CN113792932B - Shale gas yield prediction method utilizing microseism-damage-seepage relation - Google Patents
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Abstract
The invention discloses a shale gas yield prediction method utilizing a microseism-damage-seepage relationship, which comprises the following steps: step one: acquiring shale cores with target depths, and testing the permeability of the shale cores with different depths to obtain a relation between the permeability and a damage variable; step two: acquiring microseism monitoring data to obtain microseism focus parameters and obtaining damage variables containing the microseism focus parameters; step three: obtaining the relation between the microseism focus parameter and the permeability; step four: and establishing a shale reservoir geometrical model, solving the shale gas seepage model by adopting a numerical simulation method based on the shale gas seepage model and combining the microearthquake source parameter and permeability relation type and the shale reservoir geometrical model to obtain the shale gas yield. The method improves the reliability of input parameters in the process of yield prediction, greatly reduces the prediction error of shale gas yield, ensures the accuracy of yield prediction results, and has great application value for shale gas reservoir exploration and development.
Description
Technical Field
The invention relates to the field of oil and gas engineering and rock engineering, in particular to a shale gas yield prediction method utilizing a microseism-damage-seepage relation.
Background
The underground rock can generate micro-fracture under the compression deformation, the rock body fracture is a continuous process of damage fracture, crack initiation, expansion and penetration until fracture, the energy accumulated in the rock body can be released in the form of elastic waves and the like, and the micro-vibration monitoring technology is used as a three-dimensional space detection technology and used for representing the dynamic damage characteristics in the rock body. Therefore, the damage evolution analysis of the underground rock mass by using the on-site microseismic monitoring data is an important research direction. Shale is hypotonic compact sedimentary rock, and permeability is a main index of fluid seepage capability of shale gas reservoirs, and development and productivity of the shale gas reservoirs are determined. The hydraulic fracturing model needs to take into account the interplay of damage and permeability, which would otherwise lead to some degree of computational error. Therefore, the research on shale damage evolution and permeability change is indispensable, and has certain guiding significance for shale gas exploration and exploitation.
In shale gas development processes, reservoirs are often fractured in large-scale as a whole to form an effectively flowing complex fracture network system (SRV zone) as an index for evaluating oil and gas production. The SRV region can be calculated by a microseism monitoring method, a fracturing parameter estimation method and a theoretical model method. The parameters monitored by the microseism monitoring method are scattered points, only one area can be seen from the graph, the result uncertainty is large, and the reliability is poor; patent document CN111859260a and patent document CN108829945A use fracturing parameter estimation, but this kind of required parameters are many, the accuracy is poor, and the parameter dependence is strong. Therefore, the cost can be reduced and the calculation speed can be improved by adopting a theoretical model method, and patent document CN109184676A discloses a shale gas reservoir effective reconstruction volume evaluation method which comprises the following steps: step S1: introducing the fractal permeability and the fractal porosity into a fracture vertical well reconstruction volume region, wherein the fractal permeability and the fractal porosity are in a power law relation with a fractal dimension d and a fractal index theta; step S2: introducing shale gas adsorption and diffusion characteristics, and deducing an effective transformation volume analysis and evaluation model by adopting a double-hole Shan Shen model according to a power law relation to obtain a representative curve of dimensionless simulated pressure and derivative of the bottom of a shale gas reservoir fracturing vertical well; step S3: calculating the modified volume size of the homogeneous modified volume fracturing vertical well according to the curve; step S4: calculating the modified volume size of the heterogeneous modified volume fracturing vertical well according to a dimensionless pressure derivative typical curve of the bottom of the fracturing vertical well; step S5: and calculating the effective reconstruction volume size according to the mine data, and evaluating the relation between the effective reconstruction volume and the reconstruction volume. Although the mathematical model method of the invention introduces the fractal theory and shale adsorption diffusion characteristics into the double-pore medium model, the influence of the non-uniformity of physical properties in the SRV is considered, the oil gas yield which can be actually generated by the SRV region is not related to microseismic parameters, damage variables and permeability, and the simulation result is inaccurate.
