Method and system for quantitative characterization of reservoir structure porosity in high-temperature geothermal fields
By collecting and analyzing historical geological materials and on-site data of geothermal reservoirs, combined with neural network models, the problem of accurate quantification of porosity of high-temperature geothermal reservoirs is solved, the efficiency and reliability of geothermal resources are improved, and the development strategy is optimized.
Patent Information
- Application Number
- JP2023205123
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2023-07-27
- Filing Date
- 2023-12-05
- Publication Date
- 2025-02-06
- Estimated Expiration
- 2043-12-05
AI Technical Summary
The prior art is difficult to accurately characterize the porosity of high-temperature geothermal reservoirs, and cannot accurately grasp the background conditions of geological parameters, which affects the development efficiency and utilization benefits of geothermal resources.
By collecting historical geological materials from the target block, combining on-site geological surveys, nonlinear conjugation gradient inversion is performed using geological observation data and artificial neural network models, and combining core data and laboratory measurements, a porosity prediction model for geothermal reservoirs is established to accurately quantify reservoir porosity.
Accurate quantification of the porosity of high-temperature geothermal reservoirs is achieved, the efficiency and reliability of geothermal resources are improved, the development strategy is optimized, the cost is reduced and the environmental impact is reduced.
Smart Images
Figure 2025018879000001_ABST
Abstract
Description
[Technical field]
[0001] The present invention relates to the technical field of geothermal resource development, and in particular to a method and system for quantitative characterization of reservoir structure porosity in high temperature geothermal fields. [Background technology]
[0002] Geothermal energy is a non-carbon, clean energy with the advantages of stable and continuous production, which accelerates the exploration, development and utilization of geothermal energy and plays an important role in realizing the development goals of "carbon peaking out" and "carbon neutrality". The development and utilization of geothermal resources can be divided into two aspects: power generation and direct utilization. High-temperature geothermal resources are mainly used for power generation, while medium-low-temperature geothermal resources are mainly used for direct utilization. Porosity, which quantitatively characterizes the geothermal reservoir structure, is the key to the development and utilization of geothermal resources. Geothermal reservoir structures are governed by geological structures and sedimentary strata, and have various lithological combinations. As a result, the reservoir is highly heterogeneous, and weak structural surfaces such as joints, cracks, and faults are developed under the action of multi-period structures. The heterogeneous rock mass in a geothermal reservoir is the key factor governing the concentration of high-temperature geothermal fluid. The distribution law of the porosity of the geothermal reservoir and the geological parameters of the geothermal reservoir are the main governing factors for laying open geothermal wells. Therefore, how to precisely and quantitatively characterize the porosity of the geothermal reservoir and accurately grasp the background conditions of the geological parameters of the geothermal reservoir are extremely important for ensuring work efficiency and development effectiveness. Summary of the Invention
[0003] In order to solve the above-mentioned problems in the prior art, that is, how to refine and quantitatively characterize the porosity of a geothermal reservoir and accurately grasp the background conditions of the geological parameters of the geothermal reservoir, the present invention proposes a method for quantitatively characterizing the reservoir structure porosity of a high-temperature geothermal field, the method comprising: Step S100: collecting past geological data within the target block, obtaining spatial distribution characteristics of geothermal reservoir range and fractures through investigation of the past geological data and on-site geology within the target block, and obtaining structure and evolution characteristics within the target block based on the spatial distribution characteristics of geothermal reservoir range and fractures; Step S200: determining a geophysical station location that satisfies the set reservoir burial depth conditions, geothermal fluid conditions, and fracture development conditions according to the geothermal reservoir range, the spatial distribution characteristics of fractures, and the structure and evolution characteristics within the target block; Step S300: acquiring geophysical observation data of all geophysical station positions, acquiring the geothermal reservoir resistivity distribution of the geothermal field in the target block according to a nonlinear conjugate gradient inversion method, selecting the area with resistivity lower than a predetermined threshold as a reservoir with high geothermal fluid content, and determining the location of the exploration well in the target block; At the exploration well location, core sampling is performed by core boring in the entire well section to obtain core data parameters; According to the rock core data parameters, a lithology column is created. Conduct all-core logging to obtain the temperature, temperature gradient, resistivity and porosity parameters of the geothermal reservoir at the exploration well location; A step S400 of generating a geological parameter set of the geothermal reservoir according to the rock core data parameters of the exploration well position, the lithology column, the temperature of the geothermal reservoir, the temperature gradient of the geothermal reservoir, the resistivity of the geothermal reservoir, and the porosity parameters of the geothermal reservoir; Step S500 performs resistivity measurements on geothermal fluids at different temperatures and pressures in a laboratory to simulate field conditions and obtain a set of fluid resistivity data under different conditions; and step S600: using a trained reservoir porosity prediction model based on an artificial neural network based on the geological parameter collection of the geothermal reservoir to learn the quantitative relationship between the resistivity and fluid resistivity of the geothermal reservoir and the reservoir porosity, to obtain the porosity distribution of the entire geothermal reservoir and the total amount of geothermal fluid, and to realize a quantitative characterization of the porosity of the reservoir structure of the high-temperature geothermal field.
