Quantitative prediction method and quantitative prediction system for dolomite

Abstract

The present invention relates to the field of geological exploration, and provides a quantitative prediction method and a quantitative prediction system for dolomite. The method comprises: performing diagenetic mechanism identification on dolomite on the basis of multidisciplinary data analysis to determine a target region where the dolomite is present; performing multiscale characterization on a fault system in the target region to determine a multiscale fracture development plane of a target interval; establishing a one-dimensional dolomite quantitative model on a single-well control point on the basis of a multimineral model and a plurality of well logging curves, and determining a dolomite content curve of the single-well control point; and establishing a three-dimensional quantitative model of the dolomite on the basis of the one-dimensional dolomite quantitative model of a plurality of single-well control points and the multiscale fracture development plane of the target interval to perform quantitative prediction on the dolomite. In the present invention, the development characteristics and a distribution range of dolomite in a fault and a fracture development zone can be identified, and the content of the dolomite can be quantitatively analyzed, providing data support for subsequent prediction of heterogeneous reservoirs and high-permeability strips.

Description

Quantitative prediction methods and systems for dolomite

[0001] Cross-reference to related applications

[0002] This application claims the benefit of Chinese Patent Application No. 202411989809.1, filed on December 31, 2024, the contents of which are incorporated herein by reference. Technical Field

[0003] This invention relates to the field of geological exploration technology, and specifically to a quantitative prediction method and system for dolomite. Background Technology

[0004] Dolomite is closely associated with oil and gas reservoirs, especially in fractured structural zones where it exhibits high-quality reservoir characteristics. Secondary porosity and fracture systems significantly enhance reservoir permeability, making it an important oil and gas reservoir. However, due to its heterogeneity, the exploration and development of dolomite reservoirs often requires detailed geological and geophysical assessments to accurately predict reservoir distribution and storage capacity.

[0005] Current research on dolomite reservoirs mainly focuses on two aspects:

[0006] The first aspect involves analyzing the development range of dolomite, using multidisciplinary data (geology, well logging, and seismic analysis) to horizontally predict the distribution range and characteristics of dolomite samples. Specifically for hydrothermal dolomite, the focus is on the relationship between dolomite and faults and fractures, but this method does not involve the quantitative characterization of dolomite content in the reservoir. The second aspect focuses on the quality analysis of dolomite reservoirs, emphasizing the characterization of dolomite reservoir space, including pore type, distribution, and porosity prediction. A commonly used method is seismic data analysis, especially seismic inversion to predict porosity. However, this method primarily targets the reservoir space within the dolomite, not the dolomite itself.

[0007] Among these challenges, reservoir stimulation by dolomitization is one of the major obstacles to the exploration and development of carbonate oil and gas reservoirs in specific regions. The main difficulty lies in the inability to quantitatively characterize the effects of subsurface carbonate diagenesis, particularly the role of dolomitization in reservoir stimulation.

[0008] Therefore, the distribution of dolomite in strata, especially the quantitative analysis of its content, is the focus of the research and is of great significance for subsequent reservoir quality analysis. Summary of the Invention

[0009] This invention provides a quantitative prediction method and system for dolomite, particularly a quantitative analysis method for dolomite based on diagenetic mechanism for carbonate reservoirs. The aim is to identify the development characteristics and distribution range of dolomite in fault and fracture development zones, quantitatively analyze the dolomite content, and provide data support for subsequent prediction of heterogeneous reservoirs and high-permeability zones.

[0010] This invention first provides a quantitative prediction method for dolomite. The method includes: identifying the diagenetic mechanism of the dolomite based on multidisciplinary data analysis to determine the target area where the dolomite exists; characterizing the fracture system in the target area at multiple scales to determine the multi-scale fracture development plane of the target interval; establishing a one-dimensional quantitative model of dolomite at a single well control point based on a multi-mineral model and various well logging curves, and determining the dolomite content curve of the single well control point; and establishing a three-dimensional quantitative model of the dolomite based on the one-dimensional quantitative model of the dolomite at multiple single well control points and the multi-scale fracture development plane of the target interval to quantitatively predict the dolomite.

