A method and system for establishing a digital outcrop model based on laser scanning technology

Abstract

The present invention belongs to the technical field of geological exploration, and discloses a method and system for establishing a digital outcrop model based on laser scanning technology. Profile laser point cloud data and high-resolution outcrop images are acquired, the data is processed, the data is stitched for quantitative analysis of lithology, a lithology identification chart based on laser intensity characteristics is established, the profile is divided into various stratigraphic units, and a comprehensive columnar chart of digital geological information of the profile is obtained; the corresponding relationships existing between the mean curve of laser intensity and the logging curve and the hyperspectral curve are studied; a laser point cloud triangular network model is constructed and mapped, based on the Mask-RCNN model, digital outcrop holes are extracted, and combined with traditional geological research means, the digital outcrop is interpreted and labeled, so as to establish a high-precision digital outcrop model; fractures are extracted from the digital outcrop model, the development situation and scope of erosion holes in the profile are clarified, a sedimentary microfacies model is established based on laser scanning technology, and reservoir evaluation is carried out on it.

Description

Technical Field

[0001] The present invention belongs to the technical field of geological exploration, and particularly relates to a method and system for establishing a digital outcrop model based on laser scanning technology. Background Art

[0002] Carbonate reservoirs and digital outcrops are hot research fields at home and abroad. Traditional geological means are difficult to effectively characterize and evaluate carbonate reservoirs, and it is difficult to collect outcrop information in high-risk areas such as cliffs. Moreover, the resolution of well logging and seismic data varies greatly, and affected by quantity and quality, the established geological model of carbonate reservoirs is inaccurately characterized and it is difficult to achieve the effectiveness of reservoir prediction. How to establish an effective and accurate carbonate digital outcrop model, and use the established model to accurately characterize the complex morphology, configuration relationship, and internal filling characteristics of carbonate reservoirs to guide the exploration and development of carbonate oil and gas reservoirs is a problem that geological workers constantly think about.

[0003] The research in the geological field is gradually tending towards quantification and refinement. Traditional field outcrop data collection is extremely dangerous in special environments and the observed field profiles are too local to meet the current refined research. By combining traditional geological research means with advanced information means, the digitization and visualization of field profiles can be realized. Laser scanning technology can not only directly and accurately express outcrop geological features, but also more vividly express outcrop geological features in different perspectives and different profiles, which is conducive to the refined research of outcrops.

[0004] Through the above analysis, the problems and defects existing in the prior art are as follows:

[0005] (1) There are many influencing factors for laser intensity. For example, when laser scanning is carried out in areas covered by a large amount of vegetation or wire mesh, it shows low reflectivity characteristics for laser intensity.

[0006] (2) The process of collecting laser point cloud data is imperfect, resulting in inconsistent quality of the collected laser point cloud data.

[0007] In summary, how to provide a method for establishing a digital outcrop of a field profile using laser scanning technology to realize the digitization and visualization of outcrops and be able to more intuitively observe the characteristics of field outcrops is an urgently needed problem to be solved. Summary of the Invention

[0008] Aiming at the problems existing in the prior art, the present invention provides a method and system for establishing a digital outcrop model based on laser scanning technology, which can quickly establish a digital outcrop model corresponding to the profile, can intuitively feel the lithological combination characteristics of the field outcrop profile, and combine with traditional geological means for mutual verification to improve the rationality and reliability of the established model.

[0009] The present invention is implemented as follows. A method for establishing a digital outcrop model based on laser scanning technology includes the following steps:

[0010] Step 1: By combining traditional geological means with laser scanning technology, quantitatively classify the lithology of the section and divide the sedimentary microfacies, preliminarily divide the section into each stratigraphic unit, and establish a comprehensive columnar diagram of digital geological information of the section;

[0011] Step 2: In the digital outcrop bionic program, compare the laser intensity curve of the section with the well logging curves and hyperspectral curves of similar well positions in the same stratigraphic horizon in the area successively, study the correlation between the laser reflection curve and the well logging curve and the hyperspectral curve, establish a lithology identification chart of laser intensity characteristics of the section, and improve the reliability of establishing a digital outcrop model based on laser scanning;

[0012] Step 3: In the digital outcrop bionic model, splice the high-definition image of the outcrop and the processed laser point cloud data, and based on the above-divided stratigraphic units, establish a digital outcrop model of the outcrop by combining the geological means of thin section identification based on laser intensity;

[0013] Step 4: In the digital outcrop bionic program, interpret and label the digital outcrop model of the section, extract fractures from the outcrop based on the Beamlet algorithm, determine the development range of dissolution pores in the section, establish a sedimentary microfacies model, and based on the model, select important stratigraphic intervals and conduct reservoir evaluation on them in combination with various data.

[0014] Furthermore, the division of each stratigraphic unit of the section in Step 1 further includes:

[0015] Understand the basic geological information of the section through field investigations, conduct three-dimensional laser scanning on the section to obtain laser point cloud data and high-resolution outcrop images; perform noise reduction and calibration processing on the laser point cloud data, study the laser intensity range, mean value, and surface reflection characteristics corresponding to different lithofacies, and quantitatively classify the lithology. Based on this, divide the laser intensity values of the entire section and calculate the corresponding laser intensity mean values. According to the laser intensity mean values, combine the digital outcrop image information to divide the section into each stratigraphic unit;

[0016] Analyze the main developed sedimentary subfacies of the section to obtain the types of sedimentary subfacies of the section, the laser intensity ranges and mean values corresponding to each subfacies; select the stratigraphic units of the section, compare and refine them with thin section identification and field investigation data, and redefine the sedimentary microfacies of the stratigraphic units in combination with the laser intensity values.

[0017] Furthermore, in Step 2, in the digital outcrop bionic program, the comparison and research of successively combining the laser intensity curve of the section with the well logging curves and hyperspectral curves of similar well positions in the area include:

[0018] (1) Import logging curves, hyperspectral curves, and laser intensity curves into the digital outcrop bionic program to process and interpret the original data;

[0019] (2) Select typical profiles for comparative analysis. When the lithology consists of multiple lithologies, the lithology change is relatively complex, and the similarity between the laser intensity curve and the GR logging curve; when the lithology change is mostly the gravel-bearing section of the thin layer, the laser intensity curve is more similar to the RLLD logging curve;

[0020] (3) To avoid the interference factors generated by epigenetic processes, select rock samples on the fresh surfaces of each stratigraphic unit in the field profile, conduct spectral scanning and jointly analyze the hyperspectral curve and the laser intensity curve. Through the discussion of the fitting degree, the approximation degree between the laser intensity curve and the hyperspectral curve is about 0.73, which is a medium fitting degree. According to the research, the hyperspectral curve is affected by lithology to a certain extent, and the change relationship of lithology can be shown to a certain extent according to the change of the curve;

[0021] According to the grain size order from sparitic algal dolomite to micritic algal dolomite for comparison, the hyperspectral curve value is negatively correlated with the grain size.

[0022] Furthermore, select samples identified as micritic dolomite by microscopic thin sections, conduct parallel comparison of the same grain size lithology. The spectral data value is the highest for the samples with relatively low internal pores and good homogeneity; followed by the structure with dissolution but mostly filled and no oil and gas can be filled inside; the spectral data of the micritic dolomite with internal pores filled with silica is low;

[0023] Under the same lithology, the spectral data value is negatively correlated with the pore content. And there is a certain correlation between the laser scanning curve and the hyperspectral curve. Therefore, high-quality reservoirs are developed in the strata with relatively low laser intensity. However, the laser intensity of argillaceous sandy rocks also shows low values. So this method is applicable to the dolomite strata of the field profile;

[0024] The absorption peak of dolomite hyperspectral is at the minimum value at the wavelength of 2337nm, while that of limestone is at the minimum value at the wavelength of 2320nm. This point is an important index for distinguishing dolomite from limestone.

