Dynamic monitoring and evaluating method for biological yield increasing effect of coal bed gas

Abstract

The invention discloses a coal bed gas biological yield increase effect dynamic monitoring and evaluation method which comprises the following steps: arranging a multi-parameter sensor in a target coal bed gas well site, and collecting background data before bacterium injection; injecting a microbial bacterial solution and a nutrient solution into the coal seam according to the experimental design, and recording parameter information in the injection process; periodically collecting a gas production sample, and analyzing gas components; analyzing microbial community structure and abundance change in the water sample; combining the gas generation rate, the isotope characteristics and the microflora analysis result to identify a gas production path and calibrate a key functional flora; and constructing a mathematical model containing microbial activity, coal quality conversion and gas production efficiency, forming an evaluation index system, and outputting field process optimization suggestions based on the model. According to the method, a scientific basis is provided for dynamic optimization of the yield increasing process, and compared with a traditional laboratory method, the method can achieve systematic analysis of the in-situ microorganism process and quantitative evaluation of the gas production effect, and has good field adaptability and popularization value.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of coalbed methane biological stimulation, and particularly relates to a coalbed methane biological stimulation effect dynamic monitoring and evaluation method. BACKGROUND

[0002] With the increasing demand for coalbed methane resources, microbial enhanced coalbed methane (MECBM) technology, as an innovative stimulation technology, has become an important means to improve the efficiency of coalbed methane production. This technology promotes the degradation of coalbed organic matter and the generation of methane by strengthening indigenous microorganisms or introducing exogenous microbial populations. Moreover, this technology has the characteristics of low cost, simple operation and environmental protection, and has important practical significance for the stimulation and development of coalbed methane.

[0003] However, traditional coalbed methane biological stimulation technologies mostly rely on laboratory simulation experiments, which are usually carried out under standardized environmental conditions, such as constant temperature, constant pressure, and other optimal laboratory conditions. These conditions cannot accurately simulate the complex underground environment of coal seams, especially the changes in microbial activity and gas production processes under high pressure, high salinity, and low temperature geological conditions. The single condition in the laboratory cannot reflect the influence of actual geological, physical, and chemical conditions on microbial community behavior, coal substrate degradation rate, and methane production efficiency during the process of coalbed methane production. Therefore, the promotion based on laboratory results to actual application often lacks sufficient adaptability and accuracy.

[0004] The existing evaluation methods mainly focus on the increase and decrease of the total amount of methane, ignoring the dynamic changes of microbial community structure, the degradation mechanism of coal substrate, and the systematic analysis of gas production pathways. For example, the composition and succession of microbial communities in coal seams will be affected by external conditions, thereby affecting the gas production path and rate. However, the traditional evaluation system relies solely on the total amount of methane as the only standard, lacking in-depth exploration of microbial activity and community succession. This evaluation method cannot accurately reflect the key role of microorganisms in the gas production process, and cannot provide sufficient information support for optimizing the stimulation process.

[0005] In addition, the microbial enhanced coalbed methane technology in current field operation usually relies on experience to adjust process parameters. For example, the setting of process parameters such as nutrient solution concentration, inoculum ratio, and injection method is often based on experience or preliminary test results, lacking scientific dynamic adjustment basis. In addition, due to the complexity of the field environment, the response of microbial communities to injection process parameters is not static, so the traditional optimization method is difficult to adjust in real time, resulting in poor stability and repeatability of the stimulation effect. The lack of mathematical model support based on dynamic monitoring data makes the optimization process lack of accuracy and predictability, greatly affecting the scientificity and efficiency of process adjustment.

[0006] In summary, although microbial enhanced coalbed methane technology shows certain potential in theory and laboratory, traditional stimulation methods and evaluation methods cannot meet the needs of practical applications.

