Deep coal bed gas fracturing dynamic and static evaluation method

Abstract

The invention relates to the technical field of coal gas exploitation, in particular to a deep coal bed gas fracturing dynamic and static evaluation method, which can realize dynamic and static evaluation of fracturing effect and real-time guidance of on-site fracturing in a short time by connecting high-frequency pressure data acquired on site in real time with high-performance calculation through a cloud platform. A transparent stratum scene of fracturing is realized; meanwhile, deep coal-bed gas well pressure post-evaluation is achieved through seepage pressure analysis. A material balance time seepage data inversion method for fracturing evaluation is provided, and important parameters such as the fracture length, the fracture height, the permeability in an SRV area and the original formation pressure needed by deep coal-bed gas productivity calculation are given.

Description

Technical Field

[0001] The present invention relates to the technical field of coal gas mining, and in particular to a dynamic and static evaluation method for deep coalbed methane fracturing. Background Art

[0002] With improved research and advancements in exploration and development technologies, my country has achieved industrial breakthroughs in the exploration and development of coal-measure gas and deep coalbed methane, making them a key area for increasing natural gas reserves and production and developing the coalbed methane industry. Large-scale volume fracturing is also used in deep coalbed methane. The fracture morphology after volume fracturing, the formation dynamic parameters within the SRV zone (such as permeability, average pressure, and fracture conductivity), and the SRV volume have become the "primitive fingerprint" of deep coalbed methane development.

[0003] Microseismic monitoring is the most commonly used method for large-scale volume fracturing assessment. Microseismic monitoring can reveal the distribution direction, sweep length, and formation rupture energy of hydraulic fractures during the fracturing process, enabling real-time adjustments to the fracturing plan. Potential-based fracture monitoring involves supplying power to the wellbore via casing and observing the potential distribution at the surface, thereby monitoring the fracture morphology. However, microseismic and potential-based methods only obtain static parameters for the spatial distribution of fractures, equivalent to the oil (gas) drainage area in productivity equations. Deep coalbed methane development also depends on dynamic parameters such as formation permeability, fracture conductivity, and average formation pressure.

[0004] Pressure is also an effective means of evaluating fractures. For example, fracture well models used in well testing and production data analysis can invert fracture half-length, as well as parameters such as formation permeability and average pressure. However, well testing and production data analysis are based on seepage theory. When multiple fractures act together, the detailed description of each fracture is subject to multiple solutions.

[0005] However, the applicant has found that the existing technology has at least the following problems: the wellhead pressure gradually decreases over time after the pump is stopped during fracturing, and the pump-off pressure drop data can also be used to invert the SRV regional permeability and crack length. For large-scale coalbed methane fracturing, due to the use of staged fracturing technology, the pump-off pressure data inversion only analyzes the current section, which reduces the multi-solution capability. Therefore, a method is needed to quickly realize the dynamic and static evaluation of the fracturing effect. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to propose a dynamic and static evaluation method for deep coalbed methane fracturing to solve the problem of achieving dynamic and static evaluation of fracturing effects in a short time.

[0007] Based on the above objectives, the present invention provides a method for dynamic and static evaluation of deep coalbed methane fracturing, comprising:

[0008] Establishing a mathematical model of deep coal and rock gas formation flow in the cloud;

[0009] Solve the mathematical model of deep coal-rock gas formation flow;

[0010] According to the fracturing design, the target well is divided into multiple sections, and nitrogen pre-injection is used in several sections;

[0011] A high-frequency pressure gauge collects wellhead pressure during fracturing operations. High-frequency pressure data within one hour of pump shutdown is extracted from the cloud and decomposed into fluctuation and seepage data. Pump-down pressure analysis is performed on multiple sections of the target well, and cepstrum analysis, pump-down data inversion, and numerical simulation are performed.

[0012] After the pump-off pressure analysis, the cloud-based deep coal and rock gas formation flow mathematical model calculates the pressure distribution, the distribution of the fracturing fluid in the formation, and the distribution of the core SRV area through geological modeling;

[0013] The cloud constructs and generates a double logarithmic derivative summary diagram of each section of the target well, and generates a dynamic and static evaluation of the fracture of the target well based on the double logarithmic derivative summary diagram.

[0014] Optionally, establishing a deep coal-rock gas formation flow mathematical model in the cloud includes:

[0015] Conduct microscopic pore structure experiments on deep coal seams to obtain the imbibition curve of deep coal rocks in the target area;

[0016] Adsorption experiments were conducted on deep coal rocks in the target area to obtain the coal seam adsorption curve, Langmuir volume and Langmuir pressure of the deep coal rocks in the target block. At the same time, Brazilian splitting, fracture toughness, triaxial compression and Kaiser ground stress tests were carried out on the coal samples.

