A lithology combination recognition method based on discrete wavelet transform

Abstract

The application provides a lithology combination identification method based on discrete wavelet transform, and the identification method comprises the following steps: core and logging mutual calibration, determination of lithology types, characteristics and combination modes; wavelet transform of characteristic curves, determination of high-frequency signals, medium-frequency signals, low-frequency signals and lithology response characteristics, and discrete wavelet transform of multiple frequency band signals; time-frequency energy research is conducted by using the high-frequency signals, a time-frequency energy graph is prepared, and the vertical sequence of the lithology combination is finely divided according to the energy size. The indication relationship that the red high-energy part represents sandstone and the blue low-energy part represents mudstone in the wavelet time-frequency energy graph is proposed, and is used for guiding the lithology identification in the field of oil exploration and development.

Description

Technical Field

[0001] This invention relates to the field of lithology identification, and more particularly to a lithology combination identification method based on discrete wavelet transform. Background Technology

[0002] In recent years, energy demand has gradually increased across China, and traditional fossil fuels such as oil and natural gas remain important sources of energy for industrial production and daily life. In oil exploration and development research, effective detection and evaluation of formation lithology and oil and gas reservoir reserves are fundamental. Lithology identification, as a crucial link in energy exploration and development, has become a key focus of research in related fields. Lithology identification helps determine the lithology, pore structure, and permeability of reservoirs, providing crucial information for oil and gas exploration and development, and guiding well location selection, drilling design, and oil and gas production management.

[0003] Currently, traditional lithology identification methods, primarily direct and indirect methods, are still used in fossil energy exploration. The direct method involves drilling deep into the ground with specialized equipment to obtain rock samples of a fixed diameter. Based on the experience of geological exploration experts, the rock type is identified. The samples are then prepared through grinding, slicing, and other methods, and finally analyzed using precision instruments to verify the lithology. This method is cumbersome, complex, highly specialized, and costly. Furthermore, it is susceptible to subjective influences, with different interpretations from different geological engineers leading to inaccuracies. The indirect method uses well logging curves to quantitatively study the physical characteristics of geological structures, employing tomographic imaging technology to directly obtain and observe wellbore images. Well logging curve interpretation requires manual identification and labeling of lithology across multiple curves, a laborious and time-consuming process. Meanwhile, tomographic imaging technology is limited by factors such as imaging depth, hindering its widespread application.

[0004] In summary, traditional identification methods are characterized by low accuracy, slow efficiency, and significant human influence. Therefore, it is necessary to automate the reservoir characterization process. Summary of the Invention

[0005] In view of the above problems, the present invention is proposed to provide a lithological combination identification method based on discrete wavelet transform to overcome or at least partially solve the above problems.

[0006] According to one aspect of the present invention, a lithological assemblage identification method based on discrete wavelet transform is provided, the identification method comprising:

[0007] Core samples and well logs are cross-calibrated to determine lithological types, characteristics, and combination patterns;

[0008] Wavelet transform of characteristic curves is used to determine the high-frequency, mid-frequency, and low-frequency signals and lithological response characteristics, and discrete wavelet transform is performed on signals of multiple frequency bands.

[0009] The high-frequency signal was used to conduct time-frequency energy research, and a time-frequency energy map was compiled. The vertical sequence of lithological combinations was finely divided according to the energy magnitude.

[0010] Optionally, the mutual calibration between the core and the well logging specifically includes:

[0011] Well logging curve characteristics refer to the curves formed during well logging that reflect different lithologies and stratigraphic features, allowing for the identification of specific lithologies based on the curves.

[0012] Optionally, the types of logging curves include: resistivity curves, sonic curves, spontaneous potential curves, induction logging curves, neutron logging curves, three-lateral logging curves, and microsphere focusing logging.

[0013] Optionally, approximations and details are used in the wavelet transform;

[0014] The approximation represents the low-frequency information of the signal, and the detail represents the high-frequency information of the signal;

[0015] The signal is progressively refined at multiple scales using scaling and translation operations, achieving time subdivision at high frequencies and frequency subdivision at low frequencies, automatically adapting to the requirements of time-frequency signal analysis and focusing on any detail of the signal.

[0016] Optionally, the discrete wavelet transform of multiple frequency band signals specifically includes:

[0017] Discrete wavelet transform is used to filter out the low-frequency and high-frequency information of the signal;

[0018] The Haar wavelet was used to decompose the natural gamma logging signal to obtain the high-frequency signal contained in the natural gamma curve.

