A core homing method based on accurate matching of imaging logging and core facies mode

Abstract

This invention provides a core repositioning method based on precise matching of imaging logging facies and core facies patterns. The method includes: first, extracting typical imaging logging facies and core facies types to construct an imaging logging facies database and a core facies database; then, constructing an imaging logging-core facies identification pattern based on bidirectional calibration according to the characteristics of the imaging logging and core facies databases; further, establishing core facies profiles and imaging logging facies profiles based on the bidirectional calibration imaging logging-core facies identification pattern; and finally, quantitatively matching the imaging logging facies profiles and core facies profiles based on the imaging logging facies identification accuracy and matching rate thresholds to achieve accurate core depth repositioning. This invention has strong scalability and practical value, thus providing a novel and efficient solution for the development of core repositioning.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas exploration and development technology, and in particular relates to a core repositioning method based on imaging logging and precise matching of core facies patterns. Background Technology

[0002] In the field of oil and gas exploration and development, core data, as physical samples that directly reflect underground geological conditions, has a decisive impact on subsequent geological analysis, reservoir evaluation, and exploration and development due to its accuracy and reliability. However, during the core drilling process, complex geological conditions, limitations of drilling equipment and technology, etc., often lead to deviations between the retrieved core samples and the actual formation depth. Therefore, core repositioning technology is needed to reposition the core samples to their correct depth after retrieval.

[0003] Traditional core repositioning techniques primarily utilize the correlation between lithology and electrical properties, or well logging techniques such as acoustic waves, natural gamma rays, and resistivity, to determine the depth and location of cores. However, given complex geological conditions, existing core repositioning techniques have relatively high errors, making it difficult to meet the application requirements such as combined core-imaging logging for structural analysis.

[0004] Technical solution of existing technology 1

[0005] "Core Surface Gamma Measurement System and Its Application in Core Repositioning" [J]. Petroleum Instruments, 2003(02), Authors: Wang Yanqin, Yang Fuzhou, An Mingquan, et al. This system utilizes the comparison between natural gamma curves and core scanning gamma curves to achieve core repositioning. A core surface gamma measuring instrument is used to measure the surface of sequentially arranged cores, generating a core surface gamma intensity (API) curve. This curve is then compared with the well logging gamma curve to identify the similarities and differences in their trends. By comparing the correspondence of characteristic points such as peaks and valleys, the position of the core in the formation is determined, thus achieving core depth repositioning.

[0006] Disadvantages of existing technology 1

[0007] Compared to using the correlation between lithology and electrical properties for core positioning, this method is less affected by human intervention and improves the accuracy of core positioning. However, the accuracy of this method is limited by the logging response accuracy of natural gamma ray, and the application scope is relatively limited because natural gamma ray core testing increases costs to some extent.

[0008] Technical solution of existing technology 2

[0009] "Spatial Repositioning of Core from East Hole of Songke-2 Well in the Songliao Basin Scientific Drilling Project." Geological Science and Technology Information, 2017, 36(04), authors: Li Ning, Zou Changchun, Peng Cheng, et al. This article proposes a method: utilizing the high resolution, strong continuity, and intuitive accuracy of imaging logging data, core depth repositioning is achieved by comparing the characteristics of core roll scan images and electrical imaging logging images. The core roll scan image is formed by continuously rolling and scanning the core to obtain structural and tectonic features such as fractures, faults, and pores. The electrical imaging logging image is obtained by scanning the wellbore from all directions using an electrical imaging logging instrument and undergoing a series of complex data processing and image generation processes. Ignoring the size difference between the well diameter and the core diameter, the two images are flipped and adjusted to the same coordinate system. Then, based on the characteristics of color, texture, and morphology, typical geological features such as fractures, lithological abrupt changes, and bedding attitude changes are identified on the core roll scan image and the electrical imaging logging image, thus achieving core depth repositioning.

