Numerical simulation method for long-acting permeation enhancement effect of deep acidification of sandstone reservoir

Abstract

The invention discloses a sandstone reservoir deep acidification long-acting permeation enhancement effect numerical simulation method, and relates to the technical field of rock reservoir acidification transformation processes, and the method comprises the steps: collecting real-time sensor data and geological exploration data of a sandstone reservoir, and making a preliminary acidification operation plan; according to the preliminary acidification operation plan, in combination with real-time data feedback, the preliminary acid liquor concentration, the preliminary injection rate and the preliminary acid liquor injection temperature are optimized, and acidification parameters are output; based on real-time acidification operation monitoring data and sampling feedback data, a multi-field coupling numerical model is constructed and calibrated, the long-acting evolution track of the permeability of the acidified reservoir is simulated, the effective permeation increasing period and the final permeation increasing amount are evaluated, and an acidification long-acting permeation increasing effect simulation report is output. According to the method, the real-time regulation and control capability of acidification operation is improved, a scientific basis is provided for evaluating the economic validity period of acidification transformation, and an innovative solution is provided for efficient development of a deep sandstone reservoir.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of reservoir acidification reconstruction, in particular to a numerical simulation method for long-term permeability enhancement effect of deep acidification of sandstone reservoirs. BACKGROUND

[0002] As an important means to improve oil and gas recovery, the core of sandstone reservoir acidification reconstruction technology is to improve the reservoir permeability through chemical reaction between acid and reservoir minerals. The traditional acidification effect simulation method is mainly based on a static geological model, which simulates the migration law of acid in porous media by establishing a three-dimensional geological attribute model. This method couples the seepage equation and chemical reaction kinetics equation using finite element algorithm, which can better simulate the short-term acidification effect of near wellbore zone. The existing technology has realized the optimization analysis of key parameters such as acid concentration and injection rate, and corrected the key parameters such as mineral reaction rate through history matching method, and has achieved good application effect in the prediction of acidification effect of conventional sandstone reservoirs.

[0003] However, in the acidification reconstruction of deep sandstone reservoirs, the existing technology shows obvious limitations. The permeability evolution after deep reservoir acidification has obvious time-varying characteristics, and the subsequent effects such as secondary mineral precipitation and particle migration will cause the permeability enhancement effect to gradually decay over time. Although there are acidification effect evaluation methods in the existing technology that consider secondary damage, there is a lack of dynamic correlation mechanism with real-time monitoring data, resulting in a large deviation between long-term prediction results and actual permeability enhancement effect. This limitation makes it difficult for operators to accurately assess the economic effective period of deep reservoir acidification reconstruction, affecting the optimization decision of stimulation programs. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a numerical simulation method for long-term permeability enhancement effect of deep acidification of sandstone reservoirs, which solves the problem that the existing technology cannot accurately predict the long-term permeability evolution law after deep reservoir acidification.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a numerical simulation method for long-term permeability enhancement effect of deep acidizing of sandstone reservoirs, which comprises collecting real-time sensor data and geological exploration data of the sandstone reservoirs, formulating a preliminary acidizing operation plan; optimizing the preliminary acidizing concentration, the preliminary injection rate and the preliminary acidizing temperature according to the preliminary acidizing operation plan in combination with real-time data feedback, and outputting acidizing parameters; optimizing the initial injection path of the acidizing fluid in combination with the acidizing parameters, the real-time sensor data and the geological exploration data, and outputting the acidizing fluid injection path; adjusting the acidizing fluid by using the acidizing parameters, and injecting the acidizing fluid into the sandstone reservoirs according to the acidizing fluid injection path, performing acidizing operation while real-time monitoring, and outputting real-time acidizing operation monitoring data; based on the real-time acidizing operation monitoring data and the sampling feedback data, constructing a multi-field coupled numerical model and calibrating the same, simulating the long-term evolution track of the permeability of the reservoirs after acidizing, and evaluating the effective permeability enhancement period and the final permeability enhancement amount, and outputting an acidizing long-term permeability enhancement effect simulation report.

[0008] As a preferred scheme of the numerical simulation method for long-term permeability enhancement effect of deep acidizing of sandstone reservoirs, the step of formulating the preliminary acidizing operation plan comprises the following specific steps,

[0009] The geological exploration data and the real-time sensor data are collected and integrated to generate comprehensive reservoir attribute data.

[0010] Based on the comprehensive reservoir attribute data, a reservoir attribute database is established, and the physical properties in the reservoir attribute database are subjected to permeability analysis to generate acidizing operation information.

[0011] The physical property parameters are extracted from the acidizing operation information, and the acidizing effects under different sandstone reservoir conditions are simulated and compared to generate a preliminary acidizing scheme, and a preliminary acidizing operation plan is formulated.

[0012] The preliminary acidizing operation plan comprises the preliminary acidizing concentration, the preliminary injection rate and the preliminary acidizing temperature.

[0013] As a preferred scheme of the numerical simulation method for long-term permeability enhancement effect of deep acidizing of sandstone reservoirs, the step of outputting the acidizing parameters comprises the following specific steps,

[0014] Based on the preliminary acidizing operation plan, the injection concentration, the flowback concentration, the injection flow rate, the flowback flow rate, the injection pressure and the injection temperature of the acidizing fluid are collected in real time.

[0015] The acidizing fluid consumption rate is calculated according to the injection concentration, the flowback concentration, the injection flow rate and the flowback flow rate of the acidizing fluid.

[0016] The preliminary acidizing concentration of the acidizing fluid is dynamically optimized by using the acidizing fluid consumption rate and the reservoir reaction characteristics, and an optimized acidizing concentration value is outputted.

[0017] According to the injection pressure, injection flow rate and acid consumption rate of the acid liquid, the initial injection rate of the acid liquid is optimized, and the acid liquid distribution range, flow path and permeability reconstruction effect under different injection rates are evaluated in combination with the reservoir reaction characteristics, and the optimal injection rate is output;

[0018] According to the injection pressure, injection temperature, injection flow rate and injection concentration, the formation temperature distribution characteristics and the reaction activity change of the acid liquid under different temperature conditions are comprehensively evaluated;

[0019] According to the reaction activity change, the initial acid injection temperature of the acid liquid is optimized and adjusted, and the optimized acid injection temperature is output;

[0020] The optimized acid concentration value, the optimized acid injection temperature, the optimal injection rate and the real-time feedback acid consumption rate are combined into acidification parameters.

[0021] As an preferred scheme of the sandstone reservoir deep acidification long-acting permeability enhancement effect numerical simulation method, the output acid injection path has the following specific steps,

[0022] The real-time sensor data and geological exploration data are subjected to multi-objective collaborative optimization analysis to generate an acid initial injection path optimization suggestion;

[0023] Based on the acid initial injection path optimization suggestion, the acid initial injection path is optimized in terms of permeability efficiency and distribution uniformity in combination with the acidification parameters, and an acid preliminary injection path is obtained;

[0024] The permeability effect of the acid preliminary injection path under different injection rates is evaluated to obtain an acid permeation evaluation result;

[0025] The acid preliminary injection path is adjusted according to the acid permeation evaluation result, and an acid injection path is output.

[0026] As an preferred scheme of the sandstone reservoir deep acidification long-acting permeability enhancement effect numerical simulation method, the output real-time acidification operation monitoring data has the following specific steps,

[0027] The ratio parameters of the target acid liquid required are adjusted using the acidification parameters to generate the target acid liquid;

[0028] The target acid liquid is segmented and injected into the sandstone reservoir along the acid injection path, and the target acid liquid concentration and the target acid injection rate are adjusted dynamically according to the formation response to perform acidification operation, and real-time acidification operation monitoring data is obtained.

[0029] As an preferred scheme of the sandstone reservoir deep acidification long-acting permeability enhancement effect numerical simulation method, the specific steps of adjusting the ratio parameters of the target acid liquid required to generate the target acid liquid using the acidification parameters are as follows,

[0030] Extract the adjustment target of the acid liquid from the acidification parameters, and combine the real-time sensor data to obtain the dynamic feedback in the acid liquid reaction process, and calculate the optimal concentration range of each acid liquid component;

[0031] Based on the optimal concentration range, combined with real-time sensor data, the reaction rate change is calculated;

[0032] According to the reaction rate change, the direction and amplitude of the required target acid liquid ratio parameter adjustment are judged, and the target acid liquid is obtained.

