine the main controlling factors for gas-bearing properties of lithologic traps based on the analysis of sandstone reservoir space types;

[0021] Anatomy of discovered sandstone gas reservoirs in the area.

[0022] Optionally, the main controlling factors for gas-bearing properties of lithologic traps specifically include structural factors, reservoir physical property factors, and electrical property factors.

[0023] Optionally, the anatomy of discovered sandstone gas reservoirs in the area specifically includes: based on the characteristics of drilled hydrocarbon reservoirs, analyzing the sedimentary microfacies types and longitudinal development laws of gas-bearing sand bodies in the target interval, and clarifying the sedimentary microfacies types and longitudinal development characteristics of favorable sand bodies in the profile.

[0024] Optionally, the combination of well and seismic data, using the logging response model of gas-bearing layers in drilled wells, combined with the seismic forward modeling of gas-bearing layers, and the comprehensive detection of gas-bearing properties of the target interval by the seismic attribute characteristics of gas-bearing layers and fluid identification in the frequency domain specifically includes:

[0025] Build the logging response templates for pure gas zones, gas-water zones, gas-bearing water zones and water zones based on the logging response characteristics of the gas-bearing sections in the drilled target zones.

[0026] Build the wavelet spectrum templates for gas zones, gas-water zones, gas-bearing water zones and water zones through synthetic seismogram calibration.

[0027] Build the seismic forward models for gas zones, gas-water zones, gas-bearing water zones and water zones, and build the seismic reflection bright spot characteristic models for gas zones.

[0028] Extract the frequency maximum attenuation attribute, frequency fusion attribute, frequency gradient attribute and time-frequency spectrum attribute from the frequency domain data volume to detect the gas-bearing property of sandstones in the target zone section.

[0029] The present invention also provides a lithologic trap identification system for thin-layer gas-bearing sandstones, which applies the above-mentioned lithologic trap identification method for thin-layer gas-bearing sandstones. The identification system specifically includes:

[0030] A geological analysis module for analyzing the geological conditions for the development of lithologic traps according to the collected geological data of thin-layer sandstones.

[0031] A data processing module for processing the existing seismic data so that the processed seismic data meets the conditions for lithologic trap description and fluid identification.

[0032] A lithologic trap identification module for determining the seismic reflection characteristics of the target zone sequence section according to the processed seismic data, performing well-seismic comprehensive calibration, establishing an isochronous stratigraphic framework, studying the seismic facies characteristics of the target zone series under the isochronous framework, using the selected seismic attributes to depict the spatial distribution of sedimentary microfacies in the target zone section, and automatically tracking and interpreting lithologic traps using the sand body automatic tracking technology.

[0033] A gas-bearing detection module for combining well and seismic data, using the logging response pattern of the gas-bearing zones in the drilled wells, combining with the seismic forward model of the gas-bearing zones, and the seismic attribute characteristics and frequency domain fluid identification of the gas-bearing zones to comprehensively detect the gas-bearing property of the target zone section; optimizing the favorable lithologic trap targets to provide drillable exploration targets for oil and gas exploration.

[0034] Optionally, the geological analysis module specifically includes:

[0035] A single well facies and connected well facies analysis unit for the drilled wells inside and outside the area, which is used to determine the single well microfacies and connected well microfacies characteristics of sandstone reservoirs through logging, core, logging and thin section data analysis.

[0036] A reservoir space type analysis unit for determining the reservoir space type of sandstone reservoirs in the target zone series through field outcrops, cores and thin section data.

[0037] Analysis unit for the main controlling factors of gas-bearing property in lithologic traps. Based on the analysis of the types of sandstone reservoir spaces, determine the main controlling factors of the gas-bearing property in lithologic traps;

[0038] Anatomical unit of sandstone gas reservoirs discovered in the area. According to the characteristics of the drilled oil and gas reservoirs, analyze the sedimentary microfacies types and vertical development laws of the gas-bearing sand bodies in the target interval, and clarify the sedimentary microfacies types and vertical development characteristics of the favorable sand bodies in the profile.

[0039] Optionally, the lithologic trap identification module specifically includes:

[0040] Paleogeomorphology restoration unit before sandstone reservoir deposition. Use seismic interpretation horizons for layer flattening interpretation processing, make micro-paleogeomorphology maps, and analyze the characteristics of micro-paleogeomorphology and the characteristics of paleogeomorphology controlling sand;

[0041] Waveform clustering seismic facies analysis unit. Under the isochronous framework using seismic data, extract dominant favorable attributes, conduct waveform clustering seismic facies analysis, and use the drilled wells to calibrate the seismic facies types to clarify the sedimentary microfacies types in the target interval and the favorable positions where lithologic traps may develop;

[0042] Lithologic trap identification and description unit. Use the joint analysis of seismic attributes and seismic profiles to depict the boundary range of lithologic traps.