[0011] On the other hand, the present invention also provides a quantitative prediction system for dolomite, the quantitative prediction system comprising: a mechanism identification device for identifying the diagenetic mechanism of the dolomite based on multidisciplinary data analysis to determine the target area where the dolomite exists; a multi-scale characterization device for characterizing the fracture system in the target area at multiple scales to determine the multi-scale fracture development plane of the target segment; a first model construction device for establishing a one-dimensional quantitative model of dolomite at a single well control point based on a multi-mineral model and multiple well logging curves and determining the dolomite content curve of the single well control point; and a second model construction device for establishing a three-dimensional quantitative model of the dolomite based on the one-dimensional quantitative model of the dolomite at multiple single well control points and the multi-scale fracture development plane of the target segment, so as to quantitatively predict the dolomite.

[0012] Through the above technical solution, this invention provides a quantitative analysis method for dolomite based on diagenetic mechanisms. By identifying the distribution range of fracture systems at multiple scales and combining this with the dolomite content obtained from quantitative analysis in a single well, a three-dimensional quantitative model of dolomite can be established. This invention is the first to propose a quantitative analysis method for dolomite based on diagenetic mechanisms in dolomite development areas, which can accurately predict the development status and distribution range of dolomite in, for example, carbonate reservoirs, providing data support for subsequent prediction of heterogeneous reservoirs and high-permeability zones.

[0013] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0014] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0015] Figure 1 is a flowchart illustrating a quantitative prediction method for dolomite according to an embodiment of this application;

[0016] Figure 2 is a schematic diagram of the three-dimensional quantitative prediction process of dolomite based on diagenetic mechanism according to an embodiment of this application;

[0017] Figure 3 is a schematic diagram of the process for establishing a one-dimensional quantitative model of dolomite according to an embodiment of this application;

[0018] Figure 4 is a schematic diagram of the process of establishing a three-dimensional quantitative model of dolomite according to an embodiment of this application;

[0019] Figure 5a is a schematic diagram of dolomite core feature identification according to an embodiment of this application;

[0020] Figure 5b is a schematic diagram of key feature extraction of dolomite on a thin slice of cast body according to an embodiment of this application;

[0021] Figure 5c is a schematic diagram of the genetic analysis of dolomite based on carbon and oxygen isotope convergence according to an embodiment of this application;

[0022] Figure 6 is a schematic diagram illustrating a hydrothermal transport channel according to an embodiment of this application;

[0023] Figure 7 is a schematic diagram of quantitative analysis of control point dolomite according to an embodiment of this application;

[0024] Figure 8 is a schematic diagram of a one-dimensional dolomite content model according to an embodiment of this application;

[0025] Figure 9 is a schematic diagram of the quantitative analysis of the main controlling factors of dolomite according to the embodiments of this application - Manhattan distance;

[0026] Figure 10 is a schematic diagram illustrating the quantitative relationship between dolomite content and fault distance according to an embodiment of this application;

[0027] Figure 11 is a schematic diagram of multi-parameter main control factor analysis according to an embodiment of this application;

[0028] Figure 12 is a schematic diagram of the structure of a quantitative prediction system for dolomite according to an embodiment of this application. Detailed Implementation

[0029] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0030] The relationship between dolomite and reservoirs is complex and diverse. Its excellent secondary porosity and fracture systems make it an important oil and gas reservoir in many regions, but it also faces challenges due to its heterogeneity. The formation mechanism of dolomite is complex and diverse; different genesis lead to differences in its reservoir properties and reservoir performance, forming interlayers and high-permeability bands. Hydrothermal dolomite is a special type of dolomite, usually closely associated with fracture systems, especially near fault zones. Due to the porosity and fracture characteristics of hydrothermal dolomite, it often possesses good reservoir capacity. In some cases, the permeability of hydrothermal dolomite reservoirs is even better than that of surrounding undolostized carbonate reservoirs.

[0031] To address the challenge of quantitatively characterizing underground carbonate diagenesis, particularly the effect of dolomitization on reservoir modification, this invention proposes a quantitative prediction method for dolomite based on diagenetic mechanisms, providing a solution for the quantitative characterization of dolomitization and dolomite reservoirs.

[0032] As shown in Figure 1, the present invention first provides a quantitative prediction method 100 for dolomite, such as a quantitative prediction method for dolomite based on its genetic mechanism. This quantitative prediction method 100 may include steps S110-S140. The specific quantitative prediction workflow of the present invention can be seen in Figures 2-4, where Figure 2 is a schematic diagram of the three-dimensional quantitative prediction process for dolomite based on diagenetic mechanism according to an embodiment of this application; Figure 3 is a schematic diagram of the process for establishing a one-dimensional quantitative model of dolomite; and Figure 4 is a schematic diagram of the process for establishing a three-dimensional quantitative model of dolomite.