[0033] As a preferred scheme of the sandstone reservoir deep acidification long-acting permeability enhancement effect numerical simulation method, wherein: the output acidification long-acting permeability enhancement effect simulation report has the following specific steps,

[0034] Periodically sample the sandstone reservoir after acidification operation, obtain sampling feedback data, and fuse with real-time acidification operation monitoring data to generate time series input data set;

[0035] According to the real-time acidification operation monitoring data and the sampling feedback data, a multi-field coupled numerical model is constructed;

[0036] The time series input data set is input into the multi-field coupled numerical model, and the parameter estimation is carried out combined with the acid liquid reaction process and the reservoir physical property evolution law, and the parameter estimation result is output;

[0037] Calibrate the parameter estimation result by using the geological exploration data, correct the multi-field coupled numerical model parameter deviation combined with the physical constraint condition and the stratum prior information, and output the corrected multi-field coupled numerical model;

[0038] According to the real-time acidification operation monitoring data and the sampling feedback data, the evolution process of the reservoir permeability with time is simulated by using the corrected multi-field coupled numerical model, and a prediction period with a daily time step is set to generate a simulated permeability change trend curve;

[0039] According to the simulated permeability change trend curve, the permeability enhancement starting time, peak time and stable time are identified, the permeability enhancement amplitude is calculated, and the time period with the permeability enhancement amplitude exceeding the effective permeability enhancement threshold is defined as the effective permeability enhancement period;

[0040] According to the permeability enhancement in the effective permeability enhancement period, the spatial evolution relationship between the simulated reservoir permeability distribution at the end of the simulation period and the initial injection path is compared, the expansion of the acid liquid effective swept area is evaluated, and the acidification long-acting permeability enhancement effect simulation report is output.

[0041] As a preferred embodiment of the numerical simulation method for the long-term permeability enhancement effect of deep acidizing in sandstone reservoirs according to the present invention, the method involves inputting a time-series input dataset into a multi-field coupled numerical model, combining the acid reaction process with the evolution law of reservoir properties to perform parameter estimation, and outputting the parameter estimation results. The specific steps are as follows.

[0042] The time series input dataset is denoised, missing values ​​are filled and outliers are corrected to obtain a clean dataset, which is then mapped to a multi-field coupled numerical model for bidirectional variational matching to generate simulation results.

[0043] Based on the simulation results, the multi-field coupled numerical model is adjusted in reverse using inversion techniques to generate corrected values ​​for the acid diffusion coefficient and reservoir temperature gradient, and the multi-field coupled numerical model is updated to obtain parameter estimation results.

[0044] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the numerical simulation method for long-term permeability enhancement effect of deep acidizing in sandstone reservoirs as described in the first aspect of the present invention.

[0045] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the numerical simulation method for long-term permeability enhancement effect of deep acidizing in sandstone reservoirs as described in the first aspect of the present invention.

[0046] The beneficial effects of this invention are as follows: By innovatively combining dynamic optimization of acidizing operation parameters with multi-field coupled numerical simulation, precise control and long-term effect prediction of acidizing stimulation in deep sandstone reservoirs are achieved throughout the entire process. Specifically, the dynamic parameter optimization mechanism based on real-time data feedback can adaptively adjust acid parameters according to reservoir reaction characteristics, ensuring the accuracy and safety of acidizing operations. Meanwhile, the numerical simulation method integrating multi-physics coupling and inversion calibration techniques effectively solves the problem of inaccurate prediction of long-term permeability enhancement effects using traditional techniques. The synergistic effect of these two key steps not only improves the real-time control capability of acidizing operations but also provides a scientific basis for evaluating the economic effectiveness of acidizing stimulation. Overall, a closed-loop technical system is formed from operation implementation to effect evaluation, providing an innovative solution for the efficient development of deep sandstone reservoirs. Attached Figure Description

[0047] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Fig. 1 A flowchart for a numerical simulation method of the long-term permeability enhancement effect of deep acidizing in sandstone reservoirs.

[0049] Fig. 2 A flowchart was developed for data acquisition and preliminary acidification planning.

[0050] Fig. 3 A flowchart for dynamic optimization of acidification parameters.

[0051] Fig. 4 Flowchart for optimizing the acid injection path. Detailed Implementation

[0052] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0053] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0054] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0055] Reference Figs. 1-4 This is one embodiment of the present invention, which provides a numerical simulation method for the long-term permeability enhancement effect of deep acidizing in sandstone reservoirs, including the following steps:

[0056] S1: Collect real-time sensor data and geological exploration data of sandstone reservoirs to formulate a preliminary acidizing operation plan.

[0057] S1.1: Collect geological exploration data and real-time sensor data and integrate them to generate comprehensive reservoir attribute data;

[0058] Furthermore, dynamic data of the reservoir temperature field are collected through a downhole distributed temperature sensor array. This data, combined with mineral composition percentage and porosity three-dimensional distribution data obtained from logging while drilling, and reservoir structural morphology data obtained from seismic inversion, constitutes geological exploration data. Simultaneously, wellbore pressure fluctuation data and acid front propulsion velocity data are collected in real time using fiber optic pressure sensors and electromagnetic flowmeters as real-time sensor data. The Kriging spatial interpolation algorithm is used to spatially register the discrete logging data with continuous sensor monitoring data (such as dynamic data continuously collected in real time by the distributed fiber optic temperature monitoring system and wellhead pressure and flow sensors) in a unified three-dimensional coordinate system, generating comprehensive reservoir attribute data including temperature gradient field, mineral reactivity index, and real-time pressure propagation coefficient.

[0059] S1.2: Based on the comprehensive reservoir attribute data, establish a reservoir attribute database, perform permeability analysis on the physical attributes in the reservoir attribute database, and generate acidizing operation information;

[0060] Furthermore, an index table of comprehensive reservoir attribute data is established in the structured database according to well depth coordinates. The stored fields include quartz content, clay mineral dissolution rate, and initial permeability distribution. The Darcy-Fochheimer equation is solved by the finite difference method to calculate the equivalent permeability gradient of different well sections under acid treatment, and acidizing operation information marked with high reaction risk sections and potential blockage sections is generated.

[0061] S1.3: Extract physical property parameters from acidizing operation information, simulate and compare the acidizing effects under different sandstone reservoir conditions, generate preliminary acidizing schemes, and formulate preliminary acidizing operation plans;

[0062] Furthermore, physical property parameters related to acidizing operations are extracted from the acidizing operation information generated by integrating geological exploration data and real-time sensor data. These physical property parameters include basic physical properties such as reservoir porosity, permeability, mineral composition ratio, formation pressure, and formation temperature. Subsequently, based on the extracted physical property parameters, numerical simulation techniques such as the finite difference method (FDM) or the finite element method (FEM) are used to analyze the acidizing process under different sandstone reservoir conditions. By constructing a reservoir geometric model and calibrating physical parameters, inputting acidizing operation parameters, the reaction process between acid and reservoir is simulated, the acidizing effect is evaluated, permeability changes are predicted, and the effective permeability enhancement period and permeability improvement range are output. The simulation results specifically include the flow path, reaction degree, and improvement effect on reservoir permeability of acid in sandstone reservoirs under different acid concentrations, injection rates, and injection temperatures.

[0063] Based on the simulation results, the acidizing effects under different reservoir conditions are compared to evaluate the permeability enhancement potential and acid utilization efficiency of acidizing operations under different reservoir conditions. Combining the simulation results with comparative analysis, a preliminary acidizing scheme is generated. The preliminary acidizing scheme includes preliminary acidizing parameters such as recommended acid concentration range, recommended injection rate range, and recommended injection temperature range for different reservoir conditions.

