A method for increasing production and controlling water in a horizontal well of a bottom water oil and gas reservoir

Abstract

This invention discloses a method for enhancing production and controlling water in horizontal wells of bottom-water oil and gas reservoirs, relating to the field of bottom-water oil and gas reservoir development. This method dynamically couples reservoir parameters across multiple scenarios to divide onshore and offshore scenarios, obtaining comprehensive coupling parameters through parameter normalization and dynamic weight calculation. It combines perforation-fracturing parameter feedback and achieves operational parameter adaptation through multi-parameter linkage calculation. Distributed fiber optic sensing identifies high-water-producing sections, and biodegradable plugging agents are injected directionally to achieve meter-level segmented water control. A production prediction sub-model corresponding to the reservoir type is matched, and prediction accuracy is optimized through deviation correction. Production data is collected based on dual-mode transmission, processed and fused to generate control commands, and periodically optimized to form a closed-loop control system. Emergency handling and recovery procedures are also set up to ensure operational safety. This method integrates core technologies such as dynamic coupling, parameter feedback, and precise water control, solving problems such as rapid bottom-water coning, poor coordination between production enhancement and water control, and insufficient cross-scenario adaptation.

Description

Technical Field

[0001] This invention relates to the field of bottom-water oil and gas reservoir development, and more particularly to a method for increasing production and controlling water in horizontal wells of bottom-water oil and gas reservoirs. Background Technology

[0002] In the development of horizontal wells in bottom-water oil and gas reservoirs, various technologies have been developed to achieve the goals of increasing production and controlling water. Regarding reservoir parameter processing, fixed parameter models are often established for single-type reservoirs or specific scenarios. For example, static coupling methods using basic parameters such as porosity and permeability are used for onshore rare-oil reservoirs, while separate adaptation schemes are designed for offshore high-salinity reservoirs, lacking a universal parameter processing mechanism across scenarios. In terms of construction parameter optimization, perforation and fracturing are often performed in separate steps. The design of perforation density and phase angle is independent of the selection of fracturing fluid flow rate and sand ratio, and matching is done solely based on empirical values, failing to establish a logical linkage between parameters. In the water control and regulation phase, techniques such as flow control screens and chemical water shut-off are commonly used, combined with production profile monitoring for well-section-level water control operations. Simultaneously, reservoir numerical simulation is used for production prediction, but the prediction model parameters are often set only once, relying solely on manual periodic adjustments during subsequent production, lacking dynamic correlation with real-time production data.

[0003] Existing technologies have significant limitations in practical applications, making it difficult to meet the demands of efficient development of bottom-water oil and gas reservoirs. Because reservoir parameter processing lacks cross-scenario dynamic coupling, and perforation and fracturing parameters lack mutual feedback and adaptation, it's difficult to coordinate production enhancement measures with water control requirements. This results in poor adaptability when switching between onshore and offshore scenarios or developing different types of reservoirs, and is prone to bottom water intrusion due to parameter mismatch, which negatively impacts development effectiveness. Production prediction and control processes are disconnected; prediction models lack closed-loop correction with real-time production data. As reservoir conditions change, prediction deviations gradually increase, while control commands are mostly generated based on static parameters, failing to respond promptly to dynamic reservoir changes and hindering the maintenance of long-term stable development efficiency. Furthermore, the precision of high-water-producing section identification and water control execution is insufficient. Existing monitoring technologies struggle to achieve meter-level accuracy in inter-section identification, and the identification results lack a linkage mechanism with subsequent plugging agent injection and parameter adjustments. This leads to water control remaining at the well-section level with coarse regulation, failing to accurately block bottom water intrusion channels and potentially damaging high-quality oil-producing sections, thus limiting resource recovery. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the purpose of this invention is to provide a method for enhancing production and controlling water in horizontal wells of bottom water oil and gas reservoirs, so as to solve one or more problems in the prior art.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] A method for enhancing water production and controlling water in horizontal wells of bottom-water oil and gas reservoirs, comprising:

[0007] Dynamic coupling of reservoir parameters in multiple scenarios: Based on water depth and formation pressure coefficient, onshore and offshore scenarios are divided. According to the preset basic weights of each scenario, combined with the normalized values ​​of real-time parameters, the dynamic weight ratio of each parameter is calculated, and thus the comprehensive coupling parameters of the reservoir are obtained.

[0008] The perforation-fracturing parameters are fed back together. After normalizing the perforation density, perforation phase angle, fracturing fluid flow rate, fracturing sand ratio and reservoir permeability, the matching determination of perforation and fracturing parameters is achieved through multi-parameter linkage calculation.

[0009] Meter-level segmented water control uses distributed optical fiber sensing technology with a spatial resolution of 0.5m to identify high-water-producing sections. The water production rate threshold of the high-water-producing section is greater than 30%. A plugging agent is then injected into the identified high-water-producing section in a targeted manner.

[0010] Dynamic correction of multi-reservoir productivity: Based on viscosity, density and gas saturation, reservoir types are identified and corresponding sub-models are matched. The prediction deviation is calculated by the ratio of the absolute value of the difference between the predicted value and the actual value to the actual value. The sub-model parameters are adjusted according to the deviation results.

[0011] Production data closed-loop control uses fiber optic and 4G dual-mode transmission of production data. Data is fused with a weighting of 0.3 for wellhead data and 0.7 for downhole data. Control commands are generated and executed based on the fusion results.

[0012] Specifically, the dynamic weight ratio of the dynamic coupling of reservoir parameters in multiple scenarios is calculated using the following formula:

[0013] ;

[0014] in, The scenario-based weights for each parameter, Let t be the normalized value of the parameter at time t, and n be the number of parameters involved in the coupling of each scenario. For land scenarios, n=4, and for sea scenarios, n=5.

[0015] The core principle is to fix the importance benchmark of parameters by using the basic weight of the scenario, and dynamically adjust the weight ratio by combining the normalized value of the real-time parameters, so as to achieve precise coupling of reservoir parameters across scenarios.

[0016] Specifically, the adaptation determination of the perforation-fracturing parameter mutual feedback is calculated using the following formula:

[0017] .in, For the normalized perforation density, The normalized perforation phase angle, This represents the normalized fracturing fluid discharge rate. This is the normalized fracturing sand ratio. This represents the normalized reservoir permeability. The core principle is to eliminate dimensional differences through parameter normalization and quantify the matching degree between perforation and fracturing parameters through multi-parameter linkage calculation, providing a basis for parameter adjustment.

[0018] Specifically, the prediction deviation of the multi-reservoir productivity dynamic correction is calculated using the following formula:

[0019] Where Predicted represents the sub-model's predicted capacity value, and Actual represents the actual measured capacity value. The core principle is to accurately pinpoint the direction of sub-model parameter adjustments by quantifying the deviation between the predicted and actual values, thereby improving the accuracy of capacity prediction.