[0010] Disadvantages of existing technology 2

[0011] This technology recognizes the high resolution of electrical imaging logging images and clarifies the mirror correspondence between them and core roll scan images, as well as the possibility of matching their typical geological features. However, the article's technical flow does not propose specific implementation methods or quantitative standards; the comparison between core roll scan images and electrical imaging logging images often relies on the interpreter's experience and subjective judgment, and different interpreters may arrive at different conclusions due to differences in experience, knowledge, and perspectives; at the same time, imaging logging images are ambiguous, lacking a quantitative matching model between imaging logging and core lithofacies, resulting in poor generalizability. Summary of the Invention

[0012] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a core repositioning method based on accurate matching of imaging logging and core facies patterns.

[0013] This invention proposes a core repositioning method based on precise matching of imaging logging and core facies patterns. It employs a set of rules for constructing imaging logging and core facies feature patterns, establishing characteristic profiles for both cores and imaging logging, and using the matching rate between these profiles to quantify the reliability of core repositioning, thereby achieving high-precision core repositioning. This invention effectively solves the problem of accurate core repositioning under complex geological conditions, providing more accurate and reliable data support for oil and gas exploration and development.

[0014] The present invention adopts the following technical solution:

[0015] A core repositioning method based on accurate matching of imaging logging and core facies patterns includes the following steps:

[0016] Step 1. Extract typical imaging logging lithofacies and core lithofacies types respectively, and construct imaging logging lithofacies database and core lithofacies database.

[0017] (1) Constructing an imaging logging lithofacies database. Based on imaging logging images and natural gamma ray while drilling, typical lithofacies from imaging logging are identified, and an imaging logging lithofacies database is established, denoted as [database name missing]. .set up Include One element, The Middle element ( ≤ ≤ It is composed of imaging logging image features and natural gamma curve features while drilling, and does not repeat with other elements.

[0018] (2) Constructing a core lithofacies database. Based on core descriptions (color, grain size, lithology, etc.) and natural gamma curves, typical core lithofacies are identified, and a core lithofacies database is established. .set up Include one element, The number of core lithofacies types The Middle element ( ≤ ≤ It is unique and does not repeat with other elements.

[0019] Step 2. Construct an imaging logging-core lithofacies identification model based on two-way calibration.

[0020] Based on database and database Based on the lithofacies characteristics, a lithofacies identification model of imaging logging-core was constructed. A lithofacies database based on two-way calibration of imaging logging-core was established. . It has the following characteristics:

[0021] Include One element, It is based on the number of imaging logging-core lithofacies identification patterns with two-way calibration. The Middle element ( ≤ ≤ It must not repeat with other elements. Let... for arrive The mapping relationship, for any element in , There exists a unique element. ( ≤ ≤ Corresponding to this; for arrive The mapping relationship, for any element in , There exists a unique element. ( ≤ ≤ Corresponding to this. In the mapping relationship and Down, and All elements are covered, and each element can only appear once and cannot be repeated.

[0022] Step 3. Based on the bidirectional calibration imaging logging-core lithofacies identification mode, establish core lithofacies profiles and imaging logging lithofacies profiles.

[0023] (1) Establishing core facies profiles. Based on a two-way calibrated imaging logging-core facies identification mode. Based on the segmented characteristics of the core, a lithofacies profile of the core was established. The lithofacies profile of the core was constructed in the following manner:

[0024] ① For the same core sample, let the core logging depth be [γ1, γ2], where This represents the starting depth for core sampling. This is the final depth for core sampling; the core depth is [missing information]. The total length of the core sample is The core sample contained fragmentation or wear at the top and bottom. The core samples were divided into sections from shallow to deep, using core fragmentation or top and bottom abrasion as the dividing line. The segments are denoted as follows: , … .

[0025] ②Assume the rock core has a total Block. For Rock core samples, numbered from shallowest to deepest as follows: , … .

[0026] ③ Using database Based on the core facies identification standard, the core was analyzed. arrive The lithofacies were identified and denoted as b1, b2…b λ , where 1≤λ≤m.

[0027] ④Based on the core fragmentation, top and bottom wear points, and core lithofacies discontinuities, establish the core lithofacies profile map PM5.

