Prediction method and system for deep thermal storage geophysical exploration

Abstract

The invention discloses a deep thermal storage geophysical exploration prediction method and system, and relates to the technical field of deep high-temperature geothermal reservoir exploration and development, and the method comprises the steps: building a deep thermal storage typical geology-geophysical model, carrying out the forward modeling of the deep thermal storage typical geology-geophysical model, and obtaining a deep thermal storage typical geology-geophysical model; establishing a deep heat storage medium rock physics and geophysical response incidence relation model; constructing a high-precision deep thermal storage geophysical exploration inversion geologic model, and performing inversion on the high-precision deep thermal storage geophysical exploration inversion geologic model; performing sensitivity analysis on the deep thermal storage geophysical response in the inversion result to obtain sensitive geophysical attribute data; and according to the sensitive geophysical attribute data, establishing a deep thermal storage geophysical exploration prediction identification index system. According to the method, the incidence relation between the geophysical response characteristics and the deep thermal reservoir distribution can be revealed, so that the deep thermal reservoir exploration risk is reduced.

Description

Technical Field

[0001] This invention relates to the field of exploration and development technology of deep high-temperature geothermal reservoirs, and particularly to a prediction method and system for geophysical exploration of deep geothermal reservoirs. Background Technology

[0002] In the exploration and evaluation of deep geothermal resources, accurately predicting and identifying the distribution of deep geothermal reservoirs is a crucial step, but related research has yet to achieve a breakthrough. This is because the temperature field distribution of deep high-temperature geothermal bodies is exceptionally complex with depth, exhibiting peculiar nonlinear characteristics. The mechanism of geophysical response changes with temperature is unclear, making quantitative prediction and detailed characterization extremely difficult.

[0003] In existing technologies, geophysical exploration of geothermal resources plays a crucial role in shallow geothermal energy exploration. This is because the formation contains water, and the significant differences in physical properties between water and the surrounding rocks result in a marked geophysical response. Combined with geological survey information, geophysical information can be used to directly predict and identify shallow geothermal reservoirs. However, deep geothermal reservoirs are buried at great depths, primarily composed of hot, dry rocks. They are characterized by high temperature and pressure, complex and variable geological conditions, and often contain little or no water. Consequently, the contribution of temperature to the geophysical response is difficult to predict and identify, leading to significant ambiguity.

[0004] In summary, revealing the correlation between geophysical response characteristics and the distribution of deep geothermal reservoirs, thereby reducing the exploration risks of deep geothermal reservoirs, is an important issue that urgently needs to be addressed. Summary of the Invention

[0005] This invention provides a prediction method and system for geophysical exploration of deep geothermal reservoirs, which can solve the problem in the prior art that it is impossible to reveal the correlation between geophysical response characteristics and the distribution of deep geothermal reservoirs to reduce the risk of deep geothermal reservoir exploration.

[0006] This invention provides a predictive method for geophysical exploration of deep geothermal reservoirs, comprising the following steps: By utilizing the variation of rock physical parameters of deep thermal reservoir rock samples with temperature and pressure, a rock physical model of deep high-temperature thermal reservoirs is constructed. Based on the rock physics model of deep high-temperature geothermal reservoirs, as well as geological and geophysical data, a typical geological-geophysical model of deep geothermal reservoirs is established to reflect the relationship between geological physical parameters and geophysical property parameters. Forward modeling of the physical equations of the typical geological-geophysical model of deep geothermal reservoirs with selected parameter variables is performed to reveal the evolution law of geophysical response with geothermal reservoir geological characteristic parameters under different temperature, pressure and depth conditions. Based on geological and geophysical data, a high-precision geophysical exploration inversion geological model conforming to the geological structure is constructed and inverted using an inversion algorithm; sensitivity analysis is performed on the geophysical response of the deep geophysical reservoir in the inversion results to obtain sensitive geophysical attribute data; Based on sensitive geophysical attribute data, multi-source data fusion technology is used to obtain sensitive geophysical attribute data volume describing the spatial distribution of deep geothermal reservoirs, so as to establish a geophysical exploration prediction and identification index system for deep geothermal reservoirs. Based on the geophysical exploration prediction and identification index system for deep geothermal reservoirs, the deep geothermal reservoirs in the study area are classified and evaluated, and promising target areas are selected.