[0033] Step S110: Identify the diagenetic mechanism of dolomite based on multidisciplinary data analysis to determine the target area where dolomite exists.

[0034] Multidisciplinary data analysis can include core data analysis, cast thin section data analysis, and isotope data analysis.

[0035] First, the development status of dolomite in the target area can be determined based on the analysis of core data from the target area. Specifically, as shown in Figure 5a, core data is collected from the target area, and core descriptions are performed on the core data of the target stratum to determine the development status of dolomite in the target stratum.

[0036] Secondly, the development status of dolomite can be determined at the microscopic level based on the microscopic texture data in the thin section data of the casting. Specifically, as shown in Figure 5b, by analyzing the microscopic texture of dolomite through the thin section data of the casting, the characteristic texture of dolomite, such as saddle-shaped dolomite, can be identified, thereby determining the development status of dolomite at the microscopic level.

[0037] Then, isotopic analysis was performed on the core samples from the target layer. This isotopic data analysis may include carbon isotope δ¹⁴. 13 C and oxygen isotope δ 18 O, meaning that carbon and oxygen isotope analysis can be performed on core samples from the target layer. This is achieved through carbon isotope δ¹⁴... 13 C can be used to infer the sedimentary environment and the influence of organic carbon, while oxygen isotope δ¹⁴ can be used to... 18 O can be used to infer the diagenetic temperature and fluid source of dolomite. As shown in Figure 5c, cross-analysis of carbon and oxygen isotope data quantifies the diagenetic mechanism of dolomite and determines the genesis of dolomite development within the target stratigraphic section. Specifically, the isotope data analysis used in this invention is calculated using the following formula:

[0038] Among them, R sample R represents the isotope ratio of the sample. standard This is the standard isotope ratio.

[0039] Step S120: Multi-scale characterization of the fracture system in the target area is performed to determine the multi-scale fracture development plane of the target segment.

[0040] The fracture system can include first-scale fractures (large-scale fractures), second-scale fractures (medium-scale fractures), and third-scale fractures (small-scale fractures) in descending order of scale. Specifically, the scale of the first-scale fracture should be greater than a first predetermined multiple of the seismic wavelength, the scale of the third-scale fracture should be less than a second predetermined multiple of the seismic wavelength, and the scale of the first-scale fracture should be between the second predetermined multiple and the first predetermined multiple of the seismic wavelength, wherein the first predetermined multiple should be greater than the second predetermined multiple. For example, for large-scale fractures, their scale is usually more than 10 times the seismic wavelength λ (>10λ), for mesoscale fractures, their scale is generally equivalent to the seismic wavelength (λ-10λ), and for small-scale fractures, their scale is usually larger than the seismic wavelength (<λ). That is to say, the first predetermined multiple can be 10, and the second predetermined multiple can be 1.

[0041] Specifically, the applicant discovered a very close relationship between dolomite and the fracture system. The fracture system provides migration channels for hydrothermal fluids and promotes the formation of reservoir biogenic porosity during dolomitization, significantly enhancing reservoir permeability. Simultaneously, the degree of fracture development and distribution directly affect the heterogeneity and hydrocarbon accumulation potential of the dolomite reservoir. Therefore, the characterization of migration channels (fracture system) in step S120 mainly includes the following steps S121-S123:

[0042] Step S121: First-scale fractures are characterized by interpreting seismic data fractures to identify the fault distribution and strike of the fracture system on the seismic profile.

[0043] In other words, the distribution and orientation of faults can be identified on seismic profiles by interpreting the cracks in the seismic data.

[0044] Step S122: Based on one or more of the seismic properties of the seismic coherence volume, curvature volume, and chaotic volume, the second-scale cracks are characterized to identify the planar distribution of the second-scale cracks.

[0045] Secondly, mesoscale fractures can be characterized by generating various seismic attributes such as seismic coherence bodies, curvature bodies, and chaotic bodies, thereby identifying the planar distribution of mesoscale fractures.

