CN117991335A - High-frequency gyratory carbonate rock identification method based on conventional logging data - Google Patents

High-frequency gyratory carbonate rock identification method based on conventional logging data Download PDF

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CN117991335A
CN117991335A CN202211330291.1A CN202211330291A CN117991335A CN 117991335 A CN117991335 A CN 117991335A CN 202211330291 A CN202211330291 A CN 202211330291A CN 117991335 A CN117991335 A CN 117991335A
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lithology
logging
gyratory
information
recognition
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刘占国
田明智
朱超
李森明
夏志远
宋光永
王艳清
宫清顺
伍劲
唐鹏程
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Petrochina Co Ltd
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Petrochina Co Ltd
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Priority to CN202211330291.1A priority Critical patent/CN117991335A/en
Priority to PCT/CN2023/112129 priority patent/WO2024087800A1/en
Publication of CN117991335A publication Critical patent/CN117991335A/en
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/30—Analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/30—Analysis
    • G01V1/301—Analysis for determining seismic cross-sections or geostructures
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/30—Analysis
    • G01V1/306—Analysis for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/40—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging
    • G01V1/44—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging using generators and receivers in the same well
    • G01V1/48—Processing data
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/40—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging
    • G01V1/44—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging using generators and receivers in the same well
    • G01V1/48—Processing data
    • G01V1/50—Analysing data
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/30—Assessment of water resources

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  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Acoustics & Sound (AREA)
  • Environmental & Geological Engineering (AREA)
  • Geology (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Geophysics (AREA)
  • Geophysics And Detection Of Objects (AREA)

Abstract

The invention provides a high-frequency gyratory carbonate rock identification method based on conventional logging data, and belongs to the technical field of petroleum geological exploration and logging. The method comprises the following steps: determining logging data required by lithology recognition, and collecting the logging data required by lithology recognition; calculating lithology recognition factors based on logging parameters in the collected logging data; based on lithology recognition factors and the logging parameters, establishing a lithology recognition plate by combining core data; and identifying the high-frequency gyratory carbonate rock in the single well by using the lithology identification plate. According to the method, the lithology recognition factors are calculated based on the logging parameters, and the lithology recognition graph is established, so that the obtained lithology of the single well is more in line with the real lithology situation, and further, the high-frequency gyratory carbonate rock is accurately recognized by utilizing the lithology information of the single well.