Disclosure of Invention
The invention aims to solve the problem of inaccurate shale gas yield simulation results in the prior art, and provides a shale gas yield prediction method by utilizing a microseismic-damage-seepage relationship.
In order to achieve the above object, the present invention provides the following technical solutions:
a shale gas yield prediction method utilizing a microseismic-injury-seepage relationship comprises the following steps:
step one: acquiring shale cores with target depths of a target well, and testing the permeability of the shale cores with different depths to obtain a relation between the permeability and a damage variable;
step two: acquiring microseism monitoring data of a target well to obtain microseism focus parameters and damage variables containing the microseism focus parameters;
step three: combining the first step and the second step to obtain the relation between the microseism focus parameter and the permeability;
step four: and establishing a shale reservoir geometrical model, solving the shale gas seepage model by adopting a numerical simulation method based on the shale gas seepage model and combining the microearthquake source parameter and permeability relation type and the shale reservoir geometrical model to obtain the shale gas yield of the target well.
According to the prediction method, the damage variable D and the permeability k are obtained through testing of shale cores in a mining site m Is a relationship of (2); acquiring on-site microseismic monitoring data, and analyzing to obtain a damage variable D containing microseismic parameters; according to the damage variable D and the permeability k m The relation of the microseism parameters and the damage variable D containing the microseism parameters are obtained m A relationship; based on the shale gas seepage model, a research thought integrating microseism-damage-seepage-shale gas yield is established, so that new development of shale gas reservoir yield prediction and evaluation is promoted.
Further, in the first step, shale cores with target depths of a target well are obtained, the shale cores with different depths are processed into cylindrical core samples before testing, and visually defective core samples are removed.
Further, in the first step, the permeability of each shale core is tested by a transient method, and the testing process is as follows: and (3) carrying out vacuumizing saturation on the core sample according to the requirement, putting the core sample into a pressure chamber, sequentially applying hydrostatic pressure, osmotic pressure and axial pressure, and then carrying out permeation and loading and unloading tests under different stress levels.
Further, the microseismic source parameters include the microseismic energy E and the total energy E released during a rock mass microseismic break T The microseismic radiant energy is ΔU'.
Further, the relation between permeability and damage variable is:
wherein p is m Is bedrock pressure, pa; k (k) 0 The initial permeability is given in mD;e is the nondestructive elastic modulus, and the core is in a bulletModulus of sexual stage>The elastic modulus of the rock in the cyclic loading and unloading test is the damaged elastic modulus, wherein A and B are parameters related to confining pressure; d (D) 0 Is the damage threshold. Under different confining pressure conditions, the permeability characteristics of the core have differences along with the evolution trend of the damage, and parameters A, B and damage threshold are functions of effective confining pressure.
Further, the relation of the damage variable containing the microseismic source parameters is as follows:
in the middle ofIs isotropically releasable strain energy; η is the seismic efficiency coefficient, dimensionless; η=e/E T E is earthquake energy, E T Total energy released during micro-fracturing of the rock mass.
Still further, the method further comprises the steps of,the relation of (2) is:
in E 0 The initial elastic modulus of the rock is Pa; sigma (sigma) 1 、σ 2 、σ 3 Respectively 3 main stresses of the rock mass; v is poisson's ratio.