[0046] Step S123: The third-scale fracture is characterized based on imaging logging data analysis to interpret and identify the fracture parameters of the third-scale fracture. The fracture parameters may include the fracture development direction, angle and intensity.

[0047] Finally, imaging logging data analysis, as shown in Figure 6, can be used to interpret and identify the fracture parameters of microfractures, including the fracture development direction, angle, and intensity, thereby ultimately forming a multi-scale fracture development planar map of the target layer.

[0048] Step S130: Based on the multi-mineral model and various logging curves, establish a one-dimensional quantitative model of dolomite at the single-well control point and determine the dolomite content curve at the single-well control point.

[0049] In one embodiment, step S130 may include steps S131-S134:

[0050] Step S131: Determine multiple logging curves at a single well control point. That is, you can first select a combination of various logging curves to estimate mineral content, which can mainly include at least two of the following: natural gamma ray logging curves, density logging curves, neutron logging curves, sonic logging curves, and resistivity logging curves, etc.

[0051] Step S132: Determine the volumetric mineral content in the reservoir based on the multi-mineral model and multiple logging curves.

[0052] Specifically, based on multiple logging curves, including natural gamma-ray reading GR, neutron porosity reading NPHI, density logging reading RHOB, and sonic transit time logging reading DT, the mineral volume content in the reservoir can be determined by inverting the mineral volume content from the logging curves using the following multimineral model: X=A1×GR+A2×RHOB+A3×NPHI+A4×DT

[0053] Among them, A1, A2, A3, and A4 are the coefficients of the influence of each logging curve on minerals, and these coefficients can usually be determined by correction or inversion.

[0054] Step S133: Use density logging curves to determine the dolomite content in the mineral volume content.

[0055] Specifically, different logging curves can be used to estimate the mineral content of different rocks. For example, gamma-ray logging can be used to estimate clay content, while density logging can be used to estimate limestone and dolomite content based on the density differences of different minerals. Dolomite has a higher density than limestone, and its estimation formula can be found in the following formula:

[0056] Among them, V dol The content of dolomite, ρ measured The density is ρlimestonel, which represents the density of limestone (a value that can be taken as 2.71 g / cm³). 3 ), ρ dolomite The density of dolomite (can be taken as 2.85 g / cm³) 3 As shown in Figure 7.

[0057] Step S134: Based on the dolomite content, establish a one-dimensional quantitative model of dolomite at the single-well control point and determine the dolomite content in each layer of the single-well control point.

[0058] Finally, based on the analysis of dolomite at single-well control points, a corresponding one-dimensional quantitative model of dolomite can be established (Figure 8), and the content of each rock mineral in each layer can be calculated based on this model.

[0059] Step S140: Based on the one-dimensional quantitative model of dolomite with multiple single-well control points and the multi-scale fracture development plane of the target layer, a three-dimensional quantitative model of dolomite is established to make quantitative predictions on dolomite.

[0060] Hydrothermal dolomite is formed through chemical reactions under the influence of high-temperature hydrothermal fluids. Hydrothermal fluids typically originate from the deep crust, accompanied by geological tectonic activities such as the development of faults and fracture zones. These fractures provide channels for the upward movement of hydrothermal fluids. Therefore, the formation and distribution of dolomite are mainly determined by the hydrothermal fluid supply and the fracture system. Fractures, as channels for hydrothermal fluid migration, determine the reach of the hydrothermal fluids and partially determine the hydrothermal fluid supply, thereby controlling the distribution range and development intensity of dolomite. In view of this, in one embodiment, step S140 may further include steps S141-S143:

[0061] Step S141: Based on multiple fracture parameters and dolomite content of fractures at characteristic dolomite development points, determine the quantitative relationship between each fracture parameter and dolomite content, which serves as the weighting coefficient for that fracture parameter. The fracture parameters may include fault distance, fracture intensity, and fracture direction.

[0062] This step involves quantitative prediction of dolomite based on diagenetic mechanisms. Specifically, firstly, multi-parameter analysis of the main controlling factors can be performed to extract and quantify key fracture parameters, such as development intensity and fracture direction as shown in Figure 11. Then, based on multiple fracture parameters (key parameters) x and dolomite content y, a quantitative relationship between the corresponding parameters and dolomite content can be established. For example, the quantitative relationship between each fracture parameter and dolomite content can be determined using the following formula:

[0063] Where n is the number of multiple crack parameters, a k These are the weighting coefficients for the k-th crack parameter. These weighting coefficients can then be used as the basis for constructing and optimizing the 3D model.