[0043] Optionally, the gas-bearing detection module specifically includes:

[0044] Gas layer logging response pattern establishment unit. Establish logging response templates for pure gas layers, gas-water coexisting layers, gas-bearing water layers, and water layers through the logging response characteristics of the gas-bearing intervals in the drilled wells' target intervals;

[0045] Gas layer seismic response characteristic establishment unit. Through synthetic seismogram calibration, establish wavelet spectrum templates for gas layers, gas-water coexisting layers, gas-bearing water layers, and water layers, establish seismic forward models for gas layers, gas-water coexisting layers, gas-bearing water layers, and water layers, and establish seismic reflection bright spot characteristic models for gas layers;

[0046] Gas layer frequency domain attribute detection unit. Extract frequency maximum attenuation attributes, frequency fusion attributes, frequency gradient attributes, and time-frequency spectrum attributes, etc. from the frequency domain data volume to detect the gas-bearing property of sandstones in the target interval.

[0047] A method and system for identifying thin-layer gas-bearing sandstone lithologic traps provided by the present invention, the identification method comprising: analyzing the geological conditions for the development of lithologic traps according to the collected geological data of thin-layer sandstone; processing the existing seismic data so that the processed seismic data meets the conditions for lithologic trap description and fluid identification; determining the seismic reflection characteristics of the target layer sequence segment according to the processed seismic data, performing well-seismic comprehensive calibration, establishing an isochronous stratigraphic framework, studying the seismic facies characteristics of the target layer series under the isochronous framework, using optimized seismic attributes to depict the spatial distribution of sedimentary microfacies of the target layer segment, and automatically tracking and interpreting lithologic traps by using the sand body automatic tracking technology; combining well and seismic data, using the logging response pattern of the gas-bearing layer of the drilled well, combining with the seismic forward model of the gas-bearing layer, and the seismic attribute characteristics of the gas-bearing layer and the comprehensive detection of the gas-bearing property of the target layer segment by frequency-domain fluid identification; optimizing the favorable lithologic trap targets to provide drillable exploration targets for oil and gas exploration. The accuracy of thin-layer lithologic trap description is improved, laying a foundation for the efficient exploration of subtle lithologic traps.

[0048] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly, it can be implemented according to the content of the description. And in order to make the above and other purposes, features and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention are specifically exemplified below. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0050] Figure 1 It is a flowchart of a method for identifying thin-layer gas-bearing sandstone lithologic traps provided by an embodiment of the present invention;

[0051] Figure 2 It is a block diagram of the composition of a system for identifying thin-layer gas-bearing sandstone lithologic traps provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0053] The terms "comprising", "having" and any variations thereof in the description embodiments, claims and drawings of the present invention are intended to cover non-exclusive inclusion. For example, including a series of steps or units.

[0054] The following combines the drawings and embodiments to make a further detailed description of the technical solution of the present invention.

[0055] The Shibei Sag in the Junggar Basin is a residual reformation sag. There are three wells in the sag that obtained oil and gas shows in the first member of the Baijiantan Formation of the Triassic. Some wells were tested to obtain low-yield gas flows. In order to obtain oil and gas flows with commercial discoveries, a description of thin-layer sandstone lithologic traps and gas-bearing property identification in the first member of the Baijiantan Formation was specifically carried out.

[0056] Specifically, the present invention provides a system for identifying thin-layer gas-bearing sandstone lithologic traps. Figure 2 The structural schematic diagram of this system includes:

[0057] A geological analysis module 100, which is used to carry out regional geological surveys, investigate the development laws and characteristics of tectonic evolution, sedimentary background, fault systems, etc. in the study area; and is used for stratigraphic division and correlation of the target layers of wells inside and outside the area, determining the lithologic combination characteristics, electric log curve characteristics, oil and gas shows, core description and analysis and testing characteristics of the core-taking sections of marker beds and target layers.

[0058] In an implementation manner of the present invention, the geological analysis module includes: a single-well facies and connected-well facies analysis unit for wells inside and outside the area, which is used to determine the single-well microfacies and connected-well microfacies characteristics of sandstone reservoirs through mud logging, core, logging and thin-section data, and determine the sedimentary environment, genetic type of sand bodies and sedimentary interface characteristics of the target layer section.