[0025] Furthermore, step three is to establish a digital outcrop model of the outcrop, including:

[0026] (1) In the digital outcrop bionic program, perform difference and noise reduction processing on the laser point cloud data, splice the corresponding high-definition digital images in Riscan-Pro, then, the data is processed, a laser point cloud triangular mesh is constructed, model mapping of the digital outcrop is carried out, and hole extraction is performed based on the Mask-RCNN model to establish a profile digital outcrop model;

[0027] (2) In the digital outcrop bionic program, field geological information, thin section identification data, and hyperspectral data are loaded into the digital outcrop model. Through multi-data comparison, a high-precision digital outcrop model is established.

[0028] Furthermore, in step four, a profile sedimentary microfacies model is established. Important intervals are selected, and the sedimentary microfacies are refined and redefined through detailed analysis. The pore model is intercepted, and reservoir evaluation is carried out on the intervals, including:

[0029] (1) By interpreting and annotating the profile digital outcrop model, fracture extraction is performed on the model based on the Beamlet algorithm, and the development of dissolution pores in the model is determined. The corresponding sedimentary microfacies model is established, the favorable facies belts for the developed reservoir are analyzed, and important intervals of the profile are selected;

[0030] (2) In the digital outcrop bionic program, based on the sedimentary microfacies model, combined with field investigations and thin section identification data for refined comparison, the sedimentary microfacies of the important intervals are redefined.

[0031] (3) In the digital outcrop bionic program, polyline extraction is performed on the laser scan of the important intervals, their boundaries are delineated, the pore model is intercepted, and based on the sedimentary microfacies model and combined with hyperspectral data, the reservoir evaluation is carried out on the reservoir characteristics of the reservoir rock type and reservoir space type of the intervals.

[0032] Another object of the present invention is to provide a digital outcrop model establishment system based on laser scanning technology for implementing the digital outcrop model establishment method based on laser scanning technology, including:

[0033] Digital outcrop acquisition module: used for noise reduction preprocessing of laser point cloud data, establishing a lithology plate for rock identification based on laser intensity characteristics, and dividing the profile into various stratigraphic units; refining and comparing well logging curves, hyperspectral curves, and laser intensity mean curves of similar well positions in the same area, and studying the correlation between the laser intensity mean curve and the well logging curve and the hyperspectral curve;

[0034] Digital outcrop model establishment module: used for establishing a profile digital outcrop model based on laser point cloud data and high-resolution outcrop images of the profile, splicing the processed laser point cloud data and high-resolution outcrop images, using the divided stratigraphic units as a link to connect laser point cloud data, thin section identification, and basic field geological information data, synchronously refining and comparing each item of data, constructing a laser point cloud triangular network through POLYWORK and performing digital outcrop model mapping on it, and then through hole extraction, establishing a high-precision digital outcrop model;

[0035] Sedimentary microfacies model building module: used to extract fractures from the established digital outcrop model, determine the development range of dissolution pores in the digital outcrop, build sedimentary microfacies models for each stratigraphic unit in the section, load various data into the digital outcrop model, interpret and annotate the digital outcrop model, select the main stratigraphic segments of the section and redefine the sedimentary microfacies;

[0036] Pore intercepting and reservoir evaluation module: used to intercept the pore models of important segments and evaluate the reservoirs of important segments based on hyperspectral data, laser point cloud data, and thin section identification data.

[0037] Another object of the present invention is to provide a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of the digital outcrop model building method based on laser scanning technology.

[0038] Another object of the present invention is to provide a computer-readable storage medium storing a computer program. When the computer program is executed by the processor, the processor executes the steps of the digital outcrop model building method based on laser scanning technology.

[0039] Another object of the present invention is to provide an information data processing terminal, which is used to implement the digital outcrop model building system based on laser scanning technology.

[0040] Combined with the above technical solutions and the solved technical problems, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0041] First, the present invention is mainly aimed at the oil and gas geology industry, and provides a method for building a digital outcrop model based on laser scanning technology, which is used to quickly build an accurate carbonate rock digital outcrop model. The specific implementation method includes:

[0042] Obtain the laser point cloud data and high-resolution outcrop images of the section. After checking and processing the data, use Riscan-Pro for rough stitching and fine stitching. By comparing the refinement of various data with the laser intensity characteristics, conduct quantitative analysis of lithology based on laser intensity, establish a lithology identification chart based on laser intensity characteristics, divide the section into each stratigraphic unit, and establish a comprehensive digital geological information columnar chart of the section. Study the relationship between the laser intensity mean curve and the logging curve and the hyperspectral curve, and use this relationship as one of the data for building the digital outcrop model, improve the reliability of the built digital outcrop model, and combine and compare the three during subsequent reservoir prediction to improve the accuracy of the predicted reservoir, save the cost of oil and gas exploration, and to a certain extent solve the problem of inconsistent resolution between well data and seismic data.

[0043] Through the quantitative analysis of the laser intensity of the lithology of important intervals, it is found that the average laser intensities of micritic dolomite (-4.6), algal dolomite (-4.7), brecciated dolomite (-5.23), argillaceous dolomite (-5.34), and mud shale (-5.54) decrease in turn. Calculate the average area of the laser intensity for each layer with variable laser intensity, redefine the lithology identification results of this stratigraphic unit, and finally determine the lithology of the stratigraphic unit. According to the variation law of outcrop lithology and lithofacies combination, combined with the sedimentary background, determine the development law of sedimentary facies in important intervals. Through the quantitative analysis of the laser intensity of sedimentary microfacies, it is found that the laser intensity value of the shallow marine shelf facies is the lowest, mainly because this facies is mainly composed of sandstone and mudstone. Further, through the quantitative measurement of digital outcrops and the calculation of field guiding maps, the thickness of each stratigraphic unit can be obtained, and a lithology profile can be obtained.

[0044] Based on the laser intensity characteristics, load the above-mentioned various data into the spliced laser point cloud data and perform interpretation and annotation processing. Through the refined comparison of the above-mentioned various data, correct the inconsistent parts in the various data, clarify the parts where the laser intensity shows low values due to severe vegetation coverage, overcome the problem of unsatisfactory scanning effect and low modeling efficiency caused by the relatively complex environment where the profile is located, improve the reliability of the established digital outcrop model, ensure the accuracy of the stored digital outcrop model, facilitate the re-study of future geological workers, reduce the number of field trips, especially in dangerous fields such as cliffs, greatly reduce the cost of geological research, and can use the good visualization characteristics of digital outcrops for the geological education industry to cultivate compound talents in the geological industry.

[0045] Select the intervals with high-quality reservoirs developed in the profile as important intervals. Based on the digital outcrop sedimentary microfacies model, extract fractures and determine the development range of dissolution pores. Combine the above-mentioned various data to redefine the sedimentary microfacies of important intervals. Through the extraction of broken lines, clarify the development of pores. Based on the laser intensity characteristics and combined with the above-mentioned hyperspectral data, reduce the characterization difficulty of carbonate rock profiles and improve the credibility of reservoir evaluation.

