Real-time updating method and system for deep brine resource quantity based on fusion of dynamic monitoring data

Abstract

The application discloses a deep brine resource quantity real-time updating method and system fusing dynamic monitoring data, and particularly relates to the technical field of deep brine reservoir geophysical monitoring, and is used for solving the problems that the existing method is difficult to effectively extract weak geological slow-varying signals from dynamic monitoring data, leading to slow response to long-term evolution of the reservoir and inability to perform early risk warning; time series monitoring data are acquired through an intelligent sensing system, and signal components synchronized with production operations are removed to extract geological slow-varying data, then a deviation degree sequence of key parameters and theoretical constraints is calculated for the geological risks to be monitored to identify potential geological change trends, the trends are verified and spatially positioned through time series causal correlation analysis, then the verified trends and influence areas are used to make targeted correction on physical property parameters in a three-dimensional geological model, and finally, dynamic updating of resource reserve estimation is realized and graded warning information is generated.

Description

Technical Field

[0001] This invention relates to the field of geophysical monitoring technology for deep brine reservoirs, and in particular to a method and system for real-time updating of deep brine resource quantities by integrating dynamic monitoring data. Background Technology

[0002] As an important strategic mineral resource, the accurate assessment and dynamic management of deep brine resources are fundamental to their efficient development and utilization. Current technologies primarily rely on integrating geological exploration, geophysical logging, and geochemical analysis data to construct static geological models, which are then used in conjunction with resource reserve estimation methods for assessment. However, dynamic monitoring networks based on intelligent sensing systems can continuously acquire multi-parameter data such as downhole pressure, temperature, and brine ion concentration to correct model parameters, thereby reflecting reservoir changes and enabling continuous tracking of resource quantities.

[0003] However, due to the significant differences in the time scale between geological condition change signals during deep brine extraction, such as salt formation trends and slow deterioration of physical properties, and frequent data fluctuations caused by production operations, existing methods struggle to effectively distinguish and extract weak, slowly changing key signals hidden in strong background noise when processing these long-term, continuous dynamic monitoring data streams. This results in a slow response of dynamic data-based resource update and risk assessment systems to the long-term, gradual geological evolution of reservoirs, and an inability to provide early warnings of risks that could seriously affect resource recoverability and mining safety, such as a continuous decline in permeability. Consequently, the forward-looking nature and reliability of dynamic resource management are limited. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by providing a method and system for real-time updating of deep brine resources by integrating dynamic monitoring data.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: A method for real-time updating of deep brine resources by integrating dynamic monitoring data includes: S1. Acquire multi-parameter dynamic monitoring data with time-series information through an intelligent sensing system deployed in the target reservoir; S2. Based on the predetermined production operation schedule, remove signal components that change synchronously with production operation events from the multi-parameter dynamic monitoring data to obtain geologically gradual change data that is unrelated to production operations. S3. For the geological risks to be monitored, extract key parameters from the geological gradual change data and calculate the deviation sequence between the actual data and the theoretical constraints. Identify potential geological change trends based on the changing trend of the deviation sequence. S4. Based on the key parameters corresponding to the potential geological change trend, perform time-series causal correlation analysis among the nodes of the intelligent sensing system, construct the causal network topology and identify the causal source nodes, thereby verifying and locating the potential geological change trend, and obtaining the verified geological change trend and its affected area. S5. Using the verified geological change trends and their affected areas, make targeted corrections to the corresponding physical property parameters in the three-dimensional geological model of deep brine reservoirs; S6. After targeted corrections, update the resource reserve estimate and generate early warning information related to the geological risks to be monitored.

[0006] Furthermore, S1 includes: Pressure data is collected through a pressure sensor in the intelligent sensing system, temperature data is collected through a temperature sensor, and concentration data of at least two key brine ions are collected through an ion-selective electrode. Pressure data, temperature data, and concentration data are collected synchronously and marked with a unified timestamp to generate multi-parameter dynamic monitoring data with time-series information.

[0007] Furthermore, S2 includes: Based on the production operation schedule, identify data segments in the multi-parameter dynamic monitoring data that change synchronously with the water injection, brine extraction, or equipment start-up and shutdown operation periods; Remove the identified data segments from the time series of multi-parameter dynamic monitoring data; Interpolation is performed to fill in the discontinuous sequence formed after removing data segments, generating geologically variable data that is unrelated to production operations.

[0008] Furthermore, S3 includes: Based on the type of geological risk to be monitored, determine the theoretical physicochemical constraints used to characterize whether the geological risk has occurred; Pressure, temperature, or ion concentration data are selected from geological gradual change data as key parameters for calculating theoretical physicochemical constraints. The theoretical threshold that satisfies the theoretical physicochemical constraints in real time is calculated based on key parameters. The actual measured values ​​of key parameters are continuously compared with the corresponding theoretical thresholds, and the difference between the two is calculated to form a deviation sequence. By analyzing the changes in the deviation sequence over time, potential geological change trends can be identified when the deviation sequence exhibits a continuous unidirectional change and exceeds the preset fluctuation range.

[0009] Furthermore, based on the type of geological risk to be monitored, the theoretical physicochemical constraints used to characterize whether the geological risk has occurred are determined in the following ways: when the geological risk to be monitored is salt formation risk, the theoretical physicochemical constraints are selected as the relationship between the solubility product constant of a specific salt mineral and temperature; when the geological risk to be monitored is scaling risk, the theoretical physicochemical constraints are selected as the calculation formula for the saturation index of scaling minerals.

[0010] Furthermore, S4 includes: Obtain the time series of key parameters corresponding to potential geological change trends at each sensing node; Calculate the strength and direction of the causal influence between any two different node sequences in a time series; Based on the calculated causal influence strength and direction, a causal network topology is constructed with sensor nodes as vertices and causal relationships as directed edges. In a causal network topology, sensor nodes that are primarily cause-and-effect relationships but not effect-and-effect relationships are identified as causal source nodes. The spatial extent of the causal source node and its adjacent nodes that directly influence the causal network topology is defined as the influence area, and based on this, the potential geological change trend is confirmed as the verified geological change trend.

[0011] Furthermore, in the causal network topology, sensor nodes that are primarily causes rather than effects in causal relationships are identified as causal source nodes in the following way: traverse each sensor node in the causal network topology, count the number of directed edges pointing to the sensor node as the in-degree of the sensor node, and count the number of directed edges from the sensor node to other nodes as the out-degree of the sensor node; determine sensor nodes whose out-degree is significantly greater than their in-degree as nodes that are primarily causes; and determine all sensor nodes that meet the determination criteria as causal source nodes.

