Quantitative prediction method and quantitative prediction system for hydrothermal dolomite

Abstract

The invention provides a quantitative prediction method and a quantitative prediction system for hydrothermal dolomite, and belongs to the field of geological exploration. Comprising the following steps: performing diagenesis mechanism identification on the hydrothermal dolomite based on multidisciplinary data analysis to determine a target area in which the hydrothermal dolomite exists; performing multi-scale description on the fracture system in the target area to determine a multi-scale fracture development plane of the target interval; a one-dimensional dolomite quantitative model is established on the single well control point according to the multi-mineral model and the multiple logging curves, and a dolomite content curve of the single well control point is determined; and establishing a three-dimensional quantitative model of the hydrothermal dolomite based on the one-dimensional dolomite quantitative model of the plurality of single well control points and the multi-scale fracture development plane of the target interval so as to quantitatively predict the hydrothermal dolomite. According to the method, the development characteristics and the distribution range of the hydrothermal dolomite in the fault and fracture development area can be identified, the content of the hydrothermal dolomite can be quantitatively analyzed, and data support is provided for subsequent heterogeneous reservoir and high-permeability strip prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of geological exploration, and in particular to a quantitative prediction method and a quantitative prediction system for hydrothermal dolomite. Background Art

[0002] Hydrothermal dolomite is closely associated with oil and gas reservoirs, and it presents as a high-quality reservoir, particularly in tectonic zones with well-developed fractures. The secondary pore and fracture systems significantly enhance the reservoir's permeability, making it a valuable reservoir for oil and gas. However, due to its heterogeneity, the exploration and development of hydrothermal dolomite reservoirs often requires detailed geological and geophysical evaluation to accurately predict the reservoir's distribution and storage capacity.

[0003] At present, the research work on dolomite reservoirs focuses on two aspects: The first area involves analyzing the developmental range of dolomite, using multidisciplinary data (geology, well logging, and seismic) to horizontally predict the distribution range and characteristics of dolomite samples. Specifically for hydrothermal dolomite, the primary focus is on the relationship between dolomite and faults and fractures, but this method does not address the quantitative characterization of dolomite content within the reservoir. The second area focuses on analyzing dolomite reservoir quality, emphasizing the characterization of dolomite reservoir space, including pore types, distribution, and porosity prediction. Seismic data analysis, particularly seismic inversion porosity prediction, is a common method. However, this method primarily targets reservoir space within the dolomite, not the dolomite itself.

[0004] Among them, dolomitization is one of the main challenges facing the exploration and development of carbonate oil and gas reservoirs in specific areas. The main problem is the difficulty in quantitatively characterizing the underground carbonate diagenesis, especially the impact of dolomitization on reservoir transformation.

[0005] Therefore, the distribution of hydrothermal dolomite in the formation, especially the quantitative analysis of its content, is the focus of research and is of great significance to the subsequent reservoir quality analysis. Summary of the Invention

[0006] The present invention provides a quantitative prediction method and quantitative prediction system for hydrothermal dolomite, and in particular provides a quantitative analysis method for hydrothermal dolomite based on diagenetic mechanism for carbonate reservoirs. The purpose is to identify the development characteristics and distribution range of hydrothermal dolomite in fault and fracture development areas, quantitatively analyze the content of hydrothermal dolomite, and provide data support for subsequent prediction of heterogeneous reservoirs and high-permeability zones.

[0007] An embodiment of the present invention first provides a quantitative prediction method for hydrothermal dolomite, which includes: identifying the diagenetic mechanism of the hydrothermal dolomite based on multidisciplinary data analysis to determine the target area where the hydrothermal dolomite exists; performing multi-scale characterization of the fracture system in the target area to determine the multi-scale fracture development plane of the target layer; establishing a one-dimensional dolomite quantitative model at a single well control point based on a multi-mineral model and multiple logging curves and determining the dolomite content curve of the single well control point; and establishing a three-dimensional quantitative model of the hydrothermal dolomite based on the one-dimensional dolomite quantitative models of multiple single well control points and the multi-scale fracture development plane of the target layer to quantitatively predict the hydrothermal dolomite.