[0059] A reservoir space type analysis unit, which is used to determine the rock type, pore type and development characteristics of reservoir spaces in sandstone reservoirs through field outcrops, core and thin-section microscopic observations. Analyze the differences in reservoir space types of different lithologies and determine the main pore types of the target layer section.

[0060] An anatomy unit for discovered sandstone gas reservoirs in the area. According to the characteristics of discovered oil and gas reservoirs, analyze the main controlling factors of gas-bearing sand bodies in the target layer section, including tectonic factors, reservoir physical property factors and electrical property factors, etc. Specifically, it is manifested that the gas-bearing sand bodies may be located at the structural high points, the porosity and permeability of the reservoir are relatively high, and the electric log curves show the characteristics of natural potential, natural gamma box type and high resistivity. By analyzing the sedimentary microfacies types, longitudinal and planar development laws of gas-bearing sand bodies, clarify the sedimentary microfacies types and distribution areas of possible gas-bearing sand bodies.

[0061] The data processing module 200 is used to pre-process the seismic data. Generally, in order to achieve the resolution of thin-layer sandstone, the vertical resolution of the seismic data is improved by frequency enhancement and frequency extension processing. The resolution of the seismic data meets the requirement of thin-layer sand body resolution. The seismic data can be used for micro-paleomorphological feature analysis, seismic phase profile analysis, seismic attribute extraction, and identification and description of lithological traps. The seismic data can be used for frequency domain interpretive analysis.

[0062] In an embodiment of the present invention, the data processing module includes: a seismic data preprocessing unit, which performs noise reduction, fidelity and amplitude preservation processing, frequency enhancement and frequency extension processing on the existing seismic data in the study area, improves the vertical resolution of the seismic data, and can effectively identify thin-layer sand bodies.

[0063] The seismic data interpretation processing unit converts the seismic data into the frequency domain to generate a frequency domain data volume, so that frequency domain fluid detection can be carried out.

[0064] The lithologic trap identification module 300 performs small grid interpretation on the top and bottom of the target layer segment according to the isochronous stratigraphic framework of the target layer segment in the study area, and uses three-dimensional seismic data to produce micro-paleomorphological maps; seismic attribute waveform clustering analysis is used to characterize the seismic phase plane characteristics of the target layer segment. Lithologic traps are identified and described using seismic data flat section combination, attribute optimization analysis, and automatic tracking technology of geological bodies.

[0065] In the embodiment of the present invention, the lithologic trap identification module includes: a paleo-geomorphology restoration unit of the target layer section before sandstone reservoir deposition, which uses the seismic interpretation horizon to perform layer flattening processing, obtains the residual thickness of the target layer by subtracting the top and bottom surfaces of the target layer, and uses the residual thickness to make a micro-paleo-geomorphology map of the target layer, and analyzes the micro-paleo-geomorphology characteristics and the characteristics of paleo-geomorphology controlling sand.

[0066] The waveform clustering seismic facies analysis unit conducts waveform clustering analysis of seismic attributes under the established isochronous stratigraphic framework, and uses the single-well phase scale seismic facies type of the drilled target layer to clarify the sedimentary microfacies type of the target layer and the microfacies type of lithologic trap development and possible favorable locations for development.

[0067] The lithologic trap identification and description unit uses seismic profile analysis, seismic attribute analysis, seismic reservoir inversion and seismic reservoir forward modeling to jointly analyze and characterize lithologic traps. By setting the threshold of seismic attributes, lithologic traps can be automatically tracked and interpreted.

[0068] The fluid detection module 400 establishes the corresponding templates of well logging and seismic gas-bearing fluid by combining well and seismic data, and identifies the gas-bearing layer by the bright spot reflection characteristics of the seismic data of the gas-bearing layer section. The seismic data is used to interpret the frequency domain, and the gas-bearing property of the lithologic trap of the target layer section is detected by the frequency domain maximum attenuation attribute, frequency fusion attribute, frequency gradient attribute, etc.

[0069] A lithologic trap identification system for thin-layer gas-bearing sandstone provided by the present invention is a set of lithologic trap identification systems for thin-layer gas-bearing sandstone formed based on the anatomy of discovered oil and gas reservoirs, analysis of the micro-paleogeomorphic features of the target interval, seismic facies analysis, automatic tracking and interpretation of lithologic traps, and fluid identification in the frequency domain. It can identify lithologic traps relatively quickly and has important guiding significance for the well location deployment work in oil and gas exploration.