[0028] ⑤ Based on the mapping relationship in step 2 In the core facies profile Based on this, build a database Medium element-based core facies profile .

[0029] (2) Establish imaging logging lithofacies profiles.

[0030] ①Assume the core depth after repositioning is [ , ], , These represent the actual top and bottom depths of the core sample within the formation, respectively, and the repositioning offset. According to the repositioning offset Possible maximum value Determine the depth range of imaging logging lithofacies profiles , The starting depth for acquiring formation images in imaging logging. This represents the end depth of the imaging logging data recording, where μ1 = β1 - v max μ2=β2+v max Establish a continuous lithofacies profile PW4 from imaging logging, denoted as a1, a2, ..., a ω ,in ≤ ≤ .

[0031] ② Based on the mapping relationship in step 2 In imaging logging lithofacies profiles Based on this, build a database Medium element-based imaging logging profile .

[0032] Step 4. Quantitatively match the imaging logging lithofacies profile and the core lithofacies profile to achieve accurate core depth positioning.

[0033] Assuming the core is repositioned, the threshold for the match between the imaging logging facies and the core facies is... Assume the accuracy of the imaging logging image is... Assume the top depth of the logging core is... Starting from, with The step size is i, the number of moves is i, and the core facies profile is i. Move up and down by distance respectively At that time, the discontinuity point in the rock core was used as the boundary, at a depth of [ , ]and[ , Within, segmented matching of core facies profiles and imaging logging lithological profile Record the maximum match rate .

[0034] from Begin by calculating the maximum matching rate sequentially. Based on the maximum matching rate With minimum threshold The relationship between the core depth and its location is used to determine whether the core repositioning was successful. The following relationship must be satisfied:

[0035] (1). When hour, ≥ The core sample was successfully returned to its original position. The corresponding core depth is the core repositioning depth.

[0036] (2). When and ≤ hour, ≥ The core sample was successfully returned to its original position. The corresponding core depth is the core repositioning depth.

[0037] (3). When and < Then take The core depth corresponding to the maximum value is the core repositioning depth.

[0038] The beneficial effects of this invention are:

[0039] 1. Parameters are easy to obtain. The various parameters required in this invention can be directly measured through multiple methods such as imaging logging images and core sweep maps, making the parameters easy to obtain.

[0040] 2. High reliability of core repositioning results. Traditional core repositioning methods mainly rely on the correspondence between core and electrical properties or logging techniques such as acoustic waves, natural gamma rays, and resistivity, resulting in low accuracy of core repositioning results. The core repositioning method proposed in this invention, which precisely matches imaging logging and core facies models, establishes a quantitative matching model between imaging logging and core facies, ensuring high reliability of core repositioning.

[0041] 3. High scalability. The core repositioning method proposed in this invention has a clear principle, is easy to promote, and has strong scalability and practical value. Attached Figure Description

[0042] Figure 1 This is an imaging logging-core lithofacies identification mode based on two-way calibration;

[0043] Figure 2 For based on Core facies profile;

[0044] Figure 3 For based on Imaging logging profile;

[0045] Figure 4 This is an imaging logging-core lithofacies identification mode based on two-way calibration;

[0046] Figure 5 For based on Core facies profile;

[0047] Figure 6 For based on Imaging logging lithofacies profiles;

[0048] Figure 7 Let the number of moves i be the same as the matching rate. Relationship scatter plot;

[0049] Figure 8 The results show the core repositioning. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention are described clearly and completely below. Obviously, the described embodiments are only some embodiments of this invention, not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0051] This invention proposes to first extract typical imaging logging lithofacies and core lithofacies types, respectively, to construct an imaging logging lithofacies database and a core lithofacies database. Then, based on the characteristics of the imaging logging and core lithofacies databases, a bidirectional calibration-based imaging logging and core lithofacies identification model is constructed. Furthermore, based on the bidirectional calibration imaging logging-core lithofacies identification model, core lithofacies profiles and imaging logging lithofacies profiles are established. Finally, based on the imaging logging lithofacies identification accuracy and matching rate thresholds, the imaging logging lithofacies profiles and core lithofacies profiles are quantitatively matched to achieve accurate core depth localization. This invention constructs a quantitative matching model between imaging logging and core lithofacies, which can obtain better core localization results.