[0020] Specifically, in the dynamic coupling of reservoir parameters across multiple scenarios, the parameters for the terrestrial scenario include porosity, permeability, temperature, and pressure, while the parameters for the marine scenario include salinity in addition to those for the terrestrial scenario. The water depth threshold for the terrestrial scenario is less than 500m, and the formation pressure coefficient threshold is less than 1.2, while the water depth threshold for the marine scenario is greater than 500m, and the formation pressure coefficient threshold is greater than 1.2.

[0021] Specifically, the dynamic weights are updated every 7 days, and the updated data includes downhole pressure and production profile data.

[0022] Specifically, in the perforation-fracturing parameter feedback, the perforation density ranges from 12 to 20 holes / m, the perforation phase angle ranges from 120° to 180°, and the fracturing fluid discharge ranges from 8 to 12 m³ / m². 3 / min, the fracturing sand ratio ranges from 5% to 20%, and the reservoir permeability ranges from 50 to 500 mD.

[0023] Specifically, the injection rate of the plugging agent for the meter-level segmented water control is 0.5m per meter. 3 The injection rate is 0.1m. 3 The injection pressure is 1-2 MPa lower than the reservoir fracture pressure, and the degradation time of the plugging agent is 7-14 days.

[0024] Specifically, the reservoir types include heavy oil reservoirs, light oil reservoirs, and condensate gas reservoirs. The viscosity threshold for heavy oil reservoirs is greater than or equal to 50 mPa·s, for light oil reservoirs it is 1–50 mPa·s, and for condensate gas reservoirs it is less than 1 mPa·s. The corresponding sub-models include the SVR model, the Darcy modified model, and the Peng-Robinson model. The penalty coefficient of the SVR model ranges from 10 to 12, and the gamma value ranges from 0.1 to 0.15.

[0025] Specifically, the sampling frequency of the production data is 1Hz for downhole pressure and temperature, 0.1Hz for water production rate and production capacity, and 0.5Hz for perforation and fracturing parameters, with a data transmission delay of less than 2s. The cycle optimization time for the production data closed-loop control is 7 days, with data processing performed every 15 minutes and command evaluation performed every 30 minutes.

[0026] Compared with the prior art, the beneficial technical effects of the present invention are as follows:

[0027] (i) By combining the technologies of dynamic coupling of reservoir parameters in multiple scenarios, mutual feedback of perforation-fracturing parameters and meter-level segmented water control, an integrated mechanism for cross-scenario adaptation and production enhancement and water control synergy has been constructed. This is different from the limitations of single-scenario adaptation and independent operation of production enhancement and water control in existing technologies. It achieves precise adaptation of different types of bottom water oil and gas reservoirs (onshore / offshore, light oil / heavy oil / condensate gas reservoirs), while avoiding the risk of bottom water cross-flow caused by production enhancement measures, so that production enhancement and water control form a positive linkage.

[0028] (ii) By combining the technology of dynamic correction of multi-reservoir production capacity and closed-loop control of production data, and with the basic support of dynamic coupling of reservoir parameters in multiple scenarios, a full-process dynamic control system of "parameter coupling production capacity prediction, dynamic adjustment, feedback optimization" has been formed. This is different from the static prediction and non-continuous optimization mode in the existing technology. It ensures that the accuracy of production capacity prediction continues to improve with the production process, while allowing the control commands to accurately respond to the dynamic changes of the reservoir and maintain a long-term stable development effect.

[0029] (III) By combining meter-level segmented water control, perforation-fracturing parameter feedback and production data closed-loop control, a refined water control chain has been achieved, from precise identification of high-water-producing sections to dynamic parameter adjustment and real-time feedback of effects. This breaks the extensive mode of well-section level control in existing technologies. It can not only directionally block bottom water inrush channels, but also protect the production capacity of high-quality oil-producing sections, significantly improving the overall development efficiency and resource recovery rate of bottom water oil and gas reservoirs. Attached Figure Description

[0030] Figure 1 This is a flowchart of the water control method in this invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and exemplary descriptions. It should be understood that the structures, proportions, sizes, etc., illustrated in the accompanying drawings are merely for illustrative purposes to aid those skilled in the art and are not intended to limit the implementation of this invention. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in proportions, or adjustments to the size, without affecting the effects and objectives achieved by this invention, should still fall within the scope of the technical content disclosed in this invention.

[0032] Application Overview

[0033] In the field of horizontal well production enhancement and water control in bottom-water oil and gas reservoirs, the industry's conventional approaches to existing technical problems are fragmented: When faced with cross-scenario adaptation requirements, many companies use parameter models specific to a single reservoir type or a particular scenario, achieving adaptation by independently adjusting basic parameters, without forming a universal dynamic coupling mechanism; Regarding the synergistic challenges of production enhancement and water control, perforation design and fracturing operations are typically carried out step-by-step, relying on empirical values ​​to match relevant parameters, lacking a logical linkage between parameters; For high-yield water section control, many companies implement general water shut-off or screening based on well-section-level monitoring data, failing to achieve precise inter-section identification and targeted control; In terms of production prediction and control, many companies use fixed-parameter numerical simulation models for one-time prediction, subsequently adjusting control strategies manually periodically, without establishing a dynamic correlation with real-time production data.

[0034] The core flaw of conventional solutions lies in the lack of systematic technical integration and dynamic linkage mechanisms: cross-scenario adaptation relies on isolated models, resulting in poor adaptability and cumbersome adjustments when switching between different scenarios; independent design of perforation and fracturing parameters easily leads to parameter mismatch imbalances, resulting in bottom water cross-flow and making it difficult to achieve positive synergy between production enhancement and water control; water control operations remain at the well section level in a crude manner, unable to accurately distinguish between high-water-producing sections and high-quality oil-producing sections, affecting both water control effectiveness and potential damage to production capacity; production capacity prediction and regulation lack closed-loop logic, prediction models are difficult to adapt to dynamic changes in the reservoir, and the response of control commands is lagging, making it impossible to maintain long-term stable development efficiency. This invention constructs an integrated, dynamic, and refined technical system through the organic combination of multi-scenario reservoir parameter dynamic coupling, perforation-fracturing parameter mutual feedback, meter-level segmented water control, multi-reservoir production capacity dynamic correction, and production data closed-loop control. It fundamentally overcomes the limitations of the fragmented, static, and crude nature of conventional solutions, achieving technical breakthroughs in cross-scenario precise adaptation, production enhancement and water control synergy, meter-level precise water control, and full-process dynamic regulation.

[0035] Comprehensive explanation

[0036] The water control and production enhancement method for horizontal wells in bottom-water oil and gas reservoirs disclosed in this invention achieves precise cross-scenario adaptation, synergistic production enhancement and water control, and full-cycle dynamic optimization through a full-process technical system of "dynamic coupling of reservoir parameters → mutual feedback of perforation and fracturing parameters → meter-level segmented water control → dynamic correction of multi-reservoir production capacity → closed-loop control of production data". The following is a detailed description of the specific implementation process.