[0004] In some preferred embodiments, step S100 specifically includes: The spatial distribution characteristics of the fractures include the location, inclination, trend, strike and filling condition of the on-site outcrop fractures, and the on-site geological investigation includes on-site outcrop cross-section investigation, geophysical logging, rock core observation and construction verification.
[0005] In some preferred embodiments, step S200 specifically includes performing resistivity and porosity measurements on field geological outcrops of the reservoir to generate a data set of different rock resistivities and porosities.
[0006] In some preferred embodiments, the geophysical observation data specifically includes apparent resistivity and phase curves.
[0007] In some preferred embodiments, step S300 specifically includes: A 3D audio frequency terrestrial electromagnetic survey was carried out, and a "+" shaped electrode arrangement method was adopted at the geophysical station to collect two orthogonal electric field components and two orthogonal magnetic field components, (E x ,E y ) and (H x ,H y ) where x and y represent two perpendicular directions; Step S320 of obtaining apparent resistivity and phase curves at the geophysical station positions by fast Fourier transform and weighted least squares method for the electric field components and magnetic field components; Step S330 includes using a nonlinear conjugate gradient inversion method according to the obtained apparent resistivity and phase curve of the geophysical station to obtain the resistivity distribution of the geothermal reservoir, defining the area where the resistivity is lower than the preset resistivity threshold as a geothermal fluid concentrated area, obtaining the distributional characteristic of the geothermal reservoir, and selecting the geothermal fluid concentrated area where the reservoir resistivity is lower than the preset resistivity threshold and the depth is lower than the preset depth threshold to locate the exploration well position.
[0008] In some preferred embodiments, the rock core data parameters are obtained by the following method: Obtaining the lithology, lithology characteristics, water chemistry and distribution characteristics of natural fractures of the rock core through rock core observation, dividing the strata into sections at the location of the exploration well, and observing and obtaining the strata occurrence, strata contact relationship and the development of fractures and faults; In some preferred embodiments, the artificial neural network relational learning model specifically includes the following training method: A step A100 of obtaining a set of geological parameters of a geothermal reservoir with standard labels and dividing the set into a training set of data and a validation set of data; A200: based on the training set, generate a training set reservoir porosity distribution image by an untrained artificial neural network-based reservoir porosity prediction; based on the validation set, generate a validation set reservoir porosity distribution image by an untrained artificial neural network-based reservoir porosity prediction model; and step A300 of calculating a training error value based on the reservoir porosity distribution image in the training process, calculating a validation data error based on the reservoir porosity distribution image of the validation set, updating the model weight and the model deviation value through the Levenberg-Marquardt algorithm, and repeating step A200 until the training error value is lower than the preset error threshold and the validation data error starts to increase, thereby obtaining a trained reservoir porosity prediction model based on the artificial neural network.
[0009] In some preferred embodiments, obtaining the porosity distribution and total volume of geothermal fluid throughout the geothermal reservoir includes, in particular: This involves calculating the total volume of geothermal fluid in the entire geothermal field according to the following formula:
[0010]
number
[0011] In the above formula, Δx, Δy, and Δz are the intervals of the research area division cells in the x, y, and z directions, respectively, m, n, and l are the total number of division mesh units in the x, y, and z directions, respectively, k, j, and i are the division mesh units in the x, y, and z directions, respectively, and φ i represents the porosity of the geothermal reservoir in the corresponding mesh, and V W represents the total volume of geothermal fluid.
[0012] In some preferred embodiments, the method for quantitatively characterizing reservoir structure porosity of a high temperature geothermal field further comprises: It is used to lay geothermal development wells and form a high-efficiency open well network in geothermal fields.