[0052] like Figure 1As shown, the present invention provides a core repositioning method based on accurate matching of imaging logging and core facies patterns, comprising the following steps:

[0053] Step 1. Extract typical imaging logging lithofacies and core lithofacies types respectively, and construct imaging logging lithofacies database and core lithofacies database.

[0054] (1) Constructing an imaging logging lithofacies database. Based on imaging logging images and natural gamma ray while drilling, typical lithofacies from imaging logging are identified, and an imaging logging lithofacies database is established, denoted as [database name missing]. .set up Include One element, The i-th element ( ≤ ≤ It is composed of imaging logging image features and natural gamma curve features while drilling, and does not repeat with other elements.

[0055] (2) Constructing a core lithofacies database. Based on core descriptions (color, grain size, lithology, etc.) and natural gamma curves, typical core lithofacies are identified, and a core lithofacies database is established. .set up Include one element, The number of core lithofacies types The Middle element ( ≤ ≤ It is unique and does not repeat with other elements.

[0056] Step 2. Construct an imaging logging-core lithofacies identification model based on two-way calibration.

[0057] Based on database and database Based on the lithofacies characteristics, a lithofacies identification model of imaging logging-core was constructed. A lithofacies database based on two-way calibration of imaging logging-core was established. . It has the following characteristics:

[0058] Include One element, It is based on the number of imaging logging-core lithofacies identification patterns with two-way calibration. The Middle element It should not repeat with other elements. Let... for arrive The mapping relationship, for any element in , There exists a unique element. Correspondingly; for arrive The mapping relationship, for any element in , There exists a unique element. Correspondingly, in the mapping relationship and Down, and All elements are covered, and each element can only appear once; repetition is not allowed. Figure 1 As shown.

[0059] Step 3. Based on the bidirectional calibrated imaging logging-core lithofacies identification mode, establish core lithofacies profiles and imaging logging lithofacies profiles.

[0060] (1) Establishing core facies profiles. Based on a two-way calibrated imaging logging-core facies identification mode. Based on the segmented characteristics of the core, a lithofacies profile of the core is established. The lithofacies profile of the core is constructed in the following manner (e.g.) Figure 2 (As shown).

[0061] ① For the same core sample, let the core logging depth be... ,in This represents the starting depth for core sampling. This is the final depth for core sampling; the core depth is [missing information]. The total length of the core sample is The core sample contained fragmentation or wear at the top and bottom. The core samples were divided into sections from shallow to deep, using core fragmentation or top and bottom abrasion as the dividing line. The segments are denoted as follows: , … ,like Figure 2 As shown in (b).

[0062] ②Assume the rock core has a total Block. For Rock core samples, numbered from shallowest to deepest as follows: , … ,like Figure 2 As shown in (c).

[0063] ③ Using database Based on the core facies identification standard, the core was analyzed. arrive The lithofacies were identified and denoted as b1, b2…b λ , As a constant, the recognition result is as follows Figure 2 As shown in (d), where ≤ ≤ .

[0064] ④ Based on the core fragmentation, top and bottom abrasion points, and core lithofacies discontinuities, establish a core lithofacies profile diagram PM5, such as... Figure 2 As shown in (e).

[0065] ⑤ Based on the mapping relationship in step 2 In the core facies profile Based on this, build a database Medium element-based core facies profile ,like Figure 2 As shown in (f).

[0066] (2) Establish imaging logging lithofacies profiles, such as Figure 3 As shown.