Further, the expression of the shale gas seepage model is as follows:
in which Q p Is the flow, namely shale gas yield, gamma is a coefficient, gamma=m g /ZRT；M g The unit is kg/mol of the molar mass of the gas;z is a gas compression factor; r is an ideal gas constant; t is temperature, and the unit is K;is the porosity of the bedrock; p (P) L Is Langmuir pressure, in Pa; p is p m The pressure of bedrock is Pa; ρ s Is core density in kg/m 3 ；V std Is the molar volume under standard conditions, and the unit is m 3 /mol；k m Permeability of bedrock in m 2 ；μ g The unit is Pa.s, which is the viscosity of the gas; d (D) km Is the diffusion coefficient of the bedrock,c is a constant approaching 1, preferably 1m 2 /s；k mi Is the intrinsic permeability of the bedrock, the unit is m 2 。
Compared with the prior art, the invention has the beneficial effects that:
according to the method, the damage variable of the shale reservoir is obtained by combining microseism monitoring, the relation between the damage variable and the permeability is used as a bridge, a shale gas seepage model equation is synthesized, and the shale gas yield is obtained by utilizing the microseism-damage-seepage relation.
Description of the drawings:
FIG. 1 is a flow chart of a shale gas yield prediction method utilizing a microseismic-injury-seepage relationship according to the present invention;
FIG. 2 is a schematic structural diagram of a standard core sample of example 1;
FIG. 3 is a schematic view of an SRV region of a shale gas reservoir of example 1;
FIG. 4 is a graph of the pressure profile of the random fracture reservoir of example 1 over time;
FIG. 5 is a shale gas reservoir geometry model of example 1;
FIG. 6 shows daily gas production of Changning X-well obtained by the prediction method of the present invention;
FIG. 7 shows the actual daily well gas production for Changning X-well in example 1.
Detailed Description
The present invention will be described in further detail with reference to test examples and specific embodiments. It should not be construed that the scope of the above subject matter of the present invention is limited to the following embodiments, and all techniques realized based on the present invention are within the scope of the present invention.
Example 1
Step one: acquiring shale cores with target depths of a target well, and testing the permeability of the shale cores with different depths to obtain a relation between the permeability and a damage variable;
step two: acquiring microseism monitoring data of a target well to obtain microseism focus parameters and damage variables containing the microseism focus parameters;
step three: combining the first step and the second step to obtain the relation between the microseism focus parameter and the permeability;
step four: and establishing a shale reservoir geometrical model, solving the shale gas seepage model by adopting a numerical simulation method based on the shale gas seepage model and combining the microearthquake source parameter and permeability relation type and the shale reservoir geometrical model to obtain the shale gas yield of the target well.
Because shale damage is a dynamic process in actual shale exploration and development, when a rock mass is damaged, the permeability of the shale is also changed continuously, and the change of the permeability influences the shale gas yield. Taking a Sichuan chaning shale gas X well as an example, drilling a core sample with target depth on a shale exploration and development site, processing the core sample into a cylindrical sample with standard diameter of 50mm and height of 100mm, and dislocation drilling two small holes with diameter of 5mm and height of 80mm at two ends of the sample, wherein the visually defective rock sample is removed as shown in figure 2. Preferably, the permeability k of shale samples is measured by a transient method m Indoor shale permeability k test m The process is as follows: vacuum-pumping the sample for saturation, putting the rock sample in pressure chamber, sequentially applying hydrostatic pressure, osmotic pressure and axial pressure, and different stressesPermeation and loading and unloading tests at the level, the variation of the differential pressure across the sample was used to calculate the shale permeability. The permeability calculation formula is as follows:
wherein Δt is the measured duration in s; Δp i /Δp f To test the osmotic pressure difference ratio of initial and differential pressure within Δt; a is that s Is the cross-sectional area of the sample, and the unit is cm 2 ；L s The height of the sample is cm; mu is the viscosity coefficient of water, and 0.001 Pa.s is taken; beta is the volume compression coefficient of water of 0.001 Pa.s; v is the volume of the water tank, and the unit is cm 3 。
Fitting the permeability according to the permeability test data of the rock in the whole deformation and damage process to obtain the relation between the permeability and the damage variable as follows:
wherein p is m Is bedrock pressure, pa; k (k) 0 As initial permeability, mD; a and B are parameters related to confining pressure; d (D) 0 Is the damage threshold. Considering that the rock permeability characteristics have certain differences along with the damage evolution trend under different confining pressure conditions, the parameters A, B and the damage threshold are functions of effective confining pressure.