[0008] On the other hand, the present invention also provides a quantitative prediction system for hydrothermal dolomite, which includes: a mechanism identification device for identifying the diagenetic mechanism of the hydrothermal dolomite based on multidisciplinary data analysis to determine the target area where the hydrothermal dolomite exists; a multi-scale characterization device for performing multi-scale characterization of the fracture system in the target area to determine the multi-scale fracture development plane of the target layer; a first model construction device for establishing a one-dimensional dolomite quantitative model at a single well control point based on a multi-mineral model and multiple 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 hydrothermal dolomite based on the one-dimensional dolomite quantitative models of multiple single well control points and the multi-scale fracture development plane of the target layer to quantitatively predict the hydrothermal dolomite.

[0009] Through the above technical solution, the present invention provides a diagenetic-based quantitative analysis method for hydrothermal dolomite. By identifying the distribution range of fault systems at multiple scales and combining it with quantitative analysis of hydrothermal dolomite content at a single well, a three-dimensional quantitative model of hydrothermal dolomite can be established. This invention, for the first time, proposes a diagenetic-based quantitative analysis method for hydrothermal dolomite in dolomite-rich areas. This method can accurately predict the development and distribution of hydrothermal dolomite in carbonate reservoirs, for example, providing data support for subsequent predictions of heterogeneous reservoirs and high-permeability zones.

[0010] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following detailed description, they are used to explain the embodiments of the present invention, but do not constitute a limitation of the embodiments of the present invention. In the accompanying drawings: Figure 1This is a flow chart of a quantitative prediction method for hydrothermal dolomite according to an embodiment of the present application; Figure 2 Schematic diagram of a three-dimensional quantitative prediction process of hydrothermal dolomite based on diagenetic mechanism according to an embodiment of the present application; Figure 3 Schematic diagram of the process of establishing a one-dimensional dolomite quantitative model according to an embodiment of the present application; Figure 4 A schematic diagram of a process for establishing a three-dimensional quantitative model of dolomite according to an embodiment of the present application; Figure 5a Schematic diagram of hydrothermal dolomite core feature identification according to an embodiment of the present application; Figure 5b Schematic diagram of key feature extraction of hydrothermal dolomite on a cast thin section according to an embodiment of the present application; Figure 5c Schematic diagram of dolomite genesis analysis based on carbon and oxygen isotope intersection according to an embodiment of the present application; Figure 6 Schematic diagram of a hydrothermal migration channel according to an embodiment of the present application; Figure 7 Schematic diagram of quantitative analysis of dolomite at control points according to an embodiment of the present application; Figure 8 Schematic diagram of a one-dimensional dolomite content model according to an embodiment of the present application; Figure 9 A schematic diagram of Manhattan distance for quantitative analysis of the main controlling factors of hydrothermal dolomite according to an embodiment of the present application; Figure 10 Schematic diagram showing the quantitative relationship between hydrothermal dolomite content and fault distance according to an embodiment of the present application; Figure 11 Schematic diagram of multi-parameter main control factor analysis according to an embodiment of the present application; Figure 12 Schematic diagram of the structure of a quantitative prediction system for hydrothermal dolomite according to an embodiment of the present application. DETAILED DESCRIPTION

[0012] The following describes the specific implementation of the embodiment of the present invention in detail with reference to the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the embodiment of the present invention and is not used to limit the embodiment of the present invention.

[0013] The relationship between dolomite and reservoirs is complex and diverse. Its high-quality secondary pore and fracture systems make it an important oil and gas reservoir in many regions, but it also faces challenges due to heterogeneity. The formation mechanisms of dolomite are complex and diverse, and different genesis leads to differences in reservoir properties and performance, resulting in intercalations and high-permeability zones. Hydrothermal dolomite, a special type of dolomite, is often closely associated with fracture systems, especially near fault zones. Due to the pore and fracture characteristics of hydrothermal dolomite, it often has a good storage capacity. In some cases, the permeability of hydrothermal dolomite reservoirs is even better than that of surrounding non-dolomitized carbonate reservoirs.

[0014] To address the problem of underground carbonate diagenesis, especially the difficulty in quantitatively characterizing the impact of dolomitization on reservoir transformation, this paper proposes a quantitative prediction method for hydrothermal dolomite based on diagenetic mechanism, providing a solution for the quantitative characterization of dolomitization and dolomite reservoirs.

