Coal seam nanofluid fracturing and nitrogen injection displacement dynamic synergistic gas extraction method

Abstract

The present application relates to the field of coal seam gas extraction, and discloses a coal seam nanofluid fracturing and nitrogen injection displacement dynamic synergistic gas extraction method, comprising the following steps: obtaining coal rock geomechanics parameters, gas parameters and coal seam environment parameters to form a basic parameter set; establishing a four-phase dynamic coupling model of nanofluid-nitrogen-coal rock-gas; forming an exclusive parameter library suitable for the target coal seam; injecting nanofluid into the target coal seam for fracturing and seam forming and controlling the flowback ratio, and then using a gradient nitrogen injection strategy for displacement operation, and simultaneously carrying out gas extraction; predicting the operation parameter adaptability, and adjusting the operation parameters when the operation parameter deviation exceeds the preset threshold. By constructing a four-phase dynamic coupling model of nanofluid-nitrogen-coal rock-gas, and combining with orthogonal experimental data fitting correction to form an exclusive parameter library suitable for the target coal seam, the nanofluid fracturing permeability enhancement, nitrogen displacement desorption and gas extraction are dynamically coupled throughout the whole process.

Description

Technical Field

[0001] This invention relates to the field of coal seam gas extraction technology, specifically a dynamic synergistic gas extraction method combining coal seam nanofluid fracturing and nitrogen injection displacement. Background Technology

[0002] With increasing coal mining depth, the proportion of low-permeability, high-gas coal seams is rising year by year. These seams, due to their dense pore structure and complex gas occurrence, present significant challenges in gas extraction, becoming a core bottleneck restricting safe and efficient coal mining. Gas extraction, as a key technology for controlling coal seam gas disasters and ensuring mining safety, aims to improve gas desorption and migration efficiency through permeability enhancement and displacement. Nanofluid fracturing, leveraging the wettability modification and pore conductivity of nanoparticles, can effectively expand the fracture network and increase coal seam permeability; nitrogen injection displacement utilizes the low adsorption and high-pressure displacement characteristics of nitrogen to reduce gas partial pressure and enhance the desorption of adsorbed gas.

[0003] In existing technologies, the synergistic application of nanofluid fracturing and nitrogen injection displacement during coal seam gas extraction often adopts a fixed time sequence of fracturing followed by displacement. In the modeling stage, fracturing permeability enhancement models or displacement desorption models are often constructed separately. The quantitative analysis of the interaction between the dispersion stability of nanoparticles and fluid seepage and gas desorption is insufficient, resulting in insufficient depth of the synergistic mechanism and incomplete utilization of the coupling effect, which restricts the synergistic effect of permeability enhancement and displacement. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a dynamic synergistic gas extraction method combining coal seam nanofluid fracturing and nitrogen injection displacement. This method solves the problem that existing coal seam gas extraction methods suffer from insufficient depth of synergistic mechanism and incomplete coupling effect, which restricts the synergistic effect of permeability enhancement and displacement.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a dynamic synergistic gas extraction method combining coal seam nanofluid fracturing and nitrogen injection displacement, comprising the following steps:

[0006] The target coal seam is explored to obtain coal and petrology geomechanical parameters, gas parameters and coal seam environmental parameters, forming a basic parameter set;

[0007] Based on the basic parameter set, a four-phase dynamic coupling model of nanofluid-nitrogen-coal-gas is established by integrating DLVO colloidal stability theory, Buckley-Leverett two-phase flow theory and Langmuir competitive adsorption theory.

[0008] Orthogonal experiments were used to obtain multi-field coupling effect correlation data for the target coal seam. Data fitting methods were used to fit and correct the parameters of the four-phase dynamic coupling model, forming a dedicated parameter library for the target coal seam.

[0009] Based on the dedicated parameter library and the target coal seam conditions, nanofluid is injected into the target coal seam to perform fracturing and create fractures while controlling the return flow ratio. Then, a gradient nitrogen injection strategy is adopted for displacement operations, and gas extraction is carried out simultaneously.

[0010] The system collects and preprocesses construction data during the operation in real time, combines the output of the four-phase dynamic coupling model to predict the adaptability of operation parameters, adjusts operation parameters when the deviation of operation parameters exceeds the preset threshold, and stops operation when the gas extraction concentration meets the preset conditions.

[0011] By adopting the above technical solution, a four-phase dynamic coupling model covering nanofluid, nitrogen, coal and rock, and gas is constructed. Combined with orthogonal experimental data fitting and correction, a dedicated parameter library adapted to the target coal seam is formed. Based on the parameter library, the coordinated operation of nanofluid fracturing, gradient nitrogen injection displacement, and gas extraction is implemented. Dynamic control is achieved through real-time construction data preprocessing and parameter adaptability prediction. This realizes the full dynamic coupling of nanofluid fracturing permeability enhancement, nitrogen displacement desorption, and gas extraction, solving the problem that the existing coal seam gas extraction methods have insufficient depth of synergy mechanism and the coupling effect is not fully utilized, which restricts the synergistic effect of permeability enhancement and displacement.

[0012] Preferably, the formation of the basic parameter set specifically includes the following steps:

[0013] The geomechanical parameters of the target coal seam were determined using the borehole core sampling method.

[0014] Gas parameters of the target coal seam were obtained through on-site desorption experiments and indoor adsorption experiments.