In actual shale exploration and development, a microseismic monitoring system is mainly used for monitoring and counting information of cracks generated by rock fracture, and a fracture network system formed by all the cracks in a gathering mode is an SRV area. To obtain the relationship between the damage variables of the microseismic source parameters, the embodiment establishes an SRV region model, as shown in fig. 3. The reservoir region is a rectangle with the length of 100m multiplied by 50m, the bottom edge of the rectangle is a horizontal well, the point A is a fracturing point, the vertical line in the middle of the rectangle is a main fracture, and the oblique lines on two sides are secondary fractures. In order to meet the field actual operation, randomly setting the angles, lengths and numbers of the cracks to form an irregular crack spread reservoir form, and calculating to obtain a reservoir stress diagram as shown in figure 4; and calculating a stress cloud chart according to the model to obtain releasable strain energy and damage variables of each point of the SRV region, wherein the releasable strain energy and damage variables can reflect related parameters of microseisms.
Acquiring microseismic monitoring data of a target well to obtain microseismic source parameters, wherein the microseismic source parameters comprise microseismic energy E and total energy E released during the microcracking of a rock mass T The microseismic radiant energy is ΔU'. The relation of the damage variable containing the microseismic source parameters is as follows:
in the middle ofIs isotropically releasable strain energy; η is the seismic efficiency coefficient, dimensionless; η=e/E T E is earthquake energy, E T Total energy released during micro-fracturing of the rock mass. Wherein->Is as follows
In E 0 The initial elastic modulus of the rock is Pa; sigma (sigma) 1 、σ 2 、σ 3 Respectively 3 main stresses of the rock mass; v is poisson's ratio.
The damage variable containing the microseismic source parameters at each point in the model is brought into D and k obtained based on indoor tests m And (5) obtaining a trend of the permeability of the SRV region model along with the time by a relational expression.
The expression of the shale gas seepage model is:
in which Q p For flow, i.e. yield, γ is a coefficient, γ=m g /ZRT；M g The unit is kg/mol of the molar mass of the gas;z is a gas compression factor; r is an ideal gas constant; t is temperature, and the unit is K;is the porosity of the bedrock; p (P) L Is Langmuir pressure, in Pa; p is p m The pressure of bedrock is Pa; ρ s Is core density in kg/m 3 ；V std Is the molar volume under standard conditions, and the unit is m 3 /mol；k m Is the permeability, the unit is m 2 ；μ g The unit is Pa.s, which is the viscosity of the gas; d (D) km Is the diffusion coefficient of the bedrock,c is a constant approaching 1, preferably 1m 2 /s；k mi Is the intrinsic permeability of the bedrock, the unit is m 2 . Wherein T is,、P L 、p m 、ρ s 、k m 、D km And calculating according to the bedrock data actually collected in the field. The shale gas seepage model is established by assuming that the gas in the shale reservoir is stored in a natural fracture in a free state, and the free state and the adsorbed state of the gas in the bedrock coexist; only single-phase single-component gas migration exists in the shale gas reservoir; the temperature of the gas reservoir is kept unchanged in the production process, the gas meets a Langmuir isothermal adsorption equation on the surface of the bedrock, and viscous flow, knudsen diffusion, molecular diffusion and desorption mechanisms are considered in the bedrock, so that a shale gas seepage model expression is obtained.