[0013] Another aspect of the present invention provides a system for quantitative characterization of reservoir structure porosity in high temperature geothermal fields, comprising: a geophysical station location determination module configured to determine a region that satisfies a set reservoir burial depth condition, a geothermal fluid condition, and a fracture development condition as a geophysical station location according to a geothermal reservoir range, a spatial distribution characteristic of fractures, and a structure and evolution characteristic in a target block; an exploration well location arrangement module configured to acquire geophysical observation data of all geophysical station locations, acquire resistivity distribution of the geothermal reservoir of the geothermal field in the target block by a nonlinear conjugate gradient inversion method, select an area with a resistivity lower than a set threshold as a reservoir with a high geothermal fluid content, and determine an exploration well location in the target block; a geological parameter set construction module for constructing a geothermal reservoir geological parameter set, which is configured to create a lithology columnar diagram according to rock core data parameters acquired by core drilling the entire well section at the exploration well position, develop an all-core drilling log, acquire temperature, temperature gradient, resistivity and porosity parameters of the geothermal reservoir at the exploration well position, and create a geological parameter set of the geothermal reservoir according to the rock core data parameters at the exploration well position, the lithology columnar diagram, the temperature of the geothermal reservoir, the temperature gradient of the geothermal reservoir, the resistivity of the geothermal reservoir and the porosity parameters of the geothermal reservoir; a fluid resistivity acquisition module configured to perform resistivity measurements on geothermal fluids at different temperatures and pressures simulating field conditions to obtain a set of fluid resistivity data under different conditions; and a quantitative characterization module for the reservoir structure porosity of the high temperature geothermal field, which is configured to utilize a trained reservoir porosity prediction model based on an artificial neural network based on the geological parameter collection of the geothermal reservoir, to learn a quantitative relationship between the resistivity and fluid resistivity of the geothermal reservoir and the reservoir porosity, to obtain the porosity distribution and the total amount of geothermal fluid in the entire geothermal reservoir, and to realize a quantitative characterization of the porosity of the reservoir structure of the high temperature geothermal field.
[0014] The beneficial effects of the present invention are: (1) The present invention optimizes development strategies and improves development efficiency by quantitatively characterizing the reservoir structure porosity of high-temperature geothermal fields; (2) The present invention improves the accuracy of resource assessment by quantitatively characterizing the reservoir structure porosity of high-temperature geothermal fields, providing a more reliable basis for development. [Brief description of the drawings]
[0015] [Figure 1] FIG. 1 is a flow diagram of a method for quantitative characterization of reservoir structure porosity in high temperature geothermal fields. [Diagram 2] Schematic diagram of a field audio frequency earth electromagnetic "cross" data collection system. [Diagram 3] Geothermal reservoir resistivity distribution map obtained by nonlinear conjugate gradient inversion. [Figure 4] This is a three-layer neural network adopted in the relational learning model of artificial neural networks. [Diagram 5] This is the geothermal reservoir porosity distribution created. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0016] Other features, objects and advantages of the present application will become more apparent upon reading the detailed description of the non-limiting embodiments with reference to the drawings, in which:
[0017] The present application will be described in more detail below in conjunction with the drawings and examples. It will be understood that the specific examples described herein are merely for the purpose of illustrating the relevant invention, and are not intended to limit the invention. For ease of description, only parts related to the relevant invention are shown in the drawings.
[0018] In addition, where no contradiction exists, the embodiments and features in the embodiments in the present application may be combined with each other. Hereinafter, the present application will be described in detail in conjunction with the embodiments with reference to the drawings.
[0019] The method of quantitatively characterizing reservoir structure porosity of a high temperature geothermal field of the present invention comprises: Step S100: collecting past geological data within the target block, obtaining spatial distribution characteristics of geothermal reservoir range and fractures through the past geological data and on-site geological survey within the target block, and obtaining structure and evolution characteristics within the target block based on the spatial distribution characteristics of geothermal reservoir range and fractures; Step S200: determining a geophysical station location that satisfies the set reservoir burial depth conditions, geothermal fluid conditions, and fracture development conditions according to the geothermal reservoir range, the spatial distribution characteristics of fractures, and the structure and evolution characteristics within the target block; Step S300: obtaining geophysical observation data of all the geophysical station positions, obtaining the resistivity distribution of the geothermal reservoir of the geothermal field in the target block according to a nonlinear conjugate gradient inversion method, selecting the low resistivity area as the reservoir with high geothermal fluid content, and determining the exploration well position in the target block; At the exploration well location, core sampling is performed by core boring in the entire well section to obtain core data parameters; According to the rock core data parameters, a lithology column is created. All-core drilling logs are deployed to obtain the temperature, temperature gradient, resistivity and porosity parameters of the geothermal reservoir at the exploration well location; A step S400 of generating a geological parameter set of the geothermal reservoir according to the rock core data parameters of the exploration well position, the lithology column, the temperature of the geothermal reservoir, the temperature gradient of the geothermal reservoir, the resistivity of the geothermal reservoir, and the porosity parameters of the geothermal reservoir; Step S500 performs resistivity measurements on geothermal fluids at different temperatures and pressures in a laboratory to simulate field conditions and obtain a set of fluid resistivity data under different conditions; and step S600: using a trained reservoir porosity prediction model based on an artificial neural network based on the geological parameter collection of the geothermal reservoir to learn the quantitative relationship between the resistivity and fluid resistivity of the geothermal reservoir and the reservoir porosity, to obtain the porosity distribution of the entire geothermal reservoir and the total amount of geothermal fluid, and to realize a quantitative characterization of the porosity of the reservoir structure of the high-temperature geothermal field.