[0007] Therefore, it is an urgent technical problem for those skilled in the art to provide a method capable of fusing multi-source data, realizing dynamic monitoring and evaluation of microbial processes, and providing scientific basis for optimization of coalbed methane stimulation processes through quantitative analysis results. SUMMARY

[0008] Therefore, the present application provides a coalbed methane biological stimulation effect dynamic monitoring and evaluation method to realize real-time monitoring and scientific evaluation of the field application process of microbial enhanced coalbed methane technology. The method of the present application combines field monitoring sensor technology, multi-component gas online analysis, high-throughput microbial detection technology and mathematical modeling technology to dynamically collect and comprehensively analyze key biological and chemical indicators in the MECBM process.

[0009] To solve the above technical problems, the present application adopts the following technical solutions:

[0010] A coalbed methane biological stimulation effect dynamic monitoring and evaluation method, comprising the following steps:

[0011] Step 1: Field sensor layout and baseline collection

[0012] A multi-parameter sensor is laid out at the target coalbed methane well site to collect background data before injection of bacteria;

[0013] Step 2: Implementation of bacteria injection and nutrient solution injection process

[0014] According to the experimental design, microbial liquid and nutrient solution are injected into the coal seam, and parameter information during the injection process is recorded;

[0015] Step 3: Dynamic monitoring of multi-source data

[0016] Periodically collect gas production samples and analyze gas components using a gas chromatograph; use high-throughput sequencing technology to analyze the microbial community structure and abundance changes in the water sample;

[0017] Step 4: Identification of gas production path and calibration of functional bacterial flora

[0018] Combine gas generation rate, isotope characteristics and microbial community analysis results to identify the gas production path and calibrate the key functional bacterial flora;

[0019] Step 5: Mathematical model construction and evaluation index system establishment

[0020] A mathematical model including microbial activity, coal quality conversion and gas production efficiency is constructed to form an evaluation index system, and field process optimization suggestions are made based on the model output.

[0021] Preferably, in step 1, the sensor comprises a temperature sensor, a pressure sensor, a pH sensor, a conductivity sensor.

[0022] Preferably, in step 2, the parameter information comprises injection time, flow rate, concentration and ratio.

[0023] Preferably, in step 3, the gas component comprises methane, carbon dioxide, hydrogen.

[0024] Preferably, in step 3, the high-throughput sequencing technology includes but is not limited to 16S rRNA gene sequencing, metagenomic sequencing or metabolic pathway annotation analysis.

[0025] Preferably, in step 3, the gas component analysis adopts an online gas chromatograph or a portable gas chromatograph device, and the sampling period is 14-30 days.

[0026] Preferably, in step 4, the functional flora includes a methanogen group and upstream acid-producing bacteria and hydrogen-producing bacteria.

[0027] Preferably, the methanogen group includes Methanobacterium and Methanosarcina.

[0028] Preferably, in step 5, the evaluation index system includes the start-up period length, the maximum gas production rate, the cumulative gas production, the methane content change rate and the dominant functional bacteria abundance change index.

[0029] Preferably, in step 5, the constructed mathematical model is a differential equation-based kinetic model or a multi-parameter fitting model based on machine learning.

[0030] The present application has the following technical effects relative to the prior art:

[0031] (1) The present application can realize whole-process monitoring of microbial activity, coal quality conversion and gas production dynamics.

[0032] (2) The present application can reveal the gas production mechanism and microbial response law under different stimulation processes.

[0033] (3) The present application establishes a quantitative evaluation model and an optimization decision support system based on multi-source data.

[0034] (4) The present application improves the field adaptability and process control scientificity of microbial enhanced coalbed methane technology. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 The figure is a process flowchart of the present application.