[0007] Furthermore, the prediction method for deep geophysical exploration of geophysical reservoirs also includes: Based on geological and geophysical data, a multi-geophysical parameter geological attribute model with shared grid units is constructed to reveal the distribution patterns of geophysical attribute data that indicate the spatial distribution of thermal reservoir characteristic parameters.

[0008] Furthermore, the forward modeling of the physical equations for selecting parameter variables in a typical geological-geophysical model of deep geothermal reservoirs includes the following specific steps: Data on thermal reservoir properties were obtained through rock physics experiments. Based on the geological morphology, stratigraphic structure, spatial distribution, and thermal reservoir physical property data, a typical geological-geophysical model of deep thermal reservoirs is constructed. The physical equations for the corresponding variables are selected based on typical geological-geophysical models of deep geothermal reservoirs; Numerical methods are selected to iteratively solve the physical equations; the geological model is optimized based on the solution results and actual data to complete the forward modeling.

[0009] Furthermore, the specific steps for constructing a high-precision geophysical exploration inversion geological model that conforms to the geological structure and performing inversion using an inversion algorithm include: Preprocessing of geophysical observation data; Using known geophysical parameters, forward modeling studies are conducted to obtain theoretical geophysical responses and establish the correlation between observational data and subsurface geological structures; Based on the complexity of the geological structure and the required research precision, a suitable inversion algorithm should be selected. Based on the selected inversion algorithm, the preprocessed data is subjected to inversion iterative calculations to gradually optimize the inversion model parameters and obtain the inversion model. The inversion results were calibrated using known data to verify their rationality.

[0010] This invention provides a prediction system for geophysical exploration of deep geothermal reservoirs, comprising: The experimental data acquisition module is used to construct a rock physics model of deep high-temperature geothermal reservoirs by utilizing the variation law of rock physical parameters of deep geothermal reservoir rock samples with temperature and pressure. The model forward modeling module is used to establish a typical geological-geophysical model of deep geothermal reservoirs based on the rock physical model of deep high-temperature geothermal reservoirs, as well as geological and geophysical data, to reflect the relationship between geological physical parameters and geophysical attribute parameters. It also performs forward modeling on the physical equations of the typical geological-geophysical model of deep geothermal reservoirs with selected parameter variables, revealing the evolution of geophysical response with geothermal reservoir geological characteristic parameters under different temperature, pressure and depth conditions. The model inversion module is used to construct a high-precision geophysical exploration inversion geological model that conforms to the geological structure based on geological and geophysical data, and to perform inversion through inversion algorithms; it also performs sensitivity analysis on the geophysical response of deep geothermal reservoirs in the inversion results to obtain sensitive geophysical attribute data. The system construction module is used to obtain sensitive geophysical attribute data volumes describing the spatial distribution of deep geothermal reservoirs using multi-source data fusion technology, based on sensitive geophysical attribute data, in order to establish a geophysical exploration prediction and identification index system for deep geothermal reservoirs; and to classify and evaluate deep geothermal reservoirs in the study area based on the geophysical exploration prediction and identification index system for deep geothermal reservoirs, and to select promising target areas.