[0037] I. Dynamic Coupling of Reservoir Parameters in Multiple Scenarios

[0038] Dynamic coupling of reservoir parameters across multiple scenarios is the foundation of the entire technical solution. Its core lies in accurately integrating reservoir parameters through scenario classification and dynamic weight calculation, providing reliable basic data support for subsequent stages.

[0039] First, the scenarios are classified into two main categories based on water depth and formation pressure coefficient: terrestrial and marine scenarios. Terrestrial scenarios have a water depth of less than 500m and a formation pressure coefficient of less than 1.2, while marine scenarios have a water depth of more than 500m and a formation pressure coefficient of more than 1.2. The core parameters for terrestrial scenarios include porosity, permeability, temperature, and pressure, totaling four parameters. Marine scenarios, in addition to the parameters for terrestrial scenarios, include salinity, totaling five parameters, to accommodate the impact of the high-salinity marine environment on reservoir characteristics.

[0040] During the parameter acquisition phase, for land scenarios, porosity data was obtained through a combination of sonic logging and density logging, permeability was determined through core flow experiments, formation temperature was collected using a well temperature gauge, and formation pressure was recorded using a quartz manometer. For marine scenarios, salinity data was additionally obtained through formation water sampling and ion chromatography analysis. All acquired data had to meet the requirements of porosity accuracy ±1% and permeability repeatability error less than 5%. After acquisition, all parameters were normalized. The normalization rule was to subtract the minimum value of the parameter from the measured value, and then divide by the difference between the maximum and minimum values ​​of the parameter, resulting in a normalized value ranging from 0 to 1.

[0041] The dynamic weight calculation uses the "dynamic weight calculation formula":

[0042] ;

[0043] In this formula, The basic weights for each parameter are as follows: for land scenarios, the basic weights for porosity are 0.3, permeability is 0.4, temperature is 0.1, and pressure is 0.2; for marine scenarios, the basic weights for porosity are 0.25, permeability is 0.35, temperature is 0.1, pressure is 0.2, and salinity is 0.1. Here, is the normalized value of the parameter at time t; n is the number of parameters involved in coupling for each scenario, n=4 for land scenarios and n=5 for marine scenarios. The core principle is to fix the importance benchmark of parameters under different scenarios by using scenario-based weights, and dynamically adjust the weight ratio of each parameter by combining the real-time parameter normalized values, so as to achieve precise coupling of reservoir parameters across scenarios.

[0044] The weight update cycle is 7 days. Each update uses downhole pressure (1Hz sampling, continuous for 24 hours) and production profile (distributed fiber optic DAS, spatial resolution 0.5m) data. After outlier removal using the 3σ rule and missing data completion using linear interpolation (missing data rate less than 5%), the data is updated using the random forest algorithm (n_estimators=100, max_depth=5). After the update, the deviation between the coupling parameters and the core measured values ​​must be controlled within a reasonable range. If the single parameter missing rate is greater than 5%, then the parameter data from three adjacent wells in the same block should be used, weighted by distance (weight = 1 / distance). 2 Calculate and complete the target well parameters. After completion, the deviation from the average parameter value of the adjacent wells should be less than 10%.

[0045] II. Perforation-Fracturing Parameter Feedback

[0046] The mutual feedback of perforation and fracturing parameters is a key link to achieve synergistic production enhancement and water control. Through multi-parameter linkage calculation, the perforation design and fracturing operation are accurately matched to avoid bottom water flow caused by parameter imbalance.

[0047] First, the parameters related to perforation and fracturing were normalized: the perforation density ranged from 12 to 20 holes / m, and the normalization formula was (measured perforation density - 12) / (20 - 12); the perforation phase angle ranged from 120° to 180°, and the normalization formula was (measured phase angle - 120°) / (180° - 120°); the fracturing fluid displacement ranged from 8 to 12 m³ / min. 3 / min, the normalization formula is (measured displacement - 8) / (12 - 8); the value range of fracturing sand ratio is 5%~20%, the normalization formula is (measured sand ratio - 5) / (20 - 5); the value range of reservoir permeability is 50~500mD, the normalization formula is (measured permeability - 50) / (500 - 50). After normalization, all parameters are in the range of 0~1 to eliminate dimensional differences.

[0048] The parameter adaptation determination adopts the "perforation-fracturing parameter adaptation determination formula":

[0049] ;

[0050] In this formula, For the normalized perforation density, The normalized perforation phase angle, This represents the normalized fracturing fluid displacement. This is the normalized fracturing sand ratio. This refers to the normalized reservoir permeability. The core principle is to quantify the matching degree between perforation and fracturing parameters through multi-parameter linkage calculation. When the calculation result is greater than 0.6, it is considered a qualified match, and construction can be carried out according to the original parameters; when the calculation result is less than or equal to 0.6, it is considered an unqualified match, and the parameters need to be adjusted according to priority.

[0051] The parameter adjustment priority is as follows: first adjust the fracturing sand ratio by ±3%, then adjust the fracturing fluid discharge rate by ±1m. 3 The perforation density is adjusted to ±2 holes / m, and the fit judgment value needs to be recalculated after each adjustment until the result is acceptable. During fracturing operations, the fit judgment value is recalculated every 5 minutes. If the value drops below 0.5, operations must be stopped immediately and parameters readjusted to avoid continuous parameter imbalance affecting the operation results.

[0052] III. Meter-level segmented water control

[0053] Meter-level segmented water control achieves precise plugging of high-yield water sections and production capacity protection of high-quality oil-producing sections through high-precision monitoring and targeted regulation. The core lies in meter-level precision inter-segment identification and targeted plugging agent injection.

[0054] First, a distributed fiber optic sensing system was deployed, using SMF-28e+ type distributed optical fiber, laid along the outer wall of the horizontal section of the casing at a fixed interval of 0.5m. The ground end was connected to an OTDR-7200 type optical time domain reflectometer (spatial resolution 0.5m, dynamic range 40dB), and simultaneously paired with a PWS-500 type water production profiler (measurement range 0~100%, resolution 0.1m), which was lowered into the well along with the optical fiber and placed in the middle of the horizontal section to achieve meter-level accuracy in temperature and water production monitoring.

[0055] High-water-producing sections are identified based on monitoring data: temperature changes are monitored using distributed fiber optic DTS. Sections with a temperature drop greater than 3°C and a water production rate less than 20% are classified as high-quality oil-producing sections (key protection sections), while sections with a water production rate greater than 30% are classified as high-water-producing sections (water-controlled sections). After identification, a biodegradable plugging agent is directionally injected into the water-controlled section using a 50.8mm diameter coiled tubing at a rate of 0.5m per meter. 3 The injection speed is controlled at 0.1m. 3 The injection pressure is 1-2 MPa lower than the reservoir fracturing pressure to avoid further flow of bottom water caused by fracturing the formation.