[0015] Use downhole monitoring equipment to collect coal seam environmental parameters of the target coal seam;

[0016] Geomechanical parameters, gas parameters, and coal seam environmental parameters are classified, organized, and validated. After removing abnormal data, a basic parameter set is formed.

[0017] Preferably, the establishment of the four-phase dynamic coupling model of nanofluid-nitrogen-coal-gas specifically includes the following steps:

[0018] Based on the basic parameter set and combined with the DLVO colloidal stability theory, a sub-model for the dispersion stability of nanoparticles is constructed, and the critical conditions for particle dispersion are determined by calculating the total potential energy between nanoparticles.

[0019] Based on the basic parameter set and combined with the Buckley-Leverett two-phase flow theory, the flow splitting function is modified to construct a seepage coupling sub-model of nanofluid and nitrogen.

[0020] Based on the basic parameter set, combined with Langmuir competitive adsorption theory, and coupled with the wettability modification effect of nanofluids, a gas desorption synergistic sub-model is constructed.

[0021] Based on the basic parameter set, the parameter interaction relationship between the nanoparticle dispersion stability sub-model, the seepage coupling sub-model and the gas desorption synergy sub-model is clarified and integrated to form a four-phase dynamic coupling model.

[0022] Preferably, the process of forming a dedicated parameter library adapted to the target coal seam specifically includes the following steps:

[0023] Multi-field coupling effect correlation data for the target coal seam were obtained through orthogonal experiments. The independent variables of the orthogonal experiments were nanoparticle type, nitrogen injection pressure and fracturing-displacement time interval. The multi-field coupling effect correlation data included gas desorption rate, nitrogen diffusion coefficient and coal rock permeability.

[0024] The least squares method was used to fit the correlation data of multi-field coupling effect to obtain parameter correction coefficients, and the parameters in the four-phase dynamic coupling model were corrected based on the parameter correction coefficients.

[0025] The measured data from the orthogonal experiment were compared with the prediction results of the modified four-phase dynamic coupling model to verify the prediction accuracy of the modified four-phase dynamic coupling model, adapt it to the target coal seam conditions, and form a dedicated parameter library.

[0026] Preferably, the step of injecting nanofluid into the target coal seam for fracturing and controlling the flowback ratio specifically includes the following steps:

[0027] The nanofluid formulation and injection volume are determined based on a dedicated parameter library, and the nanofluid is injected. During the fracture formation stage, the appropriate viscosity and injection pressure are maintained, and proppant is added simultaneously. The fracture permeability of the target coal seam is monitored in real time.

[0028] When the crack permeability increases to the preset value set by the dedicated parameter library and the surface coverage of the nanoparticles reaches the preset requirements, the injection of nanofluid is stopped.

[0029] Based on the preset rate of fracturing fluid determined by the dedicated parameter library, the flowback ratio is controlled to be within the preset range suitable for the target coal seam.

[0030] Preferably, the gradient nitrogen injection strategy for displacement specifically includes the following steps:

[0031] Based on a dedicated parameter library, determine the range of nitrogen injection parameters for each stage in the gradient nitrogen injection strategy, and perform low-pressure pre-injection of nitrogen for an appropriate duration after fracturing and creating fractures in the target coal seam.

[0032] After the low-pressure pre-charge is completed, medium-pressure displacement nitrogen injection of appropriate duration is carried out, and the nitrogen injection pressure is gradually increased to the appropriate range to form a stable pressure difference to drive gas migration.

[0033] After the medium-pressure displacement is completed, nitrogen injection is carried out in a suitable constant pressure range to suppress crack closure and continuously replace adsorbed gas.

[0034] Preferably, the simultaneous gas extraction specifically includes the following steps:

[0035] During the displacement operation, the initial value of the extraction negative pressure is set according to the exclusive parameter library, gas extraction is carried out simultaneously, and the methane concentration and nitrogen content in the extracted gas are monitored in real time.

[0036] When the methane concentration drops to the preset concentration threshold set by the dedicated parameter library, the nitrogen injection pressure is increased by the preset pressure increment, and at the same time, the preset concentration of nanofluid is injected.

[0037] Based on the output results of the four-phase dynamic coupling model, the matching relationship between the extraction negative pressure and the nitrogen injection parameters is dynamically adjusted.

[0038] Preferably, the adaptability of the prediction operation parameters specifically includes the following steps:

[0039] Construction data during the operation is collected in real time, and abnormal data is eliminated using the 3σ criterion to obtain filtered data. The construction data includes crack propagation morphology, gas composition, injection pressure, nanofluid properties, and coal and rock permeability.

[0040] The screened data is standardized and converted into dimensionless data to form a preprocessed dataset.

[0041] With maximizing gas extraction efficiency as the objective function and a preprocessed dataset as input, an intelligent optimization model is constructed using the least squares support vector machine algorithm.

[0042] By combining the output of the four-phase dynamic coupling model with the preprocessed dataset, the adaptability of the current operation parameters is predicted through an intelligent optimization model.

[0043] When the deviation of the operation parameters exceeds the preset threshold, adjust the operation parameters and then re-verify the adaptability.