[0070] Figure 1 The following is a flowchart of a method for identifying lithologic traps of thin-layer gas-bearing sandstone provided by an embodiment of the present invention. The method for identifying lithologic traps of thin-layer gas-bearing sandstone includes:

[0071] S101: Investigate the geological conditions for the development of lithologic traps in the target formation series.

[0072] In an embodiment of the present invention, the method further includes: carrying out regional geological surveys in the study area, investigating the sedimentary background, tectonic evolution, and fault system in the study area. Obtaining seismic data and drilling data in the study area, and carrying out the optimization of the target formation series according to the oil and gas testing, oil and gas shows of the drilled wells.

[0073] In a specific embodiment, there is a 3D seismic survey of 351.8 km² in the study area. 2 In the first member of the Baijiantan Formation of the Triassic System, fluorescence-level oil and gas shows were observed in 3 wells. Well ZB1 obtained a low-yield gas flow during the testing of the Triassic Baijiantan Formation, with a converted daily gas production of 1315 m³. 3 Well ZB101, in the interval of 3617 - 3634.5 m of the Triassic Baijiantan Formation, had a daily gas production of 14782 m³ during the fracturing test, and the peak daily gas production was 35928 m³, both of which did not exceed the industrial oil flow threshold. For the core and sidewall cores of the first member of the Baijiantan Formation, physical property statistics of rock thin sections and nuclear magnetic logging show that the reservoir porosity is mainly distributed in the interval less than 10%, and the permeability is mainly distributed in the interval less than 1×10⁻³ µm². Classified according to reservoir physical properties, it has the characteristics of a tight sandstone reservoir. The first member of the Baijiantan Formation was preferably selected for lithologic trap description and fluid identification research. 3 3 -3 2 The Triassic Baijiantan Formation has experienced an evolutionary process from lowstand - transgression - highstand. In the study area, only the fan delta front sedimentary sand bodies are developed in the first member of the Baijiantan Formation, mainly developing light gray gravelly fine sandstone and light gray fine sandstone reservoir rocks; the second and third members are of shore - shallow lake facies, with dark gray silty mudstone and dark gray mudstone as cap rocks. The fault system of the Triassic Baijiantan Formation is well developed, mainly developing NW - trending normal faults. The Triassic Baijiantan Formation has the geological basic conditions for the development of large - scale lithologic traps.

[0074]

[0075] S102: Conduct quality analysis of seismic data to make the seismic data meet the requirements for thin-layer sandstone interpretation. Through the analysis of the main frequency and frequency bandwidth of the existing seismic data, and based on the sandstone velocity of the target layer, calculate the sandstone thickness that can be identified by the seismic data. When the sandstone velocities are equal, if the main frequency of the seismic data is lower, the resolvable sandstone thickness is thicker; if the main frequency of the seismic data is higher, the resolvable sandstone thickness is thinner. Therefore, the resolution of seismic data is often improved by frequency increasing and frequency broadening techniques. To detect the fluid-bearing situation of lithologic traps in the target formation system, perform frequency domain conversion on the existing seismic volume to generate a frequency domain data volume, and achieve frequency domain interpretation and fluid identification.

[0076] In a specific embodiment, comprehensively analyze the existing seismic data in the study area to determine whether the seismic resolution meets the requirements for lithologic trap identification in the study area. The sandstone thickness encountered by the wells drilled in the study area varies from 19 m to 29 m. The main frequency of the existing seismic data is 10 HZ, and the frequency bandwidth is 0 - 40 HZ. The Baijiantan Formation is at a depth of about 3600 m. Calculated with a sandstone velocity of 4000 m / s, the resolvable sandstone thickness of the seismic data is 66 m, making it difficult to predict the favorable sand bodies in the target layer. By performing frequency increasing and amplitude preservation processing on the seismic data of the target layer section in the study area, after frequency increasing, the frequency bandwidth of the seismic data is 0 - 80 HZ, and the main frequency is 40 HZ. The sandstone velocity in the target layer section is 4000 m / s. Calculated, the resolvable sandstone thickness is 25 m, meeting the technical requirements for the description of sandstone lithologic traps and fluid identification in the target layer section.

[0077] Convert the frequency-increased seismic data into a frequency domain data volume through the frequency domain to prepare for subsequent fluid identification of lithologic traps.