[0017] Optionally, establishing a deep coal-rock gas formation flow mathematical model in the cloud includes:

[0018] The water phase flow equation is established as:

[0019]

[0020] The gas phase equation is expressed as;

[0021]

[0022] Where: D is the concentration diffusion coefficient, m 2 / s,C g is the gas compressibility coefficient, 1 / Pa; μ g and μ w are respectively the gas phase and liquid phase viscosities, Pa.s; B g and B w are the volume coefficients of gas phase and liquid phase respectively; S g and Sw are gas and liquid saturations, respectively; φ is porosity; k rg and k rw are gas phase and liquid phase relative permeability curves respectively; k is the absolute permeability of the formation, m is the absolute permeability of the formation, 2 ;q ig With q iw is the contribution of gas and water source and sink caused by imbibition, which is a function of time, q w δ(t=τ,well) represents the strength of the injected fracturing fluid, which is determined by the injection volume of each fracturing stage, and τ represents the injection time of each stage; q ads is the coalbed methane source and sink term based on Langmuir adsorption, and its expression is:

[0023]

[0024] Where: ρ s is the density of coal rock, kg / m 3 ; V std is the molar volume of gas molecules under standard conditions, M is the molar molecular weight of coalbed methane; P L and V L are the corresponding Langmuir pressure and volume.

[0025] Optionally, solving the mathematical model of deep coal-rock gas formation flow includes:

[0026] Finite volume discretization is performed based on the PEBI grid. First, the water phase equation is volume integrated:

[0027]

[0028] The discretized water phase equation is:

[0029]

[0030] The gas phase equation after discretization is:

[0031]

[0032] Where:

[0033] T ij,w =λ ij,w G ij and T ij,g =λ ij,g G ij is the water and gas phase conductivity coefficient;

[0034] G ij =K ij ω ij / d ij is the geometric factor, and the relevant parameters are shown in Figure 6;

[0035] and is the relative mobility of water and gas underground;

[0036]

[0037] Δp=p j -p i ;

[0038]

[0039] K rl 、μ l and B l (l=w,g) are the relative permeability, viscosity and volume coefficient of water and gas phase respectively;

[0040] φ—porosity

[0041] C g —Gas compressibility coefficient, (1 / Pa);

[0042] V i —The volume of the i-th grid cell, (m 3 );

[0043] Δt=t n+1 -t n —Time step length between n+1 time step and n time step, (s);

[0044] Wherein the subscript w represents the water phase and g represents the gas phase.

[0045] Optionally, the high-frequency pressure meter collects the wellhead pressure during the fracturing operation, and the high-frequency pressure data within the pump-off period t is extracted on the cloud and decomposed into fluctuation and seepage data. Pump-off pressure analysis is performed on multiple sections of the target well, and cepstrum analysis, pump-off data inversion, and numerical simulation are performed, including:

[0046] Generate a time-varying graph of the target well's pressure during the shutdown period. Automatically detect significant water hammer waves after pump shutdown in the cloud. Determine the number of inlet points and inlet volumes through water hammer analysis.

[0047] Generate a double logarithmic fitting graph of the pump-off pressure and its derivative, and invert the average total length of the fracture, the total length of the stimulated area, the fracture height, the fracture backflow coefficient, the SRV area permeability, the original formation pressure, and the fracture network morphology coefficient through curve fitting.

[0048] Optionally, the calculating of pressure distribution by geological modeling using the deep coal-rock gas formation flow mathematical model in the cloud includes:

[0049] The pressure drop map during the pump-off period of fracturing is calculated using the unstructured PEBI grid numerical simulation method to generate the full-well formation pressure distribution map. The material balance time is used:

[0050]

[0051] Since the displacement changes with time during fracturing, the pressure is recalculated using the Duhamel principle:

[0052]

[0053] Where: Φ is the rate of change of pressure over time at multiple pressure sections of a horizontal well at unit flow rate.

[0054] Optionally, after the pump-off pressure analysis is performed, the cloud-based deep coal-rock gas formation flow mathematical model calculates the pressure distribution, the distribution of the fracturing fluid in the formation, and the core SRV area distribution through geological modeling, including:

[0055] The distribution of fracturing fluid in the formation is calculated based on the fracturing distribution and Darcy's law. The total volume of the core SRV area and the total volume of the total stimulated SRV are calculated based on the fracturing fluid distribution and pressure distribution. A full-well fracturing fluid distribution map is generated, and the core SRV area and secondary SRV area are marked with different colors.