[0046] Second, the three-dimensional laser scanner used in the present invention is used to obtain the three-dimensional geological information of the outcrop, which has the advantages of non-contact, high scanning progress, rich information, good data compatibility, low constraints, and can cooperate with the GPS positioning system. Due to non-contact measurement, it can complete the collection of data on dangerous targets and complex environments, further ensuring the safety of geological workers when collecting data. Compared with traditional geological research methods, the information obtained by the present invention is richer and the observation of outcrop profile characteristics is more intuitive.

[0047] The present invention uses laser point cloud data as a link to connect data such as hyperspectral data, thin section identification, and logging curves. The various data corroborate each other. For example, during the research process of the present invention, field investigations showed that the thin section identification characteristics of sparitic algal dolomite were micritic dolomite. However, as shown by the laser point cloud, its characteristics were clearly micritic dolomite, which did not match the field investigation characteristics. By corroborating each other among the various data, the establishment of the digital outcrop model is constrained to ensure its accuracy, meeting to a certain extent the accuracy required for current research on carbonate rock sections.

[0048] The carbonate rock digital outcrop model established by the present invention has the advantages of high accuracy, good visualization, and strong interactivity. The three-dimensional digital outcrop model displays the two-dimensional section in a three-dimensional form, facilitating geologists to study the carbonate rock section more stereoscopically and improving their work efficiency.

[0049] Third, the expected benefits and commercial value after the transformation of the technical solution of the present invention are as follows:

[0050] After the transformation of the technical solution of the present invention, geologists can more intuitively feel the lithological combination characteristics of the outcrop section using laser point cloud data. They can observe the outcrop section characteristics more comprehensively through the established three-dimensional digital outcrop model and can extract geological information required for research from the model at any time. In a way, the various data of the outcrop are uniformly summarized into the digital outcrop model, solving problems such as the collection, storage, and retrieval of outcrop section data during the research process. The present invention collects rich and comprehensive geological information, and at the same time has a fast collection speed, reducing the number of field trips while ensuring the integrity of geological information. Moreover, geologists can conduct geological research anytime and anywhere, improving work efficiency and reducing the time and cost required for geological investigations.

[0051] The technical solution of the present invention fills the technical gaps in the domestic and international industries:

[0052] With the further deepening of oil and gas exploration, a geological model with higher accuracy is required. Due to the large limitations of traditional geological research methods and mostly two-dimensional geological information, there is a certain one-sidedness in the research on outcrop sections. The characteristics of the present invention are the combination of traditional geological research methods and advanced geospatial mapping technologies. The established model has the advantages of strong interactivity, good visualization, and high accuracy, filling the gap in the establishment of a digital outcrop model with strong interactivity, good visualization, high accuracy, and close connection of data in the carbonate rock digital outcrop field in the domestic and international oil and gas geology industries.

[0053] The technical solution of the present invention overcomes the technical prejudice: The present invention overcomes the technical prejudice that the modeling accuracy of laser point cloud data is not high due to many interference factors. The laser point cloud data collected by a laser scanner is affected by many factors, such as: profile humidity, profile roughness, vegetation coverage, etc. When establishing a three-dimensional geological model, the present invention combines the two-dimensional geological profile of the outcrop, supplements, reconstructs and constrains the model, and eliminates the interference of the influencing factors on the modeling. In addition, in order to verify the accuracy of lithology identification based on laser point cloud, the present invention first conducts a quantitative study on the laser intensity of the lithology, and then studies the surface laser reflection characteristics of different lithologies in the outcrop profile. For example, the reflection characteristic of algal dolomite is a high reflectivity that is overall chaotic, mixed with medium and low reflectivity. By comparing and verifying the research results with on-site investigation data and thin section identification, it is found that the lithology identification based on laser point cloud basically conforms to the actual situation, and the lithology developed in the outcrop profile and its changes can be seen more intuitively and comprehensively.

[0054] Fourth, the method for establishing a digital outcrop model based on laser scanning technology according to the present invention has significant technological progress and advantages compared with traditional methods. The following are the technical problems solved by this method and the significant technological progress obtained:

[0055] 1. Improved the acquisition accuracy of geological information: Traditional geological exploration methods usually rely on manpower, with low data collection efficiency and difficult to guarantee accuracy. However, this method combines laser scanning technology and can obtain the three-dimensional shape and geological information of the outcrop surface with high precision, greatly improving the accuracy and reliability of the data.

[0056] 2. Realized the digitization of the outcrop model: The outcrop models established by traditional methods are mostly two-dimensional or simple three-dimensional models, which are difficult to fully reflect the true situation of the outcrop. However, this method can construct a high-precision three-dimensional digital outcrop model through laser scanning technology, providing more intuitive and comprehensive data support for geological research.

[0057] 3. Enhanced the ability to identify outcrop fractures and dissolution pores: By using the Beamlet algorithm to extract fractures from the outcrop, the development range of dissolution pores in the profile can be determined more accurately, which is of great significance for oil and gas exploration and reservoir evaluation.

[0058] The technological progress obtained by the present invention:

[0059] 1. Comprehensive application of various technical means: This method not only uses laser scanning technology, but also combines various technical means such as traditional geological means, logging curves, and hyperspectral curves to achieve comprehensive acquisition and analysis of outcrop geological information.

[0060] 2. Improve the automation level of outcrop model establishment: Through the digital outcrop bionic program, the laser point cloud data can be automatically processed, spliced, and modeled, greatly improving the efficiency and automation level of model establishment.

[0061] 3. Enhance the interpretation and application capabilities of the model: The digital outcrop model established by this method not only has high precision and intuitiveness, but also can further extract key information such as fractures and dissolution pores through interpretation and annotation processing, providing strong support for oil and gas exploration and reservoir evaluation.

[0062] The method for establishing a digital outcrop model based on laser scanning technology provided by the present invention solves the deficiencies of traditional methods in aspects such as geological information acquisition, model establishment, and interpretation and application, and has significant technological progress and broad application prospects. Brief Description of the Drawings

[0063] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required to be used in the embodiments of the present invention. Obviously, the following described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0064] Figure 1 It is a flowchart of the method for establishing a digital outcrop model based on laser scanning technology provided by the embodiments of the present invention;

[0065] Figure 2 It is a schematic diagram of the principle for establishing a digital outcrop model based on laser scanning technology provided by the embodiments of the present invention;

[0066] Figure 3 It is a schematic diagram of carbonate rock classification based on laser intensity of the XX section provided by the embodiments of the present invention;

[0067] Figure 4 It is a schematic diagram of the hole extraction result of a certain section of the XX section provided by the embodiments of the present invention;

[0068] Figure 5 It is a schematic diagram of the fracture extraction result of a certain section of the XX section provided by the embodiments of the present invention;

[0069] Figure 6 It is a fracture extraction density map of a certain section of the XX section provided by the embodiments of the present invention;

[0070] Figure 7 It is a laser point cloud map of the original reservoir section of a certain section of the XX section provided by the embodiments of the present invention;

[0071] Figure 8 It is a laser point cloud map of the original non-reservoir section of a certain section of the XX section provided by the embodiments of the present invention;

[0072] Figure 9 It is a comprehensive schematic diagram of digital geological information of the reservoir section of the XX profile provided by an embodiment of the present invention;

[0073] Figure 10 It is a schematic diagram of a high-precision digital outcrop model of a certain section of the XX profile provided by an embodiment of the present invention;

[0074] Figure 11 It is a structural diagram of a digital outcrop model establishment system based on laser scanning technology provided by an embodiment of the present invention;

[0075] Figure 12 It is a lithology identification plate of the XX profile provided by an embodiment of the present invention;

[0076] Figure 13 It is a spliced map of high-definition images and laser point clouds of stations 54 - 60 and 61 - 65 of the XX profile scan provided by an embodiment of the present invention. Specific embodiments

[0077] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the following further details the present invention in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0078] Aiming at the problems existing in the prior art, the present invention provides a method and system for establishing a digital outcrop model based on laser scanning technology. The following makes a detailed description of the present invention with reference to the accompanying drawings.