[0067] ① Assume the core depth after repositioning is . , , These represent the actual top and bottom depths of the core sample within the formation, respectively, and the repositioning offset. According to the repositioning offset Possible maximum value Determine the depth range of imaging logging lithofacies profiles , The starting depth for acquiring formation images in imaging logging. This represents the end depth of the imaging logging data recording, where μ1 = β1 - v max μ2=β2+v max Establish a continuous lithofacies profile PW4 from imaging logging, denoted as a1, a2, ..., a ω , where 1≤ ≤ ,like Figure 3 As shown in (d).

[0068] ② Based on the mapping relationship in step 2 In imaging logging lithofacies profiles Based on this, build a database Medium element-based imaging logging profile ,like Figure 3 As shown in (e).

[0069] Step 4. Quantitatively match the imaging logging lithofacies profile and the core lithofacies profile to achieve accurate core depth positioning.

[0070] Assuming the core is repositioned, the threshold for the match between the imaging logging facies and the core facies is... Assume the accuracy of the imaging logging image is... Assume the top depth of the logging core is... Starting from, with The step size is i, the number of moves is i, and the core facies profile is i. Move up and down by distance respectively At that time, the discontinuity point in the rock core was used as the boundary, at a depth and Internally, segmented matching of core facies profiles and imaging logging lithological profile Record the maximum match rate .

[0071] from Begin by calculating the maximum matching rate sequentially. Based on the maximum matching rate With minimum threshold The relationship between the core depth and its location is used to determine whether the core repositioning was successful. The following relationship must be satisfied:

[0072] (1). When hour, The core sample was successfully returned to its original position. The corresponding core depth is the core repositioning depth.

[0073] (2). When and hour, The core sample was successfully returned to its original position. The corresponding core depth is the core repositioning depth.

[0074] (3) When and Then take The core depth corresponding to the maximum value is the core repositioning depth.

[0075] Example

[0076] This embodiment analyzes samples from the tight sandstone oil and gas reservoir block in the Bozi area of ​​the Kuqa Depression in the Tarim Basin. Based on the core repositioning method using imaging logging and precise matching of core facies models, accurate core repositioning is achieved, including the following steps:

[0077] Step 1. Extract typical imaging logging lithofacies and core lithofacies types respectively, and construct imaging logging lithofacies database and core lithofacies database.

[0078] Based on imaging logging images and natural gamma ray while drilling, typical lithofacies were identified from imaging logging data, and an imaging logging lithofacies database was established. Based on core descriptions (color, grain size, lithology, etc.) and natural gamma curves, typical core lithofacies were identified, and a core lithofacies database was established. .database and The characteristics are as follows:

[0079] (1) Database Include Elements, by It is a high-gamma-ray bright-colored massive rock facies. It is a medium-gamma massive light to dark lithofacies. and It is a low-gamma dark massive rock facies. It is a gamma-banded lithofacies with varying elevations. and It is a gamma-ray porphyritic facies with varying elevations. Each element is composed of both imaging logging features and natural gamma-ray curve features during drilling, and does not overlap with other elements.

[0080] (2) Database It contains 11 elements. It is mudstone. It is argillaceous fine sandstone. It is coarse sandstone. It is argillaceous sandstone. It is mudstone. It is sandy mudstone. It is sandy mudstone. It is sandstone interbedded with mudstone. It is mudstone and gravel sandstone. It is fine sandstone. It is a medium conglomerate. Each element is unique and does not repeat with other elements.

[0081] Step 2. Construct an imaging logging-core lithofacies identification model based on two-way calibration.

[0082] Based on database and Based on the lithofacies characteristics, a lithofacies identification pattern database based on two-way calibration imaging logging-core was constructed. , It has the following characteristics:

[0083] It contains 5 elements, namely , … Furthermore, each element is unique. , … for arrive The mapping relationship, for Any element, There exists a unique element that corresponds to it; , … for arrive The mapping relationship, for Any element in There exists a unique element that corresponds to it. and All elements are covered, and each element appears only once, such as Figure 4 As shown.

[0084] The elemental characteristics are as follows:

[0085] ① element High-density strata with high resistivity, such as dense sandstone;

[0086] ② elements The uniform variation in color texture in the image represents the variation in the sequence combination characteristics of a single well.