[0056] Optionally, the cloud-based construction generates a double logarithmic derivative summary graph of each section of the target well, and generates a dynamic and static evaluation of the fracture of the target well based on the double logarithmic derivative summary graph, including:

[0057] Determine whether the unique dual-porosity feature of the coal seam is found based on the water conductivity curves of each section of the target well;

[0058] Determine the mobility of the periphery of the target well based on whether the late derivative is upturned;

[0059] Compare the derivative curve shapes of the sections using nitrogen pre-injection with those of other sections to see if there are any differences, and determine the impact of nitrogen pre-injection on the stimulation effect of the target well.

[0060] Beneficial effects of the present invention: The present invention provides a method for dynamic and static evaluation of deep coalbed methane fracturing. By using high-frequency pressure data collected in real time on site, the method can be connected to a cloud platform with high-performance computing, thereby enabling dynamic and static evaluation of fracturing effects in a short period of time, providing real-time guidance for on-site fracturing, and realizing the vision of "transparent formation" for fracturing.

[0061] At the same time, the seepage pressure analysis realizes the post-fracture evaluation of deep coalbed methane wells: a material balance time seepage data inversion method for fracturing evaluation is proposed, which provides important parameters required for deep coalbed methane production capacity calculation, such as fracture length, fracture height, permeability in the SRV area and original formation pressure. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0063] Figure 1 This is a flow chart of a method for dynamic and static evaluation of deep coalbed methane fracturing according to an embodiment of the present invention;

[0064] Figure 2 The mineral composition and electron microscope scanning of the coal seam;

[0065] Figure 3 This is the relationship diagram of coal seam imbibition changing with time;

[0066] Figure 4 This is the coal sample map of the deep coal seam used in the experiment;

[0067] Figure 5 This is the adsorption experimental curve of deep coal seam;

[0068] Figure 6 This is the graph showing the variation of triaxial strength of coal rock with confining pressure;

[0069] Figure 7 This is a diagram illustrating the PEBI grid point-edge relationship and parameters;

[0070] Figure 8 This is a graph showing the change in wellhead pressure over time when the pump is stopped in the Exam-1 well according to an embodiment of the present invention;

[0071] Figure 9 This is a double logarithmic fitting diagram of the pump-off pressure and derivative of the Exam-1 well in an embodiment of the present invention;

[0072] Figure 10 This is the fracturing fluid distribution diagram for the entire well of Exam-1 well in the embodiment of the present invention;

[0073] Figure 11 This is a diagram of the formation pressure distribution of the entire well Exam-1 in an embodiment of the present invention;

[0074] Figure 12 This is a summary diagram of the double logarithmic derivatives of sections 1-7 of the Exam-1 well in an embodiment of the present invention. DETAILED DESCRIPTION

[0075] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0076] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0077] Dynamic and static evaluation of deep coalbed methane fracturing involves three key technologies. Wave analysis, due to the propagation of water hammer waves in water within a circular pipe, has been the subject of numerous research papers and field applications both domestically and internationally. Pump-off pressure analysis and reservoir numerical simulation require models tailored to geological conditions. This paper applies mathematical modeling to deep coalbed methane in the Ordos Basin, part of the Sinopec North China Bureau, to conduct dynamic and static fracture evaluation.

[0078] like Figure 1 As shown, a specific embodiment of the present invention provides a method for dynamic and static evaluation of deep coalbed methane fracturing, comprising:

[0079] S101: Build a mathematical model of deep coal and rock gas formation flow in the cloud;

[0080] S201: Solve the mathematical model of deep coal-rock gas formation flow;

[0081] S301: Divide the target well into multiple sections according to the fracturing design, and use nitrogen pre-injection in several sections;

[0082] S401: A high-frequency pressure meter collects wellhead pressure during fracturing operations. High-frequency pressure data within one hour of pump shutdown is extracted from the cloud and decomposed into fluctuation and seepage data. Pump-off pressure analysis is performed on multiple sections of the target well, and cepstrum analysis, pump-off data inversion, and numerical simulation are performed.

[0083] S501: After the pump-off pressure analysis is performed, the cloud-based deep coal and rock gas formation flow mathematical model calculates the pressure distribution, the distribution of the fracturing fluid in the formation, and the distribution of the core SRV area through geological modeling;

[0084] S601: Generate a double logarithmic derivative summary diagram of each section of the target well on the cloud, and generate a dynamic and static evaluation of the fracture of the target well based on the double logarithmic derivative summary diagram.