[0044] This invention provides a dynamic synergistic method for coal seam nanofluid fracturing and nitrogen injection displacement to extract gas. It has the following beneficial effects:

[0045] 1. This invention constructs a four-phase dynamic coupling model covering nanofluids, nitrogen, coal and rock, and gas. It combines orthogonal experimental data fitting and correction to form a dedicated parameter library adapted to the target coal seam. Based on the parameter library, it implements the coordinated operation of nanofluid fracturing, gradient nitrogen injection displacement, and gas extraction. Through real-time construction data preprocessing and parameter adaptability prediction, it achieves dynamic control and realizes the full-process dynamic coupling of nanofluid fracturing permeability enhancement, nitrogen displacement desorption, and gas extraction. This solves the problem that existing coal seam gas extraction methods have insufficient depth of synergistic mechanism and the coupling effect is not fully utilized, which restricts the synergistic effect of permeability enhancement and displacement.

[0046] 2. This invention obtains multi-field coupling effect correlation data through orthogonal experiments, and uses data fitting methods to correct model parameters, forming a dedicated parameter library adapted to the target coal seam. It can determine the nanofluid formulation, fracturing-displacement parameters and extraction threshold, so that various operation parameters are matched with the geomechanical characteristics and gas occurrence state of the target coal seam, thereby improving the adaptability and targeting of the operation.

[0047] 3. This invention maintains fracture stability by controlling the backflow ratio of fracturing fluid and retaining an appropriate amount of nanofluid, and connects the various stages of gradient nitrogen injection. This achieves the organic linkage of the entire process of fracture creation, fracture stabilization, displacement and extraction, giving full play to the permeability enhancement advantage of nanofluid and the displacement advantage of nitrogen, so that the two effects are mutually reinforcing, enhancing the coupling effect of permeability enhancement and displacement, and improving the efficiency of gas desorption and migration. Attached Figure Description

[0048] Figure 1 The flowchart is a dynamic synergistic gas extraction method for coal seam nanofluid fracturing and nitrogen injection displacement proposed in this invention.

[0049] Figure 2 This is a diagram of the architecture of the dynamic synergistic gas extraction system for coal seam nanofluid fracturing and nitrogen injection displacement proposed in an embodiment of the present invention. Detailed Implementation

[0050] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] Example 1:

[0052] In the first embodiment of the present invention, the present invention provides a dynamic synergistic gas extraction method for coal seam nanofluid fracturing and nitrogen injection displacement, such as... Figure 1 As shown, it includes the following steps:

[0053] The target coal seam is explored to obtain coal and petrology geomechanical parameters, gas parameters and coal seam environmental parameters, forming a basic parameter set;

[0054] Furthermore, a basic parameter set is formed, which specifically includes the following steps:

[0055] The geomechanical parameters of the target coal seam were determined using the borehole core sampling method.

[0056] Gas parameters of the target coal seam were obtained through on-site desorption experiments and indoor adsorption experiments.

[0057] Use downhole monitoring equipment to collect coal seam environmental parameters of the target coal seam;

[0058] Geomechanical parameters, gas parameters, and coal seam environmental parameters are classified, organized, and validated. After removing abnormal data, a basic parameter set is formed.

[0059] Specifically, a basic parameter set is formed to provide data support for subsequent model construction and construction implementation. Generally, geomechanical parameters are determined using core drilling. Core samples are taken from different depths of the target coal seam to ensure core integrity, and key indicators such as coal density, porosity, and permeability are measured. Gas desorption rates are obtained in real-time through on-site desorption experiments, combined with indoor adsorption experiments to analyze parameters such as gas pressure and saturated adsorption capacity. Environmental parameters such as coal seam water pressure and formation temperature are collected using downhole pressure sensors and temperature sensors. In some embodiments, the three types of parameters are organized by category, and outliers are removed using data validity verification methods to ensure parameter accuracy and completeness, forming a basic parameter set adapted to the target coal seam, laying a reliable data foundation for the subsequent establishment of a four-phase dynamic coupling model.

[0060] Based on the basic parameter set, a four-phase dynamic coupling model of nanofluid-nitrogen-coal-gas is established by integrating DLVO colloidal stability theory, Buckley-Leverett two-phase flow theory and Langmuir competitive adsorption theory.

[0061] Furthermore, a four-phase dynamic coupling model of nanofluid-nitrogen-coal-gas is established, specifically including the following steps:

[0062] Based on the basic parameter set and combined with the DLVO colloidal stability theory, a sub-model for the dispersion stability of nanoparticles is constructed, and the critical conditions for particle dispersion are determined by calculating the total potential energy between nanoparticles.

[0063] Based on the basic parameter set and combined with the Buckley-Leverett two-phase flow theory, the flow splitting function is modified to construct a seepage coupling sub-model of nanofluid and nitrogen.

[0064] Based on the basic parameter set, combined with Langmuir competitive adsorption theory, and coupled with the wettability modification effect of nanofluids, a gas desorption synergistic sub-model is constructed.

[0065] Based on the basic parameter set, the parameter interaction relationship between the nanoparticle dispersion stability sub-model, the seepage coupling sub-model and the gas desorption synergy sub-model is clarified and integrated to form a four-phase dynamic coupling model.

[0066] Specifically, a four-phase dynamic coupling model was established to quantify the dynamic interaction between nanofluids, nitrogen, and coal-rock gas, providing theoretical support for collaborative operations.