[0012] Furthermore, S5 includes: Based on the verified geological change trends, determine at least one type of physical property parameter that needs to be corrected; Based on the spatial location of the affected area, the corresponding area to be corrected is delineated in the three-dimensional geological model of the deep brine reservoir; Based on the characteristics of the verified geological change trend, calculate the correction amount or correction direction of the physical property parameters in the area to be corrected. Based on the calculated correction amount or correction direction, the physical property parameters of the three-dimensional geological model of the deep brine reservoir in the area to be corrected are updated by assigning values, and the corrected three-dimensional geological model of the deep brine reservoir is obtained.

[0013] Furthermore, S6 includes: Based on the revised three-dimensional geological model of deep brine reservoirs, the volumetric method was used to recalculate the estimated brine resource reserves in the target reservoir. Based on the characteristics of the verified geological change trends, the early warning level classification criteria corresponding to the geological risks to be monitored are determined; Based on the matching relationship between the identified potential geological change trends and the early warning level classification criteria, an early warning information text containing risk type, trend description, affected area and early warning level is generated.

[0014] On the other hand, the present invention provides a real-time update system for deep brine resources that integrates dynamic monitoring data, including: The data acquisition module is used to acquire multi-parameter dynamic monitoring data with time-series information through an intelligent sensing system deployed in the target reservoir; The gradual change analysis module is used to remove signal components that change synchronously with production operation events from multi-parameter dynamic monitoring data according to the predetermined production operation schedule, so as to obtain geological gradual change data that is unrelated to production operations. The trend identification module is used to extract key parameters from geological gradual change data and calculate the deviation sequence between actual data and theoretical constraints for geological risks to be monitored. Based on the changing trend of the deviation sequence, potential geological change trends are identified. The regional verification module is used to perform time-series causal correlation analysis between nodes of the intelligent sensing system based on key parameters corresponding to potential geological change trends, construct causal network topology and identify causal source nodes, thereby verifying and locating potential geological change trends and obtaining the verified geological change trends and their affected areas. The model correction module is used to make targeted corrections to the corresponding physical property parameters in the three-dimensional geological model of deep brine reservoirs by utilizing the verified geological change trends and their affected areas. The early warning generation module is used to update resource reserve estimates and generate early warning information related to the geological risks to be monitored after targeted corrections.

[0015] The beneficial effects of this invention are: 1. The dynamic monitoring data stream collected by the intelligent sensing system first accurately strips away the strong interference signals brought about by short-term production operations, thereby extracting key information that purely reflects the slow evolution of the reservoir's own geological, physical, and chemical state. This directly addresses the inherent problem of mixed multi-scale signals in deep brine extraction, enabling subsequent analysis to focus on weak, slow-changing trends that have a long-term impact on resource quantity and extraction safety. This lays a clear and reliable data foundation for the early identification of potential geological risks such as salt formation, scaling, or deterioration of physical properties.

[0016] 2. By establishing a deviation quantification sequence under theoretical constraints and time-series causal verification based on monitoring networks, not only was early and sensitive capture of potential risk trends achieved, but also the objective positioning and confirmation of their spatial origin and impact range were completed. The closed-loop analysis logic from identification to verification to positioning makes the modification of physical parameters of the three-dimensional geological model more targeted, thereby significantly improving the timeliness and accuracy of dynamic updates of resource reserve estimation results. A complete technical chain from dynamic monitoring to model correction, and then to reserve updates and risk warnings has been formed, effectively improving the precision and foresight of dynamic management of deep brine resources and providing reliable technical support for the safe and efficient development of resources. Attached Figure Description

[0017] Figure 1 This is a flowchart of the method for real-time updating of deep brine resources by integrating dynamic monitoring data according to the present invention; Figure 2 This is a schematic diagram of the structure of the deep brine resource real-time update system that integrates dynamic monitoring data according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1 Figure 1 The present invention provides a method for real-time updating of deep brine resources by integrating dynamic monitoring data, including: S1. Acquire multi-parameter dynamic monitoring data with time-series information through an intelligent sensing system deployed in the target reservoir; S2. Based on the predetermined production operation schedule, remove signal components that change synchronously with production operation events from the multi-parameter dynamic monitoring data to obtain geologically gradual change data that is unrelated to production operations. S3. For the geological risks to be monitored, extract key parameters from the geological gradual change data and calculate the deviation sequence between the actual data and the theoretical constraints. Identify potential geological change trends based on the changing trend of the deviation sequence. S4. Based on the key parameters corresponding to the potential geological change trend, perform time-series causal correlation analysis among the nodes of the intelligent sensing system, construct the causal network topology and identify the causal source nodes, thereby verifying and locating the potential geological change trend, and obtaining the verified geological change trend and its affected area. S5. Using the verified geological change trends and their affected areas, make targeted corrections to the corresponding physical property parameters in the three-dimensional geological model of deep brine reservoirs; S6. After targeted corrections, update the resource reserve estimate and generate early warning information related to the geological risks to be monitored.

[0020] S1. Acquire multi-parameter dynamic monitoring data with time-series information through an intelligent sensing system deployed in the target reservoir. Specifically, this is implemented as follows: When deploying an intelligent sensing system in a target reservoir to obtain multi-parameter dynamic monitoring data with time-series information, the first step is to select several representative monitoring locations within the reservoir. These locations are typically determined based on previous geological exploration results to cover the main structural and fluid units of the reservoir. At each monitoring location, a set of sensors is installed. This set includes a pressure sensor, a temperature sensor, and multiple ion-selective electrodes. The type of ion-selective electrodes is determined based on the main ionic composition of the target reservoir brine. For example, for brine reservoirs rich in potassium and sodium ions, the ion-selective electrodes should include at least potassium and sodium ion-selective electrodes; for brine reservoirs simultaneously rich in calcium and sulfate ions, the ion-selective electrodes should also include calcium and sulfate ion-selective electrodes. The pressure sensor measures the pressure of the reservoir fluid, and its range is determined according to the target... The expected pressure range of the reservoir is selected. For example, when the expected pressure range is between 0 and 50 MPa, the pressure sensor range can be 0 to 60 MPa. The temperature sensor is used to measure the temperature of the reservoir fluid, and its measurement range is selected according to the temperature conditions of the target reservoir. For example, when the temperature range is between 0 and 150 degrees Celsius, the temperature sensor range can be -10 to 200 degrees Celsius. Ion-selective electrodes are used to measure the concentration of specific ions in the brine. Each ion-selective electrode responds to only one target ion, and its lower and upper limits of measurement are determined according to the expected concentration range of that ion in the brine. For example, when the potassium ion concentration is expected to be between 1 gram per liter and 200 grams per liter, the measurement range of the potassium ion-selective electrode should cover 0.5 grams per liter to 250 grams per liter. All sensors must be calibrated according to the standard calibration procedure before installation to ensure that their measurement accuracy meets the monitoring requirements.