[0085] In some optional specific embodiments, establishing a deep coal gas formation flow mathematical model in the cloud includes:

[0086] Conduct microscopic pore structure experiments on deep coal seams to obtain the imbibition curve of deep coal rocks in the target area;

[0087] Adsorption experiments were conducted on deep coal rocks in the target area to obtain the coal seam adsorption curve, Langmuir volume and Langmuir pressure of the deep coal rocks in the target block. At the same time, Brazilian splitting, fracture toughness, triaxial compression and Kaiser ground stress tests were carried out on the coal samples.

[0088] To establish an accurate deep coalbed methane and shale gas flow model, several experiments were conducted using deep coalbed rock samples:

[0089] Deep coal seam microscopic pore structure experiment, deep coal seam rock samples in the area were taken for whole rock mineral X-ray diffraction and scanning electron microscopy experiments, such as Figure 2 As shown. Using X-ray diffraction of coal seam samples, molecular dynamics simulation by scanning electron microscopy, and combined with nuclear magnetic resonance experiments, the imbibition curve of deep coal rock in this block was obtained.

[0090] Other experiments, Figure 4 The deep coal samples used in the experiment are given. Based on the adsorption experiment of deep coal rocks in the area, the coal seam adsorption curves, Langmuir volume and Langmuir pressure of deep coal rocks in the Shanxi Formation and Taiyuan Formation in this block are obtained. Figure 5 At the same time, the coal sample was subjected to Brazilian splitting, fracture toughness, triaxial compression and Kaiser ground stress tests. Figure 6 These experiments are an important basis for the modeling of the present invention and the relevant parameters in the equations.

[0091] In some optional specific embodiments, establishing a deep coal gas formation flow mathematical model in the cloud includes:

[0092] The water phase flow equation is established as:

[0093]

[0094] The gas phase equation is expressed as;

[0095]

[0096] Where: D is the concentration diffusion coefficient, m 2 / s,C g is the gas compressibility coefficient, 1 / Pa; μ g and μ w are respectively the gas phase and liquid phase viscosities, Pa.s; B g and B ware the volume coefficients of gas phase and liquid phase respectively; S g and S w are gas and liquid saturations, respectively; φ is porosity; k rg and k rw are gas phase and liquid phase relative permeability curves respectively; k is the absolute permeability of the formation, m is the absolute permeability of the formation, 2 ;q ig With q iw is the contribution of gas and water source and sink caused by imbibition, which is a function of time, q w δ(t=τ,well) represents the strength of the injected fracturing fluid, which is determined by the injection volume of each fracturing stage, and τ represents the injection time of each stage; q ads is the coalbed methane source and sink term based on Langmuir adsorption, and its expression is:

[0097]

[0098] Where: ρ s is the density of coal rock, kg / m 3 ; V std is the molar volume of gas molecules under standard conditions, M is the molar molecular weight of coalbed methane; P L and V L are the corresponding Langmuir pressure and volume.

[0099] Deep coalbed methane extraction generally utilizes large-scale horizontal well fracturing. The fluids in the formation are primarily fracturing fluid and coalbed methane. Experimental and molecular dynamics simulations indicate that coalbed methane experiences adsorption, imbibition, gas diffusion, and two-phase flow. This paper approximates imbibition as a source-sink term. This is because water entering the coal bedrock adds a sink term to the water-phase equation, while a source term is added to the gas-phase equation. For convenience, the reservoir is assumed to be horizontal, resulting in the above equation.

[0100] The above equation is suitable for flow calculation during fracturing, well blocking and flowback. There is a large amount of free gas in deep coalbed methane, and gas seepage and diffusion must be considered. Since the adsorption pressure in the target area is lower than 7MPa in this embodiment, the formation pressure during fracturing and well blocking is much higher than the adsorption pressure p L , q can be ignored in the equation during fracturing and well blocking ads .like Figure 3 As shown in the figure, it can be seen from the imbibition curve that imbibition almost does not occur during the fracturing operation time, and the imbibition term q in the equation and the equation can be ignored. iw and q ig .

