An isochronous stratigraphic automatic correlation method, device, medium and equipment

Abstract

The present application relates to a kind of isochronous stratum automatic correlation method, device, medium and equipment, the method includes the following steps: obtaining a well profile, select several wells as standard well, and complete stratum division to the standard well;Optimizing the well logging curve that can effectively respond to stratum marker bed, sedimentary cycle, lithologic combination, and the preferred logging curve is pretreated;With the logging curve of standard well as data basis, in turn, complete the marker bed identification of well profile on the well to be compared, sedimentary cycle identification and lithologic combination identification;According to the linear solution of stratum boundary point except marker bed identification point, sedimentary cycle identification point, lithologic combination identification point is completed according to stratum superimposed pattern;The stratum automatic division result visualization of profile is carried out.The isochronous stratum automatic correlation method is fully and reasonably integrated into rich geological expert knowledge, with stratum superimposed pattern as constraint, based on the principle and step of geological expert stratum correlation, computer algorithm is designed, and stratum automatic correlation is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to an isochronous stratum automatic correlation method, device, medium and equipment, and belongs to the technical field of oil exploration and development. BACKGROUND

[0002] After the oilfield enters the high water cut and ultra-high water cut stages, it is more and more difficult to further improve the recovery efficiency and tap the potential of remaining oil, and more accurate reservoir description must be carried out. Carrying out accurate and accurate stratum division and correlation and establishing a high-resolution isochronous stratum framework are the basis and key to realizing accurate reservoir description.

[0003] The stratum correlation process needs to rely on a large amount of geological understanding data and rich geological expert experience, and the operation time cost is high, and the stratum correlation is subjective. With the development of computer technology, since the 1970s, its efficient computing power has been gradually introduced into the stratum correlation work. At present, many automatic stratum correlation methods have been formed at home and abroad, mainly including the following systems. The first is a signal decomposition technology mainly based on wavelet transform, Walsh transform, spectral analysis and the like. This technical means takes the logging curve as the input data, carries out signal transformation on the logging curve of a single well, and obtains stratum division mark features in the logging curve under different scales. The second is a mathematical statistics method taking activity function method, intra-layer difference method, ordered cluster analysis (optimal segmentation method), extreme value variance clustering method, fuzzy pattern recognition and the like as examples, taking the reservoir heterogeneity of different sedimentary facies as the basis, and achieving the purpose of identifying small layer interfaces by statistically analyzing the incoherent points of the logging data in time sequence; the third is a stratum correlation method based on correlation analysis. The basic idea of this method is to select a reference logging curve, then construct a specific target function and error analysis index to determine the correlation of the reference curve and the correlation curve, and then carry out correlation analysis from top to bottom for the target stratum correlation layer, so as to determine the optimal curve matching and correlation scheme. The commonly used implementation methods in the correlation representation process include grey correlation degree, correlation coefficient, maximum likelihood estimation, virtual correlation degree, genetic algorithm, particle swarm optimization and the like; the fourth is a machine learning method, such as linear regression, BP neural network, quantum derivative cuckoo and other supervised learning methods, self-organizing neural network and other semi-supervised learning algorithms, multi-granularity clustering, graph clustering and other unsupervised learning algorithms, SegNet convolutional neural network, LSTM recurrent neural network and other deep learning methods.