[0064] Based on the preliminary acidizing scheme and considering the on-site operating conditions and reservoir distribution characteristics, a preliminary acidizing operation plan is formulated. The preliminary acidizing operation plan includes specific operating parameters such as the preliminary acid concentration, the preliminary injection rate, and the preliminary acid injection temperature, which are used to guide the implementation and optimization of subsequent acidizing operations.

[0065] S2: Based on the preliminary acidification operation plan and combined with real-time data feedback, optimize the preliminary acid concentration, preliminary injection rate and preliminary acid injection temperature, and output the acidification parameters.

[0066] S2.1: Based on the preliminary acidification operation plan, collect the injection concentration, backflow concentration, injection flow rate, backflow flow rate, injection pressure and injection temperature of the acid solution in real time;

[0067] Furthermore, during the acidizing operation phase, the injection and flowback concentrations of the acid are simultaneously recorded using downhole sensors and surface monitoring equipment. The injection concentration reflects the chemical composition of the acid entering the reservoir, while the flowback concentration monitors changes in the residual acid concentration in the returned fluid. High-precision flow meters are used to continuously measure the injection and flowback flow rates. The injection flow rate characterizes the volume of acid pumped into the reservoir per unit time, while the flowback flow rate reflects the formation's absorption and flowback dynamics of the acid. Injection pressure data is acquired in real time via pressure transmitters, and injection temperature changes are monitored using temperature sensors. Injection pressure reflects the flow resistance of the acid in the wellbore and near-wellbore zone, while injection temperature affects the reaction rate of the acid in the sandstone reservoir. These parameters are transmitted to the data processing terminal via a data acquisition unit at a fixed sampling frequency, forming a six-dimensional real-time monitoring data sequence including timestamps. During the data acquisition process, the injection concentration was calibrated using a combination of an online pH meter and an ion chromatograph, while the return concentration was verified through titration analysis in a sampling laboratory. The injection and return flow rates were simultaneously measured using a dual-channel electromagnetic flowmeter, with the range error controlled within ±0.5% of the example value. The injection pressure was redundantly calibrated using wellhead and downhole pressure gauges, and temperature measurement employed a sheathed thermocouple array, achieving an accuracy of ±0.5℃ of the example value. All sensor data, after time alignment, were correlated and stored with geological exploration data and parameters such as acid concentration obtained through real-time monitoring and subsequent optimization calculations to ensure synchronized parameter updates and provide input for subsequent acidizing parameter optimization.

[0068] S2.2: Calculate the acid consumption rate based on the acid injection concentration, backflow concentration, injection flow rate, and backflow flow rate;

[0069] Furthermore, the injected acid concentration and the flowback concentration are compared at fixed time intervals to reflect the changes in acid components actually participating in chemical reactions in the reservoir. Simultaneously, the relationship between injected and flowback flow rates is analyzed to characterize the formation's absorption and retention characteristics of the acid. Based on the correlation between concentration change trends and flow rates, the acid consumption rate is derived using the principle of mass balance. A moving average method is used to smooth the data during the calculation process to eliminate the influence of measurement fluctuations.

[0070] S2.3: Dynamically optimize the initial acid concentration based on acid consumption rate and reservoir reaction characteristics, and output the optimized acid concentration value;

[0071] The real-time calculated acid consumption rate is correlated with the optimized acid concentration value in geological exploration data to evaluate the reaction efficiency under the current acid concentration conditions. The acid erosion rate threshold (ERT) is set based on core flow experimental data of the target formation. By establishing the correlation between acid consumption rate and mineral solubility under different temperature and pressure conditions, the rate corresponding to the inflection point of reaction efficiency is taken as the ERT. When the acid consumption rate is consistently higher than the ERT, it indicates that the reservoir minerals are highly reactive. At this time, according to the mineral dissolution rate curve in the reservoir reaction characteristics, the acid concentration is gradually reduced to the optimized acid concentration value; conversely, the acid concentration is appropriately increased with reference to the reservoir temperature gradient data. During the optimization process, the injection pressure change and flowback ion concentration in the acidizing operation monitoring data are monitored in real time to ensure that the concentration adjustment does not lead to excessive dissolution or secondary precipitation in the near-wellbore area. Each concentration adjustment is controlled within 5%-10% of the initial value, and the effect needs to be observed after 2-3 stable monitoring cycles. The final output optimized acid concentration value must meet the dual conditions of fully dissolving the target minerals and avoiding ineffective acid consumption.

[0072] It should also be noted that the data on reservoir response characteristics come from five sources: core analysis data, including test results of sandstone reservoir samples obtained from drilling cores; well logging interpretation results, including clay mineral types and contents provided by natural gamma spectroscopy logging, mineral composition such as quartz and feldspar obtained by elemental capture spectroscopy logging, and fracture development characteristics identified by imaging logging; historical data of adjacent well acidizing operations, integrating acid formulation usage records and construction parameters of adjacent wells in the same formation; formation testing data, including reservoir temperature and pressure data obtained from well tests and fluid sampling analysis results; and geological modeling results, which come from mineral distribution predictions and pore structure analysis data in three-dimensional geological models.

[0073] S2.4: Optimize the initial injection rate of acid based on the injection pressure, injection flow rate and acid consumption rate, and evaluate the acid distribution range, flow path and permeation transformation effect under different injection rates in combination with reservoir reaction characteristics, and output the optimal injection rate.

[0074] Furthermore, the optimization process for the injection rate based on acid injection pressure, injection flow rate, and acid consumption rate is as follows: First, a real-time correlation curve between injection pressure and injection flow rate is established. When the increase in injection pressure exceeds the reservoir fracturing pressure gradient, based on experimental data and reservoir characteristics, if the acid reaction is too rapid, the injection flow rate can be appropriately reduced by 5%-10% to ensure uniform acid penetration and avoid excessive consumption. The adjustment range is set based on historical data, reservoir permeability, and reaction rate. The specific value can be dynamically optimized based on field monitoring feedback. Simultaneously, the trend of acid consumption rate changes is analyzed. If the trend of acid consumption rate changes is consistently lower than the expected value, the injection rate is appropriately increased by 3%-5% with reference to the mineral dissolution rate curve in the reservoir reaction characteristics. After each adjustment, the actual distribution range and flow path changes of acid in the reservoir are evaluated using the temperature field distribution in geological exploration data and the simulated predicted reservoir fracturing pressure gradient. Combined with the porosity and permeability distribution characteristics in geological exploration data, the adjustment of the injection rate is verified to achieve the expected ripple effect. Ultimately, the optimal injection rate was selected as the injection rate that could maintain a reasonable injection pressure while ensuring effective consumption and uniform distribution of acid. The optimal injection rate must simultaneously meet the dual requirements of preventing excessive dissolution in the near-wellbore area and achieving sufficient modification in the far-wellbore area.

[0075] It should also be noted that the reservoir fracture pressure gradient was determined based on rock mechanics parameters and fracturing data from adjacent wells.

[0076] S2.5: Based on the injection pressure, injection temperature, injection flow rate and injection concentration, comprehensively evaluate the formation temperature distribution characteristics and the changes in the reactivity of the acid under different temperature conditions;

[0077] Furthermore, the comprehensive evaluation process based on injection pressure, injection temperature, injection flow rate, and injection concentration is as follows: First, the real-time injection temperature data is compared and analyzed with the original formation temperature distribution in the geological exploration data to establish the coupling relationship between the wellbore and formation temperature fields. When the injection flow rate exceeds the critical range, the example value of the injection flow rate is immediately reduced by 5%-10% and stabilized for 2-3 monitoring cycles. The critical range is determined by historical data and experimental studies, aiming to ensure that the acid reaction is not too rapid, to ensure uniform penetration, and to avoid excessive acid consumption. When the heat exchange effect is strong, the example value of the injection flow rate is immediately adjusted by 2%-3%. This value is determined through experimental studies and reservoir simulation to balance the heat exchange effect and ensure the uniformity of the acid reaction. Simultaneously, the injection pressure data collected in real-time by downhole sensors and surface monitoring equipment is analyzed to determine the trend. The influence of injection temperature on acid viscosity and flow resistance is analyzed. When the injection pressure fluctuation exceeds the critical value determined by historical data, experimental studies, or engineering experience, the injection temperature is corrected by referring to the temperature-reactivity curve in the reservoir reaction characteristics. For acid solutions injected at different concentrations, the actual reactivity under the current temperature field is assessed by real-time monitoring of changes in different injection concentrations. If the dissolution rate of key minerals deviates from the expected value by more than the critical value determined by experimental data, historical operation data, or technical requirements, the injection temperature and flow rate are adjusted synchronously to ensure that the acid activity and the formation temperature field achieve optimal matching. This ultimately forms a temperature-pressure-flow rate coordinated control scheme that satisfies the reservoir temperature field characteristics and maintains a stable chemical reaction rate.