[0101] In some optional specific embodiments, solving the deep coal-rock gas formation flow mathematical model includes:

[0102] Finite volume discretization is performed based on the PEBI grid. First, the water phase equation is volume integrated:

[0103]

[0104] The discretized water phase equation is:

[0105]

[0106] The gas phase equation after discretization is:

[0107]

[0108] Where:

[0109] T ij,w =λ ij,w G ij and T ij,g =λ ij,g G ij is the water and gas phase conductivity coefficient;

[0110] G ij =K ij ω ij / d ij is the geometric factor, and the relevant parameters are shown in Figure 6 ;

[0111] and is the relative mobility of water and gas underground;

[0112]

[0113] Δp=p j -p i ;

[0114]

[0115] K rl 、μ l and B l (l=w,g) are the relative permeability, viscosity and volume coefficient of water and gas phase respectively;

[0116] φ—porosity

[0117] C g —Gas compressibility coefficient, (1 / Pa);

[0118] V i —The volume of the i-th grid cell, (m 3 );

[0119] Δt=t n+1 -t n—Time step length between n+1 time step and n time step, (s);

[0120] Wherein the subscript w represents the water phase and g represents the gas phase.

[0121] In some optional specific embodiments, the high-frequency pressure meter collects the wellhead pressure during the fracturing operation, and the high-frequency pressure data within 1 hour of pump shutdown is extracted on the cloud and decomposed into fluctuation and seepage data. Pump shutdown pressure analysis is performed on multiple sections of the target well, and cepstrum analysis, pump shutdown data inversion and numerical simulation are carried out, including:

[0122] Generate a time-varying graph of the target well's pressure during the shutdown period. Automatically detect significant water hammer waves after pump shutdown in the cloud. Determine the number of inlet points and inlet volumes through water hammer analysis.

[0123] Generate a double logarithmic fitting graph of the pump-off pressure and its derivative, and invert the average total length of the fracture, the total length of the stimulated area, the fracture height, the fracture backflow coefficient, the SRV area permeability, the original formation pressure, and the fracture network morphology coefficient through curve fitting.

[0124] In some optional specific embodiments, the calculation of pressure distribution by geological modeling using the deep coal and rock gas formation flow mathematical model in the cloud includes calculating the pressure drop map during the pump-off period of fracturing according to the unstructured PEBI grid numerical simulation method, generating a full-well formation pressure distribution map, and using the material balance time:

[0125]

[0126] Since the displacement changes with time during fracturing, the pressure is recalculated using the Duhamel principle:

[0127]

[0128] Where: Φ is the rate of change of pressure over time at multiple pressure sections of a horizontal well at unit flow rate.

[0129] During deep coal-rock gas fracturing construction, large-volume fracturing is used. Near the wellbore, the main fluid used is fracturing fluid. After the pump is stopped, the free gas in the coal seam can be ignored. For this purpose, the unstructured PEBI grid numerical simulation method of the present invention can be used to calculate the pressure drop diagram during the fracturing pump stop period.

[0130] Due to the large displacement during fracturing, such as 20m 3 / Min converted into daily output is 28800m 3 / D, it is not possible to directly use the well test software to analyze the data after the pump is stopped during fracturing. Because the displacement during fracturing is large, the material balance time is used:

[0131]

[0132] In the material balance time, the output in the denominator can be equivalent to the daily output, such as 100m 3 / D, which makes the material balance time very long, such as the displacement is 20m 3 / Min, daily output 28800m 3 / D, the time increases 288 times, that is, if the pump is stopped for 40 minutes, the actual material balance time is 192 hours. For large-scale volume fracturing, although the stop time is 40 minutes, the equivalent well test analysis time is 192 hours. Therefore, as long as the fluid injection rate of each section can be determined, the analysis of the pump-off pressure data can ensure the reliability of the interpretation results. Since the displacement changes with time during fracturing, the Duhamel principle is used to recalculate the pressure:

[0133]

[0134] Where: Φ is the rate of change of pressure over time at multiple pressure sections of a horizontal well at unit flow rate.

[0135] In some optional implementations, after performing the pump-off pressure analysis, the cloud-based deep coal-rock gas formation flow mathematical model calculates the pressure distribution, the distribution of the fracturing fluid in the formation, and the core SRV area distribution through geological modeling, including:

[0136] The distribution of fracturing fluid in the formation is calculated based on the fracturing distribution and Darcy's law. The total volume of the core SRV area and the total volume of the total stimulated SRV are calculated based on the fracturing fluid distribution and pressure distribution. A full-well fracturing fluid distribution map is generated, and the core SRV area and secondary SRV area are marked with different colors.

[0137] In some optional specific embodiments, the cloud constructs and generates a double logarithmic derivative summary graph of each section of the target well, and generates a dynamic and static evaluation of the fracture of the target well based on the double logarithmic derivative summary graph, including:

[0138] Determine whether the unique dual-porosity feature of the coal seam is found based on the water conductivity curves of each section of the target well;

[0139] Determine the mobility of the periphery of the target well based on whether the late derivative is upturned;

[0140] Compare the derivative curve shapes of the sections using nitrogen pre-injection with those of other sections to see if there are any differences, and determine the impact of nitrogen pre-injection on the stimulation effect of the target well.