[0011] This invention provides a prediction method and system for geophysical exploration of deep geothermal reservoirs, which has the following advantages compared with the prior art: The model established through forward modeling reveals the evolution of geophysical response under different temperature, pressure, and depth conditions with the geological characteristic parameters of the geothermal reservoir. Through inversion, sensitive geophysical attribute data are obtained to establish a geophysical exploration prediction and identification index system for deep geothermal reservoirs. Based on the index system, an evaluation model is established to classify and evaluate deep geothermal reservoirs in the study area. This can reveal the correlation between geophysical response characteristics and the distribution of deep geothermal reservoirs, thereby reducing the exploration risks of deep geothermal reservoirs. Attached Figure Description

[0012] Figure 1 A flowchart illustrating a prediction method for deep geophysical exploration of geophysical reservoirs, provided as an embodiment of the present invention; Figure 2 This diagram illustrates the execution process of a prediction method for deep geophysical exploration of geophysical reservoirs, as provided in an embodiment of the present invention. Detailed Implementation

[0013] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0014] See Figure 1 This invention provides a predictive method for geophysical exploration of deep thermal reservoirs, comprising the following steps: Step 1: Construct a rock physics model of deep high-temperature geothermal reservoirs by utilizing the variation law of rock physical parameters of deep geothermal reservoir rock samples with temperature and pressure.

[0015] Step 2: Based on the rock physics model of deep high-temperature geothermal reservoirs and geological and geophysical data, establish a typical geological-geophysical model of deep geothermal reservoirs to reflect the relationship between geological physical parameters and geophysical attribute parameters. Then, perform forward modeling on the physical equations of the typical geological-geophysical model of deep geothermal reservoirs with selected parameter variables to reveal the evolution law of geophysical response with geothermal reservoir geological characteristic parameters under different temperature, pressure and depth conditions.

[0016] Step 3: Based on geological and geophysical data, construct a high-precision geophysical exploration inversion geological model that conforms to the geological structure and perform inversion using an inversion algorithm; conduct sensitivity analysis on the geophysical response of the deep geophysical reservoir in the inversion results to obtain sensitive geophysical attribute data.

[0017] Step 4: Based on the sensitive geophysical attribute data, use multi-source data fusion technology to obtain sensitive geophysical attribute data volumes describing the spatial distribution of deep geothermal reservoirs, so as to establish a geophysical exploration prediction and identification index system for deep geothermal reservoirs; based on the geophysical exploration prediction and identification index system for deep geothermal reservoirs, conduct classification and evaluation of deep geothermal reservoirs in the study area, and select promising target areas.

[0018] The specific technical solution is as follows: (1) Physical experiments on deep thermal reservoir rocks under high temperature and pressure.

[0019] Using high-temperature and high-pressure rock physics testing equipment, we selected typical deep geothermal reservoir rock samples and carried out precise tests on rock physics parameters under different temperature and pressure conditions. We obtained high-precision rock physics experimental test data of deep geothermal reservoirs in the study area, analyzed and revealed the variation law of rock physics parameters such as resistivity, magnetic susceptibility, density, and velocity with temperature and pressure, and constructed a rock physics model of deep high-temperature geothermal reservoirs.

[0020] (2) Forward modeling of geophysical response of deep geothermal reservoir.

[0021] Utilizing experimental data and models from rock physics tests of deep geothermal reservoirs under high temperature and pressure, and combining existing geological and geophysical data, a typical geological-geophysical model of deep geothermal reservoirs was established. Large-scale forward modeling was conducted to analyze the propagation characteristics of the geophysical field, revealing the correlation between the physical properties of deep high-temperature geothermal bodies and their geophysical responses under different temperature and pressure (different depths), including geophysical response characteristic parameters such as resistivity, magnetic susceptibility, wave velocity, density, and Q value. A correlation model between the rock physics and geophysical responses of deep geothermal reservoir media was established. Specifically, the typical geological-geophysical model of deep geothermal reservoirs can be understood as follows: obtaining rock physics parameters through rock physics experiments to establish a geological physical property parameter model; and on this basis, establishing an equivalent medium model, i.e., the geophysical field calculated or summarized through various physical property parameter models, as well as various geophysical models established based on measured geophysical parameters. This model reflects the relationship between geological physical parameters and geophysical property parameters. Geological physical parameters mainly include porosity, density, resistivity, magnetic susceptibility, compressive strength, elastic modulus, and shear modulus. Geophysical parameters include gravity, magnetism, electricity, and seismicity, and their property data mainly include density, magnetic susceptibility, resistivity, velocity, and other formation parameters.