[0004] Although numerous automatic stratigraphic correlation methods have been proposed by researchers both domestically and internationally, these methods still cannot be effectively applied to practical development and production work. Currently, a systematic and effective automatic stratigraphic correlation method is still lacking. Existing automatic stratigraphic correlation technologies mainly suffer from the following problems: First, they are highly dependent on data. Previous research methods were entirely data-driven, utilizing computer technology to analyze data and derive stratigraphic correlation information. It is worth noting that data often cannot comprehensively characterize the features of underground strata, especially under conditions of strong heterogeneity, where the mapping between data and underground geological features is often not a one-to-one correspondence. Furthermore, stratigraphic data often reflects lithological characteristics rather than temporal characteristics, leading to excessive reliance on data and resulting in existing methods producing mostly isolithological correlations rather than isochronous correlations. Second, previous research methods have varying applicability across different work areas; that is, the methods have a certain degree of applicability, but their applicability is not strong. When applying existing methods to different study areas with significant differences in geological characteristics, not only is it necessary to reconstruct the method parameters, but the application effect often fails to meet the requirements. Third, they have a high demand for large amounts of data. Methods based on mathematical statistics and machine learning require large amounts of data for feature statistical analysis. Due to their limited robustness, automatic stratigraphic correlation for a specific study area necessitates obtaining extensive stratigraphic correlation results from geological experts for stratigraphic feature analysis, severely impacting the method's practicality. Fourthly, the lack of guidance from experienced geological experts is another significant factor contributing to the limited practicality of current methods. Geological experts, during stratigraphic correlation, comprehensively analyze the geological sedimentary background and stratigraphic structural characteristics of the study area, completing the correlation based on principles such as well-seismic integration, hierarchical control, model guidance, structural analysis, dynamic verification, and full-area closure. Existing stratigraphic correlation methods lack scientific correlation procedures, resulting in generally poor interpretability of their correlation results. Summary of the Invention

[0005] To address the aforementioned problems, the present invention aims to provide an automatic isochronous stratigraphic correlation method, apparatus, medium, and equipment. The method fully and rationally incorporates extensive geological expert knowledge, uses stratigraphic overlay patterns as constraints, and designs computer algorithms based on the stratigraphic correlation principles and steps of geological experts to achieve automatic stratigraphic correlation.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides an automatic isochronous stratigraphic correlation method, comprising the following steps:

[0008] Obtain a well-connected profile, select several wells as standard wells in the profile, and perform stratigraphic division on the standard wells;

[0009] Preferably, the well logging curves capable of effectively responding to the marker bed, sedimentary cycle and lithologic combination are selected, and the selected well logging curves are preprocessed;

[0010] Based on the preprocessed well logging curves of the standard well, the marker bed identification, sedimentary cycle identification and lithologic combination identification of the well to be correlated on the profile are sequentially completed;

[0011] According to the stratigraphic superimposition mode, linear solving of the stratigraphic boundary points other than the marker bed identification points, sedimentary cycle identification points and lithologic combination identification points is completed;

[0012] The visualization of the stratigraphic automatic division results of the profile is performed.

[0013] Preferably, the preprocessing is used for cleaning the local burrs, abnormal points and noises in the well logging curves affecting the stratigraphic correlation.

[0014] Preferably, the preprocessing includes the well logging curve data preprocessing based on the wavelet transform and morphological filtering.

[0015] Preferably, the marker bed identification of the well to be correlated includes the steps of:

[0016] The well logging curves are segmented in units of half wavelength based on the waveform analysis technology;

[0017] The stratigraphic boundary points having the marker bed response characteristics are selected from the standard well, and the marker bed of the study area is determined;

[0018] The well logging curve response characteristics of each marker bed of the standard well are picked up;

[0019] According to the stratigraphic superimposition mode, the marker bed positions of the well to be correlated are linearly solved, and the marker bed positions are taken as the estimated marker bed points of the well to be correlated;

[0020] A marker bed search radius is set, and the well logging response unit most conforming to the marker bed characteristics is searched within the search radius range of each estimated marker bed point;

[0021] The half amplitude point in the located well logging response unit is returned as the marker bed point, and the solving of the marker bed is completed.

[0022] Preferably, the sedimentary cycle identification and lithologic combination identification include the steps of:

[0023] Based on the stratigraphic superimposition mode, the non-marker bed stratigraphic boundary points of each well to be correlated are linearly solved as the estimated boundary points, with the marker bed identification results as the constraints;

[0024] A waveform analysis algorithm specially used for the sedimentary cycle and lithologic combination identification is designed, and the stratigraphic sedimentary cycle and lithologic combination are analyzed in a higher scale range;

[0025] Based on the standard well marker layer, a designed waveform analysis algorithm is used to analyze the sedimentary cycle and lithologic combination characteristics of the standard well;

[0026] Based on the marker layer identification result, the sedimentary cycle and lithologic combination logging responses with similar characteristics on the standard well are picked up, and the solving of the sedimentary cycle and lithologic combination is completed.