[0015] like Figure 1 As shown, the present invention first provides a quantitative prediction method 100 for hydrothermal dolomite, which may include steps S110-S140. The specific quantitative prediction workflow diagram of the present invention can be found in Figure 2-4 As shown, Figure 2 Schematic diagram of a three-dimensional quantitative prediction process of hydrothermal dolomite based on diagenetic mechanism according to an embodiment of the present application. Figure 3 Schematic diagram of the process for establishing a one-dimensional dolomite quantitative model, Figure 4 Schematic diagram of the process for establishing a three-dimensional quantitative model of dolomite.

[0016] Step S110 : Identifying the diagenetic mechanism of hydrothermal dolomite based on multidisciplinary data analysis to determine a target area where hydrothermal dolomite exists.

[0017] Among them, multidisciplinary data analysis can include core data analysis, cast thin section data analysis and isotope data analysis.

[0018] First, the dolomite development status in the target area can be determined based on the core data analysis of the target area. Figure 5a As shown, core data in the target area are collected, and core description is performed on the core data of the target layer to determine the development status of dolomite in the target layer.

[0019] Secondly, the development of hydrothermal dolomite can be determined at the microscopic level based on the microscopic texture data in the casting thin section data. Figure 5b As shown in the figure, by analyzing the microstructure of dolomite through the casting thin section data, the characteristic texture of hydrothermal dolomite, such as saddle-shaped dolomite, was identified, thereby determining the development status of hydrothermal dolomite at the microscopic level.

[0020] Then, the core samples of the target layer are analyzed for isotopes. The isotope data analysis can include carbon isotopes. and oxygen isotopes That is, the carbon and oxygen isotope analysis can be performed on the core samples of the target layer. , we can speculate the influence of sedimentary environment and organic carbon, and through oxygen isotope , we can infer the diagenetic temperature and fluid source of dolomite. Figure 5c As shown, the carbon and oxygen isotope data are intersected and analyzed to quantitatively analyze the diagenetic mechanism of dolomite and determine the origin of the dolomite development in the target layer - hydrothermal dolomite. Specifically, the isotope data analysis used in the present invention is calculated as follows:

[0021]

[0022] in, is the sample isotope ratio, is the standard isotope ratio.

[0023] Step S120 , performing multi-scale characterization on the fracture system in the target area to determine the multi-scale fracture development plane of the target layer segment.

[0024] Among them, the fault system can include first-scale cracks (large-scale cracks), second-scale cracks (medium-scale cracks) and third-scale cracks (small-scale cracks) of decreasing scale. Specifically, the scale of the first-scale cracks should be larger than the first set multiple of the seismic wavelength, the scale of the third-scale cracks should be smaller than the second set multiple of the seismic wavelength, and the scale of the first-scale cracks should be between the second set multiple and the first set multiple of the seismic wavelength, wherein the first set multiple should be larger than the second set multiple. For example, for large-scale cracks, their scale is usually larger than the seismic wavelength. λ More than 10 times (>10 λ ), for medium-scale cracks, their scale is generally comparable to the seismic wavelength ( λ -10 λ ), while for small-scale cracks, their scale is usually smaller than the seismic wavelength (< λ ). That is, the first set multiple may be 10, and the second set multiple may be 1.

[0025] Specifically, the applicant discovered a close relationship between hydrothermal dolomite and fracture systems. Fracture systems provide migration pathways for hydrothermal fluids and promote the formation of secondary pores in the reservoir during hydrothermal dolomitization, significantly enhancing reservoir permeability. Furthermore, the development and distribution of fractures directly impact the heterogeneity and oil and gas accumulation potential of hydrothermal dolomite reservoirs. Therefore, the characterization of migration pathways (fracture systems) in step S120 can primarily include the following steps, S121-S123: Step S121 , characterizing first-scale fractures through seismic data fracture interpretation, so as to identify the fault distribution and strike of the fault system on the seismic profile.

[0026] That is to say, first of all, the fractures in seismic data can be interpreted to identify the distribution and direction of faults on the seismic profile.