[0078] S103: Conduct single-well and cross-well stratigraphic division and correlation of the target layer to establish an isochronous stratigraphic framework. Subdivide sedimentary microfacies based on the sedimentary structures of drilling cores and the characteristics of electric logging curves. Conduct single-well sub-layer division and correlation and cycle division based on the morphological characteristics of GR and SP curves, the characteristics of logging lithologic combinations, core observation and description, sedimentary structure characteristics, grain size analysis characteristics, etc. Through single-well sub-layer division, establish cross-well sub-layer division, and analyze the stratigraphic characteristics and distribution of the target formation system. Combine seismic interpretation horizons and the comprehensive calibration results of drilling to establish an isochronous stratigraphic framework for the target formation system.

[0079] In a specific embodiment, the GR and SP curves of three wells in the target interval in the study area show a serrated box shape, a box-shaped and funnel-shaped composite shape, and the logging lithology shows the characteristics of thick-layered conglomerate intercalated with thin-layered siltstone. Deformed bedding, boulder block bedding, etc. are observed in the core samples, and the grain size curves show the characteristics of "one jump and one suspension" and "two jumps and one suspension". The grain size interval of the sediment is large and the sorting is poor, showing the characteristics of fan delta front sediments. According to the logging curve characteristics, lithologic combination characteristics, and sedimentary rhythm characteristics, the first member of the Baijiantan Formation is divided into three sand groups and four sedimentary cycles, showing a positive-positive-negative-positive cycle characteristic. The underwater distributary channels, frontal debris flow distributary channels, and beach bar sandstone deposits develop upward in the first member of the Baijiantan Formation in the well-to-well correlation analysis of wells ZB1-ZB101-ZB6. Well-seismic comprehensive calibration is carried out, and an isochronous stratigraphic framework of the first member of the Baijiantan Formation is established based on drilling-seismic markers.

[0080] S104: Production of the paleogeomorphological map of the target stratigraphic series and analysis of paleogeomorphological characteristics. The paleogeomorphological characteristics of the target stratigraphic series are restored using the seismic data flattening technique. First, the bottom surface T bot of the interpreted target stratigraphic series is subtracted from the top surface T top to obtain the thickness grid T H in the time domain. The thickness data grid T H is subjected to time-depth conversion with the velocity of the target interval to obtain the thickness data grid H in the depth domain. The thickness data of the target interval at the well points in the study area are used to correct H, and the corrected formation thickness data Hcal is obtained. The relative paleogeomorphological map of the first member of the Baijiantan Formation during the sedimentation period is produced using the Hcal data of the target interval, and the planar paleogeomorphological characteristics and sand control characteristics of this period are analyzed.

[0081] T H = T bot - T top , Equation 1

[0082] In a specific embodiment, the paleogeomorphology during the sedimentation of the first member of the Baijiantan Formation is restored using the flattening technique. First, the top surface T 3 b 1top and the bottom surface T 3 b 1bottom of the first member of the Baijiantan Formation are interpreted, and the thickness data grid T H of the target interval is obtained by subtracting the bottom surface of the target interval from the top surface.

[0083] T H = T 3 b 1bot - T 3 b 1top , Equation 1

[0084] The thickness data grid TH is converted into time-depth with the velocity of the target layer to obtain the depth domain thickness data grid H. The thickness data of the target layer at the well point in the study area are used to correct H to obtain the corrected formation thickness data H. The H-corrected data of the target layer are used to make a relative micro-paleomorphological map of the first section of the Baijiantan Formation during the deposition period. Multiple fault troughs and erosion valleys are identified on the micro-paleomorphological map of the first section of the Baijiantan Formation. The analysis shows that the fault troughs and erosion valleys jointly control the distribution of the reservoir sand body.

[0085] S105: Study the seismic facies of the target strata under the isochronal framework. During the implementation process, the established isochronal stratigraphic framework is used to extract a variety of attributes related to sedimentary facies. Through the intersection diagram of various attributes, the optimal seismic attributes are comprehensively selected, and single-attribute waveform clustering seismic facies analysis is carried out to generate a multi-waveform seismic facies plane map. Through the deep learning neural network analysis technology, similar facies types are merged to generate a seismic facies map after the merged waveform. The seismic facies plane map is scaled by the sedimentary microfacies of the drilled wells.

[0086] In research, seismic facies maps are often superimposed with current structural maps to study the structural environment and characteristics of sand bodies of different microfacies types. Seismic facies maps are combined with micro-paleomorphological maps to analyze the sedimentary development history, sedimentary system characteristics and lithofacies types of sedimentary sand bodies. The scaled combined seismic facies plane map can be converted into a sedimentary microfacies plane map. Combining structural characteristics, micro-paleomorphological characteristics and sedimentary microfacies plane distribution, a sand body development model map of the study area can be established to guide oil and gas exploration and well deployment.