[0022] The specific process of forward modeling includes: ① obtaining thermal reservoir property parameter data through rock physics experiments; ② constructing a geological-geophysical mathematical model based on the geological body's structural morphology, stratigraphic structure, spatial distribution, and the thermal reservoir property parameter data obtained in ①; ③ selecting appropriate physical equations for corresponding variables based on the characteristics of the geological model; ④ determining grid parameters and dividing the model into common grid cells; ⑤ using numerical methods (such as the finite difference method, finite element method, etc.) to iteratively solve the physical equations; ⑥ comparing simulation results with actual data, continuously optimizing the geological model, verifying simulation results, and improving forward modeling accuracy.

[0023] (3) Geophysical exploration and inversion study of deep geothermal reservoirs.

[0024] Based on existing geological, geophysical, and experimental test data in the study area, a high-precision geophysical exploration inversion or joint inversion geological model for deep geothermal reservoirs is established by adopting a combination of single-point control, structural constraints, and grid unit sharing. According to the actual situation of existing data, single geophysical field inversion, multi-geophysical field joint inversion, and well logging constrained inversion are selectively carried out. Using the correlation between the rock physics and geophysical response of deep geothermal reservoirs established in (2), based on the inversion data volume, sensitivity analysis of geophysical response data of deep geothermal reservoirs is carried out, and sensitive geophysical attribute data are selected and extracted.

[0025] The specific inversion process includes: ① Preprocessing geophysical observation data to meet the inversion requirements; ② Conducting forward simulation studies using known geophysical parameters to obtain theoretical geophysical responses and establish the correlation between observational data and subsurface geological structures; ③ Selecting a suitable inversion algorithm based on the complexity of the geological structure and the research precision; ④ Performing iterative inversion calculations on the preprocessed data based on the selected inversion algorithm, gradually optimizing the inversion model parameters, and obtaining the inversion model with the best fit to the geological structure; ⑤ Calibrating and scaling the inversion results using known data to verify their rationality.

[0026] (4) Establishment of comprehensive prediction and identification indicators for deep geothermal reservoirs.

[0027] Based on the sensitive geophysical attribute data obtained in (3), and according to the richness (size) of the prior knowledge sample set, a suitable data fusion method (linear or nonlinear) is selected to carry out multi-geophysical attribute data fusion processing, obtain sensitive geophysical attribute data volumes or pseudo-attribute data volumes that accurately indicate the spatial distribution of deep geothermal reservoirs, and establish a geophysical exploration prediction and identification index system for deep geothermal reservoirs. Specifically, a data volume is understood as: a data volume obtained through inversion. Earthquake inversion yields wave impedance or velocity and density volumes, gravity inversion yields density volumes, magnetic inversion yields magnetic susceptibility volumes, and electrical inversion yields resistivity volumes. The fusion of two or more of these methods can generate multiple data volumes. The establishment of a data volume involves analyzing the sensitivity of these data to temperature and establishing a relationship with the temperature field. In other words, it involves using these geophysical data to predict temperature. The identification index is the geophysical attribute data of these sensitive data. Some attribute data are not sensitive to temperature, while others are. Sensitive attribute parameters are selected.

[0028] (5) Prediction, classification and evaluation of potential target areas for deep geothermal reservoirs.

[0029] Using geological and geophysical data obtained from the study area, a multi-geophysical parameter geological attribute model of the study area with shared grid units is established to reveal the distribution pattern of geophysical attribute data indicating the spatial distribution of geothermal reservoir characteristic parameters. Based on the geophysical exploration prediction and identification index system for deep geothermal reservoirs established in (4), evaluation parameters are selected, an evaluation model is established, the weights of prediction and identification indicators are calculated, and the deep geothermal reservoirs in the study area are classified and evaluated to select the prospective target areas. Among them, the established evaluation model is to establish a mathematical model for calculating temperature using geophysical data. For linear models, mathematical statistics methods can be used to establish the model, and for nonlinear models, deep learning methods can be used to establish the model. This invention explores the use of geophysical exploration methods to directly identify geothermal anomalies, i.e., geothermal anomalies.