[0019] Optionally, the high-frequency signal includes variations at stratigraphic details for analyzing lithological changes.

[0020] Optionally, the discrete wavelet transform uses Morlet wavelets as wavelet basis functions.

[0021] Optionally, the Morlet wavelet is a one-dimensional continuous wavelet, which is a harmonic with Gaussian envelope automodulation and a period of 1 / ω0.

[0022] Optionally, in the wavelet time-frequency energy plot of the well logging curves obtained by the Morlet wavelet transform analysis, the energy of sandy sediments is higher than that of fine-grained muddy sediments. The curve of sandy sediments is represented by high-energy red, while the curve of fine-grained muddy sediments is represented by low-energy blue.

[0023] Optionally, the wavelet time-frequency energy map includes: sandy sediment energy clusters, fine-grained muddy sediment energy clusters, and wavelet energy clusters;

[0024] The low-energy region between the sandy sediment energy cluster and the fine-grained muddy sediment energy cluster corresponds to multiple adjacent geological bodies, and the vertical high-low conversion of the wavelet energy cluster corresponds to the change in sediment coarseness.

[0025] This invention provides a lithological combination identification method based on discrete wavelet transform. The method includes: mutual calibration of core samples and well logs to determine lithological types, characteristics, and combination patterns; wavelet transform of characteristic curves to determine high-frequency, mid-frequency, and low-frequency signals and lithological response characteristics; performing discrete wavelet transform on signals across multiple frequency bands; conducting time-frequency energy studies using the high-frequency signals to compile time-frequency energy maps; and finely dividing the vertical sequence of lithological combinations based on energy magnitude. An indicative relationship is proposed where the red high-energy portion represents sandstone and the blue low-energy portion represents mudstone in the wavelet time-frequency energy map, which can be used to guide lithological identification in the field of petroleum exploration and development.

[0026] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 A flowchart of a lithological combination identification method based on discrete wavelet transform provided by the present invention;

[0029] Figure 2 This is a comparison of denoised GR curves in a specific embodiment of a lithological combination identification method based on discrete wavelet transform provided by the present invention.

[0030] Figure 3 This is a high-frequency signal curve obtained by decomposing the GR curve based on discrete wavelet in a specific embodiment of the present invention;

[0031] Figure 4 This is a wavelet coefficient variance scale diagram obtained from high-frequency signal processing in a specific embodiment of the present invention;

[0032] Figure 5 This is a time-frequency energy map obtained by processing a high-frequency signal using Morlet wavelet in a specific embodiment of the present invention.

[0033] Figure 6 This is a waveform coefficient diagram showing the changes at the selected scale for the time-frequency energy diagram in a specific embodiment of the present invention.

[0034] Figure 7 This is a diagram showing the final result obtained in a specific embodiment of the present invention. Detailed Implementation

[0035] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to 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 disclosure to those skilled in the art.

[0036] The terms "comprising" and "having," and any variations thereof, in the specification, embodiments, claims, and drawings of this invention are intended to cover non-exclusive inclusion, such as including a series of steps or units.

[0037] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0038] The Zhengsha Village area in the Junggar Basin was selected as the research object. Located in the central depression zone of the Junggar Basin, Zhengsha Village is bordered by the northern Tianshan Mountains to the south, the Dabasong Uplift to the north, and the Mosuowan Uplift and Zhongguai Uplift to the east and west, currently exhibiting a monocline structure with a lower southwest and higher northeast. The strata within Zhengsha Village are well-developed, containing multiple sets of strata from the Late Paleozoic to the Mesozoic and Cenozoic eras. The Late Paleozoic deposits, from bottom to top, are Carboniferous and Permian systems. Among them, the Lower Permian Fengcheng Formation, a thick and widely deposited dark mudstone group, is an important source rock. The Mesozoic sedimentary system consists of the Triassic, Jurassic, and Cretaceous strata, from bottom to top. The mudstones of the Jurassic Badaowan and Xishanyao Formations are important source rocks, while the conglomerate at the base of the Triassic Karamay Formation and the sandstones within the Jurassic are important reservoirs. The stably distributed thick mudstones of the Cretaceous Shengjinkou and Hutubi Formations form important caprocks, creating multiple source-reservoir-caprock assemblages vertically. Currently, there are 16 wells drilled in the study area, with 9 completed exploration wells and 7 development wells, resulting in a high well density and sufficient logging data.

[0039] like Figure 1 As shown, the lithological combination identification method based on discrete wavelet transform of the present invention includes:

[0040] (1) Core and well logging are cross-calibrated to determine lithological type, characteristics and combination patterns.