Description

High-frequency gyratory carbonate rock identification method based on conventional logging data
Technical Field
The invention relates to the technical field of petroleum geological exploration and well logging, in particular to a high-frequency gyratory carbonate rock identification method based on conventional well logging data, a computer readable storage medium and an electronic device.
Background
The high-frequency gyratory carbonate rock single layer has thin thickness and large accumulated thickness, and is an important petroleum and natural gas exploration field. The traditional method for identifying the thin-layer carbonate rock relies on electric imaging and element logging data, and the logging data acquisition cost is high. And well point full coverage cannot be achieved by the electrical imaging and elemental logging data limited by the well acquisition sequence. The conventional logging data acquisition cost is low, the full coverage of the well points of a research area can be realized, and the lithology recognition by utilizing the conventional logging data has the advantages of low cost, full coverage and easy processing.
In the existing technology for carrying out lithology recognition by using logging information, some of the technology is to utilize rock core and imaging logging information to realize stratum characteristic analysis and lithology recognition, but the rock core information and the imaging logging information are high in acquisition cost and low in coverage rate, so that lithology fine recognition of all wells in a research area cannot be realized; some are facies identification using conventional logging data, but only for clastic rock or low frequency gyratory carbonate, not involving high frequency gyratory carbonate reservoirs. None of these techniques involve the identification of high frequency gyratory carbonates using conventional logging data.
Disclosure of Invention
The embodiment of the invention aims to provide a high-frequency gyratory carbonate rock identification method based on conventional logging data, a computer-readable storage medium and electronic equipment, so as to at least solve the problems that the conventional logging data cannot be utilized to realize high-frequency gyratory carbonate rock identification in the prior art.
In order to achieve the above object, a first aspect of the present invention provides a high-frequency gyratory carbonate rock identification method based on conventional logging data, comprising:
collecting logging data required for lithology recognition;
Calculating lithology recognition factors based on logging parameters in the collected logging data;
Based on lithology recognition factors and the logging parameters, establishing a lithology recognition plate by combining core data;
and identifying the high-frequency gyratory carbonate rock in the single well by using the lithology identification plate.
Optionally, the method further comprises:
determining logging information required for lithology identification, comprising: acquiring logging parameters in logging data;
conventional logging response characteristics of different rock types in the investigation region are analyzed to determine the logging parameters that are most sensitive to lithology changes.
Optionally, the logging parameters include: natural gamma curve, neutron and thorium to uranium ratio.
Optionally, the lithology recognition factor has the following calculation formula:
LITH＝(GRmax-GR)×NPHI×TH/U
Wherein LITH denotes a lithology recognition factor, GR denotes a natural gamma curve value, GR max denotes a natural gamma curve maximum value, NPHI denotes a neutron value, and TH/U denotes a thorium uranium ratio.
Alternatively, the logging parameters are obtained using a CLS-5700 device or MAXIS-500 device.
Optionally, the establishing a lithology recognition plate based on the lithology recognition factor and the logging parameter and combined with the core data includes:
Establishing a cross map by lithology recognition factors and logging parameters;
and (5) utilizing lithology data to assign lithology information of each coring position to the intersection map, and establishing a lithology recognition plate.
Optionally, the assigning lithology information of each coring position to the intersection map using lithology data includes:
The lithology information of the coring position on each rock is obtained by using core data;
determining lithology recognition factors and logging parameters of each coring position;
And matching the lithology recognition factors and the logging parameters of each coring position with the lithology recognition factors and the logging parameters in the intersection map, and further giving lithology information of each coring position to the intersection map.
Optionally, the identifying the high-frequency gyratory carbonate rock in the single well by using the lithology identification plate comprises:
calculating lithology recognition factors and logging parameters of measurement points in a single well;
the lithology recognition factors and the logging parameters of the measuring points are put into the lithology recognition plate, and lithology of the measuring points is determined;
and determining lithology information of all measurement points in the single well to obtain single well lithology information, and identifying high-frequency gyratory carbonate rock in the single well according to the single well lithology information.
A second aspect of the invention provides a computer readable storage medium having stored thereon computer instructions which, when run on a computer, cause the computer to perform the method of the first aspect.
A third aspect of the present invention provides an electronic apparatus, comprising: at least one processor, memory;
The memory stores computer-executable instructions;
the at least one processor executes computer-executable instructions stored in the memory to cause the electronic device to perform the method of the first aspect.
The beneficial effects of the invention are as follows:
According to the method, the lithology recognition factors are calculated based on the natural gamma curve, the neutron and the thorium uranium ratio which are representative of lithology, and the lithology recognition chart is established, so that the obtained lithology of the single well is more in line with the real lithology situation, and further, the high-frequency gyrocarbonate rock is accurately recognized by utilizing the lithology information of the single well.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain, without limitation, the embodiments of the invention. In the drawings:
FIG. 1 is a flow chart of a method for high frequency gyratory carbonate rock identification based on conventional well logging data, provided in one embodiment of the present invention;
FIG. 2 is a lithology recognition factor LITH-natural gamma GR intersection recognition plate provided in accordance with one embodiment of the present invention;
FIG. 3 is a graph of the lithology recognition results of an A1 well log provided by an embodiment of the present invention.