[0027] Preferably, the linear solving of the stratigraphic boundary points except the marker layer identification points, the sedimentary cycle identification points and the lithologic combination identification points is completed according to the stratigraphic superimposed mode, and the stratigraphic boundary points are obtained by using the equal proportion linear interpolation method for the aggradational superimposed stratigraphic layer.

[0028] In the second aspect, the present application provides an isochronous stratigraphic automatic correlation device, which comprises:

[0029] The first processing unit is used for obtaining a well profile, selecting a plurality of wells as standard wells on the profile, and completing stratigraphic division on the standard wells.

[0030] The second processing unit is used for preferably selecting logging curves which can effectively respond to the stratigraphic marker layer, the sedimentary cycle and the lithologic combination, and pre-processing the selected logging curves.

[0031] The third processing unit is used for sequentially completing the marker layer identification, the sedimentary cycle identification and the lithologic combination identification of the well to be correlated on the profile based on the pre-processed logging curves of the standard wells.

[0032] The fourth processing unit is used for completing the linear solving of the stratigraphic boundary points except the marker layer identification points, the sedimentary cycle identification points and the lithologic combination identification points according to the stratigraphic superimposed mode.

[0033] The fifth processing unit is used for visualizing the stratigraphic automatic division result of the profile.

[0034] In the third aspect, the present application provides a computer readable storage medium, which stores computer instructions, and the computer instructions are used for realizing the isochronous stratigraphic automatic correlation method when executed by a processor.

[0035] In the fourth aspect, the present application provides a computer device, which comprises a memory, a processor and a computer program stored on the memory and executable on the processor, and the processor realizes the isochronous stratigraphic automatic correlation method when executing the computer program.

[0036] The present application has the following advantages due to the above technical solutions:

[0037] 1) The control mode of pattern matching is adopted to reasonably predict the positions of the stratigraphic division points, and the existing qualitative and quantitative pattern information is fully integrated into the stratigraphic correlation process, so that the correlation result is closer to the actual situation underground.

[0038] 2) The method is driven by data and knowledge, and the logging curves are optimized by using the experience of geology experts. The computer algorithm is designed by using the stratigraphic correlation ideas and steps of geology experts. The logging response recognition of marker beds, sedimentary cycles and lithological combinations is realized in a hierarchical constraint manner, so that the accuracy of stratigraphic correlation is effectively improved.

[0039] 3) The stratigraphic correlation is carried out in the form of a well tie profile, the stratigraphic correlation efficiency is high, the dependence on data is low, and the stratigraphic correlation of more wells to be correlated is completed by referring to fewer standard wells.

[0040] 4) The method is simple to realize, the algorithm processing speed is fast, the stratigraphic correlation coincidence rate is high, and the correlation result is easy to explain.

[0041] 5) The stratigraphic automatic correlation method has strong robustness, wide application range and good practicability, and makes up for the shortcomings of the existing stratigraphic automatic correlation methods, such as poor correlation effect. BRIEF DESCRIPTION OF DRAWINGS

[0042] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The accompanying drawings are included to provide a description of the preferred embodiments and are not intended to limit the scope of the application. Throughout the drawings, like reference numerals will be used to designate like components.

[0043] In the drawings:

[0044] Figure 1 is the first embodiment of the well tie profile marker bed recognition effect provided by the application;

[0045] Figure 2 is the second embodiment of the well tie profile marker bed recognition effect provided by the application;

[0046] Figure 3 is the sedimentary cycle and lithological combination recognition of the COND curves of 5 wells;

[0047] Figure 4 is the sedimentary cycle and lithological combination recognition of the SP curves of 5 wells;

[0048] Figure 5 is the stratigraphic automatic correlation result of a certain profile. DETAILED DESCRIPTION

[0049] Exemplary embodiments of the present application will be described in greater detail below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it is understood that the present application can be embodied in various forms without being limited by the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the application to those skilled in the art.