[0087] In a specific embodiment, a single-attribute seismic phase waveform cluster analysis of the first section of the Baijiantan Formation was carried out using seismic interpretation software, and the seismic phase map was comprehensively analyzed with the micro-paleomorphology map and the present-day structural map. It is believed that the underwater branch channel sand bodies of the first section of the Baijiantan Formation are mainly developed in erosion valleys and fault troughs. Multiple branch channels are cross-developed in the north-south direction.

[0088] S106: Use seismic attributes to automatically track and interpret the target stratum sand bodies, and confirm the number and spatial distribution of lithologic traps. Extract favorable sweet spot seismic attributes under the isochronal framework. Select the sand body automatic tracking module, set a reasonable seismic attribute threshold range through stratum constraints, and automatically track and interpret favorable sand bodies. Select favorable geological target sand bodies by sedimentary microfacies type, distribution area and sand body scale.

[0089] In a specific embodiment, on a seismic interpretation software platform, the sweet spot attribute is used to carry out automatic tracking interpretation of the sand body of the first branch channel of the Baijiantan Formation. The sweet spot attribute is considered to have a value range greater than or equal to 5000 as a lithologic sand body development area, and 10 main lithologic traps are automatically tracked.

[0090] S107: Conduct frequency-domain fluid detection and analysis of lithologic traps. Use the frequency-domain data volume to carry out frequency-domain reservoir fluid analysis. First, perform synthetic seismogram calibration for a single well, analyze the frequency characteristics of sandstones (conglomerates) with different grain sizes and sandstones (conglomerates) with different thicknesses, and characterize the grain size, thickness, and gas-bearing property of the sandstone reservoir according to the frequency magnitude. Thick-layer coarse-grained conglomerates exhibit medium-low frequency reflection characteristics, and thin-layer siltstone and fine sandstones exhibit medium-frequency characteristics. Thin gas-bearing intervals exhibit the characteristics of high-frequency attenuation and low-frequency enhancement. Analyze the frequency characteristics of different gas-bearing intervals, and use the attenuation change of frequency to identify the gas-bearing property of sand bodies. On this basis, by extracting frequency-domain attributes, analyze the planar attenuation change of frequency in the gas-bearing interval to determine the gas-bearing area. Synthesize the frequency change characteristics of the plane and profile, combine with sweet spot attributes, and comprehensively analyze the gas-bearing property of the sand bodies in the target formation series in combination with the forward modeling characteristics of gas-bearing sand bodies.

[0091] In a specific embodiment, generate a frequency-domain data volume from the seismic data volume, calibrate the frequency characteristics of sand bodies with different thicknesses and lithologies through single-well comprehensive calibration, and optimize the frequency characteristics of favorable sand bodies. By extracting the maximum attenuation attribute in the frequency domain of the target interval, combining with the frequency attenuation characteristics in the area of drilled gas wells, and comprehensively considering the sweet spot attribute area and the bright spot reflection characteristics of the seismic profile after gas-bearing, determine whether the target lithologic trap contains gas.

[0092] S108: Optimize favorable lithologic trap targets. Comprehensively analyze the provenance direction, micro paleogeomorphic characteristics, sedimentary microfacies types, current tectonic characteristics, spatial distribution of lithologic traps, and frequency-domain fluid identification to optimize gas-bearing lithologic trap targets.

[0093] In a specific embodiment, comprehensively analyze the provenance direction, sedimentary microfacies types, micro paleogeomorphic characteristics, current tectonic characteristics, spatial distribution of lithologic traps, and gas-bearing property, and optimize 3 favorable lithologic traps with exploration value for drilling.

[0094] A method for identifying thin-layer gas-bearing sandstone lithologic traps provided by the present invention solves the problems of difficult identification of thin-layer lithologic traps and difficult identification of fluid-bearing, provides a new technical method for the efficient exploration and development of thin-layer lithologic oil and gas reservoirs, and can reduce the exploration risk of lithologic oil and gas reservoirs.

[0095] Beneficial effects: The present invention combines the planar distribution of micro paleogeomorphology and sedimentary microfacies to analyze the distribution of sedimentary sand bodies, effectively guiding the identification and description of thin-layer lithologic traps.

[0096] The present invention applies the frequency-domain gas-bearing fluid detection technology, improves the description accuracy of thin-layer lithologic traps, and lays a foundation for the efficient exploration of subtle lithologic traps.