[0064] Step S142: Based on the dolomite content curve and the fracture system, the Manhattan distance between the dolomite development point in the single well control point and the multi-scale fracture development plane is determined as the hydrothermal lateral migration path of the dolomite.

[0065] Specifically, through quantitative analysis of fault factors and dolomite content, the correlation between dolomite control points (well points with lithological and mineral data) and major faults in the study area was determined, specifically the distance from the control point to the major fault. Due to the complexity of fault system development, underground migration paths are not necessarily straight lines between two points. The Manhattan distance was used to characterize the distance from the major fault to the dolomite development point (Figure 9). The Manhattan distance is the sum of absolute axial distances in the standard coordinate system. For example, the Manhattan distance D between two points x1 and x2 can be calculated using the following formula. 12 :

[0066] Where m is the number of turns between x1 and x2, x 1k x 2k Let x1 and x2 be the coordinates of the kth rotation angle, respectively.

[0067] Step S143: Based on the dolomite content curves of multiple single-well control points, the weighting coefficients of each fracture parameter, and the hydrothermal lateral migration path of the dolomite, a vertical development model of the dolomite is established using a vertically layered approach. The relationship between the dolomite content at the dolomite development point and the distance of the hydrothermal lateral migration path should be a negative exponential relationship.

[0068] Specifically, for a single sublayer within the target segment, the fault distances from multiple control points to the main fault are calculated, and these distances are intersected with the dolomite content at the corresponding points to quantitatively fit the relationship between dolomite content and hydrothermal migration channel distance, establishing the negative exponential relationship shown in Figure 10. Then, the first two steps are repeated for other sublayers within the target segment to establish the relationship between dolomite content and fault distance for each sublayer. Finally, by integrating the longitudinal and transverse relationships between dolomite content and faults, an initial three-dimensional model of dolomite content and fault development is established.

[0069] It is evident that, based on the established three-dimensional model, it can be used to quantitatively predict the three-dimensional spatial distribution characteristics of dolomite in the target layer within the work area.

[0070] The beneficial effects that can be achieved by the present invention through the above technical solution may include:

[0071] (1) Predictive Model for Dolomite Based on Diagenetic Mechanism. This invention proposes an innovative analytical method for dolomite content based on its genetic mechanism, starting from the mechanism of dolomite formation. It comprehensively utilizes multiple data sources, including core samples, thin sections of cast bodies, and carbon and oxygen isotopes, to identify the diagenetic mechanism of dolomite. Based on this mechanism, it determines that hydrothermal supply and fracture systems are the main factors controlling dolomite production and distribution. This provides a genetic solution for quantitative prediction of dolomite, focusing the research on the dolomite itself.

[0072] (2) Quantitative analysis of dolomite content with multiple parameters. Based on well logging data and multi-mineral models, the process of hydrothermal fluid migration from bottom to top and the corresponding dynamic changes of dolomite were characterized vertically, and a one-dimensional dolomite content model was established. Based on the diagenetic mechanism of dolomite, the key parameters of the main controlling factors of dolomite genesis (fault distance, fracture intensity and fracture direction) were selected, and the Manhattan distance was innovatively used to define the lateral migration path of hydrothermal fluid, and a quantitative relationship (exponential correlation) between dolomite content and multiple key parameters was established.

[0073] (3) Quantitative prediction of dolomite in three-dimensional space. Based on the quantitative analysis of multi-parameter dolomite, a horizontal development model of dolomite is established through the key parameters of the main controlling factors. Since the hydrothermal fluid of dolomite generally enters the target stratum from deep layers through faults, a vertical development model of dolomite is established based on the one-dimensional dolomite content model and the vertical stratification method is adopted to extend the quantitative prediction model of dolomite to three-dimensional space.

[0074] In summary, this invention provides a quantitative analysis method for dolomite in carbonate reservoirs based on diagenetic mechanisms. By identifying the development characteristics and distribution range of dolomite in fault and fracture development zones, the dolomite content is quantitatively analyzed, and a three-dimensional quantitative model of dolomite is established. This invention is the first to propose a quantitative analysis method for dolomite in dolomite development areas based on diagenetic mechanisms. It innovatively uses Manhattan distance to define hydrothermal migration paths and establishes a three-dimensional model of dolomite content. This method can accurately predict the development status and distribution range of dolomite in carbonate reservoirs, providing data support for subsequent prediction of heterogeneous reservoirs and high-permeability zones.