[0020] In order to more clearly explain the method for quantitatively characterizing the reservoir structure porosity of high temperature geothermal fields of the present invention, each step in the embodiment of the present invention will be expanded and described in detail below in conjunction with FIG.
[0021] A first embodiment of the present invention provides a method for quantitative characterization of reservoir structure porosity in high temperature geothermal fields, including:
[0022] In step S100, past geological data within the target block is collected, and the spatial distribution characteristics of the geothermal reservoir range and fractures are obtained based on the past geological data and on-site geological survey within the target block, and the structure and evolution characteristics within the target block are obtained based on the spatial distribution characteristics of the geothermal reservoir range and fractures. The buried depth of the geothermal reservoir is relatively shallow, and after multiple structural uplifts and low-level erosion, the strata within the block are often exposed, and at the same time, a large amount of past geological data has been accumulated from previous geological works such as engineering construction, geological exploration and development, etc. Through comprehensive collection of past geological data within the target block, and comprehensive research on the past geological data within the target block, combined with field on-site outcrop cross-section survey, geophysical logging, rock core observation and necessary engineering verification, the distribution of stratum lithology and the geothermal reservoir range within the geothermal field area are determined, and combined with field on-site outcrop fractures, the spatial distribution characteristics of the fractures (location, inclination, trend, strike and filling situation, etc.) are obtained, and the structure and evolution characteristics within the target block are revealed.
[0023] In step S200, according to the geothermal reservoir range, the spatial distribution characteristics of the fractures, and the structure and evolution characteristics within the target block, the area that satisfies the set reservoir burial depth conditions, geothermal fluid conditions, and fracture growth conditions is determined as the geophysical station location. Satisfying the set reservoir burial depth conditions means that the reservoir is buried shallowly, satisfying the geothermal fluid conditions means that geothermal fluid is exposed at the surface, and satisfying the fracture development conditions means that fracture development is abundant. Resistivity and porosity measurements are performed on the field geological outcrop of the reservoir, and a data set of resistivity and porosity of different types of rocks is created.
[0024] In step S300, geophysical observation data of all geophysical station locations are obtained, and the resistivity distribution of the geothermal reservoir of the geothermal field in the target block is obtained according to the nonlinear conjugate gradient inversion method, and the area with resistivity lower than the set threshold is selected as a reservoir with high geothermal fluid content, and the exploration well location in the target block is determined. Among them, the geophysical observation data includes apparent resistivity and phase curve. In this embodiment, step S300 is specifically as follows: As shown in Figure 2, a 3D audio frequency terrestrial electromagnetic survey was carried out, and a "+" shaped electrode arrangement method with an inter-electrode distance of 60 m was adopted at the geophysical station location to collect two orthogonal electric field components and two orthogonal magnetic field components, (E x ,E y ) and (Hx ,H y ), x and y represent two orthogonal directions, and the observation time is 3 hours; Step S320: obtaining apparent resistivity and phase curves at the geophysical station position by fast Fourier transform and weighted least squares method for the electric field components and magnetic field components, the frequency range being 10000 to 0.1 Hz; As shown in FIG. 3 , the method includes step S330 of using a nonlinear conjugate gradient inversion method according to the apparent resistivity and phase curve of the obtained geophysical station to obtain the resistivity distribution of the geothermal reservoir, defining the area where the resistivity is lower than 10 Ωm as a geothermal fluid concentrated area, obtaining the geothermal reservoir spreading characteristics, and selecting the geothermal fluid concentrated area where the reservoir resistivity is lower than 10 Ωm and the reservoir depth is less than 500 meters to locate the exploration well position.
[0025] In step S400, core sampling is performed by core boring in the entire well section at the exploration well position to obtain rock core data parameters; According to the rock core data parameters, a lithology column is created. All-core drilling logs are deployed to obtain the temperature, temperature gradient, resistivity and porosity parameters of the geothermal reservoir at the exploration well location; A set of geological parameters of the geothermal reservoir is prepared according to the rock core data parameters of the exploration well location, the lithology column, the temperature of the geothermal reservoir, the temperature gradient of the geothermal reservoir, the resistivity of the geothermal reservoir, and the porosity parameters of the geothermal reservoir. Core drilling rock cores are the most intuitive and reliable primary geological data. Cores are collected by core drilling in the entire well section, and the rock cores are observed to describe the lithology, lithology characteristics, hydrochemistry and fracture distribution characteristics of natural fractures, and the strata are sectioned. The strata occurrence, strata contact relationships, and fracture and fault development conditions are observed. Temperature, resistivity and porosity logging is carried out on the drilling cores to obtain parameters such as temperature, temperature gradient, resistivity and porosity, and a collection of geological parameters for the geothermal reservoir is created.