For the Changning shale gas X-well, a shale reservoir geometrical model as in FIG. 5 was built to calculate shale gas production. Shale reservoir in the shale reservoir geometric model is a square area with 200m multiplied by 200m, and a shale gas well with a wellhead radius of 0.1m is arranged in the middle of the square area. And selecting a quarter area of the upper right corner according to the symmetry of the graph to perform numerical simulation. The parameters of the shale reservoir geometrical model are set, including reservoir basic physical parameters and fracturing basic parameters, and the parameters set in the embodiment are as follows: the initial formation pressure of the reservoir is 8MPa, and the gas component is considered to be CH 4 The molar mass of methane was 0.016kg/mol, the gas compression factor was 1, and the ideal gas constant was 8.314J. Cndot. Mol. K) -1 Gas reservoir temperature 323.14K, bedrock porosity 0.05, langmuir pressure 4.3MPa, core density 2500kg/m 3 Molar volume under standard conditions 0.0224m 3 Per mole, gas viscosity 1.84X10 - 5 Pa·s; the intrinsic permeability of the bedrock is 0.1e-6 mu m 2 Diffusion coefficient 0.2305 of bedrock and poisson ratio 0.3. And solving the shale gas seepage model by adopting a numerical simulation method based on an expression of the shale gas seepage model and combining the relation between the microseismic source parameters and the permeability and the shale reservoir geometric model to obtain the shale gas yield of the target well, wherein the shale gas yield is shown in figure 6. In order to evaluate the accuracy of the prediction method of the present invention, the actual daily gas production graph of the chaning X-well is shown in fig. 7, and as can be seen from comparison of fig. 6 and fig. 7, after 500 days, the single well yield starts to be gradually and relatively stable, the fitted curve of fig. 7 is basically consistent with the development trend of the shale gas yield curve obtained by the prediction method of the present invention, the daily gas production prediction accuracy is higher, and the present invention is suitable for on-site practical development.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (8)
1. A shale gas yield prediction method utilizing a microseismic-injury-seepage relationship is characterized by comprising the following steps:
step one: acquiring shale cores with target depths of a target well, and testing the permeability of the shale cores with different depths to obtain a relation between the permeability and a damage variable;
step two: acquiring microseism monitoring data of a target well to obtain microseism focus parameters and damage variables containing the microseism focus parameters;
step three: combining the first step and the second step to obtain the relation between the microseism focus parameter and the permeability;
step four: establishing a shale reservoir geometrical model, solving the shale gas seepage model by adopting a numerical simulation method based on the shale gas seepage model and combining the relation between the microseismic source parameters and the permeability and the shale reservoir geometrical model to obtain the shale gas yield of the target well;
the shale gas seepage model has the expression:
in which Q p Is the flow, namely shale gas yield, gamma is a coefficient, gamma=m g /ZRT；M g The unit is kg/mol of the molar mass of the gas; z is a gas compression factor; r is an ideal gas constant; t is temperature, and the unit is K;is the porosity of the bedrock; p (P) L Is Langmuir pressure, in Pa; p is p m The pressure of bedrock is Pa; ρ s Is core density in kg/m 3 ；V std Is the molar volume under standard conditions, and the unit is m 3 /mol；k m Is the permeability, the unit is m 2 ；μ g The unit is Pa.s, which is the viscosity of the gas; d (D) km Is the diffusion coefficient of the bedrock,c is a constant approaching 1, preferably 1m 2 /s；k mi Is the intrinsic permeability of the bedrock, the unit is m 2 。
2. The method for predicting shale gas production using microseismic-injury-percolation relationship according to claim 1, wherein in step one, shale cores of target depths of target wells are obtained, and are processed into cylindrical core samples as required, and visually defective core samples are removed.
3. The shale gas yield prediction method using the microseismic-injury-seepage relationship according to claim 1, wherein in the first step, the seepage rates of shale cores with different depths are tested by adopting a transient method, and the testing process is as follows: and (3) carrying out vacuumizing saturation on the core sample according to the requirement, putting the core sample into a pressure chamber, sequentially applying hydrostatic pressure, osmotic pressure and axial pressure, and then carrying out permeation and loading and unloading tests under different stress levels.
4. The shale gas production prediction method using a microseismic-injury-seepage relationship according to claim 1, wherein the relationship between the permeability and the injury variable is:
wherein k is m Is permeability; p is p m The pressure of bedrock is Pa; k (k) 0 The initial permeability is given in mD; a and B are parameters related to confining pressure; d is a damage variable; d (D) 0 Is the damage threshold.