[0050] The embodiment of the present application provides an isochronous stratum automatic correlation method, comprising the steps of: obtaining a well tie profile, selecting several wells as standard wells, and completing stratum division on the standard wells; preferably selecting logging curves capable of effectively responding to stratum marker layers, sedimentary cycles, and lithological combinations, and pre-processing the preferred logging curves; taking the logging curves of the standard wells as data basis, sequentially completing marker layer identification, sedimentary cycle identification, and lithological combination identification of the wells to be correlated on the profile; linearly solving stratum boundary points except for the marker layer identification points, sedimentary cycle identification points, and lithological combination identification points according to a stratum superimposition mode; and visualizing the automatic stratum division results of the profile. The isochronous stratum automatic correlation method adopts a mode fitting control mode, takes the stratum superimposition mode as a constraint, scientifically and reasonably estimates the positions of the stratum boundary points, fully integrates the existing qualitative and quantitative mode information into the stratum correlation process, and makes the correlation results closer to the actual situation underground.

[0051] Embodiment 1

[0052] The embodiment 1 of the present application provides an isochronous stratum automatic correlation method, comprising the steps of:

[0053] S1, obtaining a well tie profile, selecting 2 to 3 wells as standard wells, and completing stratum division on the standard wells;

[0054] In the range of the study area, a well tie profile containing multiple wells is obtained in the order of the source direction or in any direction. A stratum division and correlation is performed on the standard wells on the profile by a geology expert according to the stratum division standard of the study area.

[0055] S2, preferably selecting logging curves capable of effectively responding to stratum marker layers, sedimentary cycles, and lithological combinations, and pre-processing the preferred logging curves;

[0056] The logging curves capable of effectively responding to the stratum marker layers, the sedimentary cycles, and the lithological combinations are selected according to the experience of the geology expert, the correlation between the logging curves is analyzed by using a data analysis technology, and the selection of the logging curves is completed.

[0057] Effective response is effective representation, namely, the value and amplitude change of the logging curve can reflect the marker bed, lithologic combination or sedimentary cycle. For example, due to flooding, thin limestone is formed in clastic rock, so the resistivity logging value at the thin limestone is high, and the gamma logging value is low, so resistivity and gamma can effectively represent the position of the thin limestone.

[0058] The marker bed refers to a lithologic layer or lithologic interface with obvious characteristics, wide distribution and isochronous. The effective response of the marker bed refers to that the interface has obvious characteristics on the logging curve, such as value anomaly or curve amplitude anomaly.

[0059] The sedimentary cycle refers to the rock characteristics on the vertical sedimentary profile, including color, lithology, structure, sedimentary structure and the like, which are regularly repeated in a certain order. The sedimentary cycle is a phenomenon, and a regularly repeated cycle is a cycle, and the curve with the corresponding repeated change rule is an effective response curve.

[0060] The lithologic combination refers to the rock type on the profile and the arrangement relationship in the vertical direction. Due to the relative stability of the sedimentary environment in a period of time, the lithology of the stratum deposited in the same period of time has similar change rules in the vertical direction, and the similar rules can also be reflected on the logging curve, and such a curve is an effective response curve.

[0061] Based on the data preprocessing means such as wavelet transform and morphological filtering, the optimized logging curve data preprocessing is completed, so as to effectively clean the local burrs, abnormal points, noises and the like in the logging curve which affect the stratum correlation.

[0062] S3, taking the preprocessed logging curve of the standard well as the data basis, the marker bed identification, sedimentary cycle identification and lithologic combination identification of the well to be correlated on the profile are sequentially completed;

[0063] The marker bed identification comprises the steps of:

[0064] Based on the waveform analysis technology, the logging curve is segmented in units of half wavelength.

[0065] The stratum boundary point with the marker bed response characteristics is selected from the standard well, and the marker bed of the study area is determined.