[0097] The present invention has good operability and practicability and has good promotion significance.

[0098] The above specific embodiments have further elaborated in detail the purpose, technical solution and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for identifying lithologic traps in thin-layer gas-bearing sandstone, characterized in that, the identification method includes: Analyzing the geological conditions for the development of lithologic traps based on the collected geological data of thin-layer sandstone; Processing the existing seismic data to make the processed seismic data meet the conditions for lithologic trap description and fluid identification; Determining the seismic reflection characteristics of the target layer sequence segment according to the processed seismic data, calibrating well-seismic comprehensively, establishing an isochronous stratigraphic framework, studying the seismic facies characteristics of the target layer series under the isochronous framework, using optimized seismic attributes to depict the spatial distribution of sedimentary microfacies in the target layer segment, and automatically tracking and interpreting lithologic traps using sand body automatic tracking technology; Combining well and seismic data, using the logging response pattern of the gas-bearing layer in the drilled wells, combining with the forward seismic model of the gas-bearing layer, and the seismic attribute characteristics of the gas-bearing layer and the comprehensive fluid identification in the frequency domain to comprehensively detect the gas-bearing property of the target layer segment; Optimizing the favorable lithologic trap targets to provide drillable exploration targets for oil and gas exploration.

2. The method for identifying lithologic traps in thin-layer gas-bearing sandstone according to claim 1, characterized in that, the analysis of the geological conditions for the development of lithologic traps based on the collected geological data of thin-layer sandstone includes: Regional geological survey, investigating the sedimentary background, tectonic evolution, and fault system of lithologic traps; Dividing and correlating the small strata of the drilling formation inside and outside the area to obtain analysis results.

3. The method for identifying lithologic traps in thin-layer gas-bearing sandstone according to claim 2, characterized in that, the specific analysis results include: marker beds, lithologic combinations in the target layer segment, characteristics of electric logging curves and hydrocarbon shows, core descriptions, and analysis and testing characteristics of the cored sections.

4. The method for identifying lithologic traps in thin-layer gas-bearing sandstone according to claim 1, characterized in that, the optimization of the favorable lithologic trap targets specifically includes: Optimizing the favorable lithologic trap targets according to the geological conditions for the development of lithologic traps, sedimentary microfacies types and spatial distributions, reservoir physical properties, and fluid distribution characteristics.

5. The method for identifying lithologic traps in thin-layer gas-bearing sandstone according to claim 1, characterized in that, the analysis of the geological conditions for the development of lithologic traps based on the collected geological data of thin-layer sandstone further includes: Analysis of single-well facies and connected-well facies of the drilled wells inside and outside the area, which is used to determine the single-well microfacies and connected-well microfacies characteristics of sandstone reservoirs through logging, core, logging, and thin-section data analysis; Analysis of reservoir space types, which is used to determine the reservoir space types of sandstone reservoirs in the target layer series through field outcrops, cores, and thin-section data, and establish a vertical and planar development framework model of reservoir space; Analysis of the main controlling factors for the gas-bearing property of lithologic traps, and determining the main controlling factors for the gas-bearing property of lithologic traps on the basis of the analysis of the reservoir space types of sandstone; Anatomy of the discovered sandstone gas reservoirs in the area.

6. The method for identifying lithologic traps in thin-layer gas-bearing sandstone according to claim 5, characterized in that, the specific main controlling factors for the gas-bearing property of lithologic traps include tectonic factors, reservoir physical property factors, and electrical property factors.

7. The method for identifying lithologic traps in thin-layer gas-bearing sandstone according to claim 5, characterized in that, The dissection of the discovered sandstone gas reservoirs in the area specifically includes: Based on the characteristics of the drilled oil and gas reservoirs, analyzing the sedimentary microfacies types and vertical development laws of the gas-bearing sand bodies in the target interval, and clarifying the sedimentary microfacies types and vertical development characteristics of the favorable sand bodies in the profile.

8. A method for identifying thin-layer gas-bearing sandstone lithologic traps according to claim 1, wherein, the combination of well logging and seismic data, using the logging response pattern of the gas-bearing intervals in the drilled wells, combining with the seismic forward modeling of the gas-bearing intervals, and the seismic attribute characteristics and frequency-domain fluid identification of the gas-bearing intervals to comprehensively detect the gas-bearing property of the target interval specifically includes: Establishing the logging response templates for pure gas layers, gas-water layers, gas-bearing water layers, and water layers through the logging response characteristics of the gas-bearing intervals in the drilled wells; Establishing the wavelet spectrum templates for gas layers, gas-water layers, gas-bearing water layers, and water layers through synthetic seismogram calibration; Establishing the seismic forward models for gas layers, gas-water layers, gas-bearing water layers, and water layers, and establishing the seismic reflection bright spot characteristic models for gas layers; Extracting the frequency maximum attenuation attribute, frequency fusion attribute, frequency gradient attribute, and time-frequency spectrum attribute from the frequency-domain data volume to detect the gas-bearing property of the sandstone in the target interval.