5. The method for predicting shale gas yield using microseismic-injury-percolation relationship of claim 1, wherein the microseismic source parameters comprise microseismic energy E, total energy E released during a rock mass microseismic fracture T The microseismic radiant energy is ΔU'.
6. The method for predicting shale gas yield using microseismic-injury-seepage relationship according to claim 5, wherein the relationship of the injury variable containing microseismic source parameters is:
wherein D is a damage variable;is isotropically releasable strain energy; η is the seismic efficiency coefficient, dimensionless; η=e/E T E is earthquake energy, E T Total energy released during micro-fracturing of the rock mass.
7. The method for predicting shale gas yield by utilizing microseismic-injury-seepage relationship according to claim 6, wherein the method comprises the steps of,the relation of (2) is:
in E 0 The initial elastic modulus of the rock mass unit when the rock mass unit is not damaged is Pa; sigma (sigma) 1 、σ 2 、σ 3 Respectively 3 main stresses of the rock mass; v is poisson's ratio.
8. The shale gas yield prediction method utilizing the microseismic-injury-seepage relationship according to claim 1, wherein the shale reservoir geometric model is a square area, the side length is 200m, and a shale gas well with a wellhead radius of 0.1m is arranged in the middle of the square area.
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| CN104459775A (en) * | 2014-11-28 | 2015-03-25 | 中国石油集团川庆钻探工程有限公司地球物理勘探公司 | Shale gas reservoir fracture modeling method based on microseism monitoring data |
| CN108518212A (en) * | 2018-04-09 | 2018-09-11 | 西南石油大学 | A method of calculating shale gas reservoir complex fracture network unstable state yield |
| CN109488276A (en) * | 2019-01-16 | 2019-03-19 | 重庆科技学院 | The production water shale gas well shale gas production prediction method being transformed through hydraulic fracturing |
| CN110018057A (en) * | 2019-04-17 | 2019-07-16 | 山东科技大学 | A kind of microseism-shear-seepage coupling test device and test method |
| CN110631936A (en) * | 2019-09-02 | 2019-12-31 | 中国矿业大学 | Quantitative evaluation test method for coal core damage |
| CN112987106A (en) * | 2021-02-22 | 2021-06-18 | 河南理工大学 | Method for evaluating productivity potential of coal-bed gas well based on microseism static monitoring |
Family Cites Families (1)
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Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104459775A (en) * | 2014-11-28 | 2015-03-25 | 中国石油集团川庆钻探工程有限公司地球物理勘探公司 | Shale gas reservoir fracture modeling method based on microseism monitoring data |
| CN108518212A (en) * | 2018-04-09 | 2018-09-11 | 西南石油大学 | A method of calculating shale gas reservoir complex fracture network unstable state yield |
| CN109488276A (en) * | 2019-01-16 | 2019-03-19 | 重庆科技学院 | The production water shale gas well shale gas production prediction method being transformed through hydraulic fracturing |
| CN110018057A (en) * | 2019-04-17 | 2019-07-16 | 山东科技大学 | A kind of microseism-shear-seepage coupling test device and test method |
| CN110631936A (en) * | 2019-09-02 | 2019-12-31 | 中国矿业大学 | Quantitative evaluation test method for coal core damage |
| CN112987106A (en) * | 2021-02-22 | 2021-06-18 | 河南理工大学 | Method for evaluating productivity potential of coal-bed gas well based on microseism static monitoring |
Non-Patent Citations (2)
| Title |
|---|
| "页岩气水平井产量主控因素分析及产能预测";王忠东等;《测井技术》;第41卷(第05期);577-582 * |
| "An outlook into recent advances on estimation of effective stimulated reservoir volume";Ibrahim Adamu Umar etl;《 Journal of Natural Gas Science and Engineering 》;第88卷;1-18 * |
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