[0021] During data acquisition, pressure sensors, temperature sensors, and various ion-selective electrodes simultaneously perform measurements at a preset sampling frequency. This sampling frequency is set according to the rate of change in the reservoir's dynamics. For example, for deep brine reservoirs with relatively slow changes, the sampling frequency can be set to collect data once every 10 minutes. During each acquisition, the measurement values ​​acquired by each sensor at the same physical moment are combined into a data packet. This data packet contains one value for pressure, one value for temperature, and one value for the concentration of each monitored ion. Each data packet is assigned a unified timestamp, provided by a high-precision clock within the intelligent sensing system, in the format of Coordinated Universal Time (UTC) year-month-day-hour-minute-second, such as May 10, 2025, 14:30:00. In this way, each acquisition obtains a set of time-aligned pressure data, temperature data, and multiple ion concentration data. Continuous acquisition forms time series of pressure data, temperature data, and various ion concentration data, which together constitute multi-parameter dynamic monitoring data with time-series information.

[0022] To illustrate, suppose that in a deep brine reservoir, the key brine ions to be monitored are potassium, sodium, and chloride ions. In the intelligent sensing system deployed in this reservoir, each monitoring node is equipped with a pressure sensor, a temperature sensor, a potassium ion selective electrode, a sodium ion selective electrode, and a chloride ion selective electrode. The sampling frequency is set to once every 15 minutes. Starting from 00:00:00 on June 1, 2025, the intelligent sensing system simultaneously reads the readings from all sensors every 15 minutes, obtaining, for example, a pressure of 25.3 MPa, a temperature of 85.6 degrees Celsius, a potassium ion concentration of 45.2 g / L, a sodium ion concentration of 120.5 g / L, and a chloride ion concentration of... A set of measurements with a concentration of 180.7 grams per liter is recorded and timestamped as 2025-06-01 00:00:00. The next sampling time is 00:15:00 on June 1, 2025. All sensors are read simultaneously to obtain a new set of measurements, which are then timestamped. This process is repeated to generate a series of data indexed by timestamps, including pressure, temperature, potassium ion concentration, sodium ion concentration, and chloride ion concentration data. This set of sequences constitutes the multi-parameter dynamic monitoring data with time-series information. The source, collection time, and physical meaning of each data point in this data are clear and traceable, providing a complete and consistent input for subsequent data processing steps.

[0023] S2. Based on the predetermined production operation schedule, remove signal components that change synchronously with production operation events from the multi-parameter dynamic monitoring data to obtain geologically gradual change data unrelated to production operations. Specifically, this is implemented as follows: When removing signal components that change synchronously with production operation events from multi-parameter dynamic monitoring data according to the predetermined production operation schedule, it is first necessary to obtain the predetermined production operation schedule. This production operation schedule is a document or database table that records the type, start time, and duration of all planned human intervention operations in the target reservoir. For example, it may clearly record that the start time of water injection operation is 9:00:00 on July 1, 2025, with a planned duration of 6 hours, or that the start time of brine pump start-up operation is 14:00:00 on July 2, 2025, with a planned running time of 8 hours. The production operation schedule is formulated and issued by the production management department before the operation begins, and its time information is synchronized with the time base used by the intelligent sensing system, namely Coordinated Universal Time.

[0024] Based on this production operation schedule, data segments that change synchronously with the water injection, brine extraction, or equipment start-up and shutdown periods in the multi-parameter dynamic monitoring data are identified. This is achieved specifically through the following method: the start time and duration of each operation in the production operation schedule are converted into an absolute time interval. For example, a water injection operation starting at 9:00:00 on July 1, 2025 and lasting for 6 hours corresponds to the time interval from 9:00:00 on July 1, 2025 to 15:00:00 on July 1, 2025. Subsequently, the multi-parameter dynamic monitoring data with time sequence information is traversed. The monitoring data consists of a unique timestamp for each data point. All data points whose timestamps fall within any given work time interval are marked as data points that change synchronously with that type of work. These time-continuous marked data points constitute a data segment that needs to be identified. For accurate identification, a buffer period is usually added both forward and backward from the planned work time interval, for example, by 15 minutes, to cover the signal change process that may occur when the work actually starts or stops, thus ensuring that the signal components affected by the work are completely identified.

[0025] The process of removing identified data segments from the time series of multi-parameter dynamic monitoring data is as follows: First, a new empty data sequence container is created, based on all data points that have changed synchronously with the operational events. Then, each data point in the original multi-parameter dynamic monitoring data is read sequentially according to its timestamp, checking if it belongs to a data segment to be removed. If the data point is not marked, its complete content, including timestamp, pressure data, temperature data, and all ion concentration data, is copied verbatim to the new data sequence container. If the data point is marked as belonging to a data segment to be removed, it is skipped and not copied to the new container. After this process iterates through all the original data points, the new data sequence container stores the discontinuous sequence formed after removing all identified data segments from the original continuous time series. At this point, the new sequence is no longer uniform in the time dimension, and there are gaps in the data within the time period corresponding to each removed data segment.

[0026] To fill in the discontinuous sequence formed after removing data segments by interpolation and generate geologically variable data unrelated to production operations, the process is as follows: First, each data gap segment in the aforementioned discontinuous sequence needs to be processed. For a gap segment located between two known valid data points, linear interpolation is used to generate filling data for the missing moments within the gap segment. Specifically, let the timestamp of the last valid data point before the start of the gap segment be T1, and the pressure data value of this point be P1, the temperature data value be Tmp1, and the concentration data value of each ion be C1i. Let the timestamp of the first valid data point after the end of the gap segment be T2, and the corresponding values ​​of this point be P2, Tmp2, and C2i, respectively. For a missing moment Tx within the gap segment, the corresponding interpolated values ​​of each parameter are obtained through calculation.

[0027] The pressure data interpolation Px is calculated as: Px = P1 + (P2 - P1) × (Tx - T1) / (T2 - T1).

[0028] The temperature data interpolation Tmpx is calculated as follows: Tmpx = Tmp1 + (Tmp2 - Tmp1) × (Tx - T1) / (T2 - T1).

[0029] The interpolation Cxi for each ion concentration data is calculated as follows: Cxi = C1i + (C2i - C1i) × (Tx - T1) / (T2 - T1).

[0030] For each missing time point Tx within the blank segment, a complete set of interpolated data is calculated according to the original sampling frequency. This set of data, along with time point Tx, is then inserted into the sequence as a new data point. Once all blank segments are filled in this way, a temporally continuous and uniform data sequence is obtained. This sequence is geologically gradual change data unrelated to production operations. This geologically gradual change data eliminates short-term strong human operation interference and more purely reflects the slow geological, physical, and chemical changes of the reservoir itself, providing a stable basis for subsequent analysis.