Detailed Description
The following describes specific embodiments of the present invention in detail with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the invention, are not intended to limit the invention.
The related patent 2 is referred to in China about a high-frequency gyratory carbonate rock identification method, as follows:
The method is based on a neural network algorithm to identify a carbonate high-frequency sequence interface, has a good application effect on sea-phase carbonate, but only identifies the sequence interface, and does not mention carbonate lithology identification.
A method for predicting the spatial distribution form of the sand body of a lake-phase beach dam based on the high-frequency rotation of a logging curve (application number: 201610079041.3) is disclosed, which utilizes a natural gamma curve to predict the spatial distribution form of the sand body of the beach dam, but is only applied to clastic rock stratum, and does not relate to carbonate rock stratum, and only takes the inflection point of the curve as the boundary of the sand body of the beach dam, does not perform quantitative calculation on the thickness of each lithology, and has low recognition precision.
The lithology recognition of carbonate rock at abroad mainly comprises the following patent methods: lithofacies Classification SYSTEM AND Method (CA 02719537, EP09730596A, US 20080098533A), the Method integrates conventional, nuclear magnetism and ultrasonic data, realizes lithology recognition by wavelet transformation, requires special logging data, and has high acquisition cost; method For Computing Lithofacies Probability Using Lithology Proximity Models (US 201515514345A), the method utilizes LWD and MWD data to construct a model to realize lithofacies identification, and does not relate to open hole logging data; SYSTEM AND Method for Lithofacies Classification (US 202016950806 a) the method uses conventional logging data to effect facies identification, but is only applied to clastic rock and does not involve high frequency gyratory carbonate reservoirs.
In order to solve the above technical problems in the prior art, the present embodiment provides a method for identifying high-frequency gyratory carbonate based on conventional logging data, as shown in fig. 1, the method includes:
S1: the analysis determines logging data for identifying the lithology of the high-frequency carbonate, wherein the logging data comprises logging parameters capable of representing the lithology of the high-frequency carbonate.
Logging parameters in logging data are acquired, conventional logging response characteristics of different rock types in a research area are analyzed, and logging parameters most sensitive to lithology changes are determined. According to a large amount of analysis on the high-frequency carbonate rock, the natural gamma curve GR, the neutron NPHI and the thorium uranium ratio TH/U are found to be most sensitive to lithology change, so that the lithology recognition of the high-frequency gyratory carbonate rock is carried out by determining and selecting logging parameters of the natural gamma curve GR, the neutron NPHI and the thorium uranium ratio TH/U.
It should be noted that, the logging data in this embodiment is conventional logging data, and can be obtained by using ordinary logging means.
It can be understood that, in this embodiment, the basis for lithology recognition of the high-frequency gyromagnetic carbonate rock is sensitivity to lithology change by selecting three logging parameters of the natural gamma curve GR, the neutron NPHI and the thorium uranium ratio TH/U, but this does not mean that only three logging parameters of the natural gamma curve GR, the neutron NPHI and the thorium uranium ratio TH/U can be selected for lithology recognition, and the logging parameters can also be selected according to practical situations by using other conventional logging corresponding features.
S2: logging data is collected, including collecting logging parameters of a natural gamma curve GR, neutrons NPHI and a thorium-uranium ratio TH/U.
In some possible embodiments, the three parameters of the natural gamma curve GR, neutron NPHI, and thorium uranium ratio TH/U are obtained using existing CLS-5700 equipment or MAXIS-500 equipment.
S3: and calculating lithology recognition factors.
Using logging parameters in the collected logging data, lithology recognition factor LITH is calculated using the following formula:
LITH＝(GRmax-GR)×NPHI×TH/U
Wherein LITH denotes a lithology recognition factor, GR denotes a natural gamma curve value, GR max denotes a natural gamma curve maximum value, NPHI denotes a neutron value, and TH/U denotes a thorium uranium ratio.
These values are obtained when the corresponding parameters are obtained using either the CLS-5700 device or MAXIS-500 device.
The high-frequency gyratory carbonate sedimentary lithology is complex and changes longitudinally quickly, and experimental results show that the difference of various lithologies is mainly reflected in the quality of the content of the argillaceous matters and the quality of physical properties and the depth of paleo-water in the sedimentary period. Through logging response characteristics and lithology sensitivity parameter analysis, a natural gamma curve GR can represent the shale content in the rock, neutrons NPHI can represent the porosity of the rock, thorium uranium ratio TH/U can represent the paleo-water depth, and therefore the lithology recognition factor LITH is calculated by adopting the three parameters so as to recognize lithology. The formula is obtained by multiple times of calculation.
S4: and according to the logging parameters and the lithology recognition factors obtained through calculation, combining rock core data to establish a lithology recognition plate.
Specifically, in this embodiment, step S4 specifically includes:
firstly, establishing a LITH-GR intersection map by taking lithology recognition factors LITH as an ordinate and taking a natural gamma curve GR as an abscissa;
and then, the lithology information of each coring position is assigned to an intersection chart by using core data, and a lithology recognition chart is established.
The lithology recognition plate is built in a mode of 'building a graph first and assigning then', and the assigned lithology information is obtained according to real rock core data, so that the lithology information in the built lithology recognition plate is ensured to accord with the real situation.
In some possible embodiments, the specific process of assigning lithology information of each coring location to the intersection map using core data includes:
The lithology information of the coring position on each rock is obtained by using core data;
Determining a lithology recognition factor LITH value and a natural gamma curve GR value of each coring position;
And matching the lithology recognition factor LITH value and the natural gamma curve GR value of each coring position with the abscissa and ordinate of the LITH-GR intersection map, and giving the lithology information of each coring position to the LITH-GR intersection map, so that the whole LITH-GR intersection map has lithology information, and further, the lithology recognition map is established.