[0056] The degradable plugging agent used is adapted to the reservoir temperature: when the reservoir temperature is 25-60°C, the PLA-300 type plugging agent is selected, the degradation time is 14-21 days, the injection concentration is 5-6%, and 0.1% stannous octoate needs to be added as a catalyst to accelerate degradation; when the reservoir temperature is 60-120°C, the PLA-500 type plugging agent is selected, the degradation time is 7-14 days, the injection concentration is 6-8%, and no catalyst is required; when the reservoir temperature is 120-150°C, the PLA-800 type plugging agent is selected, the degradation time is 3-7 days, the injection concentration is 8-10%, and the HB-5000 type high-temperature resistant injection pump needs to be used. The water production rate of the water control section needs to be re-monitored 24 hours after the plugging agent is injected. If the water production rate drops below 20%, it is judged as qualified. If it fails to meet the standard, 50% of the first injection volume of the plugging agent needs to be re-injected.

[0057] IV. Dynamic correction of multi-reservoir productivity

[0058] The dynamic correction of multi-reservoir productivity improves the accuracy of productivity prediction through accurate identification of reservoir types and dynamic adjustment of sub-models, providing data support for the coordinated production increase and water control. The core lies in the adaptation of reservoir types and sub-models and the dynamic correction of deviations.

[0059] The identification of reservoir types is based on the threshold of reservoir fluid parameters: reservoirs with viscosity greater than or equal to 50 mPa·s, density greater than or equal to 0.92 g / cm 3 and gas saturation less than 30% are heavy oil reservoirs; reservoirs with viscosity of 1-50 mPa·s, density of 0.85-0.92 g / cm 3 and gas saturation of 30-50% are light oil reservoirs; reservoirs with viscosity less than 1 mPa·s, density less than 0.85 g / cm 3 and gas saturation greater than or equal to 60% are condensate gas reservoirs. Different types of reservoirs are matched with corresponding productivity prediction sub-models: heavy oil reservoirs are matched with the SVR model (kernel function RBF, initial penalty coefficient 10, initial gamma = 0.1), light oil reservoirs are matched with the Darcy correction model (initial skin factor 0.5, flow efficiency 0.8), and condensate gas reservoirs are matched with the Peng-Robinson model (critical temperature -40~0°C, critical pressure 4~8 MPa, initial eccentricity factor 0.1~0.3).

[0060] The calculation of productivity prediction deviation uses the "prediction deviation calculation formula":

[0061] ;

[0062] In this formula, is the productivity prediction value of the sub-model, The actual production capacity is measured (collected daily at 8:00 AM via a GL-100 crude oil metering separator, with a recorded error of less than 1%). The core principle is to accurately pinpoint the direction of sub-model parameter adjustment by quantifying the deviation between the predicted and actual values. When the absolute value of the deviation rate is greater than 10%, sub-model parameter adjustment is initiated: the SVR model adjusts the penalty coefficient to 12 and gamma to 0.15, and retrains using newly collected data (1000 iterations, convergence error less than 1e-5); the Darcy correction model calculates the new skin coefficient = original skin coefficient × (1 + deviation rate × 0.5), and recalculates it using the production capacity calculation formula; the Peng-Robinson model corrects the gas deviation factor to 0.7 × (1 + deviation rate × 0.3), and refits the PVT relationship using the Peng-Robinson state equation.

[0063] After parameter adjustment, the actual production capacity of the next day needs to be verified. If the absolute value of the deviation rate is less than 5%, it is judged as qualified for correction. If the absolute value of the deviation rate is less than 8% for three consecutive months, the correction period can be extended to 45 days to ensure that the model always adapts to the dynamic changes of the reservoir.

[0064] V. Closed-loop control of production data

[0065] Production data closed-loop control ensures the long-term stability of production increase and water control effects through full-process data collection, integration, and dynamic optimization. The core lies in the real-time transmission, rapid processing, and precise execution of instructions.

[0066] During the data acquisition phase, downhole pressure and temperature are sampled at a frequency of 1Hz using a QT-200 sensor; water production rate and production capacity are sampled at a frequency of 0.1Hz using a WS-100 sensor and a GL-100 separator; and perforation and fracturing parameters are sampled at a frequency of 0.5Hz using the device controller and fracturing truck instruments. All data is transmitted using a dual-mode fiber optic and 4G transmission: fiber optic is the primary channel (transmission delay less than 2s), and 4G is the backup channel (transmission delay less than 5s). If one channel is interrupted for 10 seconds, it automatically switches to the other channel. If both channels are interrupted, the controller's local storage is activated (the cache capacity supports 72 hours of data storage), and data is automatically retransmitted after transmission is restored.

[0067] Data processing utilizes an edge computing gateway (EC-500 model), processing the acquired data every 15 minutes. First, outliers are eliminated using the 3σ rule. Then, a weighted average is performed with a weight of 0.3 for wellhead data and 0.7 for downhole data. The resulting data is compared with preset thresholds to identify anomalies. Anomaly identification thresholds include: water production rate greater than 40% (for 5 minutes), downhole pressure fluctuation greater than 1 MPa / h (for 3 minutes), and bottom water coning velocity greater than 0.5 m / month (monthly logging comparison). Upon triggering an anomaly, the system automatically generates corresponding control commands: water production rate anomalies generate a "reduce water production rate command" (adjust perforation parameters or inject plugging agent), pressure fluctuation anomalies generate a "stabilize pressure command" (reduce fracturing fluid flow or shut in the well), and coning velocity anomalies generate a "control water command" (inject plugging agent or adjust packer opening).

[0068] Control commands are sent to the ground control valve group via industrial Ethernet with a delay of less than 5 seconds. The ground control valve group then drives the perforation adjustment device or controllable packer to perform the operation. The executing device uploads its execution progress every 30 minutes, and the edge gateway verifies whether the execution effect meets the standards. The system performs a cycle optimization every 7 days: generating a control effect report and statistically analyzing the average and deviation rates of production capacity, water production rate, and bottom water cone inlet velocity; if a certain type of anomaly is not triggered for 3 consecutive weeks, the corresponding identification threshold is relaxed by 10%; if the same anomaly is triggered for 2 consecutive weeks, the threshold is tightened by 5%; every quarter, the parameter optimization model and production capacity prediction model are retrained using cumulative production data (no less than 1000 sets) to continuously improve control accuracy.

[0069] Emergency shutdown trigger conditions include: a sudden drop in wellhead pressure greater than 5 MPa (possible well leakage), a sudden increase in water production rate greater than 50% (bottom water inrush), and a data transmission interruption exceeding 1 hour (unable to monitor). Once triggered, construction must be stopped immediately. After on-site inspection and troubleshooting, the perforation parameters should be adjusted back to the initial design values, the fracturing system should be depressurized to atmospheric pressure, and fracturing should be started again at 50% of the design displacement, increasing the displacement by 10% every 30 minutes. Water production rate and pressure should be monitored simultaneously. If no abnormalities are found, normal operation should be resumed.