[0079] The present invention is mainly oriented to the geological exploration industry, and is a method and device for collecting field outcrop data based on laser scanning technology, identifying lithology according to laser intensity and reflection characteristics, and establishing a digital outcrop model. The method includes using laser scanning technology to collect data of a field profile to obtain laser point cloud data; after processing the collected data such as noise reduction and calibration, identifying lithology according to the laser point cloud data, dividing the profile into each stratigraphic unit, restoring the sedimentary paleogeomorphology of the profile, and establishing a comprehensive digital information histogram. Using the processed laser point cloud data to construct a triangular mesh model, performing texture mapping on it, extracting holes based on the Mask-RCNN model, and establishing a high-precision digital outcrop model. Extracting fractures from the model, and based on the sedimentary microfacies model, combining with hyperspectral data, for corresponding effective reservoir prediction of the profile. Due to the heterogeneity of carbonate rocks and the difficulty of their characterization, this application mainly aims at carbonate rock outcrop profiles, quickly establishing a digital outcrop model of carbonate rock profiles, guiding the establishment of underground models, and ensuring the effectiveness and reliability of underground reservoir prediction.

[0080] Such as Figure 1As shown in the figure, a method for establishing a digital outcrop model based on laser scanning technology provided by an embodiment of the present invention includes the following steps.

[0081] S101. For the three-dimensional laser scanning technology and high-resolution image acquisition instrument, obtain the profile laser point cloud data and high-resolution outcrop image technology, perform preprocessing such as noise reduction on the laser point cloud data and correction. Stitch the processed laser point cloud data and high-resolution outcrop images, and combine traditional geological research means such as thin section identification to establish a lithology plate for identifying lithology based on laser intensity characteristics, divide the profile into each stratigraphic unit, and obtain the comprehensive columnar diagram of digital geological information of the profile.

[0082] S102. In the digital outcrop bionic program, study the corresponding relationship between the laser intensity mean curve and the well logging curve and the hyperspectral curve, and use the data as one of the data for establishing the digital outcrop model and reservoir prediction.

[0083] S103. Construct a laser point cloud triangular network in POLYWORK, perform digital outcrop model mapping on it, based on the Mask-RCNN model, extract holes from the digital outcrop, and combine traditional geological research means to interpret and label the digital outcrop to establish a high-precision digital outcrop model.

[0084] S104. Based on the Beamlet algorithm, extract fractures from the digital outcrop, and the development situation and range of dissolution holes in the profile, establish a sedimentary microfacies model. By combining the laser intensity characteristics of each stratigraphic unit with the above-mentioned various data, analyze the favorable facies belts for developing high-quality reservoirs, select important intervals, based on the established sedimentary microfacies and with the above-mentioned various data as a reference, redefine its sedimentary microfacies. In the digital outcrop bionic program, intercept its pore model by polyline extraction to clarify the pore development situation of the sedimentary microfacies model, which is used as the basis for reservoir evaluation.

[0085] As Figure 2 shown is the schematic diagram of a method for establishing a digital outcrop model based on laser scanning technology provided by an embodiment of the present invention.

[0086] An embodiment of the present invention provides a method for performing refined modeling on a profile based on laser scanning technology, including: obtaining the profile laser point cloud data and high-resolution outcrop images through a three-dimensional laser scanner and a high-resolution image acquisition device, and importing the obtained data in ASCII format into Riscan-Pro for noise reduction, correction and other processing. Since the carbonate rock profile is relatively large, the data usually needs to be mosaicked in multiple stations using Riscan-Pro. The mosaicking is divided into machine mosaicking, rough mosaicking, and fine mosaicking in sequence. When the machine mosaicking or rough mosaicking has good effects, the redundant data can be removed and exported in txt format. Otherwise, the data is calculated and adjusted until the fine mosaicking has good effects and then exported in txt format.

[0087] The traditional triangular mesh construction directly projects the laser point cloud data onto the horizontal plane to build a network. The accuracy of this method is difficult to support the refined research of carbonate rock profiles. In the present invention, each laser point cloud data is projected in various directions, and the direction with the largest projected area is selected as the best trend surface. An irregular triangular mesh is established on the best trend surface, and finally the established triangular mesh is restored to the three-dimensional space. This method can clearly represent the structural characteristics of the three-dimensional geometry of the carbonate rock digital outcrop. During the process of constructing the triangular mesh, some noise interferences caused by vegetation and other factors also need to be removed, and some holes in the digital outcrop are filled. Finally, the boundary of the model is repaired to be smooth and simplified and exported in obj format.

[0088] Since the constructed laser point cloud triangular mesh only represents the geometric characteristics of the digital outcrop and has no color information, it is necessary to process the obtained high-resolution outcrop images, select the matching points between the image and the triangular mesh model, and calculate the registration parameters to correct the image to achieve the mapping of the digital outcrop.

[0089] Since the diameters of the holes in the carbonate rock profile are not of a uniform scale, the present invention uses the Mask-RCNN, a multi-scale improved convolutional neural network image segmentation method, to extract the holes in the digital outcrop model.

[0090] After the above laser point cloud processing, triangular mesh model construction, texture mapping of the digital outcrop model, and hole extraction, finally, combined with traditional geological research methods, a high-precision digital outcrop model is established, and the model can be exported in formats such as pol, obj, and jpg.

[0091] The identification and characterization of carbonate rock fractures and cavities are basic tasks for reservoir prediction. However, due to factors such as lithology and profile roughness, automatic fracture extraction is a major problem. Currently, the mainstream fracture extraction methods mainly include two categories: based on differential operators and based on transforms. The former, such as the Canny operator, etc., although simple in calculation and fast in operation speed, is prone to distortion in the case of low signal-to-noise ratio; the latter, such as Radon, wavelet transform, etc., although insensitive to noise, has problems such as inability to determine the line segment length, starting point information, and inaccurate positioning.

[0092] The Beamlet algorithm utilized in the present invention has strong anti-noise performance. After being improved by many scholars, it has largely solved the problem of image line feature extraction in the Beamlet transform algorithm when the lines are rich. The crack extraction process is roughly divided into data preprocessing, Beamlet transform, and using the discrete Beamlet basis linking algorithm for false detection and breakpoint problems that occur, to achieve crack extraction.

[0093] According to traditional geological research methods, determine the types of reservoir rocks and reservoir spaces in the section, clarify the origin of the reservoir in the section, restore the paleogeomorphology of sedimentation in the section, determine the development range and main distribution blocks of dissolution pores, as the data support for predicting and evaluating important intervals.

[0094] Based on the established digital outcrop model and digital geological information comprehensive histogram, combined with 3D laser point cloud data, hyperspectral data lithology identification technology, and the pore and crack extraction technology described above, conduct research on redefining sedimentary microfacies, subdividing stratigraphic units, reservoir distribution laws, and reservoir evaluation of important intervals in the section.

[0095] As can be seen from the above, the present invention mainly aims at problems such as great difficulties in characterizing carbonate rock sections, provides a method for establishing a digital outcrop model based on laser scanning technology, establishes a high-precision digital outcrop model, makes the research on carbonate rock sections more refined, improves the accuracy of reservoir prediction, and also reduces the cost and danger of field research in geological research, facilitating technicians in this field to conduct oil and gas geology research unrestricted by time and space. In order to prove the creativity and feasibility of the technical solution of the present invention, the embodiments applied to the above technical solution will be described below.