[0067] Generally, a sub-model for the dispersion stability of nanoparticles is constructed based on a set of fundamental parameters and DLVO colloidal stability theory. Specifically, the critical dispersion condition is determined by calculating the total potential energy between nanoparticles, as shown in the following formula: ,in This represents the total potential energy between nanoparticles. This represents the van der Waals gravitational potential energy between nanoparticles. The formula represents the repulsive potential energy of the double electric layer of nanoparticles, and all physical quantities are determined based on coal and petrology parameters from the fundamental parameter set. By inputting data such as nanoparticle radius and coal and petrology Hamaker constant from the fundamental parameter set, the formula outputs the critical state of particle dispersion, enabling accurate prediction of the dispersion stability of nanofluids.

[0068] Based on the fundamental parameter set and the Buckley-Leverett two-phase flow theory, a seepage coupling sub-model is constructed, and the modified flow splitting function is: ,in For nanofluid splitting rate, The effective permeability of nanofluids in coal and rock. The effective permeability of nitrogen in coal and rock. For nanofluid viscosity, The nitrogen viscosity is used, and all parameters are taken from a basic parameter set or calibrated experimentally. Inputting data such as the original permeability of coal and rock from the basic parameter set allows this function to quantify the synergistic relationship between the nanofluid and nitrogen flow.

[0069] Based on the fundamental parameter set and Langmuir competitive adsorption theory, a cooperating sub-model for gas desorption is constructed by coupling the wettability modification effect of nanofluids. The formula is as follows: ,in The gas desorption rate, This represents the instantaneous adsorption capacity of methane. This represents the saturated adsorption capacity of methane. Let be the methane adsorption rate constant. Let be the methane desorption rate constant. The co-desorption coefficient, For the surface coverage of nanoparticles, For methane partial pressure, This represents the partial pressure of nitrogen. Input data such as gas adsorption capacity and pressure from the basic parameter set, and use this formula to clarify the synergistic effect of multiple factors on gas desorption.

[0070] Based on data such as coal and rock mechanical properties and gas occurrence state from the basic parameter set, the parameter interaction relationships among the three sub-models are clarified. For example, the nanoparticle coverage is simultaneously associated with dispersion stability, seepage, and desorption models. The sub-models are integrated to form a four-phase dynamic coupling model, which achieves a comprehensive characterization of multi-field coupling processes and provides a reliable theoretical basis for subsequent operation parameter optimization.

[0071] Orthogonal experiments were used to obtain multi-field coupling effect correlation data for the target coal seam. Data fitting methods were used to fit and correct the parameters of the four-phase dynamic coupling model, forming a dedicated parameter library for the target coal seam.

[0072] Furthermore, a dedicated parameter library adapted to the target coal seam is formed, which specifically includes the following steps:

[0073] Orthogonal experiments were used to obtain multi-field coupling effect correlation data for the target coal seam. The independent variables of the orthogonal experiments were nanoparticle type, nitrogen injection pressure and fracturing-displacement time interval. The multi-field coupling effect correlation data included gas desorption rate, nitrogen diffusion coefficient and coal and rock permeability.

[0074] The least squares method was used to fit the correlation data of multi-field coupling effect to obtain parameter correction coefficients, and the parameters in the four-phase dynamic coupling model were corrected based on the parameter correction coefficients.

[0075] The measured data from the orthogonal experiment were compared with the prediction results of the modified four-phase dynamic coupling model to verify the prediction accuracy of the modified four-phase dynamic coupling model, adapt it to the target coal seam conditions, and form a dedicated parameter library.

[0076] Specifically, the creation of a dedicated parameter library involves calibrating the four-phase dynamic coupling model using experimental data to ensure that the model is adapted to the actual working conditions of the target coal seam, thus providing accurate parameter support for subsequent construction.

[0077] Generally, orthogonal experiments are first conducted to obtain multi-field coupling effect correlation data. The independent variables selected for the orthogonal experiments are nanoparticle type, nitrogen injection pressure, and fracturing-displacement time interval. This selection is based on the core influencing factors determined by the four-phase dynamic coupling model. An integrated fracturing-displacement-adsorption experimental platform is built to simulate the geomechanical environment and gas occurrence state of the target coal seam. The gas desorption rate, nitrogen diffusion coefficient, and coal permeability are monitored simultaneously. These data together constitute multi-field coupling effect correlation data, providing experimental basis for model correction.

[0078] The least squares method is used to fit the correlation data of multi-field coupling effects, and the parameter correction coefficients are solved. The formula is as follows: ,in This is data on the correlation of multi-field coupling effects measured in orthogonal experiments. This is the prediction data for a four-phase dynamic coupling model. This represents the sample size for the experimental data. Input the measured gas desorption rate, nitrogen diffusion coefficient, and coal / rock permeability as... The corresponding predicted values ​​calculated based on the initial parameters of the four-phase dynamic coupling model are used as input. The formula is used to find the parameter correction coefficient that minimizes the sum of squared errors.

[0079] The obtained parameter correction coefficients are applied to the four-phase dynamic coupling model to correct key parameters such as the synergistic desorption coefficient and permeability coefficient, making the model predictions more closely match the actual conditions of the target coal seam. After correction, the model prediction accuracy needs to be verified by systematically comparing the measured data from the orthogonal experiment with the prediction results of the corrected model to analyze their consistency. By continuously optimizing the parameter correction coefficients, it is ensured that the model can accurately characterize the multi-field coupling process of the target coal seam, forming a dedicated parameter library adapted to the working conditions of the target coal seam, providing a reliable basis for setting parameters for subsequent nanofluid injection, gradient nitrogen injection, and gas extraction.