[0030] This invention provides a prediction system for geophysical exploration of deep geothermal reservoirs, comprising: The experimental data acquisition module is used to construct a rock physics model of deep high-temperature geothermal reservoirs by utilizing the variation law of rock physics parameters of deep geothermal reservoir rock samples with temperature and pressure.

[0031] The forward modeling module is used to establish a typical geological-geophysical model of deep geothermal reservoirs based on the rock physics model of deep high-temperature geothermal reservoirs and geological and geophysical data. This model reflects the relationship between geological physical parameters and geophysical attribute parameters. The module also performs forward modeling on the physical equations of the typical geological-geophysical model of deep geothermal reservoirs with selected parameter variables, revealing the evolution of geophysical response with the geological characteristic parameters of the geothermal reservoir under different temperature, pressure and depth conditions.

[0032] The model inversion module is used to construct a high-precision geophysical exploration inversion geological model that conforms to the geological structure based on geological and geophysical data, and to perform inversion through inversion algorithms; it also performs sensitivity analysis on the geophysical response of deep geothermal reservoirs in the inversion results to obtain sensitive geophysical attribute data.

[0033] The system construction module is used to obtain sensitive geophysical attribute data volumes describing the spatial distribution of deep geothermal reservoirs using multi-source data fusion technology, based on sensitive geophysical attribute data, in order to establish a geophysical exploration prediction and identification index system for deep geothermal reservoirs; and to classify and evaluate deep geothermal reservoirs in the study area based on the geophysical exploration prediction and identification index system for deep geothermal reservoirs, and to select promising target areas.

[0034] A specific example is as follows: S1. Construct a rock physics model for deep high-temperature geothermal reservoirs by utilizing the variation law of rock physics parameters of deep geothermal reservoir rock samples with temperature and pressure.

[0035] S2. Based on the rock physics model of deep high-temperature geothermal reservoirs and geological and geophysical data, establish a typical geological-geophysical model of deep geothermal reservoirs, and perform forward modeling on the typical geological-geophysical model of deep geothermal reservoirs to reveal the evolution law of geophysical response with the geological characteristic parameters of geothermal reservoirs under different temperature, pressure and depth conditions, and establish the correlation between the physical properties of deep high-temperature geothermal bodies and geophysical response.

[0036] S3. Based on geological and geophysical data, construct a high-precision geophysical exploration inversion geological model for deep geothermal reservoirs, and invert the high-precision geophysical exploration inversion geological model for deep geothermal reservoirs; conduct sensitivity analysis on the geophysical response of deep geothermal reservoirs in the inversion results to obtain sensitive geophysical attribute data.

[0037] S4. Based on sensitive geophysical attribute data, multi-source data fusion technology is used to obtain sensitive geophysical attribute data volumes describing the spatial distribution of deep geothermal reservoirs, in order to establish a geophysical exploration prediction and identification index system for deep geothermal reservoirs; based on the geophysical exploration prediction and identification index system for deep geothermal reservoirs, the deep geothermal reservoirs in the study area are classified and evaluated, and potential target areas are selected. The method and process are as follows: Figure 2 As shown.

[0038] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A predictive method for geophysical exploration of deep geothermal reservoirs, characterized in that, Includes the following steps: By utilizing the variation of rock physical parameters of deep thermal reservoir rock samples with temperature and pressure, a rock physical model of deep high-temperature thermal reservoirs is constructed. Based on the rock physics model of deep high-temperature geothermal reservoirs, as well as geological and geophysical data, a typical geological-geophysical model of deep geothermal reservoirs is established to reflect the relationship between geological physical parameters and geophysical property parameters. Forward modeling of the physical equations of the typical geological-geophysical model of deep geothermal reservoirs with selected parameter variables is performed to reveal the evolution law of geophysical response with geothermal reservoir geological characteristic parameters under different temperature, pressure and depth conditions. Based on geological and geophysical data, a high-precision geophysical exploration inversion geological model conforming to the geological structure is constructed and inverted using an inversion algorithm; sensitivity analysis is performed on the geophysical response of the deep geophysical reservoir in the inversion results to obtain sensitive geophysical attribute data; Based on sensitive geophysical attribute data, multi-source data fusion technology is used to obtain sensitive geophysical attribute data volume describing the spatial distribution of deep geothermal reservoirs, so as to establish a geophysical exploration prediction and identification index system for deep geothermal reservoirs. Based on the geophysical exploration prediction and identification index system for deep geothermal reservoirs, the deep geothermal reservoirs in the study area are classified and evaluated, and promising target areas are selected.