[0070] VI. Verification

[0071] To verify the actual impact of the core parameters in this technical solution on the production enhancement and water control effect of horizontal wells in bottom water oil and gas reservoirs, three key parameters—perforation density, fracturing fluid discharge rate, and fracturing sand ratio—were selected as variables. Quantitative tests were conducted on the daily production, water production rate, and bottom water coning speed of single wells using industry-standard specifications and effective standards. Through multiple sets of comparative experiments, the rationality of the parameter range and the effectiveness of the technical solution were clarified.

[0072] Experimental Design and Testing Standards

[0073] Test standards and methods

[0074] 1. Daily production per well: The separator metering method applicable in SY / T5267-2024 "Determination of crude oil loss in oilfields" was adopted. The GL-100 crude oil metering separator was used to continuously monitor for 72 hours, and the average value was taken as the final result.

[0075] Detailed reproduction steps:

[0076] a. Equipment calibration: Check the separator pressure gauge (±0.1MPa accuracy), thermometer (±0.5℃ accuracy), and outlet flow meter (±0.5% accuracy);

[0077] b. Process connection: Connect the separator in series to the wellhead oil outlet pipeline, and connect the outlet to the oil storage tank;

[0078] c. Monitoring and Recording: Record the oil output (t) every hour for 72 consecutive hours;

[0079] d. Calculation: Daily output = Total oil output ÷ 72 (round to two decimal places).

[0080] 2. Water production rate: In accordance with SY / T5329-2022 "Technical requirements and analysis methods for water quality indicators of clastic rock oil reservoirs", the water production data of each meter section of the horizontal segment was collected in real time using a production profiler, and the total water production rate was calculated.

[0081] Detailed reproduction steps:

[0082] a. Profiler installation: Fix the product profiler (spatial resolution 0.5m) at 0.5m intervals along the inner wall of the horizontal section of the casing.

[0083] b. Data Acquisition: Liquid production (m³) at each meter segment was collected over 24 hours at a frequency of 0.1 Hz. 3 / h) and water production (m 3 / h);

[0084] c. Total Calculation: Sum the total liquid production (Q) total ) and total water production (W) total );

[0085] d. Water production rate = (W) total / Q total ) × 100% (rounded to two decimal places).

[0086] 3. Bottom water cone advance velocity: By comparing the resistivity logging curves before and after the experiment, the vertical rise distance of the bottom water interface is identified, and the monthly average bottom water cone advance velocity is obtained by dividing it by the interval between the two monitoring sessions (statistically calculated monthly).

[0087] Detailed reproduction steps:

[0088] a. Logging instrument: RT-300 resistivity logging instrument (spatial resolution 0.5m) is used;

[0089] b. Testing time: Test once 1 day before the experiment (T0) and once 30 days after the experiment (T1);

[0090] c. Interface identification: Locate the abrupt change point (bottom water interface) where the resistivity drops sharply by ≥50% in the logging curve, and record the depths H0 (T0) and H1 (T1).

[0091] d. Speed ​​calculation: Cone advance speed = |H1-H0|÷30 (m / month, rounded to two decimal places).

[0092] Variable selection and experimental group design

[0093] Ten sets of experiments were designed, using perforation density, fracturing fluid flow rate, and fracturing sand ratio as variables:

[0094] Standard Groups (Groups 1-5): Variable values ​​are within the document's specified range (perforation density 12-20 holes / m, fracturing fluid flow rate 8-12m³). 3 / min, fracturing sand ratio 5-20%)

[0095] Control group (groups 6-9): Variable values ​​exceeded the document's specified range;

[0096] Blank group (10 groups): Using existing technology (no dynamic coupling, perforation-fracturing mutual feedback and meter-level water control), parameters are perforation density of 15 holes / m and fracturing fluid discharge of 10m³. 3 / min, fracturing sand ratio 12%).

[0097] Weighted scoring mechanism (with supplementary quantitative details)

[0098] Comprehensive score calculation formula and parameter definition:

[0099]

[0100] A (Contribution to Increased Production): Weight 0.5 (core production increase indicator);

[0101] B (Water Control Contribution): Weight 0.2 (the lower the water production rate, the larger the B value);

[0102] C (cone control contribution): The weight is 0.3 (the smaller the speed, the larger the C value, which is on the order of 200 normalized numerical values).

[0103] Experimental record form (Table 1)

[0104] Table 1. Experimental Group Data Table

[0105]

[0106] Summary of Experimental Results

[0107] 1. Conventional group (groups 1-5): The daily production per well (65.20-70.30 t / d), water production rate (40.50-45.20%), and bottom water coning speed (0.20-0.25 m / month) were all significantly better than the control group;

[0108] 2. Control group (groups 6-9): The overall performance of single well daily production (58.90-62.30t / d), water production rate (47.20-50.30%), and bottom water coning speed (0.28-0.32m / month) was better than that of the blank group;

[0109] 3. Group with the highest overall score: Group 3 (overall score 51.65);

[0110] 4. Blank group: All performance indicators are the worst.

[0111] Analysis of experimental results and principles

[0112] Based on the above experimental data and weighted scoring results, and combined with the core algorithm logic and principles of the technical solution, the following analysis will delve into the experimental results from three dimensions: performance trend characteristics, algorithm mechanism adaptability, and parameter range rationality, revealing the technical logic and intrinsic connections behind the data.

[0113] From the overall performance trend, the conventional group, whose variables are within the limits of the technical solution, showed a significant advantage in both the comprehensive score and individual indicators. The control group, whose parameters exceeded the limits, experienced a significant performance decline, while the blank group performed the worst. The core reason for this trend lies in the synergistic adaptation effect of the core algorithms in the technical solution: the multi-scenario reservoir parameter dynamic coupling algorithm, through scenario classification and real-time parameter normalization, achieved precise weighted coupling of basic parameters such as reservoir porosity and permeability, providing basic data support for the design of perforation and fracturing parameters that fits the actual reservoir conditions; the perforation-fracturing parameter feedback algorithm, through multi-parameter normalization and linkage calculation, quantified the matching degree of perforation density, fracturing fluid discharge, fracturing sand ratio, and reservoir permeability, ensuring the synergistic optimization of construction parameters and avoiding the adaptation imbalance caused by independent design of a single parameter; the meter-level segmented water control and production data closed-loop control algorithm formed a dynamic chain of "identification-regulation-feedback," accurately blocking high-water-producing sections while protecting high-quality oil-producing sections, effectively suppressing bottom water coning. In the standard group, the variables are within a limited range, which allows the above algorithm to play its full role. Data transmission, calculation and control in each link form a positive linkage, ultimately achieving comprehensive optimization of production increase, water control and conical advance control.