[0096] The method for establishing a digital outcrop model based on laser scanning technology provided by the embodiment of the present invention includes:

[0097] Obtain the 3D laser point cloud data and high-resolution outcrop images of the section through a 3D laser scanner and a high-resolution image acquisition instrument. After processing the data such as noise reduction and calibration and then splicing them, conduct quantitative analysis of the lithology in combination with traditional geological means, establish a lithology identification chart based on laser intensity, divide the section into various stratigraphic units, and obtain the digital geological information comprehensive histogram of the section.

[0098] Construct the laser point cloud triangular mesh, perform mapping and hole extraction on the digital outcrop model, establish a high-precision digital outcrop model, perform crack extraction on it based on the Beamlet algorithm, determine the development of dissolution pores in the section, select important intervals in the section to redefine sedimentary microfacies and establish the corresponding sedimentary microfacies model, and finally conduct reservoir evaluation on important intervals in combination with various data.

[0099] Since the carbonate rock profile is usually large and requires multi-station scanning to complete, after the collected lidar point cloud data is processed such as noise reduction and correction, it needs to be merged to form an overall one. First, the processed point cloud data and the corresponding high-resolution outcrop images are mosaicked by machine. If the effect is good, the redundant data can be deleted and exported in txt format. If the effect is not good, rough mosaicking is carried out. If the rough mosaicking effect is good, the redundant data is deleted and exported in txt format. If the effect is poor, the data is adjusted and then finely mosaicked and finally exported in txt format.

[0100] Combined with profile observation, based on the variation law of lithology and lithofacies combination, and combined with the regional sedimentary background, determine the sedimentary facies mainly developed in the important stratigraphic intervals, analyze the water body variation law, and obtain the laser intensity range and mean value of the sedimentary subfacies.

[0101] Clarify the laser intensity range, mean value of various lithologies, and the reflectance characteristics of the surface and hyperspectrum of different rocks. Based on the laser intensity curve, establish a lithology map recognition based on laser intensity characteristics, and combine the laser intensity curve with the well logging curve and hyperspectrum curve of similar lithologies in the same stratigraphic position in the area where the profile is located to study the relationship between the laser intensity curve and the well logging curve and hyperspectrum curve. Establish a sedimentary microfacies model and porosity model of the formation unit to predict the effective reservoir corresponding to the carbonate rock reservoir of the outcrop.

[0102] The combination of the laser intensity characteristics of the outcrop profile and the well logging curve in the same stratigraphic position in this area includes:

[0103] Select a typical profile, obtain the XYZ coordinate point cloud data and RGB information of the outcrop through 3D laser scanning, display the divided formation units on the laser model, and obtain the laser intensity mean value as the laser intensity value of this small layer according to the laser intensity value of the fresh surface of the profile, and represent it in the sedimentary histogram. Compared with the well logging curve in the same stratigraphic position in this area, the variation of the laser intensity mean value is similar to that of the well logging curves (RLLD, GR). When the lithology changes, for example, when dolomite changes to limestone or when there is a gravel section in dolomite, the laser intensity mean value can clearly show the change between lithologies. The GR curve is more sensitive to lithology changes. When the lithology of the sedimentary facies is relatively complex, the change of the GR curve is relatively more compared with the laser intensity mean value. When the small layer changes are mostly sand debris sections, the laser intensity mean value change curve is more similar to the RLLD well logging curve.

[0104] The joint analysis of the laser intensity information of the field outcrop profile and the hyperspectral data includes:

[0105] Spectral scans were performed on the fresh surfaces of each stratigraphic unit in the selected field section. The hyperspectral data curves were fitted, compared with the laser intensity curves, and the normalized goodness-of-fit was calculated to obtain the approximation between the hyperspectral curves and the laser intensity curves, inferring a certain degree of correlation between them.

[0106] The hyperspectral curves are affected by lithological changes to a certain extent, and the relationship of lithological changes can be shown according to the changes in the hyperspectral curves and laser intensity curves.

[0107] Rock samples with the same grain size were selected for comparison. Spectra with lower internal voids and better homogeneity showed higher values; those that were dissolved and filled and had no structures that could be filled with oil and gas showed lower values; the higher the internal porosity, the lower the spectral values. Since there is a certain correlation between the hyperspectral curves and the laser intensity curves, the positions with lower laser intensity under the same lithology can be used as the basis for the development of high-quality reservoirs.

[0108] The laser intensity characteristics on the outcrop section surface include:

[0109] The point cloud data obtained by three-dimensional laser scanning mainly has two display methods: the laser intensity point cloud displayed in blue - red, or the three-dimensional point cloud data displayed in RGB colors. The laser intensity point cloud can be used for image recognition through matlab, thus meeting the image recognition analysis from different angles.

[0110] Identifying different lithologies based on the surface laser intensity characteristics includes:

[0111] Select the well-exposed positions on the outcrop section for analysis. Overall, based on the corresponding laser intensity characteristics and combined with traditional geological means such as thin section identification, a lithology identification chart based on laser intensity characteristics is established.

[0112] The steps for establishing the digital outcrop model of the field outcrop section include:

[0113] To clearly express the structural characteristics of the outcrop's three-dimensional geometry, a laser point cloud triangular mesh is constructed through POLYWORK. The laser point cloud data is projected in all directions, and the direction with the largest projected area is selected as the best trend surface. All the laser point cloud data is projected onto this surface to quickly and realistically establish the surface triangular mesh model of the geological outcrop of the section. Then, multi-station polygon model merging is carried out, interference factors such as vegetation are removed, the internal holes of the model are filled, and the boundaries of the model are smoothed. After simplifying the model, it is exported in the obj format.

[0114] Since the laser point cloud triangular mesh model only represents the geometric characteristics of the digital outcrop model and has no color information itself, the high-resolution outcrop image needs to be texture-mapped to the model through the digital outcrop bionic program.

[0115] In the steps of establishing a digital outcrop model, texture mapping is performed on the triangular mesh model, including:

[0116] Studying the distribution and internal geometric structure of fractures and pores in carbonate rocks is of great significance for oil and gas exploration. Establishing a digital outcrop model based on laser scanning technology can achieve fine, quantitative, and simulation-based outcrop research. The present invention uses the Mask-RCNN model for automatic pore extraction. After identifying pores through preliminary calculations, various pore characteristic parameters such as the number, area, perimeter, area, and porosity of the pores in the section can be quantitatively characterized.

[0117] After laser point cloud data processing, constructing a laser point cloud triangular mesh, digital outcrop model texture mapping, and digital outcrop pore extraction, the data is then loaded into the digital outcrop model and interpreted and annotated to establish a high-precision digital outcrop model for facilitating subsequent reservoir evaluation.

[0118] After establishing a high-precision digital outcrop model, it further includes:

[0119] Fracture and pore identification and characterization are the basic work for studying carbonate fracture and pore systems and reservoir prediction. However, the complexity of geological phenomena and fracture formation, as well as factors such as rock lithology and roughness, make it extremely difficult to accurately characterize fractures. The multi-scale Beamlet algorithm is used to extract fractures from the digital outcrop.

[0120] Due to the influence of lithology, illumination, shadow, etc. on the high-resolution outcrop images, the images will contain complex background noise. To improve the accuracy of fracture identification, image preprocessing is required, including RGB to grayscale conversion, adaptive enhancement algorithms, and using the OTUS segmentation algorithm. After preprocessing, the Bamlet transform is performed, and finally, discrete line segment connection is carried out. To improve the credibility of this method, preliminary testing and verification of the images are needed.