[0080] Based on the dedicated parameter library and the target coal seam conditions, nanofluid is injected into the target coal seam to perform fracturing and create fractures while controlling the return flow ratio. Then, a gradient nitrogen injection strategy is adopted for displacement operations, and gas extraction is carried out simultaneously.

[0081] Furthermore, the injection of nanofluids into the target coal seam for fracturing and fracture creation, and the control of the flowback ratio, specifically includes the following steps:

[0082] The nanofluid formulation and injection volume are determined based on a dedicated parameter library, and the nanofluid is injected. During the fracture formation stage, the appropriate viscosity and injection pressure are maintained, and proppant is added simultaneously. The fracture permeability of the target coal seam is monitored in real time.

[0083] When the crack permeability increases to the preset value set by the dedicated parameter library and the surface coverage of the nanoparticles reaches the preset requirements, the injection of nanofluid is stopped.

[0084] Based on the preset rate of fracturing fluid determined by the dedicated parameter library, the flowback ratio is controlled to be within the preset range suitable for the target coal seam.

[0085] Furthermore, a gradient nitrogen injection strategy is adopted for displacement operations, specifically including the following steps:

[0086] Based on a dedicated parameter library, determine the range of nitrogen injection parameters for each stage in the gradient nitrogen injection strategy, and perform low-pressure pre-injection of nitrogen for an appropriate duration after fracturing and creating fractures in the target coal seam.

[0087] After the low-pressure pre-charge is completed, medium-pressure displacement nitrogen injection of appropriate duration is carried out, and the nitrogen injection pressure is gradually increased to the appropriate range to form a stable pressure difference to drive gas migration.

[0088] After the medium-pressure displacement is completed, nitrogen injection is carried out in a suitable constant pressure range to suppress crack closure and continuously replace adsorbed gas.

[0089] Furthermore, gas extraction is carried out simultaneously, specifically including the following steps:

[0090] During the displacement operation, the initial value of the extraction negative pressure is set according to the dedicated parameter library, gas extraction is carried out simultaneously, and the methane concentration and nitrogen content in the extracted gas are monitored in real time.

[0091] When the methane concentration drops to the preset concentration threshold set by the dedicated parameter library, the nitrogen injection pressure is increased by the preset pressure increment, and at the same time, the preset concentration of nanofluid is injected.

[0092] Based on the output results of the four-phase dynamic coupling model, the matching relationship between the extraction negative pressure and the nitrogen injection parameters is dynamically adjusted.

[0093] Specifically, based on a dedicated parameter library and the output of a four-phase dynamic coupling model, the system achieves dynamic coordination of nanofluid fracturing, gradient nitrogen injection displacement, and gas extraction, ensuring that the operation is adapted to the target coal seam conditions.

[0094] Generally, before injecting nanofluids for fracturing and fracture creation, the nanofluid formulation and injection rate are determined based on a dedicated parameter library. The formulation needs to be adjusted according to the characteristics of the target coal seam, including the type of nanoparticles and the modification method, to ensure that the particle dispersion stability meets the requirements predicted by the sub-model. In some embodiments, the nanoparticles are modified with amino functional groups, and the nanoparticles, foam stabilizer, and water are mixed according to the formulation. After stirring and dispersing, the mixture is injected into the target coal seam. During the fracture creation stage, an appropriate viscosity and injection pressure are maintained, and proppant is added simultaneously to enhance fracture stability.

[0095] Real-time monitoring of fracture permeability is achieved using distributed fiber optic sensors. The fracture permeability calculation reference formula is as follows: ,in For crack permeability, Injecting flow rate into nanofluids For nanofluid viscosity, The length of the crack. The cross-sectional area of ​​the crack. The injection pressure difference is used. Data such as injection flow rate and viscosity from a dedicated parameter library are input, and the fracture permeability is calculated in real time using this formula. When the permeability reaches the preset value set in the dedicated parameter library, and the nanoparticle surface coverage meets the preset requirements, the injection of nanofluid is stopped. Fracturing fluid is flowed back at a preset rate determined by the dedicated parameter library, controlling the flowback ratio to a preset range suitable for the target coal seam. An appropriate amount of nanofluid is retained to maintain fracture stability, laying the foundation for subsequent nitrogen injection displacement.

[0096] The parameters for each stage of the gradient nitrogen injection strategy are determined based on a dedicated parameter library. After fracturing and fracture creation, low-pressure pre-charging nitrogen injection is initiated. Low-pressure injection of an appropriate duration can rapidly fill fracture pores and reduce gas partial pressure within the fracture. In some embodiments, after low-pressure pre-charging, medium-pressure displacement nitrogen injection is initiated, gradually increasing the injection pressure to an appropriate range. The injection pressure adjustment follows the formula: ,in For instantaneous nitrogen injection pressure, For low-pressure precharge pressure, To increase the rate of pressure increase, This refers to the medium-pressure displacement duration. Inputting initial pressure values, lift rates, and other data from a dedicated parameter library, this formula achieves a stable pressure increase, creating a stable pressure difference to drive gas migration. After medium-pressure displacement, the process enters a constant-pressure, stable-fracture nitrogen injection stage. Maintaining a suitable constant-pressure range, nitrogen continuously replaces adsorbed gas using its non-adsorption properties, while simultaneously inhibiting fracture closure due to ground stress.