[0041] Well logging curve characteristics refer to the curves formed during well logging that reflect different lithologies and stratigraphic features, allowing for the determination of specific lithologies based on the obtained curves. Commonly used well logging curve types include: resistivity curves, sonic curves, spontaneous potential curves, induction logging curves, neutron logging curves, three-lateral logging curves, and microsphere focused logging.

[0042] Through comprehensive analysis of multiple well logging curves, the lithology of the Jurassic strata in Zhengsha Village was identified in detail. The Qigu Formation mudstone is purplish-red, coarse-grained, and thick, with widespread gravel development, diverse colors, and gravel diameters ranging from 1-50 mm. The gravel is poorly sorted, randomly arranged, and sub-angular to sub-rounded. The Sangonghe Formation mudstone is dark gray, without any oxidation, and fine-grained. Occasionally, torn, flattened, or rounded dark gray or yellowish-brown mudstone gravels are observed, with a maximum gravel diameter of 40 mm, generally 3-10 mm. The first section of the Sangonghe Formation is mainly composed of lacustrine mudstone interbedded with thin layers of siltstone; the second section, the second sandstone group, is mainly mudstone interbedded with thin layers of sandstone; and the first sandstone group transitions from thick sandstone to mudstone. The Badaowan Formation is characterized by thin interbedded sandstone and mudstone layers with coal seams.

[0043] (2) Wavelet transform of characteristic curves to determine the high-frequency, medium-frequency and low-frequency signals and their lithological response characteristics, and perform discrete wavelet transform on signals of different frequency bands.

[0044] For many signals, low-frequency components are crucial, containing the signal's characteristics, while high-frequency components reveal the signal's details or differences. Approximations and details are frequently used in wavelet analysis. Approximations represent the low-frequency information of a signal, while details represent its high-frequency information. By using scaling and translation operations to progressively refine the signal (function) at multiple scales, ultimately achieving time subdivision at high frequencies and frequency subdivision at low frequencies, it can automatically adapt to the requirements of time-frequency signal analysis, focusing on arbitrary details of the signal.

[0045] Discrete wavelet transform has the function of multi-level decomposition of well logging signals, filtering out low-frequency and high-frequency information. Natural gamma curves can sensitively reflect changes in clay content. In this study, Haar wavelet was used to decompose the natural gamma logging signal to obtain the high-frequency signal contained in the curve. The high-frequency signal contains more detailed transformations of the formation, which are used to analyze lithological changes.

[0046] (3) Use high-frequency signals to conduct time-frequency energy research, compile time-frequency energy maps, and finely divide the vertical sequence of lithological assemblages according to the energy magnitude.

[0047] The Morlet wavelet, a one-dimensional continuous wavelet, is a harmonic wave with Gaussian envelope automodulation and a period of 1 / ω0. The wavelet coefficient curves obtained from the Morlet one-dimensional continuous wavelet transform analysis of well logging curves exhibit good cyclicity. After multiple comparisons, the Morlet wavelet showed better plotting results, hence it was selected as the wavelet basis function. In the wavelet time-frequency energy plot of the well logging curves, sandy sediments have high energy, represented by high-energy red, while fine-grained muddy sediments have low energy, represented by low-energy blue. The low-energy regions between two energy clusters generally correspond to adjacent geological bodies of different properties, and the vertical transition of wavelet energy clusters corresponds to variations in sediment coarseness. Due to the inherent limitations of the Matlab software, after wavelet transform, the head or (and) tail of the data often appears as abnormally high-energy clusters; these abnormalities should be removed during analysis.

[0048] 1) Perform a preliminary check on the natural gamma ray logging data to remove obviously erroneous values. Import the data into Matlab software and use a moving average to filter and denoise the logging data. Here, the selected moving average window length is 9, which corresponds to 9 data points, or 0.125*(9-1)=1m in the logging data. Figure 2 As shown.

[0049] 2) Use discrete wavelets to perform signal decomposition on the processed logging curves to obtain the high-frequency components of the curves, such as... Figure 3 As shown, this part of the data is then subjected to moving average denoising again.

[0050] 3) Using Morlet wavelets as basis functions, plot the wavelet time-frequency energy diagram, such as... Figure 4 , 5 6.

[0051] Figure 7 This is a diagram of the final result obtained by the present invention.

[0052] Beneficial effects: (1) Applying wavelet transform technology to well logging data reduces the difficulty of lithology identification and the influence of subjectivity, thereby reducing the cost of lithology identification.