[0031] S3. For the geological risks to be monitored, key parameters are extracted from the geological gradual change data and the deviation sequence between the actual data and theoretical constraints is calculated. Based on the changing trend of the deviation sequence, potential geological change trends are identified. The specific implementation is as follows: When extracting key parameters from geological gradient data and calculating the deviation sequence between actual data and theoretical constraints for geological risks to be monitored, it is first necessary to clarify the specific type of geological risk to be monitored. Common types of geological risks include salt deposition risk and scaling risk. Salt deposition risk refers to the phenomenon where dissolved salt minerals in brine reach supersaturation and precipitate crystals due to changes in physicochemical conditions, which may block reservoir pores or production pipelines. Scaling risk refers to the phenomenon where scaling minerals such as calcium carbonate and calcium sulfate deposit on the surface of wellbore or equipment to form a hard scale layer. Based on the specific type of risk to be monitored, the theoretical physicochemical constraints used to characterize whether the geological risk has occurred are determined. When the geological risk to be monitored is designated as salt deposition risk, the selected theoretical physicochemical constraints are the relationship between the solubility product constant of a specific salt mineral and temperature. For example, if the target reservoir... If the brine is rich in gypsum (calcium sulfate), the corresponding theoretical physicochemical constraint is the solubility product constant of gypsum. This constant is a function of temperature, and its value decreases as temperature increases. This relationship describes that at a specific temperature, when the product of the calcium ion concentration and the sulfate ion concentration in the brine solution reaches the solubility product constant at that temperature, the gypsum minerals reach dissolution equilibrium. If the product of the ion concentrations exceeds this constant, there is a risk of oversaturation and precipitation. When the geological risk to be monitored is designated as scaling risk, such as for calcium carbonate scaling, the selected theoretical physicochemical constraint is the calculation formula for the saturation index of calcium carbonate. This calculation formula is based on a series of parameters such as the ion product constant of water, the dissociation constant of carbonic acid, the calcium ion concentration, alkalinity, and pH value to calculate the saturation index of the solution for calcium carbonate minerals. When the saturation index is greater than zero, it indicates that the solution is supersaturated and has a tendency to scale.

[0032] From the geological gradual change data, pressure, temperature, or ion concentration data are selected as key parameters for calculating the theoretical physicochemical constraints. The specific parameters selected depend entirely on the constraints determined in the previous step. For example, for the risk of gypsum scaling, the theoretical constraint of the gypsum solubility product constant directly depends on the calcium and sulfate ion concentrations, and also indirectly depends on temperature, since the solubility product constant is itself a function of temperature. Therefore, calcium ion concentration, sulfate ion concentration, and temperature data need to be selected as key parameters from the geological gradual change data. For the risk of calcium carbonate scaling, the saturation index calculation requires calcium ion concentration, alkalinity, pH, and temperature data; therefore, these parameters need to be selected as key parameters from the geological gradual change data accordingly. The geological gradual change data is a collection of time-stamped pressure data sequences, temperature data sequences, and various ion concentration data sequences. The time series of the specific parameters required are extracted by name; these extracted parameter time series are the key parameters referred to in this step.

[0033] Theoretical thresholds that satisfy theoretical physicochemical constraints in real time are calculated based on key parameters. A theoretical threshold is a boundary value calculated at each sampling moment based on the values ​​of the key parameters at that time, using the theoretical physicochemical constraint relationship. For example, in a scenario with gypsum scaling risk, for a specific sampling moment, the temperature data in the geological gradient data is first read, and the theoretical value of the solubility product constant KspT at that temperature is calculated based on the empirical relationship between the gypsum solubility product constant and temperature. KspT is the theoretical threshold at that moment, indicating that at the current temperature, when the product of the calcium ion concentration and the sulfate ion concentration reaches this value, the solution is just saturated. In a scenario with calcium carbonate scaling risk, for a specific sampling moment, key parameters such as calcium ion concentration, alkalinity, pH value, and temperature are read, and these are substituted into the theoretical calculation formula for the saturation index to calculate a theoretical saturation index SI. The state where SI equals zero is the theoretical equilibrium threshold point. In this way, a corresponding theoretical threshold is calculated for each moment in the geological gradient data time series, thus forming a theoretical threshold sequence synchronized with the time series.

[0034] The actual measured values ​​of key parameters are continuously compared with their corresponding theoretical thresholds, and the difference between the two is calculated to form a deviation sequence. Specifically, at each sampling time, the actual measured value of the key parameter is substituted into the theoretical physicochemical constraints to calculate an actual state value. For example, for the risk of gypsum salt deposition, the product of the measured calcium ion concentration and the measured sulfate ion concentration at the current time is calculated to obtain the actual ion product Q. Then, this actual ion product Q is compared with the theoretical threshold at that time, i.e., the solubility product constant KspT, and the difference is calculated. This difference is usually expressed in logarithmic or linear difference form. For example, log10(Q / KspT) is calculated, which represents the deviation at that moment. If it is greater than zero, it indicates oversaturation. For the risk of calcium carbonate scaling, the actual saturation index SIactual is calculated based on the measured key parameters. SIactual is itself a measure of deviation from the equilibrium state where SI equals 0. This calculation is repeated for each sampling moment in the time series to obtain a sequence of deviation values ​​arranged in chronological order. Each value in the deviation sequence quantitatively characterizes the degree of deviation of the actual physicochemical state of the reservoir fluid from the theoretical equilibrium conditions at that moment.

[0035] Analyzing the deviation sequence over time, when the deviation sequence exhibits a continuous unidirectional change exceeding a preset fluctuation range, potential geological change trends are identified. First, a fluctuation range threshold needs to be set for judgment. This threshold is determined based on the historical fluctuation statistical characteristics of the deviation sequence under normal production conditions; for example, the standard deviation of the deviation sequence over the past 30 days can be calculated, and the fluctuation range threshold can be set to twice the standard deviation. During analysis, the change pattern of the deviation sequence within a recent time window is examined; for example, the deviation sequence data points of the most recent 7 days are examined, and their slope or fitted trend line is calculated. If the trend line indicates deviation... If the deviation degree continuously increases in a positive direction or continuously decreases in a negative direction, and the duration of this unidirectional change exceeds a preset minimum duration of days, such as 3 days, while the absolute value of the deviation degree also exceeds a preset fluctuation range threshold, then a potential geological change trend is identified. For example, for the risk of salt formation, if the deviation degree sequence continuously and monotonically increases from 0.1 to 0.8 within 7 days, exceeding the fluctuation range threshold of 0.5 calculated based on historical data, then a potential geological change trend of continuously increasing salt supersaturation is identified. This trend indicates that the reservoir may be developing towards a risk state of large-scale salt formation, which requires further verification and attention.