[0097] After the displacement operation is initiated, the gas extraction system is simultaneously activated. An initial negative pressure value for extraction is set according to a dedicated parameter library, and the methane and nitrogen concentrations in the extracted gas are monitored in real time using a gas chromatograph. Based on the gas desorption rate formula of the four-phase dynamic coupling model, when the methane concentration drops to a preset concentration threshold set in the dedicated parameter library, the nitrogen injection pressure is increased by a preset pressure increment, while simultaneously injecting a preset concentration of nanofluid. According to the gas desorption rate output by the four-phase dynamic coupling model, the matching relationship between the extraction negative pressure and the nitrogen injection parameters is dynamically adjusted to ensure efficient transport of desorbed gas to the extraction orifice, achieving synergistic effects throughout the fracturing, displacement, and extraction process.

[0098] The system collects and preprocesses construction data during the operation in real time, combines the output of the four-phase dynamic coupling model to predict the adaptability of operation parameters, adjusts operation parameters when the deviation of operation parameters exceeds the preset threshold, and stops operation when the gas extraction concentration meets the preset conditions.

[0099] Furthermore, predicting the suitability of operational parameters specifically includes the following steps:

[0100] Construction data during the operation is collected in real time, and abnormal data is removed using the 3σ criterion to obtain filtered data. The construction data includes crack propagation morphology, gas composition, injection pressure, nanofluid properties, and coal and rock permeability.

[0101] The screened data is standardized and converted into dimensionless data to form a preprocessed dataset.

[0102] With maximizing gas extraction efficiency as the objective function and a preprocessed dataset as input, an intelligent optimization model is constructed using the least squares support vector machine algorithm.

[0103] By combining the output of the four-phase dynamic coupling model with the preprocessed dataset, the adaptability of the current operation parameters is predicted through an intelligent optimization model.

[0104] When the deviation of the operation parameters exceeds the preset threshold, adjust the operation parameters and then re-verify the adaptability.

[0105] Specifically, the predictive operation parameter adaptability is achieved through real-time data preprocessing and intelligent optimization models, combined with the output of a four-phase dynamic coupling model, to realize dynamic control of operation parameters and ensure the continuous optimal synergistic effect.

[0106] First, construction data during the operation is collected in real time. Through equipment such as distributed fiber optic sensors, gas chromatographs, and pressure sensors, data such as crack propagation morphology, gas composition, injection pressure, nanofluid properties, and coal and rock permeability are collected. These data directly reflect the real-time status of the operation and provide a basis for adaptability prediction.

[0107] Outlier data is removed using the 3σ criterion, and the formula is as follows: ,in For a single construction data point, This represents the average of construction data of the same type. This represents the standard deviation of the same type of construction data. The mean of each type of collected construction data is calculated separately. and standard deviation When a piece of data meets the above formula, it is judged as abnormal data and removed, thus obtaining the filtered data.

[0108] The screened data is standardized and converted into dimensionless data using the following formula: ,in For standardized dimensionless data, For the filtered construction data, To filter the mean of the data, To filter the standard deviation of the data, input the filtered fracture permeability, gas concentration, injection pressure, and other data. This formula eliminates the dimensional differences between different types of data, forming a preprocessed dataset that provides a uniform scale of input for the intelligent optimization model.

[0109] With maximizing gas extraction efficiency as the objective function, an intelligent optimization model is constructed using the least squares support vector machine algorithm, and the formula is as follows:

[0110] ;

[0111] ;

[0112] in For the model weight vector, For regularization parameters, As slack variables, For the adaptability of operation parameters, To preprocess the fused data from the dataset and the output of the four-phase dynamic coupling model, For kernel function mapping, For bias terms, Given the sample size, the input preprocessed dataset contains data such as crack propagation state and gas desorption rate, as well as data such as predicted desorption rate and seepage synergy coefficient output by the four-phase dynamic coupling model. The fused data is mapped to a high-dimensional feature space through kernel function mapping, and the model parameters are solved to obtain the intelligent optimization model.

[0113] By combining the output of the four-phase dynamic coupling model with the preprocessed dataset, this intelligent optimization model predicts the adaptability of the current operating parameters, i.e., the degree of matching between the output parameters and the target coal seam conditions. When the prediction results show that the deviation of the operating parameters exceeds the preset threshold, parameters such as nanofluid concentration, nitrogen injection pressure, and extraction negative pressure are adjusted according to the adaptation range. After adjustment, new construction data is re-input into the model to verify the adaptability, forming a closed-loop control of acquisition-preprocessing-prediction-adjustment-verification, ensuring that the operation is in a dynamically coordinated optimal state throughout the entire process.

[0114] Example 2:

[0115] In a second embodiment of the present invention, the present invention provides a dynamic synergistic gas extraction system for coal seam nanofluid fracturing and nitrogen injection displacement, such as... Figure 2 As shown, it includes the following modules:

[0116] Exploration module: Explorates the target coal seam, obtains coal and petrological geomechanical parameters, gas parameters and coal seam environmental parameters, and forms a basic parameter set;

[0117] Fusion Module: Based on the basic parameter set, the DLVO colloidal stability theory, Buckley-Leverett two-phase flow theory and Langmuir competitive adsorption theory are integrated to establish a four-phase dynamic coupling model of nanofluid-nitrogen-coal-gas.