[0026] The geological parameters of the geothermal reservoir provide information on various influencing factors, such as rock type, mineral composition, stress field, and from where to obtain more accurate porosity distribution information.
[0027] In step S500, in the laboratory, resistivity measurements are performed on geothermal fluids of different temperatures and pressures to simulate field conditions, and a collection of fluid resistivity data under different conditions is obtained. Through the field measurement of the rock core and all-core drilling logging, a large amount of data on the resistivity and porosity of the geothermal reservoir is obtained. At the same time, resistivity measurements are performed on geothermal fluids of different temperatures and pressures in the laboratory, and fluid resistivity is obtained.
[0028] In step S600, based on the geological parameter collection of the geothermal reservoir, a reservoir porosity prediction model based on a trained artificial neural network is used to learn the quantitative relationship between the resistivity and fluid resistivity of the geothermal reservoir and the reservoir porosity, to obtain the porosity distribution of the entire geothermal reservoir and the total amount of geothermal fluid, and to realize the quantitative characterization of the porosity of the reservoir structure of the high-temperature geothermal field.
[0029] In this embodiment, the training method of the reservoir porosity prediction model based on the artificial neural network described above specifically includes the following:
[0030] In step A100, a set of geological parameters of a geothermal reservoir with standard labels is obtained and divided into a training set of data and a validation set of data. In this embodiment, the set of geological parameters of the geothermal reservoir, i.e., the training set of parameters, is used as input data, of which 70% is used for training, 15% is used for testing, and the remaining is used for validation.
[0031] In step A200, based on the training set, an untrained artificial neural network-based reservoir porosity prediction model is used to generate a training set reservoir porosity distribution image, and based on the validation set, an untrained artificial neural network-based reservoir porosity prediction model is used to generate a validation set reservoir porosity distribution image. In step A300, a training error value is calculated based on the reservoir porosity distribution image in the training process, and a validation data error is calculated based on the reservoir porosity distribution image of the validation set. The model weight and model deviation value are updated by the Levenberg-Marquardt algorithm. Step A200 is repeated until the training error value is lower than the preset error threshold and the validation data error starts to increase, thereby obtaining a trained reservoir porosity prediction model based on an artificial neural network.
[0032] Network validation and training are performed simultaneously, and when the validation data error starts to rise, the training is stopped. The mean squared error (MSE) of the training data shows that the network reaches the optimum after 10 iterations. The correlation coefficient (R 2 ) is greater than 0.97. As can be seen from the results, the artificial neural network is highly efficient in porosity estimation.
[0033] The obtained relationship between resistivity and porosity is applied to the entire geothermal field to obtain the porosity distribution of the geothermal reservoir throughout the entire geothermal field.
[0034] The total volume of geothermal fluid in the entire geothermal field is calculated using the formula below.
[0035]
number
[0036] In the formula, Δx, Δy, and Δz are the spacings of the research area division cells in the x, y, and z directions, respectively, m, n, and l are the total numbers of division mesh units in the x, y, and z directions, respectively, k, j, and i are the division mesh units in the x, y, and z directions, respectively, and φ i represents the porosity of the geothermal reservoir in the corresponding mesh, and V W represents the total volume of geothermal fluid.
[0037] The second embodiment of the present invention proposes the application of the method for quantitative characterization of reservoir structure porosity of high temperature geothermal fields to the geothermal energy development process, specifically as follows:
[0038] According to the reservoir porosity distribution information, select areas with high porosity and permeability for drilling, and according to the spatial variation characteristics of the reservoir porosity, specify appropriate development measures, such as adjusting the amount of water injection and adopting zoned development, and continuously monitor the changes in reservoir porosity during the geothermal energy development process, such as by well logging and geophysical exploration methods, and reflect the monitoring results in the geological parameter collection of the geothermal reservoir.
[0039] In this embodiment, by applying the quantitative characterization method of the present invention to the field of geothermal energy development, the development efficiency of geothermal energy can be improved, and the development costs and environmental impacts can be reduced. In addition, the method for quantitative characterization of high-temperature geothermal field structural porosity can be continuously optimized, and further support for the sustainable development of geothermal energy can be provided.
[0040] In the above embodiment, each step is described according to the above-mentioned order, but as can be understood by those skilled in the art, in order to achieve the effect of this embodiment, different steps do not necessarily have to be executed in such order, and may be executed simultaneously (in parallel) or in reverse order, and all of these simple variations are within the scope of protection of the present invention.