[0114] From the perspective of the three core indicators of weighted scoring, the advantage of the production enhancement contribution indicator stems from the parameter optimization logic of the dynamic coupling algorithm and the perforation-fracturing feedback algorithm: the dynamic coupling algorithm, through dynamic weight updates, adapts to changes in reservoir parameters in real time, ensuring that the design of perforation and fracturing parameters always aligns with the reservoir potential; the perforation-fracturing parameter feedback algorithm, by quantifying parameter matching, avoids wasted production capacity due to parameter imbalance, enabling conventional groups to fully tap the reservoir's production enhancement potential. The advantages of water control contribution and coning control contribution lie in the precise identification and directional regulation logic of the meter-level segmented water control algorithm: this algorithm, based on meter-level precision monitoring data from distributed fiber optic sensors, accurately distinguishes between high-water-producing sections and high-quality oil-producing sections, achieving precise water control through directional injection of plugging agents. Simultaneously, the production data closed-loop control algorithm provides real-time feedback on the regulation effect, dynamically adjusting parameters to suppress bottom water coning, forming a complete logical chain of "precise identification - directional regulation - real-time optimization".

[0115] The rationality of limiting the parameter range is fully demonstrated by the performance differences between the control group and the conventional group: when the variables in the control group exceeded the limit, the adaptation logic of the core algorithm was disrupted. The weight calculation of the dynamic coupling algorithm is based on the parameter extrema and normalization rules within the limit. When the parameters exceed the limit, the effectiveness of the normalized values ​​decreases, leading to an increase in the deviation between the coupling parameters and the actual characteristics of the reservoir. The matching calculation of the perforation-fracturing parameter feedback algorithm is based on the parameter correlation rules within the limit. When the parameters exceed the limit, the synergistic effect of multi-parameter linkage weakens, and even parameter conflicts occur, leading to an increase in water production and accelerated bottom water coning. In contrast, the blank group, lacking the support of the above-mentioned core algorithms, relied solely on fixed parameters for construction, which could not adapt to dynamic changes in the reservoir, nor achieve parameter coordination and precise water control, ultimately resulting in the lowest performance levels for all parameters.

[0116] In summary, the performance trends presented in the experimental results are essentially a direct reflection of the adaptation logic of the core algorithm and parameter range in the technical solution. The core algorithm, through the organic combination of dynamic coupling, parameter feedback, precise identification, and closed-loop control, constructs a technical system adapted to reservoir characteristics and construction requirements. The defined parameter range provides the necessary conditions for the effective operation of this system. Together, they ensure the synergistic optimization of production enhancement and water control, verifying the scientific validity and practicality of the technical solution.

[0117] Exemplary Description

[0118] The aforementioned experiments were designed in strict accordance with current effective industry standards. The testing methods were standardized, and the data was objective and reliable, accurately reflecting the correspondence between variable parameters and performance effects in the technical solution. To ensure the reproducibility of the embodiments, the following embodiments directly use the ten sets of variable parameters and corresponding performance data from the experiments. The variables and performance results correspond one-to-one, and the operation steps of the embodiments fully follow the complete process of the technical solution, ensuring that those skilled in the art can directly implement them as described.

[0119] Example 1

[0120] This embodiment is an example of water control and production enhancement using a horizontal well in a bottom-water oil and gas reservoir in a terrestrial rare oil reservoir. The specifications of the horizontal well are as follows: wellbore diameter 152.4 mm, horizontal section length 1200 m, vertical depth 2500 m; reservoir temperature 60℃, formation pressure coefficient 1.0, reservoir permeability 200 mD, porosity 25%, and crude oil viscosity 20 mPa·s.

[0121] 1. Dynamic coupling of reservoir parameters in multiple scenarios

[0122] Scene classification: Water depth 400m, formation pressure coefficient 1.0, determined to be a land scene, the coupling parameters are porosity, permeability, temperature and pressure, a total of 4 items (n=4).

[0123] Parameters acquired: Porosity 25% (sonic logging + density logging), permeability 200mD (core flow experiment), temperature 60℃ (well temperature gauge), pressure 25MPa (quartz pressure gauge).

[0124] Normalized parameters: Porosity normalized value = (25-10) / (30-10) = 0.75, permeability normalized value = (200-10) / (500-10) ≈ 0.388, temperature normalized value = (60-25) / (120-25) ≈ 0.368, pressure normalized value = (25-10) / (50-10) = 0.375.

[0125] Dynamic weight calculation: using Basic weight α i With porosity 0.3, permeability 0.4, temperature 0.1, and pressure 0.2 respectively, the dynamic weight ratios of each parameter are calculated as follows: porosity 0.27, permeability 0.41, temperature 0.08, and pressure 0.24. The reservoir integrated coupling parameter is calculated as follows: 25%×0.27+200mD×0.41+60℃×0.08+25MPa×0.24 (weighted integration of normalized values).

[0126] Weight update: The weights are updated using downhole pressure (1Hz sampling) and production profile data on a 7-day cycle.

[0127] 2. Perforation-fracturing parameter feedback

[0128] Perforation parameters design: perforation density 14.00 holes / m, perforation phase angle 150°, hole depth 1.0m, hole diameter 12mm.

[0129] Fracturing parameter design: fracturing fluid displacement 9.00 m³ / h 3 / min, fracturing sand ratio 10.00%.

[0130] Normalized parameters: Perforation density normalized value = (14-12) / (20-12) = 0.25, perforation phase angle normalized value = (150-120) / (180-120) = 0.5, fracturing fluid discharge normalized value = (9-8) / (12-8) = 0.25, fracturing sand ratio normalized value = (10-5) / (20-5) ≈ 0.333, reservoir permeability normalized value = (200-50) / (500-50) ≈ 0.333.

[0131] Compatibility determination: using The calculation yields Fracture_match = (0.25 × 0.5) / (0.25 × 0.333) × 0.333 ≈ 1.5 > 0.6, indicating a qualified match. Construction should proceed according to the design parameters.

[0132] 3. Meter-level segmented water control

[0133] Sensing system layout: SMF-28e+ type distributed optical fiber is laid along the outer wall of the horizontal section conduit (fixed spacing 0.5m), connected to an OTDR-7200 type optical time domain reflectometer, and paired with a PWS-500 type water production rate profiler placed in the middle of the horizontal section. High water production section identification: The meter section with a water production rate >30% is 400-450m, which is identified as the water control section; the meter section with a temperature drop >3℃ and a water production rate <20% is 600-800m, which is identified as the key protection section.

[0134] Plugging agent injection: PLA-500 biodegradable plugging agent (concentration 6.00%) was used and injected into the water control section through a 50.8mm diameter coiled tubing at a rate of 0.5m. 3 / meter, injection speed 0.1m 3 / min, injection pressure 18MPa (2MPa lower than reservoir fracture pressure).

[0135] Effect verification: Monitoring 24 hours after injection showed that the water production rate in the water control section dropped to below 20%.

[0136] 4. Dynamic correction of production capacity in multiple reservoirs

[0137] Reservoir type identification: Crude oil viscosity 20 mPa·s, density 0.88 g / cm³ 3 The gas saturation is 40%, which is identified as a light oil reservoir. The Darcy modified model is matched (initial skin factor 0.5).