[0118] Fitting module: Obtain multi-field coupling effect correlation data for the target coal seam through orthogonal experiments, and use data fitting methods to fit and correct the parameters of the four-phase dynamic coupling model to form a dedicated parameter library for the target coal seam.

[0119] Collaborative Operation Module: Based on the exclusive parameter library and the target coal seam conditions, nanofluid is injected into the target coal seam to perform fracturing and create fractures and control the return flow ratio. Then, a gradient nitrogen injection strategy is adopted to carry out displacement operations, and gas extraction is carried out simultaneously.

[0120] Prediction and Adjustment Module: Collects and preprocesses construction data in real time during the operation, combines the output results of the four-phase dynamic coupling model to predict the adaptability of operation parameters, adjusts operation parameters when the deviation of operation parameters exceeds the preset threshold, and stops operation when the gas extraction concentration meets the preset conditions.

[0121] The deep mining area of ​​a certain coal mine is a typical low-permeability, high-gas coal seam. This coal seam has a dense pore structure, insufficiently developed fractures, and the gas is mainly in an adsorbed state with complex occurrence conditions. When using a fracturing followed by nitrogen injection extraction method, the lack of a precise synergistic mechanism means that the permeability enhancement effect of nanofluid fracturing and the desorption effect of nitrogen injection cannot be effectively coordinated. This leads to easy closure of fractures after fracturing, poor compatibility between nitrogen injection pressure and gas desorption rate, resulting in low gas extraction efficiency and long cycles, failing to meet the gas control requirements for safe coal mining. To solve these problems, the coal seam nanofluid fracturing and nitrogen injection dynamic synergistic gas extraction system provided by this invention is adopted, the architecture of which is as follows: Figure 2 As shown. The specific implementation process of this system is as follows:

[0122] First, the exploration module collects coal and rock geomechanical parameters, gas parameters, and coal seam environmental parameters through borehole core sampling, on-site desorption experiments, and underground monitoring. After verification and removal of abnormal data, a basic parameter set is formed.

[0123] Subsequently, based on this set of basic parameters, the fusion module integrates the three major theories to construct a four-phase dynamic coupling model and quantifies the dynamic interaction relationship between the four phases.

[0124] The fitting module builds an integrated experimental platform to conduct orthogonal experiments, obtains multi-field coupling effect correlation data, corrects model parameters through data fitting, and forms a dedicated parameter library adapted to the coal seam.

[0125] Based on a dedicated parameter library, the collaborative operation module injects nanofluid according to a preset formula to complete fracturing and create fractures, and controls the return flow ratio. Then, gradient nitrogen injection is initiated to displace the gas, and the gas extraction system is started simultaneously.

[0126] The prediction and adjustment module collects construction data such as crack propagation and gas composition in real time. After preprocessing, it combines the model output results and intelligently optimizes the model prediction parameter adaptability. When the parameter deviation exceeds the threshold, it automatically adjusts parameters such as nitrogen injection pressure and nanofluid replenishment amount to form a closed-loop control.

[0127] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A dynamic synergistic gas extraction method combining nanofluid fracturing and nitrogen injection displacement in coal seams, characterized in that... Includes the following steps: The target coal seam is explored to obtain coal and petrology geomechanical parameters, gas parameters and coal seam environmental parameters, forming a basic parameter set; Based on the basic parameter set, a four-phase dynamic coupling model of nanofluid-nitrogen-coal-gas is established by integrating DLVO colloidal stability theory, Buckley-Leverett two-phase flow theory and Langmuir competitive adsorption theory. Orthogonal experiments were used to obtain multi-field coupling effect correlation data for the target coal seam. Data fitting methods were used to fit and correct the parameters of the four-phase dynamic coupling model, forming a dedicated parameter library for the target coal seam. Based on the dedicated parameter library and the target coal seam conditions, nanofluid is injected into the target coal seam to perform fracturing and create fractures, and the backflow ratio is controlled. Then, a gradient nitrogen injection strategy is used for displacement operations, and gas extraction is carried out simultaneously. The system collects and preprocesses construction data during the operation in real time, combines the output of the four-phase dynamic coupling model to predict the adaptability of operation parameters, adjusts operation parameters when the deviation of operation parameters exceeds the preset threshold, and stops operation when the gas extraction concentration meets the preset conditions. The establishment of the four-phase dynamic coupling model of nanofluid-nitrogen-coal-gas specifically includes the following steps: Based on the basic parameter set and combined with the DLVO colloidal stability theory, a sub-model for the dispersion stability of nanoparticles is constructed, and the critical conditions for particle dispersion are determined by calculating the total potential energy between nanoparticles. Based on the basic parameter set and combined with the Buckley-Leverett two-phase flow theory, the flow split function is modified to construct a percolation coupling sub-model of nanofluid and nitrogen gas. The percolation synergy between nanofluid and nitrogen gas is quantified through this flow split function. Based on the fundamental parameter set, combined with Langmuir competitive adsorption theory, and coupled with the wettability modification effect of nanofluids, a gas desorption synergistic sub-model is constructed. The formula for the gas desorption synergistic sub-model is as follows: The gas desorption rate, This represents the instantaneous adsorption capacity of methane. This represents the saturated adsorption capacity of methane. Let be the methane adsorption rate constant. Let be the methane desorption rate constant. The co-desorption coefficient, For the surface coverage of nanoparticles, For methane partial pressure, The formula for the gas desorption synergistic sub-model, where nitrogen is the partial pressure, is used to clarify the synergistic effect of multiple factors on gas desorption. Based on the basic parameter set, the parameter interaction relationship between the nanoparticle dispersion stability sub-model, the seepage coupling sub-model and the gas desorption synergy sub-model is clarified and integrated to form a four-phase dynamic coupling model.