[0036] Figure 2 The injection well gas component change monitoring chart for the present application;

[0037] Figure 3 The injection well methane-producing colony structure change monitoring chart for the present application. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0039] The present application discloses a coalbed methane biological production increasing effect dynamic monitoring and evaluation method, comprising the following steps:

[0040] Step 1: field sensor layout and baseline collection

[0041] A multi-parameter sensor is laid out at the target coalbed methane well site, and background data before injection of bacteria is collected;

[0042] Step 2: implementation of bacteria injection and nutrient solution injection process

[0043] According to the experimental design, microbial liquid and nutrient solution are injected into the coal seam, and parameter information during the injection process is recorded;

[0044] Step 3: multi-source data dynamic monitoring

[0045] Periodically collect gas production samples, analyze gas components using a gas chromatograph, and use high-throughput sequencing technology to analyze microbial community structure and abundance changes in water samples;

[0046] Step 4: gas production path identification and functional bacterial group calibration

[0047] Combine gas generation rate, isotope characteristics and microbial community analysis results to identify gas production paths and calibrate key functional bacterial groups;

[0048] Step 5: mathematical model construction and evaluation index system establishment

[0049] Construct a mathematical model including microbial activity, coal quality conversion and gas production efficiency, form an evaluation index system, and output field process optimization suggestions based on the model.

[0050] In step 1, the sensors include temperature sensors, pressure sensors, pH sensors and conductivity sensors.

[0051] In step 2, the parameter information includes injection time, flow rate, concentration and ratio.

[0052] In step 3, the gas component includes methane, carbon dioxide, hydrogen.

[0053] In step 3, the high-throughput sequencing technology includes but is not limited to 16S rRNA gene sequencing, metagenomic sequencing or metabolic pathway annotation analysis.

[0054] In step 3, the gas component analysis adopts an online gas chromatograph or a portable gas chromatograph device, and the sampling period is 14-30 days.

[0055] In step 4, the functional flora includes a methanogen group and upstream acid-producing bacteria and hydrogen-producing bacteria.

[0056] The methanogen group includes Methanobacterium and Methanosarcina.

[0057] In step 5, the evaluation index system includes the start-up period length, the maximum gas production rate, the cumulative gas production, the methane content change rate and the dominant functional bacteria abundance change index.

[0058] In step 5, the mathematical model constructed is a differential equation-based kinetic model or a multi-parameter fitting model based on machine learning.

[0059] Embodiment 1:

[0060] The present application carries out a microbial enhanced coalbed methane (MECBM) field test in a certain low-yield coalbed methane well area in Inner Mongolia (the coal rank is a low-metamorphic gas coal, and the burial depth is about 400 meters).

[0061] Three low-yield coalbed methane wells are selected for modification in the test area, one of which is an injection well, and the other two are control wells. The injection well uses the method of injecting exogenous microorganisms and nutrient solution to intervene in yield increase.

[0062] The monitoring system is arranged as follows: three sets of temperature and pressure sensors (installed at 400 meters below the three wells, respectively) are used to monitor the real-time changes of temperature and pressure underground; three wellhead gas samplers are used to collect gas samples periodically; a water sampling system is arranged at the wellhead to collect coalbed water samples periodically; a portable gas chromatograph (GC) and a portable water quality analyzer are provided on the ground to analyze the concentrations of methane (CH4), carbon dioxide (CO2), hydrogen (H2) and other components on site; microbial samples are collected by filtering membranes to collect DNA in water samples, and 16S rRNA high-throughput sequencing is used for community structure analysis.

[0063] The implementation steps and key data are as follows:

[0064] (1) Bacteria injection and nutrient injection stage: the concentration of injected bacteria solution is about 1.0×10 7 CFU / mL, and organic acid nutrient solution (peptone, yeast extract) is used, and the total injection amount is about 500m3 The injection cycle is 1 day, and a one-time injection method is adopted. After the injection is completed, the well is sealed for 30 days, and the control well is opened for joint gas production monitoring.

[0065] (2) The dynamic monitoring cycle is 180 days, with water and gas samples collected every 30 days for a total of 6 times.

[0066] (3) Changes in gas composition (taking injection wells as an example, such as...) Figure 2 As shown in the figure: the initial methane concentration was about 60.14%, which increased to 76.56% on day 60, reached a peak of 85.62% on day 180, and finally stabilized at about 85%; the hydrogen and CO2 concentrations initially rose briefly and then fell, indicating that the gas production mechanism underwent a typical "fermentation-methanation" conversion process.