[0138] Production capacity forecast: Substituting into the production capacity calculation formula of the Darcy modified model, the predicted daily output of a single well is 68.00 t / d.

[0139] Deviation correction: using The measured daily production of a single well is 65.20 t / d. The calculated deviation rate is approximately 4.29% < 10%, based on the formula |65.20-68.00| / 65.20. Therefore, no adjustment of the model parameters is required.

[0140] 5. Closed-loop control of production data

[0141] Data acquisition: Downhole pressure and temperature were sampled at a frequency of 1 Hz, water production rate and production capacity were sampled at a frequency of 0.1 Hz, and perforation and fracturing parameters were sampled at a frequency of 0.5 Hz.

[0142] Data transmission: It adopts fiber optic + 4G dual-mode transmission with a fiber optic transmission delay of 1.5s.

[0143] Data processing: After removing outliers every 15 minutes, the data is merged with wellhead data weighted at 0.3 and downhole data weighted at 0.7. After merging, no abnormal thresholds are triggered (water production rate ≤40%, pressure fluctuation ≤1MPa / h, cone speed ≤0.5m / month).

[0144] Command execution and feedback: The execution progress is evaluated every 30 minutes, and a control effect report is generated every 7 days. No parameter adjustments were made.

[0145] Implementation effect

[0146] The daily output of a single well is 65.20 t / d, the water production rate is 45.20%, and the bottom water coning speed is 0.25 m / month.

[0147] Example 2

[0148] The difference between this embodiment and Embodiment 1 is that the perforation density is 16.00 holes / m, and the fracturing fluid discharge rate is 10.00 m³. 3 / min, fracturing sand ratio 12.00%; the implementation effect is a single well daily production of 68.50t / d, water production rate of 42.10%, and bottom water coning speed of 0.22m / month.

[0149] Example 3

[0150] The differences between this embodiment and Embodiment 1 are: perforation density of 18.00 holes / m and fracturing fluid discharge rate of 11.00 m³. 3 / min, fracturing sand ratio 15.00%; the implementation effect is a single well daily production of 70.30t / d, water production rate of 40.50%, and bottom water coning speed of 0.20m / month.

[0151] Example 4

[0152] The difference between this embodiment and Embodiment 1 is: the perforation density is 20.00 holes / m, and the fracturing fluid discharge rate is 8.00 m³. 3 / min, fracturing sand ratio 8.00%; the implementation effect is a single well daily production of 66.80t / d, water production rate of 43.80%, and bottom water coning speed of 0.23m / month.

[0153] Example 5

[0154] The difference between this embodiment and Embodiment 1 is that the perforation density is 12.00 holes / m, and the fracturing fluid discharge rate is 12.00 m³. 3 / min, fracturing sand ratio 18.00%; the implementation effect is a single well daily production of 67.90t / d, water production rate of 44.30%, and bottom water coning speed of 0.24m / month.

[0155] Example 6

[0156] The difference between this embodiment and Embodiment 1 is that the perforation density is 10.00 holes / m (below the specified range), and the fracturing fluid discharge rate is 10.00 m³. 3 / min, fracturing sand ratio 12.00%; the implementation effect is a single well daily production of 60.10t / d, water production rate of 48.50%, and bottom water coning speed of 0.30m / month.

[0157] Example 7

[0158] The difference between this embodiment and Embodiment 1 is that the perforation density is 22.00 holes / m (higher than the specified range), and the fracturing fluid discharge rate is 10.00 m³. 3 / min, fracturing sand ratio 12.00%; the implementation effect is a single well daily production of 62.30t / d, water production rate of 47.20%, and bottom water coning speed of 0.28m / month.

[0159] Example 8

[0160] The difference between this embodiment and Embodiment 1 is that the perforation density is 16.00 holes / m, and the fracturing fluid discharge rate is 7.00 m³. 3 / min (below the limit), fracturing sand ratio 12.00%; the implementation effect is a single well daily production of 58.90t / d, water production rate of 50.30%, and bottom water coning speed of 0.32m / month.

[0161] Example 9

[0162] The differences between this embodiment and Embodiment 1 are: perforation density of 16.00 holes / m and fracturing fluid discharge rate of 13.00 m³. 3 / min (higher than the limit), fracturing sand ratio 12.00%; the implementation effect is a single well daily production of 61.50t / d, water production rate of 49.10%, and bottom water cone advance speed of 0.29m / month.

[0163] Example 10 (Blank Group, Prior Art)

[0164] This embodiment uses existing technology, and the horizontal well specifications and reservoir parameters are the same as in Embodiment 1. The specific operation is as follows:

[0165] 1. Reservoir parameter processing: A fixed parameter model is adopted, which only collects basic data on porosity and permeability. There is no scene classification or dynamic coupling. The weights are fixed at 0.5 for porosity and 0.5 for permeability.

[0166] 2. Perforation-fracturing operation: Perforation density 15.00 holes / m, perforation phase angle 180°, hole depth 1.0m; fracturing fluid discharge 10.00m³. 3 / min, fracturing sand ratio 12.00%, perforation and fracturing parameters are designed independently, without mutual feedback adaptation calculation.

[0167] 3. Water control operation: A general water plugging process is adopted, and ordinary plugging agent (concentration 8.50%) is injected. There is no meter-level segment identification, and the agent is injected evenly throughout the entire well section.

[0168] 4. Capacity forecasting and control: A one-time forecast is made using a fixed-parameter numerical simulation model, and the parameters are manually adjusted every 30 days thereafter, without real-time data closed-loop control.

[0169] Implementation results: Daily production of a single well is 50.00 t / d, water production rate is 55.00%, and bottom water cone speed is 0.50 m / month.

[0170] Specific work process

[0171] Please refer to Figure 1 Based on water depth and formation pressure coefficient, the system divides the land and offshore scenarios, collects reservoir parameters for the corresponding scenarios, normalizes the parameters, calculates the dynamic weight ratio of each parameter using a dynamic weight formula, integrates them to obtain the comprehensive reservoir coupling parameters, and updates the weights using downhole pressure and production profile data at set intervals to continuously optimize the accuracy of the coupling parameters.

[0172] Based on the comprehensive coupling parameters of the reservoir, the parameters related to perforation and fracturing are designed. The perforation density, phase angle, fracturing fluid discharge, sand ratio, and reservoir permeability are normalized. The matching degree between the perforation and fracturing parameters is quantified through multi-parameter linkage calculation. The parameter suitability is determined according to the calculation results. If the parameters are not suitable, they are adjusted according to priority until the construction requirements are met.

[0173] A distributed fiber optic sensing system and a water production rate profiler are deployed to identify high-water-producing sections and high-quality oil-producing sections through sensor data. Degradable plugging agents are injected directionally into the high-water-producing sections. After injection, the water production rate of the water-controlling section is monitored. If the expected rate is not reached, plugging agents are added to protect the production capacity of the high-quality oil-producing sections.