The rock character information can be intuitively and accurately obtained by the core data, the rock character recognition factor LITH value and the natural gamma curve GR value of each coring position can be obtained by the core data, and the rock character recognition factor LITH value and the natural gamma curve GR value of all coring points are thrown into a LITH-GR intersection graph, so that the region of each rock character in the graph plate can be obtained.
It can be understood that in the process of constructing the intersection chart, the natural gamma curve GR may be taken as an abscissa, or neutrons NPHI or the thorium uranium ratio TH/U may be taken as an abscissa, which is only for convenience of description, in this step, the natural gamma curve GR is taken as an example to replace the logging parameters for description, and in the subsequent step S5, the logging parameters are also replaced by the natural gamma curve GR for implementation description of the technical scheme.
The rock core data is data obtained by taking rock in a single well to conduct lithology study after well construction is completed. The coring position refers to the depth of the rock core, and the depth corresponds to corresponding lithology information.
S5: and identifying the high-frequency gyratory carbonate rock in the single well by using the lithology identification plate.
Specifically, in the present embodiment, step S5 includes:
Firstly, calculating lithology recognition factors LITH and natural gamma curves GR of measurement points in a single well, so that a group of LITH-GR coordinate data can be confirmed at each measurement point;
Then, the LITH-GR coordinate value corresponding to the measuring point is put into a lithology recognition plate, and lithology of the measuring point is determined;
and finally, determining lithology information of all measurement points in the single well according to the operation, further obtaining single well lithology information, and identifying high-frequency gyratory carbonate rock in the single well according to the single well lithology information.
The measurement points are points marked at regular intervals when the measuring instrument is put into the well to measure from bottom to top after well construction is completed.
The step is based on a lithology recognition plate conforming to the real lithology information, and single-well lithology information is obtained through measurement, so that the accuracy of the result of recognizing the high-frequency gyratory carbonate rock by the single-well lithology information is ensured.
The present embodiment also provides a computer-readable storage medium, on which computer instructions are stored, which when run on a computer, cause the computer to execute the high-frequency gyratory carbonate rock identification method based on conventional logging data provided in the present embodiment.
The embodiment also provides an electronic device, including: at least one processor, memory;
The memory stores computer-executable instructions;
The at least one processor executes the computer-executable instructions stored in the memory, so that the electronic device executes the high-frequency gyratory carbonate rock identification method based on the conventional logging data provided by the embodiment.
A specific example of implementation is provided below.
Taking the recent system of the area A of the Qidamu basin as an example, taking the logging lithology recognition of the A1 well of the coring well as an example to explain the technical scheme in detail:
1. a basic geological condition is determined.
The area A is a set of high-frequency gyratory lake-phase carbonate rock stratum, the lithology is mainly microbial rock, micrite limestone and argillaceous limestone, and the geological conditions are suitable for the application of the technical scheme.
2. And acquiring logging parameters.
The natural gamma curve GR of the stratum, the neutron NPHI and the thorium uranium ratio TH/U are measured by using MAXIS-500 logging series instruments.
3. And calculating lithology recognition factors.
And selecting a natural gamma curve GR maximum GR max at the target interval, substituting the natural gamma curve GR maximum GR max into the following formula to calculate, and obtaining the rock structural factor LITH.
LITH＝(GRmax-GR)×NPHI×TH/U
For example, for natural gamma gr=90, natural gamma maximum GRmax =150, bulk density NPHI =0.05, thorium uranium ratio TH/u=3.0, LITH =9.0 is calculated using the rock structural factor formula.
4. And establishing a lithology recognition plate.
And carrying out LITH calculation on the coring well A1 well, and establishing an intersection recognition plate with the natural gamma GR, wherein the image shows the areas of different lithologies in the plate to help to recognize the lithologies, and three lithologies in a research area have corresponding areas in the plate as shown in figure 2.
5. High frequency gyratory carbonates within the single well are identified.
In the core segment 3863m-3880m, as shown in fig. 3, the coincidence rate is 84% and the application effect is good.
FIG. 3 shows the effect of identifying A1 well logging lithology in the Qaidam basin A region. The method comprises the following steps of taking a depth channel as a channel 1, taking a natural gamma curve GR as a channel 2, taking neutrons NPHI as a channel 3, taking thorium-uranium ratio TH/U as a channel 4, taking lithology recognition factors LITH as a channel 5, taking a core description section as a core section as a channel 6, and taking a logging recognition lithology section as a channel 7.
Those skilled in the art will appreciate that all or part of the steps in a method for implementing the above embodiments may be implemented by a program stored in a storage medium, where the program includes several instructions for causing a single-chip microcomputer, chip or processor (processor) to perform all or part of the steps in a method according to the embodiments of the invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The alternative embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the embodiments of the present invention are not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solutions of the embodiments of the present invention within the scope of the technical concept of the embodiments of the present invention, and all the simple modifications belong to the protection scope of the embodiments of the present invention. In addition, the specific features described in the above embodiments may be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the various possible combinations of embodiments of the invention are not described in detail.
In addition, any combination of the various embodiments of the present invention may be made, so long as it does not deviate from the idea of the embodiments of the present invention, and it should also be regarded as what is disclosed in the embodiments of the present invention.