[0027] Step S122 : characterizing the second-scale fractures based on one or more seismic attributes of the seismic coherence volume, the curvature volume, and the chaos volume, so as to identify the planar distribution of the second-scale fractures.

[0028] Secondly, mesoscale fractures can be characterized by generating various seismic attributes such as seismic coherence bodies, curvature bodies and chaos bodies, thereby identifying the planar distribution of mesoscale fractures.

[0029] Step S123 : characterizing the third-scale fractures based on the imaging logging data analysis method to interpret and identify the fracture parameters of the third-scale fractures, wherein the fracture parameters may include the development direction, angle and intensity of the fractures.

[0030] Finally, imaging logging data analysis can be used, such as Figure 6 As shown in the figure, the fracture parameters of micro-cracks are interpreted and identified, including the development direction, angle and intensity of the fractures, so as to finally form a multi-scale fracture development plan map of the target layer.

[0031] Step S130 , establishing a one-dimensional dolomite quantitative model at a single well control point based on the multi-mineral model and the multiple well logging curves, and determining the dolomite content curve at the single well control point.

[0032] In one embodiment, step S130 may include steps S131-S134: Step S131 determines multiple well logging curves at a single well control point. Specifically, a combination of multiple well logging curves may be selected to estimate mineral content, including at least two of the following: natural gamma ray logging curves, density logging curves, neutron logging curves, sonic logging curves, and resistivity logging curves.

[0033] Step S132: Determine the volume content of minerals in the reservoir based on the multi-mineral model and multiple well logging curves.

[0034] Specifically, it can be based on multiple well logs, including natural gamma ray readings , neutron porosity readings , density logging readings , sonic transit time logging readings The following multi-mineral model is used to invert the mineral volume content through the logging curve to determine the mineral volume content in the reservoir: :

[0035] in, The coefficients for the influence of each well log curve on the mineralogy can usually be determined by calibration or inversion.

[0036] Step S133: using the density logging curve to determine the dolomite content in the mineral volume content.

[0037] 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 the content of limestone and dolomite based on the density differences of different minerals. Dolomite has a higher density than limestone, and its estimation formula can be referred to as follows:

[0038] in, is the dolomite content, is the measured density, is the density of limestone (which can be taken as 2.71 g / cm³), is the density of dolomite (which can be taken as 2.85 g / cm³), such as Figure 7 shown.

[0039] Step S134: establishing a one-dimensional dolomite quantitative model at the single well control point according to the dolomite content, and determining the dolomite content in each layer of the single well control point.

[0040] Finally, based on the analysis of dolomite at a single well control point, a corresponding one-dimensional dolomite quantitative model can be established ( Figure 8 ), and then calculate the content of each rock mineral in each layer based on the model.

[0041] Step S140 , based on the one-dimensional dolomite quantitative models of multiple single-well control points and the multi-scale fracture development plane of the target layer, a three-dimensional quantitative model of hydrothermal dolomite is established to quantitatively predict the hydrothermal dolomite.

[0042] Among them, hydrothermal dolomite is formed by chemical reactions under the action of high-temperature hydrothermal fluids. Hydrothermal fluids usually 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 rise of hydrothermal fluids. Therefore, the formation and distribution of dolomite are mainly determined by the hydrothermal supply and the fracture system. Fractures, as channels for hydrothermal migration, determine the range where hydrothermal fluids can reach and partially determine the hydrothermal supply, thereby controlling the distribution range and development intensity of hydrothermal dolomite. In view of this, in one embodiment, step S140 can further include steps S141-S143: Step S141: Based on multiple fracture parameters and dolomite content of fractures at the characteristic dolomite development point, a quantitative relationship between each fracture parameter and dolomite content is determined to serve as a weight coefficient for the fracture parameter. Fracture parameters may include fault distance, fracture strength, and fracture direction.

[0043] This step is a quantitative prediction of hydrothermal dolomite based on diagenetic mechanism. Specifically, we can first conduct multi-parameter analysis of the main controlling factors to extract and quantify the key parameters of the fracture, such as Figure 11 The development intensity, crack direction, etc. Then, based on multiple crack parameters (key parameters) x and dolomite content y , establish a quantitative relationship between the corresponding parameters and the dolomite content. For example, the quantitative relationship between each fracture parameter and the dolomite content can be determined by the following formula:

[0044] is the number of multiple crack parameters, The weight coefficients of the crack parameters can be used as the basis for constructing and optimizing the three-dimensional model.