[0041] A system for quantitatively characterizing reservoir structure porosity in a high temperature geothermal field according to a third embodiment of the present invention includes: a geophysical station location determination module configured to determine a region that satisfies a set reservoir burial depth condition, a geothermal fluid condition, and a fracture development condition as a geophysical station location according to a geothermal reservoir range, a spatial distribution characteristic of fractures, and a structure and evolution characteristic in a target block; an exploration well location arrangement module configured to acquire geophysical observation data of all geophysical station locations, acquire resistivity distribution of the geothermal reservoir of the geothermal field in the target block by a nonlinear conjugate gradient inversion method, select an area with resistivity lower than a set threshold as a reservoir with high geothermal fluid content, and determine an exploration well location in the target block; a geological parameter set construction module for the geothermal reservoir, configured to create a lithology columnar diagram according to rock core data parameters acquired by core sampling through core drilling of the entire well section at the exploration well position, develop an all-core drilling log, acquire temperature, temperature gradient, resistivity and porosity parameters of the geothermal reservoir at the exploration well position, and create a geological parameter set of the geothermal reservoir according to the rock core data parameters at the exploration well position, the lithology columnar diagram, the temperature of the geothermal reservoir, the temperature gradient of the geothermal reservoir, the resistivity of the geothermal reservoir and the porosity parameters of the geothermal reservoir; a fluid resistivity acquisition module configured to perform resistivity measurements on geothermal fluids at different temperatures and pressures simulating field conditions to obtain a set of fluid resistivity data under different conditions; and a quantitative characterization module for the reservoir structure porosity of the high temperature geothermal field, which is configured to utilize a trained reservoir porosity prediction model based on an artificial neural network based on the geological parameter collection of the geothermal reservoir, to learn a quantitative relationship between the resistivity and fluid resistivity of the geothermal reservoir and the reservoir porosity, to obtain the porosity distribution and the total amount of geothermal fluid in the entire geothermal reservoir, and to realize a quantitative characterization of the porosity of the reservoir structure of the high temperature geothermal field.
[0042] As can be clearly understood by those skilled in the art, for convenience and simplification of description, the specific operation processes and related descriptions of the system described above may refer to the corresponding processes in the above method embodiments, and will not be described repeatedly here.
[0043] In addition, the system for quantitative characterization of reservoir structure porosity of high temperature geothermal field according to the above embodiment is only illustratively described by the allocation of each functional module, and in actual application, the above functions can be assigned to different functional modules as necessary to be completed, that is, the modules or steps in the embodiment of the present invention can be re-decomposed or combined, for example, the modules in the above embodiment can be merged into one module or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps in the embodiment of the present invention are merely for distinguishing each module or step, and are not to be regarded as inappropriately limiting the present invention.
[0044] The terms "first," "second," etc. are intended to distinguish between similar objects and are not intended to describe or represent a particular order or chronology.
[0045] The term "comprising" or any other similar term is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus / device comprising a set of elements not only includes those elements, but also includes other elements not expressly listed or that are inherent to the process, method, article, or device / device.
[0046] Up to now, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the drawings, but as can be easily understood by those skilled in the art, the protection scope of the present invention is obviously not limited to these specific embodiments. On the premise that it does not deviate from the principle of the present invention, those skilled in the art can make equivalent modifications or replacements to the relevant technical features, and the technical solutions after these modifications or replacements all fall within the protection scope of the present invention.