9. A system for identifying thin-layer gas-bearing sandstone lithologic traps, applying the method for identifying thin-layer gas-bearing sandstone lithologic traps according to any one of claims 1-8, wherein, the identification system specifically includes: A geological analysis module for analyzing the geological conditions for the development of lithologic traps based on the collected geological data of thin-layer sandstone; A data processing module for processing the existing seismic data so that the processed seismic data meets the conditions for lithologic trap description and fluid identification; A lithologic trap identification module for determining the seismic reflection characteristics of the target horizon sequence segment based on the processed seismic data, performing well-seismic comprehensive calibration, establishing an isochronous stratigraphic framework, studying the seismic facies characteristics of the target formation system under the isochronous framework, using the selected seismic attributes to depict the spatial distribution of the sedimentary microfacies in the target interval, and automatically tracking and interpreting lithologic traps using the sand body automatic tracking technology; A gas-bearing detection module for combining well logging and seismic data, using the logging response pattern of the gas-bearing intervals in the drilled wells, combining with the seismic forward modeling of the gas-bearing intervals, and the seismic attribute characteristics and frequency-domain fluid identification of the gas-bearing intervals to comprehensively detect the gas-bearing property of the target interval; optimizing the favorable lithologic trap targets to provide drillable exploration targets for oil and gas exploration.

10. A system for identifying thin-layer gas-bearing sandstone lithologic traps according to claim 9, wherein, the geological analysis module specifically includes: A single-well facies and connected-well facies analysis unit for the drilled wells inside and outside the area, used to determine the single-well microfacies and connected-well microfacies characteristics of the sandstone reservoir through logging, core, logging, and thin-section data analysis; A reservoir space type analysis unit for determining the reservoir space type of the sandstone reservoir in the target formation system through field outcrops, cores, and thin-section data; A main controlling factor analysis unit for the gas-bearing property of lithologic traps, which determines the main controlling factors for the gas-bearing property of lithologic traps based on the analysis of the reservoir space type of sandstone; A dissection unit for the discovered sandstone gas reservoirs in the area, which analyzes the sedimentary microfacies types and vertical development laws of the gas-bearing sand bodies in the target interval based on the characteristics of the drilled oil and gas reservoirs, and clarifies the sedimentary microfacies types and vertical development characteristics of the favorable sand bodies in the profile.

11. A thin-layer gas-bearing sandstone lithologic trap identification system according to claim 9, It is characterized in that The lithologic trap identification module specifically includes: The paleo-geomorphological restoration unit before the sandstone reservoir deposition uses the seismic interpretation horizon to perform layer flattening interpretation processing, produce micro-paleo-geomorphological maps, and analyze the micro-paleo-geomorphological characteristics and the characteristics of paleo-geomorphological control of sandstone; The waveform clustering seismic facies analysis unit uses seismic data under an isochronal framework to extract advantageous attributes and conduct waveform clustering seismic facies analysis. It uses the seismic facies types of the wells to identify the sedimentary microfacies types of the target layer and the favorable locations where lithologic traps may develop. The lithologic trap identification and description unit uses the combined analysis of seismic attributes and seismic profiles to characterize the boundary range of lithologic traps.

12. A thin-layer gas-bearing sandstone lithologic trap identification system according to claim 9, It is characterized in that The gas detection module specifically includes: The gas layer logging response mode establishment unit is established through the logging response characteristics of the gas-bearing section of the drilled target layer, and the logging response templates of the pure gas layer, gas-water layer, gas-water layer and water layer are established; The gas layer seismic response characteristic establishment unit establishes wavelet spectrum templates for gas layer, gas-water layer, gas-water layer and water layer through synthetic record calibration, establishes seismic forward model of gas layer, gas-water layer, gas-water layer and water layer, and establishes seismic reflection bright spot characteristic model of gas layer; The gas layer frequency domain attribute detection unit extracts the frequency maximum attenuation attribute, frequency fusion attribute, frequency gradient attribute and timely spectrum attribute through the frequency domain data volume to detect the gas content of the sandstone in the target layer.

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