[0045] 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 hydrothermal dolomite.

[0046] Specifically, through quantitative analysis of fault factors and dolomite content, the correlation between dolomite control points (well points with lithologic and mineral data) and major faults in the study area is determined, specifically the distance from the control point to the major fault. Due to the complexity of the development of the fault system, the migration path of underground hydrothermal fluids is not necessarily a straight line between two points. By using the selected Manhattan distance to represent the distance from the major fault to the dolomite development point ( Figure 9 ). The Manhattan distance is the sum of the absolute wheelbases in the standard coordinate system. For example, the following formula can be used to calculate the distance between two points: Manhattan distance between :

[0047] in, for The number of corners between They are No. The coordinates of the secondary corner.

[0048] In step S143, a vertical development model of the hydrothermal dolomite is established using a vertically layered approach based on the dolomite content curves of multiple single-well control points, the weight coefficients of each fracture parameter, and the hydrothermal lateral migration paths of the hydrothermal dolomite. The dolomite content at the dolomite development point and the distance along the hydrothermal lateral migration path should have a negative exponential relationship.

[0049] Specifically, for a single small layer in the target layer, the fault distances from multiple control points to the main fault are calculated and intersected with the dolomite content of the corresponding points, and the relationship between the dolomite content and the distance of the hydrothermal migration channel is quantitatively fitted to establish a Figure 10 The negative exponential relationship shown in Figure 2 is then repeated for other sublayers within the target interval to establish the relationship between dolomite content and fault distance for each sublayer. Finally, the vertical and horizontal relationships between dolomite content and faults are integrated to establish an initial 3D model of dolomite content and fault development.

[0050] It can be seen that the established three-dimensional model can be used to quantitatively predict the three-dimensional spatial distribution characteristics of dolomite in the target layer within the work area.

[0051] Through the above technical solution, the present invention can achieve the following beneficial effects: (1) Prediction model of hydrothermal dolomite based on diagenetic mechanism. Starting from the mechanism of dolomite, this paper innovatively proposes an analysis method for dolomite content based on the diagenetic mechanism. The diagenetic mechanism of hydrothermal dolomite is identified by comprehensively using multiple data such as cores, cast thin sections, and carbon and oxygen isotopes. Based on its diagenetic mechanism, it is determined that the hydrothermal supply and fracture system are the main factors controlling the production and distribution of dolomite. From the perspective of its diagenetic mechanism, a solution for quantitative prediction of dolomite is proposed, focusing the research object on the dolomite itself.

[0052] (2) Quantitative analysis of multi-parameter dolomite content. Based on well logging data and a multi-mineral model, the upward migration of hydrothermal fluids and the corresponding dynamic changes of dolomite are described vertically, and a one-dimensional dolomite content model is established. Based on the diagenetic mechanism of hydrothermal dolomite, the key parameters of the main controlling factors of hydrothermal dolomite formation (fault distance, fracture intensity, and fracture direction) are optimized. The Manhattan distance is innovatively used to define the lateral migration path of hydrothermal fluids, and a quantitative relationship (exponential correlation) between dolomite content and multiple key parameters is established.

[0053] (3) Quantitative prediction of hydrothermal dolomite in three-dimensional space. Based on the multi-parameter quantitative analysis of dolomite, a lateral development model of hydrothermal dolomite is established by using the key parameters of the main controlling factors. Since the hydrothermal fluid of hydrothermal dolomite generally enters the target stratum from deep layers through faults, a vertical development model of hydrothermal dolomite is established based on the one-dimensional dolomite content model and by adopting the vertical stratification method, thus extending the quantitative prediction model of hydrothermal dolomite to three-dimensional space.