[0078] S2.6: Optimize and adjust the initial acid injection temperature based on changes in reactivity, and output the optimized acid injection temperature;

[0079] Furthermore, based on real-time collected parameters such as acid injection temperature, injection pressure, injection flow rate, and injection concentration, combined with the formation temperature distribution characteristics of the reservoir, the changes in acid reactivity under different temperature conditions were analyzed. Subsequently, by utilizing the variation in reaction rate between acid and reservoir minerals at different temperatures, the reaction efficiency of the acid at the current initial acid injection temperature was evaluated to determine whether it is conducive to the formation of effective dissolution channels and the improvement of reservoir permeability. If the reactivity is too high, causing the acid to react rapidly and be exhausted prematurely in the near-wellbore zone, the acid injection temperature is appropriately reduced to slow down the reaction process and promote the migration of acid to deeper reservoirs; conversely, if the reactivity is too low, and the acid does not react sufficiently in the reservoir, the acid injection temperature is appropriately increased to enhance the acid-rock reaction efficiency. Combining the impact of different acid injection temperature schemes on the reaction rate, dissolution range, and permeability improvement effects, the acidizing operation effect under each temperature condition is comprehensively evaluated, and the optimized acid injection temperature that achieves the best balance between acid reaction efficiency and deep stimulation effect is finally determined. The final output of the optimized acid injection temperature, together with the optimized acid concentration and the optimal injection rate, constitutes the acidification parameters, which are used to guide the execution and real-time adjustment of subsequent acidification operations.

[0080] S2.7: Combine the optimized acid concentration value, optimized acid injection temperature, optimal injection rate, and real-time feedback acid consumption rate into acidification parameters.

[0081] Furthermore, after obtaining the four key parameters—acid concentration, injection rate, acid consumption rate, and reservoir temperature—the following steps will be taken to integrate them into a complete acidizing parameter system. First, the optimized acid concentration value will be combined with the reservoir formation temperature distribution characteristics, and the optimal range of the optimized acid concentration value will be adjusted according to the reactivity characteristics. Next, the optimized acid concentration value will be combined with the reservoir formation temperature, comprehensively considering the acid reaction efficiency and flow path evolution to ensure a uniform distribution of the acid injection rate across the reservoir temperature and maximize the permeability enhancement effect. Subsequently, the injection rate and optimized acid concentration value will be dynamically corrected based on real-time feedback of the acid consumption rate to adapt to changes in actual operations. Finally, all the above optimized parameters will be integrated, and a complete acidizing parameter system will be gradually constructed according to the order of their physical effects. This acidizing parameter system will be used to adjust and optimize various operations during the acidizing process in real time to ensure the best acidizing effect is achieved.

[0082] S3: Combine acidification parameters with real-time sensor data and geological exploration data to optimize the initial acid injection path and output the acid injection path;

[0083] S3.1: Perform multi-objective collaborative optimization analysis on real-time sensor data and geological exploration data to generate optimization suggestions for the initial acid injection path;

[0084] Furthermore, real-time sensor data and geological exploration data are integrated into a comprehensive reservoir attribute dataset, including the reservoir's physical properties and real-time monitored acid injection data. Next, a multi-objective collaborative optimization analysis method is used to optimize the permeability and uniformity of the initial acid injection path. During the optimization process, real-time sensor data feedback is incorporated to identify the permeability of acid in the reservoir under different injection paths, and the uniformity of the path distribution is evaluated. Finally, optimized suggestions for the initial acid injection path are generated. These suggestions consider the reservoir's reactivity and flow characteristics while ensuring the permeability of the injection path, striving to achieve the best acidizing effect.

[0085] S3.2: Based on the optimization suggestions for the initial acid injection path, the penetration efficiency and distribution uniformity of the initial acid injection path are optimized by combining acidification parameters to obtain the initial acid injection path;

[0086] Furthermore, based on real-time monitoring data and reservoir geological data obtained through previous geological exploration, combined with reservoir physical properties and acidizing operation feedback information, the initial acid injection path was determined. Multiple candidate paths were generated based on mission objectives, flight area limitations, and environmental data, and these candidate paths were dynamically adjusted based on real-time feedback to optimize the acid injection effect. Based on the initial acid injection path optimization recommendations and the currently determined acidizing parameters, the flow characteristics and reaction behavior of the acid under different path conditions (such as high-permeability zones, low-permeability zones, and fractured zones) were analyzed. Subsequently, the permeability efficiency of the acid in the reservoir was calculated. The permeability efficiency was evaluated based on the pressure drop change, penetration ability, and dissolution intensity of the acid during flow along the path, prioritizing paths with higher permeability efficiency. Simultaneously, the uniformity of acid distribution in each candidate path was quantitatively evaluated. The evaluation of uniformity was based on the flow distribution ratio, reaction degree differences, and impact range of the acid in each branch path within the reservoir (such as natural fractures, high-permeability zones, and low-permeability zones), selecting paths with better uniformity. Next, a multi-objective trade-off was made between penetration efficiency and distribution uniformity, and the path that achieves the overall optimality between the two was selected as the initial acid injection path.

[0087] It should also be noted that the permeability threshold is dynamically set based on the optimal injection rate range in the acidification parameters, combined with the pore throat permeability index calculated by the Darcy-Brinkman equation.

[0088] S3.3: Evaluate the acid penetration effect of the initial acid injection path at different injection rates and obtain the acid penetration evaluation results;

[0089] Furthermore, based on the initial acid injection path, multiple gradient injection rate schemes were established to simulate the flow and permeation effects of acid under different construction conditions by combining reservoir physical parameters (such as permeability and porosity), formation pressure distribution, and acid characteristics. For each gradient injection rate scheme, the flow behavior of acid along the path was simulated using reservoir physical characteristics and acid flow patterns, including acid penetration depth, sweep range, and pressure distribution along the path. Next, the reaction efficiency between acid and reservoir minerals was analyzed under each injection rate condition. The intensity of acid's dissolution effect on the reservoir was quantified by calculating the acid consumption rate and the amount of dissolution products generated, thereby evaluating the permeation effect. Simultaneously, the impact of different injection rates on the uniformity of acid distribution was considered, evaluating the rationality of flow distribution in each branch path and its effect on improving overall permeability. Then, based on the above analysis results, a comprehensive evaluation of the acid permeation effect under each gradient injection rate scheme was conducted, identifying the injection rate that achieves the best permeability improvement effect and obtaining the acid permeation evaluation results.

[0090] S3.4: Adjust the initial acid injection path based on the acid penetration assessment results and output the acid injection path;

[0091] Furthermore, based on the assessment data of acid penetration depth, sweep range, reaction efficiency, and distribution uniformity under different injection rates in the acid permeability assessment results, flow-restricted areas, inadequately reacted areas, or unevenly distributed nodes in the current initial acid injection path are identified. Subsequently, combined with the spatial distribution characteristics of reservoir physical parameters, including porosity, permeability, and mineral composition ratio, key flow nodes in the initial acid injection path are locally optimized and adjusted. The adjustment methods include expanding the acid flow channel to areas with low permeability stimulation potential, bypassing high-resistance areas, or adding branch paths to improve the acid sweep range.

[0092] The adjusted injection path was matched and validated with acidizing parameters, including the flow stability of the acid, reaction uniformity, and potential for improving reservoir permeability within the adjusted path. Finally, the final acid injection path was output, ensuring an optimal balance between physical feasibility, reaction efficiency, and distribution uniformity.