[0121] Based on the fracture extraction of the section in the present invention, fracture parameters such as fracture length, density, orientation, and spacing are also quantitatively characterized.

[0122] After fracture extraction is performed on the basis of the established digital outcrop model, the method for establishing the sedimentary microfacies model of the section includes:

[0123] Based on the fracture extraction performed on the established high-precision digital outcrop model, combined with various data such as thin section identification and hyperspectral data, the reservoir rock type, reservoir space type, and the development range of syndepositional and burial dissolution pores in the section are determined.

[0124] Load various data into the high-precision digital outcrop model, redefine the sedimentary microfacies, analyze the favorable intervals for reservoir development, and based on the sedimentary microfacies model, conduct reservoir evaluation in combination with the above data.

[0125] A method for establishing a digital outcrop model based on laser scanning technology, and conduct reservoir evaluation on the predicted important intervals:

[0126] According to the established sedimentary microfacies model, in combination with the comprehensive columnar section of digital geological information of the section, define the lithofacies information of the stratigraphic units in the favorable intervals, and intercept the pore model in the digital outcrop bionic program to clarify the pore development situation, and conduct reservoir evaluation on the important intervals with high-quality reservoirs.

[0127] The embodiment of the present invention is mainly based on the XX field section.

[0128] Go to the field for investigation and collect section laser point cloud data, high-resolution outcrop images, and rock samples, understand the basic geological information of the XX section, and initially divide the section into various stratigraphic units. In the laboratory, conduct spectral scanning and thin section identification on the rock samples to obtain spectral data and basic rock sample information. Study and process the relationships between the laser point cloud data and spectral data, and well logging data of similar well positions in the same area, and lithology identification based on the laser point cloud data and spectral data, for establishing the comprehensive columnar section of digital geological information of the section.

[0129] Process the obtained laser point cloud data and high-resolution images, construct a laser point cloud triangulation network, remove the noise points caused by vegetation, fill the internal holes of the model and conduct boundary correction processing, and export it in the obj format after simplification. Conduct texture mapping on the constructed triangulation network model, and export the simulation digital outcrop model with real texture in the pol format. To establish a high-precision digital outcrop model, it is also necessary to extract digital outcrop holes based on the Mask-PCNN model and evaluate the accuracy of the extracted holes. Use the Beamlet transform algorithm to extract fractures from the established high-precision digital outcrop model, and determine the reservoir rocks, reservoir space types, restore the paleogeomorphology of sedimentation of the section, the development range of dissolution holes, etc. according to various data, and finely depict the sedimentary microfacies model. Finally, refine and compare various data, select important intervals of the section to redefine the geological information of the corresponding sedimentary units, for conducting reservoir evaluation on it.

[0130] Below will be elaborated based on the data and charts generated during the experiment as evidence related to the embodiment of the present invention.

[0131] Lithology identification is the basis for studying carbonate rock profiles. In this invention, by studying the hyperspectral characteristics of the laser intensity of different rock samples, this embodiment mainly demonstrates the lithology identification chart based on laser intensity. By establishing the sample laser intensity identification chart, the outcrop corresponding point laser intensity identification chart, and directly counting the average laser intensity area on the outcrop, the outcrop laser intensity identification chart is established. Through these three identification charts, the lithology of dolomite and limestone can be identified, and then intelligent lithology mapping of the entire profile can be achieved quickly. As Figure 3 shown are the laser intensity values of a certain section of the XX profile and the lithology classification results based on the established chart.

[0132] Determining the pore space characteristics is one of the important contents of carbonate reservoir interpretation and evaluation. Quantitatively identifying the distribution and internal geometric characteristics of fractures and pores is of great significance for carbonate reservoir evaluation. At the same time, the scales of pores in the field profile vary and the span is usually large. Therefore, the model must have the ability to identify multi-scale objects. Based on Mask-RCNN, this invention can effectively detect object instances in high-resolution outcrop images and generate high-precision segmentation masks. And for the convolutional neural network method, the more convolutional layers there are, the stronger the abstraction ability. Although it is easier to identify complex targets, small and simple targets are likely to be lost. To improve the accuracy of pore extraction, the Mask-RCNN model of this invention can solve the multi-scale problem of convolutional layers through the feature pyramid network. Figure 4 is a schematic diagram of automatically extracting pores by using the multi-scale region convolutional neural network pore extraction method for a certain section of the XX profile. The identification ability of pores is for those with a diameter greater than 1 mm. Based on the schematic diagram, this invention also quantitatively characterized pore parameters such as the number of pores, average area, and porosity. It was found that the porosity of the 20th small layer is the largest, the 17th small layer is the smallest, there are more small karst caves in the 17th and 18th small layers, the average area of pores is small, and the spatial distribution is relatively dense. There are more large karst caves in the 19th and 20th small layers, the average area is large, and the spatial distribution is relatively sparse.

[0133] Fracture and pore identification and characterization are the cornerstones of studying and predicting carbonate reservoirs. This invention uses the Beamlet transform algorithm to extract fractures from digital outcrop models. This algorithm has been greatly improved by various scholars, and has largely solved the problem of extracting line features from images with complex background noise and rich lines. Figure 5 is Figure 4 the result map of fracture extraction using the improved image Beamlet transform fracture extraction method for the corresponding profile. The identified length of fractures is greater than 1 mm. Figure 6 is Figure 5 the fracture density map of the corresponding profile. This invention conducts quantitative characterization of fracture parameters on the basis of Figure 5 and finds that there are differences in the fracture development of the four layers. Among them, the fracture density and length of the 17th small layer are higher than those of other stratigraphic units.

[0134] Based on the laser intensity characteristics, it can be seen that Figure 7 The overall laser intensity is high, with some medium - low laser intensities interspersed. The high - laser - intensity surface has good continuity, mostly in block - strip shapes, which is tuffite; the low - intensity areas are distributed in sheets with poor continuity, and there are more low - laser - intensity areas in the middle of the formation. The laser intensity characteristics are similar to those of micritic dolomite, and it is speculated to be intraclastic dolomite formed by storm deposition.

[0135] Figure 8 The overall laser intensity shows a low value. The laser intensity characteristics are typical of dolomite, with an average laser intensity of - 4.1 dB, which is consistent with the expected tidal flat. It is speculated that the 73rd small layer contains mudstone - siltstone based on the laser intensity, which is consistent with the field investigation results. Combining thin - section identification and hyperspectral data, it is determined that this layer is mainly micritic dolomite, which is an effective pore.

[0136] Figure 9 It is a comprehensive digital histogram of the digital information of the described profile established based on the above - mentioned technical solution. It is used to analyze the reservoir distribution characteristics of the profile, and to determine the lithology change characteristics, sedimentary microfacies and reservoir prediction of the stratigraphic units based on the laser intensity.

[0137] Figure 10 It is a schematic diagram of a high - precision digital outcrop model established based on the digital outcrop bionic program of the present invention. All stratigraphic profiles related to the outcrop are displayed below the main window of the program, and the stratigraphic line vectors and sampling point files related to the outcrop can be independently loaded according to needs. The stratigraphic line vector file can be created by using the new vector in the digital outcrop bionic program, and this program supports formats such as jpg and png.