[0087] ③ Elements Formations with low resistivity and low density, such as mudstone;

[0088] ④ elements Interbedded sandstone and mudstone;

[0089] ⑤ Elements High-resistivity calcium deposits, gravel, etc. appear in relatively dark images, or low-damping gravel, nodules, etc. appear in relatively bright images;

[0090] Step 3. Based on the bidirectional calibration imaging logging-core lithofacies identification mode, core lithofacies profiles and imaging logging lithofacies profiles were established.

[0091] (1) Establish core facies profiles.

[0092] Based on two-way calibration imaging logging-core lithofacies identification mode Based on the segmented characteristics of the core, a lithofacies profile of the core is established. The lithofacies profile of the core is constructed in the following manner (e.g.) Figure 5 (as shown)

[0093] ①. Given that the core logging depth is [ ], taking the heart and pressing forward. The total length of the core sample is The core sample contained fragmentation or wear at the top and bottom. The core samples were divided into sections from shallow to deep, using core fragmentation or top and bottom abrasion as the dividing line. The segments are denoted as follows: , … ,like Figure 5 middle and As shown.

[0094] ② Core samples Blocks, numbered from lightest to darkest, are denoted as follows: , … ,like Figure 5 middle As shown.

[0095] ③ Using database Based on the core facies identification standard, the core was analyzed. arrive The lithofacies were identified, and the identification results are as follows: Figure 5 middle As shown.

[0096] ④ Based on the core fragmentation and top and bottom abrasion points, establish a core lithofacies profile, such as... Figure 5 middle As shown.

[0097] ⑤ In the core facies profile Based on this, build a database Medium element-based core facies profiles, such as Figure 5 middle As shown.

[0098] (2) Establish imaging logging lithofacies profiles, such as Figure 6 As shown.

[0099] ① Repositioning offset The range of the imaging logging lithofacies profile is determined based on the repositioning offset. Establish continuous lithofacies profiles for imaging logging, such as... Figure 6 middle As shown.

[0100] ②Based on the first Mapping relationship in the step In imaging logging lithofacies profiles Based on this, build a database Medium element-based imaging logging profile ,like Figure 6 middle As shown.

[0101] Step 4. Quantitatively match the imaging logging lithofacies profile and the core lithofacies profile to achieve accurate core depth positioning.

[0102] The coincidence threshold φ between imaging logging lithofacies and core lithofacies in the study area is 95%. Imaging logging lithofacies identification accuracy... The depth is 0.05m. Starting from the top depth of the logging core at 6795.2m, with a moving step size of 0.05m and i representing the number of moves, at the depth... Internal core facies profile The core facies profiles were matched segmentally by moving upwards and downwards by 0.05 μm, with the continuous points in the core section as the boundary. and imaging logging lithological profile .