[0093] S4: Adjust the acid solution using acidizing parameters and inject it into the sandstone reservoir according to the acid injection path to carry out acidizing operations while monitoring in real time and outputting real-time acidizing operation monitoring data.

[0094] S4.1: Adjust the ratio parameters of the target acid solution using acidification parameters to generate the target acid solution;

[0095] Furthermore, optimized acid concentration, injection temperature, optimal injection rate, and real-time acid consumption rate were extracted from the acidification parameters. These parameters, combined with dynamic parameters such as pressure distribution, temperature distribution, injection flow rate, and flowback flow rate collected from real-time sensors, were used to analyze the reaction behavior and dissolution capacity of the acid under the current injection conditions. Subsequently, based on the changes in the acid-rock reaction rate between the acid and reservoir minerals, the reaction kinetics of different acid components under the current reservoir conditions were calculated, determining the effective concentration range of each acid component under different temperature and pressure conditions. Seepage data at different acid concentrations were obtained through core flow experiments and analyzed using numerical calculation methods to evaluate the permeability of the acid in the reservoir, analyze the reaction rate between the acid and reservoir minerals, and calculate the trend of reaction rate changes. Based on the trend of reaction rate changes, the direction and magnitude of the required adjustment of the target acid ratio parameters were determined. If the reaction rate is too high, leading to premature acid consumption, the concentration ratio of highly reactive components in the acid was reduced; if the reaction rate is too low, resulting in insufficient dissolution, the concentration ratio of active components in the acid was increased. Ultimately, under the premise of ensuring that the acid has a reasonable reaction rate and dissolving ability in the reservoir, the specific component ratio and effective acid concentration of the acid are determined by comprehensively considering the acid stability, corrosion control and reservoir compatibility, and the target acid is generated for subsequent segmented injection and real-time adjustment along the acid injection path.

[0096] S4.2: Inject the target acid into the sandstone reservoir in segments along the acid injection path, and dynamically adjust the target acid concentration and injection rate according to the formation response to carry out acidizing operations, and obtain real-time acidizing operation monitoring data;

[0097] Furthermore, based on the determined acid injection path, the target acid is injected into the sandstone reservoir sequentially according to the set injection segments. Each injection segment corresponds to a different physical property region in the reservoir, including high-permeability zone, low-permeability zone, and potential dissolution enhancement zone.

[0098] During the injection process, parameters such as injection temperature, injection pressure, injection flow rate, and injection concentration are collected in real time to assess the flow state and reaction process of the acid in the reservoir. Subsequently, based on the collected reservoir geological data and real-time monitoring feedback, the acid injection path is determined and dynamically adjusted according to real-time feedback to ensure uniform acid distribution in the reservoir, thereby optimizing the injection effect. The target acid concentration is dynamically adjusted based on the changing trends of the flowback concentration and acid consumption rate. If the flowback concentration rises rapidly and the acid consumption rate decreases, it indicates that the acid reaction is nearing its end, and the acid concentration is appropriately reduced. If the flowback concentration remains at a low level and the acid consumption rate is stable, the acid concentration is maintained or slightly increased to prolong the reaction time. The fracturing test results are obtained through real-time data such as pressure and flow rate collected during the fracturing operation, reflecting the fracture characteristics of the reservoir.

[0099] Based on collected well logging data and fracturing test results, including reservoir porosity, permeability, and stress distribution information, the acid injection path is determined. The path is dynamically adjusted based on real-time monitoring results to optimize the acid injection effect. Through a combination of core experiments, formation lithology analysis, and numerical simulation, the formation fracture pressure limit is calculated and verified. Based on the formation fracture pressure limit determined by well logging data and fracturing test results, as well as historical production data and simulation results, a safety threshold is set. If the injection flow rate exceeds the set safety threshold, the injection rate will be reduced to avoid formation fracturing or fracture blockage. If the injection pressure is stable and flowback is smooth, the injection rate will be appropriately increased to expand the acid reach.

[0100] Throughout the acidizing operation, real-time acidizing monitoring data is continuously recorded and output, including the target acid concentration, target acid injection rate, actual injection pressure, actual backflow parameters, and acid consumption rate for each injection segment. This data is used for numerical simulation and effect evaluation of the long-term permeability enhancement effect of subsequent acidizing.

[0101] S5: Adjust the ratio parameters of the target acid solution using acidification parameters to generate the target acid solution;

[0102] S5.1: Extract the adjustment target of acid solution from acidification parameters, and combine it with real-time sensor data to obtain dynamic feedback in the acid reaction process;

[0103] Furthermore, control targets related to acid adjustment are extracted from the acidification parameters, including optimizing the acid concentration, the optimal injection rate, and the optimal acid injection temperature. These adjustment targets are used to dynamically regulate the acid component ratio and injection operation strategy during the acid injection process.

[0104] During acid injection operations, real-time sensor data is continuously collected to characterize the flow state, distribution characteristics, and chemical reaction process of acid within the reservoir and with reservoir minerals. Based on the difference between the collected injection concentration and the flowback concentration, and combined with the real-time comparison between the injection flow rate and the flowback flow rate, the acid consumption rate at the current injection stage is calculated. The acid consumption rate is used to quantitatively assess the degree of reaction between acid and reservoir minerals.

[0105] Based on the trends in injection and flowback pressures, the flow resistance of the acid in the reservoir is analyzed to determine whether dynamic changes occur, thereby inferring whether dissolution is underway or nearing completion. Finally, the acid consumption rate is analyzed using multiple parameters to generate dynamic feedback information on the reaction process of the acid in the reservoir.

[0106] S5.2: Calculate the optimal concentration range for each acid component based on the dynamic feedback during the acid reaction process;

[0107] Furthermore, based on dynamic feedback information, laboratory core flow experiments and numerical simulations were used to analyze the reactivity and dissolution efficiency between the acid and reservoir minerals. Subsequently, by combining the reaction kinetics of each component in the acid (such as hydrochloric acid, hydrofluoric acid, corrosion inhibitors, and stabilizers) under different temperature, pressure, and concentration conditions, the correspondence between acid component concentration and reaction rate was established. Based on the current reservoir conditions and acid reaction state, the reasonable range of action for each acid component in maintaining a reasonable reaction rate, controlling the dissolution range, and preventing excessive corrosion was determined. Combined with the trend of acid consumption rate changes, the effective concentration range that each acid component should maintain in the current injection stage was calculated. Finally, while ensuring that the overall acid reaction efficiency meets the reservoir stimulation requirements, the optimal concentration range of each acid component in the current injection stage was determined by comprehensively considering acid stability, corrosion control, and reservoir compatibility.

[0108] S5.3: Based on the optimal concentration range and combined with real-time sensor data, calculate the change in reaction rate;

[0109] Furthermore, based on the optimal concentration range of each acid component determined in the previous step, the theoretical reaction rate of each acid component at the current concentration is used as a baseline value. Subsequently, combined with real-time sensor data, the actual reaction environment conditions of the acid in the reservoir are calculated, including residence time, flow state (calculated via Reynolds number), temperature gradient, and concentration decay trend. Next, based on the known reaction kinetic characteristics of the acid components, using the law of conservation of mass and the reaction rate equation, combined with the change in the difference between the backflow concentration and the injection concentration, the actual reaction rate between the acid and reservoir minerals at the current stage is estimated. Simultaneously, the trend of injection pressure changes is used to determine whether dissolution in the reservoir continues, and the change in backflow flow rate is used to analyze whether the acid has penetrated the reaction zone or entered a new unreacted zone, thereby identifying whether the reaction rate has undergone a phased change.

[0110] The above analysis results are combined to form a dynamic projection of the reaction rate change of acid in the reservoir, which is used to guide the real-time adjustment of subsequent acid concentration and injection rate, ensuring that acidizing operations continue within the target reaction rate range.