[0141] In some optional specific embodiments, the present invention is applied using a deep coalbed methane well in the Sinopec North China Bureau as an example. For ease of description, the Exam-1 well is used to represent a deep coalbed methane well in the Sinopec North China Bureau. The effective horizontal section drilled in the Exam-1 well is 3001.00-3512.00 m, with a total length of 511 m, and a total length of coal rock encountered of 431 m. The coal seam thickness is 12.4 m, the average gas content is 26.95 m³ / t, and the coal rock principal stress is 53.66-64.82 MPa. According to the fracturing design, the Exam-1 well is divided into 7 segments with 16 segments, a cluster spacing of 18.1 m, a fracture spacing of 46.8 m, a flow rate of 20-22 m³ / min, and a sand injection rate of 500.6-750.6 m³ per segment, with an average of 580.1 m³ / segment. To study the impact of nitrogen on coalbed methane development, nitrogen pre-injection was used in segments 4-7 of the Exam-1 well.

[0142] High-frequency pressure gauges were used to determine the fracture initiation location and fluid inflow rate of each cluster in Well Exam-1:

[0143] A high-frequency pressure meter was installed at the wellhead of Well Exam-1 to collect wellhead pressure during fracturing operations. High-frequency pressure data within one hour of pump shutdown was extracted and decomposed into fluctuation and seepage data. Cepstrum analysis, pump-shutdown data inversion, and numerical simulation were performed, respectively. The first paragraph is used here as an example to illustrate this.

[0144] The amount of liquid entering the ground in the first section is 5059.6m 3 , the amount of sand entering the ground is 652.9m 3 , Figure 8 The pressure variation with time during the pump shutdown period of Exam-1 well is shown. The figure shows that there is an obvious water hammer wave in the water production after the pump is stopped. Through water hammer analysis, it is determined that there are two liquid inflow points, with liquid inflow volumes of 3390.47 and 1169.13 m3 respectively. 3 . Figure 9 A double logarithmic fitting diagram of the pump-off pressure and its derivative is given. Through curve fitting, it can be inferred that the following are the average total length of the fractures is 277.20 m; the total length of the stimulated area is 421.14 m; the fracture height is 21.14 m; the fracture conductivity is 16.54 d.cm; the SRV regional permeability is 5.61 md; the original formation pressure is 30.19 MPa; and the fracture network morphology coefficient is 0.46, indicating that the long straight fractures contribute about 54% to the productivity, the fracture network fractures contribute about 44% to the productivity, and the fracture network fractures and long straight fractures develop together.

[0145] After performing pump-off pressure analysis on the 7th section of the Exam-1 well, the numerical simulation software of the present invention can calculate the pressure distribution through geological modeling, such as Figure 11 As shown in Figure 2, the distribution of fracturing fluid in the formation can be calculated based on the fracturing distribution combined with Darcy's law, as shown in Figure 2. Figure 10According to the distribution of fracturing fluid and pressure, it can be calculated that the total volume of the core SRV area is about 1.6114 million m 3 ; Figure 10 The yellow and green areas are the areas affected by the fracturing fluid and are the secondary SRV areas. The total transformed SRV volume is 8.3471 million m 3 .

[0146] Figure 11 This is a summary of the double logarithmic derivatives for the 1st to 7th sections of the Exam-1 well. The derivative characteristics of the 1st to 7th sections are consistent. Each curve is the well reservoir section in the early stage, followed by the fracture skin effect section. It can be seen that the fracture skin is greater than 0. After the skin effect ends, the fracture linear flow section begins. After the linear flow ends, except for the first section where the derivative curve drops, the derivative curves of the other sections all rise in the later stage. From the derivative curves of the 1st to 7th sections of the Exam-1 well, we can see that:

[0147] None of the derivative curves showed the unique dual-porosity characteristic of coal seams, indicating that the stimulation effect of Well Exam-1 was good. The coal seam cleats were completely penetrated, and the coal bedrock had almost no involvement in the fracturing fluid channeling. In other words, the fracturing fluid almost did not enter the coal bedrock and was distributed in the coal seam cleats.