[0067] (4) Dynamic changes in the microbial community: Methanogens accounted for less than 1% of the initial microbial community, mainly Bacillus spp. From day 30 onwards, methanogens were significantly enriched, with the abundance of Methanosarcina and Methanobacterium increasing to 15% and 8%, respectively. Accompanied by the rapid proliferation of acid-producing bacteria such as Bacteroides and Clostridium, a typical “acid-hydrogen-methan-producing” food chain was formed.

[0068] (5) Analysis of gas production mechanism: The gas isotope δ13C-CH4 value increased from the initial -42‰ to -35‰, which is consistent with the characteristics of methanogenesis in acetic acid nutrition; combined with the results of bacterial abundance, it was determined that the main gas production pathway is acetic acid nutrition.

[0069] (6) Construction and evaluation of kinetic model: A bio-methanogenesis kinetic model based on the Logistic equation was established and fitted by integrating temperature, pressure and microbial activity parameters; the evaluation index system included: maximum gas production rate of 0.0519 m3 / d / t, start-up period shortened to 1.72 days, cumulative gas production increased by 96% compared with the control group, and Methanosarcina abundance index increased; the model prediction and measured data fit reached R2=0.93, which showed good prediction effect.

[0070] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the technical scope of the present invention. Therefore, any minor modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention shall still fall within the scope of the technical solution of the present invention.

Claims

1. A method for dynamically monitoring and evaluating the effect of coalbed gas biological stimulation, characterized in that, The method comprises the following steps: Step 1: field sensor deployment and baseline collection Deploy multi-parameter sensors at the target coalbed methane well site and collect background data before injecting microorganisms; Step 2: injection of microorganisms and nutrient solution Inject microorganism solution and nutrient solution into the coal seam according to the experimental design, and record the parameter information during the injection process; Step 3: dynamic monitoring of multi-source data Periodically collect gas samples and analyze gas components using a gas chromatograph; use high-throughput sequencing technology to analyze the microbial community structure and abundance changes in the water sample; Step 4: identification of gas production path and functional bacterial group calibration Combine gas generation rate, isotope characteristics, and microbial community analysis results to identify the gas production path and calibrate the key functional bacterial group; Step 5: construction of mathematical model and establishment of evaluation index system Construct a mathematical model including microbial activity, coal quality conversion, and gas production efficiency, form an evaluation index system, and output field process optimization suggestions based on the model.

2. The method according to claim 1, characterized in that, In step 1, the sensors include temperature sensors, pressure sensors, pH sensors, and conductivity sensors.

3. The method according to claim 1, characterized in that, In step 2, the parameter information includes injection time, flow rate, concentration, and ratio.

4. The method according to claim 1, characterized in that, In step 3, the gas components include methane, carbon dioxide, and hydrogen.

5. The method according to claim 1, characterized in that, In step 3, the high-throughput sequencing technology includes but is not limited to 16S rRNA gene sequencing, metagenomic sequencing, or metabolic pathway annotation analysis.

6. The method according to claim 1, characterized in that, In step 3, the gas component analysis uses online gas chromatography or portable gas chromatography devices, and the sampling period is 14-30 days.

7. The method according to claim 1, characterized in that, In step 4, the functional bacterial group includes methanogen groups and upstream acid-producing bacteria and hydrogen-producing bacteria.

8. The method according to claim 7, characterized in that, The methanogen groups include Methanobacterium and Methanosarcina.

9. The method according to claim 1, characterized in that, In step 5, the evaluation index system includes start-up period length, maximum gas production rate, cumulative gas production, methane content change rate, and dominant functional bacterial abundance change index.

10. The method according to claim 1, characterized in that, In step 5, the constructed mathematical model is a differential equation-based kinetic model or a multi-parameter fitting model based on machine learning.

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