[0111] S5.4: Based on the change in reaction rate, determine the direction and extent of adjustment of the required target acid solution ratio parameters, and obtain the target acid solution;

[0112] Furthermore, based on changes in the reaction rate, the analysis examines whether the current reaction intensity between the acid and reservoir minerals meets the expected dissolution target. If injection parameters (such as injection flow rate, pressure, and temperature) exceed the set safety threshold, the injection strategy is immediately adjusted to avoid damage to the formation. The critical reaction rate threshold is calculated based on the reservoir's geological characteristics and historical data, combined with historical production data and real-time sensor data, to set a reasonable upper limit for the reaction rate. If the acid reaction rate exceeds the set reasonable upper limit, it indicates that the acid may be exhausted prematurely in the near-wellbore area. In this case, it is determined that the acid concentration needs to be adjusted, prioritizing a reduction in acid concentration, and determining the specific reduction ratio based on the extent to which the acid reaction rate exceeds the limit. Conversely, if the reaction rate is below the reasonable lower limit, it indicates insufficient acid reaction and inadequate dissolution, and the direction of acid concentration adjustment is determined to be an increase in concentration, with the specific increase ratio determined based on the degree of low reaction rate.

[0113] Next, the rationality of the concentration adjustment direction is verified by combining real-time sensor data to prevent misjudgments due to uneven local reactions or changes in the flow path. Subsequently, based on determining the direction and magnitude of acid concentration adjustment, the optimal concentration range requirements of each component in the acid and its compatibility with reservoir properties are comprehensively considered to adjust the acid component ratios and generate a target acid that meets the current formation reaction conditions. The final output target acid includes the adjusted acid concentration value and the proportions of each component, which is used to guide the real-time preparation and injection control of acid in subsequent acidizing operations.

[0114] S6: Based on real-time acidizing operation monitoring data and sampling feedback data, construct and calibrate a multi-field coupled numerical model to simulate the long-term evolution trajectory of reservoir permeability after acidizing, evaluate the effective permeability enhancement period and the final permeability enhancement amount, and output a simulation report on the long-term permeability enhancement effect of acidizing.

[0115] S6.1: Periodically sample the sandstone reservoir after acidizing operations, obtain sampling feedback data, and fuse it with real-time acidizing operation monitoring data to generate a time series input dataset;

[0116] After acidizing operations are completed, sandstone reservoirs are sampled on-site at predetermined time intervals. The collected samples include core samples, flowback fluid samples, and produced fluid samples, used for laboratory analysis of changes in reservoir physical parameters, such as porosity, permeability, mineral composition changes, residual acid concentration, and types of dissolution products. Subsequently, key physical parameter change data are extracted from the laboratory analysis results to form sampling feedback data. This feedback data includes reservoir permeability changes at different sampling time points, pore structure evolution characteristics, residual acid reaction information, and dissolution intensity.

[0117] The aforementioned sampled feedback data and real-time acidification operation monitoring data were time-aligned and fused. Then, the fused data was organized into timestamped data entries according to time order. Each data entry contained the sampled feedback information and acidification operation monitoring parameters at the corresponding time point, ultimately generating a time-series input dataset for subsequent numerical simulations.

[0118] S6.2: Construct a multi-field coupled numerical model based on real-time acidification operation monitoring data and sampling feedback data;

[0119] Furthermore, based on real-time acidizing operation monitoring data, dynamic characteristic parameters of acid flow and reaction in the reservoir are extracted. Simultaneously, combined with reservoir property parameter changes provided by sampling feedback data, the acid formulation is dynamically adjusted, the injection rate is optimized, and the reaction process is monitored and potential risks are prevented, thus describing the effect of acidizing on reservoir structure modification. Subsequently, based on core flow, batch reaction, and heat conduction test results, various parameters in the acid reaction process are analyzed. Combined with the actual reservoir temperature, pressure, and acid reaction characteristics, the composition and concentration of the acid are adjusted to ensure uniform acid distribution in the reservoir, thereby improving reaction efficiency. Numerical simulations (such as multiphysics coupling models and reaction kinetic models), historical production data, and real-time sensor data are used to obtain the physical flow laws, chemical reaction kinetics, and thermodynamic conduction mechanisms in the reservoir. Control equations describing the acid flow field, reaction field, and temperature field in the reservoir are established separately, and these three are coupled through boundary conditions and initial conditions to construct a multi-field coupled numerical model covering the interactions of flow, reaction, and heat transfer.

[0120] S6.3: Input the time series input dataset into the multi-field coupled numerical model, combine the acid reaction process with the evolution law of reservoir properties to perform parameter estimation, and output the parameter estimation results;

[0121] Furthermore, the time-series input dataset is sequentially input into the constructed multi-physics coupled numerical model according to the timestamp order. Subsequently, combining the chemical kinetic characteristics of the acid reaction process with the evolution law of reservoir properties, the model parameters involved in the multi-physics coupled model are dynamically matched and estimated. During this process, the model is continuously optimized and calibrated by combining experimental data and real-time field feedback to ensure that the parameters are highly consistent with the actual situation. Then, the parameter optimization algorithm is used to iteratively calculate and adjust the errors between the output results of the multi-physics coupled model, including the pressure distribution, flow velocity distribution, reaction rate distribution, and temperature distribution of the reservoir, and the actual monitoring data, gradually approximating the optimal parameter combination that conforms to the actual acidizing process, and outputting the parameter estimation results.

[0122] S6.4: Use geological exploration data to calibrate the parameter estimation results, combine physical constraints and prior stratigraphic information to correct the parameter deviation of the multi-field coupled numerical model, and output the corrected multi-field coupled numerical model.

[0123] Furthermore, based on the parameter estimation results, prior formation information provided by geological exploration data, such as the original reservoir porosity, permeability distribution, mineral composition ratio, fracture development characteristics, and description of formation heterogeneity, is used as physical constraints to verify the rationality of the parameters in the multi-field coupled numerical model. Next, by comparing the differences between the parameter estimation results and the original reservoir state reflected in the geological exploration data, data assimilation techniques, sensitivity analysis, and optimization algorithms are used to identify parameter terms with biases in the multi-field coupled numerical model (including fluid flow, chemical reaction, and heat conduction models). Based on the reservoir physical characteristics and acid reaction laws, reservoir physical properties such as permeability, reaction rate constant, thermal conductivity, elastic modulus, and fracture pressure gradient are analyzed. These data, derived from real-time monitoring data, geological exploration data, and reservoir physical property data obtained through sampling, undergo local correction to ensure that the value range conforms to the actual reservoir physical properties and acid-rock reaction mechanism. Simultaneously, physical conservation constraints, such as mass conservation, energy conservation, and chemical equilibrium conditions, are introduced during the correction process to ensure the physical consistency of the corrected parameter combination. Finally, under the premise of satisfying the geological exploration data and physical constraints, a corrected multi-field coupled numerical model is output. The multi-field coupled numerical model includes the calibrated flow field, reaction field and temperature field control equations and coupling relationships, which are used for subsequent prediction and optimization analysis of the long-term permeability enhancement effect of acidizing.

[0124] S6.5: Based on real-time acidizing operation monitoring data and sampling feedback data, the modified multi-field coupled numerical model is used to simulate the evolution of reservoir permeability over time, and a prediction period with a daily time step is set to generate a simulated permeability change trend curve.

[0125] Furthermore, real-time acidizing operation monitoring data was used as the input boundary conditions for the corrected multi-field coupled numerical model. Subsequently, based on the calibrated flow, reaction, and temperature field control equations and coupling relationships in the multi-field coupled numerical model, and within a set spatial discrete grid and a time step of daily units, the flow, reaction, and heat transfer processes of acid in the reservoir were numerically simulated using the multiphysics coupled model. Key parameters such as reaction rate, temperature change, and heat exchange efficiency were obtained, providing a basis for subsequent optimization of the acid injection path and adjustment of the injection rate. Within each time step, the degree of reaction between the acid and reservoir minerals, the amount of dissolution products generated, and the evolution of pore structure were calculated, and the change in reservoir permeability was estimated based on the porosity-permeability conversion coefficient. Then, the permeability calculation results at each time step were concatenated in chronological order to form a simulated curve of reservoir permeability evolution over time. The final output simulated permeability trend curve covers the reservoir permeability evolution characteristics from the short to long term after acidizing operations, and is used to evaluate the long-term permeability enhancement effect of acidizing and guide the adjustment of subsequent development strategies.