[0036] S4. Based on the key parameters corresponding to potential geological change trends, perform time-series causal correlation analysis among the nodes of the intelligent sensing system, construct a causal network topology and identify the causal source nodes, thereby verifying and locating the potential geological change trends, and obtaining the verified geological change trends and their affected areas. The specific implementation is as follows: When performing time-series causal correlation analysis between nodes of the intelligent sensing system based on key parameters corresponding to potential geological change trends, it is first necessary to obtain the time series of key parameters corresponding to potential geological change trends at each sensing node. The potential geological change trend is identified in step S3, and this trend corresponds to a specific set of key parameters. For example, for the gypsum salt formation trend, the key parameters are calcium ion concentration, sulfate ion concentration, and temperature. The intelligent sensing system deploys multiple sensing nodes in the target reservoir, and each node can independently collect data. From the geologically gradual change data generated in step S2, the time series of key parameters on each sensing node are extracted according to node location and parameter type. These time series are data sequences sampled at equal time intervals, and their length needs to cover the entire time period in which the potential geological change trend is identified. For example, if the trend is identified in the last 7 days, then the time series data of key parameters in the last 7 days for each node are extracted. Each time series has a unified timestamp, thereby ensuring that the data between different nodes are strictly aligned in time.

[0037] The strength and direction of the causal influence between any two different node sequences in a time series are calculated as follows: Two different nodes are selected from all sensing nodes, denoted as node A and node B; the time series of a key parameter on node A is obtained, denoted as sequence X, and the time series of the same key parameter on node B is obtained, denoted as sequence Y; sequences X and Y are of equal length and time-aligned; to evaluate the causal influence of node A on node B, a method based on time lag correlation analysis is used; first, sequence Y is used as the current sequence, and sequence X is time-lag processed to generate a series of lag sequences, such as sequences X lagging by 1 time unit, 2 time units, up to k time units; then, the linear correlation coefficient between sequence Y and each lag sequence of sequence X is calculated; among all lags, the absolute value is taken. The correlation coefficient with the highest value serves as a basic measure of the strength of causal influence, while the lag direction corresponding to the highest correlation coefficient indicates the direction of the causal influence. For example, if the correlation coefficient between sequence X and sequence Y is the highest and positive when lagged by 3 time units, then the physicochemical state change of node A is considered to have been transmitted to node B after 3 time units, meaning that node A has a causal influence on node B, with the direction from A to B. To obtain a more robust value of the strength of causal influence, the highest correlation coefficient can be compared with a significance threshold calculated based on the correlation of the sequences themselves. Only causal relationships exceeding this significance threshold are accepted. This calculation process needs to be performed individually for each parameter type included in the key parameters and for all possible node pairs in pairs, ultimately resulting in a list recording the strength and direction of the causal influence between any two nodes.

[0038] The significance threshold is obtained by randomly rearranging the time series of key parameters to generate multiple sets of random sequences that do not have a real causal relationship, calculating the correlation coefficients between these random sequences and statistically analyzing their distribution, and taking the correlation coefficient value corresponding to the top 5 percentile of the distribution as the significance threshold.

[0039] Based on the calculated causal influence strength and direction, a causal network topology is constructed with sensor nodes as vertices and causal relationships as directed edges. Each sensor node is abstracted as a vertex. The previously calculated list of causal influence relationships is traversed. For each causal relationship deemed valid (i.e., a causal influence strength exceeding the significance threshold), a directed edge is drawn between the vertex representing the cause node and the vertex representing the result node, pointing from the cause node to the result node. A weight value can be attached to the directed edge, which is equal to the calculated causal influence strength. In this way, all sensor nodes and the confirmed causal influence relationships between them together constitute a directed weighted network, which is the causal network topology. This topology intuitively demonstrates the causal transmission path and influence strength of sensor nodes in different spatial locations regarding changes in key parameters.

[0040] In a causal network topology, sensor nodes that are primarily causes rather than effects in causal relationships are identified as causal source nodes. The specific implementation is as follows: For each sensor node in the causal network topology, the number of directed edges pointing to that sensor node is counted as its in-degree, and the number of directed edges from that sensor node to other nodes is counted as its out-degree. The in-degree reflects the degree to which the node is influenced by other nodes as an effect, while the out-degree reflects the degree to which the node influences other nodes as a cause. To determine whether a node is primarily a cause, a comparison threshold needs to be set; for example, a threshold could be set when the out-degree of a node is significantly higher than the in-degree. A node is considered a primary cause when its out-degree is greater than twice its in-degree. This 2x ratio threshold is an empirical value pre-set based on the sparsity of the network topology and the actual geological propagation characteristics. Alternatively, a node is also considered a primary cause when its out-degree is greater than one standard deviation of the average out-degree of all nodes, while its in-degree is lower than the average in-degree. All sensor nodes that meet the criteria are identified as causal source nodes. These causal source nodes appear as the starting points, not the ending points, of multiple directed edges in the causal network topology, indicating that they may be the spatial origins or main driving points of potential geological change trends.

[0041] The spatial extent encompassing the causal source nodes and their directly affected neighboring nodes within the causal network topology is defined as the influence region. Specifically, the actual locations of all sensor nodes identified as causal source nodes are first marked on a geospatial map. Then, within the causal network topology, all neighboring nodes directly connected to these causal source nodes via a directed edge are identified. The actual locations of these neighboring nodes are also marked on the geospatial map. Finally, a continuous geographical area is delineated using the smallest circumscribed convex polygon or based on actual geological boundaries for the spatial locations of all causal source nodes and their directly adjacent nodes. This area is defined as the influence region. This influence region characterizes the spatial extent directly affected by the physicochemical changes induced by the causal source nodes.

[0042] Based on this, the potential geological change trend is confirmed as a verified geological change trend. The confirmation logic is that if, after the potential geological change trend is identified, a clear causal source node and its associated influence area can be found through the above-mentioned temporal causal correlation analysis, it indicates that the trend is not caused by random fluctuations or local disturbances, but has a spatially traceable causal origin and a clear propagation path. This spatial causal consistency is consistent with the physical laws of geological change, thus constituting a strong verification of the authenticity of the potential geological change trend. At this point, the trend changes from a potential state to a verified geological change trend, and its influence area is also spatially located. The verified geological change trend and its influence area will serve as direct inputs for subsequent model corrections.