[0066] The logging curve response characteristics of each marker bed of the standard well are picked up.

[0067] The position of the marker bed of the well to be correlated is linearly solved according to the stratum superposition mode, and the position is taken as the estimated marker bed point of the well to be correlated;

[0068] The marker bed search radius is set, and the logging response unit most consistent with the marker bed characteristics is searched in the search radius range of each estimated marker bed point;

[0069] The half amplitude point is returned as a marker bed point in the located logging response unit, and the solving of the marker bed is completed.

[0070] As an example, as shown in Figure 1 FIG. 3, when S3-18-6, S3-16-11 and S3-2-12 are set as standard wells, and the remaining seven wells are to be compared, four marker beds in the standard wells can be identified, and the identification accuracy of the marker beds in the wells to be compared is 27 / 28, and the identification coincidence rate is 96.6%.

[0071] As another example, as shown in Figure 2 FIG. 4, when S3-18-6, S3-12-12 and S3-2-12 are set as standard wells, and the remaining seven wells are to be compared, three marker beds in the standard wells can be identified, and the identification accuracy of the marker beds in the wells to be compared is 21 / 21, and the identification coincidence rate is 98.9%.

[0072] The identification process of the sedimentary cycle and the lithological combination includes the following steps:

[0073] Based on the identification results of the marker beds, the non-marker bed stratigraphic boundary points of each well to be compared are linearly solved based on the stratigraphic superposition model, and the boundary points are used as the estimated boundary points.

[0074] A wave analysis algorithm specially designed for the identification of sedimentary cycles and lithological combinations is designed to analyze the sedimentary cycles and lithological combinations in a higher scale range.

[0075] Based on the marker beds of the standard wells, the designed wave analysis algorithm is used to analyze the sedimentary cycles and lithological combination characteristics of the standard wells.

[0076] Based on the identification results of the marker beds, the logging responses of the sedimentary cycles and lithological combinations with similar characteristics to the standard wells in the wells to be compared are picked up, and the solving of the sedimentary cycles and lithological combinations is completed.

[0077] As Figure 3 , Figure 4 FIGS. 5 and 6 respectively show examples of COND and SP logging curves, and the logging responses of multiple wells are analyzed by the sedimentary cycle and lithological combination identification algorithm, and the sedimentary cycles and lithological combinations in the strata can be successfully identified.

[0078] S4. Based on the stratigraphic stacking model, perform linear solutions for stratigraphic boundaries, excluding marker bedding, sedimentary cycle, and lithological assemblage identification points. Building upon the identification results of marker bedding, sedimentary cycle, and lithological assemblage boundaries, perform linear predictions for other stratigraphic boundaries based on the stratigraphic stacking model. For example, for accretive stratigraphic stacks, use a proportional linear interpolation method to obtain stratigraphic boundaries.

[0079] S5. Visualize the results of automatic stratigraphic division of the profile.

[0080] A computer algorithm is designed to visualize the stratigraphic boundaries of each well on a well-connected profile, specifically including stratigraphic boundaries identified by the method of this invention and stratigraphic boundaries identified by geological experts. Taking a certain profile as an example... Figure 5 As shown, three wells, S3-18-6, S3-12-12, and S3-2-12, were selected as standard wells, and the remaining seven wells were used as comparison wells. The method of this invention was used to automatically compare the formations of this profile. The single-well matching degree can reach 92%, and the average matching degree of the profile reaches 88%.

[0081] This invention fully incorporates the experience of geological experts, realizing a data-driven and knowledge-driven approach. It utilizes the experience of geological experts to optimize well logging curves and employs a computer algorithm designed with the stratigraphic correlation approach and steps of geological experts. Through hierarchical constraints, it progressively identifies well logging responses of marker beds, sedimentary cycles, and lithological assemblages, effectively improving the accuracy of stratigraphic correlation.