[0126] S6.6: Identify the initiation time, peak time and stabilization time of permeability enhancement based on the simulated permeability change trend curve, calculate the permeability increase, and define the time period during which the permeability increase exceeds the effective permeability enhancement threshold as the effective permeability enhancement period;

[0127] Furthermore, based on the simulated permeability trend curve, the point at which the reservoir's permeability begins to rise significantly after acidizing operations is determined as the initiation time for enhanced permeability. The difference between the average permeability between the stable point and the initiation time and the initial permeability is calculated as the permeability increase. Finally, an effective permeability threshold is set, and the period during which the permeability increase exceeds the effective permeability threshold is defined as the effective permeability enhancement period. The effective permeability enhancement threshold is determined based on the reservoir's original permeability, economic development limits, and statistical analysis of historical acidizing production enhancement effects. Typically, it is taken as an example value where the permeability increase is not less than 50% of the initial value or reaches 0.5 mD or more, to ensure that the defined effective permeability enhancement period has practical development significance.

[0128] S6.7: Based on the permeability increase during the effective permeation enhancement period, compare the spatial evolution relationship between the reservoir permeability distribution at the end of the simulation period and the initial injection path, assess the expansion of the effective acid-sweeping area, and output a simulation report on the long-term permeation enhancement effect of acidizing.

[0129] Furthermore, based on the simulated permeability values ​​corresponding to the identified initiation time, peak time, and stabilization time during the effective permeation enhancement period, the average permeability increase within the time period is statistically analyzed, and the increase factor of the permeability at the end of the simulated period relative to the initial permeability before acidizing is calculated. Subsequently, combining the initial injection path generated during the acid injection path optimization process with the spatial distribution characteristics of reservoir permeability at the end of the simulated period, the dynamic sweep range of acid in the reservoir and the changes of acid in the reservoir are identified, and the distribution change ratio of the end-permeability at each key node in the initial injection path is calculated. Finally, based on the above analysis results, a simulation report on the long-term permeation enhancement effect of acidizing is output. The simulation report includes the permeability distribution at the end of the simulated period, the permeability increase, the duration of the effective permeation enhancement period, and the evolution of the initial injection path, which is used to evaluate the long-term transformation effect of acidizing operations and guide the formulation of subsequent development strategies.

[0130] S7: Input the time series input dataset into the multi-field coupled numerical model, combine the acid reaction process with the evolution law of reservoir properties to perform parameter estimation, and output the parameter estimation results;

[0131] S7.1: Denoise the time series input dataset, fill in missing values ​​and correct outliers to obtain a cleaned dataset;

[0132] Furthermore, the time series input dataset contains multiple timestamped data entries, each of which includes dynamic parameters such as injection pressure, return pressure, injection temperature, return temperature, injection concentration, return concentration, injection flow rate, return flow rate, porosity change, and permeability evolution.

[0133] A sliding window smoothing method is used to denoise the time series of each parameter in the input time series dataset, eliminating high-frequency fluctuations caused by sensor noise or measurement errors while retaining low-frequency trend features reflecting the acidification process. Next, for time series with missing values, linear interpolation or time series similarity matching-based imputation methods are used to reasonably estimate and impute the missing values ​​based on the changing trends of data at adjacent time points.

[0134] Outlier detection was performed on the time series of each parameter. Statistical methods were used to identify data points that exceeded the normal range of variation. Physical plausibility was considered to determine whether the outliers conformed to reservoir response characteristics. Confirmed outliers were corrected using nearest neighbor substitution or trend-based fitting. Finally, the dataset, after denoising, missing value imputation, and outlier correction, was compiled into a cleaned dataset.

[0135] S7.2: Map the cleaned dataset to a multi-field coupled numerical model and perform bidirectional variational matching to generate simulation results;

[0136] Furthermore, the cleaned dataset obtained after denoising, missing value imputation and outlier correction is used as input. Then, the parameters in the cleaned dataset are mapped to the input boundary conditions of the multi-field coupled numerical model according to the timestamp. A bidirectional variational matching method is introduced in the solution process of the multi-field coupled numerical model.

[0137] The bidirectional variational matching method calculates the predicted values ​​output by the model through forward propagation and adjusts the model's internal parameters through backpropagation, enabling dynamic matching between the output of the multi-field coupled numerical model and the measured values ​​in the cleaning dataset across three dimensions: flow field, reaction field, and temperature field. During the matching process, a minimum objective function is employed to ensure that the acid flow path, reaction rate distribution, and temperature field evolution output by the multi-field coupled numerical model approximate the changing trends of the measured data as closely as possible. Finally, under the premise of satisfying physical conservation constraints and convergence conditions, simulation results that highly match the cleaning dataset are output. These simulation results include the temporal evolution data of various physical fields within the reservoir and the spatial distribution characteristics of key parameters, supporting accurate prediction of the long-term permeability enhancement effect of subsequent acidizing and iterative optimization of the model.

[0138] S7.3: Based on the simulation results, the multi-field coupled numerical model is adjusted in reverse using inversion technology to generate the acid diffusion coefficient correction value and the reservoir temperature gradient correction value;

[0139] Furthermore, based on the simulation results, a point-by-point comparison was made with the corresponding measured acid concentration, temperature change trends, and reaction progress data in the cleaning dataset. Subsequently, an inversion objective function was constructed, which uses the sum of squared residuals between the simulated and measured variables as the optimization objective, and physical conservation constraints were introduced to ensure that the inversion process conforms to the basic physical laws of the flow-reaction-heat transfer coupling process.

[0140] During the inversion process, the conjugate gradient method or quasi-Newton method is used to iteratively update the key parameters in the multi-field coupled numerical model, with a focus on adjusting the acid diffusion coefficient and reservoir temperature gradient, two parameters highly sensitive to the simulation results. Through multiple iterations, the model output gradually approximates the measured data, and correction values ​​for the acid diffusion coefficient and reservoir temperature gradient are obtained when the convergence condition is met. Finally, the reservoir temperature gradient correction value is used to update the multi-field coupled numerical model, more accurately reflecting the actual diffusion behavior and thermodynamic evolution of acid in the reservoir.

[0141] S7.4: Update the multi-field coupled numerical model using the acid diffusion coefficient correction value and the reservoir temperature gradient correction value, and obtain the parameter estimation results;

[0142] Furthermore, the original corresponding parameters are replaced to make the multi-field coupled numerical model more accurately reflect the diffusion behavior and thermodynamic evolution of acid in the reservoir. Then, based on the updated multi-field coupled numerical model, the cleaning dataset is re-inputted as boundary and initial conditions. Next, based on the updated flow, reaction, and temperature field control equations and coupling relationships in the multi-field coupled numerical model, numerical solutions are performed under a set spatial discrete grid and time step to generate new simulation outputs. Subsequently, parameter estimation methods are used, combining the simulation output with measured data from the cleaning dataset, and utilizing existing techniques such as least squares optimization or Bayesian estimation, to dynamically match and estimate other parameters in the multi-field coupled numerical model. These other parameters include the reaction rate constant, porosity-permeability conversion coefficient, and mineral solubility threshold, etc., finally outputting updated parameter estimation results.

[0143] This embodiment also provides a computer device applicable to the numerical simulation method for the long-term permeability enhancement effect of deep acidizing in sandstone reservoirs, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the numerical simulation method for the long-term permeability enhancement effect of deep acidizing in sandstone reservoirs as proposed in the above embodiment.

[0144] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0145] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the numerical simulation method for achieving long-term permeability enhancement through deep acidizing of sandstone reservoirs as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0146] In summary, this invention achieves precise control and long-term effect prediction throughout the entire process of acidizing stimulation in deep sandstone reservoirs through an innovative combination of dynamic optimization of acidizing operation parameters and multi-field coupled numerical simulation. Specifically, the dynamic parameter optimization mechanism based on real-time data feedback can adaptively adjust acid parameters according to reservoir reaction characteristics, ensuring the accuracy and safety of acidizing operations. Meanwhile, the numerical simulation method integrating multi-physics coupling and inversion calibration techniques effectively solves the problem of inaccurate prediction of long-term permeability enhancement effects using traditional techniques. The synergistic effect of these two key steps not only significantly improves the real-time control capability of acidizing operations but also provides a scientific basis for evaluating the economic effectiveness of acidizing stimulation. Overall, it forms a closed-loop technical system from operation implementation to effect evaluation, providing an innovative solution for the efficient development of deep sandstone reservoirs.