[0138] As Figure 11 shown, the digital outcrop model establishment system based on laser scanning technology provided by the embodiment of the present invention includes:

[0139] Digital outcrop acquisition module: used for denoising and pre - processing the laser point cloud data, establishing a lithology plate for identifying lithology based on laser intensity characteristics, dividing the profile into each stratigraphic unit; refining and comparing the well - logging curves, hyperspectral curves, and laser intensity average curves of similar well positions in the same area, and studying the correlation between the laser intensity average curve and the well - logging curve and hyperspectral curve;

[0140] Digital outcrop model establishment module: used for establishing a profile digital outcrop model based on the laser point cloud data and the high - resolution outcrop image of the profile, splicing the processed laser point cloud data and the high - resolution outcrop image, using the divided stratigraphic units as a link to connect the laser point cloud data, thin - section identification, and basic field geological information data, synchronously refining and comparing each piece of data, constructing a laser point cloud triangular network through POLYWORK and performing digital outcrop model mapping on it, and then extracting holes to establish a high - precision digital outcrop model;

[0141] Sedimentary microfacies model establishment module: used to extract fractures from the established digital outcrop model, determine the development range of dissolution pores in the digital outcrop, establish the sedimentary microfacies model of each stratigraphic unit in the section, load various data into the digital outcrop model, interpret and annotate the digital outcrop model, select the main section segments and redefine the sedimentary microfacies;

[0142] Pore interception and reservoir evaluation module: used to intercept the pore model of important segments and evaluate the reservoir of important segments based on hyperspectral data, laser point cloud data, and thin section identification data.

[0143] An application embodiment of the present invention provides a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of the method for establishing a digital outcrop model based on laser scanning technology.

[0144] An application embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by the processor, the processor executes the steps of the method for establishing a digital outcrop model based on laser scanning technology.

[0145] An application embodiment of the present invention provides an information data processing terminal, which is used to implement a digital outcrop model establishment system based on laser scanning technology.

[0146] Example 1 - Taking the XX section as an example

[0147] Go to the field for on-site investigation and use instruments to obtain the laser point cloud data and high-resolution outcrop images of the outcrop section. During the data acquisition process, make good GPS records of the stations for subsequent data processing. After selecting 4 to 6 control points from the laser point cloud data through the Riscan-Pro laser point cloud parsing software, perform multi-station data splicing. After splicing, organize the field-measured differential GPS control point data into a.txt format, manually mark the positions of the control points and label them. Use the coordinate transformation algorithm in Riscan-Pro to make each laser point cloud have real geographical coordinates, and obtain a complete laser point cloud data section with real geographical coordinates, providing a data basis for establishing a digital outcrop model.

[0148] Use POLYWORK software to assist in constructing a laser point cloud triangular mesh to clearly express the three-dimensional geometric features of the outcrop section. During the process of constructing the triangular mesh, it is necessary to delete the noise points generated by factors such as vegetation, fill the holes that appear inside the model, and repair the boundaries of the filled model. Manually select high-resolution outcrop images that match the triangular mesh model, and use the software to perform texture mapping on the point cloud triangular mesh model that only expresses the geometric structure of the outcrop without color. After a series of steps, a simulated digital outcrop model is initially established and can be exported in formats such as.pol,.obj,.jpg, etc.

[0149] To solve the model accuracy problem, load various data into the model, compare and verify the laser point cloud data with various data, select fresh surface samples of important intervals for spectral scanning, calculate the fitting degree between the hyperspectral curve and the average laser intensity curve, and its approximation is about 0.73, which proves that the hyperspectral curve and the laser intensity curve are correlated. The dolomite content is negatively correlated with the laser intensity, and the correlation coefficient is 0.85. Therefore, it can be used as a basis for identifying the lithology of carbonate rocks. For the same lithology, the higher the internal porosity, the lower its hyperspectral value. Therefore, in subsequent reservoir evaluation, the laser intensity can be used as an evaluation index to a certain extent.

[0150] Through further research on the quantitative analysis of lithology and combining the surface reflection characteristics of each lithology, a lithology identification chart is established. Based on this chart, re-divide the stratigraphic units of important intervals, combine with the field guiding map to obtain the thickness of each stratigraphic unit, draw the stratigraphic section of important intervals of this section, and re-determine the sedimentary microfacies corresponding to each stratigraphic unit, providing a basis for subsequent reservoir evaluation of important intervals.

[0151] Example 1

[0152] Lithology identification is the basis for studying the outcrop section. Therefore, the present invention establishes a lithology identification chart based on laser point cloud data, such as Figure 12 The XX section based on the laser point cloud data identification chart shows that the surface laser reflection characteristics of algal dolomite are overall high reflectivity, interspersed with medium and low reflectivity, and the overall reflection is chaotic; the surface reflection characteristics of crystalline dolomite are lamellar high reflectivity interspersed with lamellar low reflectivity. Different from algal dolomite, the overall reflectivity changes regularly; the surface reflection characteristics of granular dolomite are opposite to those of crystalline dolomite, that is, lamellar medium and low reflectivity interspersed with lamellar high reflectivity; the reflection characteristics of siliceous banded dolomite are similar to those of crystalline dolomite, but due to different proportions, the thickness of lamellar high reflectivity is larger.

[0153] Example 2

[0154] This lithology chart facilitates geological staff to more intuitively feel the overall lithology development characteristics of the section, such as Figure 12 , Figure 13The high-definition outcrop images and laser point cloud mosaics of stations 54-60 and 61-65 of the XX profile scan show that the surface reflectivity characteristics of the first layer are low reflectivity and high reflectivity. When the scale is enlarged separately, it is found that the high reflectivity is laminar, which is consistent with the characteristics of granular dolomite; the second layer can be seen intuitively, which is a thick laminar high reflectivity mixed with medium and low reflectivity, which is consistent with the surface laser reflection characteristics of siliceous dolomite; the surface reflection characteristics of the third layer are chaotic high reflectivity mixed with medium and low reflectivity, which is consistent with the surface reflection characteristics of algae dolomite.

[0155] Example 3

[0156] Geologists can create or load the stratigraphic line vector and sampling point vector files related to the outcrop model in the digital outcrop bionic program according to their own needs. The sampling points and stratigraphic lines are displayed with specific symbols. To facilitate geologists to query the attributes of stratigraphic lines and sampling points, after creating the vector file, the system toolbar of the program also provides relevant query tools. You can also choose to customize fields and vector information for query in the attribute table. The attribute table can store sampling points, stratigraphic data, etc., and geologists can edit it independently. Figure 10 As shown, the digital outcrop bionic system of the present invention can display all bar graphs related to the outcrop model on the right side of the program main window, the stratigraphic profile can be displayed in the main window below, and the thin-section image of the sampling point can be displayed by clicking on the model sampling point.

[0157] In the description of the present invention, unless otherwise specified, "plurality" means two or more than two; the orientations or positional relationships indicated by the terms "upper", "lower", "left", "right", "inner", "outer", "front end", "rear end", "head", "tail", etc. are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0158] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated designed hardware. Those of ordinary skill in the art can understand that the above-mentioned devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code is provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and their modules of the present invention can be implemented by hardware circuits of programmable hardware devices such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, or can be implemented by a combination of the above hardware circuits and software such as firmware.