[0054] In summary, the present invention provides a diagenetic-based quantitative analysis method for hydrothermal dolomite in carbonate reservoirs. By identifying the development characteristics and distribution range of hydrothermal dolomite in fault and fracture development zones, the content of hydrothermal dolomite is quantitatively analyzed, and a three-dimensional quantitative model of hydrothermal dolomite is established. This invention, for the first time, proposes a diagenetic-based quantitative analysis method for hydrothermal dolomite in dolomite-developed areas. It innovatively uses Manhattan distance to define hydrothermal fluid migration pathways and establishes a three-dimensional model of dolomite content. This method can more accurately quantitatively predict the development and distribution range of dolomite in carbonate reservoirs, providing data support for subsequent predictions of heterogeneous reservoirs and high-permeability zones.

[0055] On the other hand, the present invention also provides a quantitative prediction system 200 for hydrothermal dolomite, such as Figure 12 As shown, the quantitative prediction system 200 may include: The mechanism identification device 210 is used to identify the diagenetic mechanism of hydrothermal dolomite based on multidisciplinary data analysis to determine the target area where hydrothermal dolomite exists; A multi-scale characterization device 220 is used to perform multi-scale characterization of the fracture system in the target area to determine the multi-scale fracture development plane of the target layer; A first model building device 230 is used to build a one-dimensional dolomite quantitative model at a single well control point based on the multi-mineral model and multiple well logging curves and determine the dolomite content curve of the single well control point; and The second model building device 240 is used to establish a three-dimensional quantitative model of hydrothermal dolomite based on the one-dimensional dolomite quantitative model of multiple single well control points and the multi-scale fracture development plane of the target layer to quantitatively predict the hydrothermal dolomite.

[0056] The above-mentioned technical solution achieves the following beneficial effects: by identifying the development characteristics and distribution range of hydrothermal dolomite in fault and fracture zones, quantitatively analyzing the content of hydrothermal dolomite, and establishing a three-dimensional quantitative model of hydrothermal dolomite. This invention, for the first time, proposes a quantitative analysis method for hydrothermal dolomite based on diagenetic mechanisms in dolomite-rich areas. It innovatively uses Manhattan distance to define hydrothermal fluid migration pathways and establishes a three-dimensional model of dolomite content. This method can accurately quantitatively predict the development and distribution range of dolomite in carbonate reservoirs, providing data support for subsequent predictions of heterogeneous reservoirs and high-permeability zones.

[0057] An embodiment of the present invention further provides a storage medium storing a program, which implements a quantitative prediction method for hydrothermal dolomite when executed by a processor.

[0058] An embodiment of the present invention further provides a processor, which is used to run a program, wherein the program executes a quantitative prediction method for hydrothermal dolomite when running.

[0059] An embodiment of the present invention further provides a device, which may include a processor, a memory, and a program stored in the memory and executable by the processor. When the processor executes the program, each step of the aforementioned method for quantitatively predicting hydrothermal dolomite is implemented. The device herein may be a server, a PC, a PAD, a mobile phone, or the like.

[0060] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program for initializing the steps of the quantitative prediction method for hydrothermal dolomite as described above.

[0061] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0062] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0063] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0064] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0065] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0066] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0067] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can be implemented using any method or technology for information storage. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change RAM (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, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0068] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0069] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A quantitative prediction method for hydrothermal dolomite, characterized in that: The quantitative prediction method comprises: Identify the diagenetic mechanism of the hydrothermal dolomite based on multidisciplinary data analysis to determine the target area where the hydrothermal dolomite exists; Performing multi-scale characterization of the fracture system in the target area to determine the multi-scale fracture development plane of the target layer; Establishing a one-dimensional dolomite quantitative model at a single well control point based on the multi-mineral model and multiple well logging curves and determining a dolomite content curve at the single well control point; and Based on the one-dimensional dolomite quantitative models of multiple single well control points and the multi-scale fracture development plane of the target layer, a three-dimensional quantitative model of the hydrothermal dolomite is established to quantitatively predict the hydrothermal dolomite.