[0147] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A numerical simulation method for the long-term permeability enhancement effect of deep acidizing in sandstone reservoirs, characterized by: include, Collect real-time sensor data and geological exploration data of sandstone reservoirs to formulate a preliminary acidizing operation plan; Based on the preliminary acidification operation plan and combined with real-time data feedback, the preliminary acid concentration, preliminary injection rate and preliminary acid injection temperature are optimized, and acidification parameters are output. By combining acidification parameters with real-time sensor data and geological exploration data, the initial acid injection path is optimized and the acid injection path is output. The acid solution is adjusted using acidizing parameters and injected into the sandstone reservoir according to the acid injection path. The acidizing operation is carried out while real-time monitoring is performed, and real-time acidizing operation monitoring data is output. Based on real-time acidizing operation monitoring data and sampling feedback data, a multi-field coupled numerical model is constructed and calibrated to simulate the long-term evolution trajectory of reservoir permeability after acidizing, evaluate the effective permeability enhancement period and the final permeability enhancement amount, and output a simulation report on the long-term permeability enhancement effect of acidizing.

2. The numerical simulation method for long-term permeability enhancement effect of deep acidizing in sandstone reservoirs as described in claim 1, characterized in that: The specific steps for formulating a preliminary acidification operation plan are as follows. Collect and integrate geological exploration data and real-time sensor data to generate comprehensive reservoir attribute data; Based on comprehensive reservoir attribute data, a reservoir attribute database is established, and permeability analysis is performed on the physical attributes in the reservoir attribute database to generate acidizing operation information. Physical property parameters are extracted from acidizing operation information, and the acidizing effects under different sandstone reservoir conditions are simulated and compared to generate preliminary acidizing schemes and formulate preliminary acidizing operation plans. The preliminary acidification operation plan includes the preliminary acid concentration, the preliminary injection rate, and the preliminary acid injection temperature.

3. The numerical simulation method for long-term permeability enhancement effect of deep acidizing in sandstone reservoirs as described in claim 1, characterized in that: The specific steps for outputting the acidification parameters are as follows. Based on the preliminary acidification operation plan, the injection concentration, backflow concentration, injection flow rate, backflow flow rate, injection pressure, and injection temperature of the acid solution are collected in real time. The acid consumption rate is calculated based on the injected concentration, backflow concentration, injected flow rate, and backflow flow rate. The initial acid concentration is dynamically optimized based on the acid consumption rate and reservoir reaction characteristics, and the optimized acid concentration value is output. Based on the injection pressure, injection flow rate and acid consumption rate of the acid, the initial injection rate of the acid is optimized. In combination with the reservoir reaction characteristics, the distribution range, flow path and permeation transformation effect of the acid under different injection rates are evaluated, and the optimal injection rate is output. Based on the injection pressure, injection temperature, injection flow rate and injection concentration, the formation temperature distribution characteristics and the changes in the reactivity of the acid solution under different temperature conditions are comprehensively evaluated. The initial acid injection temperature is optimized and adjusted based on changes in reactivity, and the optimized acid injection temperature is output. The acidification parameters are composed of optimized acid concentration, optimized acid injection temperature, optimal injection rate, and real-time feedback of acid consumption rate.

4. The numerical simulation method for long-term permeability enhancement effect of deep acidizing in sandstone reservoirs as described in claim 1, characterized in that: The specific steps for the acid injection path are as follows. Multi-objective collaborative optimization analysis was performed on real-time sensor data and geological exploration data to generate optimization suggestions for the initial acid injection path; Based on the optimization suggestions for the initial acid injection path, the penetration efficiency and distribution uniformity of the initial acid injection path are optimized by combining acidification parameters, and the initial acid injection path is obtained. The permeability of acid was evaluated at different injection rates along the initial injection path, and the acid permeability evaluation results were obtained. Based on the acid penetration assessment results, the initial acid injection path is adjusted, and the acid injection path is output.

5. The numerical simulation method for long-term permeability enhancement effect of deep acidizing in sandstone reservoirs as described in claim 1, characterized in that: The specific steps for outputting real-time acidification operation monitoring data are as follows. The required ratio of the target acid solution is adjusted using acidification parameters to generate the target acid solution; The target acid solution is injected into the sandstone reservoir in segments along the acid injection path. The target acid solution concentration and injection rate are dynamically adjusted according to the formation response to carry out acidizing operations, and real-time monitoring data of the acidizing operations are obtained.

6. The numerical simulation method for long-term permeability enhancement effect of deep acidizing in sandstone reservoirs as described in claim 5, characterized in that: The specific steps for adjusting the ratio parameters of the target acid solution using acidification parameters to generate the target acid solution are as follows. The adjustment target of the acid solution is extracted from the acidification parameters, and combined with real-time sensor data, dynamic feedback in the acid reaction process is obtained, and the optimal concentration range of each acid solution component is calculated. Based on the optimal concentration range and combined with real-time sensor data, the change in reaction rate is estimated. Based on the change in reaction rate, determine the direction and extent of adjustment of the required target acid solution ratio parameters, and obtain the target acid solution.

7. The numerical simulation method for long-term permeability enhancement effect of deep acidizing in sandstone reservoirs as described in claim 1, characterized in that: The specific steps for generating the simulation report on the long-term acidification and permeation enhancement effect are as follows. Regularly sample the sandstone reservoir after acidizing operations to obtain sampling feedback data, and fuse it with real-time acidizing operation monitoring data to generate a time series input dataset; Based on real-time acidification operation monitoring data and sampling feedback data, a multi-field coupled numerical model was constructed. Input the time series input dataset into the multi-field coupled numerical model, combine the acid reaction process with the evolution law of reservoir properties to perform parameter estimation, and output the parameter estimation results; The parameter estimation results are calibrated using geological exploration data. Combined with physical constraints and prior stratigraphic information, the parameter deviations of the multi-field coupled numerical model are corrected, and the corrected multi-field coupled numerical model is output. Based on real-time acidizing operation monitoring data and sampling feedback data, the modified multi-field coupled numerical model is used to simulate the evolution of reservoir permeability over time and generate a simulated permeability change trend curve. Based on the simulated permeability change trend curve, the infiltration start time, peak time and stabilization time are identified, the permeability increase is calculated, and the time period when the permeability increase exceeds the effective permeability threshold is defined as the effective permeability period. Based on the permeability increase during the effective permeation enhancement period, the spatial evolution relationship between the reservoir permeability distribution at the end of the simulation period and the initial injection path is compared to assess the expansion of the effective acid-affected area, and a simulation report on the long-term permeation enhancement effect of acidizing is output.

8. The numerical simulation method for long-term permeability enhancement effect of deep acidizing in sandstone reservoirs as described in claim 7, characterized in that: The process involves inputting the time-series input dataset into a multi-field coupled numerical model, combining the acid reaction process with the evolution of reservoir properties to estimate parameters, and outputting the parameter estimation results. The specific steps are as follows: The time series input dataset is denoised, missing values ​​are filled and outliers are corrected to obtain a clean dataset, which is then mapped to a multi-field coupled numerical model for bidirectional variational matching to generate simulation results. Based on the simulation results, the multi-field coupled numerical model is adjusted in reverse using inversion techniques to generate corrected values ​​for the acid diffusion coefficient and reservoir temperature gradient, and the multi-field coupled numerical model is updated to obtain parameter estimation results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the numerical simulation method for the long-term permeability enhancement effect of deep acidizing in sandstone reservoirs as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the numerical simulation method for the long-term permeability enhancement effect of deep acidizing in sandstone reservoirs as described in any one of claims 1 to 8.

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