[0148] The upward curvature of the late derivative indicates that the mobility in the periphery of the Exam-1 well is small, that is, the remaining permeability is very small without stimulation;

[0149] Because liquid nitrogen pre-fracturing was used in sections 3-7, the pressure derivative curve for sections 3-7 is identical to that without nitrogen pre-fracturing. This suggests that nitrogen pre-fracturing had little impact on the stimulation of Well Exam-1. This is consistent with the molecular dynamics simulation results. Because both N2 and methane have relatively low molecular polarity, molecular dynamics simulations have shown that N2's ability to displace methane is far less than that of carbon dioxide.

[0150] This paper uses high-frequency pressure gauge continuous monitoring and real-time analysis of the entire process of the Exam-1 well in the Sinopec North China Bureau to propose a technology for decomposing wellhead pressure into fluctuation and seepage pressure. By combining fluctuation analysis, seepage inversion, and numerical simulation, this paper realizes dynamic and static evaluation of deep coalbed methane well fracturing, including:

[0151] Fluctuation pressure analysis can determine the number and location of fracture opening clusters and the amount of fluid inflow into each cluster; inversion of the extracted fluctuations can obtain the fluid inflow location and the amount of fluid inflow into each cluster.

[0152] A unified mathematical model for deep coalbed methane fracturing, well blocking, and flowback was established: coal samples were actually obtained from the well, macro and micro experiments were carried out, and molecular dynamics simulations were combined to finally establish a unified mathematical model for deep coalbed methane fracturing, well blocking, and flowback. A method for solving the equations was also given, and a computational model was formed in the cloud, providing a basis for the computational analysis of the present invention.

[0153] Seepage pressure analysis realizes the post-fracture evaluation of deep coalbed methane wells: a material balance time seepage data inversion method for fracturing evaluation is proposed, which provides important parameters required for deep coalbed methane production capacity calculation, such as fracture length, fracture height, permeability in the SRV area and original formation pressure.

[0154] The analysis of the Exam-1 well case study demonstrates the practical application of this method: combining geological modeling with numerical simulations enabled a comprehensive evaluation of the entire well, notably highlighting the ineffectiveness of nitrogen pre-treatment in this well. The proposed model has considerable general applicability and is applicable to post-fracture evaluation of other coalbed methane wells, providing guidance for shale gas, oil, and gas, and tight oil and gas production.

[0155] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present invention (including the claims) is limited to these examples. Within the scope of the present invention, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the present invention as described above, which are not provided in detail for the sake of simplicity.

[0156] The present invention is intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for dynamic and static evaluation of deep coalbed methane fracturing, characterized in that: include: Establishing a mathematical model of deep coal and rock gas formation flow in the cloud; Solve the mathematical model of deep coal-rock gas formation flow; According to the fracturing design, the target well is divided into multiple sections, and nitrogen pre-injection is used in several sections; A high-frequency pressure meter collects wellhead pressure during fracturing operations. High-frequency pressure data within one hour of pump shutdown is extracted from the cloud and decomposed into fluctuation and seepage data. Pump-down pressure analysis is performed on multiple sections of the target well, and cepstrum analysis, pump-down data inversion, and numerical simulation are performed. After the pump-off pressure analysis, the cloud-based deep coal and rock gas formation flow mathematical model calculates the pressure distribution, the distribution of the fracturing fluid in the formation, and the distribution of the core SRV area through geological modeling; The cloud constructs and generates a double logarithmic derivative summary diagram of each section of the target well, and generates a dynamic and static evaluation of the fracture of the target well based on the double logarithmic derivative summary diagram.

2. A method for dynamic and static evaluation of deep coalbed methane fracturing according to claim 1, characterized in that: The establishment of a deep coal-rock gas formation flow mathematical model in the cloud includes: Conduct microscopic pore structure experiments on deep coal seams to obtain the imbibition curve of deep coal rocks in the target area; Adsorption experiments were conducted on deep coal rocks in the target area to obtain the coal seam adsorption curve, Langmuir volume and Langmuir pressure of the deep coal rocks in the target block. At the same time, Brazilian splitting, fracture toughness, triaxial compression and Kaiser ground stress tests were carried out on the coal samples.