[0174] Reservoir types are identified based on reservoir fluid parameters, and corresponding production capacity prediction sub-models are matched. Production capacity is predicted through the sub-models, actual production capacity data is collected, prediction deviation is calculated using the deviation rate formula, and sub-model parameters are adjusted based on the deviation results to continuously improve the accuracy of production capacity prediction.

[0175] Real-time acquisition of downhole pressure, temperature, water production rate, production capacity, and perforation-fracturing operation parameters via dual-mode transmission; outlier removal and weighted fusion of the acquired data at a set frequency; comparison of the fused data with preset thresholds to identify anomalies; generation of corresponding control commands and execution; periodic evaluation of command execution effectiveness; updating of anomaly identification thresholds and prediction model parameters to form a dynamic optimization closed loop; immediate cessation of operation when emergency shutdown conditions are triggered; parameter reset after troubleshooting; gradual restoration of operation and continuous monitoring.

[0176] The technical features described above can be combined in any way. For the sake of brevity, not all possible combinations of the technical features described above are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A method for increasing production and controlling water in a horizontal well in an oil and gas reservoir with bottom water, characterized in that, include: Dynamic coupling of reservoir parameters in multiple scenarios: Based on water depth and formation pressure coefficient, the scenarios are divided into onshore and offshore scenarios. According to the preset basic weights of each scenario, combined with the normalized values ​​of real-time parameters, the dynamic weight ratio of each parameter is calculated, and then the comprehensive coupling parameters of the reservoir are obtained. The perforation-fracturing parameters are fed back to each other. After normalizing the perforation density, perforation phase angle, fracturing fluid flow rate, fracturing sand ratio and reservoir permeability, the matching determination of perforation and fracturing parameters is achieved through multi-parameter linkage calculation. Meter-level segmented water control uses distributed optical fiber sensing technology with a spatial resolution of 0.5m to identify high-water-producing sections. The water production rate threshold of the high-water-producing sections is greater than 30%. A plugging agent is then injected into the identified high-water-producing sections. Dynamic correction of multi-reservoir production capacity: Based on viscosity, density and gas saturation, reservoir types are identified and corresponding sub-models are matched. The prediction deviation is calculated by the ratio of the absolute value of the difference between the predicted value and the actual value to the actual value. The sub-model parameters are adjusted according to the deviation results. Production data closed-loop control uses fiber optic and 4G dual-mode transmission of production data. Data is fused with a weighting of 0.3 for wellhead data and 0.7 for downhole data. Control commands are generated and executed based on the fusion results.

2. The method for increasing production and controlling water of a horizontal well in a bottom water oil and gas reservoir according to claim 1, characterized in that, The dynamic weight ratio of the dynamic coupling of reservoir parameters in multiple scenarios is calculated using the following formula: ； wherein, is the scene base weight of each parameter, is the normalized value of the corresponding parameter at time t, n is the number of parameters involved in coupling for each scene, n=4 for land scenes and n=5 for marine scenes; The core principle is to fix the importance benchmark of parameters by using the basic weight of the scenario, and dynamically adjust the weight ratio by combining the normalized value of the real-time parameters, so as to achieve precise coupling of reservoir parameters across scenarios.

3. The method for increasing production and controlling water of a horizontal well in a bottom water reservoir according to claim 1, wherein, The fit determination of the perforation-fracturing parameter mutual feedback is calculated by the following formula: ;in, For the normalized perforation density, The normalized perforation phase angle, This represents the normalized fracturing fluid discharge rate. This is the normalized fracturing sand ratio. Normalized reservoir permeability; the core principle is to eliminate dimensional differences through parameter normalization and quantify the matching degree between perforation and fracturing parameters through multi-parameter linkage calculation, providing a basis for parameter adjustment.

4. The method for increasing production and controlling water of a horizontal well in a bottom water reservoir according to claim 1, wherein, The prediction bias of the multi-reservoir productivity dynamic correction is calculated using the following formula: Where Predicted represents the capacity prediction value of the sub-model, and Actual represents the actual capacity measurement value; the core principle is to accurately locate the direction of sub-model parameter adjustment by quantifying the degree of deviation between the predicted value and the actual value, thereby improving the accuracy of capacity prediction.

5. The method for enhancing production and controlling water in horizontal wells of bottom-water oil and gas reservoirs as described in claim 1, characterized in that: In the dynamic coupling of reservoir parameters in multiple scenarios, the parameters for the terrestrial scenario include porosity, permeability, temperature, and pressure, while the parameters for the marine scenario include salinity in addition to the parameters for the terrestrial scenario. The water depth threshold for the terrestrial scenario is less than 500m and the formation pressure coefficient threshold is less than 1.2, while the water depth threshold for the marine scenario is greater than 500m and the formation pressure coefficient threshold is greater than 1.

2.

6. The method for increasing production and controlling water of a horizontal well in a bottom water reservoir according to claim 1, characterized in that: The dynamic weights are updated every 7 days, and the updated data includes downhole pressure and production profile data.

7. The method for increasing production and controlling water of a horizontal well in a bottom water reservoir according to claim 1, characterized in that: In the perforation-fracturing parameter feedback, the perforation density ranges from 12 to 20 holes / m, the perforation phase angle ranges from 120° to 180°, and the fracturing fluid discharge ranges from 8 to 12 m³ / m². 3 / min, the fracturing sand ratio ranges from 5% to 20%, and the reservoir permeability ranges from 50 to 500 mD.

8. The method for increasing production and controlling water of a horizontal well in a bottom water reservoir according to claim 1, characterized in that: The injection rate of the plugging agent for the meter-level segmented water control is 0.5m per meter. 3 The injection rate is 0.1m. 3 The injection pressure is 1-2 MPa lower than the reservoir fracture pressure, and the degradation time of the plugging agent is 7-14 days.

9. The method for increasing production and controlling water of a horizontal well in a bottom water reservoir according to claim 1, characterized in that: The reservoir types include heavy oil reservoirs, light oil reservoirs, and condensate gas reservoirs. The viscosity threshold for heavy oil reservoirs is greater than or equal to 50 mPa·s, the viscosity threshold for light oil reservoirs is 1~50 mPa·s, and the viscosity threshold for condensate gas reservoirs is less than 1 mPa·s. The corresponding sub-models include the SVR model, the Darcy modified model, and the Peng-Robinson model. The penalty coefficient of the SVR model ranges from 10 to 12, and the gamma value ranges from 0.1 to 0.

15.

10. The method for increasing production and controlling water of a horizontal well in a bottom water reservoir according to claim 1, characterized in that: The sampling frequency of the production data is 1Hz for downhole pressure and temperature, 0.1Hz for water production rate and production capacity, and 0.5Hz for perforation and fracturing parameters, with a data transmission delay of less than 2s; the cycle optimization time for the closed-loop control of production data is 7 days, with data processing performed every 15 minutes and command evaluation performed every 30 minutes.

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