[0043] S5. Utilizing the verified geological change trends and their affected areas, targeted corrections are made to the corresponding physical property parameters in the three-dimensional geological model of deep brine reservoirs. Specifically, this is implemented as follows: When using verified geological change trends and their affected areas to specifically correct the corresponding physical property parameters in the three-dimensional geological model of deep brine reservoirs, the first step is to determine at least one type of physical property parameter that needs correction based on the verified geological change trends. The verified geological change trends are the results obtained in step S4 and have clear type characteristics, such as being verified as a trend of increased gypsum salt formation or increased calcium carbonate scaling. Physical property parameter types refer to parameters used to describe the physical properties of reservoir rocks, including but not limited to porosity, permeability, brine saturation, and rock compressibility. The specific principle for determination is that there is a known geophysical or geochemical correlation between the trend type and the target physical property parameter. For example, when the verified geological change trend is that salt minerals are supersaturated and may precipitate, the precipitated crystals may occupy reservoir pore space or block pore throats; therefore, the physical property parameter types that need correction are selected as porosity and absolute permeability. When the trend is that scaling minerals precipitate within the reservoir, it will also affect the pore structure; therefore, porosity and absolute permeability are also selected as correction targets. This determination process is a logical judgment based on the fundamental principles of reservoir geology and flow mechanics.

[0044] Based on the spatial location of the affected area, the corresponding area to be corrected is delineated in the three-dimensional geological model of the deep brine reservoir. The affected area is a polygonal region delineated in a planar geographic coordinate system obtained in step S4. The three-dimensional geological model of the deep brine reservoir is a digital model composed of numerous three-dimensional grid units, each of which has its own three-dimensional coordinates in space. The specific method for delineating the area to be corrected is to project the polygonal boundary of the affected area onto the planar coordinates of the three-dimensional geological model and determine all three-dimensional grid units whose planar coordinates fall within the projected polygonal range. At the same time, combined with the vertical stratification data of the reservoir, only grid units belonging to the development segment of the target reservoir are selected. Finally, the set of these selected three-dimensional grid units is delineated as the area to be corrected. This area represents the spatial range directly affected by the verified geological change trend within the model.

[0045] Based on the characteristics of the verified geological change trends, the correction amount or direction of the physical property parameters in the area to be corrected is calculated. The characteristics of the verified geological change trends mainly include their intensity and duration. The intensity of change can be quantified by the mean or maximum value of the deviation sequence calculated in step S3, and the duration is the length of time covered by the identification and verification of the trend. The specific method for calculating the correction amount is to establish an empirical correction coefficient table, which defines the percentage adjustment of physical property parameters corresponding to different intensity ranges and duration ranges. For example, for a salt deposition trend quantified as high intensity and lasting more than 10 days, the porosity is recommended to be reduced by 5% to 8%, and the absolute permeability is recommended to be reduced by 10% to 15%. The correction direction is directly determined by the trend type. For trends such as salt deposition or scaling that cause pore blockage, the correction direction is to reduce the values ​​of porosity and permeability downwards. For a trend of continuously rising reservoir pressure, the correction direction may be to increase the rock compressibility coefficient upwards.

[0046] Based on the calculated correction amount or direction, the physical property parameters of the three-dimensional geological model of the deep brine reservoir within the area to be corrected are updated. Specifically, each three-dimensional grid cell within the area to be corrected is traversed, and the original physical property parameter value of that cell is read, such as the original porosity value Phioriginal. Then, based on the correction amount or direction calculated for that area, new parameter values ​​are generated. For example, if it is determined that the porosity needs to be reduced by 6%, then the new porosity value Phinew = Phioriginal × 0.94. If the correction amount is a range, then that range can be used. The average value or spatial interpolation is used to allocate the values ​​based on the relative positions of the grid cells within the affected area. After traversing and recalculating all grid cells, the new physical property parameter values ​​are assigned to the corresponding grid cells one by one, overwriting their original values. Once all specified types of physical property parameters within the area to be corrected have been updated, a three-dimensional geological model of the deep brine reservoir with the distribution of physical property parameters specifically corrected is obtained. This is the corrected three-dimensional geological model of the deep brine reservoir. This model reflects the latest monitoring and verification of geological changes, providing a more accurate geological basis for subsequent resource quantity calculations.

[0047] S6. After targeted corrections, update the resource reserve estimate and generate early warning information related to the geological risks to be monitored. The specific implementation is as follows: After targeted corrections are completed, when updating resource reserve estimates and generating early warning information related to the geological risks to be monitored, the first step is to recalculate the estimated brine resource reserves in the target reservoir using the volumetric method based on the corrected three-dimensional geological model of the deep brine reservoir. The corrected three-dimensional geological model of the deep brine reservoir is the latest geological model obtained in step S5, and its internal physical properties, such as porosity and brine saturation, have been updated in specific areas based on monitoring results. The volumetric method is a fundamental method for resource reserve calculation. Its core principle is to discretize the entire reservoir into numerous tiny grid cells in three-dimensional space, calculate the brine volume contained in each cell, convert it to resource mass through brine density, and finally accumulate all cells. In practice, each three-dimensional grid cell in the model is traversed, and the three key parameter values ​​of that cell in the corrected model are read: grid cell volume, effective porosity, and halogen saturation. The grid cell volume is calculated by multiplying its length, width, and height. The effective porosity and halogen saturation values ​​are directly read from the corrected model parameter field. The volume occupied by brine within the grid cell is calculated by multiplying the grid cell volume by the effective porosity and then by the halogen saturation value. Then, the average density of the brine at that location is obtained for mass conversion. This density value is obtained in two parallel ways. The first way is through periodic laboratory analysis of brine extracted from different production wells in the target reservoir. Fresh brine samples are weighed and their volumes measured. The average of the measured densities from multiple samples is then used as the density constant representing the entire reservoir. The second approach involves establishing an empirical density calculation model, a multiple linear regression model, whose output is a dynamic density value. The establishment and coefficient acquisition of this model are achieved through the following steps: First, a large number of paired sample data are extracted from a historical database. Each pair includes the laboratory-measured density value of a brine sample, along with temperature, pressure, and total mineralization data calculated based on ion concentration, all recorded simultaneously during sample collection. This data covers various brine samples from different production periods and spatial locations within the target reservoir to ensure data accuracy. Representativeness was assessed; then, temperature, pressure, and total mineralization were used as three independent variables, and the measured density value was used as the dependent variable to construct a multiple linear regression equation. The form of this multiple linear regression equation is: predicted brine density = constant term + temperature × coefficient a + pressure × coefficient b + total mineralization × coefficient c. Next, the least squares method was used to fit all historical sample data. By minimizing the sum of squared errors between the predicted density value and the measured density value, the optimal constant term and the specific values ​​of coefficients a, b, and c that minimize the overall error were obtained. Before the model is put into use, it is also necessary to use another part of reserved historical sample data to verify the model and ensure that its prediction accuracy meets the requirements of resource reserve calculation.Finally, the obtained constant terms and the values ​​of coefficients a, b, and c are solidified into the model. During real-time calculations, only the currently monitored temperature, pressure, and total mineralization data need to be input, and the model can calculate the corresponding dynamic density value based on these coefficients.