2. The prediction method for deep geophysical exploration of geophysical reservoirs as described in claim 1, characterized in that, The prediction method for deep geophysical exploration of geophysical reservoirs also includes: Based on geological and geophysical data, a multi-geophysical parameter geological attribute model with shared grid units is constructed to reveal the distribution patterns of geophysical attribute data that indicate the spatial distribution of thermal reservoir characteristic parameters.

3. The prediction method for deep geophysical exploration of geophysical reservoirs as described in claim 1, characterized in that, The forward modeling of the physical equations for selecting parameter variables in typical geological-geophysical models of deep geothermal reservoirs includes the following steps: Data on thermal reservoir properties were obtained through rock physics experiments. Based on the geological morphology, stratigraphic structure, spatial distribution, and thermal reservoir physical property data, a typical geological-geophysical model of deep thermal reservoirs is constructed. The physical equations for the corresponding variables are selected based on typical geological-geophysical models of deep geothermal reservoirs; Numerical methods are selected to iteratively solve the physical equations; the geological model is optimized based on the solution results and actual data to complete the forward modeling.

4. The prediction method for deep geophysical exploration of geophysical reservoirs as described in claim 1, characterized in that, The specific steps of constructing a high-precision geophysical exploration inversion geological model that conforms to the geological structure and performing inversion using an inversion algorithm include: Preprocessing of geophysical observation data; Using known geophysical parameters, forward modeling studies are conducted to obtain theoretical geophysical responses and establish the correlation between observational data and subsurface geological structures; Based on the complexity of the geological structure and the required research precision, a suitable inversion algorithm should be selected. Based on the selected inversion algorithm, the preprocessed data is subjected to inversion iterative calculations to gradually optimize the inversion model parameters and obtain the inversion model. The inversion results were calibrated using known data to verify their rationality.

5. A predictive system for geophysical exploration of deep geothermal reservoirs, characterized in that, include: The experimental data acquisition module is used to construct a rock physics model of deep high-temperature geothermal reservoirs by utilizing the variation law of rock physical parameters of deep geothermal reservoir rock samples with temperature and pressure. The model forward modeling module is used to establish a typical geological-geophysical model of deep geothermal reservoirs based on the rock physical model of deep high-temperature geothermal reservoirs, as well as geological and geophysical data, to reflect the relationship between geological physical parameters and geophysical attribute parameters. It also performs forward modeling on the physical equations of the typical geological-geophysical model of deep geothermal reservoirs with selected parameter variables, revealing the evolution of geophysical response with geothermal reservoir geological characteristic parameters under different temperature, pressure and depth conditions. The model inversion module is used to construct a high-precision geophysical exploration inversion geological model that conforms to the geological structure based on geological and geophysical data, and to perform inversion through inversion algorithms; it also performs sensitivity analysis on the geophysical response of deep geothermal reservoirs in the inversion results to obtain sensitive geophysical attribute data. The system construction module is used to obtain sensitive geophysical attribute data volumes describing the spatial distribution of deep geothermal reservoirs using multi-source data fusion technology, based on sensitive geophysical attribute data, in order to establish a geophysical exploration prediction and identification index system for deep geothermal reservoirs; and to classify and evaluate deep geothermal reservoirs in the study area based on the geophysical exploration prediction and identification index system for deep geothermal reservoirs, and to select promising target areas.

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