[0075] On the other hand, the present invention also provides a quantitative prediction system 200 for dolomite, such as a quantitative prediction system for dolomite based on genetic mechanisms, as shown in Figure 12. This quantitative prediction system 200 may include:

[0076] Mechanism identification device 210 is used to identify the diagenetic mechanism of dolomite based on multidisciplinary data analysis in order to determine the target area where dolomite exists.

[0077] Multi-scale characterization device 220 is used to characterize the fracture system in the target area at multiple scales in order to determine the multi-scale fracture development plane of the target segment.

[0078] The first model construction device 230 is used to establish a one-dimensional quantitative model of dolomite at a single well control point based on a multi-mineral model and various logging curves, and to determine the dolomite content curve at the single well control point; and

[0079] The second model construction device 240 is used to establish a three-dimensional quantitative model of dolomite based on a one-dimensional quantitative model of dolomite with multiple single-well control points and a multi-scale fracture development plane of the target layer, so as to make quantitative predictions on dolomite.

[0080] The specific beneficial effects achievable by this invention through the above technical solution include: quantitatively analyzing the dolomite content by identifying the development characteristics and distribution range of dolomite in fault and fracture development zones, and establishing a three-dimensional quantitative model of dolomite. This invention is the first to propose a quantitative analysis method for dolomite based on diagenetic mechanisms in dolomite development areas. It innovatively uses Manhattan distance to define hydrothermal migration paths and establishes a three-dimensional model of dolomite content, which can accurately predict the development status and distribution range of dolomite in carbonate reservoirs, providing data support for subsequent prediction of heterogeneous reservoirs and high-permeability zones.

[0081] This invention also provides a storage medium storing a program that, when executed by a processor, implements a quantitative prediction method for dolomite.

[0082] This invention also provides a processor for running a program, wherein the program executes a quantitative prediction method for dolomite.

[0083] This invention also provides a device, which may include a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the various steps of the quantitative prediction method for dolomite described above. The device described herein may be a server, PC, PAD, mobile phone, etc.

[0084] This application also provides a computer program product that, when executed on a data processing device, is adapted to perform the steps of initializing the quantitative prediction method for dolomite as described above.

[0085] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0086] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more flowchart illustrations and / or one or more block diagrams.

[0087] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0088] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0089] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0090] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0091] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0092] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0093] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A quantitative prediction method for dolomite, characterized in that, The quantitative prediction method includes: Based on multidisciplinary data analysis, the diagenetic mechanism of the dolomite was identified to determine the target area where the dolomite exists. The fracture system in the target region is characterized at multiple scales to determine the multi-scale fracture development plane of the target segment; Based on a multi-mineral model and various logging curves, a one-dimensional quantitative model of dolomite was established at a single-well control point, and the dolomite content curve at that single-well control point was determined; and Based on the one-dimensional quantitative model of dolomite at multiple single-well control points and the multi-scale fracture development plane of the target layer, a three-dimensional quantitative model of the dolomite is established to quantitatively predict the dolomite.

2. The quantitative prediction method according to claim 1, characterized in that, The three-dimensional quantitative model of the dolomite, based on a one-dimensional quantitative model of multiple single-well control points and a multi-scale fracture development plane of the target formation, is established, including: Based on multiple fracture parameters and dolomite content of fractures at characteristic dolomite development points, a quantitative relationship between each fracture parameter and the dolomite content is determined, and this quantitative relationship is used as a weighting coefficient for the fracture parameter. The fracture parameters include fault distance, fracture intensity, and fracture direction. Based on the dolomite content curve and the fracture system, the Manhattan distance between the dolomite development point in the single-well control point and the multi-scale fracture development plane is determined as the hydrothermal lateral migration path of the dolomite; and Based on the dolomite content curves of the multiple single-well control points, the weighting coefficient of each fracture parameter, and the hydrothermal lateral migration path of the dolomite, a vertical development model of the dolomite is established using a vertically layered approach. The dolomite content at the dolomite development point has a negative exponential relationship with the distance of the hydrothermal lateral migration path.