[0048] Multiply the brine volume of each grid cell by its corresponding density value to obtain the brine resource mass of that cell; finally, sum the brine resource masses calculated from all grid cells to obtain the updated brine resource reserve estimate in the target reservoir; this estimate is closer to the actual state of the current reservoir than the initial estimate due to the targeted correction of the model's physical property parameters.

[0049] Based on the characteristics of the verified geological change trend, the early warning level classification criteria corresponding to the geological risks to be monitored are determined. The verified geological change trend is obtained from step S4, and its characteristics include change intensity and change rate. The change intensity is quantified by the average value of the deviation sequence during the trend duration in step S3, and this average value is called the trend intensity average value. The change rate is characterized by calculating the amount of change of the deviation sequence per unit time, and is called the trend change rate. The early warning level classification criteria are set to three levels: attention level, early warning level, and alarm level. To classify the levels, two sets of clear thresholds need to be set. The first set is the intensity threshold used to measure the trend intensity average value, and the second set is the rate threshold used to measure the trend change rate. The process of obtaining and setting the intensity threshold is as follows: First, collect all deviation data corresponding to the target reservoir during the long-term normal and stable production period in history, calculate the statistical distribution of the average value of these historical deviation data, and determine its upper limit value. For example, calculate its 95th percentile value and define this value as the normal fluctuation intensity threshold. The upper limit is determined by several factors. Then, based on engineering experience in classifying the severity of risk consequences, upper limits for the intensity thresholds at different levels are sequentially set above the normal fluctuation intensity threshold, creating three consecutive intensity threshold ranges. For example, if the upper limit for the normal fluctuation intensity threshold is calculated to be 0.2, the upper limit for the intensity threshold at the level of concern is set to 0.5, and the upper limit for the intensity threshold at the level of warning is set to 1.0, the intensity threshold ranges are divided as follows: a trend intensity average less than or equal to 0.2 indicates a normal state; greater than 0.2 and less than or equal to 0.5 indicates a level of concern intensity threshold range; greater than 0.5 and less than or equal to 1.0 indicates a level of warning intensity threshold range; and greater than 1.0 indicates a level of alarm intensity threshold range. A similar principle is used to set the rate threshold. By analyzing the statistical characteristics of the trend change rate in historical data, the upper limit for the rate threshold of normal fluctuations is determined, and the upper limits for the rate thresholds at the level of concern and warning are set accordingly. These specific threshold values ​​are pre-configured through the above process before the system is put into operation, and the statistical values ​​are periodically updated with new data during operation to calibrate the thresholds.

[0050] Based on the matching relationship between the identified potential geological change trends and the early warning level classification criteria, an early warning information text containing risk type, trend description, affected area, and early warning level is generated. The identified potential geological change trends are continuously output from step S3, including the calculated average trend intensity and trend change rate. The matching process involves comparing the current potential geological change trend's intensity characteristic, i.e., the average trend intensity, with the intensity threshold range determined in the previous step to determine which intensity threshold range the average trend intensity falls into. Simultaneously, the current potential geological change trend's rate characteristic, i.e., the trend change rate, is compared with the rate threshold range. The final early warning level is determined by the higher of the two: the average trend intensity and the trend change rate. For example, if the current salt deposition trend's average trend intensity is 0.6, it falls into the early warning level intensity threshold range, while its trend change rate increases by 0.25 every 10 days. If this rate value falls into the attention level rate threshold range, the higher level is used, and the final early warning level is determined to be early warning. Subsequently... A structured early warning text is automatically generated. This text follows a predefined template with fixed fields, reserving four keyword slots for filling in risk type, trend description, affected area, and warning level. The risk type is directly obtained from the geological risk types to be monitored, such as salt deposition risk. The trend description is selected from a pre-defined descriptive statement library based on the average trend intensity and the specific value of the trend change rate. For example, when the average trend intensity falls into the warning level and the trend change rate is fast, the statement "Calcium ion and sulfate ion activity product has reached a high level and continues to rise rapidly, significantly increasing the risk of oversaturation" is selected as the trend description. The affected area information is obtained from the affected area description obtained in step S4. The warning level is the matching result, such as "warning level." After filling these specific information into the corresponding fields of the template, a complete early warning text is formed. This text can be output to the monitoring system interface or sent to relevant production management personnel, providing them with real-time, quantitative risk status information for decision-making.

[0051] Example 2: Figure 2 A schematic diagram of the structure of the real-time update system for deep brine resources integrating dynamic monitoring data according to the present invention is provided. The real-time update system for deep brine resources integrating dynamic monitoring data includes: The data acquisition module is used to acquire multi-parameter dynamic monitoring data with time-series information through an intelligent sensing system deployed in the target reservoir; The gradual change analysis module is used to remove signal components that change synchronously with production operation events from multi-parameter dynamic monitoring data according to the predetermined production operation schedule, so as to obtain geological gradual change data that is unrelated to production operations. The trend identification module is used to extract key parameters from geological gradual change data and calculate the deviation sequence between actual data and theoretical constraints for geological risks to be monitored. Based on the changing trend of the deviation sequence, potential geological change trends are identified. The regional verification module is used to perform time-series causal correlation analysis between nodes of the intelligent sensing system based on key parameters corresponding to potential geological change trends, construct causal network topology and identify causal source nodes, thereby verifying and locating potential geological change trends and obtaining the verified geological change trends and their affected areas. The model correction module is used to make targeted corrections to the corresponding physical property parameters in the three-dimensional geological model of deep brine reservoirs by utilizing the verified geological change trends and their affected areas. The early warning generation module is used to update resource reserve estimates and generate early warning information related to the geological risks to be monitored after targeted corrections.

[0052] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0053] It should be noted that this invention can be deployed on the device itself to realize embedded applications, or it can run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0054] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions according to the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wireless or wired transmission; wired transmission methods include optical fiber, twisted pair, coaxial cable, etc.; wireless transmission includes infrared, microwave, etc. Computer-readable storage media can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0055] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0056] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0057] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0058] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0059] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0060] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0061] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for real-time updating of deep brine resource quantity by integrating dynamic monitoring data, characterized in that, include: S1. Acquire multi-parameter dynamic monitoring data with time-series information through an intelligent sensing system deployed in the target reservoir; S2. Based on the predetermined production operation schedule, remove signal components that change synchronously with production operation events from the multi-parameter dynamic monitoring data to obtain geologically gradual change data that is unrelated to production operations. S3. For the geological risks to be monitored, extract key parameters from the geological gradual change data and calculate the deviation sequence between the actual data and the theoretical constraints. Identify potential geological change trends based on the changing trend of the deviation sequence. S4. Based on the key parameters corresponding to the potential geological change trend, perform time-series causal correlation analysis among the nodes of the intelligent sensing system, construct the causal network topology and identify the causal source nodes, thereby verifying and locating the potential geological change trend, and obtaining the verified geological change trend and its affected area. S5. Using the verified geological change trends and their affected areas, make targeted corrections to the corresponding physical property parameters in the three-dimensional geological model of deep brine reservoirs; S6. After targeted corrections, update the resource reserve estimate and generate early warning information related to the geological risks to be monitored.