3. A method for dynamic and static evaluation of deep coalbed methane fracturing according to claim 1, characterized in that: The establishment of a deep coal-rock gas formation flow mathematical model in the cloud includes: The water phase flow equation is established as: The gas phase equation is expressed as; Where: D is the concentration diffusion coefficient, m 2 / s,C g is the gas compressibility coefficient, 1 / Pa; μ g and μ w are respectively the gas phase and liquid phase viscosities, Pa.s; B g and B w are the volume coefficients of gas phase and liquid phase respectively; S g and S w are gas and liquid saturations, respectively; φ is porosity; k rg and k rw are gas phase and liquid phase relative permeability curves respectively; k is the absolute permeability of the formation, m is the absolute permeability of the formation, 2 ;q ig With q iw is the contribution of gas and water source and sink caused by imbibition, which is a function of time, q w δ(t=τ,well) represents the strength of the injected fracturing fluid, which is determined by the injection volume of each fracturing stage, and τ represents the injection time of each stage; q ads is the coalbed methane source and sink term based on Langmuir adsorption, and its expression is: Where: ρ s is the density of coal rock, kg / m 3 ; V std is the molar volume of gas molecules under standard conditions, M is the molar molecular weight of coalbed methane; P L and V L are the corresponding Langmuir pressure and volume.

4. A method for dynamic and static evaluation of deep coalbed methane fracturing according to claim 1, characterized in that: The solving of the deep coal-rock gas formation flow mathematical model includes: Finite volume discretization is performed based on the PEBI grid. First, the water phase equation is volume integrated: The discretized water phase equation is: The gas phase equation after discretization is: Where: T ij,w =λ ij,w G ij and T ij,g =λ ij,g G ij is the water and gas phase conductivity coefficient; G ij =K ij ω ij / d ij is the geometric factor, and the relevant parameters are shown in Figure 6; and is the relative mobility of water and gas underground; Δp=p j -p i ; K rl 、μ l and B l (l=w,g) are the relative permeability, viscosity and volume coefficient of water and gas phase respectively; φ—porosity C g —Gas compressibility coefficient, (1 / Pa); V i —The volume of the i-th grid cell, (m 3 ); Δt=t n+1 -t n —Time step length between n+1 time step and n time step, (s); Wherein the subscript w represents the water phase and g represents the gas phase.

5. A method for dynamic and static evaluation of deep coalbed methane fracturing according to claim 1, characterized in that: The high-frequency pressure meter collects the wellhead pressure during the fracturing operation. The cloud extracts the high-frequency pressure data during the pump-off period t and decomposes it into fluctuation and seepage data. Pump-off pressure analysis is performed on multiple sections of the target well, and cepstrum analysis, pump-off data inversion and numerical simulation are carried out. Generate a time-varying graph of the target well's pressure during the shutdown period. Automatically detect significant water hammer waves after pump shutdown in the cloud. Determine the number of inlet points and inlet volumes through water hammer analysis. Generate a double logarithmic fitting graph of the pump-off pressure and its derivative, and invert the average total length of the fracture, the total length of the stimulated area, the fracture height, the fracture backflow coefficient, the SRV regional permeability, the original formation pressure, and the fracture network morphology coefficient through curve fitting.

6. A method for dynamic and static evaluation of deep coalbed methane fracturing according to claim 1, characterized in that: The calculation of pressure distribution by geological modeling using the deep coal and rock gas formation flow mathematical model in the cloud includes: The pressure drop map during the pump-off period of fracturing is calculated using the unstructured PEBI grid numerical simulation method to generate the full-well formation pressure distribution map. The material balance time is used: Since the displacement changes with time during fracturing, the pressure is recalculated using the Duhamel principle: Where: Φ is the rate of change of pressure over time at multiple pressure sections of a horizontal well at unit flow rate.

7. A method for dynamic and static evaluation of deep coalbed methane fracturing according to claim 1, characterized in that: After the pump-off pressure analysis, the cloud-based deep coal-rock gas formation flow mathematical model calculates the pressure distribution, the distribution of the fracturing fluid in the formation, and the core SRV area distribution through geological modeling, including: The distribution of fracturing fluid in the formation is calculated based on the fracturing distribution and Darcy's law. The total volume of the core SRV area and the total volume of the total stimulated SRV are calculated based on the fracturing fluid distribution and pressure distribution. A full-well fracturing fluid distribution map is generated, and the core SRV area and secondary SRV area are marked with different colors.

8. A method for dynamic and static evaluation of deep coalbed methane fracturing according to claim 1, characterized in that: The cloud-based construction generates a double logarithmic derivative summary diagram of each section of the target well, and generates a dynamic and static evaluation of the fracture of the target well based on the double logarithmic derivative summary diagram, including: Determine whether the unique dual-porosity feature of the coal seam is found based on the water conductivity curves of each section of the target well; Determine the mobility of the periphery of the target well based on whether the late derivative is upturned; Compare the derivative curve shapes of the sections using nitrogen pre-injection with those of other sections to see if there are any differences, and determine the impact of nitrogen pre-injection on the stimulation effect of the target well.

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