3. The quantitative prediction method according to claim 2, characterized in that, The determination of the quantitative relationship between each fracture parameter and the dolomite content based on multiple fracture parameters and dolomite content at multiple single-well control points includes: Based on multiple crack parameters x and dolomite content y, the quantitative relationship between each crack parameter and the dolomite content is determined by the following formula: Where n is the number of the plurality of crack parameters, a k The weighting coefficients for the k-th crack parameter are obtained.

4. The quantitative prediction method according to claim 1, characterized in that, The fracture system includes first-scale fractures, second-scale fractures, and third-scale fractures in descending order of scale. The multi-scale characterization of the fracture system in the target region includes: The first-scale fractures are characterized by interpreting the fracture data to identify the fault distribution and orientation of the fracture system on the seismic profile. The second-scale fractures are characterized based on one or more of the seismic properties of the seismic coherence volume, curvature volume, and chaotic volume to identify the planar distribution of the second-scale fractures; and The third-scale fractures are characterized using imaging logging data analysis to interpret and identify their development direction, angle, and intensity.

5. The quantitative prediction method according to claim 4, characterized in that, The size of the first-scale crack is greater than a first set multiple of the seismic wavelength; The scale of the third-scale crack is smaller than a second predetermined multiple of the seismic wavelength, wherein the first predetermined multiple is greater than the second predetermined multiple; and The scale of the first-scale crack is located between the second predetermined multiple and the first predetermined multiple of the seismic wavelength.

6. The quantitative prediction method according to claim 1, characterized in that, The multidisciplinary data analysis includes core data analysis, cast thin section data analysis, and isotope data analysis.

7. The quantitative prediction method according to claim 6, characterized in that, The isotopic data includes carbon isotope δ 13 C and oxygen isotope δ 18 O, the isotopic data analysis is calculated using the following formula: Among them, R sample R represents the isotope ratio of the sample. standard This is the standard isotope ratio.

8. The quantitative prediction method according to claim 1, characterized in that, The process of establishing a one-dimensional quantitative model of dolomite at the single-well control point based on the multi-mineral model and the logging curves of the single-well control point, and determining the dolomite content in each layer of the single-well control point, includes: Determine multiple logging curves at the single well control point, wherein the multiple logging curves include at least two of the following: gamma ray logging curve, density logging curve, neutron logging curve, sonic logging curve, and resistivity logging curve; Based on the multi-mineral model and the multiple logging curves, the mineral volume content in the reservoir is determined; The dolomite content in the volumetric content of the mineral was determined using density logging curves; and Based on the dolomite content, a one-dimensional quantitative model of dolomite is established at the single-well control point, and the dolomite content in each layer of the single-well control point is determined.

9. The quantitative prediction method according to claim 8, characterized in that, The determination of the mineral volume content in the reservoir based on the multi-mineral model and the multiple logging curves includes: determining the mineral volume content X of the reservoir based on the multi-mineral model and the multiple logging curves according to the following formula: X=A1×GR+A2×RHOB+A3×NPHI+A4×DT Wherein, GR is the natural gamma ray reading, NPHI is the neutron porosity reading, RHOB is the density logging reading, DT is the sonic transit time logging reading, and A1, A2, A3, and A4 are coefficients representing the influence of each logging curve on the mineral; and / or The dolomite content in the volumetric content of the mineral is determined using density logging curves, calculated using the following formula: Among them, V dol The content of dolomite, ρ measured ρlimestonel represents the measured rock density, where ρ is the density of limestone. dolomite This represents the density of dolomite.

10. A quantitative prediction system for dolomite, characterized in that, The quantitative prediction system includes: A mechanism identification device is used to identify the diagenetic mechanism of the dolomite based on multidisciplinary data analysis, so as to determine the target area where the dolomite exists. A multi-scale characterization device is used to characterize the fracture system in the target region at multiple scales in order to determine the multi-scale fracture development plane of the target segment. The first model construction device is used to establish a one-dimensional quantitative model of dolomite at a single well control point based on a multi-mineral model and various logging curves, and to determine the dolomite content curve at the single well control point; and The second model construction device is used to establish a three-dimensional quantitative model of the dolomite based on a one-dimensional quantitative model of the dolomite at multiple single-well control points and a multi-scale fracture development plane of the target layer, so as to make quantitative predictions on the dolomite.

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