Claims (9)

1. A method for identifying high frequency gyratory carbonate based on conventional logging data, comprising:
collecting logging data required for lithology recognition;
Calculating lithology recognition factors based on logging parameters in the logging data;
Based on lithology recognition factors and the logging parameters, establishing a lithology recognition plate by combining core data;
Identifying high-frequency gyratory carbonate rock in a single well by using the lithology identification plate;
The lithology recognition plate is established based on lithology recognition factors and logging parameters by combining core data, and comprises the following steps:
Establishing a cross map by lithology recognition factors and logging parameters;
And (3) utilizing core data to assign lithology information of each coring position to the intersection map, and establishing a lithology recognition plate.
2. The conventional log based high frequency gyratory carbonate rock identification method of claim 1, further comprising:
determining logging information required for lithology identification, comprising: acquiring logging parameters in logging data;
conventional logging response characteristics of different rock types in the investigation region are analyzed to determine the logging parameters that are most sensitive to lithology changes.
3. The method of high frequency gyratory carbonate rock identification based on conventional well logging data of claim 2 wherein the well logging parameters comprise: natural gamma curve, neutron and thorium to uranium ratio.
4. The method for identifying high-frequency gyratory carbonate based on conventional logging information according to claim 3, wherein the lithology identification factor is calculated as follows:
LITH＝(GRmax-GR)×NPHI×TH/U
Wherein LITH denotes a lithology recognition factor, GR denotes a natural gamma curve value, GR max denotes a natural gamma curve maximum value, NPHI denotes a neutron value, and TH/U denotes a thorium uranium ratio.
5. The method for high frequency gyratory carbonate rock identification based on conventional well logging data of claim 2 wherein the well logging parameters are obtained using CLS-5700 equipment or MAXIS-500 equipment.
6. The method for identifying high frequency gyratory carbonate based on conventional logging information of claim 1, wherein assigning lithology information of each coring location to the intersection map using lithology information comprises:
The lithology information of the coring position on each rock is obtained by using core data;
determining lithology recognition factors and logging parameters of each coring position;
And matching the lithology recognition factors and the logging parameters of each coring position with the lithology recognition factors and the logging parameters in the intersection map, and further giving lithology information of each coring position to the intersection map.
7. The method for identifying high frequency gyratory carbonate based on conventional logging information as defined in claim 6, wherein the identifying high frequency gyratory carbonate within a single well using the lithology identification plate comprises:
calculating lithology recognition factors and logging parameters of measurement points in a single well;
the lithology recognition factors and the logging parameters of the measuring points are put into the lithology recognition plate, and lithology of the measuring points is determined;
and determining lithology information of all measurement points in the single well to obtain single well lithology information, and identifying high-frequency gyratory carbonate rock in the single well according to the single well lithology information.
8. A computer readable storage medium having stored thereon computer instructions which, when run on a computer, cause the computer to perform the method of any of claims 1-7.
9. An electronic device, comprising: at least one processor, memory;
The memory stores computer-executable instructions;
the at least one processor executing computer-executable instructions stored in the memory to cause the electronic device to perform the method of any one of claims 1-7.
CN202211330291.1A 2022-10-27 2022-10-27 High-frequency gyratory carbonate rock identification method based on conventional logging data Pending CN117991335A (en)

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PCT/CN2023/112129 WO2024087800A1 (en) 2022-10-27 2023-08-10 Conventional well logging data-based method for identifying high-frequency cyclic carbonate rock

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US8126647B2 (en) * 2008-04-07 2012-02-28 Chevron U.S.A. Inc. Lithofacies classification system and method
US20210149075A1 (en) * 2019-11-19 2021-05-20 Chevron U.S.A. Inc. System and method for lithofacies classification
CN113031102A (en) * 2019-12-09 2021-06-25 中国石油大学(北京) Lithofacies well logging identification method and device for carbonate reservoir and storage medium
CN113671594B (en) * 2021-06-21 2023-06-20 成都理工大学 Automatic carbonate rock high-frequency sequence identification method based on BP neural network
CN114609675A (en) * 2022-03-14 2022-06-10 西南石油大学 Quantitative recovery method for carbonate rock stratum sedimentary micro-landform based on high-frequency cycle

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