Claims
1. 1. A method for quantitative characterization of reservoir structure porosity in a high temperature geothermal field, the method comprising: Step S100: collecting past geological data within the target block, obtaining spatial distribution characteristics of geothermal reservoir range and fractures through the past geological data and on-site geological survey within the target block, and obtaining structure and evolution characteristics within the target block based on the spatial distribution characteristics of geothermal reservoir range and fractures; Step S200: determining a geophysical station location that satisfies the set reservoir burial depth conditions, geothermal fluid conditions, and fracture development conditions according to the geothermal reservoir range, the spatial distribution characteristics of fractures, and the structure and evolution characteristics within the target block; Step S300: obtaining geophysical observation data of all geophysical station positions, obtaining resistivity distribution of the geothermal reservoir of the geothermal field in the target block by a nonlinear conjugate gradient inversion method, selecting the area with resistivity lower than a set threshold as a reservoir with high geothermal fluid content, and determining the location of the exploration well in the target block; Specifically, step S300 is as follows: A three-dimensional audio frequency terrestrial electromagnetic survey is carried out, and a "+" shaped electrode arrangement method is adopted at the geophysical station location to collect two orthogonal electric field components and two orthogonal magnetic field components, (E ｘ , E ｙ ) and (H ｘ , H ｙ ) where x and y represent two perpendicular directions; Step S320: obtaining apparent resistivity and phase curves at the geophysical station location by fast Fourier transform and weighted least squares method for the electric field components and magnetic field components; S330: according to the apparent resistivity and phase curve of the obtained geophysical station position, use a nonlinear conjugate gradient inversion method to obtain a geothermal reservoir resistivity distribution, define the area where the resistivity is lower than the preset resistivity threshold as a geothermal fluid concentrated area, obtain the geothermal reservoir extension characteristic, and select the geothermal fluid concentrated area where the reservoir resistivity is lower than the preset resistivity threshold and the depth is lower than the preset depth threshold to locate the exploration well position; At the exploration well location, core sampling is performed by core boring in the entire well section to obtain core data parameters; According to the rock core data parameters, a lithology column is created. All-core drilling logs are deployed to obtain geothermal reservoir temperature, temperature gradient, resistivity and porosity parameters; Step S400: creating a set of geological parameters of the geothermal reservoir according to the rock core parameters of the exploration well location, the lithology column, the temperature of the geothermal reservoir, the temperature gradient of the geothermal reservoir, the resistivity of the geothermal reservoir, and the porosity parameters of the geothermal reservoir; Step S500 performs resistivity measurements on geothermal fluids at different temperatures and pressures in a laboratory to simulate field conditions and obtain a set of fluid resistivity data under different conditions; S600. Based on the geological parameters of the geothermal reservoir, a trained reservoir porosity prediction model based on an artificial neural network is used to learn the quantitative relationship between the resistivity and fluid resistivity of the geothermal reservoir and the reservoir porosity, to obtain the porosity distribution of the entire geothermal reservoir and the total amount of geothermal fluid, thereby realizing a quantitative characterization of the porosity of the reservoir structure of the high temperature geothermal field; The reservoir porosity prediction model based on the artificial neural network described above is specifically trained as follows: A step A100 of obtaining a set of geological parameters of a geothermal reservoir with standard labels and dividing the set into a training set and a validation set; A200: based on the training set, a reservoir porosity distribution image is generated by an untrained artificial neural network-based reservoir porosity prediction model; based on the validation set, a reservoir porosity distribution image is generated by an untrained artificial neural network-based reservoir porosity prediction model; A300 includes calculating a training error value according to the reservoir porosity distribution image in the training process, calculating a validation data error according to the reservoir porosity distribution image of the validation set, updating the model weight and the model deviation value by the Levenberg-Marquardt algorithm, and repeating step A200 until the training error value is lower than the preset error threshold and the validation data error starts to increase, thereby obtaining a trained reservoir porosity prediction model based on the artificial neural network; Specifically, the above-mentioned method of obtaining the porosity distribution and the total amount of geothermal fluid in the entire geothermal reservoir includes the following steps: Calculating the total volume of geothermal fluid in the entire geothermal field according to the following formula: [0010] In the above, Δx, Δy, and Δz are the intervals of the research area division cells in the x, y, and z directions, respectively, m, n, and l are the total number of division mesh units in the x, y, and z directions, respectively, k, j, and i are the division mesh units in the x, y, and z directions, respectively, and φ ｉ represents the porosity of the geothermal reservoir of the corresponding mesh, and V Ｗ represents the total volume of geothermal fluid, A method for quantitative characterization of reservoir structure porosity in high temperature geothermal fields, comprising:
2. Specifically, step S100 is The spatial distribution characteristics of the fracture include the location, inclination, trend, strike and filling state of the in situ outcrop fracture; The on-site geological investigation includes on-site outcrop cross-section investigation, geophysical logging, rock core observation and construction verification; 2. The method for quantitative characterization of reservoir structure porosity of high temperature geothermal fields as claimed in claim 1.
3. Specifically, step S200 is as follows: Conducting resistivity and porosity measurements on field geological outcrops of the reservoir and developing a data set of resistivity and porosity data for different rock types; 2. The method for quantitative characterization of reservoir structure porosity of high temperature geothermal fields as claimed in claim 1.
4. Specifically, the geophysical observation data includes Includes apparent resistivity and phase curves, 2. The method for quantitative characterization of reservoir structure porosity of high temperature geothermal fields as claimed in claim 1.
5. The rock core data parameters are obtained as follows: Obtaining the lithology, lithology characteristics, water chemistry and distribution characteristics of natural fractures through rock core observation; dividing the strata into sections at the exploration well location; and observing and obtaining the strata occurrence, strata contact relationship and the development of fractures and faults.
2. The method for quantitative characterization of reservoir structure porosity of high temperature geothermal fields as claimed in claim 1.
6. The method further comprises: It is used to lay geothermal development wells and form a highly efficient geothermal field development well network.