2. The method for real-time updating of deep brine resources by integrating dynamic monitoring data according to claim 1, characterized in that, S1 includes: Pressure data is collected through a pressure sensor in the intelligent sensing system, temperature data is collected through a temperature sensor, and concentration data of at least two key brine ions are collected through an ion-selective electrode. Pressure data, temperature data, and concentration data are collected synchronously and marked with a unified timestamp to generate multi-parameter dynamic monitoring data with time-series information.

3. The method for real-time updating of deep brine resources by integrating dynamic monitoring data according to claim 1, characterized in that, S2 include: Based on the production operation schedule, identify data segments in the multi-parameter dynamic monitoring data that change synchronously with the water injection, brine extraction, or equipment start-up and shutdown operation periods; Remove the identified data segments from the time series of multi-parameter dynamic monitoring data; Interpolation is performed to fill in the discontinuous sequence formed after removing data segments, generating geologically variable data that is unrelated to production operations.

4. The method for real-time updating of deep brine resources by integrating dynamic monitoring data according to claim 1, characterized in that, S3 includes: Based on the type of geological risk to be monitored, determine the theoretical physicochemical constraints used to characterize whether the geological risk has occurred; Pressure, temperature, or ion concentration data are selected from geological gradual change data as key parameters for calculating theoretical physicochemical constraints. The theoretical threshold that satisfies the theoretical physicochemical constraints in real time is calculated based on key parameters. The actual measured values ​​of key parameters are continuously compared with the corresponding theoretical thresholds, and the difference between the two is calculated to form a deviation sequence. By analyzing the changes in the deviation sequence over time, potential geological change trends can be identified when the deviation sequence exhibits a continuous unidirectional change and exceeds the preset fluctuation range.

5. The method for real-time updating of deep brine resources by integrating dynamic monitoring data according to claim 4, characterized in that, Based on the type of geological risk to be monitored, the theoretical physicochemical constraints used to characterize whether the geological risk has occurred are determined in the following ways: when the geological risk to be monitored is salt formation risk, the theoretical physicochemical constraints are selected as the relationship between the solubility product constant of a specific salt mineral and temperature; when the geological risk to be monitored is scaling risk, the theoretical physicochemical constraints are selected as the calculation formula for the saturation index of scaling minerals.

6. The method for real-time updating of deep brine resources by integrating dynamic monitoring data according to claim 1, characterized in that, S4 include: Obtain the time series of key parameters corresponding to potential geological change trends at each sensing node; Calculate the strength and direction of the causal influence between any two different node sequences in a time series; Based on the calculated causal influence strength and direction, a causal network topology is constructed with sensor nodes as vertices and causal relationships as directed edges. In a causal network topology, sensor nodes that are primarily cause-and-effect relationships but not effect-and-effect relationships are identified as causal source nodes. The spatial extent of the causal source node and its adjacent nodes that directly influence the causal network topology is defined as the influence area, and based on this, the potential geological change trend is confirmed as the verified geological change trend.

7. The method for real-time updating of deep brine resource quantity by integrating dynamic monitoring data according to claim 6, characterized in that, In a causal network topology, sensor nodes that are primarily causes rather than effects in causal relationships are identified as causal source nodes through the following method: traversing each sensor node in the causal network topology, counting the number of directed edges pointing to the sensor node as the in-degree of the sensor node, and counting the number of directed edges from the sensor node to other nodes as the out-degree of the sensor node; sensor nodes whose out-degree is significantly greater than their in-degree are identified as nodes that are primarily causes; and all sensor nodes that meet the criteria are identified as causal source nodes.

8. The method for real-time updating of deep brine resources by integrating dynamic monitoring data according to claim 1, characterized in that, S5 include: Based on the verified geological change trends, determine at least one type of physical property parameter that needs to be corrected; Based on the spatial location of the affected area, the corresponding area to be corrected is delineated in the three-dimensional geological model of the deep brine reservoir; Based on the characteristics of the verified geological change trend, calculate the correction amount or correction direction of the physical property parameters in the area to be corrected. Based on the calculated correction amount or correction direction, the physical property parameters of the three-dimensional geological model of the deep brine reservoir in the area to be corrected are updated by assigning values, and the corrected three-dimensional geological model of the deep brine reservoir is obtained.

9. The method for real-time updating of deep brine resources by integrating dynamic monitoring data according to claim 1, characterized in that, S6 include: Based on the revised three-dimensional geological model of deep brine reservoirs, the volumetric method was used to recalculate the estimated brine resource reserves in the target reservoir. Based on the characteristics of the verified geological change trends, the early warning level classification criteria corresponding to the geological risks to be monitored are determined; Based on the matching relationship between the identified potential geological change trends and the early warning level classification criteria, an early warning information text containing risk type, trend description, affected area and early warning level is generated.

10. A real-time update system for deep brine resources integrating dynamic monitoring data, used to implement the real-time update method for deep brine resources integrating dynamic monitoring data as described in any one of claims 1-9, characterized in that, include: The data acquisition module is used to acquire multi-parameter dynamic monitoring data with time-series information through an intelligent sensing system deployed in the target reservoir; The gradual change analysis module is used to remove signal components that change synchronously with production operation events from multi-parameter dynamic monitoring data according to the predetermined production operation schedule, so as to obtain geological gradual change data that is unrelated to production operations. The trend identification module is used to extract key parameters from geological gradual change data and calculate the deviation sequence between actual data and theoretical constraints for geological risks to be monitored. Based on the changing trend of the deviation sequence, potential geological change trends are identified. The regional verification module is used to perform time-series causal correlation analysis between nodes of the intelligent sensing system based on key parameters corresponding to potential geological change trends, construct causal network topology and identify causal source nodes, thereby verifying and locating potential geological change trends and obtaining the verified geological change trends and their affected areas. The model correction module is used to make targeted corrections to the corresponding physical property parameters in the three-dimensional geological model of deep brine reservoirs by utilizing the verified geological change trends and their affected areas. The early warning generation module is used to update resource reserve estimates and generate early warning information related to the geological risks to be monitored after targeted corrections.

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