2. The method for dynamic synergistic gas extraction of coal seam nanofluid fracturing and nitrogen injection displacement as described in claim 1, characterized in that: The formation of the basic parameter set specifically includes the following steps: Geomechanical parameters of the target coal seam were determined using borehole core sampling. Gas parameters of the target coal seam were obtained through on-site desorption experiments and indoor adsorption experiments. Use downhole monitoring equipment to collect coal seam environmental parameters of the target coal seam; Geomechanical parameters, gas parameters, and coal seam environmental parameters are classified, organized, and validated. After removing abnormal data, a basic parameter set is formed.

3. The method for dynamic synergistic gas extraction of coal seam nanofluid fracturing and nitrogen injection displacement as described in claim 1, characterized in that: The process of creating a dedicated parameter library adapted to the target coal seam specifically includes the following steps: Multi-field coupling effect correlation data for the target coal seam were obtained through orthogonal experiments. The independent variables of the orthogonal experiments were nanoparticle type, nitrogen injection pressure and fracturing-displacement time interval. The multi-field coupling effect correlation data included gas desorption rate, nitrogen diffusion coefficient and coal rock permeability. The least squares method was used to fit the correlation data of multi-field coupling effect to obtain parameter correction coefficients, and the parameters in the four-phase dynamic coupling model were corrected based on the parameter correction coefficients. The measured data from the orthogonal experiment were compared with the prediction results of the modified four-phase dynamic coupling model to verify the prediction accuracy of the modified four-phase dynamic coupling model, adapt it to the target coal seam conditions, and form a dedicated parameter library.

4. The method for dynamic synergistic gas extraction of coal seam nanofluid fracturing and nitrogen injection displacement as described in claim 1, characterized in that: The process of injecting nanofluid into the target coal seam for fracturing and controlling the flowback ratio specifically includes the following steps: The nanofluid formulation and injection volume are determined based on a dedicated parameter library, and the nanofluid is injected. During the fracture formation stage, the appropriate viscosity and injection pressure are maintained, and proppant is added simultaneously. The fracture permeability of the target coal seam is monitored in real time. When the crack permeability increases to the preset value set by the dedicated parameter library and the surface coverage of the nanoparticles reaches the preset requirements, the injection of nanofluid is stopped. Based on the preset rate of fracturing fluid determined by the dedicated parameter library, the flowback ratio is controlled to be within the preset range suitable for the target coal seam.

5. The method for dynamic synergistic gas extraction of coal seam nanofluid fracturing and nitrogen injection displacement according to claim 1, characterized in that: The displacement operation using a gradient nitrogen injection strategy specifically includes the following steps: Based on a dedicated parameter library, determine the range of nitrogen injection parameters for each stage in the gradient nitrogen injection strategy, and perform low-pressure pre-injection of nitrogen for an appropriate duration after fracturing and creating fractures in the target coal seam. After the low-pressure pre-charge is completed, medium-pressure displacement nitrogen injection of appropriate duration is carried out, and the nitrogen injection pressure is gradually increased to the appropriate range to form a stable pressure difference to drive gas migration. After the medium-pressure displacement is completed, nitrogen injection is carried out in a suitable constant pressure range to suppress crack closure and continuously replace adsorbed gas.

6. The method for dynamic synergistic gas extraction of coal seam nanofluid fracturing and nitrogen injection displacement according to claim 1, characterized in that: The simultaneous gas extraction specifically includes the following steps: During the displacement operation, the initial value of the extraction negative pressure is set according to the exclusive parameter library, gas extraction is carried out simultaneously, and the methane concentration and nitrogen content in the extracted gas are monitored in real time. When the methane concentration drops to the preset concentration threshold set by the dedicated parameter library, the nitrogen injection pressure is increased by the preset pressure increment, and at the same time, the preset concentration of nanofluid is injected. Based on the output results of the four-phase dynamic coupling model, the matching relationship between the extraction negative pressure and the nitrogen injection parameters is dynamically adjusted.

7. The method for dynamic synergistic gas extraction of coal seam nanofluid fracturing and nitrogen injection displacement according to claim 1, characterized in that: The parameter adaptation for the prediction task specifically includes the following steps: Construction data during the operation is collected in real time, and abnormal data is eliminated using the 3σ criterion to obtain filtered data. The construction data includes crack propagation morphology, gas composition, injection pressure, nanofluid properties, and coal and rock permeability. The screened data is standardized and converted into dimensionless data to form a preprocessed dataset. With maximizing gas extraction efficiency as the objective function and a preprocessed dataset as input, an intelligent optimization model is constructed using the least squares support vector machine algorithm. By combining the output of the four-phase dynamic coupling model with the preprocessed dataset, the adaptability of the current operation parameters is predicted through an intelligent optimization model. When the deviation of the operation parameters exceeds the preset threshold, adjust the operation parameters and then re-verify the adaptability.

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