[0082] 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. An automatic stratigraphic correlation method based on isochronous stratigraphy, characterized in that, Including the following steps: Obtain a well-connected profile, select several wells as standard wells in the profile, and perform stratigraphic division on the standard wells; The preferred logging curves are those that can effectively respond to stratigraphic marker layers, sedimentary cycles, and lithological combinations, and the preferred logging curves are preprocessed. Based on the pre-processed logging curves of standard wells, the identification of marker layers, sedimentary cycles, and lithological combinations of wells to be compared on the profile were completed sequentially. Based on the stratigraphic superposition model, linear solutions were obtained for stratigraphic boundaries, excluding marker bed identification points, sedimentary cycle identification points, and lithological assemblage identification points. Visualize the results of automatic stratigraphic division of the cross-section.

2. The isochronous automatic stratigraphic correlation method according to claim 1, characterized in that, The preprocessing is used to clean up local burrs, anomalies, and noise in the logging curves that affect formation correlation.

3. The automatic stratigraphic correlation method according to claim 2, characterized in that, The preprocessing includes preprocessing of well logging curve data based on wavelet transform and morphological filtering.

4. The isochronous automatic stratigraphic correlation method according to claim 1, characterized in that, The identification of the marker layer of the well to be compared includes the following steps: Based on waveform analysis technology, the logging curve is divided into half-wavelength units; Select stratigraphic boundaries with marker layer response characteristics from standard wells to determine the marker layers in the study area; Pick the logging curve response characteristics of each marker layer in the standard well; The location of the marker layer of the well to be compared is linearly determined based on the stratigraphic stacking pattern, and the location of the marker layer is used as the estimated marker layer point of the well to be compared. Set the marker layer search radius, and search for the logging response unit that best matches the marker layer characteristics within the search radius of each estimated marker layer point; Within the located logging response unit, return half-amplitude points as marker layer points to complete the marker layer solution.

5. The isochronous automatic stratigraphic correlation method according to claim 4, characterized in that, The identification of sedimentary cycles and lithological assemblages includes the following steps: Using the marker layer identification results as constraints, the non-marker layer stratigraphic boundary points of each well to be compared are linearly solved based on the stratigraphic stacking model, and these boundary points are used as the predicted boundary points. A wave-trajectory analysis algorithm was designed specifically for the identification of sedimentary cycles and lithological assemblages, which can analyze stratigraphic sedimentary cycles and lithological assemblages at a high scale. Based on the marker layers of standard wells, the sedimentary cycles and lithological assemblage characteristics of the standard wells were analyzed using the designed waveform analysis algorithm. Based on the results of marker layer identification, the logging responses of sedimentary cycles and lithological combinations with similar characteristics to those of standard wells in the well to be compared are picked out, and the solutions of sedimentary cycles and lithological combinations are completed.

6. The isochronous automatic stratigraphic correlation method according to claim 1, characterized in that, The linear solution for stratigraphic boundaries, excluding marker bedding points, sedimentary cycle points, and lithological assemblage points, is completed based on stratigraphic stacking models. This includes obtaining stratigraphic boundaries using a proportional linear interpolation method for accretive stacked strata.

7. An automatic isochronous stratigraphic correlation device, characterized in that, include: The first processing unit is used to obtain a well profile, select several wells in the profile as standard wells, and perform formation division on the standard wells. The second processing unit is used to select logging curves that can effectively respond to formation marker layers, sedimentary cycles, and lithological combinations, and to preprocess the selected logging curves. The third processing unit is used to identify the marker layer, sedimentary cycle, and lithological combination of the wells to be compared on the profile, based on the pre-processed logging curves of the standard wells. The fourth processing unit is used to complete the linear solution of stratigraphic boundary points, excluding marker bed identification points, sedimentary cycle identification points, and lithological assemblage identification points, based on the stratigraphic stacking model. The fifth processing unit is used to visualize the automatic stratigraphic division results of the profile.

8. A computer-readable storage medium, characterized in that, The system stores computer instructions that, when executed by a processor, implement the isochronous stratigraphic automatic correlation method as described in any one of claims 1 to 6.

9. A computer device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the isochronous stratigraphic automatic correlation method as described in any one of claims 1 to 6.

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