[0053] Traditional lithology identification methods rely heavily on manual labeling, which is not only labor-intensive and time-consuming, but also prone to inaccuracies due to differing interpretations by various geological engineers. This research utilizes discrete wavelet decomposition of logging signals. Because high-frequency signals contain information about small-scale stratigraphic variations, they are used to create wavelet time-frequency energy maps. In these maps, sandy sediments are represented by high-energy red, while fine-grained muddy sediments are represented by low-energy blue. The low-energy regions between two energy clusters generally correspond to adjacent geological bodies of different properties, and the vertical transitions in wavelet energy clusters correspond to variations in sediment coarseness. This establishes a method for objectively identifying lithology and its transformations.

[0054] (2) By integrating geology, well logging, and wavelet transform, a lithology identification method based on discrete wavelet transform was established. An indicative relationship was proposed in the wavelet time-frequency energy map, where the red high-energy portion represents sandstone and the blue low-energy portion represents mudstone, to guide lithology identification in the field of petroleum exploration and development. This technical approach avoids the drawbacks of excessive human intervention, high technical costs, and high overall costs in previous lithology identification processes, thus demonstrating innovation and possessing significant technical advantages and practical application value.

[0055] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A lithological combination identification method based on discrete wavelet transform, characterized in that, The identification method includes: Core samples and well logs are cross-calibrated to determine lithological types, characteristics, and combination patterns; Wavelet transform of characteristic curves is used to determine the high-frequency, mid-frequency, and low-frequency signals and lithological response characteristics, and discrete wavelet transform is performed on signals of multiple frequency bands. The high-frequency signal was used to conduct time-frequency energy research, and a time-frequency energy map was compiled. The vertical sequence of lithological combinations was finely divided according to the energy magnitude.

2. The lithological combination identification method based on discrete wavelet transform according to claim 1, characterized in that, The mutual calibration between core samples and well logs specifically includes: Well logging curve characteristics refer to the curves formed during well logging that reflect different lithologies and stratigraphic features, allowing for the identification of specific lithologies based on the curves.

3. The lithological combination identification method based on discrete wavelet transform according to claim 2, characterized in that, The types of logging curves include: resistivity curves, sonic curves, spontaneous potential curves, induction logging curves, neutron logging curves, three-lateral logging curves, and microsphere focusing logging.

4. The lithological combination identification method based on discrete wavelet transform according to claim 1, characterized in that, The wavelet transform uses approximations and details; The approximation represents the low-frequency information of the signal, and the detail represents the high-frequency information of the signal; The signal is progressively refined at multiple scales using scaling and translation operations, achieving time subdivision at high frequencies and frequency subdivision at low frequencies, automatically adapting to the requirements of time-frequency signal analysis and focusing on any detail of the signal.

5. The lithological combination identification method based on discrete wavelet transform according to claim 1, characterized in that, The discrete wavelet transform of signals across multiple frequency bands specifically includes: Discrete wavelet transform is used to filter out the low-frequency and high-frequency information of the signal; The Haar wavelet was used to decompose the natural gamma logging signal to obtain the high-frequency signal contained in the natural gamma curve.

6. The lithological combination identification method based on discrete wavelet transform according to claim 5, characterized in that, The high-frequency signal includes variations at the stratigraphic level, which is used to analyze lithological changes.

7. The lithological combination identification method based on discrete wavelet transform according to claim 1, characterized in that, The discrete wavelet transform uses Morlet wavelets as wavelet basis functions.

8. The lithological combination identification method based on discrete wavelet transform according to claim 7, characterized in that, The Morlet wavelet is a one-dimensional continuous wavelet, which is a harmonic with Gaussian envelope automodulation and a period of 1 / ω0.

9. A lithological combination identification method based on discrete wavelet transform according to claim 7, characterized in that, In the wavelet time-frequency energy plot of the well logging curves analyzed by Morlet wavelet transform, the energy of sandy sediments is higher than that of fine-grained muddy sediments. The curve of sandy sediments is represented by high energy red, while the curve of fine-grained muddy sediments is represented by low energy blue.

10. A lithological combination identification method based on discrete wavelet transform according to claim 9, characterized in that, The wavelet time-frequency energy map includes: sandy sediment energy clusters, fine-grained muddy sediment energy clusters, and wavelet energy clusters; The low-energy region between the sandy sediment energy cluster and the fine-grained muddy sediment energy cluster corresponds to multiple adjacent geological bodies, and the vertical high-low conversion of the wavelet energy cluster corresponds to the change in sediment coarseness.

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