[0103] When the number of moves i=40, the logging core depth shifts downwards by 1m, the matching rate reaches the threshold of 95%, and the core is successfully returned to its original position. Figure 7 As shown. The core repositioning results are as follows. Figure 8 As shown.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A core repositioning method based on accurate matching of imaging logging and core facies patterns, characterized in that, Includes the following steps: Step 1. Extract typical imaging logging lithofacies and core lithofacies types respectively, and construct imaging logging lithofacies database and core lithofacies database. (1). Constructing an imaging logging lithofacies database: Based on imaging logging images and natural gamma ray during drilling, typical lithofacies were identified from the imaging logging data, and an imaging logging lithofacies database was established, denoted as [database name missing]. ,set up Include One element, The Middle element , ≤ ≤ It is composed of imaging logging image features and drilling natural gamma curve features, and does not repeat with other elements; (2). Constructing a core-lithological database: Based on core descriptions and natural gamma-ray curve data, typical core lithofacies are identified, and a core lithofacies database is established. ,set up Include one element, The number of core lithofacies types The Middle element , ≤ ≤ It is unique and does not repeat with other elements; Step 2. Construct an imaging logging-core lithofacies identification model based on two-way calibration. Based on imaging logging lithofacies database and core facies database Based on the lithofacies characteristics, an imaging logging-core lithofacies identification model was constructed, and a lithofacies database based on two-way calibration of imaging logging-core was established. , It has the following characteristics: Include One element, It is based on the number of imaging logging-core lithofacies identification patterns with two-way calibration. The Middle element , ≤ ≤ It does not repeat with other elements; let for arrive The mapping relationship, for any element in , There exists a unique element. , ≤ ≤ Corresponding to this; for arrive The mapping relationship, for any element in , There exists a unique element. , ≤ ≤ Correspondingly, in the mapping relationship and Down, and All elements are covered, and each element can only appear once and cannot be repeated; Step 3. Based on the bidirectional calibrated imaging logging-core lithofacies identification mode, establish core lithofacies profiles and imaging logging lithofacies profiles. (1) Establishing core facies profiles: a two-way calibrated imaging logging-core facies identification mode Based on the segmented characteristics of the core, a lithofacies profile of the core is established. The lithofacies profile of the core is constructed in the following manner: ① For the same core sample, let the core logging depth be [γ1, γ2], where This represents the starting depth for core sampling. This is the final depth for core sampling; the core depth is [missing information]. The total length of the core sample is The core sample contained fragmentation or wear at the top and bottom. The core samples were divided into sections from shallow to deep, using core fragmentation or top and bottom abrasion as the dividing line. The segments are denoted as follows: , … ; ②Assume the rock core has a total Block, for Rock core samples, numbered from shallowest to deepest as follows: , … ; ③ Using core and lithofacies databases Based on the core facies identification standard, the core was analyzed. arrive The lithofacies were identified and denoted as b1, b2…b λ , where 1≤λ≤m; ④Based on the core fragmentation, top and bottom wear points, and core lithofacies discontinuities, establish the core lithofacies profile diagram PM5; ⑤ Based on the mapping relationship in step 2 In the core facies profile Based on this, a lithofacies database of imaging logging-core samples with two-way calibration is constructed. Medium element-based core facies profile ; (2) Establishing imaging logging lithofacies profiles: ① Assume the core depth after repositioning is . , , These represent the actual top and bottom depths of the core sample within the formation, respectively, and the repositioning offset. According to the repositioning offset Get the maximum value Determine the depth range of the imaging logging lithofacies profile. , The starting depth for acquiring formation images in imaging logging. This is the end depth of the imaging logging data recording, where , A continuous lithofacies profile PW4 was established using imaging logging, denoted as a1, a2…a ω ,in ≤ ≤ ; ② Based on the mapping relationship in step 2 In imaging logging lithofacies profiles Based on this, build a database Medium element-based imaging logging profile ; Step 4. Quantitatively match the imaging logging lithofacies profile and the core lithofacies profile to achieve accurate core depth positioning. Assuming the core is repositioned, the threshold for the match between the imaging logging facies and the core facies is... Assume the accuracy of the imaging logging image is... Assume the top depth of the logging core Starting from, with The step size is i, the number of moves is i, and the core facies profile is i. Move up and down by distance respectively At that time, the discontinuity point in the rock core was used as the boundary, at a depth of [ , ]and[ , Within, segmented matching of core facies profiles and imaging logging lithological profile Record the maximum match rate ; from Begin by calculating the maximum matching rate sequentially. Based on the maximum matching rate With minimum threshold The relationship between the core depth and its positioning is used to determine whether the core repositioning was successful, and the following relationship must be satisfied: (1). When hour, ≥ The core sample was successfully returned to its original position. The corresponding core depth is the core repositioning depth; (2). When and ≤ hour, ≥ The core sample was successfully returned to its original position. The corresponding core depth is the core repositioning depth; (3). When and < Then take The core depth corresponding to the maximum value is the core repositioning depth.

2. The method according to claim 1, characterized in that, The core description in step 1 (2) includes: color, grain size, and lithology.

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