[0159] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for establishing a digital outcrop model based on laser scanning technology, characterized in that: The following steps are involved: Step 1: By combining traditional geological methods with laser scanning technology, the lithology of the profile is quantitatively classified and the sedimentary microfacies is divided, the profile is preliminarily divided into various stratigraphic units, and a comprehensive columnar diagram of the digital geological information of the profile is established; Step 2: In the digital outcrop bionic program, the profile laser intensity curve is compared with the well logging curve and hyperspectral curve of the same layer and similar well position in the area, the correlation between the laser reflection curve and the well logging curve and hyperspectral curve is studied, and the profile laser intensity feature identification lithology plate is established to improve the reliability of the digital outcrop model established based on laser scanning; Step 3: In the digital outcrop bionic model, the outcrop high-definition image and the processed laser point cloud data are spliced, and the digital outcrop model of the outcrop is established based on the stratigraphic units divided above and the laser intensity combined with the geological means of thin section identification; Step 4: In the digital outcrop bionic program, the digital outcrop model of the section is interpreted and annotated, the outcrop fractures are extracted based on the Beamlet algorithm, the development range of the dissolution holes in the section is determined, and a sedimentary microfacies model is established. Based on the model, important layers are selected and reservoir evaluation is performed in combination with various data; In step 2, in the digital outcrop bionic program, the profile laser intensity curve is combined with the well logging curve and hyperspectral curve of similar well locations in the area for comparative study, including: (1) Import logging curves, hyperspectral curves, and laser intensity curves into the digital outcrop bionic program and process and interpret the raw data; (2) A comparative analysis was performed by selecting typical sections. When the lithology is composed of multiple lithologies, the lithology changes are relatively complex, and the laser intensity curve is similar to the GR logging curve. When the lithology changes are mostly small layers of gravel-bearing sections, the laser intensity curve is more similar to the RLLD logging curve. (3) In order to avoid the interference factors caused by epigenetic effects, rock samples were selected from the fresh surface of each stratigraphic unit in the field section, and spectral scanning was performed. The hyperspectral curve and the laser intensity curve were jointly analyzed. Through the discussion of the fitting degree, the approximation degree between the laser intensity curve and the hyperspectral curve was 0.73, which is a medium fitting degree. According to the research, the hyperspectral curve is affected by the lithology to a certain extent, and the change relationship of the lithology can be shown according to the change of the curve. According to the grain size order, the hyperspectral curve values ​​are negatively correlated with the grain size.

2. The method for establishing a digital outcrop model based on laser scanning technology according to claim 1, characterized in that: Each stratigraphic unit of the section division in step 1 also includes: Through field investigation, the basic geological information of the dissected surface was investigated, and the section was scanned by 3D laser to obtain laser point cloud data and high-resolution outcrop images. The laser point cloud data was de-noised and corrected, and the laser intensity range, mean value and surface reflection characteristics corresponding to different lithofacies were studied to quantitatively classify the lithology. Based on this, the laser intensity value of the entire section was divided and the corresponding laser intensity mean value was calculated. According to the laser intensity mean value, combined with the digital outcrop image information, the section was divided into various stratigraphic units. The sedimentary subfacies developed in the profile were analyzed to obtain the sedimentary subfacies types, the laser intensity range and mean corresponding to each subfacies; the stratigraphic units of the profile were selected and compared and refined with thin section identification and field investigation data, and the sedimentary microfacies of the stratigraphic units were redefined in combination with the laser intensity value.

3. The method for establishing a digital outcrop model based on laser scanning technology according to claim 1, characterized in that: Samples identified as micrite dolomite under microscope thin sections were selected for parallel comparison of lithology with the same grain size. The spectral data value of the samples with lower internal porosity and better homogeneity was the highest; the next was the structure with dissolution but mostly filled, with no oil or gas inside to fill; the spectral data of the micrite dolomite with internal pores filled with silica was low; Under the same lithology, the spectral data value is negatively correlated with the pore content; while the laser scanning curve has a certain correlation with the hyperspectral curve, so the formation with relatively low laser intensity develops high-quality reservoirs, but the laser intensity of muddy and sandy rocks also shows low values, so this method is suitable for dolomite sections in field sections; The dolomite hyperspectral absorption peak has a minimum value at a wavelength of 2337nm, while the limestone has a minimum absorption peak at a wavelength of 2320nm. The minimum absorption peak is an important indicator for distinguishing dolomite from limestone.

4. The method for establishing a digital outcrop model based on laser scanning technology according to claim 1, characterized in that: Step 3: Establish a digital outcrop model of the outcrop, including: (1) The laser point cloud data was interpolated and denoised in the digital outcrop bionic program, and the corresponding high-definition digital images were spliced ​​in Riscan-Pro. Then, the data was processed, the laser point cloud triangulation network was constructed, the digital outcrop was modeled, and the holes were extracted based on the Mask-RCNN model to establish the cross-section digital outcrop model. (2) In the digital outcrop bionic program, field geological information, thin section identification data, and hyperspectral data are loaded into the digital outcrop model, and multiple data are compared to establish a high-precision digital outcrop model.

5. The method for establishing a digital outcrop model based on laser scanning technology according to claim 1, characterized in that: Step 4: Establish a profile sedimentary microfacies model, select important layers, refine the analysis and redefine the sedimentary microfacies, extract the pore model, and conduct reservoir evaluation on the layers, including: (1) By interpreting and annotating the digital outcrop model of the cross-section, the fractures in the model are extracted based on the Beamlet algorithm, and the development of the dissolution pores in the model is determined. The corresponding sedimentary microfacies model is established, the favorable phase belts for reservoir development are analyzed, and the important layers of the cross-section are selected; (2) In the digital outcrop bionic program, based on the sedimentary microfacies model, combined with field investigation and thin section identification data, the sedimentary microfacies of important layers are redefined; (3) In the digital outcrop bionic program, the laser scanning of important layers is used to extract broken lines, characterize their boundaries, and intercept the pore model. Based on the sedimentary microfacies model and combined with hyperspectral data, the reservoir characteristics of the layer reservoir rock type and reservoir space type are determined to conduct reservoir evaluation.

6. A digital outcrop model building system based on laser scanning technology that implements the digital outcrop model building method based on laser scanning technology as claimed in any one of claims 1 to 5, characterized in that: include: Digital outcrop acquisition module: used to perform noise reduction preprocessing on laser point cloud data, establish lithology identification plates based on laser intensity characteristics, and divide the profile into stratigraphic units; refine and compare well logging curves, hyperspectral curves, and laser intensity mean curves of similar well locations in the same area, and study the correlation between the laser intensity mean curve and the well logging curve and hyperspectral curve; Digital outcrop model building module: used to build a digital outcrop model of the profile based on laser point cloud data and high-resolution outcrop images of the profile. The processed laser point cloud data and high-resolution outcrop images are spliced, and the laser point cloud data, thin section identification, and basic field geological information are connected in series with the divided stratigraphic units. The various data are simultaneously refined and compared. The laser point cloud triangulation network is constructed through POLYWORK and the digital outcrop model is mapped to it. After hole extraction, a high-precision digital outcrop model is established. Sedimentary microfacies model building module: used to extract fractures from the established digital outcrop model and determine the development range of dissolution holes in the digital outcrop, establish sedimentary microfacies models for each stratigraphic unit in the profile, load various data into the digital outcrop model, interpret and annotate the digital outcrop model, select profile segments and redefine sedimentary microfacies; Pore ​​interception and reservoir evaluation module: used to intercept the pore model of important layers and conduct reservoir evaluation on important layers based on hyperspectral data, laser point cloud data and thin section identification data.

7. A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for establishing a digital outcrop model based on laser scanning technology as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the steps of the method for establishing a digital outcrop model based on laser scanning technology as claimed in any one of claims 1 to 5.

9. An information data processing terminal, used for implementing the digital outcrop model building system based on laser scanning technology as claimed in claim 6.

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