2. The quantitative prediction method according to claim 1, characterized in that The one-dimensional dolomite quantitative model based on the multiple single-well control points and the multi-scale fracture development plane of the target layer is used to establish the three-dimensional quantitative model of the hydrothermal dolomite, 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 used as a weight coefficient for the fracture parameter, wherein the fracture parameters include fault distance, fracture strength, and fracture direction; Determining the Manhattan distance between the dolomite development point in the single well control point and the multi-scale fracture development plane as the hydrothermal lateral migration path of the hydrothermal dolomite based on the dolomite content curve and the fracture system; and Based on the dolomite content curves of the multiple single-well control points, the weight coefficient of each fracture parameter, and the hydrothermal lateral migration path of the hydrothermal dolomite, a vertical development model of the hydrothermal dolomite is established in a vertically layered manner, wherein there is a negative exponential relationship between the dolomite content of the dolomite development point and the distance of the hydrothermal lateral migration path.

3. The quantitative prediction method according to claim 2, characterized in that The determining of the quantitative relationship between each fracture parameter and the dolomite content based on the plurality of fracture parameters and dolomite content of the fractures at the plurality of single well control points comprises: Based on multiple crack parameters and dolomite content , the quantitative relationship between each fracture parameter and the dolomite content is determined by the following formula: in, is the number of the multiple crack parameters, To obtain the The weight coefficient of each crack parameter.

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 of decreasing scales. The multi-scale characterization of the fracture system in the target area includes: Characterizing the first-scale fractures through seismic data fracture interpretation to identify the fault distribution and strike of the fracture system on a seismic profile; Characterizing the second-scale cracks based on one or more seismic attributes of a seismic coherence body, a curvature body, and a chaotic body to identify the planar distribution of the second-scale cracks; and The third-scale fractures are characterized based on imaging logging data analysis to interpret and identify the development direction, angle and intensity of the third-scale fractures.

5. The quantitative prediction method according to claim 4, characterized in that: The scale 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 set multiple of the seismic wavelength, wherein the first set multiple is larger than the second set multiple; and The scale of the first-scale crack is between the second set multiple and the first set 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, casting thin section data analysis and isotope data analysis.

7. The quantitative prediction method according to claim 6, characterized in that: The isotope data includes carbon isotopes and oxygen isotopes , the isotope data analysis is calculated as follows: in, is the sample isotope ratio, is the standard isotope ratio.

8. The quantitative prediction method according to claim 1, characterized in that The method of establishing a one-dimensional dolomite quantitative model at the single well control point based on the multi-mineral model and the well logging curve of the single well control point and determining the dolomite content in each layer of the single well control point includes: Determining a plurality of well logging curves at the single well control point, wherein the plurality of well logging curves include at least two of the following: a gamma ray well logging curve, a density well logging curve, a neutron well logging curve, a sonic well logging curve, and a resistivity well logging curve; determining a volumetric content of minerals in a reservoir based on the multi-mineral model and the plurality of well logs; determining the dolomite content of the mineral by volume using a density log; and According to the dolomite content, the one-dimensional dolomite quantitative model 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 method of determining the volume content of minerals in the reservoir based on the multi-mineral model and the plurality of well logging curves includes: determining the volume content of minerals in the reservoir based on the multi-mineral model and the plurality of well logging curves according to the following formula: : in, Readings for natural gamma rays, is the neutron porosity reading, is the density logging reading, For the sonic time difference logging reading, A coefficient for the mineral effect of each well log; and / or The dolomite content in the mineral volume content is determined using the density logging curve and is calculated using the following formula: in, is the dolomite content, is the measured rock density, is the density of limestone, is the density of dolomite.

10. A quantitative prediction system for hydrothermal dolomite, characterized in that: The quantitative prediction system includes: a mechanism identification device for identifying the diagenetic mechanism of the hydrothermal dolomite based on multidisciplinary data analysis to determine a target area where the hydrothermal dolomite exists; A multi-scale characterization device, used for performing multi-scale characterization of the fracture system in the target area to determine the multi-scale fracture development plane of the target layer; A first model building device is used to build a one-dimensional dolomite quantitative model at a single well control point based on a multi-mineral model and a plurality of well logging curves, and to determine a dolomite content curve at the single well control point; and The second model building device is used to establish a three-dimensional quantitative model of the hydrothermal dolomite based on the one-dimensional dolomite quantitative models of multiple single well control points and the multi-scale fracture development plane of the target layer to quantitatively predict the hydrothermal dolomite.

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