2. The method for quantitative characterization of reservoir structure porosity of high temperature geothermal fields as claimed in claim 1.
7. 1. A system for quantitative characterization of reservoir structure porosity in a high temperature geothermal field, the system comprising: a geophysical station location determination module configured to collect past geological data within a target block, obtain spatial distribution characteristics of a geothermal reservoir range and fractures from the past geological data within the target block and on-site geological surveys, obtain structural and evolutionary characteristics within the target block based on the spatial distribution characteristics of the geothermal reservoir range and fractures, and determine, as a geophysical station location, an area that satisfies set reservoir burial depth conditions, geothermal fluid conditions, and fracture development conditions according to the geothermal reservoir range, spatial distribution characteristics of fractures, and structural and evolutionary characteristics within the target block; The geophysical observation data of all geophysical stations are obtained, and the resistivity distribution of the geothermal reservoir of the geothermal field in the target block is obtained by the nonlinear conjugate gradient inversion method. The area with resistivity lower than the set threshold is selected as the reservoir with high geothermal fluid content, and the exploration well location in the target block is determined. Specifically, a three-dimensional audio frequency earth electromagnetic exploration is carried out, and a "+" shaped electrode arrangement method is adopted at the geophysical station position to collect two orthogonal electric field components and two orthogonal magnetic field components (E ｘ , E ｙ ) and (H ｘ , H ｙ ), where x and y represent two perpendicular directions; Obtaining apparent resistivity and phase curves at the geophysical station location by fast Fourier transform and weighted least squares method for the electric field components and magnetic field components; According to the apparent resistivity and phase curve of the obtained geophysical station position, a nonlinear conjugate gradient inversion method is used to obtain a geothermal reservoir resistivity distribution, and an area where the resistivity is lower than a preset resistivity threshold is defined as a geothermal fluid concentrated area, to obtain a geothermal reservoir spreading characteristic, and a geothermal fluid concentrated area where the reservoir resistivity is lower than the preset resistivity threshold and the depth is lower than a preset depth threshold is selected to locate an exploration well position; a geological parameter set construction module for the geothermal reservoir, configured to create a lithology columnar diagram according to rock core data parameters acquired by core sampling through core drilling of the entire well section at the exploration well position, develop an all-core drilling log, acquire temperature, temperature gradient, resistivity and porosity parameters of the geothermal reservoir at the exploration well position, and create a geological parameter set of the geothermal reservoir according to the rock core data parameters at the exploration well position, the lithology columnar diagram, the temperature of the geothermal reservoir, the temperature gradient of the geothermal reservoir, the resistivity of the geothermal reservoir and the porosity parameters of the geothermal reservoir; a fluid resistivity acquisition module configured to perform resistivity measurements on geothermal fluids at different temperatures and pressures simulating field conditions to obtain a set of fluid resistivity data under different conditions; According to the geological parameter collection of the geothermal reservoir, a reservoir porosity prediction model based on a trained artificial neural network is used to learn the quantitative relationship between the resistivity and fluid resistivity of the geothermal reservoir and the reservoir porosity, to obtain the porosity distribution of the entire geothermal reservoir and the total amount of geothermal fluid, and to realize the quantitative characterization of the porosity of the reservoir structure of the high temperature geothermal field, specifically: A step A100 of obtaining a set of geological parameters of a geothermal reservoir with standard labels and dividing the set into a training set and a validation set; A200: based on the training set, a reservoir porosity distribution image is generated by an untrained artificial neural network-based reservoir porosity prediction model; based on the validation set, a reservoir porosity distribution image is generated by an untrained artificial neural network-based reservoir porosity prediction model; A300: calculating a training error value according to the reservoir porosity distribution image in the training process, calculating a validation data error according to the reservoir porosity distribution image of the validation set, updating the model weight and the model deviation value by the Levenberg-Marquardt algorithm, and repeating step A200 until the training error value is lower than a preset error threshold and the validation data error starts to increase, thereby obtaining a trained reservoir porosity prediction model based on an artificial neural network; Specifically, the above-mentioned method of obtaining the porosity distribution and the total amount of geothermal fluid in the entire geothermal reservoir includes the following steps: Calculating the total volume of geothermal fluid in the entire geothermal field according to the following formula: [0025] In the above, Δx, Δy, and Δz are the intervals of the research area division cells in the x, y, and z directions, respectively, m, n, and l are the total number of division mesh units in the x, y, and z directions, respectively, k, j, and i are the division mesh units in the x, y, and z directions, respectively, and φ ｉ represents the porosity of the geothermal reservoir of the corresponding mesh, and V Ｗ represents the total volume of geothermal fluid, A system for quantitative characterization of reservoir structure porosity in high temperature geothermal fields, characterized by: